From 9d330454f936da3267592f084ddcab9de82b6963 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 24 Mar 2022 19:25:58 +0000 Subject: [PATCH 01/81] Add evaluation. --- src/av2/evaluation/__init__.py | 3 + src/av2/evaluation/detection/__init__.py | 3 + src/av2/evaluation/detection/constants.py | 156 +++++++++ src/av2/evaluation/detection/eval.py | 207 +++++++++++ src/av2/evaluation/detection/utils.py | 409 ++++++++++++++++++++++ src/av2/geometry/iou.py | 26 ++ src/av2/utils/constants.py | 8 +- 7 files changed, 811 insertions(+), 1 deletion(-) create mode 100644 src/av2/evaluation/__init__.py create mode 100644 src/av2/evaluation/detection/__init__.py create mode 100644 src/av2/evaluation/detection/constants.py create mode 100644 src/av2/evaluation/detection/eval.py create mode 100644 src/av2/evaluation/detection/utils.py create mode 100644 src/av2/geometry/iou.py diff --git a/src/av2/evaluation/__init__.py b/src/av2/evaluation/__init__.py new file mode 100644 index 00000000..8109497b --- /dev/null +++ b/src/av2/evaluation/__init__.py @@ -0,0 +1,3 @@ +# + +"""Dataset evaluation subpackage.""" diff --git a/src/av2/evaluation/detection/__init__.py b/src/av2/evaluation/detection/__init__.py new file mode 100644 index 00000000..281dc033 --- /dev/null +++ b/src/av2/evaluation/detection/__init__.py @@ -0,0 +1,3 @@ +# + +"""Detection evaluation subpackage.""" diff --git a/src/av2/evaluation/detection/constants.py b/src/av2/evaluation/detection/constants.py new file mode 100644 index 00000000..b6653fe8 --- /dev/null +++ b/src/av2/evaluation/detection/constants.py @@ -0,0 +1,156 @@ +# + +"""3D object detection evaluation constants.""" + +from enum import Enum, unique +from typing import Final, Tuple + +from av2.utils.constants import PI + +MAX_SCALE_ERROR: Final[float] = 1.0 +MAX_YAW_ERROR: Final[float] = PI + +# Higher is better. +MIN_AP: Final[float] = 0.0 +MIN_CDS: Final[float] = 0.0 + +# Lower is better. +MAX_NORMALIZED_ATE: Final[float] = 1.0 +MAX_NORMALIZED_ASE: Final[float] = 1.0 +MAX_NORMALIZED_AOE: Final[float] = 1.0 + +# Max number of boxes considered per class per scene. +MAX_NUM_BOXES: Final[int] = 500 + +NUM_DECIMALS: Final[int] = 3 + + +@unique +class EvaluationColumns(str, Enum): + """Columns needed for evaluation in the input detection and ground truth dataframes.""" + + LOG_ID = "log_id" + TIMESTAMP_NS = "timestamp_ns" + CATEGORY = "category" + LENGTH_M = "length_m" + WIDTH_M = "width_m" + HEIGHT_M = "height_m" + QW = "qw" + QX = "qx" + QY = "qy" + QZ = "qz" + TX_M = "tx_m" + TY_M = "ty_m" + TZ_M = "tz_m" + SCORE = "score" + + @classmethod + @property + def DIMENSION_NAMES(cls) -> Tuple[str, str, str]: + return cls.LENGTH_M.value, cls.WIDTH_M.value, cls.HEIGHT_M.value + + @classmethod + @property + def QUAT_COEFFICIENTS_XYZW(cls) -> Tuple[str, str, str, str]: + return cls.QX.value, cls.QY.value, cls.QZ.value, cls.QW.value + + @classmethod + @property + def QUAT_COEFFICIENTS_WXYZ(cls) -> Tuple[str, str, str, str]: + return cls.QW.value, cls.QX.value, cls.QY.value, cls.QZ.value + + @classmethod + @property + def TRANSLATION_NAMES(cls) -> Tuple[str, str, str]: + return cls.TX_M.value, cls.TY_M.value, cls.TZ_M.value + + +@unique +class TruePositiveErrorNames(str, Enum): + """True positive error names.""" + + ATE = "ATE" + ASE = "ASE" + AOE = "AOE" + + +@unique +class MetricNames(str, Enum): + """Metric names.""" + + AP = "AP" + ATE = TruePositiveErrorNames.ATE.value + ASE = TruePositiveErrorNames.ASE.value + AOE = TruePositiveErrorNames.AOE.value + CDS = "CDS" + + +@unique +class CompetitionCategories(str, Enum): + """Sensor dataset annotation categories.""" + + ANIMAL = "ANIMAL" + ARTICULATED_BUS = "ARTICULATED_BUS" + BICYCLE = "BICYCLE" + BICYCLIST = "BICYCLIST" + BOLLARD = "BOLLARD" + BOX_TRUCK = "BOX_TRUCK" + BUS = "BUS" + CONSTRUCTION_BARREL = "CONSTRUCTION_BARREL" + CONSTRUCTION_CONE = "CONSTRUCTION_CONE" + DOG = "DOG" + LARGE_VEHICLE = "LARGE_VEHICLE" + MESSAGE_BOARD_TRAILER = "MESSAGE_BOARD_TRAILER" + MOBILE_PEDESTRIAN_CROSSING_SIGN = "MOBILE_PEDESTRIAN_CROSSING_SIGN" + MOTORCYCLE = "MOTORCYCLE" + MOTORCYCLIST = "MOTORCYCLIST" + OFFICIAL_SIGNALER = "OFFICIAL_SIGNALER" + PEDESTRIAN = "PEDESTRIAN" + RAILED_VEHICLE = "RAILED_VEHICLE" + REGULAR_VEHICLE = "REGULAR_VEHICLE" + SCHOOL_BUS = "SCHOOL_BUS" + SIGN = "SIGN" + STOP_SIGN = "STOP_SIGN" + STROLLER = "STROLLER" + TRAFFIC_LIGHT_TRAILER = "TRAFFIC_LIGHT_TRAILER" + TRUCK = "TRUCK" + TRUCK_CAB = "TRUCK_CAB" + VEHICULAR_TRAILER = "VEHICULAR_TRAILER" + WHEELCHAIR = "WHEELCHAIR" + WHEELED_DEVICE = "WHEELED_DEVICE" + WHEELED_RIDER = "WHEELED_RIDER" + + +@unique +class DimensionColumnNames(str, Enum): + pass + + +@unique +class AffinityType(str, Enum): + """Affinity types for assigning detections to ground truth cuboids.""" + + CENTER = "CENTER" + + +@unique +class DistanceType(str, Enum): + """Distance types for computing metrics on true positive detections.""" + + TRANSLATION = "TRANSLATION" + SCALE = "SCALE" + ORIENTATION = "ORIENTATION" + + +@unique +class InterpType(str, Enum): + """Interpolation type for interpolating precision over recall samples.""" + + ALL = "ALL" + + +@unique +class FilterMetricType(str, Enum): + """Metric used to filter cuboids for evaluation.""" + + EUCLIDEAN = "EUCLIDEAN" diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py new file mode 100644 index 00000000..aabe3b34 --- /dev/null +++ b/src/av2/evaluation/detection/eval.py @@ -0,0 +1,207 @@ +# + +"""Argoverse 3D object detection evaluation. + +Evaluation: + + Precision/Recall + + 1. Average Precision: Standard VOC-style average precision calculation + except a true positive requires a bird's eye view center distance of less + than a predefined threshold. + + True Positive Errors + + All true positive errors accumulate error solely when an object is a true positive match + to a ground truth detection. The matching criterion is represented by `tp_thresh` in the DetectionCfg class. + In our challenge, we use a `tp_thresh` of 2.0 meters. + + 1. Average Translation Error: The average Euclidean distance (center-based) between a + detection and its ground truth assignment. + 2. Average Scale Error: The average intersection over union (IoU) after the prediction + and assigned ground truth's pose has been aligned. + 3. Average Orientation Error: The average angular distance between the detection and + the assigned ground truth. We choose the smallest angle between the two different + headings when calculating the error. + + Composite Scores + + 1. Composite Detection Score: The ranking metric for the detection leaderboard. This + is computed as the product of mAP with the sum of the complements of the true positive + errors (after normalization), i.e.: + - Average Translation Measure (ATM): ATE / TP_THRESHOLD; 0 <= 1 - ATE / TP_THRESHOLD <= 1. + - Average Scaling Measure (ASM): 1 - ASE / 1; 0 <= 1 - ASE / 1 <= 1. + - Average Orientation Measure (AOM): 1 - AOE / PI; 0 <= 1 - AOE / PI <= 1. + + These (as well as AP) are averaged over each detection class to produce: + - mAP + - mATM + - mASM + - mAOM + + Lastly, the Composite Detection Score is computed as: + CDS = mAP * (mATE + mASE + mAOE); 0 <= mAP * (mATE + mASE + mAOE) <= 1. + + ** In the case of no true positives under the specified threshold, the true positive measures + will assume their upper bounds of 1.0. respectively. + +Results: + + The results are represented as a (C + 1, P) table, where C + 1 represents the number of evaluation classes + in addition to the mean statistics average across all classes, and P refers to the number of included statistics, + e.g. AP, ATE, ASE, AOE, CDS by default. +""" +import logging +from typing import Optional, Tuple + +import numpy as np +import pandas as pd + +from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames +from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision +from av2.utils.typing import NDArrayBool, NDArrayFloat + +logger = logging.getLogger(__name__) + + +def evaluate( + dts: pd.DataFrame, + gts: pd.DataFrame, + cfg: DetectionCfg, + poses: Optional[pd.DataFrame] = None, +) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """Evaluate a set of detections against the ground truth annotations. + + Each sweep is processed independently, computing assignment between detections and ground truth annotations. + + Args: + dts: (N,15) Table of detections. + gts: (M,15) Table of ground truth annotations. + cfg: Detection configuration. + poses: (K,8) Table of city egopose. + + Returns: + (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. + K refers to the number of evaluation metrics. + + Raises: + ValueError: If ROI filtering is enabled, but no egoposes are provided. + """ + if cfg.eval_only_roi_instances and poses is None: + raise ValueError("Poses must be provided for ROI cuboid filtering!") + + # (log_id, timestamp_ns) uniquely identifies a sweep. + uuid_columns = ["log_id", "timestamp_ns"] + + # Set index and sort for performance. + dts = dts.set_index(keys=uuid_columns).sort_index() + gts = gts.set_index(keys=uuid_columns).sort_index() + + # Find unique list of uuid tuples. + uuids = gts.index.unique().tolist() + + # Construct arguments for multiprocessing. + args_list = [(dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg, poses) for uuid in uuids] + + # Accumulate and gather the processed detections and ground truth annotations. + dts_list, gts_list = zip(*[accumulate(*x) for x in args_list]) + + # Concatenate the detections and ground truth annotations into DataFrames. + dts_processed: pd.DataFrame = pd.concat(dts_list).reset_index(drop=True) + gts_processed: pd.DataFrame = pd.concat(gts_list).reset_index() + + # Compute summary metrics. + summary_metrics = summarize_metrics(dts_processed, gts_processed, cfg).round(NUM_DECIMALS) + summary_metrics.loc["AVERAGE_METRICS"] = summary_metrics.mean().round(NUM_DECIMALS) + + # Return results. + return dts, gts, summary_metrics + + +def summarize_metrics( + dts: pd.DataFrame, + gts: pd.DataFrame, + cfg: DetectionCfg, +) -> pd.DataFrame: + """Calculate and print the 3D object detection metrics. + + Args: + dts: (N,15) Table of detections. + gts: (M,15) Table of ground truth annotations. + cfg: Detection configuration. + + Returns: + The summary metrics. + """ + # Sample recall values in the [0, 1] interval. + recall_interpolated: NDArrayFloat = np.linspace(0, 1, cfg.num_recall_samples) + + # Initialize the summary metrics. + summary = pd.DataFrame( + {s.value: cfg.metrics_defaults[i] for i, s in enumerate(tuple(MetricNames))}, index=cfg.categories + ) + + average_precisions = pd.DataFrame({t: 0.0 for t in cfg.affinity_thresholds_m}, index=cfg.categories) + for category in cfg.categories: + # Find detections that have the current category. + is_category_dts = dts["category"] == category + + # Only keep detections if they match the category and have NOT been filtered. + is_valid_dts = np.logical_and(is_category_dts, dts["is_evaluated"]) + + # Get valid detections and sort them in ascending order. + category_dts = dts.loc[is_valid_dts].sort_values(by="score", ascending=False).reset_index(drop=True) + + # Find annotations that have the current category. + is_category_gts = gts["category"] == category + + # Compute number of ground truth annotations. + num_gts = gts.loc[is_category_gts, "is_evaluated"].sum() + + # Cannot evaluate without ground truth information. + if num_gts == 0: + continue + + for affinity_threshold_m in cfg.affinity_thresholds_m: + true_positives: NDArrayBool = category_dts[affinity_threshold_m].to_numpy() + + # NaN values correspond to detections that weren't true positives. + # is_valid = ~np.isnan(true_positives) + # true_positives = true_positives[is_valid].astype(bool) + + # Continue if there aren't any true positives. + if len(true_positives) == 0: + continue + + # Compute average precision for the current threshold. + threshold_average_precision, _ = compute_average_precision(true_positives, recall_interpolated, num_gts) + + # Record the average precision. + average_precisions.loc[category, affinity_threshold_m] = threshold_average_precision + + mean_average_precisions = average_precisions.loc[category].mean() + + # Select only the true positives for each instance. + middle_idx = len(cfg.affinity_thresholds_m) // 2 + middle_threshold = cfg.affinity_thresholds_m[middle_idx] + is_tp_t = category_dts[middle_threshold] + + # Initialize true positive metrics. + tp_errors = cfg.tp_normalization_terms + + # Check whether any true positives exist under the current threshold. + has_true_positives = np.any(is_tp_t) + + # If true positives exist, compute the metrics. + if has_true_positives: + tp_errors = category_dts.loc[is_tp_t, tuple(x.value for x in TruePositiveErrorNames)].mean(axis=0) + + # Convert errors to scores. + tp_scores = 1 - np.divide(tp_errors, cfg.tp_normalization_terms) + + # Compute Composite Detection Score (CDS). + cds = mean_average_precisions * np.mean(tp_scores) + summary.loc[category] = [mean_average_precisions, *tp_errors, cds] + + # Return the summary. + return summary diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py new file mode 100644 index 00000000..1e4a24ff --- /dev/null +++ b/src/av2/evaluation/detection/utils.py @@ -0,0 +1,409 @@ +# + +"""Detection utilities for the Argoverse detection leaderboard. + +Accepts detections (in Argoverse ground truth format) and ground truth labels +for computing evaluation metrics for 3d object detection. We have five different, +metrics: mAP, ATE, ASE, AOE, and DCS. A true positive for mAP is defined as the +highest confidence prediction within a specified Euclidean distance threshold +from a bird's-eye view. We prefer these metrics instead of IoU due to the +increased interpretability of the error modes in a set of detections. +""" + +import logging +from dataclasses import dataclass +from typing import Optional, Tuple + +import numpy as np +import pandas as pd +from scipy.spatial.distance import cdist +from scipy.spatial.transform import Rotation + +from av2.evaluation.detection.constants import ( + MAX_NORMALIZED_ASE, + MAX_SCALE_ERROR, + MAX_YAW_ERROR, + MIN_AP, + MIN_CDS, + AffinityType, + CompetitionCategories, + DistanceType, + EvaluationColumns, + FilterMetricType, + InterpType, + TruePositiveErrorNames, +) +from av2.geometry.geometry import quat_to_mat, wrap_angles +from av2.geometry.iou import iou_3d_axis_aligned +from av2.geometry.se3 import SE3 +from av2.map.map_api import ArgoverseStaticMap, RasterLayerType +from av2.structures.cuboid import CuboidList +from av2.utils.constants import EPS, NAN +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt + +logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class DetectionCfg: + """Instantiates a DetectionCfg object for configuring a evaluation. + + Args: + affinity_thresholds_m: Affinity thresholds for determining a true positive (in meters). + affinity_type: Type of affinity function to be used for calculating average precision. + num_recall_samples: Number of recall points to sample uniformly in [0, 1]. + tp_threshold_m: Center distance threshold for the true positive metrics (in meters). + categories: Detection classes for evaluation. + filter_metric: Detection metric to use for filtering of both detections and ground truth annotations. + max_range_m: Max distance (under a specific metric in meters) for a detection or ground truth cuboid to be + considered for evaluation. + tp_normalization_terms: Normalization constants for ATE, ASE, and AOE. + metrics_defaults: Evaluation summary default values. + eval_only_roi_instances: Only use dets and ground truth that lie within region of interest during eval. + splits: Tuple of split names to evaluate. + """ + + affinity_thresholds_m: Tuple[float, ...] = (0.5, 1.0, 2.0, 4.0) # Meters + affinity_type: AffinityType = AffinityType.CENTER + num_recall_samples: int = 101 + tp_threshold_m: float = 2.0 # Meters + categories: Tuple[str, ...] = tuple(x.value for x in CompetitionCategories) + filter_metric: FilterMetricType = FilterMetricType.EUCLIDEAN + max_range_m: float = 200.0 # Meters + tp_normalization_terms: Tuple[float, ...] = ( + tp_threshold_m, + MAX_SCALE_ERROR, + MAX_YAW_ERROR, + ) + metrics_defaults: Tuple[float, ...] = ( + MIN_AP, + tp_threshold_m, + MAX_NORMALIZED_ASE, + MAX_YAW_ERROR, + MIN_CDS, + ) + eval_only_roi_instances: bool = True + splits: Tuple[str, ...] = ("val",) + + +def accumulate( + dts: pd.DataFrame, + gts: pd.DataFrame, + cfg: DetectionCfg, + poses: Optional[pd.DataFrame] = None, + avm: Optional[ArgoverseStaticMap] = None, +) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Accumulate the true / false positives (boolean flags) and true positive errors for each class. + + Args: + dts: (N,len(EvaluationColumns)) Detections of shape. Must contain all of the columns in EvaluationColumns. + gts: (M,len(EvaluationColumns) + 1) Ground truth labels. Must contain all of the columns in EvaluationColumns + and the `num_interior_points` column. + cfg: Detection configuration. + poses: (N,) Detections city egopose. + avm: Argoverse map object. + + Returns: + The detection and ground truth cuboids augmented with assigment and evaluation fields. + + Raises: + ValueError: If all of the evaluation columns are not present in the detections or the + ground truth labels. + """ + required_dts_cols = set(x.value for x in EvaluationColumns) + required_gts_cols = set(tuple(x.value for x in EvaluationColumns) + ("num_interior_points",)) + + missing_dts_cols = required_dts_cols - set(dts.columns) + missing_gts_cols = required_gts_cols - set(gts.columns) + + # if len(missing_dts_cols) > 0: + # raise ValueError(f"Missing the following columns in your detections: {missing_dts_cols}!") + # if len(missing_gts_cols) > 0: + # raise ValueError(f"Missing the following columns in your ground truth labels: {missing_gts_cols}!") + + dts.sort_values("score", ascending=False, inplace=True) + dts.reset_index(drop=True, inplace=True) + + # Filter detections and ground truth annotations. + dts.loc[:, "is_evaluated"] = compute_evaluated_cuboids_mask(dts, cfg) + gts.loc[:, "is_evaluated"] = compute_evaluated_cuboids_mask(gts, cfg) + + if cfg.eval_only_roi_instances and avm is not None and poses is not None: + dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, poses, avm) + + # Initialize corresponding assignments + errors. + dts.loc[:, cfg.affinity_thresholds_m] = False + dts.loc[:, tuple(x.value for x in TruePositiveErrorNames)] = cfg.metrics_defaults[1:4] + for category in cfg.categories: + is_eval_dts = np.logical_and(dts["category"] == category, dts["is_evaluated"]) + is_eval_gts = np.logical_and(gts["category"] == category, gts["is_evaluated"]) + + category_dts = dts.loc[is_eval_dts].reset_index(drop=True) + category_gts = gts.loc[is_eval_gts].reset_index(drop=True) + + # Skip if there are no detections for the current category left. + if len(category_dts) == 0: + continue + + assignments = assign(category_dts, category_gts, cfg) + dts.loc[is_eval_dts, assignments.columns] = assign(category_dts, category_gts, cfg).to_numpy() + return dts, gts + + +def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFrame: + """Attempt assignment of each detection to a ground truth label. + + Args: + dts: (N,23) Detections of shape. Must contain all columns in EvaluationColumns and the + additional columns: (is_evaluated, *cfg.affinity_thresholds_m, *TruePositiveErrorNames). + gts: (M,23) Ground truth labels. Must contain all columns in EvaluationColumns and the + additional columns: (is_evaluated, *cfg.affinity_thresholds_m, *TruePositiveErrorNames). + cfg: Detection configuration. + + Returns: + The (N,K+S) confusion table containing the true and false positives augmented with the true positive errors where + K is the number of thresholds and S is the number of true positive error names. + """ + # Construct all columns. + cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + + M = len(cols) # Number of columns. + N = len(dts) # Number of detections. + K = len(cfg.affinity_thresholds_m) # Number of thresholds. + + # Construct metrics table. + # 0.5 1.0 2.0 4.0 ATE ASE AOE (default columns) + # 0 False False False False 0.0 0.0 0.0 + # 1 False False False False 0.0 0.0 0.0 + # ... + # N False False False False 0.0 0.0 0.0 + metrics_table = pd.DataFrame({c: np.zeros(N) for c in cols}) + metrics_table = metrics_table.astype({tx_m: bool for tx_m in cfg.affinity_thresholds_m}) + metrics_table.iloc[:, K : K + M] = cfg.metrics_defaults[1:4] + metrics_table.loc[:, "score"] = dts["score"] + + if len(gts) == 0: + return metrics_table + + affinity_matrix = compute_affinity_matrix(dts, gts, cfg.affinity_type) + + # Get the GT label for each max-affinity GT label, detection pair. + idx_gts = affinity_matrix.argmax(axis=1)[None] + + # The affinity matrix is an N by M matrix of the detections and ground truth labels respectively. + # We want to take the corresponding affinity for each of the initial assignments using `gt_matches`. + # The following line grabs the max affinity for each detection to a ground truth label. + affinities: NDArrayFloat = np.take_along_axis(affinity_matrix.transpose(), idx_gts, axis=0)[0] + + # Find the indices of the _first_ detection assigned to each GT. + assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) + + idx_gts, idx_dts = assignments + for i, threshold_m in enumerate(cfg.affinity_thresholds_m): + # `is_tp` may need to be defined differently with other affinities. + is_tp = affinities[idx_dts] > -threshold_m + + # Record true positives. + metrics_table.iloc[idx_dts, i] = is_tp + if threshold_m != cfg.tp_threshold_m: + continue # Skip if threshold isn't the true positive threshold. + if not np.any(is_tp): + continue # Skip if no true positives exist. + + idx_tps_dts = idx_dts[is_tp] + idx_tps_gts = idx_gts[is_tp] + + tps_dts = dts.iloc[idx_tps_dts] + tps_gts = gts.iloc[idx_tps_gts] + + translation_errors = distance(tps_dts, tps_gts, DistanceType.TRANSLATION) + scale_errors = distance(tps_dts, tps_gts, DistanceType.SCALE) + orientation_errors = distance(tps_dts, tps_gts, DistanceType.ORIENTATION) + + # Assign errors. + metrics_table.loc[idx_tps_dts, tuple(x.value for x in TruePositiveErrorNames)] = np.vstack( + [translation_errors, scale_errors, orientation_errors] + ).transpose() + return metrics_table + + +def interpolate_precision(precision: NDArrayFloat, interpolation_method: InterpType = InterpType.ALL) -> NDArrayFloat: + r"""Interpolate the precision at each sampled recall. + + This function smooths the precision-recall curve according to the method introduced in Pascal + VOC: + + Mathematically written as: + $$p_{\text{interp}}(r) = \max_{\tilde{r}: \tilde{r} \geq r} p(\tilde{r})$$ + + See equation 2 in http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.167.6629&rep=rep1&type=pdf + for more information. + + Args: + precision: Precision at all recall levels (N,). + interpolation_method: Accumulation method. + + Returns: + (N,) The interpolated precision at all sampled recall levels. + + Raises: + NotImplementedError: If the interpolation method is not implemented. + """ + if interpolation_method == InterpType.ALL: + precision_interpolated: NDArrayFloat = np.maximum.accumulate(precision[::-1])[::-1] + else: + raise NotImplementedError("This interpolation method is not implemented!") + return precision_interpolated + + +def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: AffinityType) -> NDArrayFloat: + """Calculate the affinity matrix between detections and ground truth annotations. + + Args: + dts: (N,) Detections. + gts: (M,) Ground truth annotations. + metric: Affinity metric type. + + Returns: + The affinity scores between detections and ground truth annotations (N,M). + + Raises: + NotImplementedError: If the affinity metric is not implemented. + """ + if metric == AffinityType.CENTER: + dts_xy_m = dts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]] + gts_xy_m = gts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]] + affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) + else: + raise NotImplementedError("This affinity metric is not implemented!") + return affinities + + +def compute_average_precision( + tps: NDArrayBool, recall_interpolated: NDArrayFloat, num_gts: int +) -> Tuple[float, NDArrayFloat]: + """Compute precision and recall, interpolated over N fixed recall points. + + Args: + tps: True positive detections (ranked by confidence). + recall_interpolated: Interpolated recall values. + num_gts: Number of annotations of this class. + + Returns: + The average precision and interpolated precision values. + """ + cum_tps: NDArrayInt = np.cumsum(tps) + cum_fps: NDArrayInt = np.cumsum(~tps) + cum_fns: NDArrayInt = num_gts - cum_tps + + # Compute precision. + precision = cum_tps / (cum_tps + cum_fps + EPS) + + # Compute recall. + recall = cum_tps / (cum_tps + cum_fns) + + # Interpolate precision -- VOC-style. + precision = interpolate_precision(precision) + + # Evaluate precision at different recalls. + precision_interpolated: NDArrayFloat = np.interp(recall_interpolated, recall, precision, right=0) + + average_precision: float = np.mean(precision_interpolated) + return average_precision, precision_interpolated + + +def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDArrayFloat: + """Distance functions between detections and ground truth. + + Args: + dts: (N,D) Detections where D is the number of attributes. + gts: (N,D) Ground truth labels where D is the number of attributes. + metric: Distance function type. + + Returns: + (N,) Distance between the detections and ground truth under the specified metric. + + Raises: + NotImplementedError: If the distance type is not supported. + """ + if metric == DistanceType.TRANSLATION: + dts_xyz_m: NDArrayFloat = dts.loc[:, EvaluationColumns.TRANSLATION_NAMES].to_numpy() + gts_xyz_m: NDArrayFloat = gts.loc[:, EvaluationColumns.TRANSLATION_NAMES].to_numpy() + translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) + return translation_errors + elif metric == DistanceType.SCALE: + dts_lwh_m: NDArrayFloat = dts.loc[:, EvaluationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() + gts_lwh_m: NDArrayFloat = gts.loc[:, EvaluationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() + scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) + return scale_errors + elif metric == DistanceType.ORIENTATION: + dts_quats_xyzw = dts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_XYZW] + gts_quats_xyzw = gts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_XYZW] + yaws_dts: NDArrayFloat = Rotation.from_quat(dts_quats_xyzw).as_euler("zyx")[:, 0] + yaws_gts: NDArrayFloat = Rotation.from_quat(gts_quats_xyzw).as_euler("zyx")[:, 0] + orientation_errors = wrap_angles(yaws_dts - yaws_gts) + return orientation_errors + else: + raise NotImplementedError("This distance metric is not implemented!") + + +def compute_objects_in_roi_mask( + cuboids_dataframe: pd.DataFrame, poses: pd.DataFrame, avm: ArgoverseStaticMap +) -> NDArrayBool: + """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. + + Args: + cuboids_dataframe: Dataframes containing cuboids. + poses: Poses of the cuboids in the city frame. + avm: Argoverse map object. + + Returns: + The boolean mask indicating which cuboids will be evaluated. + """ + cuboids_dataframe = cuboids_dataframe.sort_values("timestamp_ns").reset_index(drop=True) + poses = poses.sort_values("timestamp_ns").reset_index(drop=True) + + cuboid_list_ego = CuboidList.from_dataframe(cuboids_dataframe) + timestamp_ns = poses.loc[:, "timestamp_ns"].unique() + quat_wxyz = poses.loc[timestamp_ns, EvaluationColumns.QUAT_COEFFICIENTS_WXYZ] + translation = poses.loc[timestamp_ns, EvaluationColumns.TRANSLATION_NAMES[:2]] + rotation = quat_to_mat(quat_wxyz) + + city_SE3_ego = SE3(rotation=rotation, translation=translation) + cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) + cuboid_list_vertices_m = cuboid_list_city.vertices_m + + vertices_within_roi = avm.get_raster_layer_points_boolean(cuboid_list_vertices_m[..., :2], RasterLayerType.ROI) + vertices_within_roi = vertices_within_roi.reshape(-1, 4) + is_within_roi: NDArrayBool = vertices_within_roi.any(axis=1) + return is_within_roi + + +def compute_evaluated_cuboids_mask( + cuboids: pd.DataFrame, + cfg: DetectionCfg, +) -> NDArrayBool: + """Compute the evaluated cuboids mask. + + Valid cuboids meet _two_ conditions: + 1. The cuboid's centroid (x,y,z) must lie within the maximum range in the detection configuration. + 2. The cuboid must have at _least_ one point in its interior. + + Args: + cuboids: Dataframes containing cuboids. + cfg: Detection configuration object. + + Returns: + The boolean mask indicating which cuboids will be evaluated. + + Raises: + ValueError: If all of the columns aren't in the cuboids table. + """ + # if not set(translation_names + ("num_interior_points",)).issubset(cuboids.columns): + # raise ValueError("All of the columns must be present in `cuboids`!") + # is_not_empty: NDArrayBool = cuboids.loc[:, "num_interior_points"] > 0 + + norm: NDArrayFloat = np.linalg.norm(cuboids.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) + is_within_radius: NDArrayBool = norm < cfg.max_range_m + is_evaluated: NDArrayBool = is_within_radius + return is_evaluated diff --git a/src/av2/geometry/iou.py b/src/av2/geometry/iou.py new file mode 100644 index 00000000..ca20a526 --- /dev/null +++ b/src/av2/geometry/iou.py @@ -0,0 +1,26 @@ +# + +"""Intersection over Union (IoU) methods.""" + +import numpy as np + +from av2.utils.typing import NDArrayFloat + + +def iou_3d_axis_aligned(src_dims_m: NDArrayFloat, target_dims_m: NDArrayFloat) -> NDArrayFloat: + """Compute 3d, axis-aligned (vertical axis alignment) intersection-over-union (IoU) between two sets of cuboids. + + Both objects are aligned to their +x axis and their centroids are placed at the origin + before computation of the IoU. + + Args: + src_dims_m: (N,3) Source dimensions (length,width,height). + target_dims_m: (N,3) Target dimensions (length,width,height). + + Returns: + (N,) Intersection-over-union between the source and target dimensions. + """ + inter: NDArrayFloat = np.minimum(src_dims_m, target_dims_m).prod(axis=1) + union: NDArrayFloat = np.maximum(src_dims_m, target_dims_m).prod(axis=1) + iou: NDArrayFloat = np.divide(inter, union) + return iou diff --git a/src/av2/utils/constants.py b/src/av2/utils/constants.py index f6de7e93..912a9965 100644 --- a/src/av2/utils/constants.py +++ b/src/av2/utils/constants.py @@ -1,11 +1,17 @@ # -"""Common mathematical and computing system constants used in AV2.""" + +"""Common mathematical and computing system constants.""" import math import multiprocessing as mp +import sys from pathlib import Path from typing import Final +# Difference between 1.0 and the least value greater than 1.0 that is representable as a float. +# https://docs.python.org/3/library/sys.html#sys.float_info +EPS: Final[float] = sys.float_info.epsilon + # 3.14159 ... PI: Final[float] = math.pi From f7d708dd2926be1731e795f9b892da027ed4c475 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 24 Mar 2022 23:33:36 +0000 Subject: [PATCH 02/81] Small fixes. --- src/av2/datasets/tbv/splits.py | 1 - src/av2/evaluation/detection/eval.py | 8 +++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/src/av2/datasets/tbv/splits.py b/src/av2/datasets/tbv/splits.py index 358814b2..253274cb 100644 --- a/src/av2/datasets/tbv/splits.py +++ b/src/av2/datasets/tbv/splits.py @@ -1,4 +1,3 @@ # """Splits for the Trust, but Verify (Argoverse 2 Map Change) dataset.""" - diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index aabe3b34..e4854c53 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -111,11 +111,13 @@ def evaluate( gts_processed: pd.DataFrame = pd.concat(gts_list).reset_index() # Compute summary metrics. - summary_metrics = summarize_metrics(dts_processed, gts_processed, cfg).round(NUM_DECIMALS) - summary_metrics.loc["AVERAGE_METRICS"] = summary_metrics.mean().round(NUM_DECIMALS) + metrics = summarize_metrics(dts_processed, gts_processed, cfg) + metrics.loc[:, "AOE"] = np.rad2deg(metrics["AOE"]) + metrics.loc["AVERAGE_METRICS"] = metrics.mean() + metrics = metrics.round(NUM_DECIMALS) # Return results. - return dts, gts, summary_metrics + return dts, gts, metrics def summarize_metrics( From 74d0eb0f15ba3f04677ac64d816b4308c4f18575 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Fri, 25 Mar 2022 13:35:31 +0000 Subject: [PATCH 03/81] Add unit tests + eval mask fixes. --- src/av2/evaluation/detection/eval.py | 5 +- src/av2/evaluation/detection/utils.py | 23 +- .../detection/data/detections.feather | Bin 0 -> 7258 bytes .../data/detections_assignment.feather | Bin 0 -> 7274 bytes .../data/detections_identity.feather | Bin 0 -> 7234 bytes .../evaluation/detection/data/labels.feather | Bin 0 -> 7234 bytes tests/evaluation/detection/test_eval.py | 443 ++++++++++++++++++ 7 files changed, 450 insertions(+), 21 deletions(-) create mode 100644 tests/evaluation/detection/data/detections.feather create mode 100644 tests/evaluation/detection/data/detections_assignment.feather create mode 100644 tests/evaluation/detection/data/detections_identity.feather create mode 100644 tests/evaluation/detection/data/labels.feather create mode 100644 tests/evaluation/detection/test_eval.py diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index e4854c53..7a9c5ca5 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -60,6 +60,7 @@ from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision from av2.utils.typing import NDArrayBool, NDArrayFloat +from joblib import delayed, Parallel logger = logging.getLogger(__name__) @@ -104,7 +105,7 @@ def evaluate( args_list = [(dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg, poses) for uuid in uuids] # Accumulate and gather the processed detections and ground truth annotations. - dts_list, gts_list = zip(*[accumulate(*x) for x in args_list]) + dts_list, gts_list = zip(*Parallel(n_jobs=-1)(delayed(accumulate)(*x) for x in args_list)) # Concatenate the detections and ground truth annotations into DataFrames. dts_processed: pd.DataFrame = pd.concat(dts_list).reset_index(drop=True) @@ -112,7 +113,7 @@ def evaluate( # Compute summary metrics. metrics = summarize_metrics(dts_processed, gts_processed, cfg) - metrics.loc[:, "AOE"] = np.rad2deg(metrics["AOE"]) + metrics.loc[:, "AOE"] = np.rad2deg(metrics["AOE"]) # Report orientation error in degrees. metrics.loc["AVERAGE_METRICS"] = metrics.mean() metrics = metrics.round(NUM_DECIMALS) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 1e4a24ff..28490dd6 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -82,6 +82,7 @@ class DetectionCfg: MAX_YAW_ERROR, MIN_CDS, ) + max_num_dts_per_category: int = 100 eval_only_roi_instances: bool = True splits: Tuple[str, ...] = ("val",) @@ -105,22 +106,7 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. - - Raises: - ValueError: If all of the evaluation columns are not present in the detections or the - ground truth labels. """ - required_dts_cols = set(x.value for x in EvaluationColumns) - required_gts_cols = set(tuple(x.value for x in EvaluationColumns) + ("num_interior_points",)) - - missing_dts_cols = required_dts_cols - set(dts.columns) - missing_gts_cols = required_gts_cols - set(gts.columns) - - # if len(missing_dts_cols) > 0: - # raise ValueError(f"Missing the following columns in your detections: {missing_dts_cols}!") - # if len(missing_gts_cols) > 0: - # raise ValueError(f"Missing the following columns in your ground truth labels: {missing_gts_cols}!") - dts.sort_values("score", ascending=False, inplace=True) dts.reset_index(drop=True, inplace=True) @@ -399,11 +385,10 @@ def compute_evaluated_cuboids_mask( Raises: ValueError: If all of the columns aren't in the cuboids table. 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interpolate_precision, +) +from av2.geometry.geometry import wrap_angles +from av2.geometry.iou import iou_3d_axis_aligned +from av2.utils.constants import PI +from av2.utils.typing import NDArrayFloat + +TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" + +TRANSLATION_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m"] +DIMS_COLS: Final[List[str]] = ["length_m", "width_m", "height_m"] +QUAT_COLS: Final[List[str]] = ["qw", "qx", "qy", "qz"] +ANNO_COLS: Final[List[str]] = ["timestamp_ns", "category"] + DIMS_COLS + QUAT_COLS + TRANSLATION_COLS + + +def _get_summary_identity() -> pd.DataFrame: + """Define an evaluator that compares a set of results to itself.""" + detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections_identity.feather") + _, _, summary = evaluate(dts, dts, detection_cfg) + return summary + + +def _get_summary_assignment() -> pd.DataFrame: + """Define an evaluator that compares a set of results to one with an extra detection to check assignment.""" + detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections_assignment.feather") + gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") + _, _, summary = evaluate(dts, gts, detection_cfg) + return summary + + +def _get_summary() -> pd.DataFrame: + """Get a dummy summary.""" + detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections.feather") + gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") + _, _, summary = evaluate(dts, gts, detection_cfg) + return summary + + +def test_affinity_center() -> None: + """Initialize a detection and a ground truth label. + + Verify that calculated distance matches expected affinity under the specified `AffFnType`. + """ + columns = DIMS_COLS + QUAT_COLS + TRANSLATION_COLS + dts = pd.DataFrame([[5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], columns=columns) + gts = pd.DataFrame([[5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0]], columns=columns) + + expected_result = -5.0 + assert compute_affinity_matrix(dts, gts, AffinityType.CENTER) == expected_result + + +def test_translation_distance() -> None: + """Initialize a detection and a ground truth label with only translation parameters. + + Verify that calculated distance matches expected distance under the specified `DistFnType`. + """ + columns = TRANSLATION_COLS + dts = pd.DataFrame([[0.0, 0.0, 0.0]], columns=columns) + gts = pd.DataFrame([[5.0, 5.0, 5.0]], columns=columns) + + expected_result: float = np.sqrt(25 + 25 + 25) + assert np.allclose(distance(dts, gts, DistanceType.TRANSLATION), expected_result) + + +def test_scale_distance() -> None: + """Initialize a detection and a ground truth label with only shape parameters. + + Verify that calculated scale error matches the expected value. + + NOTE: We only provide shape parameters due to alignment assumption. + """ + columns = DIMS_COLS + dts = pd.DataFrame([[5.0, 5.0, 5.0]], columns=columns) + gts = pd.DataFrame([[10.0, 10.0, 10.0]], columns=columns) + + expected_result: float = 1 - 0.125 + assert np.allclose(distance(dts, gts, DistanceType.SCALE), expected_result) + + +def test_orientation_quarter_angles() -> None: + """Initialize a detection and a ground truth label with only orientation parameters. + + Verify that calculated orientation error matches the expected smallest angle ((2 * PI) / 4) + between the detection and ground truth label. + """ + # Check all of the 90 degree angles + expected_result: float = (2 * PI) / 4 + quarter_angles: List[NDArrayFloat] = [np.array([0, 0, angle]) for angle in np.arange(0, 2 * PI, expected_result)] + + for i in range(len(quarter_angles) - 1): + quat_xyzw_dts = Rotation.from_rotvec(quarter_angles[i : i + 1]).as_quat() + quat_xyzw_gts = Rotation.from_rotvec(quarter_angles[i + 1 : i + 2]).as_quat() + + quat_wxyz_dts = quat_xyzw_dts[..., [3, 0, 1, 2]] + quat_wxyz_gts = quat_xyzw_gts[..., [3, 0, 1, 2]] + + columns = QUAT_COLS + dts = pd.DataFrame(quat_wxyz_dts, columns=columns) + gts = pd.DataFrame(quat_wxyz_gts, columns=columns) + + assert np.isclose(distance(dts, gts, DistanceType.ORIENTATION), expected_result) + assert np.isclose(distance(gts, dts, DistanceType.ORIENTATION), expected_result) + + +def test_orientation_eighth_angles() -> None: + """Initialize a detection and a ground truth label with only orientation parameters. + + Verify that calculated orientation error matches the expected smallest angle ((2 * PI) / 8) + between the detection and ground truth label. + """ + expected_result: float = (2 * PI) / 8 + eigth_angles: List[NDArrayFloat] = [np.array([0, 0, angle]) for angle in np.arange(0, 2 * PI, expected_result)] + + for i in range(len(eigth_angles) - 1): + quat_xyzw_dts = Rotation.from_rotvec(eigth_angles[i : i + 1]).as_quat() + quat_xyzw_gts = Rotation.from_rotvec(eigth_angles[i + 1 : i + 2]).as_quat() + + quat_wxyz_dts = quat_xyzw_dts[..., [3, 0, 1, 2]] + quat_wxyz_gts = quat_xyzw_gts[..., [3, 0, 1, 2]] + + columns = QUAT_COLS + dts = pd.DataFrame(quat_wxyz_dts, columns=columns) + gts = pd.DataFrame(quat_wxyz_gts, columns=columns) + + assert np.isclose(distance(dts, gts, DistanceType.ORIENTATION), expected_result) + assert np.isclose(distance(gts, dts, DistanceType.ORIENTATION), expected_result) + + +def test_wrap_angle() -> None: + """Test mapping angles to a fixed interval.""" + theta: NDArrayFloat = np.array([-3 * PI / 2]) + expected_result: NDArrayFloat = np.array([PI / 2]) + assert np.isclose(wrap_angles(theta), expected_result) + + +def test_accumulate() -> None: + """Verify that the accumulate function matches known output for a self-comparison.""" + cfg = DetectionCfg(eval_only_roi_instances=False) + gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") + + for _, group in gts.groupby(["log_id", "timestamp_ns"]): + job = (group, group, cfg) + dts, gts = accumulate(*job) + + # Check that there's a true positive under every threshold. + assert np.all(dts.loc[:, cfg.affinity_thresholds_m]) + + # Check that all error metrics are zero. + assert (dts.loc[:, tuple(x.value for x in TruePositiveErrorNames)] == 0).all(axis=None) + + # Check that there are 2 regular vehicles. + assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 + + # Check that there are no other labels. + assert gts["category"].value_counts().sum() == 2 + + +def test_assign() -> None: + """Verify that the assign functions as expected by checking ATE of assigned detections against known distance.""" + cfg = DetectionCfg(eval_only_roi_instances=False) + + columns = DIMS_COLS + QUAT_COLS + TRANSLATION_COLS + ["score"] + + dts = pd.DataFrame( + [ + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 10.0, 10.0, 10.0, 1.0], + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 20.0, 20.0, 20.0, 1.0], + ], + columns=columns, + ) + + gts = pd.DataFrame( + [ + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, -10.0, -10.0, -10.0, 1.0], + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 1.0], + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 10.1, 10.0, 10.0, 1.0], + ], + columns=columns, + ) + + metrics = assign(dts, gts, cfg) + # if these assign correctly, we should get an ATE of 0.1 for the first two + expected_result: float = 0.1 + ATE_COL_IDX = 4 + + assert math.isclose(metrics.iloc[0, ATE_COL_IDX], expected_result) # instance 0 + assert math.isclose(metrics.iloc[1, ATE_COL_IDX], expected_result) # instance 1 + assert math.isnan(metrics.iloc[2, ATE_COL_IDX]) # instance 32 + + +def test_interp() -> None: + """Test non-decreasing `interpolation` constraint enforced on precision results.""" + prec: NDArrayFloat = np.array([1.0, 0.5, 0.33, 0.5]) + expected_result: NDArrayFloat = np.array([1.0, 0.5, 0.5, 0.5]) + assert np.isclose(interpolate_precision(prec), expected_result).all() + + +def test_iou_aligned_3d() -> None: + """Initialize a detection and a ground truth label with only shape parameters. + + Verify that calculated intersection-over-union matches the expected + value between the detection and ground truth label. + + NOTE: We only provide shape parameters due to alignment assumption. + """ + columns = DIMS_COLS + dt_dims: NDArrayFloat = pd.DataFrame([[4.0, 10.0, 3.0]], columns=columns).to_numpy() + gt_dims: NDArrayFloat = pd.DataFrame([[9.0, 5.0, 2.0]], columns=columns).to_numpy() + + # Intersection is 40 = 4 * 5 * 2 (min of all dimensions). + # Union is the sum of the two volumes, minus intersection: 270 = (10 * 3 * 4) + (5 * 2 * 9) - 40. + expected_result: float = 40 / 270.0 + assert iou_3d_axis_aligned(dt_dims, gt_dims) == expected_result + + +def test_assignment() -> None: + """Verify that assignment works as expected; should have one duplicate in the provided results.""" + expected_result: float = 0.999 + assert _get_summary_assignment().loc["AVERAGE_METRICS", "AP"] == expected_result + + +def test_ap() -> None: + """Test that AP is 1 for the self-compared results.""" + expected_result: float = 1.0 + assert _get_summary_identity().loc["AVERAGE_METRICS", "AP"] == expected_result + + +def test_translation_error() -> None: + """Test that ATE is 0 for the self-compared results.""" + expected_result_identity: float = 0.0 + expected_result_det: float = 0.017 # 0.1 / 6, one of six dets is off by 0.1 + assert _get_summary_identity().loc["AVERAGE_METRICS", "ATE"] == expected_result_identity + assert _get_summary().loc["AVERAGE_METRICS", "ATE"] == expected_result_det + + +def test_scale_error() -> None: + """Test that ASE is 0 for the self-compared results.""" + expected_result_identity: float = 0.0 + expected_result_det: float = 0.033 # 0.2 / 6, one of six dets is off by 20% in IoU + assert _get_summary_identity().loc["AVERAGE_METRICS", "ASE"] == expected_result_identity + assert _get_summary().loc["AVERAGE_METRICS", "ASE"] == expected_result_det + + +def test_orientation_error() -> None: + """Test that AOE is 0 for the self-compared results.""" + expected_result_identity = 0.0 + expected_result_det = 0.524 # pi / 6, since one of six dets is off by pi + + assert _get_summary_identity().loc["AVERAGE_METRICS", "AOE"] == expected_result_identity + assert _get_summary().loc["AVERAGE_METRICS", "AOE"] == expected_result_det + + +# def test_filter_instances() -> None: +# """Generate 100 different detections and filter them based on Euclidean distance.""" +# columns = ["category"] + DIMS_COLS + QUAT_COLS + TRANSLATION_COLS + +# dts = pd.DataFrame( +# [["REGULAR_VEHICLE", 0.0, 0.0, 0.0, 0.0, 5.0, 2.0, 3.0, i, i, 0] for i in range(100)], +# columns=columns, +# ) +# cfg = DetectionCfg(eval_only_roi_instances=False) + +# expected_result: int = 71 +# assert len(is_filter(dts, cfg)) == expected_result + + +# def test_filter_objs_to_roi() -> None: +# """Use the map to filter out an object that lies outside the ROI in a parking lot.""" +# avm = av2Map() + +# # should be outside of ROI +# outside_obj = { +# "center": { +# "x": -14.102872067388489, +# "y": 19.466695178746022, +# "z": 0.11740010190455852, +# }, +# "rotation": { +# "x": 0.0, +# "y": 0.0, +# "z": -0.038991328555453404, +# "w": 0.9992395490058831, +# }, +# "length": 4.56126567460171, +# "width": 1.9370055686754908, +# "height": 1.5820081349372281, +# "track_label_uuid": "03a321bf955a4d7781682913884abf06", +# "timestamp": 315970611820366000, +# "label_class": "VEHICLE", +# } + +# # should be inside the ROI +# inside_obj = { +# "center": { +# "x": -20.727430239506702, +# "y": 3.4488006757501353, +# "z": 0.4036619561689685, +# }, +# "rotation": { +# "x": 0.0, +# "y": 0.0, +# "z": 0.0013102003738908123, +# "w": 0.9999991416871218, +# }, +# "length": 4.507580779458834, +# "width": 1.9243189627993598, +# "height": 1.629934978730058, +# "track_label_uuid": "bb0f40e4f68043e285d64a839f2f092c", +# "timestamp": 315970611820366000, +# "label_class": "VEHICLE", +# } + +# log_city_name = "PIT" +# lidar_ts = 315970611820366000 +# dataset_dir = TEST_DATA_LOC / "roi_based_test" +# log_id = "21e37598-52d4-345c-8ef9-03ae19615d3d" +# city_SE3_egovehicle = get_city_SE3_egovehicle_at_sensor_t(lidar_ts, dataset_dir, log_id) + +# dts = np.array([json_label_dict_to_obj_record(item) for item in [outside_obj, inside_obj]]) +# dts_filtered = filter_objs_to_roi(dts, avm, city_SE3_egovehicle, log_city_name) + +# assert dts_filtered.size == 1 +# assert dts_filtered.dtype == "O" # array of objects +# assert isinstance(dts_filtered, np.ndarray) +# assert dts_filtered[0].track_id == "bb0f40e4f68043e285d64a839f2f092c" + + +# def test_AP_on_filtered_instances() -> None: +# """Test AP calculation on instances filtered on region-of-interest.""" +# dt_fpath = TEST_DATA_LOC / "remove_nonroi_detections" +# gt_fpath = TEST_DATA_LOC / "remove_nonroi_ground_truth" +# fig_fpath = TEST_DATA_LOC / "test_figures" + +# cfg = DetectionConfig(eval_only_roi_instances=True) +# evaluator = DetectionEvaluator(dt_fpath, gt_fpath, fig_fpath, cfg) +# metrics = evaluator.evaluate() + +# assert metrics.AP.loc["Vehicle"] == 1.0 + + +# def test_rank() -> None: +# """Test ranking of detections and scores during detection evaluation.""" +# columns = [ +# "uuid", +# "length", +# "width", +# "height", +# "qw", +# "qx", +# "qy", +# "qz", +# "x", +# "y", +# "z", +# "score", +# ] +# dts = pd.DataFrame( +# [ +# [ +# "00000000-0000-0000-0000-000000000000", +# 5.0, +# 5.0, +# 5.0, +# 1.0, +# 0.0, +# 0.0, +# 0.0, +# 0.0, +# 0.0, +# 0.0, +# 0.7, +# ], +# [ +# "00000000-0000-0000-0000-000000000001", +# 5.0, +# 5.0, +# 5.0, +# 1.0, +# 0.0, +# 0.0, +# 0.0, +# 10.0, +# 10.0, +# 10.0, +# 0.9, +# ], +# [ +# "00000000-0000-0000-0000-000000000002", +# 5.0, +# 5.0, +# 5.0, +# 1.0, +# 0.0, +# 0.0, +# 0.0, +# 20.0, +# 20.0, +# 20.0, +# 0.8, +# ], +# ], +# columns=columns, +# ) + +# ranked_dts = rank(dts) +# uuids = ranked_dts["uuid"] +# expected_track_ids = np.array( +# [ +# "00000000-0000-0000-0000-000000000001", +# "00000000-0000-0000-0000-000000000002", +# "00000000-0000-0000-0000-000000000000", +# ] +# ) +# expected_scores = np.array([0.9, 0.8, 0.7]) +# assert np.all(uuids == expected_track_ids) and np.all(ranked_dts["score"] == expected_scores) From 5589bf51655f9c0b90b3c3a9a8c5a60f85ea4357 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Fri, 25 Mar 2022 14:19:55 +0000 Subject: [PATCH 04/81] Update detection unit tests. --- src/av2/evaluation/detection/eval.py | 3 +- src/av2/evaluation/detection/utils.py | 60 +++++++++++++----- .../evaluation/detection/data/labels.feather | Bin 7234 -> 7722 bytes tests/evaluation/detection/test_eval.py | 6 +- 4 files changed, 49 insertions(+), 20 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 7a9c5ca5..b187ccd2 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -56,11 +56,11 @@ import numpy as np import pandas as pd +from joblib import Parallel, delayed from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision from av2.utils.typing import NDArrayBool, NDArrayFloat -from joblib import delayed, Parallel logger = logging.getLogger(__name__) @@ -113,7 +113,6 @@ def evaluate( # Compute summary metrics. metrics = summarize_metrics(dts_processed, gts_processed, cfg) - metrics.loc[:, "AOE"] = np.rad2deg(metrics["AOE"]) # Report orientation error in degrees. metrics.loc["AVERAGE_METRICS"] = metrics.mean() metrics = metrics.round(NUM_DECIMALS) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 28490dd6..d884bd39 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -33,7 +33,7 @@ InterpType, TruePositiveErrorNames, ) -from av2.geometry.geometry import quat_to_mat, wrap_angles +from av2.geometry.geometry import mat_to_xyz, quat_to_mat, wrap_angles from av2.geometry.iou import iou_3d_axis_aligned from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap, RasterLayerType @@ -99,7 +99,7 @@ def accumulate( Args: dts: (N,len(EvaluationColumns)) Detections of shape. Must contain all of the columns in EvaluationColumns. gts: (M,len(EvaluationColumns) + 1) Ground truth labels. Must contain all of the columns in EvaluationColumns - and the `num_interior_points` column. + and the `num_interior_pts` column. cfg: Detection configuration. poses: (N,) Detections city egopose. avm: Argoverse map object. @@ -107,12 +107,14 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ + # avm = ArgoverseStaticMap.from_json() + dts.sort_values("score", ascending=False, inplace=True) dts.reset_index(drop=True, inplace=True) # Filter detections and ground truth annotations. - dts.loc[:, "is_evaluated"] = compute_evaluated_cuboids_mask(dts, cfg) - gts.loc[:, "is_evaluated"] = compute_evaluated_cuboids_mask(gts, cfg) + dts.loc[:, "is_evaluated"] = compute_evaluated_dts_mask(dts, cfg) + gts.loc[:, "is_evaluated"] = compute_evaluated_gts_mask(gts, cfg) if cfg.eval_only_roi_instances and avm is not None and poses is not None: dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, poses, avm) @@ -323,10 +325,10 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) return scale_errors elif metric == DistanceType.ORIENTATION: - dts_quats_xyzw = dts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_XYZW] - gts_quats_xyzw = gts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_XYZW] - yaws_dts: NDArrayFloat = Rotation.from_quat(dts_quats_xyzw).as_euler("zyx")[:, 0] - yaws_gts: NDArrayFloat = Rotation.from_quat(gts_quats_xyzw).as_euler("zyx")[:, 0] + dts_quats_xyzw = dts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() + gts_quats_xyzw = gts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() + yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts_quats_xyzw))[..., 2] + yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts_quats_xyzw))[..., 2] orientation_errors = wrap_angles(yaws_dts - yaws_gts) return orientation_errors else: @@ -365,8 +367,8 @@ def compute_objects_in_roi_mask( return is_within_roi -def compute_evaluated_cuboids_mask( - cuboids: pd.DataFrame, +def compute_evaluated_dts_mask( + dts: pd.DataFrame, cfg: DetectionCfg, ) -> NDArrayBool: """Compute the evaluated cuboids mask. @@ -376,7 +378,7 @@ def compute_evaluated_cuboids_mask( 2. The cuboid must have at _least_ one point in its interior. Args: - cuboids: Dataframes containing cuboids. + dts: Dataframes containing cuboids. cfg: Detection configuration object. Returns: @@ -385,10 +387,36 @@ def compute_evaluated_cuboids_mask( Raises: ValueError: If all of the columns aren't in the cuboids table. """ - is_not_empty: NDArrayBool = cuboids.loc[:, "num_interior_points"] > 0 - - norm: NDArrayFloat = np.linalg.norm(cuboids.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) + norm: NDArrayFloat = np.linalg.norm(dts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) is_within_radius: NDArrayBool = norm < cfg.max_range_m - is_evaluated: NDArrayBool = is_within_radius & is_not_empty - is_evaluated[:, cfg.max_num_dts_per_category :] = False # Limit the number of detections. + is_evaluated: NDArrayBool = is_within_radius + is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. + return is_evaluated + + +def compute_evaluated_gts_mask( + gts: pd.DataFrame, + cfg: DetectionCfg, +) -> NDArrayBool: + """Compute the evaluated cuboids mask. + + Valid cuboids meet _two_ conditions: + 1. The cuboid's centroid (x,y,z) must lie within the maximum range in the detection configuration. + 2. The cuboid must have at _least_ one point in its interior. + + Args: + gts: Dataframes containing ground truth cuboids. + cfg: Detection configuration object. + + Returns: + The boolean mask indicating which cuboids will be evaluated. + + Raises: + ValueError: If all of the columns aren't in the cuboids table. + """ + norm: NDArrayFloat = np.linalg.norm(gts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) + is_valid_radius: NDArrayBool = norm < cfg.max_range_m + is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"] > 0 + + is_evaluated: NDArrayBool = is_valid_radius return is_evaluated diff --git a/tests/evaluation/detection/data/labels.feather b/tests/evaluation/detection/data/labels.feather index 7ca96dbc9ef89fafaf56ac6a52c8985045815480..6374178040fdb30258b3f2d928e07187ab7362cc 100644 GIT binary patch delta 667 zcmX?PvC4+eF(}AC+>n9cKM)vjPvp~MJTuW&pKra z#TS$mE9odGrDdk(q{QbX=B6rHDJY?!Q^)mhV^E826~1{wLnb;iFqlB z#qnjSMa7x3wQ0C4~iO91f;Mh1p0K->YuF+gkp z#4JF31*m=r5SIY43rH^z`~3a?pMimq31pTER18QdfelA@Ay9(h1y}J#$?Z&& zYq&TVXH0J8Qf7QGc_o)LW5nd6T=I;{o1bzCGpVisIu04Y_$Ek#0+avpe4Z@8E3o+k z?+WJ083O$BvOsBJ21W*9Mh*sHCLm@8VipdD28g!FeS%hk4%`sV0;m-aCf^j4ADMB$ L6#)zjNO}YS!a<;P delta 313 zcmZ2wbI5|vF(}AC+>n9cKM)jfP2|&K^qH(Etj{R6IZ*g0+vMp|hOFj#26_gQ&q_(V z87kE>Fz_=lFdSiGU}yp25Fi!+;v0+%3@d=R1c*I=SO$ncfYbr;6d+CkViO={0pgCy z4>`m)FW^|h#F#PJm0Ou{#pFzGX-135t=#gAikp{m3p23-l`-&7zRUZ0^8>yW%##lY h83;CTfdm;C3Ya1Eiph+^@^o|T944+AKnFlV3jjG}L1q8| diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 55331509..aca8dcdc 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -41,7 +41,9 @@ def _get_summary_identity() -> pd.DataFrame: """Define an evaluator that compares a set of results to itself.""" detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections_identity.feather") - _, _, summary = evaluate(dts, dts, detection_cfg) + gts: pd.DataFrame = dts.copy() + gts.loc[:, "num_interior_pts"] = [1, 1, 1, 1, 1, 1] + _, _, summary = evaluate(dts, gts, detection_cfg) return summary @@ -213,7 +215,7 @@ def test_assign() -> None: assert math.isclose(metrics.iloc[0, ATE_COL_IDX], expected_result) # instance 0 assert math.isclose(metrics.iloc[1, ATE_COL_IDX], expected_result) # instance 1 - assert math.isnan(metrics.iloc[2, ATE_COL_IDX]) # instance 32 + assert math.isclose(metrics.iloc[2, ATE_COL_IDX], 2.0) # instance 32 def test_interp() -> None: From 244100d78271f712ac03f2316f76c5be545cfd4f Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Fri, 25 Mar 2022 14:33:18 +0000 Subject: [PATCH 05/81] Fix typing + update pyproject. --- pyproject.toml | 14 ++++++++++++++ src/av2/evaluation/detection/eval.py | 17 +++++++++-------- src/av2/evaluation/detection/utils.py | 3 +-- .../evaluation/detection/data/labels.feather | Bin 7722 -> 7722 bytes 4 files changed, 24 insertions(+), 10 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6e1524f7..5076ff26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,3 +22,17 @@ ignore_missing_imports = true disallow_untyped_decorators = false plugins = "numpy.typing.mypy_plugin" strict = true + +[tool.pyright] +include = ["src"] + +reportMissingTypeStubs = false +reportUnknownMemberType = false +reportUntypedFunctionDecorator = false + +[tool.pytest.ini_options] +minversion = "6.0" +addopts = "--cov-report term-missing:skip-covered --cov src/av2" +testpaths = [ + "tests", +] \ No newline at end of file diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index b187ccd2..74b77861 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,7 +52,7 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Optional, Tuple +from typing import List, Optional, Tuple import numpy as np import pandas as pd @@ -87,6 +87,7 @@ def evaluate( Raises: ValueError: If ROI filtering is enabled, but no egoposes are provided. + RuntimeError: If parallel processing fails to complete. """ if cfg.eval_only_roi_instances and poses is None: raise ValueError("Poses must be provided for ROI cuboid filtering!") @@ -105,7 +106,13 @@ def evaluate( args_list = [(dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg, poses) for uuid in uuids] # Accumulate and gather the processed detections and ground truth annotations. - dts_list, gts_list = zip(*Parallel(n_jobs=-1)(delayed(accumulate)(*x) for x in args_list)) + results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel(n_jobs=-1)( + delayed(accumulate)(*x) for x in args_list + ) + if results is None: + raise RuntimeError("Accumulation of dts and gts has failed!") + + dts_list, gts_list = zip(*results) # Concatenate the detections and ground truth annotations into DataFrames. dts_processed: pd.DataFrame = pd.concat(dts_list).reset_index(drop=True) @@ -115,8 +122,6 @@ def evaluate( metrics = summarize_metrics(dts_processed, gts_processed, cfg) metrics.loc["AVERAGE_METRICS"] = metrics.mean() metrics = metrics.round(NUM_DECIMALS) - - # Return results. return dts, gts, metrics @@ -167,10 +172,6 @@ def summarize_metrics( for affinity_threshold_m in cfg.affinity_thresholds_m: true_positives: NDArrayBool = category_dts[affinity_threshold_m].to_numpy() - # NaN values correspond to detections that weren't true positives. - # is_valid = ~np.isnan(true_positives) - # true_positives = true_positives[is_valid].astype(bool) - # Continue if there aren't any true positives. if len(true_positives) == 0: continue diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index d884bd39..9e0103d0 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -417,6 +417,5 @@ def compute_evaluated_gts_mask( norm: NDArrayFloat = np.linalg.norm(gts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) is_valid_radius: NDArrayBool = norm < cfg.max_range_m is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"] > 0 - - is_evaluated: NDArrayBool = is_valid_radius + is_evaluated: NDArrayBool = is_valid_radius & is_valid_num_points return is_evaluated diff --git a/tests/evaluation/detection/data/labels.feather b/tests/evaluation/detection/data/labels.feather index 6374178040fdb30258b3f2d928e07187ab7362cc..4ecfe8773fcccbac344208a7ee74200d79e184e3 100644 GIT binary patch delta 48 zcmZ2wv&v?}1ztw8%@=u(vB=0XFfb@FGB7d-F|ji!Gca>72(th&D+faZ+vd$e<=g-m Cl?hV- delta 48 zcmZ2wv&v?}1ztvz%@=u(vB=0WFfa%+Ffs@;axe%p0WmWWvv4pp0L3?N7Aofk02X@* ACjbBd From 5314ee49124733912a97b8c5b4b86338b8629eb6 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Fri, 25 Mar 2022 14:35:40 +0000 Subject: [PATCH 06/81] Run autoflake. --- src/av2/evaluation/detection/utils.py | 5 ++--- tutorials/generate_egoview_overlaid_vector_map.py | 1 - tutorials/untar_tbv.py | 1 - 3 files changed, 2 insertions(+), 5 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 9e0103d0..d2d77ac3 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -4,7 +4,7 @@ Accepts detections (in Argoverse ground truth format) and ground truth labels for computing evaluation metrics for 3d object detection. We have five different, -metrics: mAP, ATE, ASE, AOE, and DCS. A true positive for mAP is defined as the +metrics: mAP, ATE, ASE, AOE, and CDS. A true positive for mAP is defined as the highest confidence prediction within a specified Euclidean distance threshold from a bird's-eye view. We prefer these metrics instead of IoU due to the increased interpretability of the error modes in a set of detections. @@ -17,7 +17,6 @@ import numpy as np import pandas as pd from scipy.spatial.distance import cdist -from scipy.spatial.transform import Rotation from av2.evaluation.detection.constants import ( MAX_NORMALIZED_ASE, @@ -38,7 +37,7 @@ from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList -from av2.utils.constants import EPS, NAN +from av2.utils.constants import EPS from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt logger = logging.getLogger(__name__) diff --git a/tutorials/generate_egoview_overlaid_vector_map.py b/tutorials/generate_egoview_overlaid_vector_map.py index 057ed7db..8d43339d 100644 --- a/tutorials/generate_egoview_overlaid_vector_map.py +++ b/tutorials/generate_egoview_overlaid_vector_map.py @@ -9,7 +9,6 @@ import os import sys import time -from enum import Enum from pathlib import Path from typing import Final, List, Tuple diff --git a/tutorials/untar_tbv.py b/tutorials/untar_tbv.py index 10fc4d76..df564359 100644 --- a/tutorials/untar_tbv.py +++ b/tutorials/untar_tbv.py @@ -7,7 +7,6 @@ from typing import Final, Optional, Tuple import click -import joblib from joblib import Parallel, delayed NUM_TBV_SHARDS: Final[int] = 21 From 4c0e6bbcbbc4f7e2760ed48c8266fb835c0ca35c Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 26 Mar 2022 00:28:19 +0000 Subject: [PATCH 07/81] Add ROI pruning. --- src/av2/evaluation/detection/eval.py | 7 +----- src/av2/evaluation/detection/utils.py | 31 +++++++++++++------------ src/av2/structures/cuboid.py | 33 +++++++++++++++++++++++++++ 3 files changed, 51 insertions(+), 20 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 74b77861..b089a4ad 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -69,7 +69,6 @@ def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, - poses: Optional[pd.DataFrame] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: """Evaluate a set of detections against the ground truth annotations. @@ -86,12 +85,8 @@ def evaluate( K refers to the number of evaluation metrics. Raises: - ValueError: If ROI filtering is enabled, but no egoposes are provided. RuntimeError: If parallel processing fails to complete. """ - if cfg.eval_only_roi_instances and poses is None: - raise ValueError("Poses must be provided for ROI cuboid filtering!") - # (log_id, timestamp_ns) uniquely identifies a sweep. uuid_columns = ["log_id", "timestamp_ns"] @@ -103,7 +98,7 @@ def evaluate( uuids = gts.index.unique().tolist() # Construct arguments for multiprocessing. - args_list = [(dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg, poses) for uuid in uuids] + args_list = [(dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg) for uuid in uuids] # Accumulate and gather the processed detections and ground truth annotations. results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel(n_jobs=-1)( diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index d2d77ac3..896f6065 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -12,6 +12,8 @@ import logging from dataclasses import dataclass +from optparse import Option +from pathlib import Path from typing import Optional, Tuple import numpy as np @@ -38,6 +40,7 @@ from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList from av2.utils.constants import EPS +from av2.utils.io import read_city_SE3_ego from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt logger = logging.getLogger(__name__) @@ -62,6 +65,7 @@ class DetectionCfg: splits: Tuple of split names to evaluate. """ + dataset_dir: Optional[Path] = None affinity_thresholds_m: Tuple[float, ...] = (0.5, 1.0, 2.0, 4.0) # Meters affinity_type: AffinityType = AffinityType.CENTER num_recall_samples: int = 101 @@ -90,7 +94,6 @@ def accumulate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, - poses: Optional[pd.DataFrame] = None, avm: Optional[ArgoverseStaticMap] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. @@ -100,22 +103,24 @@ def accumulate( gts: (M,len(EvaluationColumns) + 1) Ground truth labels. Must contain all of the columns in EvaluationColumns and the `num_interior_pts` column. cfg: Detection configuration. - poses: (N,) Detections city egopose. avm: Argoverse map object. Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ - # avm = ArgoverseStaticMap.from_json() - dts.sort_values("score", ascending=False, inplace=True) dts.reset_index(drop=True, inplace=True) # Filter detections and ground truth annotations. dts.loc[:, "is_evaluated"] = compute_evaluated_dts_mask(dts, cfg) gts.loc[:, "is_evaluated"] = compute_evaluated_gts_mask(gts, cfg) + if cfg.eval_only_roi_instances: + log_id = gts.log_id.unique().item() + log_dir = cfg.dataset_dir / log_id + avm_dir = log_dir / "map" + avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) - if cfg.eval_only_roi_instances and avm is not None and poses is not None: + poses = read_city_SE3_ego(log_dir) dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, poses, avm) # Initialize corresponding assignments + errors. @@ -348,21 +353,19 @@ def compute_objects_in_roi_mask( The boolean mask indicating which cuboids will be evaluated. """ cuboids_dataframe = cuboids_dataframe.sort_values("timestamp_ns").reset_index(drop=True) - poses = poses.sort_values("timestamp_ns").reset_index(drop=True) cuboid_list_ego = CuboidList.from_dataframe(cuboids_dataframe) - timestamp_ns = poses.loc[:, "timestamp_ns"].unique() - quat_wxyz = poses.loc[timestamp_ns, EvaluationColumns.QUAT_COEFFICIENTS_WXYZ] - translation = poses.loc[timestamp_ns, EvaluationColumns.TRANSLATION_NAMES[:2]] - rotation = quat_to_mat(quat_wxyz) + timestamp_ns = cuboids_dataframe.timestamp_ns.unique().item() + city_SE3_ego = poses[timestamp_ns] - city_SE3_ego = SE3(rotation=rotation, translation=translation) cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) cuboid_list_vertices_m = cuboid_list_city.vertices_m - vertices_within_roi = avm.get_raster_layer_points_boolean(cuboid_list_vertices_m[..., :2], RasterLayerType.ROI) - vertices_within_roi = vertices_within_roi.reshape(-1, 4) - is_within_roi: NDArrayBool = vertices_within_roi.any(axis=1) + is_within_roi = avm.get_raster_layer_points_boolean( + cuboid_list_vertices_m.reshape(-1, 3)[..., :2], RasterLayerType.ROI + ) + is_within_roi = is_within_roi.reshape(-1, 8) + is_within_roi: NDArrayBool = is_within_roi.any(axis=1) return is_within_roi diff --git a/src/av2/structures/cuboid.py b/src/av2/structures/cuboid.py index 0dd6f867..8a6786d4 100644 --- a/src/av2/structures/cuboid.py +++ b/src/av2/structures/cuboid.py @@ -380,3 +380,36 @@ def from_feather(cls, annotations_feather_path: Path) -> CuboidList: ) cuboid_list.append(cuboid) return cls(cuboids=cuboid_list) + + @classmethod + def from_dataframe(cls, data: pd.DataFrame) -> CuboidList: + """Read annotations from a feather file. + + Args: + annotations_feather_path: Feather file path. + + Returns: + Constructed cuboids. + """ + rotation = quat_to_mat(data.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy()) + translation_m = data.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy() + length_m = data.loc[:, "length_m"].to_numpy() + width_m = data.loc[:, "width_m"].to_numpy() + height_m = data.loc[:, "height_m"].to_numpy() + category = data.loc[:, "category"].to_numpy() + timestamp_ns = data.loc[:, "timestamp_ns"].to_numpy() + N = len(data) + + cuboid_list: List[Cuboid] = [] + for i in range(N): + ego_SE3_object = SE3(rotation=rotation[i], translation=translation_m[i]) + cuboid = Cuboid( + dst_SE3_object=ego_SE3_object, + length_m=length_m[i], + width_m=width_m[i], + height_m=height_m[i], + category=category[i], + timestamp_ns=timestamp_ns[i], + ) + cuboid_list.append(cuboid) + return cls(cuboids=cuboid_list) From 8e598679a153e8633b7e61bdc1d4465f0e1b13aa Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 26 Mar 2022 00:44:33 +0000 Subject: [PATCH 08/81] Remove arg. --- src/av2/evaluation/detection/utils.py | 61 ++++++++++++++------------- 1 file changed, 32 insertions(+), 29 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 896f6065..c20faae1 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -36,11 +36,10 @@ ) from av2.geometry.geometry import mat_to_xyz, quat_to_mat, wrap_angles from av2.geometry.iou import iou_3d_axis_aligned -from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList from av2.utils.constants import EPS -from av2.utils.io import read_city_SE3_ego +from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt logger = logging.getLogger(__name__) @@ -53,48 +52,53 @@ class DetectionCfg: Args: affinity_thresholds_m: Affinity thresholds for determining a true positive (in meters). affinity_type: Type of affinity function to be used for calculating average precision. - num_recall_samples: Number of recall points to sample uniformly in [0, 1]. - tp_threshold_m: Center distance threshold for the true positive metrics (in meters). categories: Detection classes for evaluation. + eval_only_roi_instances: Only use dets and ground truth that lie within region of interest during eval. filter_metric: Detection metric to use for filtering of both detections and ground truth annotations. max_range_m: Max distance (under a specific metric in meters) for a detection or ground truth cuboid to be considered for evaluation. - tp_normalization_terms: Normalization constants for ATE, ASE, and AOE. - metrics_defaults: Evaluation summary default values. - eval_only_roi_instances: Only use dets and ground truth that lie within region of interest during eval. + num_recall_samples: Number of recall points to sample uniformly in [0, 1]. splits: Tuple of split names to evaluate. + tp_threshold_m: Center distance threshold for the true positive metrics (in meters). """ - dataset_dir: Optional[Path] = None - affinity_thresholds_m: Tuple[float, ...] = (0.5, 1.0, 2.0, 4.0) # Meters + affinity_thresholds_m: Tuple[float, ...] = (0.5, 1.0, 2.0, 4.0) affinity_type: AffinityType = AffinityType.CENTER - num_recall_samples: int = 101 - tp_threshold_m: float = 2.0 # Meters categories: Tuple[str, ...] = tuple(x.value for x in CompetitionCategories) + dataset_dir: Optional[Path] = None + eval_only_roi_instances: bool = True filter_metric: FilterMetricType = FilterMetricType.EUCLIDEAN - max_range_m: float = 200.0 # Meters - tp_normalization_terms: Tuple[float, ...] = ( - tp_threshold_m, - MAX_SCALE_ERROR, - MAX_YAW_ERROR, - ) - metrics_defaults: Tuple[float, ...] = ( - MIN_AP, - tp_threshold_m, - MAX_NORMALIZED_ASE, - MAX_YAW_ERROR, - MIN_CDS, - ) max_num_dts_per_category: int = 100 - eval_only_roi_instances: bool = True + max_range_m: float = 200.0 + num_recall_samples: int = 101 splits: Tuple[str, ...] = ("val",) + tp_threshold_m: float = 2.0 + + @property + def metric_defaults(self) -> Tuple[float, ...]: + """Return the evaluation summary default values.""" + return ( + MIN_AP, + self.tp_threshold_m, + MAX_NORMALIZED_ASE, + MAX_YAW_ERROR, + MIN_CDS, + ) + + @property + def tp_normalization_terms(self) -> Tuple[float, ...]: + """Return the normalization constants for ATE, ASE, and AOE.""" + return ( + self.tp_threshold_m, + MAX_SCALE_ERROR, + MAX_YAW_ERROR, + ) def accumulate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, - avm: Optional[ArgoverseStaticMap] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. @@ -103,7 +107,6 @@ def accumulate( gts: (M,len(EvaluationColumns) + 1) Ground truth labels. Must contain all of the columns in EvaluationColumns and the `num_interior_pts` column. cfg: Detection configuration. - avm: Argoverse map object. Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. @@ -340,7 +343,7 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr def compute_objects_in_roi_mask( - cuboids_dataframe: pd.DataFrame, poses: pd.DataFrame, avm: ArgoverseStaticMap + cuboids_dataframe: pd.DataFrame, poses: TimestampedCitySE3EgoPoses, avm: ArgoverseStaticMap ) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. @@ -365,7 +368,7 @@ def compute_objects_in_roi_mask( cuboid_list_vertices_m.reshape(-1, 3)[..., :2], RasterLayerType.ROI ) is_within_roi = is_within_roi.reshape(-1, 8) - is_within_roi: NDArrayBool = is_within_roi.any(axis=1) + is_within_roi = is_within_roi.any(axis=1) return is_within_roi From 343c219d2b04b92ed0bbb19744c21e67f53a946b Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 26 Mar 2022 01:10:19 +0000 Subject: [PATCH 09/81] Fix typo. --- src/av2/evaluation/detection/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index c20faae1..30403a99 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -75,7 +75,7 @@ class DetectionCfg: tp_threshold_m: float = 2.0 @property - def metric_defaults(self) -> Tuple[float, ...]: + def metrics_defaults(self) -> Tuple[float, ...]: """Return the evaluation summary default values.""" return ( MIN_AP, From 6a34f5bcff021912d8823c221c81f4f1f3e84a1f Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 28 Mar 2022 18:46:12 +0000 Subject: [PATCH 10/81] Speed up argoverse maps. --- src/av2/evaluation/detection/eval.py | 26 +++++++++++++++++++++----- src/av2/evaluation/detection/utils.py | 7 +++---- 2 files changed, 24 insertions(+), 9 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index b089a4ad..2f0f76d3 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,7 +52,7 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import List, Optional, Tuple +from typing import Dict, List, Optional, Tuple import numpy as np import pandas as pd @@ -60,11 +60,13 @@ from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision +from av2.map.map_api import ArgoverseStaticMap from av2.utils.typing import NDArrayBool, NDArrayFloat logger = logging.getLogger(__name__) +@profile def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -96,14 +98,28 @@ def evaluate( # Find unique list of uuid tuples. uuids = gts.index.unique().tolist() + log_ids = gts.index.get_level_values(level=0).unique() + + log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} + if cfg.eval_only_roi_instances: + log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} + for log_id in log_ids: + log_dir = cfg.dataset_dir / log_id + avm_dir = log_dir / "map" + avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) + log_id_to_avm[log_id] = avm # Construct arguments for multiprocessing. - args_list = [(dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg) for uuid in uuids] + args_list = [ + (dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg, log_id_to_avm.get(uuid[0], None)) + for uuid in uuids + ] # Accumulate and gather the processed detections and ground truth annotations. - results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel(n_jobs=-1)( - delayed(accumulate)(*x) for x in args_list - ) + # results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel(n_jobs=-1, backend="threading")( + # delayed(accumulate)(*x) for x in args_list + # ) + results = [accumulate(*x) for x in args_list] if results is None: raise RuntimeError("Accumulation of dts and gts has failed!") diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 30403a99..fff580e2 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -95,10 +95,12 @@ def tp_normalization_terms(self) -> Tuple[float, ...]: ) +@profile def accumulate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, + avm: Optional[ArgoverseStaticMap] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. @@ -117,12 +119,9 @@ def accumulate( # Filter detections and ground truth annotations. dts.loc[:, "is_evaluated"] = compute_evaluated_dts_mask(dts, cfg) gts.loc[:, "is_evaluated"] = compute_evaluated_gts_mask(gts, cfg) - if cfg.eval_only_roi_instances: + if cfg.eval_only_roi_instances and avm is not None: log_id = gts.log_id.unique().item() log_dir = cfg.dataset_dir / log_id - avm_dir = log_dir / "map" - avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) - poses = read_city_SE3_ego(log_dir) dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, poses, avm) From 23b2fec5f5bab8b685f080f7de161e4c05314b19 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 28 Mar 2022 19:05:53 +0000 Subject: [PATCH 11/81] Speed up evaluation. --- src/av2/evaluation/detection/eval.py | 25 +++++++++++++++++-------- src/av2/evaluation/detection/utils.py | 10 ++-------- 2 files changed, 19 insertions(+), 16 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 2f0f76d3..f4c71e3f 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -61,12 +61,12 @@ from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision from av2.map.map_api import ArgoverseStaticMap +from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego from av2.utils.typing import NDArrayBool, NDArrayFloat logger = logging.getLogger(__name__) -@profile def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -101,19 +101,28 @@ def evaluate( log_ids = gts.index.get_level_values(level=0).unique() log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} + timestamped_city_SE3_egoposes: TimestampedCitySE3EgoPoses = {} if cfg.eval_only_roi_instances: - log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} for log_id in log_ids: log_dir = cfg.dataset_dir / log_id avm_dir = log_dir / "map" avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) log_id_to_avm[log_id] = avm - - # Construct arguments for multiprocessing. - args_list = [ - (dts.loc[uuid].reset_index().copy(), gts.loc[uuid].reset_index().copy(), cfg, log_id_to_avm.get(uuid[0], None)) - for uuid in uuids - ] + timestamped_city_SE3_egoposes[log_id] = read_city_SE3_ego(log_dir) + + args_list = [] + for (log_id, timestamp_ns) in uuids: + sweep_dts = dts.loc[(log_id, timestamp_ns)].reset_index().copy() + sweep_gts = gts.loc[(log_id, timestamp_ns)].reset_index().copy() + + sweep_map = None + sweep_poses = None + if log_id in log_id_to_avm: + sweep_map = log_id_to_avm[log_id] + if log_id in timestamped_city_SE3_egoposes: + if timestamp_ns in timestamped_city_SE3_egoposes[log_id]: + sweep_poses = timestamped_city_SE3_egoposes[log_id][timestamp_ns] + args_list.append([sweep_dts, sweep_gts, cfg, sweep_map, sweep_poses]) # Accumulate and gather the processed detections and ground truth annotations. # results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel(n_jobs=-1, backend="threading")( diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index fff580e2..96b2f3f2 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -95,12 +95,12 @@ def tp_normalization_terms(self) -> Tuple[float, ...]: ) -@profile def accumulate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, avm: Optional[ArgoverseStaticMap] = None, + poses: Optional[TimestampedCitySE3EgoPoses] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. @@ -120,9 +120,6 @@ def accumulate( dts.loc[:, "is_evaluated"] = compute_evaluated_dts_mask(dts, cfg) gts.loc[:, "is_evaluated"] = compute_evaluated_gts_mask(gts, cfg) if cfg.eval_only_roi_instances and avm is not None: - log_id = gts.log_id.unique().item() - log_dir = cfg.dataset_dir / log_id - poses = read_city_SE3_ego(log_dir) dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, poses, avm) # Initialize corresponding assignments + errors. @@ -342,7 +339,7 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr def compute_objects_in_roi_mask( - cuboids_dataframe: pd.DataFrame, poses: TimestampedCitySE3EgoPoses, avm: ArgoverseStaticMap + cuboids_dataframe: pd.DataFrame, city_SE3_ego: TimestampedCitySE3EgoPoses, avm: ArgoverseStaticMap ) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. @@ -357,9 +354,6 @@ def compute_objects_in_roi_mask( cuboids_dataframe = cuboids_dataframe.sort_values("timestamp_ns").reset_index(drop=True) cuboid_list_ego = CuboidList.from_dataframe(cuboids_dataframe) - timestamp_ns = cuboids_dataframe.timestamp_ns.unique().item() - city_SE3_ego = poses[timestamp_ns] - cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) cuboid_list_vertices_m = cuboid_list_city.vertices_m From ae4271821cf861ba73b797a43f5e3351ee6d6aa9 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 28 Mar 2022 19:56:32 +0000 Subject: [PATCH 12/81] Small fixes. --- src/av2/evaluation/detection/eval.py | 26 +++++++++++++------------- src/av2/evaluation/detection/utils.py | 6 +++--- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index f4c71e3f..fb74bc7f 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -57,6 +57,7 @@ import numpy as np import pandas as pd from joblib import Parallel, delayed +from rich.progress import track from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision @@ -97,21 +98,21 @@ def evaluate( gts = gts.set_index(keys=uuid_columns).sort_index() # Find unique list of uuid tuples. - uuids = gts.index.unique().tolist() - log_ids = gts.index.get_level_values(level=0).unique() + uuids: List[Tuple[str, int]] = gts.index.unique().tolist() + log_ids: List[str] = gts.index.get_level_values(level=0).unique() log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} - timestamped_city_SE3_egoposes: TimestampedCitySE3EgoPoses = {} - if cfg.eval_only_roi_instances: + log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} + if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: for log_id in log_ids: log_dir = cfg.dataset_dir / log_id avm_dir = log_dir / "map" avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) log_id_to_avm[log_id] = avm - timestamped_city_SE3_egoposes[log_id] = read_city_SE3_ego(log_dir) + log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) args_list = [] - for (log_id, timestamp_ns) in uuids: + for (log_id, timestamp_ns) in track(uuids, "Loading maps ..."): sweep_dts = dts.loc[(log_id, timestamp_ns)].reset_index().copy() sweep_gts = gts.loc[(log_id, timestamp_ns)].reset_index().copy() @@ -119,16 +120,15 @@ def evaluate( sweep_poses = None if log_id in log_id_to_avm: sweep_map = log_id_to_avm[log_id] - if log_id in timestamped_city_SE3_egoposes: - if timestamp_ns in timestamped_city_SE3_egoposes[log_id]: - sweep_poses = timestamped_city_SE3_egoposes[log_id][timestamp_ns] + if log_id in log_id_to_timestamped_poses: + if timestamp_ns in log_id_to_timestamped_poses[log_id]: + sweep_poses = log_id_to_timestamped_poses[log_id][timestamp_ns] args_list.append([sweep_dts, sweep_gts, cfg, sweep_map, sweep_poses]) # Accumulate and gather the processed detections and ground truth annotations. - # results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel(n_jobs=-1, backend="threading")( - # delayed(accumulate)(*x) for x in args_list - # ) - results = [accumulate(*x) for x in args_list] + results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel( + n_jobs=-1, backend="multiprocessing", verbose=True + )(delayed(accumulate)(*x) for x in args_list) if results is None: raise RuntimeError("Accumulation of dts and gts has failed!") diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 96b2f3f2..7ccf02a1 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -12,7 +12,6 @@ import logging from dataclasses import dataclass -from optparse import Option from pathlib import Path from typing import Optional, Tuple @@ -36,6 +35,7 @@ ) from av2.geometry.geometry import mat_to_xyz, quat_to_mat, wrap_angles from av2.geometry.iou import iou_3d_axis_aligned +from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList from av2.utils.constants import EPS @@ -137,7 +137,7 @@ def accumulate( continue assignments = assign(category_dts, category_gts, cfg) - dts.loc[is_eval_dts, assignments.columns] = assign(category_dts, category_gts, cfg).to_numpy() + dts.loc[is_eval_dts, assignments.columns] = assignments.to_numpy() return dts, gts @@ -339,7 +339,7 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr def compute_objects_in_roi_mask( - cuboids_dataframe: pd.DataFrame, city_SE3_ego: TimestampedCitySE3EgoPoses, avm: ArgoverseStaticMap + cuboids_dataframe: pd.DataFrame, city_SE3_ego: SE3, avm: ArgoverseStaticMap ) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. From a02e45ced3ce5d5a0071c50f1ac86604537a1bcc Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 28 Mar 2022 21:27:54 +0000 Subject: [PATCH 13/81] Fix lint. --- src/av2/evaluation/detection/constants.py | 15 ++----- src/av2/evaluation/detection/eval.py | 1 - src/av2/evaluation/detection/utils.py | 51 +++++++++++------------ src/av2/structures/cuboid.py | 2 +- 4 files changed, 29 insertions(+), 40 deletions(-) diff --git a/src/av2/evaluation/detection/constants.py b/src/av2/evaluation/detection/constants.py index b6653fe8..73ac6747 100644 --- a/src/av2/evaluation/detection/constants.py +++ b/src/av2/evaluation/detection/constants.py @@ -26,7 +26,7 @@ @unique -class EvaluationColumns(str, Enum): +class AnnotationColumns(str, Enum): """Columns needed for evaluation in the input detection and ground truth dataframes.""" LOG_ID = "log_id" @@ -47,21 +47,19 @@ class EvaluationColumns(str, Enum): @classmethod @property def DIMENSION_NAMES(cls) -> Tuple[str, str, str]: + """Return the column names for the evaluation cuboids dataframes.""" return cls.LENGTH_M.value, cls.WIDTH_M.value, cls.HEIGHT_M.value - @classmethod - @property - def QUAT_COEFFICIENTS_XYZW(cls) -> Tuple[str, str, str, str]: - return cls.QX.value, cls.QY.value, cls.QZ.value, cls.QW.value - @classmethod @property def QUAT_COEFFICIENTS_WXYZ(cls) -> Tuple[str, str, str, str]: + """Return the quanternion coefficient names (scalar first) for the evaluation cuboids dataframes.""" return cls.QW.value, cls.QX.value, cls.QY.value, cls.QZ.value @classmethod @property def TRANSLATION_NAMES(cls) -> Tuple[str, str, str]: + """Return the translation component names for the evaluation cuboids dataframes.""" return cls.TX_M.value, cls.TY_M.value, cls.TZ_M.value @@ -121,11 +119,6 @@ class CompetitionCategories(str, Enum): WHEELED_RIDER = "WHEELED_RIDER" -@unique -class DimensionColumnNames(str, Enum): - pass - - @unique class AffinityType(str, Enum): """Affinity types for assigning detections to ground truth cuboids.""" diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index fb74bc7f..19b2345b 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -81,7 +81,6 @@ def evaluate( dts: (N,15) Table of detections. gts: (M,15) Table of ground truth annotations. cfg: Detection configuration. - poses: (K,8) Table of city egopose. Returns: (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 7ccf02a1..42a3e0ad 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -100,7 +100,7 @@ def accumulate( gts: pd.DataFrame, cfg: DetectionCfg, avm: Optional[ArgoverseStaticMap] = None, - poses: Optional[TimestampedCitySE3EgoPoses] = None, + city_SE3_ego: Optional[SE3] = None, ) -> Tuple[pd.DataFrame, pd.DataFrame]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. @@ -109,6 +109,8 @@ def accumulate( gts: (M,len(EvaluationColumns) + 1) Ground truth labels. Must contain all of the columns in EvaluationColumns and the `num_interior_pts` column. cfg: Detection configuration. + avm: Argoverse static map for the log. + city_SE3_ego: Egovehicle pose in the city reference frame. Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. @@ -119,8 +121,8 @@ def accumulate( # Filter detections and ground truth annotations. dts.loc[:, "is_evaluated"] = compute_evaluated_dts_mask(dts, cfg) gts.loc[:, "is_evaluated"] = compute_evaluated_gts_mask(gts, cfg) - if cfg.eval_only_roi_instances and avm is not None: - dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, poses, avm) + if cfg.eval_only_roi_instances and city_SE3_ego is not None and avm is not None: + dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) # Initialize corresponding assignments + errors. dts.loc[:, cfg.affinity_thresholds_m] = False @@ -152,8 +154,8 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr cfg: Detection configuration. Returns: - The (N,K+S) confusion table containing the true and false positives augmented with the true positive errors where - K is the number of thresholds and S is the number of true positive error names. + The (N,K+S) confusion table containing the true and false positives augmented with the true positive errors + where K is the number of thresholds and S is the number of true positive error names. """ # Construct all columns. cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) @@ -184,10 +186,10 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr # The affinity matrix is an N by M matrix of the detections and ground truth labels respectively. # We want to take the corresponding affinity for each of the initial assignments using `gt_matches`. # The following line grabs the max affinity for each detection to a ground truth label. - affinities: NDArrayFloat = np.take_along_axis(affinity_matrix.transpose(), idx_gts, axis=0)[0] + affinities: NDArrayFloat = np.take_along_axis(affinity_matrix.transpose(), idx_gts, axis=0)[0] # type: ignore # Find the indices of the _first_ detection assigned to each GT. - assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) + assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) # type: ignore idx_gts, idx_dts = assignments for i, threshold_m in enumerate(cfg.affinity_thresholds_m): @@ -240,8 +242,9 @@ def interpolate_precision(precision: NDArrayFloat, interpolation_method: InterpT Raises: NotImplementedError: If the interpolation method is not implemented. """ + precision_interpolated: NDArrayFloat if interpolation_method == InterpType.ALL: - precision_interpolated: NDArrayFloat = np.maximum.accumulate(precision[::-1])[::-1] + precision_interpolated = np.maximum.accumulate(precision[::-1])[::-1] else: raise NotImplementedError("This interpolation method is not implemented!") return precision_interpolated @@ -262,8 +265,8 @@ def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: Affini NotImplementedError: If the affinity metric is not implemented. """ if metric == AffinityType.CENTER: - dts_xy_m = dts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]] - gts_xy_m = gts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]] + dts_xy_m = dts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES[:2])] + gts_xy_m = gts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES[:2])] affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) else: raise NotImplementedError("This affinity metric is not implemented!") @@ -288,10 +291,10 @@ def compute_average_precision( cum_fns: NDArrayInt = num_gts - cum_tps # Compute precision. - precision = cum_tps / (cum_tps + cum_fps + EPS) + precision: NDArrayFloat = cum_tps / (cum_tps + cum_fps + EPS) # Compute recall. - recall = cum_tps / (cum_tps + cum_fns) + recall: NDArrayFloat = cum_tps / (cum_tps + cum_fns) # Interpolate precision -- VOC-style. precision = interpolate_precision(precision) @@ -318,18 +321,18 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr NotImplementedError: If the distance type is not supported. """ if metric == DistanceType.TRANSLATION: - dts_xyz_m: NDArrayFloat = dts.loc[:, EvaluationColumns.TRANSLATION_NAMES].to_numpy() - gts_xyz_m: NDArrayFloat = gts.loc[:, EvaluationColumns.TRANSLATION_NAMES].to_numpy() + dts_xyz_m: NDArrayFloat = dts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES)].to_numpy() + gts_xyz_m: NDArrayFloat = gts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES)].to_numpy() translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) return translation_errors elif metric == DistanceType.SCALE: - dts_lwh_m: NDArrayFloat = dts.loc[:, EvaluationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() - gts_lwh_m: NDArrayFloat = gts.loc[:, EvaluationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() + dts_lwh_m: NDArrayFloat = dts.loc[:, list(EvaluationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() + gts_lwh_m: NDArrayFloat = gts.loc[:, list(EvaluationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) return scale_errors elif metric == DistanceType.ORIENTATION: - dts_quats_xyzw = dts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() - gts_quats_xyzw = gts.loc[:, EvaluationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() + dts_quats_xyzw = dts.loc[:, list(EvaluationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() + gts_quats_xyzw = gts.loc[:, list(EvaluationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts_quats_xyzw))[..., 2] yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts_quats_xyzw))[..., 2] orientation_errors = wrap_angles(yaws_dts - yaws_gts) @@ -345,7 +348,7 @@ def compute_objects_in_roi_mask( Args: cuboids_dataframe: Dataframes containing cuboids. - poses: Poses of the cuboids in the city frame. + city_SE3_ego: Egovehicle pose in the city reference frame. avm: Argoverse map object. Returns: @@ -381,9 +384,6 @@ def compute_evaluated_dts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. - - Raises: - ValueError: If all of the columns aren't in the cuboids table. """ norm: NDArrayFloat = np.linalg.norm(dts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) is_within_radius: NDArrayBool = norm < cfg.max_range_m @@ -408,12 +408,9 @@ def compute_evaluated_gts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. - - Raises: - ValueError: If all of the columns aren't in the cuboids table. """ - norm: NDArrayFloat = np.linalg.norm(gts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) + norm: NDArrayFloat = np.linalg.norm(gts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES[:2])], axis=1) is_valid_radius: NDArrayBool = norm < cfg.max_range_m - is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"] > 0 + is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"].to_numpy() > 0 is_evaluated: NDArrayBool = is_valid_radius & is_valid_num_points return is_evaluated diff --git a/src/av2/structures/cuboid.py b/src/av2/structures/cuboid.py index 8a6786d4..70ab84a0 100644 --- a/src/av2/structures/cuboid.py +++ b/src/av2/structures/cuboid.py @@ -386,7 +386,7 @@ def from_dataframe(cls, data: pd.DataFrame) -> CuboidList: """Read annotations from a feather file. Args: - annotations_feather_path: Feather file path. + data: (N,12) Dataframe containing the cuboids and their respective parameters . Returns: Constructed cuboids. From 2f208d5e1c80d223e674b10da5ae7475f37a7ba5 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 28 Mar 2022 22:21:15 +0000 Subject: [PATCH 14/81] Small lint fixes. --- src/av2/evaluation/detection/utils.py | 37 +++++++++++++++------------ 1 file changed, 20 insertions(+), 17 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 42a3e0ad..80076dbe 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -26,9 +26,9 @@ MIN_AP, MIN_CDS, AffinityType, + AnnotationColumns, CompetitionCategories, DistanceType, - EvaluationColumns, FilterMetricType, InterpType, TruePositiveErrorNames, @@ -105,8 +105,8 @@ def accumulate( """Accumulate the true / false positives (boolean flags) and true positive errors for each class. Args: - dts: (N,len(EvaluationColumns)) Detections of shape. Must contain all of the columns in EvaluationColumns. - gts: (M,len(EvaluationColumns) + 1) Ground truth labels. Must contain all of the columns in EvaluationColumns + dts: (N,len(AnnotationColumns)) Detections of shape. Must contain all of the columns in AnnotationColumns. + gts: (M,len(AnnotationColumns) + 1) Ground truth labels. Must contain all of the columns in AnnotationColumns and the `num_interior_pts` column. cfg: Detection configuration. avm: Argoverse static map for the log. @@ -147,9 +147,9 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr """Attempt assignment of each detection to a ground truth label. Args: - dts: (N,23) Detections of shape. Must contain all columns in EvaluationColumns and the + dts: (N,23) Detections of shape. Must contain all columns in AnnotationColumns and the additional columns: (is_evaluated, *cfg.affinity_thresholds_m, *TruePositiveErrorNames). - gts: (M,23) Ground truth labels. Must contain all columns in EvaluationColumns and the + gts: (M,23) Ground truth labels. Must contain all columns in AnnotationColumns and the additional columns: (is_evaluated, *cfg.affinity_thresholds_m, *TruePositiveErrorNames). cfg: Detection configuration. @@ -265,8 +265,8 @@ def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: Affini NotImplementedError: If the affinity metric is not implemented. """ if metric == AffinityType.CENTER: - dts_xy_m = dts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES[:2])] - gts_xy_m = gts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES[:2])] + dts_xy_m = dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])] + gts_xy_m = gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])] affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) else: raise NotImplementedError("This affinity metric is not implemented!") @@ -300,7 +300,7 @@ def compute_average_precision( precision = interpolate_precision(precision) # Evaluate precision at different recalls. - precision_interpolated: NDArrayFloat = np.interp(recall_interpolated, recall, precision, right=0) + precision_interpolated: NDArrayFloat = np.interp(recall_interpolated, recall, precision, right=0) # type: ignore average_precision: float = np.mean(precision_interpolated) return average_precision, precision_interpolated @@ -321,18 +321,18 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr NotImplementedError: If the distance type is not supported. """ if metric == DistanceType.TRANSLATION: - dts_xyz_m: NDArrayFloat = dts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES)].to_numpy() - gts_xyz_m: NDArrayFloat = gts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES)].to_numpy() + dts_xyz_m: NDArrayFloat = dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES)].to_numpy() + gts_xyz_m: NDArrayFloat = gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES)].to_numpy() translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) return translation_errors elif metric == DistanceType.SCALE: - dts_lwh_m: NDArrayFloat = dts.loc[:, list(EvaluationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() - gts_lwh_m: NDArrayFloat = gts.loc[:, list(EvaluationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() + dts_lwh_m: NDArrayFloat = dts.loc[:, list(AnnotationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() + gts_lwh_m: NDArrayFloat = gts.loc[:, list(AnnotationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) return scale_errors elif metric == DistanceType.ORIENTATION: - dts_quats_xyzw = dts.loc[:, list(EvaluationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() - gts_quats_xyzw = gts.loc[:, list(EvaluationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() + dts_quats_xyzw = dts.loc[:, list(AnnotationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() + gts_quats_xyzw = gts.loc[:, list(AnnotationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts_quats_xyzw))[..., 2] yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts_quats_xyzw))[..., 2] orientation_errors = wrap_angles(yaws_dts - yaws_gts) @@ -385,7 +385,9 @@ def compute_evaluated_dts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - norm: NDArrayFloat = np.linalg.norm(dts.loc[:, EvaluationColumns.TRANSLATION_NAMES[:2]], axis=1) + norm: NDArrayFloat = np.linalg.norm( # type: ignore + dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])], axis=1 + ) is_within_radius: NDArrayBool = norm < cfg.max_range_m is_evaluated: NDArrayBool = is_within_radius is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. @@ -409,8 +411,9 @@ def compute_evaluated_gts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - norm: NDArrayFloat = np.linalg.norm(gts.loc[:, list(EvaluationColumns.TRANSLATION_NAMES[:2])], axis=1) - is_valid_radius: NDArrayBool = norm < cfg.max_range_m + norm: NDArrayFloat = np.linalg.norm( # type: ignore + gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])], axis=1 + ) is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"].to_numpy() > 0 is_evaluated: NDArrayBool = is_valid_radius & is_valid_num_points return is_evaluated From c7797a5d73babaadbb6483996be32fda43e9764f Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 29 Mar 2022 11:55:47 -0400 Subject: [PATCH 15/81] Fix filtering. --- src/av2/evaluation/detection/utils.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 80076dbe..b8093b39 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -414,6 +414,7 @@ def compute_evaluated_gts_mask( norm: NDArrayFloat = np.linalg.norm( # type: ignore gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])], axis=1 ) + is_within_radius: NDArrayBool = norm < cfg.max_range_m is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"].to_numpy() > 0 - is_evaluated: NDArrayBool = is_valid_radius & is_valid_num_points + is_evaluated: NDArrayBool = is_within_radius & is_valid_num_points return is_evaluated From 0b152485bd96953282191b2c227b94b64a1517c7 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 29 Mar 2022 12:23:26 -0400 Subject: [PATCH 16/81] Small fixes. --- src/av2/evaluation/detection/constants.py | 21 ++++----------------- src/av2/evaluation/detection/utils.py | 4 ++-- 2 files changed, 6 insertions(+), 19 deletions(-) diff --git a/src/av2/evaluation/detection/constants.py b/src/av2/evaluation/detection/constants.py index 73ac6747..98806642 100644 --- a/src/av2/evaluation/detection/constants.py +++ b/src/av2/evaluation/detection/constants.py @@ -44,23 +44,10 @@ class AnnotationColumns(str, Enum): TZ_M = "tz_m" SCORE = "score" - @classmethod - @property - def DIMENSION_NAMES(cls) -> Tuple[str, str, str]: - """Return the column names for the evaluation cuboids dataframes.""" - return cls.LENGTH_M.value, cls.WIDTH_M.value, cls.HEIGHT_M.value - - @classmethod - @property - def QUAT_COEFFICIENTS_WXYZ(cls) -> Tuple[str, str, str, str]: - """Return the quanternion coefficient names (scalar first) for the evaluation cuboids dataframes.""" - return cls.QW.value, cls.QX.value, cls.QY.value, cls.QZ.value - - @classmethod - @property - def TRANSLATION_NAMES(cls) -> Tuple[str, str, str]: - """Return the translation component names for the evaluation cuboids dataframes.""" - return cls.TX_M.value, cls.TY_M.value, cls.TZ_M.value + DIMENSION_NAMES = (LENGTH_M, WIDTH_M, HEIGHT_M) + TRANSLATION_NAMES = (TX_M, TY_M, TZ_M) + QUAT_COEFFICIENTS_WXYZ = (QW, QX, QY, QZ) + @unique diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index b8093b39..460d25b5 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -126,7 +126,7 @@ def accumulate( # Initialize corresponding assignments + errors. dts.loc[:, cfg.affinity_thresholds_m] = False - dts.loc[:, tuple(x.value for x in TruePositiveErrorNames)] = cfg.metrics_defaults[1:4] + dts.loc[:, [x.value for x in TruePositiveErrorNames]] = np.array(cfg.metrics_defaults[1:4]) for category in cfg.categories: is_eval_dts = np.logical_and(dts["category"] == category, dts["is_evaluated"]) is_eval_gts = np.logical_and(gts["category"] == category, gts["is_evaluated"]) @@ -323,7 +323,7 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr if metric == DistanceType.TRANSLATION: dts_xyz_m: NDArrayFloat = dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES)].to_numpy() gts_xyz_m: NDArrayFloat = gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES)].to_numpy() - translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) + translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) # type: ignore return translation_errors elif metric == DistanceType.SCALE: dts_lwh_m: NDArrayFloat = dts.loc[:, list(AnnotationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() From c7a66de43f9841019a2d266d765cc556241e5a7d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 29 Mar 2022 13:32:49 -0400 Subject: [PATCH 17/81] Fix enums. --- src/av2/evaluation/detection/constants.py | 26 ++++++++++++++++------- src/av2/evaluation/detection/utils.py | 12 +++++------ 2 files changed, 24 insertions(+), 14 deletions(-) diff --git a/src/av2/evaluation/detection/constants.py b/src/av2/evaluation/detection/constants.py index 98806642..0cd78963 100644 --- a/src/av2/evaluation/detection/constants.py +++ b/src/av2/evaluation/detection/constants.py @@ -3,7 +3,7 @@ """3D object detection evaluation constants.""" from enum import Enum, unique -from typing import Final, Tuple +from typing import Final, List, Tuple from av2.utils.constants import PI @@ -44,10 +44,24 @@ class AnnotationColumns(str, Enum): TZ_M = "tz_m" SCORE = "score" - DIMENSION_NAMES = (LENGTH_M, WIDTH_M, HEIGHT_M) - TRANSLATION_NAMES = (TX_M, TY_M, TZ_M) - QUAT_COEFFICIENTS_WXYZ = (QW, QX, QY, QZ) + @classmethod + @property + def TRANSLATION_NAMES(cls) -> List[str]: + return [cls.TX_M, cls.TY_M, cls.TZ_M] + @classmethod + @property + def DIMENSION_NAMES(cls) -> List[str]: + return [cls.LENGTH_M, cls.WIDTH_M, cls.HEIGHT_M] + + @classmethod + @property + def QUAT_COEFFICIENTS_WXYZ(cls) -> List[str]: + return [cls.QW, cls.QX, cls.QY, cls.QZ] + + # DIMENSION_NAMES = [LENGTH_M, WIDTH_M, HEIGHT_M] + # TRANSLATION_NAMES = [TX_M, TY_M, TZ_M] + # QUAT_COEFFICIENTS_WXYZ = [QW, QX, QY, QZ] @unique @@ -74,7 +88,6 @@ class MetricNames(str, Enum): class CompetitionCategories(str, Enum): """Sensor dataset annotation categories.""" - ANIMAL = "ANIMAL" ARTICULATED_BUS = "ARTICULATED_BUS" BICYCLE = "BICYCLE" BICYCLIST = "BICYCLIST" @@ -89,15 +102,12 @@ class CompetitionCategories(str, Enum): MOBILE_PEDESTRIAN_CROSSING_SIGN = "MOBILE_PEDESTRIAN_CROSSING_SIGN" MOTORCYCLE = "MOTORCYCLE" MOTORCYCLIST = "MOTORCYCLIST" - OFFICIAL_SIGNALER = "OFFICIAL_SIGNALER" PEDESTRIAN = "PEDESTRIAN" - RAILED_VEHICLE = "RAILED_VEHICLE" REGULAR_VEHICLE = "REGULAR_VEHICLE" SCHOOL_BUS = "SCHOOL_BUS" SIGN = "SIGN" STOP_SIGN = "STOP_SIGN" STROLLER = "STROLLER" - TRAFFIC_LIGHT_TRAILER = "TRAFFIC_LIGHT_TRAILER" TRUCK = "TRUCK" TRUCK_CAB = "TRUCK_CAB" VEHICULAR_TRAILER = "VEHICULAR_TRAILER" diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 460d25b5..7e455fae 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -321,18 +321,18 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr NotImplementedError: If the distance type is not supported. """ if metric == DistanceType.TRANSLATION: - dts_xyz_m: NDArrayFloat = dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES)].to_numpy() - gts_xyz_m: NDArrayFloat = gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES)].to_numpy() + dts_xyz_m: NDArrayFloat = dts.loc[:, AnnotationColumns.TRANSLATION_NAMES].to_numpy() + gts_xyz_m: NDArrayFloat = gts.loc[:, AnnotationColumns.TRANSLATION_NAMES].to_numpy() translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) # type: ignore return translation_errors elif metric == DistanceType.SCALE: - dts_lwh_m: NDArrayFloat = dts.loc[:, list(AnnotationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() - gts_lwh_m: NDArrayFloat = gts.loc[:, list(AnnotationColumns.DIMENSION_NAMES)].reset_index(drop=True).to_numpy() + dts_lwh_m: NDArrayFloat = dts.loc[:, AnnotationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() + gts_lwh_m: NDArrayFloat = gts.loc[:, AnnotationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) return scale_errors elif metric == DistanceType.ORIENTATION: - dts_quats_xyzw = dts.loc[:, list(AnnotationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() - gts_quats_xyzw = gts.loc[:, list(AnnotationColumns.QUAT_COEFFICIENTS_WXYZ)].to_numpy() + dts_quats_xyzw: NDArrayFloat = dts.loc[:, AnnotationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() + gts_quats_xyzw: NDArrayFloat = gts.loc[:, AnnotationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts_quats_xyzw))[..., 2] yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts_quats_xyzw))[..., 2] orientation_errors = wrap_angles(yaws_dts - yaws_gts) From 957e016ea12c945a2f52e4b3f9ef68c79db69fea Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 29 Mar 2022 13:34:49 -0400 Subject: [PATCH 18/81] Remove unused lines. --- src/av2/evaluation/detection/constants.py | 8 ++------ src/av2/evaluation/detection/utils.py | 6 +++--- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/src/av2/evaluation/detection/constants.py b/src/av2/evaluation/detection/constants.py index 0cd78963..55847d3a 100644 --- a/src/av2/evaluation/detection/constants.py +++ b/src/av2/evaluation/detection/constants.py @@ -3,12 +3,12 @@ """3D object detection evaluation constants.""" from enum import Enum, unique -from typing import Final, List, Tuple +from typing import Final, List from av2.utils.constants import PI MAX_SCALE_ERROR: Final[float] = 1.0 -MAX_YAW_ERROR: Final[float] = PI +MAX_YAW_RAD_ERROR: Final[float] = PI # Higher is better. MIN_AP: Final[float] = 0.0 @@ -59,10 +59,6 @@ def DIMENSION_NAMES(cls) -> List[str]: def QUAT_COEFFICIENTS_WXYZ(cls) -> List[str]: return [cls.QW, cls.QX, cls.QY, cls.QZ] - # DIMENSION_NAMES = [LENGTH_M, WIDTH_M, HEIGHT_M] - # TRANSLATION_NAMES = [TX_M, TY_M, TZ_M] - # QUAT_COEFFICIENTS_WXYZ = [QW, QX, QY, QZ] - @unique class TruePositiveErrorNames(str, Enum): diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 7e455fae..eae27246 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -22,7 +22,7 @@ from av2.evaluation.detection.constants import ( MAX_NORMALIZED_ASE, MAX_SCALE_ERROR, - MAX_YAW_ERROR, + MAX_YAW_RAD_ERROR, MIN_AP, MIN_CDS, AffinityType, @@ -81,7 +81,7 @@ def metrics_defaults(self) -> Tuple[float, ...]: MIN_AP, self.tp_threshold_m, MAX_NORMALIZED_ASE, - MAX_YAW_ERROR, + MAX_YAW_RAD_ERROR, MIN_CDS, ) @@ -91,7 +91,7 @@ def tp_normalization_terms(self) -> Tuple[float, ...]: return ( self.tp_threshold_m, MAX_SCALE_ERROR, - MAX_YAW_ERROR, + MAX_YAW_RAD_ERROR, ) From b8ab038295706065b74746cb501245467b28f601 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 29 Mar 2022 14:16:45 -0400 Subject: [PATCH 19/81] Mypy fixes. --- src/av2/evaluation/detection/constants.py | 37 ++--------------------- src/av2/evaluation/detection/utils.py | 28 ++++++++--------- 2 files changed, 16 insertions(+), 49 deletions(-) diff --git a/src/av2/evaluation/detection/constants.py b/src/av2/evaluation/detection/constants.py index 55847d3a..f0181adb 100644 --- a/src/av2/evaluation/detection/constants.py +++ b/src/av2/evaluation/detection/constants.py @@ -24,40 +24,9 @@ NUM_DECIMALS: Final[int] = 3 - -@unique -class AnnotationColumns(str, Enum): - """Columns needed for evaluation in the input detection and ground truth dataframes.""" - - LOG_ID = "log_id" - TIMESTAMP_NS = "timestamp_ns" - CATEGORY = "category" - LENGTH_M = "length_m" - WIDTH_M = "width_m" - HEIGHT_M = "height_m" - QW = "qw" - QX = "qx" - QY = "qy" - QZ = "qz" - TX_M = "tx_m" - TY_M = "ty_m" - TZ_M = "tz_m" - SCORE = "score" - - @classmethod - @property - def TRANSLATION_NAMES(cls) -> List[str]: - return [cls.TX_M, cls.TY_M, cls.TZ_M] - - @classmethod - @property - def DIMENSION_NAMES(cls) -> List[str]: - return [cls.LENGTH_M, cls.WIDTH_M, cls.HEIGHT_M] - - @classmethod - @property - def QUAT_COEFFICIENTS_WXYZ(cls) -> List[str]: - return [cls.QW, cls.QX, cls.QY, cls.QZ] +TRANSLATION_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m"] +DIMENSION_COLS: Final[List[str]] = ["length_m", "width_m", "height_m"] +QUAT_WXYZ_COLS: Final[List[str]] = ["qw", "qx", "qy", "qz"] @unique diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index eae27246..26a6adcc 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -20,13 +20,15 @@ from scipy.spatial.distance import cdist from av2.evaluation.detection.constants import ( + DIMENSION_COLS, MAX_NORMALIZED_ASE, MAX_SCALE_ERROR, MAX_YAW_RAD_ERROR, MIN_AP, MIN_CDS, + QUAT_WXYZ_COLS, + TRANSLATION_COLS, AffinityType, - AnnotationColumns, CompetitionCategories, DistanceType, FilterMetricType, @@ -265,8 +267,8 @@ def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: Affini NotImplementedError: If the affinity metric is not implemented. """ if metric == AffinityType.CENTER: - dts_xy_m = dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])] - gts_xy_m = gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])] + dts_xy_m = dts.loc[:, TRANSLATION_COLS[:2]] + gts_xy_m = gts.loc[:, TRANSLATION_COLS[:2]] affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) else: raise NotImplementedError("This affinity metric is not implemented!") @@ -321,18 +323,18 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr NotImplementedError: If the distance type is not supported. """ if metric == DistanceType.TRANSLATION: - dts_xyz_m: NDArrayFloat = dts.loc[:, AnnotationColumns.TRANSLATION_NAMES].to_numpy() - gts_xyz_m: NDArrayFloat = gts.loc[:, AnnotationColumns.TRANSLATION_NAMES].to_numpy() + dts_xyz_m: NDArrayFloat = dts.loc[:, TRANSLATION_COLS].to_numpy() + gts_xyz_m: NDArrayFloat = gts.loc[:, TRANSLATION_COLS].to_numpy() translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) # type: ignore return translation_errors elif metric == DistanceType.SCALE: - dts_lwh_m: NDArrayFloat = dts.loc[:, AnnotationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() - gts_lwh_m: NDArrayFloat = gts.loc[:, AnnotationColumns.DIMENSION_NAMES].reset_index(drop=True).to_numpy() + dts_lwh_m: NDArrayFloat = dts.loc[:, DIMENSION_COLS].reset_index(drop=True).to_numpy() + gts_lwh_m: NDArrayFloat = gts.loc[:, DIMENSION_COLS].reset_index(drop=True).to_numpy() scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) return scale_errors elif metric == DistanceType.ORIENTATION: - dts_quats_xyzw: NDArrayFloat = dts.loc[:, AnnotationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() - gts_quats_xyzw: NDArrayFloat = gts.loc[:, AnnotationColumns.QUAT_COEFFICIENTS_WXYZ].to_numpy() + dts_quats_xyzw: NDArrayFloat = dts.loc[:, QUAT_WXYZ_COLS].to_numpy() + gts_quats_xyzw: NDArrayFloat = gts.loc[:, QUAT_WXYZ_COLS].to_numpy() yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts_quats_xyzw))[..., 2] yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts_quats_xyzw))[..., 2] orientation_errors = wrap_angles(yaws_dts - yaws_gts) @@ -385,9 +387,7 @@ def compute_evaluated_dts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - norm: NDArrayFloat = np.linalg.norm( # type: ignore - dts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])], axis=1 - ) + norm: NDArrayFloat = np.linalg.norm(dts.loc[:, TRANSLATION_COLS], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m is_evaluated: NDArrayBool = is_within_radius is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. @@ -411,9 +411,7 @@ def compute_evaluated_gts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - norm: NDArrayFloat = np.linalg.norm( # type: ignore - gts.loc[:, list(AnnotationColumns.TRANSLATION_NAMES[:2])], axis=1 - ) + norm: NDArrayFloat = np.linalg.norm(gts.loc[:, TRANSLATION_COLS], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"].to_numpy() > 0 is_evaluated: NDArrayBool = is_within_radius & is_valid_num_points From ceff8fa86487cf94e1d21d86cd442dded4f2aacb Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 29 Mar 2022 14:50:10 -0400 Subject: [PATCH 20/81] Fix click mypy error. --- tutorials/generate_egoview_overlaid_lidar.py | 3 ++- tutorials/generate_egoview_overlaid_vector_map.py | 3 ++- tutorials/generate_forecasting_scenario_visualizations.py | 3 ++- tutorials/generate_per_camera_videos.py | 3 ++- tutorials/map_teaser_notebook.py | 3 ++- tutorials/untar_tbv.py | 3 ++- 6 files changed, 12 insertions(+), 6 deletions(-) diff --git a/tutorials/generate_egoview_overlaid_lidar.py b/tutorials/generate_egoview_overlaid_lidar.py index 8e91a8aa..101cfe27 100644 --- a/tutorials/generate_egoview_overlaid_lidar.py +++ b/tutorials/generate_egoview_overlaid_lidar.py @@ -195,4 +195,5 @@ def run_generate_egoview_overlaid_lidar( if __name__ == "__main__": - run_generate_egoview_overlaid_lidar() + # https://github.com/pallets/click/issues/2227 + run_generate_egoview_overlaid_lidar() # type: ignore diff --git a/tutorials/generate_egoview_overlaid_vector_map.py b/tutorials/generate_egoview_overlaid_vector_map.py index 8d43339d..759b1903 100644 --- a/tutorials/generate_egoview_overlaid_vector_map.py +++ b/tutorials/generate_egoview_overlaid_vector_map.py @@ -339,4 +339,5 @@ def run_generate_egoview_overlaid_map( if __name__ == "__main__": - run_generate_egoview_overlaid_map() + # https://github.com/pallets/click/issues/2227 + run_generate_egoview_overlaid_map() # type: ignore diff --git a/tutorials/generate_forecasting_scenario_visualizations.py b/tutorials/generate_forecasting_scenario_visualizations.py index f94de042..4c753543 100644 --- a/tutorials/generate_forecasting_scenario_visualizations.py +++ b/tutorials/generate_forecasting_scenario_visualizations.py @@ -119,4 +119,5 @@ def run_generate_scenario_visualizations( if __name__ == "__main__": - run_generate_scenario_visualizations() + # https://github.com/pallets/click/issues/2227 + run_generate_scenario_visualizations() # type: ignore diff --git a/tutorials/generate_per_camera_videos.py b/tutorials/generate_per_camera_videos.py index 52497f27..7650d3f8 100644 --- a/tutorials/generate_per_camera_videos.py +++ b/tutorials/generate_per_camera_videos.py @@ -110,4 +110,5 @@ def run_generate_per_camera_videos(data_root: str, output_dir: str, num_workers: if __name__ == "__main__": - run_generate_per_camera_videos() + # https://github.com/pallets/click/issues/2227 + run_generate_per_camera_videos() # type: ignore diff --git a/tutorials/map_teaser_notebook.py b/tutorials/map_teaser_notebook.py index d37fc942..0d5acdc9 100644 --- a/tutorials/map_teaser_notebook.py +++ b/tutorials/map_teaser_notebook.py @@ -369,4 +369,5 @@ def run_map_tutorial(data_root: str, log_id: str, save_figures: bool) -> None: if __name__ == "__main__": - run_map_tutorial() + # https://github.com/pallets/click/issues/2227 + run_map_tutorial() # type: ignore diff --git a/tutorials/untar_tbv.py b/tutorials/untar_tbv.py index df564359..e048bed7 100644 --- a/tutorials/untar_tbv.py +++ b/tutorials/untar_tbv.py @@ -85,4 +85,5 @@ def run_untar_tbv_dataset(num_workers: int, shard_dirpath: str, desired_tbv_data if __name__ == "__main__": - run_untar_tbv_dataset() + # https://github.com/pallets/click/issues/2227 + run_untar_tbv_dataset() # type: ignore From c31477521d4b46481aea35bcad401ec78dd243de Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Wed, 30 Mar 2022 08:48:06 -0400 Subject: [PATCH 21/81] Pytype fixes. --- src/av2/evaluation/detection/utils.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 26a6adcc..90aa5191 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -66,7 +66,7 @@ class DetectionCfg: affinity_thresholds_m: Tuple[float, ...] = (0.5, 1.0, 2.0, 4.0) affinity_type: AffinityType = AffinityType.CENTER - categories: Tuple[str, ...] = tuple(x.value for x in CompetitionCategories) + categories: Tuple[str, ...] = tuple(x.value for x in list(CompetitionCategories)) dataset_dir: Optional[Path] = None eval_only_roi_instances: bool = True filter_metric: FilterMetricType = FilterMetricType.EUCLIDEAN @@ -128,7 +128,7 @@ def accumulate( # Initialize corresponding assignments + errors. dts.loc[:, cfg.affinity_thresholds_m] = False - dts.loc[:, [x.value for x in TruePositiveErrorNames]] = np.array(cfg.metrics_defaults[1:4]) + dts.loc[:, [x.value for x in list(TruePositiveErrorNames)]] = np.array(cfg.metrics_defaults[1:4]) for category in cfg.categories: is_eval_dts = np.logical_and(dts["category"] == category, dts["is_evaluated"]) is_eval_gts = np.logical_and(gts["category"] == category, gts["is_evaluated"]) @@ -160,7 +160,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr where K is the number of thresholds and S is the number of true positive error names. """ # Construct all columns. - cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + cols = cfg.affinity_thresholds_m + tuple(x.value for x in list(TruePositiveErrorNames)) M = len(cols) # Number of columns. N = len(dts) # Number of detections. @@ -216,7 +216,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr orientation_errors = distance(tps_dts, tps_gts, DistanceType.ORIENTATION) # Assign errors. - metrics_table.loc[idx_tps_dts, tuple(x.value for x in TruePositiveErrorNames)] = np.vstack( + metrics_table.loc[idx_tps_dts, tuple(x.value for x in list(TruePositiveErrorNames))] = np.vstack( [translation_errors, scale_errors, orientation_errors] ).transpose() return metrics_table @@ -267,8 +267,8 @@ def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: Affini NotImplementedError: If the affinity metric is not implemented. """ if metric == AffinityType.CENTER: - dts_xy_m = dts.loc[:, TRANSLATION_COLS[:2]] - gts_xy_m = gts.loc[:, TRANSLATION_COLS[:2]] + dts_xy_m = dts.loc[:, TRANSLATION_COLS[:2]] # pytype: disable=unsupported-operands + gts_xy_m = gts.loc[:, TRANSLATION_COLS[:2]] # pytype: disable=unsupported-operands affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) else: raise NotImplementedError("This affinity metric is not implemented!") From 2e7af38aa9aca13b58aae4a05c4a797bfd12cbe2 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Wed, 30 Mar 2022 09:08:02 -0400 Subject: [PATCH 22/81] Fix pytype. --- src/av2/evaluation/detection/eval.py | 3 ++- src/av2/evaluation/detection/utils.py | 1 - 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 19b2345b..4f862446 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -216,7 +216,8 @@ def summarize_metrics( # If true positives exist, compute the metrics. if has_true_positives: - tp_errors = category_dts.loc[is_tp_t, tuple(x.value for x in TruePositiveErrorNames)].mean(axis=0) + tp_error_cols = tuple(x.value for x in list(TruePositiveErrorNames)) + tp_errors = category_dts.loc[is_tp_t, tp_error_cols].mean(axis=0) # Convert errors to scores. tp_scores = 1 - np.divide(tp_errors, cfg.tp_normalization_terms) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 90aa5191..dbcfcc52 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -41,7 +41,6 @@ from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList from av2.utils.constants import EPS -from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt logger = logging.getLogger(__name__) From 43f938a6b55509647b228660e1e5c3e520ab758b Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Wed, 30 Mar 2022 09:10:30 -0400 Subject: [PATCH 23/81] Remove pytype. --- noxfile.py | 18 ------------------ src/av2/evaluation/detection/utils.py | 8 ++++---- 2 files changed, 4 insertions(+), 22 deletions(-) diff --git a/noxfile.py b/noxfile.py index 2285f87c..644a1619 100644 --- a/noxfile.py +++ b/noxfile.py @@ -14,8 +14,6 @@ PYTHON: Final[List[str]] = ["3.8", "3.9", "3.10"] nox.options.sessions = ["black", "isort", "lint", "mypy", "pytest"] -if platform != "win32": # pytype isn't supported on windows. - nox.options.sessions.append("pytype") def _setup(session: Session) -> None: @@ -107,22 +105,6 @@ def mypy(session: Session) -> None: session.run("mypy", ".") -@nox.session(python=["3.8", "3.9"]) # pytype doesn't support 3.10. -def pytype(session: Session) -> None: - """Run `pytype` against `av2`. - - Args: - session: `nox` session. - """ - env = [ - "git+https://github.com/google/pytype@fc08da16d4eab1deace406c52130457a7da66020", # Fixes typing.Final issue. - "pytest", # Required for analysis in the test directory. - ] - _setup(session) - session.install(*env) - session.run("pytype", ".") - - @nox.session(python=PYTHON) def pytest(session: Session) -> None: """Run `pytest` against `av2`. diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index dbcfcc52..85795d55 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -65,7 +65,7 @@ class DetectionCfg: affinity_thresholds_m: Tuple[float, ...] = (0.5, 1.0, 2.0, 4.0) affinity_type: AffinityType = AffinityType.CENTER - categories: Tuple[str, ...] = tuple(x.value for x in list(CompetitionCategories)) + categories: Tuple[str, ...] = tuple(x.value for x in CompetitionCategories) dataset_dir: Optional[Path] = None eval_only_roi_instances: bool = True filter_metric: FilterMetricType = FilterMetricType.EUCLIDEAN @@ -127,7 +127,7 @@ def accumulate( # Initialize corresponding assignments + errors. dts.loc[:, cfg.affinity_thresholds_m] = False - dts.loc[:, [x.value for x in list(TruePositiveErrorNames)]] = np.array(cfg.metrics_defaults[1:4]) + dts.loc[:, [x.value for x in TruePositiveErrorNames]] = np.array(cfg.metrics_defaults[1:4]) for category in cfg.categories: is_eval_dts = np.logical_and(dts["category"] == category, dts["is_evaluated"]) is_eval_gts = np.logical_and(gts["category"] == category, gts["is_evaluated"]) @@ -159,7 +159,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr where K is the number of thresholds and S is the number of true positive error names. """ # Construct all columns. - cols = cfg.affinity_thresholds_m + tuple(x.value for x in list(TruePositiveErrorNames)) + cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) M = len(cols) # Number of columns. N = len(dts) # Number of detections. @@ -215,7 +215,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr orientation_errors = distance(tps_dts, tps_gts, DistanceType.ORIENTATION) # Assign errors. - metrics_table.loc[idx_tps_dts, tuple(x.value for x in list(TruePositiveErrorNames))] = np.vstack( + metrics_table.loc[idx_tps_dts, tuple(x.value for x in TruePositiveErrorNames)] = np.vstack( [translation_errors, scale_errors, orientation_errors] ).transpose() return metrics_table From 90c791f55b77efcefea7e08e659057dce4e43386 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Wed, 30 Mar 2022 09:27:59 -0400 Subject: [PATCH 24/81] Small typing fixes. --- src/av2/evaluation/detection/eval.py | 4 +- tests/evaluation/detection/test_eval.py | 77 ------------------------- 2 files changed, 2 insertions(+), 79 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 4f862446..5287dcc5 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -98,7 +98,7 @@ def evaluate( # Find unique list of uuid tuples. uuids: List[Tuple[str, int]] = gts.index.unique().tolist() - log_ids: List[str] = gts.index.get_level_values(level=0).unique() + log_ids: List[str] = gts.index.get_level_values(level=0).unique().to_numpy().tolist() log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} @@ -111,7 +111,7 @@ def evaluate( log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) args_list = [] - for (log_id, timestamp_ns) in track(uuids, "Loading maps ..."): + for (log_id, timestamp_ns) in track(uuids, "Loading maps and egoposes ..."): sweep_dts = dts.loc[(log_id, timestamp_ns)].reset_index().copy() sweep_gts = gts.loc[(log_id, timestamp_ns)].reset_index().copy() diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index aca8dcdc..7d020454 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -366,80 +366,3 @@ def test_orientation_error() -> None: # metrics = evaluator.evaluate() # assert metrics.AP.loc["Vehicle"] == 1.0 - - -# def test_rank() -> None: -# """Test ranking of detections and scores during detection evaluation.""" -# columns = [ -# "uuid", -# "length", -# "width", -# "height", -# "qw", -# "qx", -# "qy", -# "qz", -# "x", -# "y", -# "z", -# "score", -# ] -# dts = pd.DataFrame( -# [ -# [ -# "00000000-0000-0000-0000-000000000000", -# 5.0, -# 5.0, -# 5.0, -# 1.0, -# 0.0, -# 0.0, -# 0.0, -# 0.0, -# 0.0, -# 0.0, -# 0.7, -# ], -# [ -# "00000000-0000-0000-0000-000000000001", -# 5.0, -# 5.0, -# 5.0, -# 1.0, -# 0.0, -# 0.0, -# 0.0, -# 10.0, -# 10.0, -# 10.0, -# 0.9, -# ], -# [ -# "00000000-0000-0000-0000-000000000002", -# 5.0, -# 5.0, -# 5.0, -# 1.0, -# 0.0, -# 0.0, -# 0.0, -# 20.0, -# 20.0, -# 20.0, -# 0.8, -# ], -# ], -# columns=columns, -# ) - -# ranked_dts = rank(dts) -# uuids = ranked_dts["uuid"] -# expected_track_ids = np.array( -# [ -# "00000000-0000-0000-0000-000000000001", -# "00000000-0000-0000-0000-000000000002", -# "00000000-0000-0000-0000-000000000000", -# ] -# ) -# expected_scores = np.array([0.9, 0.8, 0.7]) -# assert np.all(uuids == expected_track_ids) and np.all(ranked_dts["score"] == expected_scores) From 515c6f2e5a1861d34519761f5771f706ac8d825c Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Wed, 30 Mar 2022 20:00:20 -0400 Subject: [PATCH 25/81] Add unit tests. --- tests/evaluation/detection/test_eval.py | 45 ++++++++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 7d020454..8d1d7493 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -12,6 +12,7 @@ import numpy as np import pandas as pd +from av2.evaluation.detection.utils import compute_evaluated_dts_mask, compute_evaluated_gts_mask from scipy.spatial.transform import Rotation from av2.evaluation.detection.constants import AffinityType, DistanceType, TruePositiveErrorNames @@ -27,7 +28,7 @@ from av2.geometry.geometry import wrap_angles from av2.geometry.iou import iou_3d_axis_aligned from av2.utils.constants import PI -from av2.utils.typing import NDArrayFloat +from av2.utils.typing import NDArrayBool, NDArrayFloat TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" @@ -280,6 +281,48 @@ def test_orientation_error() -> None: assert _get_summary().loc["AVERAGE_METRICS", "AOE"] == expected_result_det +def test_compute_evaluated_dts_mask() -> None: + """Unit test for computing valid detections cuboids.""" + columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS + dts = pd.DataFrame( + [ + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # In bounds with at least 1 point. + [175, 175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # Out of bounds with at least 1 point. + [-175.0, -175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # Out of bounds with at least 1 point. + [1.0, 1.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # In bounds with at least 1 point. + ], + columns=columns, + ) + detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) + dts_mask_: NDArrayBool = np.array([True, False, False, True]) + np.testing.assert_array_equal(dts_mask, dts_mask_) + + dts = pd.DataFrame(np.random.rand(1000, 10), columns=columns) + dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) + dts_mask_ = np.zeros(len(dts)) + dts_mask_[: detection_cfg.max_num_dts_per_category] = True + np.testing.assert_array_equal(dts_mask, dts_mask_) + + +def test_compute_evaluated_gts_mask() -> None: + """Unit test for computing valid ground truth cuboids.""" + columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS + ["num_interior_pts"] + gts = pd.DataFrame( + [ + [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # In bounds with at least 1 point. + [175, 175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # Out of bounds with at least 1 point. + [-175.0, -175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # Out of bounds with at least 1 point. + [1.0, 1.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0], # In bounds with at least 1 point. + ], + columns=columns, + ) + detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + gts_mask = compute_evaluated_gts_mask(gts, detection_cfg) + gts_mask_: NDArrayBool = np.array([True, False, False, False]) + np.testing.assert_array_equal(gts_mask, gts_mask_) + + # def test_filter_instances() -> None: # """Generate 100 different detections and filter them based on Euclidean distance.""" # columns = ["category"] + DIMS_COLS + QUAT_COLS + TRANSLATION_COLS From 8c5d1bc22e0f8e152d2556e2ad1a6216652488d6 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Wed, 30 Mar 2022 20:47:28 -0400 Subject: [PATCH 26/81] Fix typing. --- src/av2/evaluation/detection/utils.py | 17 ++++++-------- tests/evaluation/detection/test_eval.py | 30 ++----------------------- 2 files changed, 9 insertions(+), 38 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 85795d55..ecc839fe 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -160,10 +160,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr """ # Construct all columns. cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) - - M = len(cols) # Number of columns. N = len(dts) # Number of detections. - K = len(cfg.affinity_thresholds_m) # Number of thresholds. # Construct metrics table. # 0.5 1.0 2.0 4.0 ATE ASE AOE (default columns) @@ -173,7 +170,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr # N False False False False 0.0 0.0 0.0 metrics_table = pd.DataFrame({c: np.zeros(N) for c in cols}) metrics_table = metrics_table.astype({tx_m: bool for tx_m in cfg.affinity_thresholds_m}) - metrics_table.iloc[:, K : K + M] = cfg.metrics_defaults[1:4] + metrics_table.loc[:, tuple(x.value for x in TruePositiveErrorNames)] = np.array(cfg.metrics_defaults[1:4]) metrics_table.loc[:, "score"] = dts["score"] if len(gts) == 0: @@ -195,20 +192,20 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr idx_gts, idx_dts = assignments for i, threshold_m in enumerate(cfg.affinity_thresholds_m): # `is_tp` may need to be defined differently with other affinities. - is_tp = affinities[idx_dts] > -threshold_m + is_tp: NDArrayBool = affinities[idx_dts] > -threshold_m # Record true positives. - metrics_table.iloc[idx_dts, i] = is_tp + metrics_table.iloc[idx_dts, i] = is_tp.tolist() if threshold_m != cfg.tp_threshold_m: continue # Skip if threshold isn't the true positive threshold. if not np.any(is_tp): continue # Skip if no true positives exist. - idx_tps_dts = idx_dts[is_tp] - idx_tps_gts = idx_gts[is_tp] + idx_tps_dts: NDArrayInt = idx_dts[is_tp] + idx_tps_gts: NDArrayInt = idx_gts[is_tp] - tps_dts = dts.iloc[idx_tps_dts] - tps_gts = gts.iloc[idx_tps_gts] + tps_dts = dts.loc[idx_tps_dts] + tps_gts = gts.loc[idx_tps_gts] translation_errors = distance(tps_dts, tps_gts, DistanceType.TRANSLATION) scale_errors = distance(tps_dts, tps_gts, DistanceType.SCALE) diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 8d1d7493..f2ba867d 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -12,7 +12,6 @@ import numpy as np import pandas as pd -from av2.evaluation.detection.utils import compute_evaluated_dts_mask, compute_evaluated_gts_mask from scipy.spatial.transform import Rotation from av2.evaluation.detection.constants import AffinityType, DistanceType, TruePositiveErrorNames @@ -22,6 +21,8 @@ accumulate, assign, compute_affinity_matrix, + compute_evaluated_dts_mask, + compute_evaluated_gts_mask, distance, interpolate_precision, ) @@ -323,20 +324,6 @@ def test_compute_evaluated_gts_mask() -> None: np.testing.assert_array_equal(gts_mask, gts_mask_) -# def test_filter_instances() -> None: -# """Generate 100 different detections and filter them based on Euclidean distance.""" -# columns = ["category"] + DIMS_COLS + QUAT_COLS + TRANSLATION_COLS - -# dts = pd.DataFrame( -# [["REGULAR_VEHICLE", 0.0, 0.0, 0.0, 0.0, 5.0, 2.0, 3.0, i, i, 0] for i in range(100)], -# columns=columns, -# ) -# cfg = DetectionCfg(eval_only_roi_instances=False) - -# expected_result: int = 71 -# assert len(is_filter(dts, cfg)) == expected_result - - # def test_filter_objs_to_roi() -> None: # """Use the map to filter out an object that lies outside the ROI in a parking lot.""" # avm = av2Map() @@ -396,16 +383,3 @@ def test_compute_evaluated_gts_mask() -> None: # assert dts_filtered.dtype == "O" # array of objects # assert isinstance(dts_filtered, np.ndarray) # assert dts_filtered[0].track_id == "bb0f40e4f68043e285d64a839f2f092c" - - -# def test_AP_on_filtered_instances() -> None: -# """Test AP calculation on instances filtered on region-of-interest.""" -# dt_fpath = TEST_DATA_LOC / "remove_nonroi_detections" -# gt_fpath = TEST_DATA_LOC / "remove_nonroi_ground_truth" -# fig_fpath = TEST_DATA_LOC / "test_figures" - -# cfg = DetectionConfig(eval_only_roi_instances=True) -# evaluator = DetectionEvaluator(dt_fpath, gt_fpath, fig_fpath, cfg) -# metrics = evaluator.evaluate() - -# assert metrics.AP.loc["Vehicle"] == 1.0 From e7f2925c01499b5961b395635501676bc9bd36b6 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 31 Mar 2022 11:16:13 -0400 Subject: [PATCH 27/81] Remove click typing issue. --- src/av2/evaluation/detection/utils.py | 9 +++++---- tests/evaluation/detection/test_eval.py | 6 +++--- tutorials/generate_egoview_overlaid_lidar.py | 3 +-- tutorials/generate_egoview_overlaid_vector_map.py | 3 +-- .../generate_forecasting_scenario_visualizations.py | 3 +-- tutorials/generate_per_camera_videos.py | 3 +-- tutorials/map_teaser_notebook.py | 3 +-- tutorials/untar_tbv.py | 3 +-- 8 files changed, 14 insertions(+), 19 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index ecc839fe..b927f3ad 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -72,7 +72,7 @@ class DetectionCfg: max_num_dts_per_category: int = 100 max_range_m: float = 200.0 num_recall_samples: int = 101 - splits: Tuple[str, ...] = ("val",) + split: str = "val" tp_threshold_m: float = 2.0 @property @@ -159,7 +159,8 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr where K is the number of thresholds and S is the number of true positive error names. """ # Construct all columns. - cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + tp_error_cols = tuple(x.value for x in TruePositiveErrorNames) + cols = cfg.affinity_thresholds_m + tp_error_cols N = len(dts) # Number of detections. # Construct metrics table. @@ -170,7 +171,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr # N False False False False 0.0 0.0 0.0 metrics_table = pd.DataFrame({c: np.zeros(N) for c in cols}) metrics_table = metrics_table.astype({tx_m: bool for tx_m in cfg.affinity_thresholds_m}) - metrics_table.loc[:, tuple(x.value for x in TruePositiveErrorNames)] = np.array(cfg.metrics_defaults[1:4]) + metrics_table.loc[:, tp_error_cols] = np.array(cfg.metrics_defaults[1:4]) metrics_table.loc[:, "score"] = dts["score"] if len(gts) == 0: @@ -212,7 +213,7 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr orientation_errors = distance(tps_dts, tps_gts, DistanceType.ORIENTATION) # Assign errors. - metrics_table.loc[idx_tps_dts, tuple(x.value for x in TruePositiveErrorNames)] = np.vstack( + metrics_table.loc[idx_tps_dts, tp_error_cols] = np.vstack( [translation_errors, scale_errors, orientation_errors] ).transpose() return metrics_table diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index f2ba867d..29cf904f 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -297,13 +297,13 @@ def test_compute_evaluated_dts_mask() -> None: detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) dts_mask_: NDArrayBool = np.array([True, False, False, True]) - np.testing.assert_array_equal(dts_mask, dts_mask_) + np.testing.assert_array_equal(dts_mask, dts_mask_) # type: ignore dts = pd.DataFrame(np.random.rand(1000, 10), columns=columns) dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) dts_mask_ = np.zeros(len(dts)) dts_mask_[: detection_cfg.max_num_dts_per_category] = True - np.testing.assert_array_equal(dts_mask, dts_mask_) + np.testing.assert_array_equal(dts_mask, dts_mask_) # type: ignore def test_compute_evaluated_gts_mask() -> None: @@ -321,7 +321,7 @@ def test_compute_evaluated_gts_mask() -> None: detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) gts_mask = compute_evaluated_gts_mask(gts, detection_cfg) gts_mask_: NDArrayBool = np.array([True, False, False, False]) - np.testing.assert_array_equal(gts_mask, gts_mask_) + np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore # def test_filter_objs_to_roi() -> None: diff --git a/tutorials/generate_egoview_overlaid_lidar.py b/tutorials/generate_egoview_overlaid_lidar.py index 101cfe27..8e91a8aa 100644 --- a/tutorials/generate_egoview_overlaid_lidar.py +++ b/tutorials/generate_egoview_overlaid_lidar.py @@ -195,5 +195,4 @@ def run_generate_egoview_overlaid_lidar( if __name__ == "__main__": - # https://github.com/pallets/click/issues/2227 - run_generate_egoview_overlaid_lidar() # type: ignore + run_generate_egoview_overlaid_lidar() diff --git a/tutorials/generate_egoview_overlaid_vector_map.py b/tutorials/generate_egoview_overlaid_vector_map.py index 759b1903..8d43339d 100644 --- a/tutorials/generate_egoview_overlaid_vector_map.py +++ b/tutorials/generate_egoview_overlaid_vector_map.py @@ -339,5 +339,4 @@ def run_generate_egoview_overlaid_map( if __name__ == "__main__": - # https://github.com/pallets/click/issues/2227 - run_generate_egoview_overlaid_map() # type: ignore + run_generate_egoview_overlaid_map() diff --git a/tutorials/generate_forecasting_scenario_visualizations.py b/tutorials/generate_forecasting_scenario_visualizations.py index 4c753543..f94de042 100644 --- a/tutorials/generate_forecasting_scenario_visualizations.py +++ b/tutorials/generate_forecasting_scenario_visualizations.py @@ -119,5 +119,4 @@ def run_generate_scenario_visualizations( if __name__ == "__main__": - # https://github.com/pallets/click/issues/2227 - run_generate_scenario_visualizations() # type: ignore + run_generate_scenario_visualizations() diff --git a/tutorials/generate_per_camera_videos.py b/tutorials/generate_per_camera_videos.py index 7650d3f8..52497f27 100644 --- a/tutorials/generate_per_camera_videos.py +++ b/tutorials/generate_per_camera_videos.py @@ -110,5 +110,4 @@ def run_generate_per_camera_videos(data_root: str, output_dir: str, num_workers: if __name__ == "__main__": - # https://github.com/pallets/click/issues/2227 - run_generate_per_camera_videos() # type: ignore + run_generate_per_camera_videos() diff --git a/tutorials/map_teaser_notebook.py b/tutorials/map_teaser_notebook.py index 0d5acdc9..d37fc942 100644 --- a/tutorials/map_teaser_notebook.py +++ b/tutorials/map_teaser_notebook.py @@ -369,5 +369,4 @@ def run_map_tutorial(data_root: str, log_id: str, save_figures: bool) -> None: if __name__ == "__main__": - # https://github.com/pallets/click/issues/2227 - run_map_tutorial() # type: ignore + run_map_tutorial() diff --git a/tutorials/untar_tbv.py b/tutorials/untar_tbv.py index e048bed7..df564359 100644 --- a/tutorials/untar_tbv.py +++ b/tutorials/untar_tbv.py @@ -85,5 +85,4 @@ def run_untar_tbv_dataset(num_workers: int, shard_dirpath: str, desired_tbv_data if __name__ == "__main__": - # https://github.com/pallets/click/issues/2227 - run_untar_tbv_dataset() # type: ignore + run_untar_tbv_dataset() From 14f11416475b9a8a6e26bb4a71b82c9d36a77f0a Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 31 Mar 2022 11:45:33 -0400 Subject: [PATCH 28/81] Fix mypy. --- src/av2/utils/io.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/av2/utils/io.py b/src/av2/utils/io.py index 5e024308..31a0be3d 100644 --- a/src/av2/utils/io.py +++ b/src/av2/utils/io.py @@ -206,7 +206,7 @@ def write_img(img_path: Path, img: NDArrayByte, channel_order: str = "RGB") -> N cv2.imwrite(str(img_path), img) -def read_json_file(fpath: Path) -> Any: +def read_json_file(fpath: Path) -> Dict[str, Any]: """Load dictionary from JSON file. Args: @@ -216,7 +216,8 @@ def read_json_file(fpath: Path) -> Any: Deserialized Python dictionary. """ with open(fpath, "rb") as f: - return json.load(f) + data: Dict[str, Any] = json.load(f) + return data def save_json_dict( From 7d4139cb3202491c994a7dc1e295fed3b92d797c Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 01:41:04 -0400 Subject: [PATCH 29/81] Detection eval speed up. --- pyproject.toml | 2 +- src/av2/evaluation/detection/eval.py | 61 ++++----- src/av2/evaluation/detection/utils.py | 143 ++++++++++----------- src/av2/utils/typing.py | 1 + tests/evaluation/detection/test_eval.py | 158 +++++++++--------------- 5 files changed, 150 insertions(+), 215 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5076ff26..322104c3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ reportUntypedFunctionDecorator = false [tool.pytest.ini_options] minversion = "6.0" -addopts = "--cov-report term-missing:skip-covered --cov src/av2" +addopts = "--cov-report term-missing:skip-covered --cov av2" testpaths = [ "tests", ] \ No newline at end of file diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 5287dcc5..dcf366e3 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -51,7 +51,9 @@ in addition to the mean statistics average across all classes, and P refers to the number of included statistics, e.g. AP, ATE, ASE, AOE, CDS by default. """ +import functools import logging +from collections import defaultdict from typing import Dict, List, Optional, Tuple import numpy as np @@ -89,16 +91,7 @@ def evaluate( Raises: RuntimeError: If parallel processing fails to complete. """ - # (log_id, timestamp_ns) uniquely identifies a sweep. - uuid_columns = ["log_id", "timestamp_ns"] - - # Set index and sort for performance. - dts = dts.set_index(keys=uuid_columns).sort_index() - gts = gts.set_index(keys=uuid_columns).sort_index() - - # Find unique list of uuid tuples. - uuids: List[Tuple[str, int]] = gts.index.unique().tolist() - log_ids: List[str] = gts.index.get_level_values(level=0).unique().to_numpy().tolist() + log_ids: List[str] = gts["log_id"].unique().tolist() log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} @@ -110,38 +103,34 @@ def evaluate( log_id_to_avm[log_id] = avm log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) - args_list = [] - for (log_id, timestamp_ns) in track(uuids, "Loading maps and egoposes ..."): - sweep_dts = dts.loc[(log_id, timestamp_ns)].reset_index().copy() - sweep_gts = gts.loc[(log_id, timestamp_ns)].reset_index().copy() - - sweep_map = None - sweep_poses = None - if log_id in log_id_to_avm: - sweep_map = log_id_to_avm[log_id] - if log_id in log_id_to_timestamped_poses: - if timestamp_ns in log_id_to_timestamped_poses[log_id]: - sweep_poses = log_id_to_timestamped_poses[log_id][timestamp_ns] - args_list.append([sweep_dts, sweep_gts, cfg, sweep_map, sweep_poses]) + dts_mapping = {uuid: x for uuid, x in dts.groupby(["log_id", "timestamp_ns"])} + gts_mapping = {uuid: x for uuid, x in gts.groupby(["log_id", "timestamp_ns"])} + args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] # Accumulate and gather the processed detections and ground truth annotations. - results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel( - n_jobs=-1, backend="multiprocessing", verbose=True - )(delayed(accumulate)(*x) for x in args_list) - if results is None: - raise RuntimeError("Accumulation of dts and gts has failed!") - + # results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel( + # n_jobs=-1, backend="multiprocessing", verbose=True + # )(delayed(accumulate)(*x) for x in args_list) + # breakpoint() + results = [accumulate(*x) for x in args_list[:100]] dts_list, gts_list = zip(*results) + dts_processed: NDArrayFloat = np.concatenate(dts_list) + gts_processed: NDArrayFloat = np.concatenate(gts_list) + + columns = list(cfg.affinity_thresholds_m) + [x.value for x in TruePositiveErrorNames] + ["is_evaluated"] + gts_processed = pd.DataFrame(gts_processed, columns=columns[:4] + ["is_evaluated"]) + dts_processed = pd.DataFrame(dts_processed, columns=columns) + + gts_processed = pd.concat([gts[:len(gts_processed)], gts_processed], axis=1) + dts_processed = pd.concat([dts[:len(dts_processed)], dts_processed], axis=1) - # Concatenate the detections and ground truth annotations into DataFrames. - dts_processed: pd.DataFrame = pd.concat(dts_list).reset_index(drop=True) - gts_processed: pd.DataFrame = pd.concat(gts_list).reset_index() + breakpoint() # Compute summary metrics. metrics = summarize_metrics(dts_processed, gts_processed, cfg) metrics.loc["AVERAGE_METRICS"] = metrics.mean() metrics = metrics.round(NUM_DECIMALS) - return dts, gts, metrics + return dts_processed, gts_processed, metrics def summarize_metrics( @@ -175,7 +164,7 @@ def summarize_metrics( # Only keep detections if they match the category and have NOT been filtered. is_valid_dts = np.logical_and(is_category_dts, dts["is_evaluated"]) - # Get valid detections and sort them in ascending order. + # Get valid detections and sort them in descending order. category_dts = dts.loc[is_valid_dts].sort_values(by="score", ascending=False).reset_index(drop=True) # Find annotations that have the current category. @@ -189,7 +178,7 @@ def summarize_metrics( continue for affinity_threshold_m in cfg.affinity_thresholds_m: - true_positives: NDArrayBool = category_dts[affinity_threshold_m].to_numpy() + true_positives: NDArrayBool = category_dts[affinity_threshold_m].astype(bool).to_numpy() # Continue if there aren't any true positives. if len(true_positives) == 0: @@ -206,7 +195,7 @@ def summarize_metrics( # Select only the true positives for each instance. middle_idx = len(cfg.affinity_thresholds_m) // 2 middle_threshold = cfg.affinity_thresholds_m[middle_idx] - is_tp_t = category_dts[middle_threshold] + is_tp_t = category_dts[middle_threshold].astype(bool) # Initialize true positive metrics. tp_errors = cfg.tp_normalization_terms diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index b927f3ad..5464d601 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -13,9 +13,10 @@ import logging from dataclasses import dataclass from pathlib import Path -from typing import Optional, Tuple +from typing import Any, Dict, Final, List, Optional, Tuple import numpy as np +import numpy.lib.recfunctions as rf import pandas as pd from scipy.spatial.distance import cdist @@ -41,10 +42,12 @@ from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList from av2.utils.constants import EPS -from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt, NDArrayObject logger = logging.getLogger(__name__) +CUBOID_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m", "length_m", "width_m", "height_m", "qw", "qx", "qy", "qz"] + @dataclass(frozen=True) class DetectionCfg: @@ -102,7 +105,7 @@ def accumulate( cfg: DetectionCfg, avm: Optional[ArgoverseStaticMap] = None, city_SE3_ego: Optional[SE3] = None, -) -> Tuple[pd.DataFrame, pd.DataFrame]: +) -> Tuple[NDArrayFloat, NDArrayFloat]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. Args: @@ -116,35 +119,48 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ - dts.sort_values("score", ascending=False, inplace=True) - dts.reset_index(drop=True, inplace=True) - - # Filter detections and ground truth annotations. - dts.loc[:, "is_evaluated"] = compute_evaluated_dts_mask(dts, cfg) - gts.loc[:, "is_evaluated"] = compute_evaluated_gts_mask(gts, cfg) - if cfg.eval_only_roi_instances and city_SE3_ego is not None and avm is not None: - dts.loc[:, "is_evaluated"] &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) - - # Initialize corresponding assignments + errors. - dts.loc[:, cfg.affinity_thresholds_m] = False - dts.loc[:, [x.value for x in TruePositiveErrorNames]] = np.array(cfg.metrics_defaults[1:4]) + dts = dts.sort_values("score", ascending=False).reset_index(drop=True) + dt_records: NDArrayObject = dts.to_records() + gt_records: NDArrayObject = gts.to_records() + + dt_params: NDArrayFloat = rf.structured_to_unstructured(rf.repack_fields(dt_records[CUBOID_COLS])) + gt_params: NDArrayFloat = rf.structured_to_unstructured(rf.repack_fields(gt_records[CUBOID_COLS])) + + dt_results: NDArrayFloat = np.zeros((len(dts), 8)) + gt_results: NDArrayFloat = np.zeros((len(gts), 5)) for category in cfg.categories: - is_eval_dts = np.logical_and(dts["category"] == category, dts["is_evaluated"]) - is_eval_gts = np.logical_and(gts["category"] == category, gts["is_evaluated"]) + category_dts_mask: NDArrayBool = dt_records["category"] == category + category_gts_mask: NDArrayBool = gt_records["category"] == category + + if not np.any(category_dts_mask): + continue + if not np.any(category_gts_mask): + continue + + is_evaluated_dts = category_dts_mask & compute_evaluated_dts_mask(dt_params, cfg) + is_evaluated_gts = category_gts_mask & compute_evaluated_gts_mask( + gt_params, gt_records["num_interior_pts"], cfg + ) + + category_dts_params = dt_params[is_evaluated_dts] + category_gts_params = gt_params[is_evaluated_gts] - category_dts = dts.loc[is_eval_dts].reset_index(drop=True) - category_gts = gts.loc[is_eval_gts].reset_index(drop=True) + dt_results[:, -1] = np.logical_or(dt_results[:, -1], is_evaluated_dts) + gt_results[:, -1] = np.logical_or(gt_results[:, -1], is_evaluated_gts) # Skip if there are no detections for the current category left. - if len(category_dts) == 0: + if len(category_dts_params) == 0: + continue + if len(category_gts_params) == 0: continue - assignments = assign(category_dts, category_gts, cfg) - dts.loc[is_eval_dts, assignments.columns] = assignments.to_numpy() - return dts, gts + dts_assignments, gts_assignments = assign(category_dts_params, category_gts_params, cfg) + dt_results[is_evaluated_dts, :-1] = dts_assignments + gt_results[is_evaluated_gts, :-1] = gts_assignments + return dt_results, gt_results -def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFrame: +def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDArrayFloat, NDArrayFloat]: """Attempt assignment of each detection to a ground truth label. Args: @@ -158,25 +174,6 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr The (N,K+S) confusion table containing the true and false positives augmented with the true positive errors where K is the number of thresholds and S is the number of true positive error names. """ - # Construct all columns. - tp_error_cols = tuple(x.value for x in TruePositiveErrorNames) - cols = cfg.affinity_thresholds_m + tp_error_cols - N = len(dts) # Number of detections. - - # Construct metrics table. - # 0.5 1.0 2.0 4.0 ATE ASE AOE (default columns) - # 0 False False False False 0.0 0.0 0.0 - # 1 False False False False 0.0 0.0 0.0 - # ... - # N False False False False 0.0 0.0 0.0 - metrics_table = pd.DataFrame({c: np.zeros(N) for c in cols}) - metrics_table = metrics_table.astype({tx_m: bool for tx_m in cfg.affinity_thresholds_m}) - metrics_table.loc[:, tp_error_cols] = np.array(cfg.metrics_defaults[1:4]) - metrics_table.loc[:, "score"] = dts["score"] - - if len(gts) == 0: - return metrics_table - affinity_matrix = compute_affinity_matrix(dts, gts, cfg.affinity_type) # Get the GT label for each max-affinity GT label, detection pair. @@ -191,12 +188,16 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) # type: ignore idx_gts, idx_dts = assignments + K = len(cfg.affinity_thresholds_m) + dts_table: NDArrayFloat = np.zeros((len(dts), K + 3)) + dts_table[:, 4:] = cfg.metrics_defaults[1:4] + gts_table: NDArrayFloat = np.zeros((len(gts), K)) for i, threshold_m in enumerate(cfg.affinity_thresholds_m): - # `is_tp` may need to be defined differently with other affinities. is_tp: NDArrayBool = affinities[idx_dts] > -threshold_m - # Record true positives. - metrics_table.iloc[idx_dts, i] = is_tp.tolist() + dts_table[idx_dts[is_tp], i] = True + gts_table[idx_gts, i] = True + if threshold_m != cfg.tp_threshold_m: continue # Skip if threshold isn't the true positive threshold. if not np.any(is_tp): @@ -205,18 +206,15 @@ def assign(dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg) -> pd.DataFr idx_tps_dts: NDArrayInt = idx_dts[is_tp] idx_tps_gts: NDArrayInt = idx_gts[is_tp] - tps_dts = dts.loc[idx_tps_dts] - tps_gts = gts.loc[idx_tps_gts] + tps_dts = dts[idx_tps_dts] + tps_gts = gts[idx_tps_gts] - translation_errors = distance(tps_dts, tps_gts, DistanceType.TRANSLATION) - scale_errors = distance(tps_dts, tps_gts, DistanceType.SCALE) - orientation_errors = distance(tps_dts, tps_gts, DistanceType.ORIENTATION) + translation_errors = distance(tps_dts[:, :2], tps_gts[:, :2], DistanceType.TRANSLATION) + scale_errors = distance(tps_dts[:, 3:6], tps_gts[:, 3:6], DistanceType.SCALE) + orientation_errors = distance(tps_dts[:, 6:10], tps_gts[:, 6:10], DistanceType.ORIENTATION) - # Assign errors. - metrics_table.loc[idx_tps_dts, tp_error_cols] = np.vstack( - [translation_errors, scale_errors, orientation_errors] - ).transpose() - return metrics_table + dts_table[idx_tps_dts, 4:] = np.stack((translation_errors, scale_errors, orientation_errors), axis=-1) + return dts_table, gts_table def interpolate_precision(precision: NDArrayFloat, interpolation_method: InterpType = InterpType.ALL) -> NDArrayFloat: @@ -249,7 +247,7 @@ def interpolate_precision(precision: NDArrayFloat, interpolation_method: InterpT return precision_interpolated -def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: AffinityType) -> NDArrayFloat: +def compute_affinity_matrix(dts: NDArrayFloat, gts: NDArrayFloat, metric: AffinityType) -> NDArrayFloat: """Calculate the affinity matrix between detections and ground truth annotations. Args: @@ -264,8 +262,8 @@ def compute_affinity_matrix(dts: pd.DataFrame, gts: pd.DataFrame, metric: Affini NotImplementedError: If the affinity metric is not implemented. """ if metric == AffinityType.CENTER: - dts_xy_m = dts.loc[:, TRANSLATION_COLS[:2]] # pytype: disable=unsupported-operands - gts_xy_m = gts.loc[:, TRANSLATION_COLS[:2]] # pytype: disable=unsupported-operands + dts_xy_m = dts[:, :2] + gts_xy_m = gts[:, :2] affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) else: raise NotImplementedError("This affinity metric is not implemented!") @@ -305,7 +303,7 @@ def compute_average_precision( return average_precision, precision_interpolated -def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDArrayFloat: +def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDArrayFloat: """Distance functions between detections and ground truth. Args: @@ -320,20 +318,14 @@ def distance(dts: pd.DataFrame, gts: pd.DataFrame, metric: DistanceType) -> NDAr NotImplementedError: If the distance type is not supported. """ if metric == DistanceType.TRANSLATION: - dts_xyz_m: NDArrayFloat = dts.loc[:, TRANSLATION_COLS].to_numpy() - gts_xyz_m: NDArrayFloat = gts.loc[:, TRANSLATION_COLS].to_numpy() - translation_errors: NDArrayFloat = np.linalg.norm(dts_xyz_m - gts_xyz_m, axis=1) # type: ignore + translation_errors: NDArrayFloat = np.linalg.norm(dts - gts, axis=1) # type: ignore return translation_errors elif metric == DistanceType.SCALE: - dts_lwh_m: NDArrayFloat = dts.loc[:, DIMENSION_COLS].reset_index(drop=True).to_numpy() - gts_lwh_m: NDArrayFloat = gts.loc[:, DIMENSION_COLS].reset_index(drop=True).to_numpy() - scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts_lwh_m, gts_lwh_m) + scale_errors: NDArrayFloat = 1 - iou_3d_axis_aligned(dts, gts) return scale_errors elif metric == DistanceType.ORIENTATION: - dts_quats_xyzw: NDArrayFloat = dts.loc[:, QUAT_WXYZ_COLS].to_numpy() - gts_quats_xyzw: NDArrayFloat = gts.loc[:, QUAT_WXYZ_COLS].to_numpy() - yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts_quats_xyzw))[..., 2] - yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts_quats_xyzw))[..., 2] + yaws_dts: NDArrayFloat = mat_to_xyz(quat_to_mat(dts))[..., 2] + yaws_gts: NDArrayFloat = mat_to_xyz(quat_to_mat(gts))[..., 2] orientation_errors = wrap_angles(yaws_dts - yaws_gts) return orientation_errors else: @@ -368,7 +360,7 @@ def compute_objects_in_roi_mask( def compute_evaluated_dts_mask( - dts: pd.DataFrame, + dts: NDArrayFloat, cfg: DetectionCfg, ) -> NDArrayBool: """Compute the evaluated cuboids mask. @@ -384,7 +376,7 @@ def compute_evaluated_dts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - norm: NDArrayFloat = np.linalg.norm(dts.loc[:, TRANSLATION_COLS], axis=1) # type: ignore + norm: NDArrayFloat = np.linalg.norm(dts[:, :3], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m is_evaluated: NDArrayBool = is_within_radius is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. @@ -392,7 +384,8 @@ def compute_evaluated_dts_mask( def compute_evaluated_gts_mask( - gts: pd.DataFrame, + gts: NDArrayFloat, + num_interior_pts: NDArrayFloat, cfg: DetectionCfg, ) -> NDArrayBool: """Compute the evaluated cuboids mask. @@ -408,8 +401,8 @@ def compute_evaluated_gts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - norm: NDArrayFloat = np.linalg.norm(gts.loc[:, TRANSLATION_COLS], axis=1) # type: ignore + norm: NDArrayFloat = np.linalg.norm(gts[:, :3], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m - is_valid_num_points: NDArrayBool = gts.loc[:, "num_interior_pts"].to_numpy() > 0 + is_valid_num_points: NDArrayBool = num_interior_pts > 0 is_evaluated: NDArrayBool = is_within_radius & is_valid_num_points return is_evaluated diff --git a/src/av2/utils/typing.py b/src/av2/utils/typing.py index 7277d9e3..e3b501ef 100644 --- a/src/av2/utils/typing.py +++ b/src/av2/utils/typing.py @@ -14,3 +14,4 @@ NDArrayFloat = npt.NDArray[np.float64] NDArrayByte = npt.NDArray[np.uint8] NDArrayInt = npt.NDArray[np.int64] +NDArrayObject = npt.NDArray[np.object_] diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 29cf904f..4c25bdd9 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -44,7 +44,7 @@ def _get_summary_identity() -> pd.DataFrame: detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections_identity.feather") gts: pd.DataFrame = dts.copy() - gts.loc[:, "num_interior_pts"] = [1, 1, 1, 1, 1, 1] + gts.loc[:, "num_interior_pts"] = np.array([1, 1, 1, 1, 1, 1]) _, _, summary = evaluate(dts, gts, detection_cfg) return summary @@ -54,6 +54,7 @@ def _get_summary_assignment() -> pd.DataFrame: detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections_assignment.feather") gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") + _, _, summary = evaluate(dts, gts, detection_cfg) return summary @@ -63,6 +64,7 @@ def _get_summary() -> pd.DataFrame: detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections.feather") gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") + _, _, summary = evaluate(dts, gts, detection_cfg) return summary @@ -72,9 +74,8 @@ def test_affinity_center() -> None: Verify that calculated distance matches expected affinity under the specified `AffFnType`. """ - columns = DIMS_COLS + QUAT_COLS + TRANSLATION_COLS - dts = pd.DataFrame([[5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]], columns=columns) - gts = pd.DataFrame([[5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0]], columns=columns) + dts: NDArrayFloat = np.array([[0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0]]) + gts: NDArrayFloat = np.array([[3.0, 4.0, 0.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0]]) expected_result = -5.0 assert compute_affinity_matrix(dts, gts, AffinityType.CENTER) == expected_result @@ -85,9 +86,8 @@ def test_translation_distance() -> None: Verify that calculated distance matches expected distance under the specified `DistFnType`. """ - columns = TRANSLATION_COLS - dts = pd.DataFrame([[0.0, 0.0, 0.0]], columns=columns) - gts = pd.DataFrame([[5.0, 5.0, 5.0]], columns=columns) + dts: NDArrayFloat = np.array([[0.0, 0.0, 0.0]]) + gts: NDArrayFloat = np.array([[5.0, 5.0, 5.0]]) expected_result: float = np.sqrt(25 + 25 + 25) assert np.allclose(distance(dts, gts, DistanceType.TRANSLATION), expected_result) @@ -100,9 +100,8 @@ def test_scale_distance() -> None: NOTE: We only provide shape parameters due to alignment assumption. """ - columns = DIMS_COLS - dts = pd.DataFrame([[5.0, 5.0, 5.0]], columns=columns) - gts = pd.DataFrame([[10.0, 10.0, 10.0]], columns=columns) + dts: NDArrayFloat = np.array([[5.0, 5.0, 5.0]]) + gts: NDArrayFloat = np.array([[10.0, 10.0, 10.0]]) expected_result: float = 1 - 0.125 assert np.allclose(distance(dts, gts, DistanceType.SCALE), expected_result) @@ -119,18 +118,14 @@ def test_orientation_quarter_angles() -> None: quarter_angles: List[NDArrayFloat] = [np.array([0, 0, angle]) for angle in np.arange(0, 2 * PI, expected_result)] for i in range(len(quarter_angles) - 1): - quat_xyzw_dts = Rotation.from_rotvec(quarter_angles[i : i + 1]).as_quat() - quat_xyzw_gts = Rotation.from_rotvec(quarter_angles[i + 1 : i + 2]).as_quat() + quat_xyzw_dts: NDArrayFloat = Rotation.from_rotvec(quarter_angles[i : i + 1]).as_quat() + quat_xyzw_gts: NDArrayFloat = Rotation.from_rotvec(quarter_angles[i + 1 : i + 2]).as_quat() quat_wxyz_dts = quat_xyzw_dts[..., [3, 0, 1, 2]] quat_wxyz_gts = quat_xyzw_gts[..., [3, 0, 1, 2]] - columns = QUAT_COLS - dts = pd.DataFrame(quat_wxyz_dts, columns=columns) - gts = pd.DataFrame(quat_wxyz_gts, columns=columns) - - assert np.isclose(distance(dts, gts, DistanceType.ORIENTATION), expected_result) - assert np.isclose(distance(gts, dts, DistanceType.ORIENTATION), expected_result) + assert np.isclose(distance(quat_wxyz_dts, quat_wxyz_gts, DistanceType.ORIENTATION), expected_result) + assert np.isclose(distance(quat_wxyz_gts, quat_wxyz_dts, DistanceType.ORIENTATION), expected_result) def test_orientation_eighth_angles() -> None: @@ -149,12 +144,8 @@ def test_orientation_eighth_angles() -> None: quat_wxyz_dts = quat_xyzw_dts[..., [3, 0, 1, 2]] quat_wxyz_gts = quat_xyzw_gts[..., [3, 0, 1, 2]] - columns = QUAT_COLS - dts = pd.DataFrame(quat_wxyz_dts, columns=columns) - gts = pd.DataFrame(quat_wxyz_gts, columns=columns) - - assert np.isclose(distance(dts, gts, DistanceType.ORIENTATION), expected_result) - assert np.isclose(distance(gts, dts, DistanceType.ORIENTATION), expected_result) + assert np.isclose(distance(quat_wxyz_dts, quat_wxyz_gts, DistanceType.ORIENTATION), expected_result) + assert np.isclose(distance(quat_wxyz_gts, quat_wxyz_dts, DistanceType.ORIENTATION), expected_result) def test_wrap_angle() -> None: @@ -174,50 +165,46 @@ def test_accumulate() -> None: dts, gts = accumulate(*job) # Check that there's a true positive under every threshold. - assert np.all(dts.loc[:, cfg.affinity_thresholds_m]) + assert np.all(dts[:, :4]) # Check that all error metrics are zero. - assert (dts.loc[:, tuple(x.value for x in TruePositiveErrorNames)] == 0).all(axis=None) + assert (dts[:, 4:] == 0).all(axis=None) # Check that there are 2 regular vehicles. - assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 + # assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 # Check that there are no other labels. - assert gts["category"].value_counts().sum() == 2 + # assert gts["category"].value_counts().sum() == 2 def test_assign() -> None: """Verify that the assign functions as expected by checking ATE of assigned detections against known distance.""" cfg = DetectionCfg(eval_only_roi_instances=False) - columns = DIMS_COLS + QUAT_COLS + TRANSLATION_COLS + ["score"] - - dts = pd.DataFrame( + dts: NDArrayFloat = np.array( [ - [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], - [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 10.0, 10.0, 10.0, 1.0], - [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 20.0, 20.0, 20.0, 1.0], + [0.0,0.0, 0.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], + [10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], + [20.0, 20.0, 20.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], ], - columns=columns, ) - gts = pd.DataFrame( + gts: NDArrayFloat = np.array( [ - [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, -10.0, -10.0, -10.0, 1.0], - [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 1.0], - [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 10.1, 10.0, 10.0, 1.0], + [-10.0, -10.0, -10.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], + [0.1, 0.0, 0.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], + [10.1, 10.0, 10.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], ], - columns=columns, ) - metrics = assign(dts, gts, cfg) + dts_assignments, _ = assign(dts, gts, cfg) # if these assign correctly, we should get an ATE of 0.1 for the first two expected_result: float = 0.1 ATE_COL_IDX = 4 - assert math.isclose(metrics.iloc[0, ATE_COL_IDX], expected_result) # instance 0 - assert math.isclose(metrics.iloc[1, ATE_COL_IDX], expected_result) # instance 1 - assert math.isclose(metrics.iloc[2, ATE_COL_IDX], 2.0) # instance 32 + assert math.isclose(dts_assignments[0, ATE_COL_IDX], expected_result) # instance 0 + assert math.isclose(dts_assignments[1, ATE_COL_IDX], expected_result) # instance 1 + assert math.isclose(dts_assignments[2, ATE_COL_IDX], 2.0) # instance 32 def test_interp() -> None: @@ -324,62 +311,27 @@ def test_compute_evaluated_gts_mask() -> None: np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -# def test_filter_objs_to_roi() -> None: -# """Use the map to filter out an object that lies outside the ROI in a parking lot.""" -# avm = av2Map() - -# # should be outside of ROI -# outside_obj = { -# "center": { -# "x": -14.102872067388489, -# "y": 19.466695178746022, -# "z": 0.11740010190455852, -# }, -# "rotation": { -# "x": 0.0, -# "y": 0.0, -# "z": -0.038991328555453404, -# "w": 0.9992395490058831, -# }, -# "length": 4.56126567460171, -# "width": 1.9370055686754908, -# "height": 1.5820081349372281, -# "track_label_uuid": "03a321bf955a4d7781682913884abf06", -# "timestamp": 315970611820366000, -# "label_class": "VEHICLE", -# } - -# # should be inside the ROI -# inside_obj = { -# "center": { -# "x": -20.727430239506702, -# "y": 3.4488006757501353, -# "z": 0.4036619561689685, -# }, -# "rotation": { -# "x": 0.0, -# "y": 0.0, -# "z": 0.0013102003738908123, -# "w": 0.9999991416871218, -# }, -# "length": 4.507580779458834, -# "width": 1.9243189627993598, -# "height": 1.629934978730058, -# "track_label_uuid": "bb0f40e4f68043e285d64a839f2f092c", -# "timestamp": 315970611820366000, -# "label_class": "VEHICLE", -# } - -# log_city_name = "PIT" -# lidar_ts = 315970611820366000 -# dataset_dir = TEST_DATA_LOC / "roi_based_test" -# log_id = "21e37598-52d4-345c-8ef9-03ae19615d3d" -# city_SE3_egovehicle = get_city_SE3_egovehicle_at_sensor_t(lidar_ts, dataset_dir, log_id) - -# dts = np.array([json_label_dict_to_obj_record(item) for item in [outside_obj, inside_obj]]) -# dts_filtered = filter_objs_to_roi(dts, avm, city_SE3_egovehicle, log_city_name) - -# assert dts_filtered.size == 1 -# assert dts_filtered.dtype == "O" # array of objects -# assert isinstance(dts_filtered, np.ndarray) -# assert dts_filtered[0].track_id == "bb0f40e4f68043e285d64a839f2f092c" +def test_val_identity() -> None: + root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" + paths = sorted(root_dir.glob("*/annotations.feather")) + + annotations = [] + for p in paths: + df = pd.read_feather(p) + df["log_id"] = p.parent.stem + annotations.append(df) + annotations = pd.concat(annotations).reset_index(drop=True) + annotations = annotations.drop("track_uuid", axis=1).drop_duplicates().reset_index(drop=True) + dts = annotations.copy() + dts["score"] = 1.0 + annotations["num_interior_pts"] = 1 + # annotations = pd.concat([pd.read_feather(x) for x in paths]).reset_index(drop=True) + + # dts = dts.drop("track_uuid", axis=1).drop_duplicates() + detection_cfg = DetectionCfg(eval_only_roi_instances=False) + dts_processed, gts_processed, metrics = evaluate(dts, annotations, detection_cfg) + breakpoint() + + +if __name__ == "__main__": + test_val_identity() From c9de7cb05a95e01c35fd1e796fcf6143ba6377d4 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 10:45:07 -0400 Subject: [PATCH 30/81] Rewrite detection eval for major speedup. --- src/av2/evaluation/detection/eval.py | 27 ++++++----- src/av2/evaluation/detection/utils.py | 41 +++++++++------- tests/evaluation/detection/test_eval.py | 63 ++++++++++--------------- 3 files changed, 67 insertions(+), 64 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index dcf366e3..1d01f5cd 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -91,6 +91,7 @@ def evaluate( Raises: RuntimeError: If parallel processing fails to complete. """ + dts = dts.sort_values("score", ascending=False).reset_index(drop=True) log_ids: List[str] = gts["log_id"].unique().tolist() log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} @@ -103,6 +104,7 @@ def evaluate( log_id_to_avm[log_id] = avm log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) + # dts = dts.sort_values("score", "") dts_mapping = {uuid: x for uuid, x in dts.groupby(["log_id", "timestamp_ns"])} gts_mapping = {uuid: x for uuid, x in gts.groupby(["log_id", "timestamp_ns"])} args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] @@ -112,25 +114,28 @@ def evaluate( # n_jobs=-1, backend="multiprocessing", verbose=True # )(delayed(accumulate)(*x) for x in args_list) # breakpoint() - results = [accumulate(*x) for x in args_list[:100]] + results = [accumulate(*x) for x in args_list] dts_list, gts_list = zip(*results) - dts_processed: NDArrayFloat = np.concatenate(dts_list) - gts_processed: NDArrayFloat = np.concatenate(gts_list) - columns = list(cfg.affinity_thresholds_m) + [x.value for x in TruePositiveErrorNames] + ["is_evaluated"] - gts_processed = pd.DataFrame(gts_processed, columns=columns[:4] + ["is_evaluated"]) - dts_processed = pd.DataFrame(dts_processed, columns=columns) + dts = pd.concat(dts_list).reset_index(drop=True) + gts = pd.concat(gts_list).reset_index(drop=True) - gts_processed = pd.concat([gts[:len(gts_processed)], gts_processed], axis=1) - dts_processed = pd.concat([dts[:len(dts_processed)], dts_processed], axis=1) + # breakpoint() + # dts_processed: NDArrayFloat = np.concatenate(dts_list) + # gts_processed: NDArrayFloat = np.concatenate(gts_list) + + # columns = list(cfg.affinity_thresholds_m) + [x.value for x in TruePositiveErrorNames] + ["is_evaluated"] + # gts_processed = pd.DataFrame(gts_processed, columns=columns[:4] + ["is_evaluated"]) + # dts_processed = pd.DataFrame(dts_processed, columns=columns) - breakpoint() + # gts_processed = pd.concat([gts], gts_processed], axis=1) + # dts_processed = pd.concat([dts[:len(dts_processed)], dts_processed], axis=1) # Compute summary metrics. - metrics = summarize_metrics(dts_processed, gts_processed, cfg) + metrics = summarize_metrics(dts, gts, cfg) metrics.loc["AVERAGE_METRICS"] = metrics.mean() metrics = metrics.round(NUM_DECIMALS) - return dts_processed, gts_processed, metrics + return dts, gts, metrics def summarize_metrics( diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 5464d601..f48b25da 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -119,7 +119,8 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ - dts = dts.sort_values("score", ascending=False).reset_index(drop=True) + # breakpoint() + # dts = dts.sort_values("score", ascending=False).reset_index(drop=True) dt_records: NDArrayObject = dts.to_records() gt_records: NDArrayObject = gts.to_records() @@ -129,24 +130,26 @@ def accumulate( dt_results: NDArrayFloat = np.zeros((len(dts), 8)) gt_results: NDArrayFloat = np.zeros((len(gts), 5)) for category in cfg.categories: - category_dts_mask: NDArrayBool = dt_records["category"] == category - category_gts_mask: NDArrayBool = gt_records["category"] == category + category_dts_idx: NDArrayBool = np.where(dt_records["category"] == category)[0] + category_gts_idx: NDArrayBool = np.where(gt_records["category"] == category)[0] - if not np.any(category_dts_mask): + if not np.any(category_dts_idx): continue - if not np.any(category_gts_mask): + if not np.any(category_gts_idx): continue - is_evaluated_dts = category_dts_mask & compute_evaluated_dts_mask(dt_params, cfg) - is_evaluated_gts = category_gts_mask & compute_evaluated_gts_mask( - gt_params, gt_records["num_interior_pts"], cfg + is_evaluated_dts = compute_evaluated_dts_mask(dt_params[category_dts_idx], cfg) + is_evaluated_gts = compute_evaluated_gts_mask( + gt_params[category_gts_idx], gt_records["num_interior_pts"][category_gts_idx], cfg ) + category_dts_idx = category_dts_idx[is_evaluated_dts] + category_gts_idx = category_gts_idx[is_evaluated_gts] - category_dts_params = dt_params[is_evaluated_dts] - category_gts_params = gt_params[is_evaluated_gts] + category_dts_params = dt_params[category_dts_idx] + category_gts_params = gt_params[category_gts_idx] - dt_results[:, -1] = np.logical_or(dt_results[:, -1], is_evaluated_dts) - gt_results[:, -1] = np.logical_or(gt_results[:, -1], is_evaluated_gts) + dt_results[category_dts_idx, -1] = True + gt_results[category_gts_idx, -1] = True # Skip if there are no detections for the current category left. if len(category_dts_params) == 0: @@ -155,9 +158,15 @@ def accumulate( continue dts_assignments, gts_assignments = assign(category_dts_params, category_gts_params, cfg) - dt_results[is_evaluated_dts, :-1] = dts_assignments - gt_results[is_evaluated_gts, :-1] = gts_assignments - return dt_results, gt_results + dt_results[category_dts_idx, :-1] = dts_assignments + gt_results[category_gts_idx, :-1] = gts_assignments + + cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + ("is_evaluated",) + dts.loc[:, cols] = dt_results + gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gt_results + # dts_results = pd.concat([dts, dt_results], axis=1) + # breakpoint() + return dts, gts def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDArrayFloat, NDArrayFloat]: @@ -209,7 +218,7 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA tps_dts = dts[idx_tps_dts] tps_gts = gts[idx_tps_gts] - translation_errors = distance(tps_dts[:, :2], tps_gts[:, :2], DistanceType.TRANSLATION) + translation_errors = distance(tps_dts[:, :3], tps_gts[:, :3], DistanceType.TRANSLATION) scale_errors = distance(tps_dts[:, 3:6], tps_gts[:, 3:6], DistanceType.SCALE) orientation_errors = distance(tps_dts[:, 6:10], tps_gts[:, 6:10], DistanceType.ORIENTATION) diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 4c25bdd9..d6afa3ba 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -165,16 +165,16 @@ def test_accumulate() -> None: dts, gts = accumulate(*job) # Check that there's a true positive under every threshold. - assert np.all(dts[:, :4]) + assert np.all(dts.loc[:, cfg.affinity_thresholds_m]) # Check that all error metrics are zero. - assert (dts[:, 4:] == 0).all(axis=None) + assert (dts.loc[:, tuple(x.value for x in TruePositiveErrorNames)] == 0).all(axis=None) # Check that there are 2 regular vehicles. - # assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 + assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 # Check that there are no other labels. - # assert gts["category"].value_counts().sum() == 2 + assert gts["category"].value_counts().sum() == 2 def test_assign() -> None: @@ -183,7 +183,7 @@ def test_assign() -> None: dts: NDArrayFloat = np.array( [ - [0.0,0.0, 0.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], + [0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], [10.0, 10.0, 10.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], [20.0, 20.0, 20.0, 5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 1.0], ], @@ -272,65 +272,54 @@ def test_orientation_error() -> None: def test_compute_evaluated_dts_mask() -> None: """Unit test for computing valid detections cuboids.""" columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS - dts = pd.DataFrame( + dts: NDArrayFloat = np.array( [ [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # In bounds with at least 1 point. [175, 175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # Out of bounds with at least 1 point. [-175.0, -175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # Out of bounds with at least 1 point. [1.0, 1.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # In bounds with at least 1 point. ], - columns=columns, ) detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) dts_mask_: NDArrayBool = np.array([True, False, False, True]) np.testing.assert_array_equal(dts_mask, dts_mask_) # type: ignore - dts = pd.DataFrame(np.random.rand(1000, 10), columns=columns) - dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) - dts_mask_ = np.zeros(len(dts)) - dts_mask_[: detection_cfg.max_num_dts_per_category] = True - np.testing.assert_array_equal(dts_mask, dts_mask_) # type: ignore - def test_compute_evaluated_gts_mask() -> None: """Unit test for computing valid ground truth cuboids.""" - columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS + ["num_interior_pts"] - gts = pd.DataFrame( + # columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS + ["num_interior_pts"] + gts: NDArrayFloat = np.array( [ [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # In bounds with at least 1 point. [175, 175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # Out of bounds with at least 1 point. [-175.0, -175.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # Out of bounds with at least 1 point. [1.0, 1.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0], # In bounds with at least 1 point. ], - columns=columns, ) detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) - gts_mask = compute_evaluated_gts_mask(gts, detection_cfg) + + gts_mask = compute_evaluated_gts_mask(gts[:, :-1], gts[:, -1], detection_cfg) gts_mask_: NDArrayBool = np.array([True, False, False, False]) np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -def test_val_identity() -> None: - root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" - paths = sorted(root_dir.glob("*/annotations.feather")) - - annotations = [] - for p in paths: - df = pd.read_feather(p) - df["log_id"] = p.parent.stem - annotations.append(df) - annotations = pd.concat(annotations).reset_index(drop=True) - annotations = annotations.drop("track_uuid", axis=1).drop_duplicates().reset_index(drop=True) - dts = annotations.copy() - dts["score"] = 1.0 - annotations["num_interior_pts"] = 1 - # annotations = pd.concat([pd.read_feather(x) for x in paths]).reset_index(drop=True) - - # dts = dts.drop("track_uuid", axis=1).drop_duplicates() - detection_cfg = DetectionCfg(eval_only_roi_instances=False) - dts_processed, gts_processed, metrics = evaluate(dts, annotations, detection_cfg) - breakpoint() +# def test_val_identity() -> None: +# root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" +# paths = sorted(root_dir.glob("*/annotations.feather")) + +# annotations = [] +# for p in paths: +# df = pd.read_feather(p) +# df["log_id"] = p.parent.stem +# annotations.append(df) +# annotations = pd.concat(annotations).reset_index(drop=True) +# dts = annotations.copy() +# dts["score"] = 1.0 +# annotations["num_interior_pts"] = 1 + +# detection_cfg = DetectionCfg(eval_only_roi_instances=False, max_num_dts_per_category=1000) +# dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) if __name__ == "__main__": From 59a65f83d21ab256cbb716b789996d590be18936 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 10:54:33 -0400 Subject: [PATCH 31/81] Typing fixes. --- src/av2/evaluation/detection/eval.py | 33 ++++++--------------------- src/av2/evaluation/detection/utils.py | 2 +- 2 files changed, 8 insertions(+), 27 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 1d01f5cd..7b262209 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -51,15 +51,11 @@ in addition to the mean statistics average across all classes, and P refers to the number of included statistics, e.g. AP, ATE, ASE, AOE, CDS by default. """ -import functools import logging -from collections import defaultdict -from typing import Dict, List, Optional, Tuple +from typing import Dict, List, Tuple import numpy as np import pandas as pd -from joblib import Parallel, delayed -from rich.progress import track from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision @@ -110,27 +106,12 @@ def evaluate( args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] # Accumulate and gather the processed detections and ground truth annotations. - # results: Optional[List[Tuple[pd.DataFrame, pd.DataFrame]]] = Parallel( - # n_jobs=-1, backend="multiprocessing", verbose=True - # )(delayed(accumulate)(*x) for x in args_list) - # breakpoint() results = [accumulate(*x) for x in args_list] dts_list, gts_list = zip(*results) dts = pd.concat(dts_list).reset_index(drop=True) gts = pd.concat(gts_list).reset_index(drop=True) - # breakpoint() - # dts_processed: NDArrayFloat = np.concatenate(dts_list) - # gts_processed: NDArrayFloat = np.concatenate(gts_list) - - # columns = list(cfg.affinity_thresholds_m) + [x.value for x in TruePositiveErrorNames] + ["is_evaluated"] - # gts_processed = pd.DataFrame(gts_processed, columns=columns[:4] + ["is_evaluated"]) - # dts_processed = pd.DataFrame(dts_processed, columns=columns) - - # gts_processed = pd.concat([gts], gts_processed], axis=1) - # dts_processed = pd.concat([dts[:len(dts_processed)], dts_processed], axis=1) - # Compute summary metrics. metrics = summarize_metrics(dts, gts, cfg) metrics.loc["AVERAGE_METRICS"] = metrics.mean() @@ -195,30 +176,30 @@ def summarize_metrics( # Record the average precision. average_precisions.loc[category, affinity_threshold_m] = threshold_average_precision - mean_average_precisions = average_precisions.loc[category].mean() + mean_average_precisions: NDArrayFloat = average_precisions.loc[category].to_numpy().mean() # Select only the true positives for each instance. middle_idx = len(cfg.affinity_thresholds_m) // 2 middle_threshold = cfg.affinity_thresholds_m[middle_idx] - is_tp_t = category_dts[middle_threshold].astype(bool) + is_tp_t = category_dts[middle_threshold].to_numpy().astype(bool) # Initialize true positive metrics. - tp_errors = cfg.tp_normalization_terms + tp_errors = np.array(cfg.tp_normalization_terms) # Check whether any true positives exist under the current threshold. has_true_positives = np.any(is_tp_t) # If true positives exist, compute the metrics. if has_true_positives: - tp_error_cols = tuple(x.value for x in list(TruePositiveErrorNames)) - tp_errors = category_dts.loc[is_tp_t, tp_error_cols].mean(axis=0) + tp_error_cols = [str(x.value) for x in TruePositiveErrorNames] + tp_errors: NDArrayFloat = category_dts.loc[is_tp_t, tp_error_cols].to_numpy().mean(axis=0) # Convert errors to scores. tp_scores = 1 - np.divide(tp_errors, cfg.tp_normalization_terms) # Compute Composite Detection Score (CDS). cds = mean_average_precisions * np.mean(tp_scores) - summary.loc[category] = [mean_average_precisions, *tp_errors, cds] + summary.loc[category] = np.array([mean_average_precisions, *tp_errors, cds]) # Return the summary. return summary diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index f48b25da..a60e7436 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -105,7 +105,7 @@ def accumulate( cfg: DetectionCfg, avm: Optional[ArgoverseStaticMap] = None, city_SE3_ego: Optional[SE3] = None, -) -> Tuple[NDArrayFloat, NDArrayFloat]: +) -> Tuple[pd.DataFrame, pd.DataFrame]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. Args: From 4327278a761c3acacaf605171640e74b609caedb Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 14:13:27 -0400 Subject: [PATCH 32/81] Typing fixes. --- src/av2/evaluation/detection/eval.py | 6 ------ src/av2/evaluation/detection/utils.py | 29 ++++++++++++++------------- 2 files changed, 15 insertions(+), 20 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 7b262209..681da72a 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -83,13 +83,8 @@ def evaluate( Returns: (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. - - Raises: - RuntimeError: If parallel processing fails to complete. """ - dts = dts.sort_values("score", ascending=False).reset_index(drop=True) log_ids: List[str] = gts["log_id"].unique().tolist() - log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: @@ -100,7 +95,6 @@ def evaluate( log_id_to_avm[log_id] = avm log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) - # dts = dts.sort_values("score", "") dts_mapping = {uuid: x for uuid, x in dts.groupby(["log_id", "timestamp_ns"])} gts_mapping = {uuid: x for uuid, x in gts.groupby(["log_id", "timestamp_ns"])} args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index a60e7436..5bb275ab 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -119,34 +119,37 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ - # breakpoint() - # dts = dts.sort_values("score", ascending=False).reset_index(drop=True) - dt_records: NDArrayObject = dts.to_records() - gt_records: NDArrayObject = gts.to_records() + dts = dts.sort_values("score", ascending=False).reset_index(drop=True) + dt_records: NDArrayObject = dts[CUBOID_COLS].to_numpy() + gt_records: NDArrayObject = gts[CUBOID_COLS].to_numpy() - dt_params: NDArrayFloat = rf.structured_to_unstructured(rf.repack_fields(dt_records[CUBOID_COLS])) - gt_params: NDArrayFloat = rf.structured_to_unstructured(rf.repack_fields(gt_records[CUBOID_COLS])) + dts_categories: NDArrayObject = dts["category"].to_numpy() + gts_categories: NDArrayObject = gts["category"].to_numpy() + + num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() dt_results: NDArrayFloat = np.zeros((len(dts), 8)) gt_results: NDArrayFloat = np.zeros((len(gts), 5)) for category in cfg.categories: - category_dts_idx: NDArrayBool = np.where(dt_records["category"] == category)[0] - category_gts_idx: NDArrayBool = np.where(gt_records["category"] == category)[0] + category_dts_mask: NDArrayBool = dts_categories == category + category_gts_mask: NDArrayBool = gts_categories == category + category_dts_idx: NDArrayBool = np.where(category_dts_mask)[0] + category_gts_idx: NDArrayBool = np.where(category_gts_mask)[0] if not np.any(category_dts_idx): continue if not np.any(category_gts_idx): continue - is_evaluated_dts = compute_evaluated_dts_mask(dt_params[category_dts_idx], cfg) + is_evaluated_dts = compute_evaluated_dts_mask(dt_records[category_dts_idx], cfg) is_evaluated_gts = compute_evaluated_gts_mask( - gt_params[category_gts_idx], gt_records["num_interior_pts"][category_gts_idx], cfg + gt_records[category_gts_idx], num_interior_pts[category_gts_idx], cfg ) category_dts_idx = category_dts_idx[is_evaluated_dts] category_gts_idx = category_gts_idx[is_evaluated_gts] - category_dts_params = dt_params[category_dts_idx] - category_gts_params = gt_params[category_gts_idx] + category_dts_params = dt_records[category_dts_idx] + category_gts_params = gt_records[category_gts_idx] dt_results[category_dts_idx, -1] = True gt_results[category_gts_idx, -1] = True @@ -164,8 +167,6 @@ def accumulate( cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + ("is_evaluated",) dts.loc[:, cols] = dt_results gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gt_results - # dts_results = pd.concat([dts, dt_results], axis=1) - # breakpoint() return dts, gts From df479b9d1440d2bf4b3b10cacb63e5bafb1e065b Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 14:57:30 -0400 Subject: [PATCH 33/81] Switch from record arrays to numpy arrays. --- src/av2/evaluation/detection/utils.py | 33 ++++++++++++--------------- 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 5bb275ab..6882b470 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -13,22 +13,18 @@ import logging from dataclasses import dataclass from pathlib import Path -from typing import Any, Dict, Final, List, Optional, Tuple +from typing import Final, List, Optional, Tuple import numpy as np -import numpy.lib.recfunctions as rf import pandas as pd from scipy.spatial.distance import cdist from av2.evaluation.detection.constants import ( - DIMENSION_COLS, MAX_NORMALIZED_ASE, MAX_SCALE_ERROR, MAX_YAW_RAD_ERROR, MIN_AP, MIN_CDS, - QUAT_WXYZ_COLS, - TRANSLATION_COLS, AffinityType, CompetitionCategories, DistanceType, @@ -120,8 +116,8 @@ def accumulate( The detection and ground truth cuboids augmented with assigment and evaluation fields. """ dts = dts.sort_values("score", ascending=False).reset_index(drop=True) - dt_records: NDArrayObject = dts[CUBOID_COLS].to_numpy() - gt_records: NDArrayObject = gts[CUBOID_COLS].to_numpy() + dts_npy: NDArrayObject = dts[CUBOID_COLS].to_numpy() + gts_npy: NDArrayObject = gts[CUBOID_COLS].to_numpy() dts_categories: NDArrayObject = dts["category"].to_numpy() gts_categories: NDArrayObject = gts["category"].to_numpy() @@ -141,31 +137,32 @@ def accumulate( if not np.any(category_gts_idx): continue - is_evaluated_dts = compute_evaluated_dts_mask(dt_records[category_dts_idx], cfg) + is_evaluated_dts = compute_evaluated_dts_mask(dts_npy[category_dts_idx], cfg) is_evaluated_gts = compute_evaluated_gts_mask( - gt_records[category_gts_idx], num_interior_pts[category_gts_idx], cfg + gts_npy[category_gts_idx], num_interior_pts[category_gts_idx], cfg ) category_dts_idx = category_dts_idx[is_evaluated_dts] category_gts_idx = category_gts_idx[is_evaluated_gts] - category_dts_params = dt_records[category_dts_idx] - category_gts_params = gt_records[category_gts_idx] + # Skip if there are no detections for the current category left. + if len(category_dts_idx) == 0: + continue + if len(category_gts_idx) == 0: + continue + + category_dts_params = dts_npy[category_dts_idx] + category_gts_params = gts_npy[category_gts_idx] dt_results[category_dts_idx, -1] = True gt_results[category_gts_idx, -1] = True - # Skip if there are no detections for the current category left. - if len(category_dts_params) == 0: - continue - if len(category_gts_params) == 0: - continue dts_assignments, gts_assignments = assign(category_dts_params, category_gts_params, cfg) dt_results[category_dts_idx, :-1] = dts_assignments gt_results[category_gts_idx, :-1] = gts_assignments - cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + ("is_evaluated",) - dts.loc[:, cols] = dt_results + cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + dts.loc[:, cols + ("is_evaluated",)] = dt_results gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gt_results return dts, gts From 4c40a5f30aba660574347b929373c33b1a5738d7 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 16:07:38 -0400 Subject: [PATCH 34/81] Temp changes. --- src/av2/evaluation/detection/eval.py | 49 +++++++++++++++++-------- src/av2/evaluation/detection/utils.py | 20 ++++++---- tests/evaluation/detection/test_eval.py | 40 ++++++++++---------- 3 files changed, 66 insertions(+), 43 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 681da72a..586b48c2 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -65,7 +65,7 @@ logger = logging.getLogger(__name__) - +@profile def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -84,27 +84,44 @@ def evaluate( (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. """ - log_ids: List[str] = gts["log_id"].unique().tolist() - log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} - log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} - if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: - for log_id in log_ids: - log_dir = cfg.dataset_dir / log_id - avm_dir = log_dir / "map" - avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) - log_id_to_avm[log_id] = avm - log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) - - dts_mapping = {uuid: x for uuid, x in dts.groupby(["log_id", "timestamp_ns"])} - gts_mapping = {uuid: x for uuid, x in gts.groupby(["log_id", "timestamp_ns"])} + # dts_npy = dts.to_numpy() + # gts_npy = gts.to_numpy() + # log_ids: List[str] = gts["log_id"].unique().tolist() + # log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} + # log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} + # if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: + # for log_id in log_ids: + # log_dir = cfg.dataset_dir / log_id + # avm_dir = log_dir / "map" + # avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) + # log_id_to_avm[log_id] = avm + # log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) + # dts = dts.sort_values(["log_id", "timestamp_ns"], ascending=True).reset_index(drop=True) + # breakpoint() + # uuids = gts.loc[:, ["log_id", "timestamp_ns"]].drop_duplicates().melt() + # breakpoint() + + # dts = dts.set_index(["log_id", "timestamp_ns"]).sort_values("score", ascending=False) + # gts = gts.set_index(["log_id", "timestamp_ns"]) + # breakpoint() + dts_mapping = {uuid: x for uuid, x in dts.groupby(["log_id", "timestamp_ns", "category"])} + gts_mapping = {uuid: x for uuid, x in gts.groupby(["log_id", "timestamp_ns", "category"])} args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] # Accumulate and gather the processed detections and ground truth annotations. results = [accumulate(*x) for x in args_list] dts_list, gts_list = zip(*results) - dts = pd.concat(dts_list).reset_index(drop=True) - gts = pd.concat(gts_list).reset_index(drop=True) + cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) + dts_npy = np.concatenate(dts_list) + gts_npy = np.concatenate(gts_list) + + dts.loc[:len(dts_npy) - 1, cols + ("is_evaluated",)] = dts_npy + gts.loc[:len(gts_npy) - 1, cfg.affinity_thresholds_m + ("is_evaluated",)] = gts_npy + # breakpoint() + + # dts = pd.concat(dts_list).reset_index(drop=True) + # gts = pd.concat(gts_list).reset_index(drop=True) # Compute summary metrics. metrics = summarize_metrics(dts, gts, cfg) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 6882b470..254f9ade 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -94,7 +94,7 @@ def tp_normalization_terms(self) -> Tuple[float, ...]: MAX_YAW_RAD_ERROR, ) - +@profile def accumulate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -115,9 +115,14 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ + breakpoint() + perm = (-dts["score"]).to_numpy().argsort() + dts = dts.iloc[perm] + # dts = dts[] + # breakpoint() dts = dts.sort_values("score", ascending=False).reset_index(drop=True) - dts_npy: NDArrayObject = dts[CUBOID_COLS].to_numpy() - gts_npy: NDArrayObject = gts[CUBOID_COLS].to_numpy() + dts_npy: NDArrayFloat = dts[CUBOID_COLS].to_numpy() + gts_npy: NDArrayFloat = gts[CUBOID_COLS].to_numpy() dts_categories: NDArrayObject = dts["category"].to_numpy() gts_categories: NDArrayObject = gts["category"].to_numpy() @@ -126,6 +131,8 @@ def accumulate( dt_results: NDArrayFloat = np.zeros((len(dts), 8)) gt_results: NDArrayFloat = np.zeros((len(gts), 5)) + + for category in cfg.categories: category_dts_mask: NDArrayBool = dts_categories == category category_gts_mask: NDArrayBool = gts_categories == category @@ -161,10 +168,9 @@ def accumulate( dt_results[category_dts_idx, :-1] = dts_assignments gt_results[category_gts_idx, :-1] = gts_assignments - cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) - dts.loc[:, cols + ("is_evaluated",)] = dt_results - gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gt_results - return dts, gts + _, inv = np.unique(perm, return_index=True) + dt_results = dt_results[inv] + return dt_results, gt_results def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDArrayFloat, NDArrayFloat]: diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index d6afa3ba..1dac3004 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -165,16 +165,16 @@ def test_accumulate() -> None: dts, gts = accumulate(*job) # Check that there's a true positive under every threshold. - assert np.all(dts.loc[:, cfg.affinity_thresholds_m]) + assert np.all(dts[:, :4]) # Check that all error metrics are zero. - assert (dts.loc[:, tuple(x.value for x in TruePositiveErrorNames)] == 0).all(axis=None) + assert (dts[:, 4:7] == 0).all() - # Check that there are 2 regular vehicles. - assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 + # # Check that there are 2 regular vehicles. + # assert gts["category"].value_counts()["REGULAR_VEHICLE"] == 2 - # Check that there are no other labels. - assert gts["category"].value_counts().sum() == 2 + # # Check that there are no other labels. + # assert gts["category"].value_counts().sum() == 2 def test_assign() -> None: @@ -304,22 +304,22 @@ def test_compute_evaluated_gts_mask() -> None: np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -# def test_val_identity() -> None: -# root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" -# paths = sorted(root_dir.glob("*/annotations.feather")) +def test_val_identity() -> None: + root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" + paths = sorted(root_dir.glob("*/annotations.feather")) -# annotations = [] -# for p in paths: -# df = pd.read_feather(p) -# df["log_id"] = p.parent.stem -# annotations.append(df) -# annotations = pd.concat(annotations).reset_index(drop=True) -# dts = annotations.copy() -# dts["score"] = 1.0 -# annotations["num_interior_pts"] = 1 + annotations = [] + for p in paths: + df = pd.read_feather(p) + df["log_id"] = p.parent.stem + annotations.append(df) + annotations = pd.concat(annotations).reset_index(drop=True) + dts = annotations.copy() + dts["score"] = 1.0 + annotations["num_interior_pts"] = 1 -# detection_cfg = DetectionCfg(eval_only_roi_instances=False, max_num_dts_per_category=1000) -# dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) + detection_cfg = DetectionCfg(eval_only_roi_instances=False, max_num_dts_per_category=1000) + dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) if __name__ == "__main__": From eccd4f0ab016c2f7718634a40c109b0ce6da789d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 16:56:18 -0400 Subject: [PATCH 35/81] Improve readability. --- src/av2/evaluation/detection/eval.py | 27 ++++------ src/av2/evaluation/detection/utils.py | 68 ++++++++----------------- tests/evaluation/detection/test_eval.py | 1 + 3 files changed, 31 insertions(+), 65 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 586b48c2..f67ef937 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -56,6 +56,7 @@ import numpy as np import pandas as pd +from rich.progress import track from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision @@ -65,7 +66,7 @@ logger = logging.getLogger(__name__) -@profile + def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -96,32 +97,22 @@ def evaluate( # avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) # log_id_to_avm[log_id] = avm # log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) - # dts = dts.sort_values(["log_id", "timestamp_ns"], ascending=True).reset_index(drop=True) - # breakpoint() - # uuids = gts.loc[:, ["log_id", "timestamp_ns"]].drop_duplicates().melt() - # breakpoint() - - # dts = dts.set_index(["log_id", "timestamp_ns"]).sort_values("score", ascending=False) - # gts = gts.set_index(["log_id", "timestamp_ns"]) - # breakpoint() - dts_mapping = {uuid: x for uuid, x in dts.groupby(["log_id", "timestamp_ns", "category"])} - gts_mapping = {uuid: x for uuid, x in gts.groupby(["log_id", "timestamp_ns", "category"])} + + task_columns = ["log_id", "timestamp_ns", "category"] + dts_mapping = {uuid: x for uuid, x in dts.groupby(task_columns, sort=False)} + gts_mapping = {uuid: x for uuid, x in gts.groupby(task_columns, sort=False)} args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] # Accumulate and gather the processed detections and ground truth annotations. - results = [accumulate(*x) for x in args_list] + results = [accumulate(*x) for x in track(args_list)] dts_list, gts_list = zip(*results) cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) dts_npy = np.concatenate(dts_list) gts_npy = np.concatenate(gts_list) - dts.loc[:len(dts_npy) - 1, cols + ("is_evaluated",)] = dts_npy - gts.loc[:len(gts_npy) - 1, cfg.affinity_thresholds_m + ("is_evaluated",)] = gts_npy - # breakpoint() - - # dts = pd.concat(dts_list).reset_index(drop=True) - # gts = pd.concat(gts_list).reset_index(drop=True) + dts.loc[:, cols + ("is_evaluated",)] = dts_npy + gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gts_npy # Compute summary metrics. metrics = summarize_metrics(dts, gts, cfg) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 254f9ade..85fa7247 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -94,14 +94,14 @@ def tp_normalization_terms(self) -> Tuple[float, ...]: MAX_YAW_RAD_ERROR, ) -@profile + def accumulate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, avm: Optional[ArgoverseStaticMap] = None, city_SE3_ego: Optional[SE3] = None, -) -> Tuple[pd.DataFrame, pd.DataFrame]: +) -> Tuple[NDArrayFloat, NDArrayFloat]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. Args: @@ -115,60 +115,34 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ - breakpoint() - perm = (-dts["score"]).to_numpy().argsort() - dts = dts.iloc[perm] - # dts = dts[] - # breakpoint() - dts = dts.sort_values("score", ascending=False).reset_index(drop=True) - dts_npy: NDArrayFloat = dts[CUBOID_COLS].to_numpy() - gts_npy: NDArrayFloat = gts[CUBOID_COLS].to_numpy() - - dts_categories: NDArrayObject = dts["category"].to_numpy() - gts_categories: NDArrayObject = gts["category"].to_numpy() + scores: NDArrayFloat = dts["score"].to_numpy() + perm: NDArrayInt = np.argsort(-scores) + dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy()[perm] + gts_npy: NDArrayFloat = gts.loc[:, CUBOID_COLS].to_numpy() num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() + is_evaluated_dts = compute_evaluated_dts_mask(dts_npy, cfg) + is_evaluated_gts = compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) + dt_results: NDArrayFloat = np.zeros((len(dts), 8)) gt_results: NDArrayFloat = np.zeros((len(gts), 5)) + if is_evaluated_dts.sum() == 0 or is_evaluated_gts.sum() == 0: + return dt_results, gt_results - - for category in cfg.categories: - category_dts_mask: NDArrayBool = dts_categories == category - category_gts_mask: NDArrayBool = gts_categories == category - category_dts_idx: NDArrayBool = np.where(category_dts_mask)[0] - category_gts_idx: NDArrayBool = np.where(category_gts_mask)[0] - - if not np.any(category_dts_idx): - continue - if not np.any(category_gts_idx): - continue - - is_evaluated_dts = compute_evaluated_dts_mask(dts_npy[category_dts_idx], cfg) - is_evaluated_gts = compute_evaluated_gts_mask( - gts_npy[category_gts_idx], num_interior_pts[category_gts_idx], cfg - ) - category_dts_idx = category_dts_idx[is_evaluated_dts] - category_gts_idx = category_gts_idx[is_evaluated_gts] - - # Skip if there are no detections for the current category left. - if len(category_dts_idx) == 0: - continue - if len(category_gts_idx) == 0: - continue - - category_dts_params = dts_npy[category_dts_idx] - category_gts_params = gts_npy[category_gts_idx] - - dt_results[category_dts_idx, -1] = True - gt_results[category_gts_idx, -1] = True + dts_npy = dts_npy[is_evaluated_dts] + gts_npy = gts_npy[is_evaluated_gts] + dt_results[is_evaluated_dts, -1] = True + gt_results[is_evaluated_gts, -1] = True - dts_assignments, gts_assignments = assign(category_dts_params, category_gts_params, cfg) - dt_results[category_dts_idx, :-1] = dts_assignments - gt_results[category_gts_idx, :-1] = gts_assignments + dts_assignments, gts_assignments = assign(dts_npy, gts_npy, cfg) + dt_results[is_evaluated_dts, :-1] = dts_assignments + gt_results[is_evaluated_gts, :-1] = gts_assignments - _, inv = np.unique(perm, return_index=True) + # Permute the detections according to the original ordering. + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(perm, return_index=True) + _, inv = outputs dt_results = dt_results[inv] return dt_results, gt_results diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 1dac3004..0b57f542 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -320,6 +320,7 @@ def test_val_identity() -> None: detection_cfg = DetectionCfg(eval_only_roi_instances=False, max_num_dts_per_category=1000) dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) + breakpoint() if __name__ == "__main__": From 72dc702438155fd2e5b65fb169d48dac3eb67219 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 17:01:36 -0400 Subject: [PATCH 36/81] Add comments. --- src/av2/evaluation/detection/utils.py | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 85fa7247..0d4f741c 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -115,35 +115,39 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ + # Sort the detections by score in _descending_ order. scores: NDArrayFloat = dts["score"].to_numpy() - perm: NDArrayInt = np.argsort(-scores) - dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy()[perm] + permutation: NDArrayInt = np.argsort(-scores) + + dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy()[permutation] gts_npy: NDArrayFloat = gts.loc[:, CUBOID_COLS].to_numpy() num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() - is_evaluated_dts = compute_evaluated_dts_mask(dts_npy, cfg) is_evaluated_gts = compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) - dt_results: NDArrayFloat = np.zeros((len(dts), 8)) - gt_results: NDArrayFloat = np.zeros((len(gts), 5)) + N, M = len(dts), len(gts) + dt_results: NDArrayFloat = np.zeros((N, 8)) + gt_results: NDArrayFloat = np.zeros((M, 5)) if is_evaluated_dts.sum() == 0 or is_evaluated_gts.sum() == 0: return dt_results, gt_results dts_npy = dts_npy[is_evaluated_dts] gts_npy = gts_npy[is_evaluated_gts] - dt_results[is_evaluated_dts, -1] = True - gt_results[is_evaluated_gts, -1] = True - + # Compute true positives through assigning detections and ground truths. dts_assignments, gts_assignments = assign(dts_npy, gts_npy, cfg) dt_results[is_evaluated_dts, :-1] = dts_assignments gt_results[is_evaluated_gts, :-1] = gts_assignments + # `is_evaluated` boolean flag is always the last column of the array. + dt_results[is_evaluated_dts, -1] = True + gt_results[is_evaluated_gts, -1] = True + # Permute the detections according to the original ordering. - outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(perm, return_index=True) - _, inv = outputs - dt_results = dt_results[inv] + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(permutation, return_index=True) + _, inverse_permutation = outputs + dt_results = dt_results[inverse_permutation] return dt_results, gt_results From fddd8991158b623f38f8495bb04bec663ccf5fb2 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 18:33:23 -0400 Subject: [PATCH 37/81] Modularize evaluate. --- src/av2/evaluation/detection/eval.py | 56 ++++++++++++++----------- src/av2/evaluation/detection/utils.py | 31 +++++++++++--- tests/evaluation/detection/test_eval.py | 2 +- 3 files changed, 58 insertions(+), 31 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index f67ef937..03fbd9ce 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,14 +52,19 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple import numpy as np import pandas as pd from rich.progress import track from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames -from av2.evaluation.detection.utils import DetectionCfg, accumulate, compute_average_precision +from av2.evaluation.detection.utils import ( + DetectionCfg, + accumulate, + compute_average_precision, + load_mapped_avm_and_egoposes, +) from av2.map.map_api import ArgoverseStaticMap from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego from av2.utils.typing import NDArrayBool, NDArrayFloat @@ -85,31 +90,32 @@ def evaluate( (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. """ - # dts_npy = dts.to_numpy() - # gts_npy = gts.to_numpy() - # log_ids: List[str] = gts["log_id"].unique().tolist() - # log_id_to_avm: Dict[str, ArgoverseStaticMap] = {} - # log_id_to_timestamped_poses: Dict[str, TimestampedCitySE3EgoPoses] = {} - # if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: - # for log_id in log_ids: - # log_dir = cfg.dataset_dir / log_id - # avm_dir = log_dir / "map" - # avm = ArgoverseStaticMap.from_map_dir(avm_dir, build_raster=True) - # log_id_to_avm[log_id] = avm - # log_id_to_timestamped_poses[log_id] = read_city_SE3_ego(log_dir) - - task_columns = ["log_id", "timestamp_ns", "category"] - dts_mapping = {uuid: x for uuid, x in dts.groupby(task_columns, sort=False)} - gts_mapping = {uuid: x for uuid, x in gts.groupby(task_columns, sort=False)} - args_list = [(dts_mapping[uuid], sweep_gts, cfg, None, None) for uuid, sweep_gts in gts_mapping.items()] - - # Accumulate and gather the processed detections and ground truth annotations. - results = [accumulate(*x) for x in track(args_list)] - dts_list, gts_list = zip(*results) + log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None + log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None + + # Load maps and egoposes if roi-pruning is enabled. + if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: + log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) + + column_names = ["log_id", "timestamp_ns", "category"] + uuid_to_dts = {uuid: x for uuid, x in dts.groupby(column_names, sort=False)} + uuid_to_gts = {uuid: x for uuid, x in gts.groupby(column_names, sort=False)} + + dts_list = [] + gts_list = [] + for uuid, sweep_gts in track(uuid_to_gts.items()): + args = uuid_to_dts[uuid], sweep_gts, cfg, None, None + if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: + avm = log_id_to_avm[uuid[0]] + city_SE3_ego = log_id_to_timestamped_poses[uuid[0]][uuid[1]] + args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego + dts_accum, gts_accum = accumulate(*args) + dts_list.append(dts_accum) + gts_list.append(gts_accum) cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) - dts_npy = np.concatenate(dts_list) - gts_npy = np.concatenate(gts_list) + dts_npy: NDArrayFloat = np.concatenate(dts_list) + gts_npy: NDArrayFloat = np.concatenate(gts_list) dts.loc[:, cols + ("is_evaluated",)] = dts_npy gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gts_npy diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 0d4f741c..4a17bb78 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -13,7 +13,7 @@ import logging from dataclasses import dataclass from pathlib import Path -from typing import Final, List, Optional, Tuple +from typing import Dict, Final, List, Optional, Tuple import numpy as np import pandas as pd @@ -38,6 +38,7 @@ from av2.map.map_api import ArgoverseStaticMap, RasterLayerType from av2.structures.cuboid import CuboidList from av2.utils.constants import EPS +from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt, NDArrayObject logger = logging.getLogger(__name__) @@ -115,16 +116,25 @@ def accumulate( Returns: The detection and ground truth cuboids augmented with assigment and evaluation fields. """ + N, M = len(dts), len(gts) + # Sort the detections by score in _descending_ order. scores: NDArrayFloat = dts["score"].to_numpy() - permutation: NDArrayInt = np.argsort(-scores) + permutation: NDArrayInt = np.argsort(-scores).tolist() + dts = dts.iloc[permutation, :] + + is_evaluated_dts: NDArrayBool = np.ones(N, dtype=bool) + is_evaluated_gts: NDArrayBool = np.ones(M, dtype=bool) + if avm is not None and city_SE3_ego is not None: + is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) + is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy()[permutation] + dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy() gts_npy: NDArrayFloat = gts.loc[:, CUBOID_COLS].to_numpy() num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() - is_evaluated_dts = compute_evaluated_dts_mask(dts_npy, cfg) - is_evaluated_gts = compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) + is_evaluated_dts &= compute_evaluated_dts_mask(dts_npy, cfg) + is_evaluated_gts &= compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) N, M = len(dts), len(gts) dt_results: NDArrayFloat = np.zeros((N, 8)) @@ -397,3 +407,14 @@ def compute_evaluated_gts_mask( is_valid_num_points: NDArrayBool = num_interior_pts > 0 is_evaluated: NDArrayBool = is_within_radius & is_valid_num_points return is_evaluated + + +def load_mapped_avm_and_egoposes( + gts: pd.DataFrame, dataset_dir: Path +) -> Tuple[Dict[str, ArgoverseStaticMap], Dict[str, TimestampedCitySE3EgoPoses]]: + log_ids: List[str] = gts["log_id"].unique().tolist() + log_id_to_timestamped_poses = {log_id: read_city_SE3_ego(dataset_dir / log_id) for log_id in log_ids} + log_id_to_avm = { + log_id: ArgoverseStaticMap.from_map_dir(dataset_dir / log_id / "map", build_raster=True) for log_id in log_ids + } + return log_id_to_avm, log_id_to_timestamped_poses diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 0b57f542..e05e13b4 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -318,7 +318,7 @@ def test_val_identity() -> None: dts["score"] = 1.0 annotations["num_interior_pts"] = 1 - detection_cfg = DetectionCfg(eval_only_roi_instances=False, max_num_dts_per_category=1000) + detection_cfg = DetectionCfg(eval_only_roi_instances=True, max_num_dts_per_category=1000, dataset_dir=root_dir) dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) breakpoint() From faa83171846aeb0a7de24d4ba47105d2fcb3bb26 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 20:10:11 -0400 Subject: [PATCH 38/81] Additional speedups. --- src/av2/evaluation/detection/eval.py | 74 +++++++++++++++++++------ src/av2/evaluation/detection/utils.py | 21 ++++--- tests/evaluation/detection/test_eval.py | 5 +- 3 files changed, 74 insertions(+), 26 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 03fbd9ce..aa7db0b8 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,7 +52,7 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Dict, List, Optional, Tuple +from typing import Dict, Final, List, Optional, Tuple import numpy as np import pandas as pd @@ -67,11 +67,19 @@ ) from av2.map.map_api import ArgoverseStaticMap from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego -from av2.utils.typing import NDArrayBool, NDArrayFloat +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayObject logger = logging.getLogger(__name__) +import numba +@numba.njit +def groupby(uuids, values): + N = len(uuids) + for i in range(N): + uuid = uuids[i] + +CUBOID_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m", "length_m", "width_m", "height_m", "qw", "qx", "qy", "qz"] def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -90,29 +98,61 @@ def evaluate( (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. """ - log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None - log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None + column_names = ["log_id", "timestamp_ns", "category"] + dts = dts.sort_values(column_names) + gts = gts.sort_values(column_names) - # Load maps and egoposes if roi-pruning is enabled. - if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: - log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) + dts_npy = dts.loc[:, CUBOID_COLS + ["score"]].to_numpy() + gts_npy = gts.loc[:, CUBOID_COLS].to_numpy() - column_names = ["log_id", "timestamp_ns", "category"] - uuid_to_dts = {uuid: x for uuid, x in dts.groupby(column_names, sort=False)} - uuid_to_gts = {uuid: x for uuid, x in gts.groupby(column_names, sort=False)} + dts_uuids: NDArrayFloat = dts[column_names].to_numpy() + dts_uuids = dts_uuids.astype("U64") + + gts_uuids: NDArrayFloat = gts[column_names].to_numpy() + gts_uuids = gts_uuids.astype("U64") + + unique_dts_uuids, dts_indices = np.unique(dts_uuids, axis=0, return_index=True) + unique_gts_uuids, gts_indices = np.unique(gts_uuids, axis=0, return_index=True) + + dts_groups = np.split(dts_npy, dts_indices[1:]) + gts_groups = np.split(gts_npy, gts_indices[1:]) + + uuid_to_dts = {tuple(unique_dts_uuids[i].tolist()): x for i, x in enumerate(dts_groups)} + uuid_to_gts = {tuple(unique_gts_uuids[i].tolist()): x for i, x in enumerate(gts_groups)} dts_list = [] gts_list = [] - for uuid, sweep_gts in track(uuid_to_gts.items()): - args = uuid_to_dts[uuid], sweep_gts, cfg, None, None - if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: - avm = log_id_to_avm[uuid[0]] - city_SE3_ego = log_id_to_timestamped_poses[uuid[0]][uuid[1]] - args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego - dts_accum, gts_accum = accumulate(*args) + for k, v in track(uuid_to_gts.items()): + dts_accum, gts_accum = accumulate(uuid_to_dts[k], v, cfg, None, None) dts_list.append(dts_accum) gts_list.append(gts_accum) + # breakpoint() + + + # log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None + # log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None + + # # Load maps and egoposes if roi-pruning is enabled. + # if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: + # log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) + + # column_names = ["log_id", "timestamp_ns", "category"] + # uuid_to_dts = {uuid: x for uuid, x in dts.groupby(column_names, sort=False)} + # uuid_to_gts = {uuid: x for uuid, x in gts.groupby(column_names, sort=False)} + + # dts_list = [] + # gts_list = [] + # for uuid, sweep_gts in track(uuid_to_gts.items()): + # args = uuid_to_dts[uuid], sweep_gts, cfg, None, None + # if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: + # avm = log_id_to_avm[uuid[0]] + # city_SE3_ego = log_id_to_timestamped_poses[uuid[0]][uuid[1]] + # args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego + # dts_accum, gts_accum = accumulate(*args) + # dts_list.append(dts_accum) + # gts_list.append(gts_accum) + # breakpoint() cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) dts_npy: NDArrayFloat = np.concatenate(dts_list) gts_npy: NDArrayFloat = np.concatenate(gts_list) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 4a17bb78..56178a54 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -118,10 +118,14 @@ def accumulate( """ N, M = len(dts), len(gts) + # breakpoint() + # breakpoint() + # Sort the detections by score in _descending_ order. - scores: NDArrayFloat = dts["score"].to_numpy() + scores: NDArrayFloat = dts[..., -1] permutation: NDArrayInt = np.argsort(-scores).tolist() - dts = dts.iloc[permutation, :] + # dts = dts.iloc[permutation, :] + dts = dts[permutation] is_evaluated_dts: NDArrayBool = np.ones(N, dtype=bool) is_evaluated_gts: NDArrayBool = np.ones(M, dtype=bool) @@ -129,12 +133,14 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy() - gts_npy: NDArrayFloat = gts.loc[:, CUBOID_COLS].to_numpy() + # dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy() + # gts_npy: NDArrayFloat = gts.loc[:, CUBOID_COLS].to_numpy() + dts_npy = dts + gts_npy = gts - num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() - is_evaluated_dts &= compute_evaluated_dts_mask(dts_npy, cfg) - is_evaluated_gts &= compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) + # num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() + # is_evaluated_dts &= compute_evaluated_dts_mask(dts_npy, cfg) + # is_evaluated_gts &= compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) N, M = len(dts), len(gts) dt_results: NDArrayFloat = np.zeros((N, 8)) @@ -214,6 +220,7 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA scale_errors = distance(tps_dts[:, 3:6], tps_gts[:, 3:6], DistanceType.SCALE) orientation_errors = distance(tps_dts[:, 6:10], tps_gts[:, 6:10], DistanceType.ORIENTATION) + dts_table[idx_tps_dts, 4:] = np.stack((translation_errors, scale_errors, orientation_errors), axis=-1) return dts_table, gts_table diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index e05e13b4..10faa082 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -38,6 +38,8 @@ QUAT_COLS: Final[List[str]] = ["qw", "qx", "qy", "qz"] ANNO_COLS: Final[List[str]] = ["timestamp_ns", "category"] + DIMS_COLS + QUAT_COLS + TRANSLATION_COLS +CUBOID_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m", "length_m", "width_m", "height_m", "qw", "qx", "qy", "qz"] + def _get_summary_identity() -> pd.DataFrame: """Define an evaluator that compares a set of results to itself.""" @@ -64,7 +66,6 @@ def _get_summary() -> pd.DataFrame: detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) dts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "detections.feather") gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") - _, _, summary = evaluate(dts, gts, detection_cfg) return summary @@ -161,7 +162,7 @@ def test_accumulate() -> None: gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") for _, group in gts.groupby(["log_id", "timestamp_ns"]): - job = (group, group, cfg) + job = (group.loc[:, CUBOID_COLS].to_numpy(), group.loc[:, CUBOID_COLS].to_numpy(), cfg) dts, gts = accumulate(*job) # Check that there's a true positive under every threshold. From f81ae17e84727ea8f1398f71f84b9655efe876cb Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 20:32:57 -0400 Subject: [PATCH 39/81] Cleanup code. --- src/av2/evaluation/detection/eval.py | 59 ++++++++++++--------------- src/av2/evaluation/detection/utils.py | 29 ++++++------- 2 files changed, 39 insertions(+), 49 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index aa7db0b8..02e5ddde 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -67,19 +67,24 @@ ) from av2.map.map_api import ArgoverseStaticMap from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego -from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayObject +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt, NDArrayObject + +TP_ERROR_COLUMNS: Final[Tuple[str, ...]] = tuple(x.value for x in TruePositiveErrorNames) logger = logging.getLogger(__name__) -import numba -@numba.njit -def groupby(uuids, values): - N = len(uuids) - for i in range(N): - uuid = uuids[i] + +def groupby_mapping(uuids: NDArrayObject, values: NDArrayFloat) -> Dict[Tuple[str, ...], NDArrayFloat]: + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(uuids, axis=0, return_index=True) + unique_items, unique_items_indices = outputs + dts_groups: List[NDArrayFloat] = np.split(values, unique_items_indices[1:]) + uuid_to_groups = {tuple(unique_items[i].tolist()): x for i, x in enumerate(dts_groups)} + return uuid_to_groups CUBOID_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m", "length_m", "width_m", "height_m", "qw", "qx", "qy", "qz"] + + def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -98,27 +103,21 @@ def evaluate( (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. """ - column_names = ["log_id", "timestamp_ns", "category"] - dts = dts.sort_values(column_names) - gts = gts.sort_values(column_names) + uuid_column_names = ("log_id", "timestamp_ns", "category") + dts = dts.sort_values(uuid_column_names) + gts = gts.sort_values(uuid_column_names) - dts_npy = dts.loc[:, CUBOID_COLS + ["score"]].to_numpy() - gts_npy = gts.loc[:, CUBOID_COLS].to_numpy() + dts_cols = CUBOID_COLS + ["score"] + gts_cols = CUBOID_COLS + dts_npy = dts.loc[:, dts_cols].to_numpy() + gts_npy = gts.loc[:, gts_cols].to_numpy() - dts_uuids: NDArrayFloat = dts[column_names].to_numpy() - dts_uuids = dts_uuids.astype("U64") + # Convert UUID into fixed bitwidth for unique computation. + dts_uuids: NDArrayFloat = dts[uuid_column_names].to_numpy().astype("U64") + gts_uuids: NDArrayFloat = gts[uuid_column_names].to_numpy().astype("U64") - gts_uuids: NDArrayFloat = gts[column_names].to_numpy() - gts_uuids = gts_uuids.astype("U64") - - unique_dts_uuids, dts_indices = np.unique(dts_uuids, axis=0, return_index=True) - unique_gts_uuids, gts_indices = np.unique(gts_uuids, axis=0, return_index=True) - - dts_groups = np.split(dts_npy, dts_indices[1:]) - gts_groups = np.split(gts_npy, gts_indices[1:]) - - uuid_to_dts = {tuple(unique_dts_uuids[i].tolist()): x for i, x in enumerate(dts_groups)} - uuid_to_gts = {tuple(unique_gts_uuids[i].tolist()): x for i, x in enumerate(gts_groups)} + uuid_to_dts = groupby_mapping(dts_uuids, dts_npy) + uuid_to_gts = groupby_mapping(gts_uuids, gts_npy) dts_list = [] gts_list = [] @@ -127,9 +126,6 @@ def evaluate( dts_list.append(dts_accum) gts_list.append(gts_accum) - # breakpoint() - - # log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None # log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None @@ -152,13 +148,12 @@ def evaluate( # dts_accum, gts_accum = accumulate(*args) # dts_list.append(dts_accum) # gts_list.append(gts_accum) - # breakpoint() - cols = cfg.affinity_thresholds_m + tuple(x.value for x in TruePositiveErrorNames) dts_npy: NDArrayFloat = np.concatenate(dts_list) gts_npy: NDArrayFloat = np.concatenate(gts_list) - dts.loc[:, cols + ("is_evaluated",)] = dts_npy - gts.loc[:, cfg.affinity_thresholds_m + ("is_evaluated",)] = gts_npy + COLS = cfg.affinity_thresholds_m + TP_ERROR_COLUMNS + ("is_evaluated",) + dts.loc[:, COLS] = dts_npy + gts.loc[:, COLS] = gts_npy # Compute summary metrics. metrics = summarize_metrics(dts, gts, cfg) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 56178a54..cb39b022 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -97,8 +97,8 @@ def tp_normalization_terms(self) -> Tuple[float, ...]: def accumulate( - dts: pd.DataFrame, - gts: pd.DataFrame, + dts: NDArrayFloat, + gts: NDArrayFloat, cfg: DetectionCfg, avm: Optional[ArgoverseStaticMap] = None, city_SE3_ego: Optional[SE3] = None, @@ -118,9 +118,6 @@ def accumulate( """ N, M = len(dts), len(gts) - # breakpoint() - # breakpoint() - # Sort the detections by score in _descending_ order. scores: NDArrayFloat = dts[..., -1] permutation: NDArrayInt = np.argsort(-scores).tolist() @@ -133,10 +130,8 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - # dts_npy: NDArrayFloat = dts.loc[:, CUBOID_COLS].to_numpy() - # gts_npy: NDArrayFloat = gts.loc[:, CUBOID_COLS].to_numpy() - dts_npy = dts - gts_npy = gts + # dts_npy = dts.copy() + # gts_npy = gts.copy() # num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() # is_evaluated_dts &= compute_evaluated_dts_mask(dts_npy, cfg) @@ -144,15 +139,15 @@ def accumulate( N, M = len(dts), len(gts) dt_results: NDArrayFloat = np.zeros((N, 8)) - gt_results: NDArrayFloat = np.zeros((M, 5)) + gt_results: NDArrayFloat = np.zeros((M, 8)) if is_evaluated_dts.sum() == 0 or is_evaluated_gts.sum() == 0: return dt_results, gt_results - dts_npy = dts_npy[is_evaluated_dts] - gts_npy = gts_npy[is_evaluated_gts] + # dts_npy = dts_npy[is_evaluated_dts] + # gts_npy = gts_npy[is_evaluated_gts] # Compute true positives through assigning detections and ground truths. - dts_assignments, gts_assignments = assign(dts_npy, gts_npy, cfg) + dts_assignments, gts_assignments = assign(dts[is_evaluated_dts], gts[is_evaluated_gts], cfg) dt_results[is_evaluated_dts, :-1] = dts_assignments gt_results[is_evaluated_gts, :-1] = gts_assignments @@ -195,10 +190,11 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) # type: ignore idx_gts, idx_dts = assignments - K = len(cfg.affinity_thresholds_m) - dts_table: NDArrayFloat = np.zeros((len(dts), K + 3)) + K = len(cfg.affinity_thresholds_m) + 3 + dts_table: NDArrayFloat = np.zeros((len(dts), K)) dts_table[:, 4:] = cfg.metrics_defaults[1:4] gts_table: NDArrayFloat = np.zeros((len(gts), K)) + gts_table[:, 4:] = cfg.metrics_defaults[1:4] for i, threshold_m in enumerate(cfg.affinity_thresholds_m): is_tp: NDArrayBool = affinities[idx_dts] > -threshold_m @@ -220,7 +216,6 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA scale_errors = distance(tps_dts[:, 3:6], tps_gts[:, 3:6], DistanceType.SCALE) orientation_errors = distance(tps_dts[:, 6:10], tps_gts[:, 6:10], DistanceType.ORIENTATION) - dts_table[idx_tps_dts, 4:] = np.stack((translation_errors, scale_errors, orientation_errors), axis=-1) return dts_table, gts_table @@ -341,7 +336,7 @@ def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDAr def compute_objects_in_roi_mask( - cuboids_dataframe: pd.DataFrame, city_SE3_ego: SE3, avm: ArgoverseStaticMap + cuboids_dataframe: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap ) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. From d49105ac0d38805b780e2f71993fd92417d48308 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 20:50:56 -0400 Subject: [PATCH 40/81] Additional speedup. --- src/av2/evaluation/detection/eval.py | 17 ++++++++--------- src/av2/evaluation/detection/utils.py | 4 ---- 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 02e5ddde..38924650 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,7 +52,7 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Dict, Final, List, Optional, Tuple +from typing import Any, Dict, Final, List, Optional, Tuple import numpy as np import pandas as pd @@ -74,8 +74,8 @@ logger = logging.getLogger(__name__) -def groupby_mapping(uuids: NDArrayObject, values: NDArrayFloat) -> Dict[Tuple[str, ...], NDArrayFloat]: - outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(uuids, axis=0, return_index=True) +def groupby_mapping(uuids: List[str], values: NDArrayFloat) -> Dict[Tuple[str, ...], NDArrayFloat]: + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(uuids, return_index=True) unique_items, unique_items_indices = outputs dts_groups: List[NDArrayFloat] = np.split(values, unique_items_indices[1:]) uuid_to_groups = {tuple(unique_items[i].tolist()): x for i, x in enumerate(dts_groups)} @@ -103,7 +103,7 @@ def evaluate( (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. """ - uuid_column_names = ("log_id", "timestamp_ns", "category") + uuid_column_names = ["log_id", "timestamp_ns", "category"] dts = dts.sort_values(uuid_column_names) gts = gts.sort_values(uuid_column_names) @@ -112,12 +112,11 @@ def evaluate( dts_npy = dts.loc[:, dts_cols].to_numpy() gts_npy = gts.loc[:, gts_cols].to_numpy() - # Convert UUID into fixed bitwidth for unique computation. - dts_uuids: NDArrayFloat = dts[uuid_column_names].to_numpy().astype("U64") - gts_uuids: NDArrayFloat = gts[uuid_column_names].to_numpy().astype("U64") + dts_uuids: List[str] = dts[uuid_column_names].to_numpy().astype(str).tolist() + gts_uuids: List[str] = gts[uuid_column_names].to_numpy().astype(str).tolist() - uuid_to_dts = groupby_mapping(dts_uuids, dts_npy) - uuid_to_gts = groupby_mapping(gts_uuids, gts_npy) + uuid_to_dts = groupby_mapping(["".join(x) for x in dts_uuids], dts_npy) + uuid_to_gts = groupby_mapping(["".join(x) for x in gts_uuids], gts_npy) dts_list = [] gts_list = [] diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index cb39b022..061f01c4 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -121,7 +121,6 @@ def accumulate( # Sort the detections by score in _descending_ order. scores: NDArrayFloat = dts[..., -1] permutation: NDArrayInt = np.argsort(-scores).tolist() - # dts = dts.iloc[permutation, :] dts = dts[permutation] is_evaluated_dts: NDArrayBool = np.ones(N, dtype=bool) @@ -130,9 +129,6 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - # dts_npy = dts.copy() - # gts_npy = gts.copy() - # num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() # is_evaluated_dts &= compute_evaluated_dts_mask(dts_npy, cfg) # is_evaluated_gts &= compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) From 195b46a28c407c340b76b8f0f0dc53c37316f0ef Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 21:43:12 -0400 Subject: [PATCH 41/81] Add roi pruning back. --- src/av2/evaluation/detection/eval.py | 75 +++++++++++++------------ src/av2/evaluation/detection/utils.py | 55 ++++++++++++------ src/av2/structures/cuboid.py | 17 +++--- tests/evaluation/detection/test_eval.py | 2 +- 4 files changed, 88 insertions(+), 61 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 38924650..c9d06ea4 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,7 +52,7 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Any, Dict, Final, List, Optional, Tuple +from typing import Dict, Final, List, Optional, Tuple import numpy as np import pandas as pd @@ -60,6 +60,7 @@ from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import ( + ORDERED_CUBOID_COL_NAMES, DetectionCfg, accumulate, compute_average_precision, @@ -74,17 +75,23 @@ logger = logging.getLogger(__name__) -def groupby_mapping(uuids: List[str], values: NDArrayFloat) -> Dict[Tuple[str, ...], NDArrayFloat]: - outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(uuids, return_index=True) +def groupby(names: List[str], values: NDArrayFloat) -> Dict[str, NDArrayFloat]: + """Groups a set of values by their corresponding names. + + Args: + names: String which maps data to a "bin". + values: Data which will be grouped by their names. + + Returns: + Dictionary mapping the group name to the corresponding group. + """ + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(names, return_index=True) unique_items, unique_items_indices = outputs dts_groups: List[NDArrayFloat] = np.split(values, unique_items_indices[1:]) - uuid_to_groups = {tuple(unique_items[i].tolist()): x for i, x in enumerate(dts_groups)} + uuid_to_groups = {unique_items[i]: x for i, x in enumerate(dts_groups)} return uuid_to_groups -CUBOID_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m", "length_m", "width_m", "height_m", "qw", "qx", "qy", "qz"] - - def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -104,49 +111,45 @@ def evaluate( K refers to the number of evaluation metrics. """ uuid_column_names = ["log_id", "timestamp_ns", "category"] + + # Sort both the detections and annotations by in lexicographic order for grouping. dts = dts.sort_values(uuid_column_names) gts = gts.sort_values(uuid_column_names) - dts_cols = CUBOID_COLS + ["score"] - gts_cols = CUBOID_COLS + dts_cols = ORDERED_CUBOID_COL_NAMES + ["score"] + gts_cols = ORDERED_CUBOID_COL_NAMES + ["num_interior_pts"] dts_npy = dts.loc[:, dts_cols].to_numpy() gts_npy = gts.loc[:, gts_cols].to_numpy() dts_uuids: List[str] = dts[uuid_column_names].to_numpy().astype(str).tolist() gts_uuids: List[str] = gts[uuid_column_names].to_numpy().astype(str).tolist() - uuid_to_dts = groupby_mapping(["".join(x) for x in dts_uuids], dts_npy) - uuid_to_gts = groupby_mapping(["".join(x) for x in gts_uuids], gts_npy) + # We merge the unique identifier -- the tuple of ("log_id", "timestamp_ns", "category") + # into a single string to optimize the subsequent grouping operation. + # `groupby_mapping` produces a mapping from the uuid to the group of detections / annotations + # which fall into that group. + uuid_to_dts = groupby([":".join(x) for x in dts_uuids], dts_npy) + uuid_to_gts = groupby([":".join(x) for x in gts_uuids], gts_npy) + + log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None + log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None + + # # Load maps and egoposes if roi-pruning is enabled. + if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: + log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) dts_list = [] gts_list = [] - for k, v in track(uuid_to_gts.items()): - dts_accum, gts_accum = accumulate(uuid_to_dts[k], v, cfg, None, None) + for uuid, sweep_gts in track(uuid_to_gts.items()): + log_id, timestamp_ns, _ = uuid.split(":") + args = uuid_to_dts[uuid], sweep_gts, cfg, None, None + if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: + avm = log_id_to_avm[log_id] + city_SE3_ego = log_id_to_timestamped_poses[log_id][int(timestamp_ns)] + args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego + dts_accum, gts_accum = accumulate(*args) dts_list.append(dts_accum) gts_list.append(gts_accum) - - # log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None - # log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None - - # # Load maps and egoposes if roi-pruning is enabled. - # if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: - # log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) - - # column_names = ["log_id", "timestamp_ns", "category"] - # uuid_to_dts = {uuid: x for uuid, x in dts.groupby(column_names, sort=False)} - # uuid_to_gts = {uuid: x for uuid, x in gts.groupby(column_names, sort=False)} - - # dts_list = [] - # gts_list = [] - # for uuid, sweep_gts in track(uuid_to_gts.items()): - # args = uuid_to_dts[uuid], sweep_gts, cfg, None, None - # if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: - # avm = log_id_to_avm[uuid[0]] - # city_SE3_ego = log_id_to_timestamped_poses[uuid[0]][uuid[1]] - # args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego - # dts_accum, gts_accum = accumulate(*args) - # dts_list.append(dts_accum) - # gts_list.append(gts_accum) dts_npy: NDArrayFloat = np.concatenate(dts_list) gts_npy: NDArrayFloat = np.concatenate(gts_list) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 061f01c4..a7293024 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -14,6 +14,7 @@ from dataclasses import dataclass from pathlib import Path from typing import Dict, Final, List, Optional, Tuple +from unicodedata import category import numpy as np import pandas as pd @@ -30,20 +31,30 @@ DistanceType, FilterMetricType, InterpType, - TruePositiveErrorNames, ) from av2.geometry.geometry import mat_to_xyz, quat_to_mat, wrap_angles from av2.geometry.iou import iou_3d_axis_aligned from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap, RasterLayerType -from av2.structures.cuboid import CuboidList +from av2.structures.cuboid import Cuboid, CuboidList from av2.utils.constants import EPS from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego -from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt, NDArrayObject +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt logger = logging.getLogger(__name__) -CUBOID_COLS: Final[List[str]] = ["tx_m", "ty_m", "tz_m", "length_m", "width_m", "height_m", "qw", "qx", "qy", "qz"] +ORDERED_CUBOID_COL_NAMES: Final[List[str]] = [ + "tx_m", + "ty_m", + "tz_m", + "length_m", + "width_m", + "height_m", + "qw", + "qx", + "qy", + "qz", +] @dataclass(frozen=True) @@ -129,19 +140,15 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - # num_interior_pts: NDArrayFloat = gts["num_interior_pts"].to_numpy() - # is_evaluated_dts &= compute_evaluated_dts_mask(dts_npy, cfg) - # is_evaluated_gts &= compute_evaluated_gts_mask(gts_npy, num_interior_pts, cfg) + num_interior_pts: NDArrayFloat = gts[..., -1] + is_evaluated_dts &= compute_evaluated_dts_mask(dts, cfg) + is_evaluated_gts &= compute_evaluated_gts_mask(gts, num_interior_pts, cfg) - N, M = len(dts), len(gts) dt_results: NDArrayFloat = np.zeros((N, 8)) gt_results: NDArrayFloat = np.zeros((M, 8)) if is_evaluated_dts.sum() == 0 or is_evaluated_gts.sum() == 0: return dt_results, gt_results - # dts_npy = dts_npy[is_evaluated_dts] - # gts_npy = gts_npy[is_evaluated_gts] - # Compute true positives through assigning detections and ground truths. dts_assignments, gts_assignments = assign(dts[is_evaluated_dts], gts[is_evaluated_gts], cfg) dt_results[is_evaluated_dts, :-1] = dts_assignments @@ -331,9 +338,7 @@ def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDAr raise NotImplementedError("This distance metric is not implemented!") -def compute_objects_in_roi_mask( - cuboids_dataframe: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap -) -> NDArrayBool: +def compute_objects_in_roi_mask(cuboids: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. Args: @@ -344,9 +349,27 @@ def compute_objects_in_roi_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ - cuboids_dataframe = cuboids_dataframe.sort_values("timestamp_ns").reset_index(drop=True) - cuboid_list_ego = CuboidList.from_dataframe(cuboids_dataframe) + translation = cuboids[:, :3] + length_m, width_m, height_m = cuboids[:, 3:6].T + quat_wxyz = cuboids[:, 6:10] + + cuboid_list: List[Cuboid] = [] + for c in cuboids: + translation = c[:3] + length_m, width_m, height_m = c[3:6] + quat_wxyz = c[6:10] + cuboid = Cuboid( + dst_SE3_object=SE3(rotation=quat_to_mat(quat_wxyz), translation=translation), + length_m=length_m, + width_m=width_m, + height_m=height_m, + category="", + timestamp_ns=0, + ) + cuboid_list.append(cuboid) + cuboid_list_ego = CuboidList(cuboid_list) + cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) cuboid_list_vertices_m = cuboid_list_city.vertices_m diff --git a/src/av2/structures/cuboid.py b/src/av2/structures/cuboid.py index 70ab84a0..97dbb7a3 100644 --- a/src/av2/structures/cuboid.py +++ b/src/av2/structures/cuboid.py @@ -20,7 +20,7 @@ from av2.rendering.color import BLUE_BGR, TRAFFIC_YELLOW1_BGR from av2.rendering.vector import draw_line_frustum from av2.utils.io import read_feather -from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat +from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat, NDArrayInt, NDArrayObject @dataclass @@ -391,13 +391,14 @@ def from_dataframe(cls, data: pd.DataFrame) -> CuboidList: Returns: Constructed cuboids. """ - rotation = quat_to_mat(data.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy()) - translation_m = data.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy() - length_m = data.loc[:, "length_m"].to_numpy() - width_m = data.loc[:, "width_m"].to_numpy() - height_m = data.loc[:, "height_m"].to_numpy() - category = data.loc[:, "category"].to_numpy() - timestamp_ns = data.loc[:, "timestamp_ns"].to_numpy() + quat_wxyz: NDArrayFloat = data.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy() + rotation = quat_to_mat(quat_wxyz) + translation_m: NDArrayFloat = data.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy() + length_m: NDArrayFloat = data.loc[:, "length_m"].to_numpy() + width_m: NDArrayFloat = data.loc[:, "width_m"].to_numpy() + height_m: NDArrayFloat = data.loc[:, "height_m"].to_numpy() + category: NDArrayObject = data.loc[:, "category"].to_numpy() + timestamp_ns: NDArrayInt = data.loc[:, "timestamp_ns"].to_numpy() N = len(data) cuboid_list: List[Cuboid] = [] diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 10faa082..1cf457ef 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -162,7 +162,7 @@ def test_accumulate() -> None: gts: pd.DataFrame = pd.read_feather(TEST_DATA_DIR / "labels.feather") for _, group in gts.groupby(["log_id", "timestamp_ns"]): - job = (group.loc[:, CUBOID_COLS].to_numpy(), group.loc[:, CUBOID_COLS].to_numpy(), cfg) + job = (group.loc[:, CUBOID_COLS].to_numpy(), group.loc[:, CUBOID_COLS + ["num_interior_pts"]].to_numpy(), cfg) dts, gts = accumulate(*job) # Check that there's a true positive under every threshold. From dc2a954551813fe923a3fbd69622daad1a5a73ae Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 22:52:06 -0400 Subject: [PATCH 42/81] Add multiprocessing. --- src/av2/evaluation/detection/eval.py | 16 +++++++++++----- src/av2/evaluation/detection/utils.py | 9 +++++---- 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index c9d06ea4..c829d25a 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -56,6 +56,7 @@ import numpy as np import pandas as pd +from joblib import Parallel, delayed from rich.progress import track from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames @@ -66,9 +67,10 @@ compute_average_precision, load_mapped_avm_and_egoposes, ) +from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap -from av2.utils.io import TimestampedCitySE3EgoPoses, read_city_SE3_ego -from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt, NDArrayObject +from av2.utils.io import TimestampedCitySE3EgoPoses +from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt TP_ERROR_COLUMNS: Final[Tuple[str, ...]] = tuple(x.value for x in TruePositiveErrorNames) @@ -140,6 +142,7 @@ def evaluate( dts_list = [] gts_list = [] + args_list: List[Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]]] = [] for uuid, sweep_gts in track(uuid_to_gts.items()): log_id, timestamp_ns, _ = uuid.split(":") args = uuid_to_dts[uuid], sweep_gts, cfg, None, None @@ -147,9 +150,12 @@ def evaluate( avm = log_id_to_avm[log_id] city_SE3_ego = log_id_to_timestamped_poses[log_id][int(timestamp_ns)] args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego - dts_accum, gts_accum = accumulate(*args) - dts_list.append(dts_accum) - gts_list.append(gts_accum) + args_list.append(args) + + outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel( + n_jobs=-1, backend="multiprocessing", verbose=10 + )(delayed(accumulate)(*args) for args in args_list) + dts_list, gts_list = zip(*outputs) dts_npy: NDArrayFloat = np.concatenate(dts_list) gts_npy: NDArrayFloat = np.concatenate(gts_list) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index a7293024..5a776181 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -14,10 +14,10 @@ from dataclasses import dataclass from pathlib import Path from typing import Dict, Final, List, Optional, Tuple -from unicodedata import category import numpy as np import pandas as pd +from joblib import Parallel, delayed from scipy.spatial.distance import cdist from av2.evaluation.detection.constants import ( @@ -435,7 +435,8 @@ def load_mapped_avm_and_egoposes( ) -> Tuple[Dict[str, ArgoverseStaticMap], Dict[str, TimestampedCitySE3EgoPoses]]: log_ids: List[str] = gts["log_id"].unique().tolist() log_id_to_timestamped_poses = {log_id: read_city_SE3_ego(dataset_dir / log_id) for log_id in log_ids} - log_id_to_avm = { - log_id: ArgoverseStaticMap.from_map_dir(dataset_dir / log_id / "map", build_raster=True) for log_id in log_ids - } + avms: List[ArgoverseStaticMap] = Parallel(n_jobs=-1, backend="threading", verbose=10)( + delayed(ArgoverseStaticMap.from_map_dir)(dataset_dir / log_id / "map", build_raster=True) for log_id in log_ids + ) + log_id_to_avm = {log_ids[i]: avm for i, avm in enumerate(avms)} return log_id_to_avm, log_id_to_timestamped_poses From 06e2e9659cd563788cfa2a18da06afea94d69c2f Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 23:06:26 -0400 Subject: [PATCH 43/81] Add verbosity. --- src/av2/evaluation/detection/eval.py | 10 ++++++---- src/av2/evaluation/detection/utils.py | 4 +++- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index c829d25a..d6a35b71 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -138,12 +138,13 @@ def evaluate( # # Load maps and egoposes if roi-pruning is enabled. if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: + logger.info("Loading maps and egoposes ...") log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) dts_list = [] gts_list = [] args_list: List[Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]]] = [] - for uuid, sweep_gts in track(uuid_to_gts.items()): + for uuid, sweep_gts in uuid_to_gts.items(): log_id, timestamp_ns, _ = uuid.split(":") args = uuid_to_dts[uuid], sweep_gts, cfg, None, None if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: @@ -152,9 +153,10 @@ def evaluate( args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego args_list.append(args) - outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel( - n_jobs=-1, backend="multiprocessing", verbose=10 - )(delayed(accumulate)(*args) for args in args_list) + logger.info("Starting evaluation ...") + outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel(n_jobs=-1, verbose=1)( + delayed(accumulate)(*args) for args in args_list + ) dts_list, gts_list = zip(*outputs) dts_npy: NDArrayFloat = np.concatenate(dts_list) gts_npy: NDArrayFloat = np.concatenate(gts_list) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 5a776181..7e98beed 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -435,8 +435,10 @@ def load_mapped_avm_and_egoposes( ) -> Tuple[Dict[str, ArgoverseStaticMap], Dict[str, TimestampedCitySE3EgoPoses]]: log_ids: List[str] = gts["log_id"].unique().tolist() log_id_to_timestamped_poses = {log_id: read_city_SE3_ego(dataset_dir / log_id) for log_id in log_ids} - avms: List[ArgoverseStaticMap] = Parallel(n_jobs=-1, backend="threading", verbose=10)( + avms: Optional[List[ArgoverseStaticMap]] = Parallel(n_jobs=-1, backend="threading", verbose=1)( delayed(ArgoverseStaticMap.from_map_dir)(dataset_dir / log_id / "map", build_raster=True) for log_id in log_ids ) + if avms is None: + raise RuntimeError("Map and egopose loading has failed!") log_id_to_avm = {log_ids[i]: avm for i, avm in enumerate(avms)} return log_id_to_avm, log_id_to_timestamped_poses From 54901b27a1abd80150372dd633c478d6743fd978 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 2 Apr 2022 23:28:19 -0400 Subject: [PATCH 44/81] Mypy fixes. --- src/av2/evaluation/detection/eval.py | 43 ++++++++--------------- src/av2/evaluation/detection/utils.py | 46 +++++++++++++++++++------ tests/evaluation/detection/test_eval.py | 35 +++++++++---------- 3 files changed, 67 insertions(+), 57 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index d6a35b71..3f639d4e 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,12 +52,11 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Dict, Final, List, Optional, Tuple +from typing import Dict, Final, List, Optional, Tuple, Union import numpy as np import pandas as pd from joblib import Parallel, delayed -from rich.progress import track from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import ( @@ -65,35 +64,19 @@ DetectionCfg, accumulate, compute_average_precision, + groupby, load_mapped_avm_and_egoposes, ) from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap from av2.utils.io import TimestampedCitySE3EgoPoses -from av2.utils.typing import NDArrayBool, NDArrayFloat, NDArrayInt +from av2.utils.typing import NDArrayBool, NDArrayFloat TP_ERROR_COLUMNS: Final[Tuple[str, ...]] = tuple(x.value for x in TruePositiveErrorNames) logger = logging.getLogger(__name__) -def groupby(names: List[str], values: NDArrayFloat) -> Dict[str, NDArrayFloat]: - """Groups a set of values by their corresponding names. - - Args: - names: String which maps data to a "bin". - values: Data which will be grouped by their names. - - Returns: - Dictionary mapping the group name to the corresponding group. - """ - outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(names, return_index=True) - unique_items, unique_items_indices = outputs - dts_groups: List[NDArrayFloat] = np.split(values, unique_items_indices[1:]) - uuid_to_groups = {unique_items[i]: x for i, x in enumerate(dts_groups)} - return uuid_to_groups - - def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, @@ -139,13 +122,13 @@ def evaluate( # # Load maps and egoposes if roi-pruning is enabled. if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: logger.info("Loading maps and egoposes ...") - log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(gts, cfg.dataset_dir) + log_ids = gts["log_id"].unique().tolist() + log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(log_ids, cfg.dataset_dir) - dts_list = [] - gts_list = [] args_list: List[Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]]] = [] for uuid, sweep_gts in uuid_to_gts.items(): log_id, timestamp_ns, _ = uuid.split(":") + args: Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]] args = uuid_to_dts[uuid], sweep_gts, cfg, None, None if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: avm = log_id_to_avm[log_id] @@ -157,13 +140,15 @@ def evaluate( outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel(n_jobs=-1, verbose=1)( delayed(accumulate)(*args) for args in args_list ) + if outputs is None: + raise RuntimeError("Accumulation has failed! Please check the integrity of your detections and annotations.") dts_list, gts_list = zip(*outputs) - dts_npy: NDArrayFloat = np.concatenate(dts_list) - gts_npy: NDArrayFloat = np.concatenate(gts_list) COLS = cfg.affinity_thresholds_m + TP_ERROR_COLUMNS + ("is_evaluated",) - dts.loc[:, COLS] = dts_npy - gts.loc[:, COLS] = gts_npy + dts_metrics: NDArrayFloat = np.concatenate(dts_list) # type: ignore + gts_metrics: NDArrayFloat = np.concatenate(gts_list) # type: ignore + dts.loc[:, COLS] = dts_metrics + gts.loc[:, COLS] = gts_metrics # Compute summary metrics. metrics = summarize_metrics(dts, gts, cfg) @@ -237,7 +222,7 @@ def summarize_metrics( is_tp_t = category_dts[middle_threshold].to_numpy().astype(bool) # Initialize true positive metrics. - tp_errors = np.array(cfg.tp_normalization_terms) + tp_errors: NDArrayFloat = np.array(cfg.tp_normalization_terms) # Check whether any true positives exist under the current threshold. has_true_positives = np.any(is_tp_t) @@ -245,7 +230,7 @@ def summarize_metrics( # If true positives exist, compute the metrics. if has_true_positives: tp_error_cols = [str(x.value) for x in TruePositiveErrorNames] - tp_errors: NDArrayFloat = category_dts.loc[is_tp_t, tp_error_cols].to_numpy().mean(axis=0) + tp_errors = category_dts.loc[is_tp_t, tp_error_cols].to_numpy().mean(axis=0) # Convert errors to scores. tp_scores = 1 - np.divide(tp_errors, cfg.tp_normalization_terms) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 7e98beed..83e9c0ac 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -140,9 +140,9 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - num_interior_pts: NDArrayFloat = gts[..., -1] + # num_interior_pts: NDArrayFloat = gts[..., -1] is_evaluated_dts &= compute_evaluated_dts_mask(dts, cfg) - is_evaluated_gts &= compute_evaluated_gts_mask(gts, num_interior_pts, cfg) + is_evaluated_gts &= compute_evaluated_gts_mask(gts, cfg) dt_results: NDArrayFloat = np.zeros((N, 8)) gt_results: NDArrayFloat = np.zeros((M, 8)) @@ -159,7 +159,7 @@ def accumulate( gt_results[is_evaluated_gts, -1] = True # Permute the detections according to the original ordering. - outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(permutation, return_index=True) + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(permutation, return_index=True) # type: ignore _, inverse_permutation = outputs dt_results = dt_results[inverse_permutation] return dt_results, gt_results @@ -342,14 +342,13 @@ def compute_objects_in_roi_mask(cuboids: NDArrayFloat, city_SE3_ego: SE3, avm: A """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. Args: - cuboids_dataframe: Dataframes containing cuboids. + cuboids: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. city_SE3_ego: Egovehicle pose in the city reference frame. avm: Argoverse map object. Returns: The boolean mask indicating which cuboids will be evaluated. """ - translation = cuboids[:, :3] length_m, width_m, height_m = cuboids[:, 3:6].T quat_wxyz = cuboids[:, 6:10] @@ -407,7 +406,6 @@ def compute_evaluated_dts_mask( def compute_evaluated_gts_mask( gts: NDArrayFloat, - num_interior_pts: NDArrayFloat, cfg: DetectionCfg, ) -> NDArrayBool: """Compute the evaluated cuboids mask. @@ -417,7 +415,7 @@ def compute_evaluated_gts_mask( 2. The cuboid must have at _least_ one point in its interior. Args: - gts: Dataframes containing ground truth cuboids. + gts: (N,11) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES` + "num_interior_pts". cfg: Detection configuration object. Returns: @@ -425,15 +423,26 @@ def compute_evaluated_gts_mask( """ norm: NDArrayFloat = np.linalg.norm(gts[:, :3], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m - is_valid_num_points: NDArrayBool = num_interior_pts > 0 + is_valid_num_points: NDArrayBool = gts[:, -1] > 0 is_evaluated: NDArrayBool = is_within_radius & is_valid_num_points return is_evaluated def load_mapped_avm_and_egoposes( - gts: pd.DataFrame, dataset_dir: Path + log_ids: List[str], dataset_dir: Path ) -> Tuple[Dict[str, ArgoverseStaticMap], Dict[str, TimestampedCitySE3EgoPoses]]: - log_ids: List[str] = gts["log_id"].unique().tolist() + """Load the maps and egoposes for each log in the dataset directory. + + Args: + log_ids: List of the log_ids. + dataset_dir: Directory to the dataset. + + Returns: + A tuple of mappings from log id to maps and timestamped-egoposes, respectively. + + Raises: + RuntimeError: If the process for loading maps and timestamped egoposes fails. + """ log_id_to_timestamped_poses = {log_id: read_city_SE3_ego(dataset_dir / log_id) for log_id in log_ids} avms: Optional[List[ArgoverseStaticMap]] = Parallel(n_jobs=-1, backend="threading", verbose=1)( delayed(ArgoverseStaticMap.from_map_dir)(dataset_dir / log_id / "map", build_raster=True) for log_id in log_ids @@ -442,3 +451,20 @@ def load_mapped_avm_and_egoposes( raise RuntimeError("Map and egopose loading has failed!") log_id_to_avm = {log_ids[i]: avm for i, avm in enumerate(avms)} return log_id_to_avm, log_id_to_timestamped_poses + + +def groupby(names: List[str], values: NDArrayFloat) -> Dict[str, NDArrayFloat]: + """Group a set of values by their corresponding names. + + Args: + names: String which maps data to a "bin". + values: Data which will be grouped by their names. + + Returns: + Dictionary mapping the group name to the corresponding group. + """ + outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(names, return_index=True) # type: ignore + unique_items, unique_items_indices = outputs + dts_groups: List[NDArrayFloat] = np.split(values, unique_items_indices[1:]) # type: ignore + uuid_to_groups = {unique_items[i]: x for i, x in enumerate(dts_groups)} + return uuid_to_groups diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 1cf457ef..316464d4 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -300,29 +300,28 @@ def test_compute_evaluated_gts_mask() -> None: ) detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) - gts_mask = compute_evaluated_gts_mask(gts[:, :-1], gts[:, -1], detection_cfg) + gts_mask = compute_evaluated_gts_mask(gts, detection_cfg) gts_mask_: NDArrayBool = np.array([True, False, False, False]) np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -def test_val_identity() -> None: - root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" - paths = sorted(root_dir.glob("*/annotations.feather")) +# def test_val_identity() -> None: +# root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" +# paths = sorted(root_dir.glob("*/annotations.feather")) - annotations = [] - for p in paths: - df = pd.read_feather(p) - df["log_id"] = p.parent.stem - annotations.append(df) - annotations = pd.concat(annotations).reset_index(drop=True) - dts = annotations.copy() - dts["score"] = 1.0 - annotations["num_interior_pts"] = 1 +# annotations = [] +# for p in paths: +# df = pd.read_feather(p) +# df["log_id"] = p.parent.stem +# annotations.append(df) +# annotations = pd.concat(annotations).reset_index(drop=True) +# dts = annotations.copy() +# dts["score"] = 1.0 +# annotations.loc[:, "num_interior_pts"] = 1 - detection_cfg = DetectionCfg(eval_only_roi_instances=True, max_num_dts_per_category=1000, dataset_dir=root_dir) - dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) - breakpoint() +# detection_cfg = DetectionCfg(eval_only_roi_instances=True, max_num_dts_per_category=1000, dataset_dir=root_dir) +# dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) -if __name__ == "__main__": - test_val_identity() +# if __name__ == "__main__": +# test_val_identity() From 856b4d429c53ddee425faa31f6a4bb2ea838119b Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sun, 3 Apr 2022 13:30:42 -0400 Subject: [PATCH 45/81] Update cuboid fields. --- src/av2/evaluation/detection/eval.py | 4 +- src/av2/evaluation/detection/utils.py | 40 ++------------ src/av2/structures/cuboid.py | 75 ++++++++++++++------------- 3 files changed, 45 insertions(+), 74 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 3f639d4e..91aea0eb 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,7 +52,7 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging -from typing import Dict, Final, List, Optional, Tuple, Union +from typing import Dict, Final, List, Optional, Tuple import numpy as np import pandas as pd @@ -60,7 +60,6 @@ from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import ( - ORDERED_CUBOID_COL_NAMES, DetectionCfg, accumulate, compute_average_precision, @@ -69,6 +68,7 @@ ) from av2.geometry.se3 import SE3 from av2.map.map_api import ArgoverseStaticMap +from av2.structures.cuboid import ORDERED_CUBOID_COL_NAMES from av2.utils.io import TimestampedCitySE3EgoPoses from av2.utils.typing import NDArrayBool, NDArrayFloat diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 83e9c0ac..a7d3bd84 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -43,19 +43,6 @@ logger = logging.getLogger(__name__) -ORDERED_CUBOID_COL_NAMES: Final[List[str]] = [ - "tx_m", - "ty_m", - "tz_m", - "length_m", - "width_m", - "height_m", - "qw", - "qx", - "qy", - "qz", -] - @dataclass(frozen=True) class DetectionCfg: @@ -338,37 +325,20 @@ def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDAr raise NotImplementedError("This distance metric is not implemented!") -def compute_objects_in_roi_mask(cuboids: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap) -> NDArrayBool: +def compute_objects_in_roi_mask( + cuboids_parameters: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap +) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. Args: - cuboids: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. + cuboids_parameters: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. city_SE3_ego: Egovehicle pose in the city reference frame. avm: Argoverse map object. Returns: The boolean mask indicating which cuboids will be evaluated. """ - translation = cuboids[:, :3] - length_m, width_m, height_m = cuboids[:, 3:6].T - quat_wxyz = cuboids[:, 6:10] - - cuboid_list: List[Cuboid] = [] - for c in cuboids: - translation = c[:3] - length_m, width_m, height_m = c[3:6] - quat_wxyz = c[6:10] - cuboid = Cuboid( - dst_SE3_object=SE3(rotation=quat_to_mat(quat_wxyz), translation=translation), - length_m=length_m, - width_m=width_m, - height_m=height_m, - category="", - timestamp_ns=0, - ) - cuboid_list.append(cuboid) - cuboid_list_ego = CuboidList(cuboid_list) - + cuboid_list_ego: CuboidList = CuboidList([Cuboid.from_numpy(params) for params in cuboids_parameters]) cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) cuboid_list_vertices_m = cuboid_list_city.vertices_m diff --git a/src/av2/structures/cuboid.py b/src/av2/structures/cuboid.py index 97dbb7a3..da5a935f 100644 --- a/src/av2/structures/cuboid.py +++ b/src/av2/structures/cuboid.py @@ -8,7 +8,7 @@ from enum import Enum from functools import cached_property from pathlib import Path -from typing import List, Optional, Tuple +from typing import Final, List, Optional, Tuple import numpy as np import pandas as pd @@ -22,6 +22,19 @@ from av2.utils.io import read_feather from av2.utils.typing import NDArrayBool, NDArrayByte, NDArrayFloat, NDArrayInt, NDArrayObject +ORDERED_CUBOID_COL_NAMES: Final[List[str]] = [ + "tx_m", + "ty_m", + "tz_m", + "length_m", + "width_m", + "height_m", + "qw", + "qx", + "qy", + "qz", +] + @dataclass class Cuboid: @@ -42,8 +55,8 @@ class Cuboid: length_m: float width_m: float height_m: float - category: Enum - timestamp_ns: int + timestamp_ns: Optional[int] = None + category: Optional[Enum] = None @property def xyz_center_m(self) -> NDArrayFloat: @@ -134,25 +147,27 @@ def transform(self, target_SE3_dst: SE3) -> Cuboid: ) @classmethod - def from_series(cls, data: pd.Series) -> Cuboid: - """Convert a Pandas series to a Cuboid. + def from_numpy( + cls, params: NDArrayObject, category: Optional[Enum] = None, timestamp_ns: Optional[int] = None + ) -> Cuboid: + """Convert a set of cuboid parameters to a `Cuboid` object. + + NOTE: Category and timestamp may be optionally provided. Args: - data: Row from a Pandas DataFrame. + data: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. + category: Category name of the cuboid. + timestamp_ns: Sweep at which the cuboid was annotated or detected. Returns: Constructed cuboid. """ - translation = data.loc[["tx_m", "ty_m", "tz_m"]].to_numpy() - quat_wxyz = data.loc[["qw", "qx", "qy", "qz"]].to_numpy() - length_m = data["length_m"] - width_m = data["width_m"] - height_m = data["height_m"] + translation = params[:3] + length_m, width_m, height_m = params[3:6] + quat_wxyz = params[6:10] rotation = geometry_utils.quat_to_mat(quat_wxyz) ego_SE3_object = SE3(rotation=rotation, translation=translation) - category = data["category"] - timestamp_ns = data["timestamp_ns"] return cls( dst_SE3_object=ego_SE3_object, length_m=length_m, @@ -386,31 +401,17 @@ def from_dataframe(cls, data: pd.DataFrame) -> CuboidList: """Read annotations from a feather file. Args: - data: (N,12) Dataframe containing the cuboids and their respective parameters . + data: (N,12) Dataframe containing the cuboids and their respective parameters. Returns: Constructed cuboids. """ - quat_wxyz: NDArrayFloat = data.loc[:, ["qw", "qx", "qy", "qz"]].to_numpy() - rotation = quat_to_mat(quat_wxyz) - translation_m: NDArrayFloat = data.loc[:, ["tx_m", "ty_m", "tz_m"]].to_numpy() - length_m: NDArrayFloat = data.loc[:, "length_m"].to_numpy() - width_m: NDArrayFloat = data.loc[:, "width_m"].to_numpy() - height_m: NDArrayFloat = data.loc[:, "height_m"].to_numpy() - category: NDArrayObject = data.loc[:, "category"].to_numpy() - timestamp_ns: NDArrayInt = data.loc[:, "timestamp_ns"].to_numpy() - N = len(data) - - cuboid_list: List[Cuboid] = [] - for i in range(N): - ego_SE3_object = SE3(rotation=rotation[i], translation=translation_m[i]) - cuboid = Cuboid( - dst_SE3_object=ego_SE3_object, - length_m=length_m[i], - width_m=width_m[i], - height_m=height_m[i], - category=category[i], - timestamp_ns=timestamp_ns[i], - ) - cuboid_list.append(cuboid) - return cls(cuboids=cuboid_list) + cuboids_parameters: NDArrayFloat = data.loc[:, ORDERED_CUBOID_COL_NAMES].to_numpy() + categories: NDArrayObject = data.loc[:, "category"].to_numpy() + timestamps_ns: NDArrayInt = data.loc[:, "timestamp_ns"].to_numpy() + + cuboid_list = [ + Cuboid.from_numpy(params, category, timestamp_ns) + for params, category, timestamp_ns in zip(cuboids_parameters, categories, timestamps_ns) + ] + return cls(cuboid_list) From be5a89c49a15edecae9861ec9be04c05045cff8d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sun, 3 Apr 2022 13:45:37 -0400 Subject: [PATCH 46/81] Lint fixes. --- src/av2/evaluation/detection/eval.py | 7 +++++-- src/av2/structures/cuboid.py | 4 ++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 91aea0eb..bdf13cbb 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -94,6 +94,9 @@ def evaluate( Returns: (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. K refers to the number of evaluation metrics. + + Raises: + RuntimeError: If accumulation fails. """ uuid_column_names = ["log_id", "timestamp_ns", "category"] @@ -103,8 +106,8 @@ def evaluate( dts_cols = ORDERED_CUBOID_COL_NAMES + ["score"] gts_cols = ORDERED_CUBOID_COL_NAMES + ["num_interior_pts"] - dts_npy = dts.loc[:, dts_cols].to_numpy() - gts_npy = gts.loc[:, gts_cols].to_numpy() + dts_npy: NDArrayFloat = dts.loc[:, dts_cols].to_numpy() + gts_npy: NDArrayFloat = gts.loc[:, gts_cols].to_numpy() dts_uuids: List[str] = dts[uuid_column_names].to_numpy().astype(str).tolist() gts_uuids: List[str] = gts[uuid_column_names].to_numpy().astype(str).tolist() diff --git a/src/av2/structures/cuboid.py b/src/av2/structures/cuboid.py index da5a935f..c23640d6 100644 --- a/src/av2/structures/cuboid.py +++ b/src/av2/structures/cuboid.py @@ -155,9 +155,9 @@ def from_numpy( NOTE: Category and timestamp may be optionally provided. Args: - data: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. + params: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. category: Category name of the cuboid. - timestamp_ns: Sweep at which the cuboid was annotated or detected. + timestamp_ns: Sweep timestamp at which the cuboid was annotated or detected (in nanoseconds). Returns: Constructed cuboid. From 5cb43eadf2dde7e8b4aade64b6aefa99f02f490d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sun, 3 Apr 2022 14:20:41 -0400 Subject: [PATCH 47/81] Fix map tutorial issues. --- tutorials/map_tutorial.ipynb | 99 +++++++++++++++++++++++++----------- 1 file changed, 70 insertions(+), 29 deletions(-) diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb index 29c82ce8..31a079e7 100644 --- a/tutorials/map_tutorial.ipynb +++ b/tutorials/map_tutorial.ipynb @@ -10,41 +10,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "f25e1e66", "metadata": {}, "outputs": [], "source": [ - "from types import SimpleNamespace" + "from argparse import Namespace\n", + "from pathlib import Path" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "d70c7396", "metadata": {}, "outputs": [], "source": [ "# path to where the logs live\n", - "dataroot = \"/Users/jlambert/Downloads/av2-v7-2022-01-15\"\n", + "dataroot = \"/Users/admin/data/datasets/av2/sensor/val/\"\n", "\n", "# unique log identifier\n", - "log_id = \"adcf7d18-0510-35b0-a2fa-b4cea13a6d76\"" + "log_id = \"36aec72e-5086-376c-b109-295b128e77e1/\"" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "f919b29b", "metadata": {}, "outputs": [], "source": [ - "args = SimpleNamespace(**{\"dataroot\": dataroot, \"log_id\": log_id})" + "args = Namespace(**{\"dataroot\": Path(dataroot), \"log_id\": Path(log_id)})" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "15a8196d", "metadata": {}, "outputs": [], @@ -62,7 +63,7 @@ "\n", "import av2.geometry.polyline_utils as polyline_utils\n", "import av2.rendering.vector as vector_plotting_utils\n", - "from av2.datasets.sensor_dataset.av2_sensor_dataloader import AV2SensorDataLoader\n", + "from av2.datasets.sensor.av2_sensor_dataloader import AV2SensorDataLoader\n", "from av2.map.map_api import ArgoverseStaticMap, LaneSegment" ] }, @@ -76,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "3a02b3b8", "metadata": {}, "outputs": [], @@ -91,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "8d5ae748", "metadata": {}, "outputs": [], @@ -102,23 +103,20 @@ " in a bird's eye view.\n", " \"\"\"\n", " log_map_dirpath = Path(args.dataroot) / args.log_id / \"map\"\n", - " avm = ArgoverseStaticMap.from_json(log_map_dirpath, build_raster=False)\n", + " avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)\n", "\n", " fig = plt.figure(figsize=(10, 10))\n", " ax = fig.add_subplot()\n", "\n", - " for id, ls in avm.vector_lane_segments.items():\n", + " for _, ls in avm.vector_lane_segments.items():\n", "\n", " # right_ln_bnd\n", " # left_ln_bnd\n", " vector_plotting_utils.draw_polygon_mpl(ax, ls.polygon_boundary, color=\"g\", linewidth=0.5)\n", " vector_plotting_utils.plot_polygon_patch_mpl(ls.polygon_boundary, ax, color=\"g\", alpha=0.2)\n", "\n", - " x, y, z = ls.polygon_boundary.mean(axis=0)\n", - " # plt.text(x, y, str(id))\n", - "\n", " # plot all pedestrian crossings\n", - " for id, pc in avm.vector_pedestrian_crossings.items():\n", + " for _, pc in avm.vector_pedestrian_crossings.items():\n", " vector_plotting_utils.draw_polygon_mpl(ax, pc.polygon, color=\"m\", linewidth=0.5)\n", " vector_plotting_utils.plot_polygon_patch_mpl(pc.polygon, ax, color=\"m\", alpha=0.2)\n", "\n", @@ -127,17 +125,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "4e27fe09", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "single_log_teaser(args)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "56a98bd9", "metadata": {}, "outputs": [], @@ -148,7 +157,7 @@ " Also, visualize side-by-side plots of the 3 raster arrays -- ground height, drivable area, ROI.\n", " \"\"\"\n", " log_map_dirpath = Path(args.dataroot) / args.log_id / \"map\"\n", - " avm = ArgoverseStaticMap.from_json(log_map_dirpath, build_raster=True)\n", + " avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=True)\n", "\n", " height_array = avm.raster_ground_height_layer.array\n", " ax = plt.subplot()\n", @@ -179,17 +188,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "2bdc3926", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize_raster_layers(args)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "f45e82da", "metadata": {}, "outputs": [], @@ -246,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "4f7dec27", "metadata": {}, "outputs": [], @@ -264,7 +294,7 @@ "\n", " log_map_dirpath = Path(args.dataroot) / args.log_id / \"map\"\n", "\n", - " avm = ArgoverseStaticMap.from_json(log_map_dirpath, build_raster=False)\n", + " avm = ArgoverseStaticMap.from_map_dir(log_map_dirpath, build_raster=False)\n", "\n", " # retain every pose first.\n", " traj_ns = loader.get_subsampled_ego_trajectory(args.log_id, sample_rate_hz=1e9)\n", @@ -307,10 +337,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "ac070630", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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lX/4l5DUaGxuLX/ziFyHnnxmAg914443IysoKeZM0MjICm82GqqqqwHkTEhLmfX0RUeSt+TA2Dx7X6tHe3i4OHz4sdDpd4FCpVOLrX/+6ePvtt0VKSopITk4Wx44dE1//+tcFAHH48GHR19cnysvLhcFgEE8++eS8UywkSRJ/+Zd/GVj5v337dtHX1xeysM1/aVqr1Yry8nIxODgYWAx28uTJQCWFsrIy0djYKI4dOyYACKPRKDo6OsQDDzwg1Gq1UKvVYtu2bYEpBv/5n/8pXnvtNZGZmSlkWRY7d+4MWWj4xS9+Ubz55puz+rx//34xMjIy58p+WZbFmTNnxN///d8LvV4vZFkWVVVVIQvDgo/KykpRXV0tZFkWsbGx4lvf+pZobGwUarVaABC33Xab6O/vFxUVFSIhIUG8/vrrIZflW1tbxRe+8AWhUqlEfHy8ePbZZ8XPfvYzAfimYzQ3N4svf/nLQqVSiRtvvFFMTU0FFqzNPG655RaxadMmIcuyMJlM4jvf+Y7o7e0NqRii1+vF+Pi42L9//6znP/nkk+L3v/+9MBqNIjs7WzQ0NAQWrN17772iq6srUEWjqKgoUJVk5vHII48Il8slPvCBDwiVSiU+97nPifb29sDPRKVSCZ1OJ77xjW+In/zkJ4HXJACRkpIiJiYmxN133y10Op34t3/7t5DpGoWFhSIpKUnIsiwOHz4shoeHRWVl5byv/w9/+MOiv79/3mkpmzZtEmq1WphMJvGtb31LvPXWW4H79u3bJ3JzcwUAkZOTI954442QqTZHjhwR3d3doqKiQhgMBvHMM8+Ip556KnC/RqMROp1OvPXWW+JjH/uY0Ol0QpKkRV+Di70utm7dKrq6usSOHTsE4JtOc8cddwSmNQG+KTMWiyXktuDjscceE08++eSs27/97W+LX/7ylyI1NVUAvkW3hw4dCtyv1WqFTqcT3d3d4tZbb41qNRoePK7DY807wIPHNXu0t7eLmb7+9a8LnU4nvvOd74i+vj7R19cnvvOd74T8sfuHf/gH0d/fL3p7e8Vf/dVfCSGEyMnJmXV+SZLEiy++KEZHR4XZbBZNTU3ii1/8YshjsrKyxIsvvijMZrNobW0Vf/mXfxm4b8+ePaKhoUGYzWZx9OhR8bWvfS0QkAFfJYc//OEPYmhoSIyMjIjXX39dVFdXC8AX9r71rW+Jnp4eMTExId58882QFffBAS/4eOONN4Tb7RZmszlwvPDCC4H7MzMzxdNPPy36+/vF2NiYOH78+Lyl8vbv3y8aGxuFxWIRg4OD4je/+Y0oLi4OecznPvc5MTAwICYnJ8Xjjz8eEuKrq6vFkSNHxNjYmBgeHhbPPPNMIJwAvgD+zjvvCIvFIurq6sRdd90177/1vffeG/hZDg0Nieeff35WdYP77rtPdHR0zPl8k8kkfv7zn4upqSnR1dUVUgIOgPj4xz8uGhsbhdlsFrW1tWLTpk1znmdmFYuzZ8+GVLx45JFHZr0mH3nkkcD9Bw8eFA0NDcJms4kjR44E5msDEB/84AdFb2+vsFqt4ty5cyHhba6jra1NuFyukH/rH/zgB4H7n376aTExMSEmJibEL37xi5Cf/ec+9znR09MjrFar6OrqEt/97ndDAmdwFYvJyUnx3HPPhcwjPnLkyKzv0z9vd7HX4GKvi9tuu02cOnUqUP3lmWeeCenbD3/4Q/GTn/xkzp+JTqcT4+Pjc1Ys0el04l/+5V9Ea2urmJycFPX19SFvduf6/0nwvw8PHjzCd0jTnxDRFaq8vBwXL16ETqeD1+td6+4QERFd8zgHmegKdNddd0Gj0SAhIQGPPvoofv/73zMcExERRQkDMtEV6OMf/ziGh4fR2toKr9cbsuiJiIiIIotTLIiIiIiIgnAEmYiIiIgoCAMyEREREVEQBmQiIiIioiAMyEREREREQRiQiYiIiIiCMCATEREREQVRr3UHiIiIiNZaYmIiPvvZz6KgoACSJK11dyiMhBDo6OjAt7/9bYyPjy/pOayDTERERNe9r33tazhz5gxeeOEF7lx6jVGpVLjzzjuxdetWPPLII0t6DqdYEBER0XWvoKCA4fga5fV68fzzz6OgoGDJz2FAJiIiouueJEkMx9cwr9e7rKkzDMhEREREREEYkImIiIiuAB6PB+fOnQscX/jCF9akH2azec7bP/7xj+PBBx+c93n5+fmora2NVLeiilUsiIiIiK4AdrsdmzdvXutuzOuxxx5b6y5EDUeQiYiIiK5gt99+OxoaGnDs2DF85zvfwe9//3sAvtJ0v/nNb1BTU4Pjx49jw4YNs577V3/1V3j00UcDXz/88MP47ne/CwD4yEc+gpMnT+LcuXP44Q9/CFm+HAv/+Z//GefPn8fx48eRlpYGAHjkkUfw+c9/HgBQVFSEV199FefPn8e7776LwsLCkHZlWcY3v/lNnDp1CjU1NfjLv/zLWX3Lz89HQ0MD/ud//gc1NTX41a9+BYPBAAA4cOAAzp49iwsXLuDHP/4xtFotAOBf//VfUVdXh5qaGvz7v/87ACAlJQW//vWvcerUKZw6dQo33njjyn7QMwgePHjw4MGDB4/r+fjJT34S+PxbgDgS5uNbS+iDx+MR586dCxwf+tCHhE6nE11dXaKgoEAAEE8//bT4/e9/LwCI7373u+IrX/mKACD2798vzp07N+ucKSkporm5OfD1Cy+8IG666SZRXl4unnvuOaFWqwUA8f3vf188+OCDAoAQQoj3vOc9AoB49NFHxT/+4z8KAOKRRx4Rn//85wUAceLECXHXXXcJAEKn0wmDwSDy8/NFbW2tACD+4i/+IvA8rVYrTp8+Hfge/Ed+fr4QQogbb7xRABA//vGPxec///nA91xSUiIAiCeffFJ85jOfEYmJiaKxsTHw/Pj4eAFAPPXUU+Kmm24SAERubq6or69f9N94sYNTLIiIiIiuAHNNsaiurkZbWxs6OjoAAD//+c8Do7G7d+/GPffcAwA4cuQIkpOTERcXh6mpqcDzR0ZG0NbWhhtuuAHNzc0oKyvD22+/jU996lPYunUrTp8+DQAwGAwYGhoCADidTvzhD38AALz77ru49dZbQ/pkNBqRnZ2N3/72t4HHz3To0CFs3LgR9957LwAgPj4eJSUlge/Dr6urC++88w4A4Gc/+xk+/elP49VXX0V7ezuam5sBAE8++SQ+9alP4T//8z/hcDjwox/9CM8//3ygj7fccgsqKysD54yLi4PRaITFYlnsRz4vBmQiIiKiIJ9b6w4EWag02Vz3CSHw+OOPY/Pmzejr68Odd96JX/7yl/jQhz6ExsZG/OY3vwk898knn8SXvvSlWedwu92Bz71eL9Tq0Li4lHJpkiThb/7mb/DKK68s+DghxKyv5zu/1+vFjh07cPDgQdx3333467/+axw8eBCyLGPXrl1wOByL9mupOAeZiIiI6ArV2NiIwsJC5OfnAwD+5E/+JHDf0aNH8ZGPfAQAsG/fPoyMjMBsNuPP//zPsXnzZtx5550AgGeffRZ33XUX7r//fvzyl78EALz++uu49957kZqaCsA3nzkvL29JfTKbzejp6cH73/9+AIBWqw3MHfZ7+eWX8YlPfCIQrktKShATEzPrXPn5+di5cycA4P7778dbb72FxsZGFBQUoKioCADw4IMP4s0330RsbCzi4+Px4osv4rOf/Sw2bdoEAHjllVfw13/914FzVldXL+n7WAhHkImIiIiuAAaDAefOnQt8/dJLL+GLX/wiPvnJT+Kll17CyMgITp06Fbj/q1/9Kp544gnU1NTAZrPh4YcfnvO8ExMTqK+vR2VlZWBKRUNDA7785S/jlVdegSzLcLvd+NSnPoWurq4l9fXBBx/EY489hn/6p3+C2+3GBz/4QSiKErj/Rz/6EQoKCnD27FlIkoTh4WHcdddds85TX1+Phx9+GI899hiam5vxgx/8AE6nE3/2Z3+GX/3qV1Cr1Th9+jR++MMfIikpCb/73e+g1+shSRI+9znfWP+nP/1pfP/730dNTQ3UajWOHj2KT3ziE0v6Phay5hPjefDgwYMHDx481vJYzgKuaB+xsbGBz7///e+Lz372s2vep3AcwYv6rrR/Y06xICIiIrqC/cVf/AXOnTuHuro6xMfHX1f1iNcKp1gQERERXcG+/e1v49vf/vZadyPsOjs756zdfCXgCDIRERFd94QQUKlUa90NihCVSjWrYsZCGJCJiIjoutfR0YE777yTIfkapFKpcOedd86qwbwQCb7JyERERETXrcTERHz2s59FQUHBkur80tVDCIGOjg58+9vfxvj4+JKew4BMRERERBSEUyyIiIiIiIIwIBMRERERBWFAJiIiIiIKwoBMRERERBSEAZmIiIiIKAgDMhERERFREAZkIiIiIqIgDMhEREREREEYkImIiIiIgjAgExEREREFYUAmIiIiIgrCgExEREREFIQBmYiIiIgoCAMyEREREVEQBmQiIiIioiAMyEREREREQRiQiYiIiIiCMCATEREREQVhQCYiIiIiCsKATEREREQUhAGZiIiIiCgIAzIRERERURAGZCIiIiKiIAzIRERERERBGJCJiIiIiIIwIBMRERERBWFAJiIiIiIKwoBMRERERBSEAZmIiIiIKAgDMhERERFREAZkIiIiIqIgDMhEREREREEYkImIiIiIgjAgExEREREFYUAmIiIiIgrCgExEREREFIQBmYiIiIgoCAMyEREREVEQBmQiIiIioiAMyEREREREQRiQiYiIiIiCMCATEREREQVhQCYiIiIiCsKATEREREQUhAGZiIiIiCgIAzIRERERURAGZCIiIiKiIAzIRERERERBGJCJiIiIiIIwIBMRERERBWFAJiIiIiIKwoBMRERERBSEAZmIiIiIKAgDMhERERFREAZkIiIiIqIgDMhEREREREEYkImIiIiIgjAgExEREREFYUAmIiIiIgrCgExEREREFIQBmYiIiIgoCAMyEREREVEQBmQiIiIioiAMyEREREREQRiQiYiIiIiCMCATEREREQVhQCYiIiIiCsKATEREREQUhAGZiIiIiCgIAzIRERERURAGZCIiIiKiIAzIRERERERBGJCJiIiIiIIwIBMRERERBWFAJiIiIiIKwoBMRERERBSEAZmIiIiIKAgDMhERERFREAZkIiIiIqIgDMhEREREREEYkImIiIiIgjAgExEREREFYUAmIiIiIgrCgExEREREFIQBmYiIiIgoCAMyEREREVEQBmQiIiIioiAMyEREREREQRiQiYiIiIiCMCATEREREQVhQCYiIiIiCsKATEREREQUhAGZiIiIiCgIAzIRERERURAGZCIiIiKiIAzIRERERERBGJCJiIiIiIIwIBMRERERBWFAJiIiIiIKwoBMRERERBSEAZmIiIiIKAgDMhERERFREAZkIiIiIqIgDMhEREREREEYkImIiIiIgjAgExEREREFYUAmIiIiIgrCgExEREREFIQBmYiIiIgoCAMyEREREVGQVQdkWWbGJiIiIqJrh3qhOzUaDWRZhkqWIatkyHLocfvtt8NqteHZZ5+NVn+JiIiIiCJqwYBckJ836zZJkgKH2+3G+vXrcezYMQwPD0esk0RERERE0bLg/AiVSjXrkGUZkiQBAOrr6uDxeHD48OGodJaIiIiIKNJWNYHY5XKhsaEeRUVFyMubPdpMRERERHS1WfUKu6amJthsNo4iExEREdE1YdUB2ev1ou7iRWRlZaG8vDwcfSIiIiIiWjNhqdHW1taKqakpHLr11sD8ZCIiIiKiq1FYArIQAhcu1CApORlbNm8OxymJiIiIiNZE2Hb56OnuxsjICPbv3w+1esHqcUREREREV6ywboN3oeY8jCYTdu3aGc7TEhERERFFTVgD8tDQEPr6+rB7927o9fpwnpqIiIiIKCrCGpAB3yiyTqfHvn37wn1qIiIiIqKIC3tAnpiYQEdHO7Zv3w6TyRTu0xMRERERRVTYAzIA1F64AEmScMstt0Ti9EREREREERORgGy1WtHS0oINGzYgOTk5Ek0QEREREUVERAIyANTXXYTX68Xhw7dFqgkiIiIiorCLWEB2Op1obGhASUkpcnJyItUMEREREVFYRSwgA0BTUyPsdjsOHz4cyWaIiIiIiMImogHZ4/Ggru4icnJyUFpaGsmmiIiIiIjCIqIBGQDaWlthNptx6NAhSJIU6eaIiIiIiFYl4gFZURTUXriAlJQUVFdXR7o5IiIiIqJViXhABoCurk6MjY3hwIEDUKlU0WiSiIiIiGhFohKQAaCm5jzi4uKwc+fOaDVJRERERLRsUQvIgwMDGBjox+7du6HT6aLVLBERERHRskQtIANATU0NDAYD9u7dG81miYiIiIiWLKoBeXxsDJ2dndixYweMRmM0myYiIiIiWpKoBmQAqL1wASqVCgcPHox200REREREi4p6QLZYzGhtbUF1dTWSkpKi3TwRERER0YKiHpABoO7iRSiKgkOHDq1F80RERERE81qTgOxwONDU2Ijy8nJkZWWtRReIiIiIiOa0JgEZABoa6uFwOHD48G1r1QUiIiIiolnWLCB7PB7U19chLy8fxcXFa9UNIiIiIqIQaxaQAaCluRkWiwWHDh2CJElr2RUiIiIiIgBrHJAVRcHF2gtIS0vDhvXr17IrREREREQA1jggA0BnZyfGx8dx8JZboFKp1ro7RERERHSdW/OALITAhZoaxMfHY/v27WvdHSIiIiK6zq15QAaA/v4+DA4OYt/evdBqtWvdHSIiIiK6ji0YkGVZiVY/cKHmPAwxMdi9e3fU2iQiIiIimmnBgPze945Gqx8YHR1Fd1cXdu3aiZiYmKi1S0REREQUbMGAfPP+CQBRHEW+cAEqlRoHDhyIWptERERERMEWDMh6vcD+/ZPR6gvM5im0t7Vh8+bNSEhIiFq7RERERER+CwZkRQFuvyN60ywA4OLFWgghcOjQoai2S0REREQELBKQGxsMMJkUbN5ijlZ/YLfbcelSEyoqKpCRkRG1domIiIiIgEUC8lNPpUAI4O67h6PVHwBAQ309XC4XDt92W1TbJSIiIiJaMCBv3mxHZ6cOyckeFBfbotUnuN1u1NfXoWDdOqxbty5q7RIRERERLRiQD98+hqefSgMA3Hf/UFQ65Nd86RKsVitu4ygyEREREUXRggHZaFSQnu7C0JAGWVkuZGQ4o9UvKIqCi7W1yMjIQFVVVdTaJSIiIqLr24IBWQjgAx8YxTO/TAUAfPgjg1HplF9HRzsmJiZw6623QJaviF2xiYiIiOgat2Dq7OjQITHJA7cbmJxUobjYAZPJE62+QQiBCxdqkJCQiG3btkWtXSIiIiK6fi1cxeJn6QCA++4fwe9+lwxJiv4ocl9vL4aGhrBv3z5oNJqotk1ERERE158FA3Jvrx4DAxpkZrrQ0a6HzSZj40YrtNrobT8NABdqahAbG4ubbropqu0SERER0fVn0Ym9v3rGV8XigQeH8OqrCZBl4IMfim5Fi5GRYfT09GDXrl0wGAxRbZuIiIiIri+LBuSGhlhMTKhQWOjAO28b4XJJ2LlzCrIc3VHk2gs10Gg02L9/f1TbJSIiIqLry5JKQ/z2NymQJOCBB0fw1ltxUKuB97x3NNJ9CzE5OYmOjnZs3boF8fHxUW2biIiIiK4fSwrIp07Fw2qVUVVlw/N/SITXC+zfPwEguqPIF2trAUi49dZbo9ouEREREV0/llxc+JVXEiHLwD33juLsWSP0eoF9N09Gsm+z2Gw2NF+6hMrKSqSlpUW1bSIiIiK6Piw9IL+cCJdLwo4dZvzyF8lQFOCOO8Yi2bc51dfXwePxcAtqIiIiIoqIZWxPJ+PYsXio1cCthybR2GhAXJwXmzebI9e7ObhcLjQ01KOoqAh5eXlRbZuIiIiIrn3L2r/52f9NgccD3HzzJJ76WQqEAO6+ZzhSfZvXpaYm2Gw2HD58OOptExEREdG1bVkBWVFknD1rgk4nsGmTHV1dOiQne1BYZItU/+bk9XpRd7EWWVlZqKioiGrbRERERHRtW1ZABoCfP50KRQEO3z6Gp55KBQDcf390Nw4BgLa2NkxNTeHWW2+FJElRb5+IiIiIrk3LDsgOhxoNDTEwmRSkpboxNKRBdrYL6enOSPRvXkIIXKipQVJSErZs2RLVtomIiIjo2rXsgAwAT/0sDUIAH7h7FL96xjeK/JEHBsPasaXo6enGyMgI9u/fD7VaHfX2iYiIiOjas6KAPD6uRUeHDklJHrhcwOSkCsXFDphMnnD3b1EXas7DaDRi165dUW+biIiIiK49KwrIAPD0U+kAgD+5bwS/fy4JkgR8+CPRH0UeGhpCX18fdu++CXq9PurtExEREdG1ZcUBuadHj8FBDbKyXGhrM8Bmk7FhgxVabXS3nwZ8o8harQ779u2LettEREREdG1ZcUAGgGd+6dvu+SMPDOG1VxOgUgEf/GD0K1pMTEygs7MD27dvh8lkinr7REREdHWKj49f6y7QFWhVK9saGmIxMaFCUZED/++xdBy+fRw37JzCz3+eBkVZVfZettoLF5Cbm4dbbrkFv/nNb6LaNhER0dWusLAQnZ2d8Hq9ABAooTrXR8n3yeWPvk9mfz3HRwkSEPSxsqISe/buxU9/8hM4nI5Z98/6KEmQIPk+Sv5+BH2N4Nv9/ZmjzwCq1m/ApupqfOvb34bVao3oz5euLqsu/fC736XgT/90EB95YBhvv23C/v1TeM97xvDccynh6N+SWa1WtLS0YMOGDTh27BhGRkai2j4REdHVKjs7Gw899BDOnTuLpsZGCCFC7p/5te82AJh9+3IlJiUiNjYW733f+/DmH48Ebp+5x0Ek9jwY6O+HvGULbr75Zjz//PNhPz9dvVY9zHvyRDysVhkbNtjwh98nwesF9h8YBxD9ucj1dRfh9Xhw2223Rb1tIiKiq1Vvby862ttRVbUeOp1uejT28iHL8qxDpZKhUqlWfbS3tcHhcCArKwsxMTGB88/sQyRYLGZ0dLRj8+bNMBqNEWmDrk5hmQfx6iuJkGXgnntGce6cEXq9wL59k+E49bI4nU40NjaipKQEOTk5UW+fiIjoavWH55+HWq3G+vUbot72uXNnIUkSdu26Mept1128CFmWsX///qi3TVeusATkl19OhMslYccNZvzi58lQFOCOO8fCcepla2pqhN1ux+23H16T9omIiK5GIyMjOH/+PIqKixEXFxfVtjs7OmC32ZCWno6YmJiotm21WtHe3oZNm6qj/n3TlStMK+lkvHUsHmo1cMutU2hqNCAuzotNm8zhOf0yeDwe1NVdRHZ2DkpLS6PePhER0dXqtddeg9vtxqbNW6Le9rtn312zUeT6ujpIEkeR6bKwlZr4zW9S4PUCN988gaeeSoEQwD33DIfr9MvS1toKs9mM2w4diti8JSIiomuNzWbDsWPHkJWVhczMzKi23dPdDavVipTUVBiN0S3ZarVa0dbaio0bN7LsGwEIY0D2eGS8+64Jer3Axo12dHXpkJziQWGRLVxNLJmiKKi9cAHJKSnYtGlT1NsnIiK6Wh0/fhyTExPYtHlz1AeZzpw5DQDYdeOuqLYLAPX1dQDAUWQCEMaADAC//EUqFAW4/Y4xPPVUKgDg/vuiv3EIAHR1dWJsbBQHDhyASqVakz4QERFdbbxeL15+5RXExyegqLg4qm339/XBarUiKSk56iO5NpstMIqckJAQ1bbpyhPWgGyzqdHYaIDJpCA1xYPhYQ2yc1xITXOFs5klq6mpgclkws6dO9ekfSIioqtRfX09erq7sX79Bmg0mqi2ferkSQDAzrWYi1xfByEEDhw4EPW26coS9u3ufvbTdAgB3H3PCJ75ZSokCXjggYFwN7MkgwMDGOjvx549e6DT6dakD0RERFejPzz/PHQ6HarWr49qu0NDgzCbzUhISEBiYmJU27bb7WhpacH69euj3jZdWcIekMfHtejs1CEpyQOnU8LkpAolJQ6YTJ5wN7UkNTXnodfrsW/f3jVpn4iI6Go0MDCA2tpalJSURn3R3KmTJwAAN+yM/lzkhvo6KIqCgwcPRr1tunKEPSADwFM/SwcA3Hf/MJ57LgmSBHz4w4ORaGpR4+Pj6OzswPbtO7hLDhER0TK88sorUBQF1VFe8D4yMoLJyUnEx8cjJSUlqm07HA60tLSgsrISSUlJUW2brhwRCcg9PXoMDWqQleVCa6sBNpuMDRutUKujv/00ANReuACVSoVb+G6QiIhoySwWC95++23k5uYiLS0tqm2fPHEcALDjhuivI2poqIeiKMwN17GIBGQAeOYZ3y/SAw8M4fXXEqBSAR/60NrURbZYLGhtacHG6mq+GyQiIlqGt956C2azGZs2b4lq2bfx8XGMj4/DZDIhPT0jau0CgNPhQHPzJZRXVER9BJuuDBELyPX1sZicUKGoyIFjx0xwuSTs3DUFWV6bUeS6uotQFAW33XbbmrRPRER0NfJ4PHj11VeRlJSEdevWRbVt/yjy9h07otouADQ2NMDr9eKWWziKfD2KWEAGgN89l+ybf/yREbzzjgkajcAdd45Fssl5ORwONDY2oKysDFlZWWvSByIioqvRhQsX0N/fjw0bq6FWq6PW7uTkJEZHRxEbGxv1v91OpxPNzZdQVlaO1NTUqLZNay+iAfnE8QRYrTI2brTiud8lw+sFDhwYj2STC2psaIDD4cDtt9++Zn0gIiK6Gj3//PMwGAyoqKyMarsnjvtGkbdu2x7VdgFfbvB4PLjlllui3jatrYgGZAB49dVEyDJwz70jOH/OCINBYO/eiUg3OyePx4P6ujrk5uaiOMq7AxEREV3Nenp60NDQgPLyCsTExEStXYvFjJHhYcTGxiI3Nzdq7QKAy+XCpaYmlJWVRX2RIq2tiAfkl19KhMsl4YYbzPj5z5OhKMCd7xmNdLPzamlphsViwW2HDkV9j3kiIqKr2UsvvQQhBKqrN0W13ePH34EQAlu2botquwDQ1NQIl8uFW2+9Nept09qJeEAGZLz9VhzUauDgLVO41GRAXJwX1dXmyDc9B0VRUFt7Aalpadi4YcOa9IGIiOhqNDk5iZMnTyK/oADJydGr7mCz2TA0OAiDwYCCgoKotQtcHkUuKSlBRkZ0q2nQ2olCQAaefTYVXi+wf/8EfvazFAgB3HPv2pR8A4DOjg6Mj4/jwMGDUKlUa9YPIiKiq82bb74Jq9WKzVu2RLVd/yjyps3RbRcIGkXmXOTrRlQCsscj4+y7Ruj1Ahs22NHVpUNKigeFhbZoND+npsYGxMfH4zOf+cya9YGIiOhq43K58PrrryMlJQX5+flRa9fhcKC/vw96vR5FRdFdR+R2u9HU2Iii4mJWwrpORCUgA8AvfpEGRQHuuHMMP3/aVy7lvvuHotV8CCEE2tvbIYSAyWTiFtRERETLcO7cOQwNDWFj9aaoXok9cfz49Bzo6qi16dfU1Ain08mKFteJqAVkm02NpiYDTCYFSckeDA9rkJPjQmqaK1pdgBACQggAgCRJ6OvrgyRJuO+++6LWByIioqudEAIvPP88YmNjUV5eEbV2XS4Xenp6oNXpUFpWFrV2AV8lrKbGBhQWFiI7OzuqbVP0RS0gA8BTP0uHEMDdd4/gV79KgSQBDzwwEPF2ZwZjf/WKxoZ6AEB2djaLgBMRES1DR2cnmpubUVFZCb3BELV2T544DkVRsGF99BfaX7p0CQ6HgxUtrgNRDcijo1p0duqQnOyB3SZjclKFkhIHjEZPRNqbLxj7jYyMwO12AwA+9KEPRaQPRERE16oXXngBkiRh48aNUWvT4/Ggu6sLGq0WFZVVUWvX33ZjYwMKCgqiXpOZoiuqARkAnn7aV2j7/g8P47nnkqa3oh4M2/n9oVgIEQjF89U7FkKgv78PQgikpKREvXQMERHR1Wx8fBzvnjmDdesKkZiYGLV2T58+BUVRUFUV3V39AKDZP4rMucjXtKgH5O4uA4aGNMjKcqG11QC73bcVtVqtrOq8i40Wz6e/rx+y7Psx3H333avqAxER0fXm9TfegMPhiGr5NY/Hg46OdqjVGqzfEL3RawDwer1oaKhHXn5+VKt4UHRFPSADwK+e8c33/chHhvDaawlQqYAPfWhldZFXGoz9+vv7AAA2qxVxcXHYwM1DiIiIlszpdOKPf/wj0tPTkZOTE7V2z5w+DUVRUF5eHrU2/Vqam2G321nR4hq2JgG5rs6IyUkViosdOPqmCW63hJ27piDLSx9FXm0w9nM4HBgbG4Pd4YAQAnfeeeeKzkNERHS9On36NEZHR1G9aXPgqmykKYqC1tYWqNVqVG/aHJU2/bxeLxrq65Gbm4t1nJ55TVqTgAwAz/0uGZIEfOSBEbzzjgkajcDtd4wt+rxwBeNg/X19SEpKChQg37N796rPSUREdL1QFAUvvvgiTCYTSkpLo9buubNn4fV6UVpaGrVg7tfa2gKbzYZbWNHimrRmAfn48QTYbL75x7/7bTK8XuDgwfE5H7uchXcr0d/fB1mW0dXZCUVRsO/mm8N6fiIiomtdS0sL2tvbUFW1HjqdLiptKoqC5uZmqFQqbN68NqPI2dnZKCoqimrbFHlrFpAB4NVXEyHLwN33jOL8eSMMBoG9eycC90ditHguo6OjcDqdSEtPR1tbK9RqNe64446ItEVERHStev75F6BWq6O6cO78ubPweDwoLCpek1Fkq9WKWw4ejGq7FHlrGpBfejERLpeEnTun8MtfJEMI4M73jEYtGPsJITAwMIDMzCy8e+YMvB4Ptm7dCo1GE9F2iYiIriUjIyM4f/4cioqKEB8fH7V2LzU1QaVSYeu27VFrE/CNYDfU1yEzKwvFxcVRbZsia8GAHPlpBjLefisOajVw3/17cPbsxxAX50VFhTUqwThYf18fDAYDEhISUF9fD1mWcc89LPtGRES0HK+++hrcbjc2RXHKw4ULNfB4PFi3bh3UanXU2gWAtrY2WK0WVrS4xiwYkKOxccazz6bC6wVMphFYLNn44x+3oLExJuLtztQ/0A8AyMzMQl3dRbhcLpSVlcNoNEa9L0RERFcru92Oo0ePIjMzC5mZWVFrt6G+DrIsY9sajCLX1dUhIyMDpVFcoEiRtWBArqisitgorn8ahdst4ffPJaOjQw1AgqLcsVi3IsLpcGB0dBSZWb5f5vPnzkGSJLzvfe+Nel+IiIiuZidOnMDExAQ2bd4ctavBdXV1cLvdyMvPj/oocntbGywWCw5yLvI1Y8EkGhcXh7y8vLA2ONf84pdfTsZLL/b4OiSr1mzub39fH5KTk6HVatHW1gqv14uiomLo9fo16Q8REdHVyOv14uWXX0J8fDyKi0ui1m7dxVrIsowdN+yMWpuAL9vU1V1Eenr6mmxcQuG3YEAeHR1FRWVVWBpabOGdzWYLlHHbdeNNYWlzufzl3jIyMgEA42NjUKlU2Ldv35r0h4iI6GrV0NCI7u5urN+wIWoDX42NjXC5XMjNzYVWq41Km34d7e0wm80cRb5GLBiQ//jHPyIhIQE5ubkrbmA5FSlsViuEEMjIyFhxe6sxNjYGp9MRmGYxNDwEIQS2b98Ok8m0Jn0iIiK6Wj3//PPQarWoWr8ham3WXqiBJEm4YecajCJfvIjU1FRUVlZGtW0KvwUD8sWLFzE+Po6qqvXLOulKN/YYHh6CJEmQZTmq+7n7CSHQ3z+AzMzLI8j+vnN1KhER0fIMDAygtrYWpaWlURtoam5uhtPpQFZWdtSnSHZ2dmBqagoHDhzghmNXuQUDshACR48eRWJiIrKzsxc92WrrF3f39AQ+37Jl67KeGy79fb7tppOSkjA+7tvZb3h4GBs2bEBycvKa9ImIiOhq9corr8Dr9aJ606aotXnu3HlIkoSdO3dFrU3g8ihySkoKqqrCM0WV1sai5SJqampgNpuxrnD+bRTDtbFHf19f4FyGmBjExsau6Dyr0d/fDyEEMjOzYLFY4HK5YLNZ4fV6cdttt0W9P0RERFczi8WCt99+Gzk5uUhLT49Kmx3tbbDb7UjPyEBMTHRLx3Z1dWJycpKjyFe5RQOyoijo7+9HXFxcyO0rnUaxWFsulwuAL2hXrV/e1I5wcLmcIeXexsfHERcXj6bGRpSWli5pJJ2IiIgue+utt2A2m7F585aohcazZ8+u6ShyUlISNmyI3txrCq8lFRweGRmB0WiELMsR3wZ6anIy8HlBwTqoVKqwnn8p+vv95d50GB8fQ0JCApqaGuFwOHD48OGo94eIiOhq5vF48MorryAxMRHr1hVGpc3urk7YbDakpqVFfdOv7u4uTExMYP/+/RxFvkotKSCnp6dDlmWUV/hWZUZyG+iBwQFIkgQhBGRZRl5+fkTaWUh/Xx8kSUJmZibGx8ahVqsRExODurqLyM3N5X7rREREy1RbW4u+vj5s2Lgxaht5nDl9GgCwc1f0R5EvXqxFYmIiqquro9o2hceSAvLw8DAAID0tLeLvhDo7OgD4iowDWHYFjXAYGxuDw+FAZlYmxsfHAACJSUlobWmBxWLBbbcd4jtCIiKiZXr++edhMBhQGaY9FhbT19cLm9WK5OQUxMXHR6VNv57uboyPj+Pmm2+GLEd/h2BanSX9izU3NwMATDPmIUeCxWKB1+uFoigAAKPRiOSUlIi3O1N/fz8yMzMD/YmPj4eiKKitvYDU1DTOKyIiIlqm3t5e1NfXo6y8PGoL8U+fPgUAUZ+LDAAXL9YiISGBo8hXoSUFZI/HAwAwGAxRmRNss9mg0WgC853LSssi3uZM/f190On0SEhMhNk8hbg43zvPzo4OjI+P4+DBg2syP5qIiOhq9tJLL0EIgY1RCo0DAwOwWMxITExEQmJiVNr06+3pwdjYGEeRr0JL+tfyj+ZKkoT0KOxyNzY6CkmSYDabIUkScnJzo17se6C/H4qiICszC5OTU4gPGj2/UFOD+Ph47Ni+Pap9IiIiutpNTU3hxIkTyM8viNoV4pMnTwJYo1Hk2lrEx8djy+bNUW+bVm5ZI8hCCGRNlz+LpJ6ebgC+XyIAkGUZRUXRXRjncrkC5d6mJicRazQGRoz7+/swODiIvXv3Rn2vdyIioqvdm2++CavVis2bt0SlvZHhYUxNTSE+Pj7qm3719fVidHQUe/ft45Xnq8iSAvLkdOk1SZKQlRX5OsC9vb0QQiA2NjYQzotLSqK+MM5f7s1mt0OSpJA52BdqzsMQE4Pdu3dHtU9ERERXO7fbjddffx0pKSnILyiISpsnT5wAANxww86otBfsYm0t4uLisGVLdN4Q0OotKSDbbLZAVYmYmBgkJCREsk9QFAVutxsmkwntbW0AfPOfc3JyItruTP19fQAAvU4HACHTLEZHR9Hd3YWdO3dGfZceIiKiq925c+cwNDSEjRurozKyOjY2iomJCZji4pCWlhbx9oL19/dhZGQEe/fu5SjyVWLJM8btdnvg82iMIk9NTkKlUuHixYuBxXolpaURbzfY+Pg47HY7EhIToChKYKGeX+2FC1Cr1Thw4EBU+0VERHS1E0Lg+eefR2xsLMrLK6LS5onjxwEA23fcEJX2gl1qaoLJZMLHPvaxqLdNy7fkgGwxmwEAExMTUZmHPDA4GFig51+sl5aWjvgIj17P1N/fh4wMX7m3mTUUp6am0N7ehs2bNyM+yvUViYiIrnadnZ24dOkSKiorYTAYIt7e5OQExsbGYDQakZGRGfH2/IQQ6OzsgKIoyMhIj9pGKbRySw7Ik9ML5ibGx5GckgKtVhexTgFAZ2cHACArKwvnz50F4HuBlZSURLTdmfr7+qHT6WC32xEfP7sO9MXaWgghcOjQoaj2i4iI6Frw4osvQpIkbNwYnbJvJ46/AwDYHoVKVP4r4IBvHVd9fT0kScadd94Z8bZpdZYckMfGRgH4NvKQJAmZWZF952WemoKiKEhMTERfX19gDnRBwTpoNJqIth1sYMBX7k2WJBiNpll1DO12O5qbL6GysjLqc5qIiIiuduPj4zh9+jTWFRYiMQp1is1mM0ZHRhBrNCI7QmubZgZjf5GBuou18Hq92LhxI+ciX+EWDMga6XIJM6vVBsC38tRut0dlmoXNZgtccunt7YEkSVCr1VhXWBjxtv3cbjdGR0ZgiImBLMtzLshrqK+H2+XC4cOHo9YvIiKia8WRI0dgt9uxOUpVHo4ffwdCCGzdui2s550vGAff39raCpVKhVsOHgxr2xReC06C+ffNv4RXKLB7LUiKTUBSRzz0rjyoWyQUJG+CM0YLh2KHSzjg9H8UdriEHU7FAS88q+rc6Oiob6vp5GRcqKlBXl7+9DSLUlxqalrVuZejr78P1dWbAGDOeUMulwsNDQ2o3rQJ+fn56OzsjFrfiIiIrnZOpxNHjhzBHXfcgZyc3MB+CJFitVoxNDSI9PQM5OXno2sVf7f9gRjAksrR1pw/h+LiYmzfsR2vvvZaYDM2urIsGJAHHN2QIEElqRGjy4HOaUSGVABlSkGKOg2HkjLhcjkhoEDA/wKRIEOCBBleeOBU7LALK+yKFXbFAptihk0xw6pMwSUcvkPxBWtnUNBW4EVvbw/y8/ORl5+Pc2fPwu12Q6PRwGQyITMzE/39/VH4EfnKvV0OyHNP77h0qQklpaU4fPgwHnvssaj0i4iI6Fpx5swZ3HDDDdi0aRP6+nojHhxPHD+O973/LmzZsnVFAVlRlEAgXs4+DV6vF91dXcgvKMDu3btx9OjRZbdNkbfoMkoBAY9wQxurhlvlgFfrxMjUCIzperhjrBizj8z7XAkSVFBDK+lgUMVCVmdCBTVUkgoyVIFgHRyufdFahke44LQ5kNWaiTyxAznxm5HZnQu1SYZbdmBP7h04M3rCF6qFHS7FERjBvny+8JiYmIDD4YBer4dGM/ePzOv1ou5iLbbvuAEVFRVoaGgIax+IiIiuZYqi4MUXX8QDDzyA0rIyNEb476jdbsfAQD8yM7NQWFiEtrbWJT1voSkUS3X27LvIy8/H7t278dZbb3EU+Qq0pDojGq0WGrUGHo8barUGQihwOByIiYnB6Oj8zxMQ8MANCLf/hiWTIEMFFWS3CvFIRb62HCkTqYh1GCFBRr6kIC95MzxeDyCmX6jTY9cu4YRT2OBQfCPXNsUCmzDD5p2CXVjhVHyj1b4RbN90EKewwy2c84br4aEh5OblLbhAsK2tDWXlFbj11lvR2NgYctmFiIiIFtbS0oK2tjZUVVWhva0NTqczou2dOH4Cd33gA6jetGnRgLzcqRQLcTqd6O/vR1ZWFnbs2IET07v80ZVjSQHZZDQCwPQUB99TbFYrklNSoZ4OzuEmoMADBXavDXq9DlYxBZUbUMX73mXKsgRhcmNidPYItgwZMtQwyEYYVQm+qC2poYIKEmQIKFAgACEA6fK4NQC4hRN2YYXDaw1MDbEpUzD2xSAlLg9VJjckbcz0vGv/nGuHL1wLgQs1Ndi9Zw+2bNmCd999N+w/FyIiomvZCy+8gE9+8pPYsGEjzpw5HdG2nE4H+vp6kZ2dg5KSEjQ3N896TDiDcbCz755BZuZ7cfPNN+PkyZMcVLvCLC0gm0wQQoHT6YRerwcAWKYDsslkxPj4eMQ6aLfboNfrYTIZYbNagZRUuN0u6HR6xMfHY2xsDEKEXppQoECBCx6BZY1aA5gO0yrEquIRJyVBBTVkSQWtQ4v0vgxkeKtQlrA7cOLgcO0UdjjsVqTWp+CvK7bj9fFXMekYg8UzAZvXikn3GBqnzoXhp0JERHRtGhkZwbmzZ7F5yxY0N1/C5ORkRNs7eeIE7vrA3diwsTokIEcqGPtZLBaMjo4iJSUFmzdvxtmzZ8PeBq3ckuogx8bGwuFwwO12Q5JkqFRqeDxu2B02mEyzN88IJ4vFt4NfTEws3B43AAFZluF2uyBJMkwmU1jbU+CFW7jgFDbYFDPMyjgmvSMYcfXDrjPDE2vHhHcIE95hTHiHMe4dwph3AGPeATgUC2RJBceIC4neDOxbdzv2pb8H78v5U/xJ/qfwiZKvoio+vCVliIiIrjWvvf463G4XNm2OfNk3l8uFnu5uaLVa3HrroUCpNv8c40iEY78LNecBAAcOHIhoO7R8iwZknU4HgyEGdrsdHrdvKoV/Hq5lygyNRgudTh+xDrpcLgiIQBuKokClUmNkeBgAkJiYFLG2g/kXE6pUaqhU8yzUgxdu4cSEYwyTrjGY0g0Y9wxj0NGDIUcPptzjuDv3Y1BJ3GKSiIhoPna7HUePHkNmZiYyo7DvwpkzpyGEQFJycsRDcbChoSHY7XYYjUasX78+Km3S0iwakFNSUgAITE1Nwe3x1TUOBGSrBUIoYR/Fncnr8UCl9u044/F4IMsSbHYbXC4n1Gr1nJt3RIKYXmUau4T2RkdHoVKpkZl5ecdBq2cKydp03JDM4uBEREQLOX78OCYmxrFp0+aIBVb/aLHT6YTX64UkSajetCkibc3Hv6/DwYMHOYp8BVk0IJvi4qZfOJ7pEWQR2CxDURRYrVYYjUYAkftHdTgckCBBp9XB5XIB058PDQ0BEEhJSY1Y28G8igKhKIiJXTwgu1xOWCxmpKWlhVS+GHMN433ZDyFGZYxkV4mIiK5qiqLgpZdeRnx8PIqLS8J67rl2vOvs6IAQAqWlZWFtazFt7W1QFAUJCQmoqqqKats0vwUDsizLiDEYYLfbAUyXbfN4QwKf2WyGShXZUVybzQoAMJpMcDgcAABDTAycTgdcLhc0Gk1g8WAkCUWBR/HCYIjFUt4QjI2OApKE7OzswG0uxQGtyoD96XdFrqNERETXgMbGRnR1dWH9hg3QarWrPt9CW0FPTE5AkiSoVCqUlpauuq2lcjoc6O3tgaIouPnmmzmKfIVYMCDHGo2QJBl2uy1wm8fjhjposwybzQav1xPRaRYWixWAgMFggNvlAoDAL4pvFBlITU2PWPt+iqJMl5iTYVhCIHd73JianERScjJ0QY8fdQ7gQPpdSNZmRLK7REREV73nn38eGo0GVauYo7uUhXcWsyXw2A0bq1fc1kq0trRClmWkpKSgoqIiqm3T3BYMyMlJSRAQsNsdgdvcbjc0IdstC1gsFsTGxkKWl1QUY9mE8AVTjUYbqNqmUvnmJPtGkd3QarURH0VWFF/tZCEUxMTGLuk54+PjEIpAbk5O4Dav8EBA4M7sj0Sqq0RERNeEwcFB1NbWoqSkdFl/5/2hODgYL8RfNcu354MGObm5q+r3cgwM9MNqtcDj8WD//puj1i7Nb+GAnJwC89RUSJ1hj9sNtVoNKWiKgdlshiTJ03ORI8PtdkOWL7/Ag8P46IivokVaWmRHkYXim8Dv30VwKbxeDyYnxhEfn4CYmMuhetQ5gM2Ju5EfG73LOERERFejU6dOBUZYF7PQNIqFWK1WKIqC/v4+AMD2bdtX3uEVaGttg1qtRmpqGsrLy6PaNs22YEB2OOyBcmp+vkoWUmChHoDAXOBI1kT2TfOQYDAYAIQGZJvdBo/HDY1GC6MxclM9FOGrwWyzWaHV6kJ+BgsZn5iA1+tBbu7lUWQBAafXhg/kfDRS3SUiIromDA4OQlEUJCbNX9p1pcHYT1EU2Gw2QPgG5XR6PVLT0lbV7+Voa2uFovg2ZTtw4EDU2qW5LRiQBwYGIGZsReeeroWs1mhCbjdbpqDXG6BWh94eLubpuUH+yyszp3OMjY0BAFJT05YcXJfLf5nGavXNyV7qKLIQCsbHx2E0mhAXFx+4fcI9ioLYMqyP3xGR/hIREV0LPB4PxsbGkDTH3gerDcbBLGYzjCYTamsvAAB27ty14nMtl91uR39fHyRJQlpaWlQXCtJsCwZkr9cz6zaPx79ZSGgItZh9c3citVjP5XJCQAQqaEgzArLFbIZX8UKSJaSlZyASZef8AdntdsHjcYdMmVjM1OQk3B43cnJzQvo25R7HPbkfg1qKzBsLIiKia8HAwEDICHI4g7Gf2WKGyWTCpaYmeL1exMTEID4+fvEnhklbWyu0Wi3sdjtHkdfYslfVeTxeCKHMGkH2eDyw220RrWbh9XgCi/NmjiALCExNTkICYNAbkJCYEPb2/QEZAKw2GwyGmJC52As+FwLjY6Mw6A1ISkoM3G7zmpGgTcWulFvD3l8iIqJrhd1uh16vR2pqasS2graYLdBqtdBqtWhuboYkSbjxppvCdv7F9Pf3w+VywWqxICMjAyUl4a3/TEu3grITAh6PZ0YlCx+z2bf1tD5CW087nU74R18lSLNC8uTkJAQAt9uFpKRk6LS6sLYvhBL4RbRZrZBlGfrpOdFLYTZb4HQ5kZ2dE/ILPe4awnuyH0SsKrI7EhIREV2trFbfnggVFRURqxVsnq5kYTSZcP7cWSiKgri4eMQusXLVaimKgt6eHsTFx8NqtXIUeQ2tqC7bfAHZYvFtPW2M0Ciy/5fDTyWrQr72ej2wWixQqdTwejxIz8iAJIWv9JzvSo4ECRLsdruv3NuyNkgRGBsdhVarRWrq5Yn/LsUJjazFwYy7w9ZXIiKia8nbb78NIQSSkhevZLFSgemi0wv+u7u7IUkSdu66MWJtztTV1QmtVou+vl5kZmaiqKgoam3TZQumR5OciBjZBJ1kgBqawHQCtzt0sxA/Iaa3njaZljz1YDksFgsAMX0Asko16zETExOQZRlWqxUajQbJSygJs1SBcneSBCEUOOyOZc1DBny7AtodNmRmZkIOCvijzgHsS38fUnTcPISIiGgmt9sNs9kMvV6PlJTUiLThG+gTMJp8ZWtPnzoJIQRSUlIiumNwsIGBATidTmi1WthsVhw8yFHktbBgQG51XsSwuxcOxQa1pEWCKhVJqgzoXSbEeOORqE5DgioNSap0JKsykaBKhXdKgk6JQYIxOSRUh4N/Mv7lzUJmd9/pdATqFE9MTCA+Ln7ZIXb+9n0f5cA8ZCu0Wu2yK3eMjYxCrVYjI+Ny3Wav8EJRvHhv1sNh6SsREdG1pqOjA0IIFJcUR+T8/lJv/hFkj8eD4aEhSJKEbVGqiyyEQE93N7KystHY0IisrGwUrlsXlbbpsgXrob0w9T+zbtNIWhTpS7Fr3W6803kcbqsXejkGeikGRlUCYh3xqM7cAn2cFjarBkY5ATJUEFB8JeMEAEmC7JtFDC888ArP5Y/Tn88sLwdgugayBMk/gizPHkEGgMnJCaSnZ8BuH4EhJgZpaWno7u6esyrHcgSPIAO+bbYBX7m3qanJJZ/H4XTAarUgPT0dQ0ND8Hh8/RpzDWJj0k6sG6pAu7VhVX0lIiK61nR0dGDjxo3Izc3D2XfPwuVyhr0Ni8UcMlX0xInjeO/73o/MrCzo9Xo4HI4Fnh0eXV2dKCouht1hh81mw4GDB9D2ox9HvF26bNkFg93ChSFrP+z6KZgNI+gb7531mE7rGWysrsYf2p+DxWKBRtJCKxmgl2Kglw3QSTHQyzHQSQYY5QQYVfGIleMQIxthkE0wSgmQJRUEBASUQKhONCRC74iBkAWEpCBGMsItzQ7VVosFnmQPEhISMDgwgNzcXKSlpU/vjjM7eC+VfwRbliR44VsM6Pa4lx2QAWB0dBS5eXnIyspCV1eX7/wQcHhs+EDuR/Gtxr+b800CERHR9aq7uxsAoFKpsK5wHZoaG8PehsVsQXbO5Y29bDYbJicnER8fj+pNm3HyxPGwtznT0NAQHA47cnPz0NjQgC1btyI/Px+dnZ0Rb5t8VrSjhtXi27Qj1jj31IX29jas37AB6woLUXvhAtzCBbdwwYpJwLvUjmmgmx6Z1ssG6OQYHKw+BNu4Exnx2dC49RgaGYZXETDIRhilBKgkNRQoEFDgmQAS41LhkSW4hoHEhFRoUvQYGRsKGqn2+gL4EgllOrAGrZ61Wa2Ii4ubHtdeeqB1u12wTE0hJTUVk5OTmJz0BexJ9yjyYopRnbAL5yfeWfL5iIiIrnX+xfpmsxnFxSURCchWmxV6vR6yLENRfBnh1KmTuPXWQ8jPz8e5s5EZuQ4mhEB3VzfWFRbizJnTqKisxMGDB/H4449HtF26bEUB2el0wuPxwBhrnPN+u92O/v5+FK4rxMXa2sDI63J44IZHmQyE6oyMDJhTBnCs/k3sLt4LSZLwWv2rGBm7vBW2L1T7RqiN5jjcWnYYY9IkOho7UKKtQGFGKQRcsA3bYJCNiJXioZY0gVB9efqHDECCgstB2is8kIUaEFJgDjLge2cZH58AvcEwvR320o2MjkCr06GoqAjNzc0wT6+enXKP4QO5H0Xd5Bm4hWvZPzsiIqJrkdfrG2UbGRnBunXrkJ6ejsHBwbC2MVdmGRsdhd1mQ0xsLNavX4+zZ98Na5tz6erqRElpKTIyMtDY0IDNW7YgLy8vcNWZImvFezJbrZZ5R5ABoK21Bdl79yEzMwt9fbOnYSxXfn4B3G53oIi2TqeD0xk6D8gXqt2wYgpj9gGcHTSipLQUjY2ncO7Sm9hs3IqyLWWoqTmPhvp6AIAKauhk3/QP34i1b7TaIMUiRjbBKCcgVhWHWDkOeqRC5zEgUZUOp8oJAQWSS4LOGYM0fQ4sLjOU6TDtmTWnevZItaIo6O/rQ1Z2NoqLi3Hp0iVYrVbYvBak63NxU+ph/HHouVX/7IiIiK4F/oA8NTUJp9OB4uKSsAfk+Zw7dxY37d6DouJi1NZegNvtjmh7w8PDsNlsyMvLw/F33gmMIj/xxBMRbZd8VhyQLRYrYucZQQaAvr4+2O12FBYVrTogGwwG5OXno62tFYqiwG63Q6fTQafTBUZd53KpqQklJaUoLStDzfnzOHf2Xeh0WlRXb4LL6URrayu88MCmmGGDedHpH1n6LOwtvxmvdb4K24QjEKp3Fe6GKTEe50/WwCjHwygnIEZlQqxkQozKhBgpERpJOz1SHVSmDjIkSYZtwIWkVCMqCzeita0ZFrsZk65R3J71YZwZexMWz/LmNxMREV2L/AFZlmS0tbWhrKwceoMBDrs94m13d3fD5XJBq9WirLwcF2trI95mT3c3CqfrIDc2NmDTps3IyclBT09PxNu+3i0YkN/z3vfB7XbB5XLD7XLBFfS5Rq2GyWRCVlY2XC7X9ON8h9frhRAC7e1tKC+vWPWLt3x615zGBl9lB5vNhoSEBGRlZWNkZGTe51mtVnR3d6OoqBh1Fy/C4/Hg5IkT0Gq02LptO5wuF3qmJ/wvjW9qhVeEhuraUQ22bt2GFvnsrM1M/Pwj1TrJML1A8fKCxVg5DknuVKwvqkZ2aQa6mxVILjW0sg55scWon4z8pRwiIqIrnRACiqJAlmW0trSioqIShYWFqK+ri0r79XUXsWnzFpSXV6CxoSFQhSpS+vp6UVpWhrS0dLQ0N6O8vAK3HDyI/3nyyYi2S4sE5OHhYWg1Gmi0WhhNpsDnGs3lur979+2b9TxFUeByueDxeCDLMg4eOIjx8XG43C64XW5fkPZ/Ph2sfQHcF779k+IBQKfToaioGB0d7YHw6XI6IYRAWnr6rLZnamxsQH5+PgqLinCpqQlCCLz99lu4ef9+7Np1I466/rjkyzPz7WzZ39cHbAUys7LQ0tw852MWHam2Am+6TLh5/37o1unxu9/9DvU1DfCKyP7yERERXU2EEJBkCRaLGQMD/SgqKkZDff2K1jstV2NjIzZsrIZarUZxcQkaGyNbktVfCjYzKwv9/X1oampEdfUmZGdno7d39dNXaX4LBuT5SplIkoT8/ALs3LULx4+/A4fDAa1GA61WC41WC61m+qNWA41Gg5jYWAgIaDRaaLVaqObYAS+Yx+MJjFhrNL7Ha7U6bNm6DW6XC4lJSQCAhIQEpKWn+x47fXg8npBfkvGxMQwNDaKsrBzNly5BCAGv14ujb76Jg7fcgt179uLIG69jbGxsCT8uX0Ke+UtosVhgNpuRlTl/QF4Ks9mMV15+Gbv37ME999yDtLRjeP3111d8PiIiomuJJElQqVSBqRYtzS3YvWcPMjMz0dfXF5U+9PR0Iz+/AJVVlWhuvhToSyQoioKBgQFkZWXh7LtA86VLKC+vwMGDB/GTn/wkYu3SCucgCyEwMTEOwDcfaHBgYN7H5hcUYNeuG3Hm9GkMDQ0B8NUv1MwI1FqtJihca6DVaKHT6ZCV7ZvCkRAfj9TUVGg0Gsiybwc9tVqNAwcOzmrTN+XDHRiZlmUZsbGx2LNnL0bHRqdvd6OxsREbN1bj5v37ceL4CQwNDS54uWS+EWTAN+e6qKgo5Bd3JZxOJ4688Qa279iBPXv2oKCgACdOHEdjY1NEfwmDqSQVjOoExGkSEa9JRJwmCam6TKTqs5Ciy0C8Jhn/0/bvaDKfj0p/iIiIAN/ffeDyXOTe3h7Y7XYUFZdELSDX19UhP78AWq0OhYVFaG6+FNH2+vv6kJOTg7i4OExNTaGpyZddMjMz0d/fH9G2r2erqGLhm+4wX6k3v57ubri2ulBYVBwIyF6vF16vd9HdaDZWVyNHzsUbb7yOyYmJwO3vfd/74fV6ERcXh4u1tRgfH5sesQ4N2P5RbJVKDUVRkJmVhazs7Dnb8k8VURQlMO/68txq39QPnV4HAMjNzUVcfHzQyLUboyMjKCsrQ2paGgZW+YJVFAUnT5zA2OgYKior8cEPfgh2ux0XL17EqVOnMDw8vPhJFmBQxSJuOvjGaRKRpE1Fmi4HKfoMJGvTYNIkQBFKoK6zSlLBIzxwKU64vU7oVQYUm6oYkImIKKr8Uzy9Hl9AFkKgrbUVlVVViImJCexwG0mTk5Ow2+0wGAyoWr8era0tIVNDw823yZlvGufU1BSaL11CWVk5bjl4ED/92c8i1u71bsUB2e12w+l0LljqDfCF4Y6ODhQVFeHsu1q4XEur61taWobKyiq0t7eHhGPANy+5p7sbcXFxMMXF4eLFxVeSFhYVYceOG/DGG69jYnzcN3o9PYptiIlBZWUV4uLiMDoygomJiaDArYEhPsb3uVYLAKhav2Hedvbu3Qen0xkyr/ry5zPnYLtCR7vd7pDpG83Nl9DS0oyMjEwUFhZi69at2L59OwYGBtDY2Iiuri709fUt+EYjRmXEnVkPIEmXiiRdOpK0aVBJKijCC0CCLPlG4/3h16U4MehYeHWsxqNFTkzRoj9zIiKicArsaDt9JRkAWltbUFlVhaIiX/m1aGhvb0NFRSX0ej3WrVuH1tbWiLVls9kwMTGOrKxsNDU2wu1249KlJmzYsBEZGRkYWOAqPq3cigMy4BtFXqjUm19bawtKS0uRX1CA5kuLX4ooKyvH5i1b0N3VhVMnT4TcZzKZoFarMTg4gPz8fCQnJy+prx3t7di4sRplZeU4dvTNWUG9q7MT27ZtR2FREewOB06eOD5rSkNubh5u2r0br7/2KhwOJ7Ta4GkiGpSWlUGv16Ovt9c3iq31TRMxmUzT87G1Ib/Uc/GH5Zmj1za7DU1NjTAZTUhMSsLNN98ceM7U1BQGBwfR1dWFrq6uQK1oALgp9Xa8N+dBdFtb4VKcGHcOQVnG7oFzcSp2ZBnyV3UOIiKi5bLb7XA6HDCaLmcPm82Gvr4+FBYV4eLFlW1OtlxdXV2orKwC4Bs0a2tri2i7fX19KC+vgEaj8QXkpiaUlZXj4MGDeOqppyLW7vVsdQHZYkF8Qvyij5uYmMDoyAhKSkoWDcjl5RXYtHkzuro6cfydd2a94PwL9MbHx2F3OBATE7OkviqKguZLl7Bh48bAPJ6Z9586dRKTU5PYtGkzjLGxOHbsKOxB5emk6UnITqcTZnPo86cfgG3btqOhoQEWy9z1mdVqdcjo9cx5177bLn8eGxsLbUJCYER7LnFxcYiLi0NJSQkABBYiWiwWSD2x8Iw5oFGpIHk0kD0SPG433G4PsIytsYO5FCfS9anQyjq4lMhut0lERBTM6XRCown9e9ja0ozsfTcjOzsHPT3LKd+6MhPj47BYLIiNjUVMTAzyCwrQ0d4esfb6+/pQWVmF9IwM9HR3B0aR16/fgLS0tMAUVgqfVQVki9Uy75zemZqbm7Fz164Ft4Usr6jApk2b0dnZiRPHZ4djAEhMTITX68Xk5CTGx8eRnZ0NU1wczFNzBNY5+lBRWYnyigqcOnlyzsc0NTbCPGXGrhtvxK2HbsOxo29ifNy3IHG6iMW87xL7+/zzhDLRfGnugOzxeFZcN1GSpEDADh659n9uMBhgMpkQExOLGIMB8fHxiNEnIKU9BSatYY4zTs8ynv5+FCEgFAEhFCjK5cM/Z9zfd7fHg1glBpuKtqFjshkWiyUq876IiIh0ej1cztDBmf7+flitVhSXFEclIANAd3cXyssrIIRAVdV6dHZ0RGwUeWRkBC6XC1lZWYH9Gy41NaGstAwHDx7Ez3/+84i0ez1b5QiyFSqVakkbgXR1dWLT5s0oKSmdFZDVajXWr9+A8ooKdHZ04MSJ4/O+yBITEzE5MQEhBPr6+pCdnY38/AJcXMK8I5fLiba2VhQXl+Bibe28oa6vrxevvfYq9u7dh4O33IoTJ46jp7sbsuQv8zb3+a1WK6amJpGVlbWkqSTLJYSA2+2G2+2ed0OSmdKTspATvx1W1QRklew7ZBmydPmjJEuQJN8hq2RAUuFywY65S3foXbH48PaHMGHqD/TN/9FfyN0frj0eT6DfLpcLTqcDDocTdrsdNpvNt7329Eer1Qqz2bzoAk4iIrr+yLIMnU43a5qkEAKtrS3YuLEaRqNp3qu44dTd3Y2KikoIIWAymZCbm4eurs6ItCWEQH9/PzIzswK3uVwuXGq+hMrKKqSmpq56AT+FWuUcZAsAwBgbu2hAVhQFba2tKK+oCKw0lWUZRUXFqFq/Hnq9Hi0tzXj3zJkF34ElJiYF3j11dXVi27ZtyEhPxxLW6QEAGhsaUFxcgrLycpw7e3bex01OTOCVV17Gnj17sXv3HnR2dASmVTid84e3/r5+FJeUrLrcW7gMjvVhVB6GU9jgxfJHriVIkFUqqFUqqNRqqFQqqFQy4gGMdZrRbeyARquBRq2BSq2GOvAYFVSyDJVWC71eH5ieIi1UKy/IXIE7ZCR7OnA7XU44nb761yqVCo2NjYF32r4w7gzUxyYioqubXq8H4JtmMVNbayvWr9+AouIi1Jw/v+I2dDp9YEfghYyNjsJmtQKSBIPBgMqqqogFZMB3lTo/Px8JiYmYmL6y3dTYhNLpUeRf/OIXEWv7erS6KRYW3yhmrNG44JbPfi0tzSivqEBxcQnGx8ewsXoTTCYTBgcHcfTNPy66WUdMTAx0Oh3Gx32Pc09vax0Xv/g8aD+bzYbOjg4UFRWjvq5uzl8yP6fDgTdefw0VlZWByfherxdut3ve5/T196GsvBxp6emBKRdrbdTbjyRVOuzCsuznCgh4vR54vR7Adfln5ZUlWIddOD71zrLOJ8sy9Ho9DAYDdDo99HodtDoddFotNFqdb7dGjQZqjRpqtQZqtQoq1eXQrdFoAqPdwOzAXVZWNme7vvJ97qCKIZdL+bmmQ7b/o9PphNPphMPhCHzuD9r+0L3Qa4CIiCLDYPBNF5yrIpbD4UBvTw8KCwtRe+HCikuvJSUlYnx8fEnTJbp7elBcXAxJkpCQkIDs7Bz09i5cCWqlhoZ8V99TU1IDAdnlcqK5+RIqKiqRkpKypCxGSxOmEeTFK1kAvnA6MjKC8ooKyLKMiYkJvPnHPwZq/C0mMfHyAj0/s9mMhIQEyLK85F+GhoZ6FKxbh9LSskVLwiiKgrqLF9HV2YkDB2+BwWDALbfcitOnT2FycnLW44f920JmZl0xAXnI040MTT7s3uUH5Pm4hAOpmqXNPw+mKApsNlvY5iyr1Wro9XrExsYiLi4eZrMZgIBarYHGH7I1amjUGqjV6sufa3zPMxqN0KjVUE8H88V2efQLnu4SXHlk5si1y+mEIyhkOx0OOGaEbf/n0Vh5TUR0NfMHZKdr7sGtlpZm5OblITc3D52dHStqIzExCe1LXHDX092FsrIyTExMIC4uDlXr10csINtsNtisVqSkpoZsTtLU2Iji4hK8973vxRNPPBGRtq9HqwrIiqLAbrMtWgs5JiYGWdnZyM3JRVpaGgCgtaUFZ86cXlYoSExMhKIomAiqizwyPITExERkZ2eju3tpE/OnpqbQ09ODktJSNDTUL+nyu9lshnlqCm63G6Y4E247fDsaGxtQd/FiyFQKRVEwGLQt5JVg2N0LlWFV/9SzuIQDCao0SJACG4qsBY/HA4vFAovFgoGBgSVP4ZiPfyGkRuMP1BpfgA4Ebv9tMwL39OLJmJiYkMDt3/VpKeYK3L7P5x7hnjnKPTN0R7JwPRHRWvBXrpq5SM9vcHAQZrMZxcXFKwrIJlMcNBoNxhe5ou3nn9Jns9mQkJCApKSkiO5wNzwygtSUlJDbnE4nLlyowbZt21FdXY2ampqItH29WXVqssxTCzk5ORlZ2dnIyspGYmIiAF8wrTl/HusK1yEhIWHZI2aJiYkwm6dCAmlnVxdKSsuQk5O75IAMAA31dcjNzUVxcQkaGxuW9By9wYCJ8XGcOXMGmzZvQmVlFfLy8vHumdMhvwx9/X3IzsmByWSaHtFcW5PeMSgI73xoAQEJgFFOgFkZX/TxV4vgkeFwkCQpMDVkuYHbP8qtVidC43/e9BSTpQheHOkP2v6dH/0hOjhcOx0O2KcD98zRbafTeUXMqSei65vR6MsbC2061tLSjM2btyA+Pn7OK70LSUr2XakeGxtd0uOFEBgeHobRaMTExDji4uJRVbU+YgF5ZHgY+fn5s3YNbG1pQUHBOhw+fBsuXboUUqKWVmbVAdlqtSA1JRVqtRrpGRnIzs5GVlYW9HoDFEXByPAwzp07i77e3kBY9Hg82LptG5KSkhaddxwsMSkJQzMqYIwMD0MIgaQlbhjiNzY2hoGBAZSVl+PSpaYljbYZDAb09/XB5XLi1MmTaG9rx7bt27Hv5v3o7e1FfX0dRkdGgsq9ZcHc1LSsfkXCpHcEEhbeoGQlBAQSVCnXVEAONyHEqkr7zWW+wB08fWTuwK1BbKwR8fGXR8g1Gs2im9f4KYoS2B1yrmklLqcTTlfoCHfwKPfM0M153ES0XIWFhVAUZcFKR+1tvo3BioqLcfbd5V3KTUpKgsfjmbVXwkJGhoeRnZ2N06dPYfv2HUhJTUVaevqsvBIOIyO+ShUpqano6ry8IFAIgTOnT+HQbYdx+PBh/OY3vwl729ebZQdktVoNk8kU2OY5MSERMbGx+MDd90ClUsHlcqG/vw99vb0hO7oFa29vw8bqapSUlOLkjJ3y5qPX6xETE4Ox8dmB2uGwL3nDkGD19XU4cOAg1hUWorWlZcHH+sOE3XH5Xdnw8BBefulFlJeXo6y8ArfeeghDQ0NoqK/H5OQEsrKycOkKCMhmZQKACPt0CBky4lUp6HY3h+2ctDh/NY9wkWV54cCtVs8zn1sTqL0dHLiXOo9bURR4PJ5AWHa7XHDODN3TodrhcMDlcsHhcAQ+n2vhJOdxE127KisrsX79ejTULzw10uVyoru7CwUF61Bz/vyy/n+ZlJi05AV6fsPDvk06nA4nrFYr9Ho9qqqqIhKQJyYm4Ha7kZISGpD99zU1NaK6uhpn330XnV1dYW//erJgQM7IyIApLg5xprhAIA4OooqiwOl0QpIktLe1oaurE8PTI7oL8Xg86GhvR2FREc6dOwfXPJPtg6WkpgIARoZnr9AcH/ftUW40GmGxLH0h2tDgIEZHRlBRUYm21tYF++1fGDDzsoWiKKivr0dTUxOKiopRVl6OfTffDIfDEZjLtNYjZQIKJrwj0Eg6uET46gt74UWaJhtgyeKrmv/3eKGKLsshy7IvQC9lKsmMwK3T6RAbGxsSuFcyj/vy/O3ZI9wulxMOx+Ij3JzHTXTlMBqNeN/73oexsdFFF9cDQEuzb8pBfn4+2traltSGJElITEpCW2vrsvo2NjYGr9eLlNRUNDU2YsvWrUhPz4hIVQkhBEZHR5GamjLn/Rdra5GXl4f333UXvv/973Nq3Cos+Jfn5v0HAPjm+kxNTWFwcABTU1MwT01hamoKFosFKSkpOHDwFnR3dy1rq8Pm5maUlJaisKgQjQ2LzwFOSUmFx+MJlHgL1t/Xj6ysbOQXFKDu4sUl9wEA6uvrsWfvXuTl5aGzc/76hf6APF+9Z6/Xi0uXmtDS0oy8/HysX78Ber0ed9z5HtRdvIj29rY1faEOe3pRoK0Ia0B2KQ6kqfPCdj66NiiKEgiY4eBfOLmSwO2vchII3Cudxz09tWS+aiUzp5T4R7nVajXcbjfGxsbgmi5LSUTLI0kSPvjBe6FWq3H8neNLeuM6MjKMiYkJFBWXLDkgm0xxUKvVy5r6Cfj+nzc6OoLU1FS8caEGVVVVUGs0qKxaj6Nv/nFZ51qKkeFhVFZVQa1WzxpJ93q9ePfMGezddzP27NmDP/4x/O1fLxYMyFar1TcS43LB7fGNgur1BsiSDJ3e4Ks/PD3qapyuZ7xUU1OTGBwcQElJKZoaGxcddU5NScHY2Oicj+vs7MCWrVuRkZ6x7IDc29uDyckJVFRWLRiQ9fOMIM+kKAo62tvR1dmJu++5FwCwbft2rN+wHs2XmtHZ2bGsUe5wGXR3o0S3OazndAkHktUZYT0n0UyhCyfDs/BkOYE7+DEatcZXGjBQp3t587j9ddRnVSrxj27PmsftmA7dzlmj3Fw4SdeLG2+8Efn5BThz+lRgw66laG1pwdZt25CYlLSkqhRJSctboBdseGgYFZWVkCQJTU2N2Fi9CVlZWUtuezlGRoYhyzKSU1IwODAw6/6+vj50dXViz549qK2txejo8r8fWiQgGwyGwJSKxUZctm/fgW3btgPArK2GA7ufedzwuD0hIzGxsbHYvHkL+vr64HA6YLfZZ+1Up1KpkJiUNG+1Cf/IzHI2DAnWUN+Anbt2ITs7G729vXM+JjbGV8puqStDFUXBQH8/EpOS8Pprr6KisgobNm7Eho0bMTY6iq7uLnR3dS15y+jVmvAOAQjv5WIvPNBJBuilGDhEeOoaE0XD5YWT4bmiolKpAqE7eD53YmIiDAYDzGbzHIHb9zEmxoC4uLjA6LZ/N8qlWNLCyRmhe6ENcLjjJF1p0tPTceDAAfT29qBlkbVCM3V0tKN60yYUFxXj9NipRR+flJQEt9u9oupTw8PDqJJlJCcno7m5GRWVlZBlFaoqq/DWW8eWfb6FjIyMQFEUpKakzhmQAeDs2bNIT8/AQw89hP/3//5f1LLGtWTBgPzMLy9vW+jf/Uzv3wFNp4VO59sFLT+/AG63G1arNfBHInjnM51Ot+BWw6VlZSgN2gFt5jbDQgjIsox16wqRlZUNj9sNl9sFl9MF5/R8QpfTCUNMDOLjE2Cz2+BexuXdzs4OVK1fj/UbNs4bkBOTkmCxWJY1n7i/vw85ublwOp04+uYfERMTg9y8POTl5WPTps3YtGkzRkZG0NXVie6uroiWZZn0zj36vloKvIhXpcDh4WIAun75BwJmzuMe6O+HEGLZ9bl987jnHt3WzAjZcy2cjIszhYxwLytwL7rj5PxTSmaOcq/1+gu6usXFxeFDH/ogXC4XTp08ueznu91udHZ2IL+gAOfPn1vw9SjLMrJzcjA6urK/lSMjw77QmpqGwcFBNF/yheSc3FzEJyRgMmj/htXyeDyYnJxEyjzzkAHfdNCjb76J/QcO4M/+7E/xox/9eMHKHzTbkle/+P8niPHZJb2MsUao1Gq89uorizeoVkOvN0Bv0MOg1yMnNxf5+QXo6uqC4vVCo/VvsOD7A6BS+TZgAACdThfYh32+Pzi333EHgNCQPddIttvlDqyYdzodGBkZwbp161BZWYWOjnY4HI6QeU7JycnLnmzvr4OYmZWFqakp2Gw2NDU2oqmxEbGxsYGwvGXLVmzZshXDw0Po6uxCd0/3vHOdV2rSOwpZWtofyeWQICFelYxBBmSiWVb6ptQ3j9u5pAXMSxG8cHJWxZLA/GzNnFNL9DpdyI6T/vMs9fvwz+P2T9dzTgfv4LDtr1Ay18LJmYGblUqubZIkYf369di2bRvy8vIghMCxo0dXvIi4taUFRUXFyC8oQEvz/BWX1q1bh9jYWJw+vfhI81w8Hg8mJiaQmuYrKNDU1Iiy8nJIkoTKyiocf+ftFZ13PiMjIygoKFjwMaOjI3jr2DHs2bsXDz74IJ544gleJVqGsGyvZrFakJW1tG2HfTufmWGx+C5h9PX1ISMjA7Is452335rzOXv27oPRaMSLLzwfuE2r1SImNhYxBgP0egOSU1JQVFQEi8WMqakpaLVa3//g1Wqopke0/Qt2JEmaN2BvrK7GxupqAKEj2CqVClqtFu957/um/4cfOnfQ4XDC6bDDZrPDbrfDZvdtpzwxMYGszCw0NTaGtGO1WtHY0IDGhgYYjSbk5eUhLz8PW7dtw9Zt2zA1NYXh4SEMDw9jeGho1ZdHnMIOp7BBBTW8COcviIQkVXoYz0d0bVnt7o7hEKmFk3OF7Xnrck+Hbp1OB2Ns7OVgvoxKJf756B6PBzqdLrBtvdfrhdfjgWd6IMT/uX8qjTfoc9/XHnjcvs/d0wF+5mNnfgz+nCE9/BITE7F9+3Zs2LABJpMJFosFdXUX0dHevqp1O2NjYxgbG0NxcfG8AVmWZVRWVmFkZAQDq9jgY3h4CEVFxZBlGU6nE60tLSgpLUVeXh4u1l4I68ZhExPj0GhKZm0YMtPAQD9OnjiBXTfeiPvvvx9PPfUUq/MsUVgCstVihcFggEqlWvaiEa/Xi+ZLzVi/YQNMprg5J+CnpKSgtyd0b3P//+wnpke029paUVhYCCEEjr755qLtyrKMmJgYGAwGGGJioNfrkZaWhpycXIyNjcLtdkOj0UKjUUOr1UGlUgWes9B0kWCB/4nGx+PeD35o0VHsttY2QAJMRhOMRiNycnJRVFQMwLcH+/DwcCA0r+RyzYinH0mqdNhF+BYJuoQD6RpWsiC6nkRix8nQEW5/sJ47dOv0OqSmpsFut8Pr9UKlkqHRGCDLMlQqGbKsCvw/O/hjuN6s+K9KKl4vvNOf+wO0d0ZID3weFNrnC+zu6a8XCuxz3XY1B/aUlBQcOHAA5eXlAIDBwQFcqKlBd3f4rkq2tDRjx44bkJySgtE5rgQXrFuHWKMRp8+cXlU7w0PDKCsrR2JiIkZHR9HY2IDikhIAQGVVFU6eWNq+D0sxOeHbITA+PmHBgAwAXV2d0Go12LZ9B+655x78+te/vqpfM9ESnoBs9QWu2NjYZe0+49fcfAnlFRUoLy+fdXkjLi4OOp0Ow9O7xyzE4XAgZnox3WIURYHFYgl5Z3qpqQm3Hb4dGo0Wr77ySuAFtH79BlRWVeE3z/5vyOWJmaPYBoMeWp0eOp0WWq3v0OsNMBqNUBQlcJlzsVHsYP4+6PV65OXlIT8/P9D/y9sHu4M2UbDDZrXCbLZgamoSdrs9cI5hTy8yNPmwe8MbkFPVWWE7HxFdf4ID91LXYqxkbrd/63dZliGrVFAFBWj/14GP/pCtkgO3y7IMley/Lfij/7n+5wUHdlXo8yMc2D3BIX2uwD5HUA8J7G7fVvR2hyPwBqiwsBAjIyOYnJyEzWaD3W6H1WoN+fvi/9kGf39+sixDkiTfz3n6MBgM2H3TTSgpLQ2USW1qbIzIWpyuzk5s3rwFxcUlswKyf/R4dJWjx8Dl6hcJ0wHZZrOho6N9uh5zAS7W1oZtsdzk5AQAID4hHv39fYs+vqWlBTqdHhs2bkRcnAnPP/8CBuZZ4Ec+4ZliYfH9g8cajSsKyE6nEx3t7VhXWIgLtRfgDJpInl9QACHEvCs1g01MjCMzMwuxsbErfhHWXriAvfv2Yd26dYHaiUnJyZiampo1d2fmKPZcJEnCB+6+B91dXbPC/1yj2Hq93rf4UauDVqsJjGKrVOqQ//lIkgSt1rdQcjH+/4GljBcgeyAXydr4wNQRRQgIRYFXUaB4PfB6Fbinq4345w0KMf/lGLdwIlGVHoGpG0RE4eXf+v1KcTmwq6ZHvucL7DNDeWhg9wd5WZ4R8mcEdtWMxwS3t5wNeYKt5I1K8HOdTiecDgdSU9OQkpwCRShQlMuVsMT0R9/tl+8TIbf5H3v5eYpQAn/bhKJgeGgIeXl56OnuCmwCpChKYJOxuou1iImJCTmfoiiBv5VLYbPZ4Ha7ERcXF7itob4e69b5rm5XVFbizOnVjVL7ud1u2Gw2xMcnLPk5dXUX4XQ6sWHjRnz84x/HxYsX8fLLL69J6dmrQVhHkI2xxhWfo6mpEUXFxSgtKQ3skiPLMoqKitDX27voJQTAtyguMzMLBQUFqKurW1E/+vp6MToygqr1G9DR0QFFUZCcnDRvdYvF+MN9ZmbmrPvmGsVeLp1OB5PJBJMpDrGxsdNB2x+wtYHLlrIsw66xAJIIGb1WYSn/YxOhn03/z8IfrvUuI95T+QGMi2E4HY7A6ILFasHkxCQcjshV5yAiulpdDuxXRmj3j+76q6b4grUGObl5UBQFkiSFVlFR+f62KIH1OkrI2h3fMm7A/5/gvz2SBDidrul2JcjSdKCX5EAFLF9/JEiS702BFDQC7R8oWmqFFr89e/fNefsNO3fN+5zg0rW+0KxA8SpQZt4+Pbe3oGAd4uLiA7fZbDYYDAYUFhZBrdb4Bp5mBHyhhL4x8Af8y+cXgcf7b7fZrEhKSkJcXLyvT8rsw993v5YW334MVVXrUVlZifLycpw4cQJvv/02q1zMEJaA7HA44PF4EGtc2vSGuZjNZvT29KC4pAT19XXwer3Izc2FXm9A8wIrT4N1dvguo6SnZ6w4IANAbe0F3Lz/AAoLi9Df3wedTr+qQtv9/X3IzctDfHw8JicnV3yeufhXfS+lwka8Khl/nlyCce/lDV0kSFBrNNBqtdPz/S6PVvv/Z+X/H5EkSUBwuJYkQJahVjRIN+XAYNTM2e7Md99z1cl2u11wu9xwOJ2+aSI2GyxmMywWC8xmMzdEICKKMP//lz0eDxxBm/IMDQ1dEYtN5xM8fUMKBO3QKR2SLGPHjhug1Wpx6uQJyLKMzMwslJaVob6+HlOTk76gHhzEpeDnB51PCj1vcMD3eDy+v6dBAd9fqlYIgZycHHi9HsiyKqTfq3HHnXcu+pi5Ar7T6YRGo8GePXuwZ8+eoL/HvqvHExMTqK+vh8ViCXm+1+udFcTnum2+42qZ/xyWgAz4qjLErmIEGQAaGhuQk5uLwsIiNDdfQnFJCcxmMwYGljYvyOl0wOv1Ij4hYVX9GBgYwPDQECqrqgJzsMZWEZD7gsq9hTsgL4fZOwHAF4rF9KiwgJiuyLHy1e2pmmxcGmhBV0w9Yo2xMOgN0AWmivgW3AQuGwa96/ePEixnLrb/8+CQ7fF4oFKpoCgKenq6ffWxnY5AqSi73VdZ5Eq6tEpEROEhhAiEu4U0NTZg564bIQCMjo5i27btGB0dxYWa82HrS2VlFTZWV+OPR94I+Zuzd+8+pGf4dp598YUXZo3WBv99lGeMlAeCuCQHQrwv4GeirLwCZ989A6fT5RttD37eIgHfd5sqML3Tf9XZYDAgNjYWiYmJWLduXdh+NsH8f9PdbveskO3/t1QUL7zeyyPm/uky3uBRd6EEHjPz/tDDd66Q26YX2Z45c2bOPoYvIFssMK5iBBkARkdGMDI8jLLycoyMDCM1NQ3nzp5d1jksFgviV7ijXrALFy7g4C23oLCoCF6vd1XB1mG3Y3x8HFmZWWhsmHs3wGhQ4MWkdxgaSQeXCN+lFKfXDq0jDm0DrSt6vkql8k0TiYuDMTYWBkMMDAY9dDo9NMEj27Jq1iU2wLdY0h+yS0vL5m1nZm3skFFsj38FedCOZE4XXO7p+qzTdcDtdvusGtlERHTl6+7uxuYtTpSUlAY2Pjt+/J2wtuFfh2UymTAetD6ppuY8Dmf59mkoKy9HzfnzIc/z/z1aDqfLhbLyCthsNvTMqPS1WnqDAQnx8UhNS4NarfYFd0kGJAmyBN/VZEiQ5OmPgavMgCTJ8D1Emn789NVnIPB4jUYDrU6H3t5eXyndmW8Gpg+VLENS+f72+948+Kbc+N9ISNPnl+QZtwVd+V5shD7iAdlitSIlNXXV52lobMCePXtRXb0JHo8H7e1ty3r+yMgIEhISkJWVjb6+lc0bBnz1DAcGBpCWlobh4eFVB6L+vj6UV1RAo9Gs6e5Sw55+5GvLwhuQhQPpmpwVP9/r9WJiYgITq9hpKC4uDgkJCXC5XL6Fjno9dNPzsDVa7fSCx8uj2Wq1KlAb238pDFh6zVp/2L48x8v37nS+bdXdrukNEPyj29Oj2pzzRUQUHV6vFx0d7SgtLYMQAm+/9dayNwBbzNSUbzAtLi4+JCBPTk6irbUVhUVFKC4uQUN9w6o3ApqanIQQAvHxCWEPyA67HQN2e0QrXaxmgedyzRWaFwvPYZxiYQnMu1lNAOzr7YXZbEZ6Rgba29qWXdi+p7sLxcXFyM3NXVVABny1lTMyMsIy/7W/vw+VVVVIT89AT0/3qs+3UoPuThTrqsN6TrdwrPlmIZOTkyuqoDKTLMvTl5sMoVVFdDrodFpoNFpoNZc3PVCrVFBNL4JcyrbqM83cVn3m3GxP0Ki2e7qcn9PlgsvphMPpgNPhhN3hC9vL2V6diOh6I0kS4uPiIUkSuru70dsb3lAJIDBfN7iShV9t7QXkFxRAo9GgrKwsUJBgpbxer++qecLqr5pf6/zTcJYjrFMsAMBoNIa8a1ouIQTGRkdhMpkwMbH88wwMDEAIgeSU5BX3wS8+3lcOLT09HUajcVXVJkZGRuByuZCVlRWWgBzv9eKuySnstVqhFQKdGg1+nRCP83q9b3nwPMa9wwiuShEOChTIkhqxcjysytrNsQ4HZXrF8VKqpizGX/NabzDAoPfVyNb7w7ZWMz19RDO9o5gqpJSf/5LWasP25TqoHng8Xnjcbrg901NIXO7poO2Ey+mA3eEb2fYvuiUiupbs2HEDMjIzMTU1heSkJEiSFPYFY/7qVHMFZIfDgfq6i9hYvQmlZWVobGxY9RXlycmJsEwrpdnCOsUCAGJjVxeQVSoVklNS4PV6UbCuEJcuXVr2OZxOJ1IkGQ+NjcOskmGRZZhlVeDzKVmGRaWCe5HQkZubh+HhISQmJmHTps14661jK/22IITAwED/nOXelusWsxlfHxjEUWMsfpKYAJsso8rhwL/1D6BFp8XfZmbCMc9lg0nvKATCP39WCAUJquSrPiCHk29b9dWV8fPTarXTQdsAvV4HnU4fGNXWarTQBE0hUasvB+3ghZBL3ZwGWGC+dmCTgdApJK7pUW3f4kjn9HxtG5xOJ+drE13lruQKFku1ecsWrCssRO2FC5iamsRNu/cgIyNzSZtsLNfU1CTi4mcHZABoampCSWkZDAYDSkpLUb+Kilu+tqaQlZW9qnPQ3MI+gryaUm8AsGHjRhiNRtTX16GysgrZ2TnLvgwyMTGOdYrAXw4vvPueQ5JglqcDtGo6RMsyLCoZLkMMMs+eRfPgIJx6PdaXliJWp0P35CTM04HbopLhWcb/OPr7+pCXl4+EhIQVz7fdarPhkcEhfDQ3B/V6feD20zEx+FliIv61fwCP9g/gM9lz72435R2FjOXVjVwKWVIhXpWCXvfy5ozT0vhD6FQYqqDo9frAAhW9Xg/d9IYzWp0uaAqJJlBD2xe0fRsJaLUaSNLy52sHH/7VyR6PfxqJf2GkG27/1usuZ9DCSH9FEs7XJloL0ZwrGgnr129AWVk5GhsbUVd30bcvgN2O4uLiCAXkKWRmZs05Qu31enH+3DnsuvFGVFRU4lJT06qu2NlsNsiyDJ1OB6dzdXOaKVTYArJ/5f9qNgtJTklBWVk5mpsvofbCBeTk5GL9hg3LDsgD/f3or96EB++4A32NDTB5FZgUL0yKApNXgTHkc2X6c99t6R4PTIoX8RYr9AMD2O8/6blzuG+OtmySNB2wVdMj1XLQSLUvRPtCuArOxkbkp2fgxpgY1AwNwzL9OO8y/sfzqdExfDM1NSQc+3kkCV/KzMBLbe2odDjmfIxD2OAU9rDvfKfAi5QwbTmtVxTstlqR4FUwrlLhrdgYOFdZJ5Iuc0wHT6ziSg9web62P2zrdLrpUkG+8n7+NQlqtQYatRqq6bDtm6+thU4nh3G+tgdutwdej29U2+3ylS50+hdFOl1w2G2w2x2w2awc1Sa6DsTFxWHbtu1IS09HW1srzp/zVcVSFAVtba2oqKhETExMWKbUBZuamoJKpYLRaITZbJ51f2dnByqrqhAfH4+SklI0NNSvuC3/1twGg4EBOczCFpAB3+T0WOPKArJKpcINN+yEzWpFzfnzEEKg7uJF7LrxRuTk5C5r3m5HRweqN22GKT8Pfa0twNz7Vyzozve8F/apKZw98gaMXi+KUlKxs7ISvXV1MPf0wKh4ERcI2F7fR68vbGd6PIjz+m4zzJzf1PHkrLaskjQdpFVBAVs1Y3RbhloRqHI48P+SElHmcAaCuEWWoUwHDLck4ZmEeNwzOTlnQAaAUc8AElWpsIvwbS/pEg6kq3NXdQ6VEPjU6Cjum5hAvU6PAY0aWW4P/mlwEE8lJOCHyUmB75PWXrjnaxsMBhgMBuj0Buh1Wuh0+sDCSI3WN7KtUqtDtl73b5ErSfoVhW1/0Xx/BRJ/0Pa43dPTR5xwOi8virTb7HA4HbBZrcteQExE0aFSqVBVtR5l5eXweDw4feokWltDy5C2tfoCcklJKWrCWAcZAGzTU04NBsOcARkATp86hVtuvRWVVVW4dKlpxcUAAgE5JmZVlaBotrAGZKvVgri4lU0WX79hA+Li4nDkjdcDlxu6ujpRWVWF9Rs2LCsg++vULmeP8mAJiYkwmUxoqK/HhEqFCZUKPVOTkAwGJN56K178w++X/MdRLQSMQSPYG/MLUJqRgbq330KM2x00iu0NBOwErxe5LndgdFs/I2Q/0TO7Ood/9Noiy5CEQJLiRbxXCQRus6wKhO007xvIjNmKUckNpzoGTlUMXCodIK18lNalOJCsXvn8akkIPNo/gASvF/fm56NPc/ldTY7LhX8eGESu240vZqQvuAiRrk4ejwdms3nePybLodVqL9fSDq5EMl32z1/yT6PWBEa1Ly+M1C95rvZc87SDq4/463MPDQ4GgrZzejGk3WZjTW2iCMjKysKWrdtgNBrR3taG8+fPzTmyarVa0dPdjaLiYtTVXQzrwmT/wju1Zv7RuZGRYQwODiI9PR2VVetRe6FmRW05gkaQKbzCG5AtVmRmLv8ye3Kyb2pFS3MzBgcvb4MshMDFi7W46abdyM3LQ3dX1zL6YoFpjlWkS5E3ve/8zLqC586+i9sO3471Gzbi7LtzF5aeySNJmFCrMDE973fQ6wEqK3FqbAzd3Uv7fjTTQXmbzY7Pjozgn9LTA6PWcUFTRozTAXudywWVANY7HIGQrQ0J2c9MH5cJSHCqDHCqY+BQxfg+TodnhzoGDnVs4L7Ln8cGHueWBeJUydBK+hXVWL7VYsE6lwv35+XCNWM6RY9Wi7/KycYznV242WrFH1d4lSKSCp1O7LbafBVFtBr80WhcdBEoRYZ/vvZqpmvPnD6i1xug1+mg0/tDtm/6iEajDVkUOVepv/yCggXbCp6fHVJ5xOOBe3oxpDN4IaTLOV1H27cQkrtEEgExMTHYsmUrcnJzMTk5iddfew3Dw0MLPqehsQF5+fkoKi5GU2Nj2PriD8jaBQIyABx/5228933vR0VFBZoaG1Z0Vcput0MIwYAcAeGdYmG1BEpbLXUDBJVKhRt23gC73Y7z58/Nur+7qwsTVRNYv34Derq7l1ySZXhkBHHx8cjIyFzyVtWA7w9aXn5+YNQn2OTkJFpaWlBcXIzWluYV7a43NjoKp9OJzKzMJQdktyxjTJbxhsmIfxwawohahRO6mHkf/8OeXjyelIjfBpV+0frnWisKSuQcHDIchsc1AJ3XBp3HBp3XPv3RBv30xwTHMPQeq+8x3oX/Pb2SCi5VLA7LbozLCsyyClMq3zxs/0dzyNe+edv+j/dPTODHSYmzwrGfQ5bxeFIi7p+YuKICcpbbjX8eGESx04nXTEbYJBm7x634v4ND+F5KMn61ym3PaW2Ea/qIwWCAVqsLCtq6wNQRrVYXWBAZPG1ErVYte/OamRvXBJf3c7uDdoh0uuB0OqeP6dFsu51TRuiqFRcXj3Xr1qG4pASSJKHm/Hk0NTUu6crM+NgYBgcHfGufLl0K29Ucf0DWaLQLPs7hcKCm5jy2bNmKm/cfwCsvv7TstoQQcDgciDHMnwloZcI+ggwAsUbjkgOyb2pFPI7M2Lc82MWLtdi9ew/y8vLR2dmxpPP29HSjqKgIuXl5ywrI+fkFMBqNOHf23bn7UnsB+fn52LJlK44ceWPJ5/W7XO5t+SPtHknCLxMS8PnhEfx1dtaci/t2Wm2odDjwmazQ6Q4uWcbo/8/ee4c3kqbXvb+qQs4AM5vN1Impc7NznJ68Wmm9uw7atWVLV5KvZUm+DvK1ZK8kW5Lta0uWrHyVr2zZWml3tbvS7oTumekwnaZzYEc2MwkmEDmjqu4fBYBgM5PoMDM4z4MGulD1oQCiUOd767zniCI+wC8pdLh34pcXnl0XQlBljJk4JjmqEehMDFMmikmefuxQVCZjtxAyPhyyQk1Gk5A4Z1WwZ0MFGpMp/k/f1AxCHSwg1ikBdsfidMZihEWRUJZgR0WxyM7OS0NlOs3/GBjkz9wu/nHdmhkV4w3JJL8+PIJZUflTj/s57N00yjMZ3LJMQJKY0BX1kC9hAaiqSiwrpQgGAyseRys6mLFYsiTbaMJoMmpJkUbjjJRI3VOUjGTSBTHsKc0/O5HIEu2SZKSEZwiTyUR9QwONjU14PB4URWF4aIgbN64Tzep/l4r79+5x5Ogx6hsa6OvtLcr+TRPkxRugHj54wKZNLXg8HjZtauHBg+VXsuPxOKZSBbnoKLoGGcBmteFbQnzjppYWWlvbNGnFAnGGQ4OD+P1+2js6GBjoX1IV2TsygqqqVJSXL3n/BUGgrb0dv3+K4eG5U/hSqRS3b91iV2cnDY2N9Pf1LXn8wn1raGjE7XYv2zP698s8/ObwML89PMx/Ly/PN+JZZZnPh0L8n74p/lltzYKuD2HZD1p6OuoSqaUqSCT0NhL6+au3Hqmas5Fvczl24omNVYyqikNRcGTdQpzZe4es4JAz/IRvivNWCzZFxa7IVGUyrE9Nr1/4bv50cKb0RYas1lrKEueZFeuwKBKUpr2wn6xix7M58cvFP5v08W2Hgz/yeGY998ho5IfW1vFXff18x2HH9xyI6YFolP9jaorWRJJJnY7yTIYHRiN/7HFz+gWqwn+SUQxrLM1PO0wksnJ9ds4GymKxzCsZMegNeXu/hSQji2ExyUjO0i9VkAiZSCRIlCQjJSwASZKoq1tLY1MjVVXViKKIz+fj2tWr9A/0k1xiUe5JeL1eAoEALS2tRSPIue/9UggywNUrlzl85Chbt23D55tcdvx1PB4vSSyeAhY8ax8+ckS7JJfSKgXxeJxEPEY0FiMei836EcvN3JbihdzS0sq27dsZ6O/n6hL0vHdu3+bQ4cM0NjbSu8QvcTKZXJYvc31DAw6Hg7Nnzyy43uPH3TQ2NrJjxw5Gvd5lW6t4vVpFu6a2dtkEOS0I/PiaNfzDKT+/MTxCShCIiiJ16TTnrRZ+qK6OBybjgmMoyATlSfSCcUV64fmQUhNU6edwshAEkoLAhCjOW8F8NRLlbxwOLlhn/70EVcWqKLwajvAP/X5+qaoyT5wdsmbR51QUHAWNjo2pFI6sfZ91kQlVCuYn1wVV7MKqtYKmm/5MY8O843r1et612/hiMMj/W7b6ZMfl4Mt+Pz805edXKso5UWcnLQjoVZXj4Qj/bnycxlSa/+85VLabk0k2pFIowA2zuVTRfkZQFIV4PJ7veF8pphsgzZgtWjU7H1gzr2REV3TJiCLLWG1WQsEQoVBII93pFKlkKnuvVbiT2Qp3CR8/CIKA3W7H4/FQVV1NXd1a9Ho90WiUe/fu0t/XRygUKspr3b93j7379lFTU5M/P68W6XRqyQR5ZGQEv9+Pw+Fg/4GDvPP2W8viFfF4DM8chZoXGR8Hb+0Fz06LpbPMVSlQFIVNm1qoqa4hlc7q3bI/UrF4nHgsRk1tLW1t7fT393HxwoUlVYSHh4eY8vno2LyFgYGBJVmiBIMBKiurlqSJFgSB9vZ2/H4/w0ML+y6rqspHH33Ea6+/zvYdO7h44cKi+1KIZDKJz+ejtqZ2RSk6aUHgD8o8/JHHzbpUCoOqMqLT4V8G2ZjMeKk3bCg6Qa7QrSzR56suJ//I7+eCxTKrmqtmJwGfDYf4Y4+bjywztVaLHWg6VcUuy1nCrBFpR9ZZxJEl1LmKdm5Z7RLlIWd6eolnA2dCBZrqUNYxxCErbIuH8Um6GZrswor3cnywl4K2RIIf9U3x/Q31M9xA0oLA2w4718wmvjowyA2ziZvPqOqwJR7nX01M0pBKcd1sRq+q/MLoGOetFv5zRQXjSzyRrAbmbAOrqEKvQU9UKn5gzicd0w2QgRWPMUsyYjJpRNtgWLZkxGy2UFVdvehrzuehrSgKSk5Gkq1yZzIZ0hlNt51KpUmnU6RTaZKp5HTVO5EgmUyWKt1FgkaGHXg8HtweDx6PG5fLnSeYqVSKgYF++nr7Fm28WwkGBvrZsmULLa1tRSPIqVQavWHpv2tdXXc4ePAQRqORYy+9xOXLl5d0JR6yEguT6alEZ3+asSCj+vrXv4bFbMFizVYMsjG3BoMRo9GoxdvqtIrB9A+YgMFgoLyiYtHZQX19A2uzjhEawc6Qycik09qPknYZTiPYiUSC4eFhNm/ZQsfmzdy+dWtRrduod5SqqmoaGxu5v0iH6tr6ehwO55LjpEOhIPfu3qVj82b6+/qWfVB5R0Zoa2/HYDCsuDlGEQQeGReuFs+HsfQA642bgeJFQ6fVJC6pEgFx2XHW33Q4+HwwyE+PT/DLlRUz9Lx6ReFnxicwKCrfsduXvV8ZQcCv07HSWAyDohHlQpK9PR7jzXCEbzgdmkwkW712KAplmQyNyjQRl4BfKHBneRK5+PM8yc7KQXIkO5INmolkUx5z3tc5W7+YKKIWfF5f9gf4U7d7BjkuxLhez5+43XzZH3gmBLkzFuPXRrz8PxUVfNdhz6dP2mSZf+T3878GBvly/VrGnhJJdsky/8Tn43uDIbx6PSqwJp3mHbud3y7zFP11jYrCkWiUynSaqCRxympd1uT1k45iSUacThc6XVYKYjBiMmp+2XqdPnuv06rZBSE1Op0OURTzHtqSJCEWSEhgebKYJ4n3rGj2gsp3Jke+07mYdo145zTdyXwDZYJUKvWJ1HIbTSasFgsWiwWLxYrNZsPt8eB2u9Flj5FMJoPf76e3p4cp/xT+qSlCodBTJX6KovDg4QO2b9+Bx+Nhampq1WNm0ulFm/QKMTQ4SDAYRJIkDAYjr7zyKn19vdy8cWPRKz/xeBxBEDCbzUUPPfk0Y8Ff7fa2dkZHRxkbHV3ywbp3337Ky8v5m7/+NiaTCavVitliwWwyUbe2nqqqKqLRKJFIJGuTpM/+yOlmpGst9CPV2tpGa2tbwY9SodG/nP0BSpPJVpk3bNwIgpC3RorFZ0pEtOpxB4FAgKHBpfst373bxdr6enZ17uat735nWdWEEe8IHZs3U11Tw0B//5K3Kxb88njRG9s0PbOKQ3ITlH3L2jYpivxIXR3/j3eU9x738G2HA282KOR7QyFumE1aI9xzSNRLieIsechts4kvBYJ81eUiuEAl8udGxwhIEl91ObOSEDmrxZ6uVk+Ta+2+Np3BISdxKArWJ/TXc0EBolnCHBVFmlIprphNtCWTeSIdnUG2RUZ0Ov6vSITGVCq/TnKFWuyFIGX9rX+qpprzT8hnIpLEb5aXoyDwM+MT88ajrwaeTIb/MTjIJYuFLzRO+2uXZzL8A7+f/z0wyA+srWPIsPQT2bxQVX5kys8/8vu5ZzTSa9BTlkjwb8YnOGGz8R+rKok9+f1VVfbE4nx/IMDWRAJBVXHIMoURP0FB4LX6tYTmCf75NEJRFKamfE/lEq3eYMBoMOaLQTkJiV6vWftNR7FLBV7aIpKUI98iOp2EIOhnnMuWu69zVb1z5zpFyRFvhUwmnSXfslbtTme0yPZckSmZyBLvFIlEcfXdWlCPJqnJnc/1egNmizlPgnOE2GyxID3xW5lOpwkE/Dx+/Bj/1BRTU1OEw0+XDM+Hx93dtLd30NLayvlz51Y9XjqdRq9f3sT4blcX+/bv59yHH+Jyu2hpaWXNmjru3u3iwf35nTmSCU2OYTQaSwS5iFjwr7dh40ZaWlvJZDKMjo7iHRnB6x1Z8A8QjUSor69HEITpSFufj/b2Dqqqqujt7eWjSxcXPQBEUczq3Cx503+T0YTVZmXt2nqSCc2iSK/XoZN0SLr5G0qsVhvbtm2f9RqqqoKq0TpRFEmnUnz2e78vO8ufnt0nU8n86yXicWIFtkgffXSJl19+hc1btnD92rXFPu88/FNTJJMJampqnwtBDsq+JTfoLQcqKk6pbNkEGSAkSfyTujWsSyZ5IxymOZViSpL4B/Vr6SsGgSkiApLEKZuVv+/381vzNIJWptO8EQ7z2aZGJnQ65m9DnR+CqmJRpiPRbblblmBbC5Zbs3KRdakUVkWhMpnALmsk2zLP8fZWb1/+cRqyFWppmlRnCXWuch0WpRlku5CYJ0SRhCCQEATtCoAgcCwSYUSvm0WOC/EnHjfvPe6hKp0uejX358fGOWGz82sVM/9Gkzodv1pRwYROx3/xjvKlhvpVv9ZPT0ywNZ7g++vXMmAw5KU/Tlnmp8Yn+P3BIX5obV2+gVZUVX5xdIxt8Th/6nYzJIr8UIGmMicic6oqF/sHeN9s5sfrV5dWWcLiSKdSpFOpVVW454IoihgMuYTIAt220YhBr8sTcJ1eh06ny8pLpiUmoihmY9r1CIJhxeS78NyrPVbRToWzq+Gqqub144XkLPfaC71uroKezrqfTE1NaefTZIJktqcpmUrOaOLU63U4HI5p55Ts7Vkgk8nQ3f2IlpZWbDYbkcjqkmZT6TR20/KueA4M9NOxeTOtbW28+87b9Dx+zPbtO9i6dRubNrUwPDTE4NAg42NjM/4esqL9WohiSTZWTCxIkL/x9a9RWVlFTW0ttbW11NXVARAI+BkZGWFkeASfb3LGAReNRhBFEYvFQjQaRa/Xs2PHTpqam+nt7eGjS5eWNDtUFIVoNDqnZUt0a5S2tnbOnj2z4KUQk8nEy6+8itVq5cqVK5hziVpGI0aDIS8Rsdps2pdNEDCZTEtuJil8Hxs3bqKpqTl7QGszek23VtgwEs+naMViMbxeLzU1K0+fWw2Csg9x0drk8iEg4pTKgYcrHuOx0chvrlA68izx6+Xl/NnAIAFJ4s9drhla4sZUit8YHuEPPJ5VNaKpgkBUkohKEvOLNGbirZ5e/kNVNXfM0xVHXbbR0ZZNbmxPxPkXkz7+c2VFfpl207TY1qyUpDqTKSDlMkudpshAQhCQgKQg8N2eXhLZKnVCFEgIGplOZh8HJYmvjI1z12QiLgokBTG73vTjpCAQz40hCCTnIOSFqE2n6YzF+L9r5teo/i+Xix+c8tOWSMwbzb4UbI7HOR6O8LnGBiJPVMmCksRXqqv4reER/l4gmG+O/IlJHzWZNF9obGBdNMrPZsnxloZ6BIOBX/V6GdPp+IXKSm4/fMRL8Tj/5/gEv1tZseL9LOH5QVGU6aJRkZHLHyg8vxkMmgwyF2qTq/RqBSUdOklCEEVEQUAQRbRDaPoKrigIkCXlQP6+ELlz4FznykJCv1o8yRmm/78wuVcVBaXgSrOizJTBqIVyGEXJv49Dh48wPj423VuVu0KtyMgZGVlRkDPpGRH1hRr2jCyTyaSX/d5VVeXu3S727Nmbbxj88MOzVFZVsW7dunyoSSqVYmRkmKHBQbxeL0r2arkkPfsrrJ9kCDB/GbFl08YZ/3c4HFmyvIaKigpEUSQej9Pf10dPTw+hUJDKqipeeuk477//HqIgsHv3HkxmM/fu3uXOndtFuXSi0+n4zPd8lkgkwnsnTyy47p49e2lqbubUB+8zOoeVXGNjI3v37ef8+XOzKrk6nU6TiJgtWCxmjFkNtuY/Om2LpNfrsZjNIAgo2YNsKd6juQrTk5q1aa1aoQ5bu0yW60TPhRisRqf24xX/lbgSRmZlGfBzwSmVcSd+iffCXy3amIvheXbDrk2l+E+jo9SmM7xrtxEVRToSCToSSX6rzMP/cj97p4gf9k3RlErxbxcghj8/Osa4Tsdvly/DXUNVMajqdMU6S6JzBNqoKpgVFaOqxaMbFZVD0SgRUcSr12vLFc3yz5SNUM89dskyAiv3ncwR8hxpTgpCdh8UHpqMswh5soB8747FiIsiJ+y2FRPy/+gdpdtomGH59+T3cls8zn/2jvJGUyNmVeW9xz18rrGBMb2emw8eogfaGuohS9Qdssy7Pb282dTIlE7H3QcPUYH2J36XP634OHTBf1KwlM9ak1tMa71zJHy6Ci4i6fRIooiUlVVKWUmKKElIooQoiVlira0vCmKeaAtiwWNBQBSFPKEvfPzk+XexSvvT/A7NxXcKyXwhwS+8GbJXoBLxuEbw80RfycpRtYmPJEl5wi9JEomEpl/Pad1zvuWZdKqAwMvTRbysv3kqlcqvryjKM6nav0jH7/0Hcxf0lnU+CoU0S50H9++j1+upqq6moaGRjZs20dLais/nY3hI0/DmJBXBYJAPT5xgamr5l9znQyaT4fatm+zes3fRCOrBwUGampupW1s/iyCbzWZ27NzF5OTknGNkMhmCweCSEvNqa2s5fOQoXV138s4UWiXdisVi1uQiZrN2ac1kyjc6lpeX57+UK/EdndNztPCLn7U+yln1JRNxYrE48XgMvzKGQ/AQV5dnrL4QkkqCSl1d0cZ70TFoMPD36+tpSSTyUdPfdjj4yVrbgl7UTxNfczn5el8/nwsGZ6Qp5vCZUIgj0ShfWK6sQBBICUI+cGYpGPHr2B5P8K9q579SIqoqb/f28c9ra7hnNOaJrTlLsk2qkiXS2mNTAQk3PUHIzaqS3UZlfTKJCuhUqJAz2hgFzxsVZUZF/OgyAgYKCXlaAE9GZlCv5+VwhJgoEhcFYoJIPPdYFIkjUJHJ8OVAgPpUin6Dnvp0mnJZRg9EAadeT0xRSGedUT6wWXkjHObP3G5uGgxsTaUQk0mUj8EVlhI+XVAU5WOZxpgj3TlZi8Pp5NChw/T39dE/0I9OkjQCnyXxki57n1su5gi+gFhA8j0eDzqdnkDAnyX1heS+oFI/47GAIGikV5IkzAWOTU9ygUKOkNN2m0wmTEXqVVhKMfPJSn5uWe6Gqs6q4OecY8wWM7FYnIDfj4qare4r+W0URQU1OzHIVf5VFVWRUQFFnp40TGv01fxVgekrBbnXzi6H7D4o+eXzYcXXftPpNEODgwwNDmI0GmlobKSpqZktW7ehqiqVlZUMDgxw6dLFpzIb6e3tZcPGjWzbtp2R4eF5bd9GRoZRVZXyOXSiu/fsQRTFJVvNLYSRkRH6+/tpb+/Am/U0VBRl0W7tl195FYCTJ96d83mz2ZxvcDCbTFoV+wmZiHZgTzuJGAx6LMLiMpE13irKAg2kDNFp39HsF0xVVBRV+xLJinZpSckUWCFlK92yPPNvm1ITlOuej2zkeeK+ycT9F6SJKiBJ/EjdGv7f4WHeCIf5mtPJiF5PbTrNF4IhNiST/GjdGqaegbPCXzsc/OSkj5p0Gu88lxtfikQIiiJdRiMIAjFBI5QrdR3J4Vgkwg9N+fkHC+h2JVXl10ZGuGo2867dPoOQm5VpYv4kITfnKuGqZgP4eijMqF6HgIBVUSjPaATfoiiYs2Q/N136t+MT+dcvDL2xARe7H8/YvzMWC+UZ7bftHzQ2cOvhI44kEnxQIsgllFAU5IhTjqdEIhEGBwepXbOGq1ev5FPxlosjR4+h1+s5eWLhq9xzQRRFPvvZ7yUUDvHB+/Mn9uZ06i6Xi2MvvcTNmzeYnJhAkjQeoMluTOgNhmw1XyPwM1xcshX7HFmfTdjF7MWyuavzGrR1F8JcXMRoNOF+DldZn8T1GzfnXF6UM2QymeRxdzdWixWXywVoX7q19fW4PR7u37tLT09PUW1rVFXl2tVrHH/5ZTa1tCzoJ5xKpbA9kRy2bv16ampquXL5ctGaMa5euUxFeXne6HspEwPviOZmYTQa5zQGz5v7+1ZWgTcYDFisVixmzXNUI9kmjAYjUsaBK1VHinT2QNAOBlGSQCeQPQQWeQV1+t/sTNGUtPG5XV8gpkRn2/UlE8SziVmxaLQo0bQv0qWaFwU9RiOfaWzkjXCEvxMI4pFl/JLEdxz2RZMWi4mQJPF7Hg+/OzTMj9atmdWEty0e5+fGxvlXNTVFd9A4a7Xyc2NjbEwkeThPeE55JkNnLM6/qa5elS+yRVG4bLbwv92u/LIZ30tVZUsiwW8Mj/D3167ls+EQO+IJft/jwaIq/NbwCF06Hd/2uDErCuUZmX8QCJDINkQCNGW1q0PPwDO6hBI+zbjbdYf6+no2bNy4oqwC0MhrJrMycq0oCvfu32PHjp2Ul5fPm6yXk2PmUozjsTgTExNzrvsiIGcDbDQacbrcyHKGeCxWQMxFRFGYWWmfVXXXdPOiJCIAgihpOnpBQBCz2vncOFkpXG5c4QmSnxt7PhSFINfVrWXrtm3Y7XYePnyIy+lElCQe3L/PppYWdnXupr1jMw8e3Kf70aOiVZQnJsYZHBigra2dnp4eEvN4BQYDASoqKzEaTSSTCWw2G9u3ayl43d2PirIvoBHx8+fP89Lx43R27ubChfOLbjPiHWHzli1U19SsKLZ6KfuUSqUIzJHYN2zwUeFqISDPf0AJwvSlp5wdn1Q4ExWlaW1YwZfUra/CaPEvO5p22jdUzjY65uyKUnmCnYjHSSQS+UTHlc7wP+lIiSLfcjr4ltPxXPfjDz1uBOBbff18YLNx2WLGoKgcj0RoSSb5d9VVXLJaFh1nucgIAr9VVsavekf4obq6WeTcJcv8+sgI/5/bverQkD93ufjZsXG+7nSQmmvyIQh8ORDgz10uBo0G/lxy8QP+Pu6ZjISyr92WyfC3C6opv1JRzvs9vfzXrAPHVwc0+dojS/E/qxJKKGEagUCA4aEhNm1q4eGDByviLDqdbsXx16DZzrW1tdPW3sGZ06cWXDd3BV18wZv0VFXNWg4mCQaDL3xha1UEuay8nG3btlNRUUEwGOCD999jbGyM3bv3UFNby+DgAIODA1RWVtLW1s62bdtpa2vn4cMHPHzwkFRqeRHNc+HGjevUrlnD1q3buHRx7kS7sbFRKquqaGhs4NHDh+zduw9FUbh06eKqX/9JTE5OcOf2bbZs3crY2Cg9PT0Lru+fmiKRiFNTU/tUCPJCCMlTLFYhVlUl6625dG2ZW6ri/P1LPEheRRRFTCYTFqsVs8mE2WzRLvvkPEYN+qy9kXapSLcCHXYikeAXfuEXUFU135yQzKZdxWIx3nn7bX7nd357yftfQpEhaMmPX3M5+b5giJ2xOLIA33Y4+DG77al6W/+ly4VdUfhmXz9/7XBwymZFBfbFYnw+GOKbDge/U7b6iNaPzGbuG438+oiXf11TnSe9AHpV5ccmfbQlkvxCZSUAUzodJ+w2fmZ8nJ+urqZbr2d9Os2/9o7yX2qqQVX5lxOTXDGbGTIYOBQMYgJKDqcllPBs0HW3i1dffY316zdw//69ZW+v1+lWVQyUZZkH9++zdds23B4P/gUcu3JXYaWSzVtRsSKCbLPZ2bp1K2vr64nHYnx06RK9vT15HWskGsFsNiNJErIsMz4+zvj4OB6Ph9a2djo6NtPS0srj7m7u37+3aErMQohGo9y/f4/29g76ensYmyOxrK+vj81btlJTU4MkSpRXVHDh/LlVve5CuHfvLpVVldkGQB+h0MJNfl6vl9raNc88JjIkTyEuKqFYCVTKdNWQ1A7cnOPGSmE0mrBkjedNZgsmk1GLpjUaMej1RGMxent7MWdjaw0GA3a7HbfbjSiKjI+Pr/hz/Zmf+be8dPw48Xg8H3ATDocJh4IEAkF+9Vf/24rf16cNAUnKW5w9S/yRx8N37Xb+TjDID0/5EVC5azTx5fq19BfLX1sQ+Dc11fzf4xO829PLezYbvQY95bLMm6Ew941G/uHauhkWcP+xspLfHRrm94aG+aWqSv5oaJh/FArxuXCY6yYTFarC/1Fby7cf97A+e6LdVQS/5hJKKGFxTPl8jHq9tLS28OjRw3n7nOaDTq8jvcqr5Y8ePaSltZX2tvYFU34/LhXkjxuWRZANBiMdHR2s37ABWZa5fesW9+/fm/XFiUa0bnCr1UqowPh+amqKcx+exeFw0NrWxoaNG1m3fj337t6dc5ylouvOHdauradz9x7e+u53Zu9PNIosy3g8ZVRWVjEw0E//UwznUFWVixcu8Pobb3Dg4AHefeedBd+bd8RLU1MzHo8H3wq1xiuBgkxQnkIvGEmrq6/m55BSE1TpixdokEwmSCYT+OeQieR0nt/72e9ZcIyVXMpRVZXa2lqqqqqQJGmWdZ+qqvy3//YrSxrrydfX6XT88q/8N0KhEMFggIDfj29qCt+kj4mJcQYGBkqJSEXEqF7Pr88T6FIsZASBX6qq5HfLPLwZDlORzuCXJP7R2jp65miqi4siP1y3hi8GQ/zM+ETems6lqhzLTt4v9fQCWlritgILuBJKKOHpo6vrDsdffoXm5nU8erQ8b3+dTr9iDXIOmUyGhw8fsHnzFpwuF8FAYM71ShXkp4MlEWRJkti4cROtbW3odDp6Hj/mzp3b8xqeR7KCcavVNoMg5xAKhbh08SJ3bt9m67btbN6yhabmZm5cv87Q0NKjnnNQFIWPLl3i5VdeYcuWrVy/PjvRLhaLYbPZSCQSXLl8ZdmvsVwkEgkuXLjA0aPH2LFjJ5cvfzTvuqOjXhRFoaa29pkSZIDJzAh1hvVFJ8jlujVFG+95QRAEfuInfnzGMovFQkVFJRWVFbhcriUT7ycr2JWVlbz22mvzrv/eyZP803/6Y3Pu02LYunUrbrebyclJRsfGmPL5itogW8LC8Ol0/A+3e0nNo2lR5H+7XfxvlzOfdhhPJvna6Ci1mQwBQeTv1q0pxUyXUMJzwMTEBOPj47S2tfH4cfeSf0cFQcg26a2+3+rRw4e0tLTS1tbOhfNzR2DnCfIqeylKmIkFCbJOp2Pd+vVs2tSCxWJheGiImzdvzEl6CxHNRjRabfPHy4JW2T1/7kO6H2lyhIOHDjE2Nsq1q1eX5D1ciMnJCR4+fMjGTZsYGOifQTRzdiaCIPDgwf2iaJ+XgrHRUe7e7aK9vYOx8bF5I6VTqRQ+n4+amlru3L79TPYtv4+ZAdYZO4iyvM97IaTVFB6pGh16MnyyGuhisRj9/X309/cta7snidLo6CivvfoKFRWVlJeXUVZWhtvtweV24XQ6OXHi5JzkailSkV/4xV9iw4YNs7ZRFIV4PM6unTte+OaITx2y9nYAmM18oanp+e5PCSWUAGhV5GPHXqKpqYnHjx8vvgHTRLUYBDmVSvHo0UNaW9u4c/sW4fBs163c7/mzlGh+GrAgQf7e7/scBoOBsbFRLl44z/j4+JIGTSQSZDIZbFbb4isD4+PjvPP2W6xbt57NW7bw2utv0N3dzZ3bt5ZlPH7r5g3WrFnD7j17eeftt/Kpdnv37cOS7fy2Whcm7cXGndu3qayspLNzNwG/f97JhXdkhC1bt2I0mVbV+bpc+DPjqPOHKa4YCgoOycOUvNSA5E8XFEVhYGCAgQVCbubCUojtb//2b7Fj+w4cTicOhwObzYbVasVisZBOp1esdW9ra+fr3/hGPpAml9KUa4j8kz/+Y7761T9f9rgllFBCCS8qxkZH8U1O0pp1y1rKb6cu65iTSRfHsevB/fts3LiJtvZ2Ll2cbS6gy3rap5bRTF/C4liQII+NjnLv3r0VpeBFo9FFK8iFUFWV7u5HDAz007F5C+vXr6ehoYFrV68uuVqXyWS4cvkjjhw9Rlt7O3du32ZXZyf19Q3cuH6drdu2UVFRuez3shqoqsqF8+d55dXXOHzkKCdOvDsnAfZ6NYJcU11DX1/vM9u/gDz5FOixBpdUXiLIzwFvv/UWb7/11oLrrKSCHIvFuHu3C4vZgimXCmk0YrFY0Ol01DfUL4t4F0oQ/t/f+31aWlpIJDSP7Gg0pjVEhkIMDw/za7/2q8ve3+eJkjd3CSV8ctDV1cXhI0doaGykr3fx87Mx2/ybXqUGOYdkMkl3dzcbN27kzu3bRJ9I/TTkXi/1ybpi+7yxIEE+d+7DFQ8cjUaxLrGCXIhUKsW1q1d43P2IXZ272bd/PzW1tVy9cnlJfrder5fe3l7a2tqxWKw0NzfTdecO9+/fo7WtbVZgyLNALBbjzJnTHD/+MocPH+H9907Oatrz+/3E43FqamufKUEOyj5Eit/5KiLilJ5uU1QJzxb9/X188QtfWHCd5ZDCwnUdDgculyvvsV34fDKZXNAtpJCMPvn6TU1N/Juf/hnCoRDBYJBAMIDf72fK52N8YoKHDx4sKhkroYQSPt0YGRnG7/fT1tZOf1/fooUAh0Pzni/mb8v9+/fYsGEDrW1tXLl8ecZz+mzFejl2rCUsjqeWNRuNROaMd14qgsEg7793kra2dto7OigvL+fihfPzJsoU4vq1q9TV1dHc3Myjhw+5ffsWAKFgkPKKCgwGwzPPjPdPTXH+3DkOHjrE/v0H+PDDs7MOMq93hDVr6p6p3VtCjZJWk4hIKKzMRWQupNUkVbqSJVUJS8OXvv/vzVrmdrupqqrCZDYvSLyfdBYpxOYtWzh8+PC82/7O7/w2//3Xfi2/beFYi5H948dfxmwx45ucZGJigtHRUSLZ/osSSijhk4W7XXc4cPAQa9fWMzCwsAuW0+VCURTCRSTIiXicnsePaV63jrtdXTNcjvT6bAW5FJpVVDw1ghyJRjAYDOj1+hX/0VRVpavrDqOjo+zbv5+Xjr/M3a4uurruLEgg6+vr8zOqnKMGwOjYKBWVlTQ0NC7bsqUYGBkZ5tq1q+za1cn2HTu4dvXqjOe9IyM0N6+jrKxsSROBYsGX8eKQykio0cVXXiJSaoJKfV3Rxivh0we/3z+ntd9CeJLUfvtb3+LUBx9QWVlJRUUFZeXllJeV43a7cbmcvP/ee3NWn3PJjgvhF37xF3EXJN8Vbjc5OcnhQwfn3KeFYDAYMJlMpap2CSW8YBgcHCQYDNDe0cHg4MCCvw9Op4tIJLJi69r5cO/eXdatX09LSyvXrk3zB71B4zupEkEuKp5iBTnrhWyzzRlzvBz4fJO8/dZ32bmrk47Nm6murubChfOzdDgADQ0N7NzVyfDQECoqmzdvwTsyQigUor+vn82bt1BTW/NcCDJA96NH2Kw2WlpbiUaiPHhwP//c6OioZvdWU/tMCfJ4Zphy3ZoiE+QkHl0VAsJTaQIsoYSlIhQKEQqF6O7uXvI2SyG1v/RLv0hzczNutwenw4HdYcdms2GxWBkbG8tfCVrO1aDv/9KX+Df/5qfz2ymKkk+HTKWS/OxXvsL777+/5PFKKKGE4uHO7dscOHiI+oaGBZNvnU7nvJ7Fq0EsFqO3t0erIt/tylvtGnISi5IGuah4egQ5W7m1Wa2rJsigNeBdungBr3eEXbs6ef2NN7ly+fKMBr5NLS1s3bqNifFxzp8/h16v5/U33mT/gYOcePcdIpEwsizPqvo8a9y4cR2L1cq27duJRqN57+d0Os3k5CQ1tbV5WcizwHhmiC3CgaKOqaIgIGAVnUSUQFHHLqGEFwHf+Zu/mfe5nFxjuY16d27f4cyZM9hsViwWzXnEZDLlmyFZhfzqxMn3cLvdpJJJEslkvhkyGo3S1XWHX/nlX17RuCWU8GnB4OAgfr+fjo7NDPT3z3ksSpKE3W6f19Z1tbh39y5NTc1samnh5o0bQKHEoqRBLiaensQi74Vc3Ka4gf5+Jicm2LdvP/v278ftcdN15w6du3dTX9/A4OAAly5eRJZlZFnm4kUtrGP79h1cuXI5HxjyvHHxwnnM5uPs3bePDz6I48tWjL0jI2zdtg2TyTRvEEuxEZQnUSl+kISKilMqKxHkEkpYIq5evcI//tGFg4xW6o4RiURwOByYzGZsdjuSJCGKIoIg0NzczC//1/+66BiqCrmXz+3HK6++xo/8yI9oUezhMKFQiEAwSCDgxzc5ycmTJ0uSkRI+Mbhz+xaHDh+hsamJ3p6eWc87HA4EQSDwFCrIoB3HAwP9rF+/gXt375FKJfMSi5IGubh4agQ555G6EieLxRCLxXj//ffYvmMHLS2trFu3Hp1Ox43r17l//96MdUe9Xu7du0traxtjY6NM+XzY7XbKKyqYnJgo+r4tFYqicPbMGV5+5RWOHDnKqVMfMOXz4fVqBLmmppbe3tkH39NASPY9FRFEzsliOL00c/USSijh6eHzf+tzcy43GAxYLJYlEe/CVXLVsy2bN9PR0THv9pOTPk6fPjVj2ULOI4X4kR/9UQRBwD81hc/nY3x8nPHxcSYnJ4uu7yyhhKVgeHgYn89HR3sH/X19s9L1nC4XAMFg4Kntw92uLhoaGtm0aRO3b99Cr9eTyWRKialFxlMjyKDJLGxPKZhDVVXGx8by5LhQqvAkbt28SUVFJZ2793Dr1k0aGhtpqK9/rgQZIJVKcuqD9zn20nGOHXuJM6dPMTExQSwWo6b2WRJk/1OxelOQqdDVFn3cHEpesyWUsHqkUqkVufrkjr1f+ZVf5ld+5ZcxGAyUlZVpDZGVlZR5yvB4PNy+fWvWcbqQ80ghfvIn/1k+BOFJBINBjhw+RDweX3D8+fBHf/wnZDJpUskkyaQWeBOPx0kkk3x06SKnT59edIwSPp24c/sWR44eo6m5mcdP9Da4nC5kWX6qjjahUIjBwUE2bNzI/fv3MOgNperxU8CCBLmjYzPpdJp0OkUqpd1rleHpxwvNWCKRaN4PsJgQBIEtW7bS2taGb3KS+/fvsatzN6+8+hrnPjw7K/FPC+s4x2uvv0FTUzOqqlL+jAND5kMsFuP9905y9NhLHDl6jLNnTuP1jrB2bf0zs3uTyRCSfegFI2m1eDHcSSVBpW5t0cYroYQSXlykUim8Xi9er3dZ2y1EZn/sn/wTqqur8HjKcLvdOJ1O7A47drudVCpNMplEFGdP7hf73RRFkX379s37fEd7O6dOnVryeyh83d/4jd/k2EsvoSgKiiIjy0pe8pfJZDh29MiKK31f+tKXiMXjxGMxYrE4sViMaDRCNBrF5/PNsP4qJkqFiJnwer1MTkzQ3t5Bb0/PjL+n0+UkGAw+9XP33a471NfXs2HjRgxG4zO3rv00YGGCvHnzogPkOqzT6TTpVJZAZx9bLRbsdjsbNmzME+2c9CK/TTq9rC+S0Whk//4DVFVX0/3oEdeuXUVRFPx+P4cOH+HosZe4euXyrMz0aDTKR5cucfDQIWRZxm63L/k1nzbi8Tjvv/8ex44d4/CRo9qM0GCgvLyciWdU5Z6UvazRrysqQU6pCcqfYgW5hBJK+GTj7Nkz8z630BWkxQidqqq0t7XmY9hzN5vNhtliYWhwcEWkUBAEBocGGR8fQ6fTo9Pp8uE3er0eQRBWLA0xGAx85Wd/bt7nL168wD/6h/9wxrL5PqMnl/2Lf/Eveen4S9niVzp/VSGVShGPx/kX//z/mvM1165dy6aWFqKRCJHsLZFIkM5kSCWTn2jt+e3btzj20nHWrV/Po4fTrlhOp4uxsaefIBsIBBgeGmLTphaSycQn+rN+XhBgfvlpa8smdDoder0evcGAQa9HrzegN+gx6A3Z5TMf67OPDXo9BqMxn4q1EHJEeQZxTqU0op1Okc5WrM0WC+vWrUev19N15w59fb2kUikyGS3vXK/Xs2//AWpra3n44AHXr1+bRb537trFhg0bUVWVb3zj66RfoFmXwWDg6NFjOF0uBEHg/v173Lp585m89j7rG+y1voG/yNHQHqma35z4KVJq8RsOSxKLEl5UlL6bTx+fts9YFEV+8Ad/EJPJjMlswmQyaY+zke+nTn3AN77xjSWN9eR58ff/4A85cODAnEE5qqrS2rJpznH+w3/4Bf7O3/27877Ol7/8Ja5eWbjpdD6cv3ARs9kMqCiKmrc+VFWVGzdu8I9/9EdWNO5nvud7+NKXvpSt6it57W4mk0GRZX7xF3+RsbHRJY310kvHsTsc/M1ffxtZlvmez34Wm81OMpkgFo2SychZjpJmbHycnsfF7cfxlJXx6quvoaoq9+7e5datZ8MXioEX6fi9/2Bu298FK8iqquYJKyu4dFNTW6s1oH3wAZFIOE+0NQJtyBLq2Y9NRiN2u11bX6+fk2Rv2bqVLVu3AuS/3DmCHYtF2bhpE2vq6hgZGSaVTJHKVq8nxieor2/AaDTS3tbO/fv3SKfTL0TDRyqV4oMP3ufIkaOUlZfT0ND4zAjyVObpzHgVVcYllTOeGXoq45dQQgklfBqgKAp/+Id/WJSxniQmP/ojPzxrHZPJhNWq2Q3OR2T+6pt/RSQaxWw2YzaZMJnN6HUSoqRDp5MYHhpeMQny+6eQZWfeaUUUxfxjSRIXvPKsPaXO+V737NnD9u075t2vP/zDP2B0dGkyoVu3bvLyK6+yfsMGHty/z+c//wXeffdd/vk//xdUVFTMWLe/v5/XX3t1SeM+iX/3la/w/d//JWBmiJGqqvzJn/wJIyMjhMKhWZ/JUj77v/jLr2Gz2TRJkCwjF9xfu3qV//yf/9OK9nn7jh289NJLpNMZ5EyGdCaNnMmQTKWQMzKnz5xmZHh4RWM/KzzdJr2sSN1oNDA6unzBul6vp729gw0bN6IoCo+7uxkaGsxWteci2FqVW/MCFLBaraxbtx5gTp1aS2srLa2tAMiyPHf1+gnN9fRzBZKS7PPF0Byl02lOnfqA115/A7vdzqaWFh7cv7/4hqtEUPY9lUAPQRBwSmUlglxCCSWU8DFCIpEgkUgwOTk5L9G6cf06N65ffyqv/z2f+cyCzy8cPw/aBfLZ+NmvfIWf/cpXAPJXyI1GIzq9HqPBkA/5WQomJycZ9XppbW3jcXc3J06cQBJFvva1r6HX65AkHTpJQqfXr+o8/uDBQx53dyNKIqIgImYtGkVRoKamhpGRERwO56z9XgonaW5uxmQyAdOfae7eYjavmNd87nN/i87OTnw+Hz6fj6mpKSYnJ4nFE6jA3r37+PrX/nLJ492+05UPT5JlOX+fyWR49513+IVf+A8r2s+Ojo6VVZA7O3dz48b1FXdH5pLulmv1pvlyrmPzli0YjUZ6eh5z+9atZfsCr1+/nl2du/F6R7h44UJeB6bX6zn+8isIgoDP52NoaDC73IChQCZiMjnycdm56OqFkMlkZhLorDwklSfXMx+n06m8Xjtfqc+Oc/7cOV57/XW2b9+B3WbPa62fFkKKD2GeH5TVQcAtvRgNkSWUUEIJJZSQQyaTIZPJEI/HVzzGrdu3ePXV12htbUWWZe7cvVv0oK+//Iuv8pd/8dU5n2vv6KCjYzONjY3cuX1rBk9YCtHftXPHgs8vNIZer8dut2N3OLR7e+7ezr1797h3T7PdzZFs7UqAgF6v5+yZM8u6ujDq9Wqafp0OXcHNaDTidLnylfXlXrH4qZ/613zt63NLkxYkyE3NzaypW8P1a9foX0EqjCzLxONxrLalW71VVVWxfccOXC434+NjXL92Df8Kk/i6u7tRFIXO3XvYv/8AZ86cznf5RqNRrFYrZWVljHq9i0oZBEHIE2W9Xl9AnGdrsnOVbIPegNVqy28zn11RDoWSlnQ6lZ8lrd+wgbq1axkcGCAai2bJdZZsP0G0VyoViSkRMmoaEQmF4slN0mqCKn1D0cYroYSPA14kfV0JJZTw9DDl8zE8PMzGTS2IosjgPHazTwtOh5NEIoHFYqGpqWmWQcFqIYoiNptNI78OO44cCXbYMZnM+fUURSEWjRIKh5mYGCccChMOhwiHw3O6qyz3N/LVV19Z8PmVJJcC/O7v/s68zy3I2N595206O3ezb/8BmpqauXLl8rK9/aLRKLYlVJBtNjvbtm+nrq6OSCTCh2fPzutrvBz0ZC1Ydu/Zy5GjRzlz+jSZTAafz4fNZmNocJD2jg4i0cicqTg5qKq6Yr/QHERRfKJSPbtqPd0MqcdgMGI2m4nH45hMJtZv2LDoF0BRlCdcQlIFhLqwWj3bui8s+rDrPGSkJJIkIQhCXotUeEljgb7OWUipiafqhVxCCS8iSuS4hBI+Pbh96yavvf4GqVSKwAoLeiuFw+lkamoKo9FIa1s7PT09K5JFWCyWWZVgh8OBxWKZIVGNx+OEw2GGh4dnkOBIJPKxDCq5cOHCvM8tSJADgQAnT55g3fr1bN26jdffeJPbt28tS0sTjUQoKy+f9/nKykrWrd/A2rVrkWWZmzdu8ODB/aJ+0H19fSiqyt69+zhy9BhnTp9iZHiIhoYGorEYXq+Xzs7dxKLRp2rPoigKyWSSZHJpVmpr1qzh0OEjXDh/jlgsxoEDB3F7PFy+fJmPPvoIo9GIyWTCYrHkGyRMJhNmkwmjyaR1N5tMWKxWjEZjvuo9H6q8HjzBelL6hRsyVVVByWqBlALiLMsZMhntPtchrMgybqGi6JXpEkoooYQSSngRkJOT6nQ6bDY7kUj4mbyuIAjY7Xa8IyNMTIxz+MhRGhob6evtnXN9g8GA3e7A4SiQQzjs2Gz2GVe40+k04XAYn2+Svt4w4XCYUDhEJBz+VAWSLNqkp6oq3Y8eMTw0xM6du9i+fQdlnjIuXbq4pMv5kWiEtfUzQy8MBgNNTc2sW78eh8NBMpnk4cOH3L93d9k646VioL8fRVbYf+AAR48e48yZ06iqSkVFBR+8/x7HX36FAwcPcfLECUKh4FPZh+VibGwMWZapqa3l5o0bnDx5gp27dtHZ2UlVZSV//tWvzrh0odfrqaiooKamhpqaGqxWCy6XC4vFMmvsVEpLjkpnbfIycgYidmzRaiI67eAWRBFJFBHF6YYA7TKGiCSAJEqgW1ybbUzZ+Pv7f4i4LpQX1ecbHlMpkqkkiXiCRCJOLB4nFo0Si8VIrUIyUkIJzwsleUUJJXy6sGbNmrzH9bZt2/jww7PP5HVtNjuSJBEMBhkZGcHvn6KtrZ2AP4DNbsOR1wdrZNhoNOa3VRSFSCRCOBRi1DuarwSHwmESq9Bkf5KwoA9yy6aNs5e1trJ16zb8fj8fnj2zaHJPc/M6du/Zw19/+1uYzWbWr9/A2vp6JEliYmKCx93dDA4OPDMiVLtmDQcOHCQYCGCz2xEEga9/7S+xWCy88uprKLLMiRPvPjWivlwcPfYSJpOJt9/6bn5Zc3MzO3d1kk6nefz4MSaTifLychwOR/7EnMlkCAaDBIMBgsEg0UiEeCJBIh4nkUjM+Xk3Glr5nPMfE1AWCyfRLHekrO3OdEetiKST0ElZc3xRQpRELLKT+zWniTgmkbLrLpVAaMJ7ZUYaVTqdpry8HLfbTf3atYRCIQLBAIFAAL/fj29yEp/Px8TExIr16yWUsFKUCPKzQelzfjYofc6L4+DBQ3jKyuh+9JAtW7fx/nsnZyX6FhOCIGCxWGhubqa9YzMDA/0YDAZcLnfekSKHWCymkd+QVgUOh8OEQ2Gi0cgzSeqdDy/S92pFLhZzDnTvHsFgkP37D/DKq6/x4dmz+HyT866fSGpE89hLx7HZbKRSKR4/fszj7kcEg8++UjsyPMyHH57l0KHDZDKZfCdkLBbjzOlTHH/5FQ4dPsL77518IaqXXu8I27fvwGKxkEqlqK9voLm5OZ/O1N7eTiqVYnxsjIH+fgLBAMFAgGg0uuwvf1D25fxxFoGKosgoytI+H4+U5sbjO9yKf5hfVugoYjSZsJjNmM1mTGaLJh0xGjU9tsGg/Y0kCZ1OwmAwYLFY8jryH/uxH1t4T7OfQWHUa05LnkgkiMdj2bjWaPYHJEwoGMTv9+P3+5mammJicoLJyUmmfL6PpcaqhBJKKKGEpwOdTkd1TQ09jx/z4MED1q3fwLbtOzjx7jurJqBGk0mrAj/hFGGz2WbkQ1RX12QrwV6qa6pRFJUPz54hFArlg9RKWD5W5IPsHRnhxLvvcOjwEV46fpwrlz+it0DzIkkS5RUV1NfX09DQmF/+0aWL9Pf3P3fi6R0Z4aNLl9i7bx8ADQ2NPH7cjd/v5/z5cxw6dJj9Bw5y7sOzz50QeUc0grxv/wHcbjc6nY5gMMj169eYGJ/IR31WVFYwNDzE8NDK/YZD8hQis/2iV4uMmqZKt3bGshxhTSQShMPL12uJokh5eQXXrl2lrKycsjIPHrcHl8uF0+XCYbdjs9ux2ayaRttsxmQyY8iSbovFkp9kwNKaqnI/doX+i+l0WtOVJxLEEwlisVg+cjUUChIKhQgGAkxNTTE1NYXP52N8fAKfb7L0w1VCCSWU8DHHmro6dDodg4ODWh/VzRvs33+AxsYmenvnb/zPQafTzbRIK9AHGwyG/HqyLGsSiFCI4eEhwuEwmza1oMgy7777Tn69mpoajhw9Rm3tGqampp7Ke/60YMVBIaFQiBPvvsP+AwfZs3cfVdU1BIMBqqqqqaioQJIk0uk0/X19NK9bR39fHz0LuEQ8a/T19eJ0OWltbaOltZXHj7sBrcJ85fJlOnfv5uDBQ3z4nEiy2WymsamJ5uZ1AJSVldHT85ienh6mfL78elNTPvr6etnVuZu9e/fR1NTElcuXV0Q6ZTKElSn0gpG0urRGwqVAc7KoK9p4oP1YjI+PMT5enKZKl8tFeXk5FRUVeMrKcLvduN1unE4njqyGy2q1YrVYMFssmEymfOOj0+lA8njy0pHlyEcURcknQebJdjJJPB4nHo8TjUaJhLUmiWAoRCDgx+8P4Pdrpuu+yUkmJiZeGElQCSWUUMKnBYIg0N7eQSAQYHJSkyYO9PezceMmtmzZwuDgAJlMBkEQZlilFRLiwh4hVVWJRaOEw2H6+nqzLhGaU0QsFptRkTYajezevYc7d27P2Cev10tfby+tbW0MDg48lyv1nxSsmCBbrVaqq2vy3ruNjY0ABAJ+Hj16yNjoKOPj48iyTHVNzbK8kJ8Vbt64QUtLK3a7nbb2du52dQFkybJK5+49HDx0mHMfnn0mVW9RFKmtraW5eR3VNTWIosj42BjRaISysnKuXZ07LCQYDPLeyROsW7eerds0t5G7XV3cu3d32eR+IjPCGn1z0Qlyma66aOM9DQQCmoa5u7t71WPZbDYqKiooKy+nvLwcj9uNy+XC5XLjcDiw2W3ZH0arFtFqNufJtt1ux+Vy5W32lqfVVvOVbU1KkiSRSJLIVrZjsWi2KUOrQgSDAab8AfxTPnxTU0yMTzAxMb5oX0EJJZRQQgnQ1NSMw+HgbLbp32w2Y7fbGR8fo62tnVdfex3QzgmFVmm5K6ejo96sJnjaKm2pXKO6pgZBEPCOjMx67tq1a1TX1LB79x5OnjzxXLXGH2csmSAbDEaqqqqorq6mqqoKm90OQCQS4d69exgMBjZu3Mj42Pis6MloJLLsNL1nhVg0isVqZcuWrSQSCXqyJtuPHz9GUVV2797DoUOHOXv2zFMlyWvXrmXrtu3YbDZisRj37t6lt7eHSCRCbe0aDh+poaKiYkEbusePuxkeHmL7jp1s3rKF+oYGrl+7tuRceYDx9BBNhjaihIrxtgBQkNELeiyinZjybOxvnidyEoveeax2lgOTyURFRQXlFRWUlZXhdnvwuN04XS5cTid2ux2rzYbVqklJTCYTJpMpKyOx4nA4V0y2NSlJhnQ6k3c9SeQq23kpSTjbDBokEAgwVRApOj4+TihUvO9RCSWUUMLzhpYe58DhdLB9+3aSySQdmzezd9/+GTaqiqJgt9sZGRlhcGAgXwkOh8OrylPIobamlkQiPqeMIpVKcu3qVfYfOMCGjRt5+ODBql/v04gFCfLWbdtwOpw4nE5sNo3gplIpBgYGeHz+PA8fPsRXcLn/zTffZPfu3WTkzIxkukg0Qk11zVN6C6uDb2oKq83G+Pg4u3Z1kkwkGR7WdLy9WcPtPXv2cvjwEc6cOV10kuzxlLF9x3YqKirx+/2cOX0Kr9c7Y8Y3Njaat3tbzKc5kUhw4fw5+np72Lmrk6PHjuGbnKSrq4uRkeFF92dKHmW+DPvVQEHBKZV/KghyMZFIJBgcHGRwcPWhOQaDgbKysryUxO3x4Ha5cLndOBzOfERojmznKts5v+1cY8hyXUhgdpOkJiVJEIvFC5okQ/kmySm/n0DAj883hW9ygomJiRm/NSWUUEIJTwv59LhsY9x86XGguUTE4wnGx8bzBDgncXzzM99DOpUqevS0IAhU19QwMjz/OX1goJ+Gxka2bNnK8NBQ3qu5hKVjQYK8ceMm/H4/Q0NDjI6O0t3dzcjIyLyX7b/73e9i0OvZtn07mUwmL1mIRqKYs01Rz7tB70mMDA9RX1+P3z+FKIrsP3CAUx+8z8SEpifq6+3Nk+QjR45y5szpojRXWSwWtmzdSmNjE/F4nI8uXaK3d+4EHE1vO05NTe2s6vx88Hq9fPc7f0NTUxOtbe0cPnIEv99PV9cdhhYgW0HZh0rxNdciIk6pDG969VXVElaGVCqF1+vF6136FYX5oDVJahKSsrJyysrLcLtyUhKn1mltt2Gz2TCbc02S07ptq9WaJ9uwsibJXFpkoW5bk5HkpCRBgkFNSuKbmsKfb5IcZ3Jy8rk34JZQQgnPF4XpcY68Pni+9LgQw0PDhMNhotEonbs78fmmOH3qg3nHf/DgPu3tHTx8+JCpqeJN8D1lZRiNRkbmkFcU4srlj3jzM99D5+49nPrg/aK9/qcFCxLkX/qlX1r2SeRb3/42eoOBLVu2kslkePjgAdGoFk9tsVgJh1+sS65DQ0P5wJBTH3yQt3l77+SJvLi9v68PVVHZu28fR44c5fTpUysmyTqdjpbWVlpaWhEEga6uO9y7e3fR8bzeEXbs2InVal3yTFBRFB4/1hr7GhobaWtr5+DBQwSDQe52dTEw0D+LkAflyafiZKGiUi69mFcRSlg+FEVhfHy8aF6fbrebioqK6cq2243b7cHpcOB0OrFlK9tWq3UG2dbr9ZjN5hmV7eU2SeZSIFOpdL66HY/FiMXjeblMOBzOe4oH/H58Ph8+n4/JbJNkMS6ZllBCCcWHwWDA7nBk7dIWSY8LhfBNTtKXlUOEsvrgJ8/PHR2bMRiM3Lp5Y8HXvnf3Ls3N69i+YwfvnTxRtPdUW1OLoiiLyifj8Tg3blyns3M3Tc3N9L5ARgkfByxIkFdSYVFVla9//evodDp27NiJnMkQDGqk2GZ78Qiy1tCUxm53kEqlOH3qA15+5VUOHT7Cu++8nT/xaWRSYd/+6SS+5ZwUBUGgsamJLVu2Yjab6e/v4+aNG0tuiPKOjMCOndTU1Cy7kUxVVfp6e+nv62Pt2nra2tvZt38/HZs3c/duF/19ffm/dUyJkFbTRY+GTqoJqvT1RRuvhE8Wcr7TDx/Obdi+HDgcDsorKijPykncWfs/l9s1fYK027IyEi2m3Zj13bbb7bjd7hWR7VyTJMAP/eAPcuXK5VW/lxJKKGFxSJKELSsRK6wEz50epwVleL3efGhGOBxashOQ0WhkU0sLAwMDiwZRZTIZbt26yZ49e4tKUGtqa5mcnFxS7PPj7m4aGhrYvn0H3pGRkuPRMrAgQW5ubs5fvkwmtW74pfxBFEXhL/7iL/jyl7/Mrs7dXLt6BeCFbdQLhUJ4PGX5wJAPPzzL8eMvs//AAU6fOpWvsg4ODqKe+5B9+w/w6quvcfbsmSVZqHjKyujs7MTt9jA5OblouMpcCIfDRMJhamrXrNhpQVVVBgb6GRjoZ01dHe3tHezZs5etW7fR399PX2+PRlQyY9gkJwm1eG4GKSVBua5UQS7h6SMUChEKhfINt6uBxWKhoqJScyMpK9MaJV0unE4nTqcTu92G1ZbTbWuVbafTRTgcyv9uLCcx6gd/6P/gX//rf11g/5cmnc7kf4N//ud+lnPnzq36fZVQwscNgiBgtVqf8Ax25PsmCpGzShsYGMinyIXDoRUFaD2JtrZ2JEni9q2l6Yp7e3poampm27btjAwPk0yuziHKZDbj8Xi4eePGkre5/NFHvP7Gm+zctYtzH364+AYlAIsQ5B/4gR+YtUxRFNKpFMlsGlkhgc6R6Nztzu3bOBwOtm3fgaIouNwuDAYD6XT6hbIdGR8bp6ysnLX19fRmfYavXL7Mnr0aebxxY1r3OzQ0xHvvneTQwUO8/MqrXLx4Yd5wjpxHYlt7O4l4nPPnzjEw0L/i/RzxjtDcvA5RFFetnxwe0kJFqqtraF7XzPr169m0aROBgB8epTFFrCTSxSPIGVI4xGr0goG0WrocXcLHA7FYjP7+Pvr7+5a8TY4Q50jxcuJUH3d3c+fOnSzZNucdSXL2fxardcW/ne+88y4ut3tGsE00qum179y5w6//919b0bgllFBMTKfHzfQMfjI9LpVKEQqFGB8fn0GCw+HwU+t1slqtrN+wgb7e3mVdDb9y+SNee/0Ntm/fwcWLF1a1DzU1WqFpxLuw/rgQ4XCYO7dvs3XbNurq6hhaRaDYpwkLEuSTJ06gN+jzkcDTNwMGvR5d9v9mszn/nMFgmCFuL8T69RtYv34DoOl9Muk0qex9uuBxKp0mnU7lm3DSqTTpTPa+YHmxDoL+gX5a29qorV2TvwTS29uD2+2mpbUVv98/4wQ55fPxzrvvcOjgIQ4dOsztW7fo6rozY0ybzc6+ffsoKy+nt7eXa1evLKn6vhC8I142btxEZWUlo6Ojqxorh9FRL6OjXgwGA/X19TQ2NeNeZ2XNaB1mWU84lJ11s/oJjYKMQyzDJ6++SayEEj6JOHv2DGfPnllwneUQ7kKkMxkkScLhcKB7Ithm48aN/Pdf+9VljZfbjzfffJMf+dF/TDyetf4LRwiGQvnI9m9965uLXoou4dOFGelxBXKIRdPjCkjwaiuxy4UkSRw8dBhZlmeFcyyGUCjEvXt36ejYTG9vz6JuVAuhuamZSCRCMBBY1nb3799jbX09uzo7mZycLEktloAFCXIuGWa5kCRpBqF2ezzs2tWJoijcunkTnV43g2znHpstlhnbLQZFUaZJdI44p9IFy5547kkinkqRyWQI+P0oioLH45kx/vXr13C5XHTu3k0oFJzxI5+Ix3nvvZN07t7N5i1bcLldXLp4kUwmw7p169i+YyeyLHPuww8ZHBxY0ef4JMbHx8hkMtTU1haNIOeQSqXo7u6mu7sbr8vPF8rWYXAaqKquQVFkIpEI0WiUeDyOqq60ei3glEoEuYQSngc++z2fmXN5ztJvucQ7V8ne1NLKxo0b59VsP+p+xIdnzy441nyv/dM//TPoDQYCAS090u/345ucZGpqkrGxIYLBGKpafFvKElaPudLjcnZp5ifS46Kz0uM0EvxketzzxO49e3G5XJw+fWpFYUp3u7qor29gV+du3n7ruysq8Hk8ZVRUVnLt2tVlb6uqKhcvXODV115j7779nD71wQvz2b6oEGD+0mDLpo1Fe6Gjx45RXV3Dw4cPuHZ18T+uIAjodDoMBgM6vR7DHFXs/GODHr0ue1+4XK+ft5pdiHQ6nQ9T8Pl8M0i1oig0NDSiqip37twmHovNIuANjY1s3bqNcDhEPB6nurqG0VEvly5eJB6PF+Pjy+PwkaPYbDa++52/Keq4hfBIVfxA2c8QkCe0ZCCHHZvVhiCIqKpSkMwWI5Va+izeI1VzOvJXXI2t3m5mObrOEkp4lvg0fzcdDgeVlZpmu6y8HLfbzbe/9a1FA2Pm02vf6bo7w2mgEPH4Yy5dWo+iQCYjIMuCdp+BTO7xE/fa8wJOVxm1tQ0oioQii8iKiCwLyLJ2f+XKDeLxDHJGICOTvRdm3heOW7BOWVk1mTQkkzKJhEwikcnfnobH/NPGYt9n7RzhmKkNttvnSY8LzWiMC4XCRCLhF95ysaW1lW3btnPjxnXu37u34nEqq6p46aXjdHXdWbKGuRD79h+gpqaGb3/rmyt20mpuXsfuPXu4dfMmd+92rWiMTxruP5i7OXzFUdPLxejoKNXVNZq38tTUoiljqqrmCehqMF3NNsxBpqfJdl1dXTYMRcWg12OxWDRSbjDkf6B37tw17+soioLD4cThcJJIJBAFkZ27OueWjBRKRwqWZzKZRWd03pERdu7ahc1mIxKJrOqzmQ9B2Ze1elOJx2PE4zEmhAlMJhMWiwWLxaJ535aBLGeIxWOaLVYsjizPf9Cm1SRVuuI4WXyaSUgJJbyoyDVILreReD699t/63PdRVVWF2+PB4ynL+my7cLttpJLXOXW6DJ2kIulUdLlb7v8S2fvp541GBZ1VxeWKYTINA2kgg6qmgTSqmkJRUrz++mrke/P3maiqRDKpzCLuOSKeKSDeOcKd+7/bXcmaNY0oyjSRz2+b0Uh9IpHJEnZhbnI/16RBBpvVRTKpkEhktImBPPM8lEuPm1EJzlqlFV7tzWQyhMNhAn5/Nj1Os0qLFCk97nmguqaGrVu3MdDfvypyDDA+NkZvTw+trW309/UTCi3e5J+DxWJh7dq1PHhwf1VZDD09j6mqrqJj8+asJ/zKlAKfBjwzghzNkrnJyUl2de4mGAzOGZFYbOQSvBbT24SCAXbv2cvE+Dg3C1IAQfvRbm5eR+fu3fT19fG4+1GecBuNRuobGigvLyeVSiIIIkajEavNhj6VWlE1e055SFY6kjuBdHRsZmhocLZ2O5Va9WxcJkNYCcxoqFNVJU+WfT6QJJ12adZiwWK2YLc5AEimNA/ZXNOmNsHRfmxTaoIK/ZpV7VsOJXJcQgmffORkX3NBmySXrfIV5j4FCoKKJGmkWpLQ7rNkW1v2xH0BId+5YwtGo4ReDzo96HTkyfvk5Cgjw/2ziHtunNwySdLIfO61dZKK3TGB3R5GVdPZWwq1oOF5bVFdNEUEwcDkZBt37nyWwsp3YREnZ22o2RwqqCr5IkplVVXe/rDwBiqqoqLMWK5o94qKoirT96qKoqioijJjuebyovmYa8+pqIqMnHV/URUFWVZQFBkl63euyHLe91xRlPy6SpYj5JbnbqD1Eu3ff4BAIMClSxeL8slev36d2jW1dO7evSxv5I2bNgHwqAhWmJc/+giPp4z9+/fz9ttvPfPJy8elwPXMJBZuj4fXXnudCxcusGXzZgRR5J133ib5ggjFdTodX/ji32ZqyseJd9+dc51t27bT0trKpUsX6e3pwWg0cvjIUTweD/fv3+P2rVuIosiOnTtpbl6H3+/n4sULeTG9JEnoDYZ8BTvX6GhYVDKiyz8/3+XGQhSmjM0k2pklNT+m02k+a/1havSNRJWldeoaDUaNLFssmEwmBEGbDKiqQjKZIplMkEqmMKZt/DfvTyArK6/Q5H6gPw4HWAmfLnxcfvg/CSh91gAqBoMOs1kinY7PJu5PkPs5Sb5OpbNzO0ajOE3mJZAkhXTayuOedUSjKum0lCeOOb25IJD/rS9cBrNdXAr/Vtoqc//tXpS/6XxXc5+cIMx/r6KqzJ4gqCqSTofJZCIej5NMJuecKKjq9GRBRaW6uoZ4PM7kxER2kjA9cVBkZeay/PLcpEGbQCgFEwer1crOXZ1MTExw7eqV/HJZVpDlzIyJwtP4bF+UvzPML7F4ZgTZYDDw+S98kWvXrjI+NsbLr7yKf2qKDz54/4XRH33hi38bVJWvf/1rcz4vCAJHjh6loqKScx+eZfuOnZjNZs6fO8fIyMxM9No1a+js3I3BYODOndvcv3evKIJ4QRDYsXMXTU1NvP/+e0iiNE2q59FozyDiWcnIYtXs2vE2qn2bSOjD2dm5dvAWzrC1W+6Ays7CMxlkRclLW4xGIwajEaPBiCiKmFJWbja/xUTMm2+6CQWDRKNRYrHYkr4LTmeKaFRHJlP8xL8SSlgNXrQf/k8ySp/1s8Hz+JxFUUTS6ZBEUXssSYiihCgKBY9FJEl7XpQkJFFEEMT8ciG7viiKiIKIIIpIoqCtI4mIgvZYEEXE3HJR0NYVBJwuzZY2HAqRkeUC1xdtUjA9IRDyhH++ZdmH5CrxSwkhelG+28uZKOQmBfllquZ/VfgdEgCDMY3VOsHatX+GLJPV/+ekPzA2ZmBszDBDZlRfnyCTFkilRdJpgVRKIJ0SSaVEAgGJYFBPOiMgidoOJJMiyaRAMinAIunAz12DnEppFUqb1cbDwAM++ugS+/cfoKNjM7du3Vx8gGeAcDiM2+1Gp9PNqfFRVZXz587xxpuf4eChw6TTaT54//05Qz9Ghod5a+K77Nq1i61bt1G3po6LFy+uOklQVVWGh4bYsGEDRoMBr3dhRwidTkdlZVU+SczlcmGz2fPPZzIZIpEI8VgMURSz1n06LOoEVcpGVBXth0QnsrKvS9YgTlU1Mq0o6OImbDYbDoeD5uZ1M95bKpXSPFrjccKRMMFgiHA4RCwaJRqNIssy//E/9SGKZGfnuYOL7EEjkkyKJBIC8bhELCYSjUhEIhLhsI5gUCIY1OH3a48XO3BKKGE5KJG2Ej5peB7fZ0VRUFIpVteBtHJ0bN5MZVUV165e5eHDB0/lNex2O6+/8SZDQ0NcOD93+E9ucvD6G28Sj8e5eOHC9KQgP3EQEUUpvzw3MZCyEwdRyC1/coKgLauursZsNjM+NkY6k5meJBRMFgQxR/ynJwZifvn0/uQmEYWPYfZ3SFV1ZDJ2ysrS2QnH9HOCABs3FldZME3aIRyWyKSnibfJpPDFL8693TMjyACRSASrTUvTG+jvp6qqipbWVkZGhpmcXF6y3NPA+NgYHo+Huro6+vr65lzH5Xaj1+sRBIGJiYkFE/FSqSTnz59jcGiQXbs6ee3117l16yYPH6zugMvbvdXULkiQq6ur6dy9J58yFA6HCQQCDPQPEAj4CQQC8zb61eibcLibCMiFAn4hPyMXBO3gnHkT8zP7Jw+Y3MElChIWxUFc55910AiCgNFoxGg04nA6qaJ61n6pqsrFi2Fstgc4nScxmRSMBgW9QcVgUDGbM4gisw66uZA7aBSF6YaVtEAyJWRJtkgsJhKLSUSjEuGQRCgkEQppBDsQ0BGLiZRIdgk5lMhxCZ80fJomfTmJ5Pr1G+jpefzUyDFo5+O7XV1s3rKF/v4+RoaHZ62jKApr6uqwWq1cvXqFSCRc9P0wGAy8/vobWG023nn7rVnFQZPJpCWFWq1YLdl7qxWLxYLVbJ3hWw2axFNzudKKWrFojGgsmi9yxWIxZFlGEAT+5I9nqxQEQUGnUxFFIa/dl3QK1VVpDAYle1MxGBX0ehW9XiUaFYmEdUg6lerqFBXlaXR6ZbpxVweSpCKKKn195hkNveXl8+uvnylBjkYjM6qX169do6qqmr179/H2HH+YZ42BgQFaWlupXTM3QV5bX8/evfsIh8OMer20tLbS2NhEX9/CjhyDAwNMjI/TuXsPO3bspG5NHR99dGnFLhSKojA2NkZNbS3M4Yeo0+nYvn0H69avJxgMcuqD95mcnFzW5xuUJ7NOFoVQ840RACsxGHFK5dzrecQH4a9rFWudHpPJhNlswmQyYTSaCmQZBowmI0ajKa+/1kh4hlRKR0+PkUhERzgkzVEZBotFweXO4HbJOJwZHPYMDoeM1apgscpYLIpGsI0KRoOKTq9iMitYbSo5BcpSSLaqziTZ6ZRAMqUR7ERCJB4TiUS1KnYkou1nMCgRCOjxT5WkIiWUUEIJzxsGg5GDhw5SWVnF3a6uZ3Jl+969u9StrWP37j28/dZ35zQTaGlpJRQKzUmgVwtRFDEYDNy7f5/t27fzyiuv4pvy5YmwxWKZkV4ImhogR3QnJiYKiHCUaCxGYpXWtqoq5rlFYRbMlM8w9wZFwXOWWABEI1Gqq2vy/89kMly8cIHjL7/M9u07uHz5o2e5O7MwNeVDURTKnggMAdiwYSM7du5kYmKCs2dOk8lk8JR52LlrFz7fJOHwwjO7RCLB2TOnaWpqYvuOnbzx5mfo7n5E1507K+og9Y6MsGbNGmw2+4xZZVV1Nbt378FsNnPv7l1u3761Io13TAkjqxlERBSKpxFPKQkqdWsBjeinUklSqeSS7W6qqpL8/L9fOK57qZXhyQk90aioVYXDOkJBHf6ARMCvVYZFEex2Gbc7g9OZwemUsTsy2GwyNpuCxaJgNsuYTFrHucGgzVitNhWHJC+pip3bX00qou1vOq1pqzSSLRCPF1SxwxKRcGEVW3usKCWSXUIJJZSwEjidTg4dPoLJZOLC+XP09y98jikWFEXhwvnzvPra6+zZu4/Tpz6Y8XxHx2bKysq4eGFl8dR6vf6J6q8lW/3VqsBms3nG+s5snH3OZWxocHBW9Xe11rsfJzxbiUU0gk6nw2g05mMiJycnuHfvLm1t7QyPDD+VWdJykEgkZqT8AGzZspW29naGBge5cOF8PgHnwvnzvP7Gm+zbf4CTJ95dEhHt7e3F6/XSsXkzGzZspKmpma6uOzx6+HBZRNabzWGvra3l4cMH6HQ6tm3fzvr1GwiFgrx38gQ+n28Z73w2puQxrKKDhLr81KD5kFITlOtqV7z92JiR/+ufNVFWJuNyyTicaZwOOUtaZSxWGbNZwWxSMBqnL8M8jcpwLCbi9xu1ynA4V8XWKsNT2cqwTqfgcmXyN4dDxmbPYLcrWC0yFquC2axgMir5fV2tVCRHslPZCUE8Lk6T7IhEOCJlq+6aTCQQ0BGJlKQiJZRQwqcPa9asYe++/WTSad5/7+QzsZ8tRCgU4sb1a+zq3M3GjZvyso6amhraOzro7e2Z9yq12Wyel/xardZZicSyLOeJrndkZBb5bWltY/369fT39xXFTu7jjmdeQQaw2mwzctTv3L5NTU0tu3fv5q3vfveZZ6wXwu+fYs2aOpxOJ8FgkB07d7Jx4ya6Hz3i6tUrMzo34/E4H126yKHDR9i6dRvXr19b0mskEgmuXL7MwwcP2bZtG9u372DDho3cunmTgYGlzVyj0SjBYJCa2lr8/in27t2HxWrl3r273Ll9e0Uxlk9iLD1Ei2lHUQmyTAajYMYkWFY8biKhY2REz8jISvdCweFQcLnSuFwZnC4Zuz2DzZoj2VnSmpVfaDonjWDbRTlPXBfDUirDkYjE2JiecFgiHNIRCkl5fXMoJKEoIiZTBrdb1vbVqd2sNgWbTcY6Qyqi5vfXZFKwWpc3IYAsyc4IpDPCdNNjQiCe0Ai2RrJFQtl9DYZ0BPw6/H49qVSJYJdQQgkfH7S1tbNl61Z8Ph9nz55ZtTxgpeju7qamdg1bt21jbHyMTDrN3n37CYVCDPQP0NzcPEsDbDabZ8kfkskksViMSCTC+NhYAQHWNMGL5UFcvXIZk9HIjh07icfjDA0OPs23/cLjmdm8gXYZ4403P8P5c+dmEUGn08mrr72O1zvCh2fPFvV1l4PmdevYvXsPd7u6SKaSbN++g/v373Hj+vV5t9mxYycbN23izOlTjKyAtVVVVbFt+3bcbg++yUlu3LjOxMTi6Tbbtu9gw4YNqKpKLBbj0sWLCzYNLhfbzUc4Zv8iU/Jo0cYEcEkV/O+p/8ZYZmBF278ITSOiqOB2Z/LyC4cjK7+wKhpptcqYTDNJdq4xoNiV4WhU0poUIhKhoGZ3EwhKTPlyTYRgt2v768ySbIc9W3W3y1mpyPS+GgzahECnA1FUlz0hyEX/ZjIawU6lBE2LHReJxyQi0WwlOywRyOqxgwFNMvJxl4q8CN/NTwtKn/WzwSfxc5Ykid179tDQ0EhfXx+XP7pUlKLScmAwGLKk14LFas27Oqmqmm9wL4SqqsTj8QK970zyG41Gi9LHJUkSR4+9hMfj4dQH7y+JiywXL9p36rnbvIFW9QSw2qyzngsGg9y6dZPt23fQ1NxMb0/Ps9y1PAb6++ns3E3d2rU4HA4GBvoXJMcAN25cp6Kykj179/L2W28RX+YsdGxsjHfefpvGxia2bN3K8ZdfYWhwkFu3bhIKzW8LFwwEkCSJeCzGeydPFL3yHpAnUIuoP85BQMQpla2YIL8IUBQRn8+AbxWNAyZTtiqck1/YM9jtOX2zjNmiyUWMRjXfubvSyrAsgyLPXxkeG9VrBDurbw4FtUp2rjIsigpOVwaPW6u4Oxxa1d1um2561PZ1en91OhWTaelSkdz+KgooikAmA+m0mJ8QJJMFriIRkXC2QTMUkvAHdAT8esLhklSkhBJKmB9ut5vOzt24PR5u3Li+6vjouSAIgiZ/sFjyGuBp6YMFi2W2/CGTyZBMJrFkJZ59vb2Mjo5ON8HFYkXJUlgMsixz9sxpjr/8CocOH+G9kycIBpceif1JwjMlyJlMhkQigc1qm/P5B/fvU1u7hh07dmqXB7KE+lkik8kgyzJ2u52JiQkuXVw8XlJRFM6f+5DXXn+Dffv288EH76/oi9zX18vg4ACbNm2ita2dN9euZWRkhIcP7jM6OrOKa7FY2LxlC6qqMjo6+lRkKUF5dRrm+SAAHqnqqYz9cUIiITE6KjE6alzhCAo2m4Lbk9akFw4Zh1PGbstgyVayZ1eGVaxWBbt9+VKRhSrDsZjExLg+6zetSS+CAYlAQWXYYFBwu7OyFqfmLGLPVrGtVm1SYDJpkwCDISsVMSpYLCuvumdyE4KkSCIpTFv3RXVEslX3YFBr0AwEtElBIvFMfxZLKKGEZwCn00nH5i2sXbuWZDLJ2TNnZgV8LRWSJGUJr+UJ6YNGfi0Wy6wKcDKZJBqNEg6FGR0dK9D+avfJZJLm5nXs3rMHgP7+vkVzDp4WUqkUp099wCuvvMqRI0c5efIEsVjxpJYfFzzzM0E0EpmzgpzDpYsXeP2NN9m7dx/vv//eM5kxFcJms+W/2Oc+PLvkyy7hcJirVy6zZ+8+2tra6eq6s6LXl2WZu3fv0t39mA0bNrB+wwaOHnuJQCDAwwcP6O/vQ5J0HDl6DJ1Ox/j4OBWVlSt6rcUQkqcQnkI1LqkmqNLXF33cTx9EIhGRSETHSpVioqjgdOakIlnSai9oerQUSkU06YWmcS5+ZVhrFsy6dIQ1uUggoMfv12Urw5p1nyZtkXE609jtMvasdV9uQmA2a97YBoOK3qBiNmt67ZU2aKbThSQ7JxXRpC2FVXc5A5GISF+fiUymRLJLKOFFgMPhoKNjM/UNDaRSKW7fvsXDBw8WdGMwGIzT5Ddf/bXkibDJZJqxvqIoxONxYtEok5OTs8hvLBZbVP7g9miuWKNeLyazmT179z7XnqxYLMbp06c4/vIrHD5ylPdOnvhUOVjAcyDIkWgUzxw2ajnEYjGuXb3K3n372LBh41M16n4SBoOBw0eOoiiKlkBXVcXAMuxeent7qarWOk+9Xi9TUyuvwKZSSbq67nDv3l3qGxrYtKmF3Xv2sGXrVhRFwWg0cuqD93E4nXR27sZud6w6pe9JZEgTUYLoBQNpdflWdPMhpSaoWIWTRQnFg6KI+P0G/P6VS0UMhpz8Ip23wnPYtUZCzalj2govp8c2PqXK8Pi4PpucKGb9pnXZKrZEIqFDFJW8dV/OVcThyOSr2FbrtKzFZFTQG6b3V3KuxLpvekKQTmv7OZ9UJJDz8p7Sl1xFSihhlbDZbHliLMsyXV13eHD/Pul0GrPZjCtraZbTABc6QOh0M6lRJpPJE16/3z+D/EajUeLx+KqKeQaDgYMHDpJIJDh//jxms4lXX3ud3Xv2cvbM6dV+FCtGIBDg7NkzHDlylCNHj3Hm9KkV2dJ+XPHsK8jRCHV1dQiCMO8Xqq+vl4bGBjZv2cLA4MAz6SwVRZGDhw5jtVq5dvUKnbv3sGZN3bIIMmhdoJUVFezZu5d33n5rRR7EhVAUhb7eXvp6e6mqqmLf/gOYzWYURaGpuTnv11hbW8uDB8UlyACTmRGq9Q1FJchpNYlbqkJCh8zzDYcpYfVIpUQmxg1MjK+UZCv5ynBOk223y1ol21pg3fcMKsOxqIjPl5WKZPXNuQCaQGDaui+3r4XWfc3NCSor04yNGaalLdmUx9yEwO1ewYTgCVcRzWZQIBaViMay3tilAJoSSgC0ivHmzVtYU1eHqqpMTfmIxeJUVlTS2NiE2WyeJX9IJBLEolFCoRCjXu8M+7NoNEYq9fSquIIgsG//fkxmM++dPJHPBrhx4zo7d+5i/YYNdD969NRefzGMj41x/vw59u8/wMuvvMqZ06dWHHL2ccNzkVhIkoTZbF5Q03L1yhXeePMz7Ni+g/Pz5JQXE3v27KWysjLvsLFrVydlZWXLHiedTvPRRx9x9NgxNm/Zws0bN4q2j03N6zCZTNy8cQOzxUJzczPNzevIZDKsW7+egYH+ZTcILobx9CANhk3EKC75VlFxSB788viyt32Rul9LKAZyFVUdK7VBn6synAt1sVrlp1IZlmWmGx6TIhaLjCCohMMSo6N6wuGZKY9TU3pCoZlSEZdLxuVaQCpSBO14oc1gbl81m0GJWFSTioQjEuFwoW681PBYwosJo9E4y/vXarFidziw2WyzrM88njLMphjRWIyJ8fGC6m8s//hZO1gUor29g5qaWi5/dGmGB/Ojhw+pqall+/Yd+Kf8RXWoWi6Gh4b44P33OHT4MC+/8ipnz5xedc7CxwHPRWIBYLXaFiTIkUgkn1Pe09PD6OjTE6tv3ryFhsZGbt64kbefiycS+W7S5WJ01Mvj7m5aWloZGhrCN7n6L3Z7eweNjY3cvHmDe/fuAnD71k2am9fR0tqKw+Hge7/vc0yMj9Pf38/g4EBRLoVMyt6nokNGVXFK5SsiyC+aRUwJzx+KIhIMigSD+sVXngfTleF01qmjsIlwmrTmmggNhrlDXTo756+uLEUqEo1KjI/riYSnQ10CAR2BoI4p37SryHQVe1omYi+QiphMWmBOsQJocvuq6caFvG1f9Elv7LyspeSNXcLyIAgCFoslL3WwFOh+cw1wT8ofcldpRVFEURQmJyfo7+vD7w8Qi61e/vA0UVNbS8fmzfT0PObx48eznr944Tyvvvoahw4f4t133nmujXKTk5OcePcER44e5dhLx7l44TxDQ0PPbX+eBRYkyG3t7SQSCZKJBIlEgng8TiKRWJVsIJotzdtsVhaz17t37y4NjY3s3LWLt9/67lOZ5dXV1dHe0cHjx9154gng9/tZs2YNdoeD8AJWa/Ph+vVrVFdXs2ePJrVYzb7X1GgHUW9vD/fuTu9jOp3mwYP7+P1TvHT8Zfr7+zULm92782L//oF+hoeGVuyPGJKnUCj+5y4KEi6pvOjjllDCSpHJiExMGJiYWLke225PYreruFxaE6FWyZ5ZxTaZssmJRZCKFLqKJLMEO5GYHUCjSS902YbH6QAaT1kGl1PGmbUZdDimbftyATTF3FdN1lJYxda02JGIlJW1aCT7yX0t4ZMBnU43k/wWVoItVkxzyh/iRKMxgoEg4+PjGPQGrDYbTqcTnU6HLMuMjY4yMjLCyMjwomEYLwqsViv79u3H75/i6pUrc66TSqU4c+Y0L7/yKocOH+bkiRPPtdodiYQ5eeJdDh0+woGDh7h+/RoPHzy7PrFnjQUJ8pYtW+dcrqpqwU1BlhVkWUaWM2QyMpl0mlQ6TTqVIpVOkUwmSSWTJBIJEskkqqpis9kX3TlFUbh65TLHXjpOa2sbd+7cXtm7nAd2u509e/fh8/lmfUFHvSOsWbOGhoZG7ty+teyxM5kMH310iWMvHWfzlq3cWGLK3pOw2Wzs27+fQMDPlcuX51xncnKSdDpNJp3mre9+B5fLRUNDI/UNDexbs4ZMJsPIyDCDA4OMjY0tS08VlCefSgU5o6ao1K0t+rgllPC8oKoq4bCRcJgVpzyKooLDkcHjmekqYrfnUhPlgir2yvym561iPxFAMzGuJxKViIS16nAullyzwpOy+ypn9zWbSGnL5KvYc3lj6/XFqWLnLAYvXnTw/nvzN32X8GxhNJlmSR8KI5CNxpmWloqiEIvFiEWjjI2PEcvKHnIa4Hg8jtPponZNLbW1a1jr1tyPIpEIPY8fMzIywvj42Kp7fZ41JEni4KFDAHx4dmG3rFAoxIXz5zh0+Ah79+3n3IfPL0gNNLu6D95/j71797Fjx05sVhvXr197Yav0q8GCBPmjSxcxGk0YjEaMRgN6vQGDXo9Or0ev0yHpdEiShCSK6IxGBMGUv/S92CXw9o4O2trbAe0gUVUVRZGRZZlMJnufTpNOp4lEIrS1t2MwGgmHQiSSSZKJOPG4dltJdVSn03Hg4CFkWebch2dnHWB9/f3s2LmL6qoqVsrLx8bGePTwIZs2bWJ4aHDZiTSSJHHg4CFUVV3wIFIUhdHRUWpqNWeIQCBAIHCDmzdvUF5eTn1DI/X19dTXN6CqKn6/n7HRUUbHRpmcmFjw4IwqIRQ1g4iIUsTQkJSaoEpfV7TxSijhkwBFEQkEDAQCqw+gcbszOLLR5DabjN2mhc9YLJr0wmia1jdrleHlBdAs6o0d1byxC/XNmha7MEZdxuXO4C6IUbdlY9RzVWxz1mIwJxUp3Ne9e7Wre6oioKjZe2V631S0gBwQkLM2g+SeU7X/5+6V3PLcY1n7EGRletxZ68u5ccS83ls7lWififY3zS37eFfCRVHEbDbngy+eJL9Wq3WW/jedTueT3zT7sxjRnPVZVIs+1sa1YLFowRpmiwW3243FYqGsvByTyZSXTty4fp2RkeEFA7Q+Dti5axdut4fTp08tKe/B6/Vy48Z1duzYyebNW7i9gqJdMSHLMufOfci22HZaWlopKy/nxvXrTEwsXzL5ImNBgtyzijQ7g8GA2WzBbDZhMmk3g9GIwWCkrq4OURQJh0PodHp0koSk0yGKInq9AaNRnJNob9w4d/R1buaikWwFRclVtGUymQyZjEa006k0qVSKVCpFTW0NTqeTmzdvoNfrMRgMMzS76VQKWZZxOB0r/gwAbt68QU1tLbv37F22TKRz925cLteSDiKvd4S1a9ficDgJhaZTbyYnJ5mcnOT6tat4PB6qqquprqpm46ZNtLa1IcsykxMTjI6NMjY6it/vnzUTnJLHsYh2kmrx9E8pNVEKCynhE4UXRRu/2gCahRoec97YWhVbzdr2Ld8be94Y9aRAMjVdxfZN6olGRUIhiVBYRzCgIxCUcDkz/OQ/87J2bYq1a59f89Jysdwi21zrL7ZMeyzMs3zm45nLhFnP3bjxL5FlS37Zk9/vwnNuLglOO+dmSKVS+eAtTVtsxW53oNPp0Ov16HQ6DAbDrEQ50KqU8XicsbExhoeG8HpHPjEevOvWraO5eR137tzGu4xLTQ8fPMDpdNLe0UEoFMw7WD1P3Lh+Hf+Un61bt3L85ZcZHhri5s0bH/sJTA4C2iR7TrRsmpuQrhZ79uylqrqab3/rm4uuq9PpMFssbFi/gY2bNtHb20ssFsVoMKI3GDAY9NmDTTvgJElCkqR8lvlSK9owm2jnts+ZfGcyaTLpDKl0lmgnEySTSRLJFIl4nEQyQTwWJ5mcqYGqqKjk+Msv8/DBA65du7qkz2jDho3s3LWL27duLSl0xGw2832f+1vcuH6d+/cXj86UJImKykqqq6qpqq7G7XYDmuZpbGyUsbExpqamCAYCvGL5fjaathNSphYZdXlwS1X83uRXiCrLi7F8UYhICSUUovS9nMbM1MRpv+mcN3ZhjHrOG1unU5Gk5VWx79wxc++eFUEAUQBRUhEAIUvURUEFASQREFQEASRRWyaK2glQyC4v3ObJ7YX8TZ3eTsy+lqCNndtvbZKgzpgoiKKaf0+FYxWus9hyUBG1Yri2jOn3lF+P3PNqwePp5WTfb+HnO/t++vnLl/9vFMU4Jyl/lt/1WZfvVTVPXHJyT+2xkr26oeavTOduOSI//ZyCImvLlKxUVFtHRpYVVEUmI2v/V7IFt8JbRpaRsxOATCaDrChk0mkymUx+wvAkPB4Px19+hfHxMc6cPr1sWYIoihw9doyysnLee+8kUy+Ik4QkSWzcqBXddDodvT093L59a149+Iv2W3n/wcM5lz8Xgtze0UFHx2b+8i++umTtkCAIvPLKq5gtFr77nb9Z9mxSp9NhMpmprq5mx86dhIJBhoaGMBoNGAxG9E8QbZ0kYTAYkHQ6FEVZMdHOHYyCIKDT6bKRkgmtop0n2imSqVS+GdJkMrGrsxPvyAhnz55Z8nt8/Y03svqg95f12YBmnVNVVU1VdRXV1TVYrVraoaIo2IdqaZzYgS/jJZlMkkwli6L5cokV/GXg1xlOL+9KxYt2cJVQApS+l8WFgt1eQLJdMna75ipitcpYrApms8x3/qacu3fnT2YtYfVY6HstimK+KKXT6bSrxHoD+mxlWKeT0Ov0WUKZQc7I2jY6CUnSIYkikiQiShKSKCFKYra4JWnLs4UuURARJRFByP1fQCi4FwRh3hsw7/n7aR+v8xHgmX1c89wUBaXgPkfqVRVcLheCIDA+Pk46nUZVFGRFRpEVZEVBkTPIioosyyjKtGxVzpJ6bVkmT/ILH6/m3G4wGGnvaGf9+g2oqsqjhw+ZmBgnGAzOuAr+ov1WvlAEubGxib379vGdv/lrwuHwkrdzezy8+uprPHr0kGtXl1aJLYTJZOK1198gk8nw7jtvL0qyKyoqOP7yK/T19XLxwgVgWodlNpsxmc0YjSZMRqOm0zboC34Y9Oj1Ou1HIFvRLrSnWeqXY64ZcK4hMp0ulI+ksDscuN0ebly/RjQWI5HVaK/EecRiseB2e3B73Gy0bGN37HtIm6c9ltOZNKlEQiP2ySTJZBJZXp4W3CNV83bof3A38dGytnvRDq4SSoDS9/JZo/R5Pxt80j/n3LlZl+2rykk+dVniP+OxJCFKUpbA50i81oclimL2OXH6/6KIIIoYDAasViuCIORt53IEXrtSDUK23P+8if1cpP7Jol/h40LDBkWZft5gMGAwGGaMkclkSKfTGI0JjMa7SNIlUikx33ibSmp9DMPDRmIxKd/fkMkIGAxKPok0kRAopqZ/PoL8zH2QQUvTA80LeTkE2T81xaNHj1i/fgO9PT34/f4lbysIAvsPHESv13Pqg/eXVIGemJhAVVXKyqbtyBRFycdLLhfl5eUcf/kVursfcf3aNYxGI2azJa/RNppMNDc1YbXZGB8fRxQEdNlZeI5o52bqCzVE7ti5a9Zr5760uUtMMy4VZTJaQ2RGq2inUymSyRTJZIJgIMCdsWs0S3vwyxMYDQYMJiNGgxGj0Yi1wI1Eu6yUzlfHc84a6XSadCbDk3MxBZnyUuR0CSWUUMILiU86OQbtvJXrTXoaaGtrp2PzZsLhMOc+PFs0fW5dXR0HDh5idNTLzRs3EEVJ4woFUlMpR+R1049zVX+tai8gFlTuJVFCEIV8FT9H4EVRQBC0Sv2TVfsnpayFj58k23q9Pqs5t2AwbKSz8+SK33+hVj5nbZlOCzidGXQ6FVmebqzNBTuFQhIDA+Zp4p0WsDsy/Pt/P/drPBeCnIsptNqWf2ns9q2brF27lp27Ojl54t0lb7dly1YtKe/8OYLBpWteE4n4igNDnsTk5CQP7t+npbWVwYEBxsfHZyTfecrK2Lp165J1xzmYTKZsRdvCgQMHCAYCml+k0ajN4vR6dHrdTJ22KKIzGPJfdJh/VioqElX3a3AabJDVp6mo2W5weZr2qqo229bpsVgFbUacewoVOZMhncmQTqVIp1NIaZFGaRPXBTuJROIT04RRQgkllFDCpxsGg4G9+/ZTW1tLf18fly9/tOI8grkwNDTE1atX2LWrE1mWOX/u3Atvd5eT1zgdEhZrmvdOrsVkKrCCNGqPfZN6VAR0Oq03Qa9TaG2LodNN9ytoy1UkHQSDmlOOXqclpWJSkCQVnS7XN6Bp9R0OmaqqdEHPg7ZfLxRBjsfjyLKMzWpb9rbpdJpbN2+wZ+8+6hsaGFhCJ6fL5WJTSwuPu7uXtH4hAv4ANbW12Gy2ouSP3759i7q6OnZ17ubtt7474wu9dctWEokEDx7cX9aYiax2Gb8f78gIZeXl3Lx5Y9n7pjmPaPIRY66qnXUeaUr5EDKgSoo2m8zpv7IzSoRCOjybaAsIWYKux2wya8sUiXK1jNTGzwIzL9nkqtu5SnQy66Mdj0UpKy9nZHiY8fFxAoHAst9nCSWUUEIJJTwteDxlHDh4EJPJxJXLH9Hd3f1UXqf70SNQYVdnJwcPHuLDOSxrXyQoioKSUpiYSCFMCoB5ydueO+cq+v4IgorJpACzUwzhORFkgGg0uqIKMkBvby8bNm5i69ZtDA8NLWqdtnNXJ6lUakWkcWRkhJraWhoaG+m6s/Sq7nyQZZkrVy5z9NhLtLW158NPqqqqqKqu5trVq6uaZY54R1hbX4/T5SK4TPKYu8w0V4W9wdVJlb6emLL45SFByGq6JF22GSOn3dIu4eQu5YiiiFV2IScUVN20a0jusk2OsD8JSZL4nd/5XWBmI6QsZ3j8uIcvfP5vLet9l1BCCSWUUEIxsGHDRrZt304iHue9kyeYmiqu+9OT6O5+hKIqdHbu5tDhI3x49sxzTdv7OEFVBeJxad7nny9BXkEFOYcb16/x0vGX2bhp04z45SfR1NxMRUUFly5eXJHGqL+/jx07d1JdVV0UggwwOjpKX18frW1t9Pf3Ew6H2LJ1G9FolO7uR6sa2+v1AlBTU7NsgrwQxtODrDVsYClOyKqqkE5rMorF4JJifHDvDJOZ2X6QmvOICYvFgt3uwGqzYrFYOHjwEH/z13+N3eHAZrNhtVryWu5IJLziRJ9/8mM/xo//+E/MUbnWok6/8u/+LXcX+K6VUEIJJZTw6YROp6Nz924aGhoZHh7m0sULT03X/CR6Hj9GVRR279nL4cNHOHPmdIkkFwHPjyBHInjqVx41PD4+ztDQIG1t7fT09JCcw2/PYDCwdes2JiYm6O1dWehJKh8Y4lzxvs6F69euUlNTQ+fuTh4+eEhZWRmXLl5Y9eWRRDyO3z9FbW0t9+8t7oe8VEzKXkTmn2mtHAJOqXxOgpzJZIhEIkQiEcbHpxN6Lpw/v2DjyEqbSkZGRhgYGNAkJln9ttlsRqcrRxRF3B7Pksl3YXOLIAh846++ic1qJRaPE4tFiUQihENhgqEQt27d5Jt/9Vcr2ucSSiihhBKeLzxlZezZsxe73c7NmzcWLNo9LfT29qIoKnv27uXI0WOcOX2qqJrnTyOeG0GORCMYjSZ0Ot2K/4g3rt/gzc98hs2bN3Pl8uVZz2/ZshWDwcCVK7OfW9a+RiI4nU5EUSyavieZTHLzxg1279mD3e4gGAzS19dXlLFHRkZobW1Dr9cXrfEtJPtQ1OJrm0RE3FJF0cddCb79rW/x7W99a8F1lkq+C9dTVZWqqiocDscMGUkOBw4c4K++8Y1ljwvw0ksv8b3f9zlCoRDBYICA349vagrfpI+JyQm6Hz0q/UiWUEIJJTwFOJ1ONm/eQt3atcTjcU598P6MYs6zRn9/H4qqsG/ffo4ePcbp06dKze+rwHOsIGs2aVabbcVSgEgkzKOHD9mwcSOPHj6coZ31eMpYt349Dx88WLXUYHJiApfLRU1tLcNDQ6saqxA9PY9pbW3F7nBw6+aNFUsDnoR3xEt7ewdV1dUMDQ4WZcyA7Hsqdj8pNUG1vqHo475IEASBA/v3zVjmcDiorKykoqKCWCy25ACaJ78jr772Oq+99tq82/ztL36R27dvZbcvTMta/PU+8z3fQzwWY2JigvHxCXy+yRLZLqGEEj71sNlsdHRspqGxkXQ6ze1bt3jw4P4L8fs4ODCAqijsP3CQo0ePcerUByWSvEI8Rw2y5ghhs1pXRWC7uu7Q2NTE9u07OHXqA0A7+e/q7CQRj+fJwWowMDDA+g0bWFtXV1SCLIoiOr0eVVWprKqmt7e3KOP6fJOkUilqa2qLRpCjShBFlREQUSleJTmlJqjQrSnaeB8XhEIhQqHQsrqb5yK1P/9zP8tv/9ZvUllVRXl5OWWeMtweNy6XC6fDycBAf4HUY3q7pUzG/st/+a+I4kwz9lxD5NUrV/iBH/gHn3iP1BJKKKGEHMxmM23tHaxbtw5FUbh/7x737t19ZlrjpWJoaIgPPzzLgQMHOfbScT48e4ZYbCkdRM8GH5fzxvOTWBRUkFeDVCpF15077Ni5k5qaGrxeL+vWrcfj8XD+/LmizOjGx8e0wJDy8sVXXgaam5sxm8309fbS1NREX28PY2Njqx5XVVVGvV5qaosbwuGXxzCLNpJqfPGVl4i0msQlVRSdeH9akEgkGBgYYGBgYFnbLeUH6td+7Vcp85ThdDqxO+zY7XasVisWm+imsgAAYLRJREFUs4XHPT1zGsEvBb/5m7/F8Zdfztv55dKVclZ+f+dvf7Fk31dCCSW8MDAYjLS2tbJhw0ZEUeRxdzddd7tIxIt3Liw2RoaH+fDsGfYfOMhrr7/BpYsXGRkZft67BXx8AmieG0FOpZKk0+lVOVnk0N39iPUbNrBt+w78/vfZsnUrY6Ojy/Y8XgiJRAKLZWW2dPNhw8ZNTPl8XL78EWXl5Xlv5GJ0n454R6hvaMDldhNYRuLgQpjIjLDeuKWoBFnNxow4JDdB2Ve0cUtYPX7/935v0XVW8iN38eJFKquqsFgsmM2a37bBYMRut+N2u/NRrCvBxUsfIcsZzTM7niAWi2kNkeEwH7z/Pt/61jdXNG4JJZTw6YNOp2NTSwstLa1IkkR/fx93bt9eUZLu84DX6+Wdt9/mwMEDHD5yhPv373Hr5s0X2iv5RcJzI8igySxs1tWTTkVRuHnjOocOH+HAwYNIksSVK1eKsIfTCAT81NTUYrVai3JwVFdX43Q6uXjhvOaNfPkjjr10nLa29qLIQnJ2b7U1NUUjyGPpQVpNnUUZqxAqKk6prESQPyX4sz/7n/zZn/3PBddZCfHORaIajVYcDieSJM1oiHQ6HXzzmwu7hcyn0/7hH/5hDh0+TDgc1txHgkH8fj9Tfj9TPh/vvXeSqupqpny+F0KHWEIJJawcbo+H+vp6mpubMRpNDA4McPv2raLFRD9LRCJhTrz7Ltu276ClpZWK8grOnz/3sSH5zxPPlSBHIlFsq5RY5DA8PMzk5CTl5eU87u4mHC7uF3l0dJSaGi0w5G5X16rH27hxE/F4PH9pfGxsjN7eXlpaW+nv71v1gZhMJJjy+aiprS2ad29AnngqMggBEadUDjxc0vofl8szJTxbKIrCnt27Zy03m81UVFSQSCQW/d7Mp9M+duwlduzcOec2qqqyb+8eTp8+k/+/Flyj+WmnUim+/vWv8Su//MsreFcllFDCs4DL5aK+voH6+npsdjuyLOP1eunquoP/KYd9PG0oisK1q1cYHxtj9549vPb6G3x06SJDReyp+iTiuVeQq6qqijZeKpUEeCp2ZH29fWzbtp3qqupVE2SbzU7tmjXcuX17xqWOG9evUVtbS2fnbt577+Rqdxmv10trW/Hs3oKyj+L4bMyEQoZKXd1TGLmEEpgxEV0OCsn0l7/8JXQ6HWVl5VRWVlBRUUFZWRlujwerxYqiKLx38gQ2ux2r1abJR0wmjCYTBoMBk9GIoigrmtj9/u//ATt37SKVSk1HrsdjRKMxpnw+fvInf2LZY5ZQQgngcDipb6invr4eh8OJoiiMjY3RdbeLocHBT5z7w9DQIH7/FPsPHOTgocM8fPiAG9evlyQX8+D5EuRIFL1ej9FoJJlMrmosh8NJTU0twWCAdevW8+D+/aJeQkgmE8iyjNPlWvVYGzduRJblWal5yWSSG9evs2fvXpqb19HTM3c++FIxMjJCe0cH1TU1DK6AIDyJkOJDRFx8xWUiqSSo1K08NKaEEp4FMpkMY2OjjI2NznpOVVV+/Md/fMHtn3QEWSqCwSCJeByD0YjZbM5Ht4NWGVquXjtH0isrK/m93/8DotEIkUiUcChEMBgkEAzg9/s5f/48PY9X9xtUQgkvGux2O/X1Daytr8flcqEoChMT4zy4/4ChocFVc5EXHdFolPdOnmDL1m20tLRQXl7B+XPniETCz3vXXjg8X4lF1urNarWt+ku5efNmMpkM58+d59XXXmPrtm2cP3euGLuZRzQaxeFwrGoMvV5PU3MzAwMDJOZI/+vt7aGpqYmt27YxNDS4KvuYqSkfyWSS2praohDktJoiqoTQoSdD8WbWKTVBma66aOOVUMInCT/1U/9q3udcLteyq9I5Ql1dXc2GDRtmBdfk8LWv/SX/7t/+2wXHmu+1P//5z1O3th6/f4qpqSkmxseZmJhgbGzshbKbKuGTD71ej8vloryigvr6etxuDwAT4+NcvXKFwcG5z8WfZCiKwo3r1xgfH2PPnr28+ZnP0NfXy727dwmHS0Q5hwUJ8p69e0mlUqSSSRKJJIlEPHt5T7tfbVk+GskSZJuVqamVN2i53W7W1tdz5/ZtQqEg9+/do2PzZu7dvYu/SA1qABMTEzidTmpr16zYLqWpqRm9Xs/Dhw/mXefq1Su89vobbN6ylaurSAGctnurWfEYT2IqM0qFfg0ZpXgEWUFGLxgxCVYSaqlxoIQSloqV2OHlSO2tW7dob2sFNKKdC64pL6/A4/Fw6dLFBcn3XME1OfzTH/8JauexmUylUmzZ3DHnPi0Gk8lEXV0d4+PjM4KhSigBtH4Dt9uN2+3B5Xbhdrmx2e355ycnJ7l27SqDAwPEX2CLtmeFkeFh3nrru7S1ttG8bh2NjU0MDg5y927XqgPWPglYkCA3NTUvuHHuBzLXlJJrTJHlDOl0hkw6TTqTzmrnUqRSmn4umbVgymmGbau0euvYvIVkMsmDB/cBuH//Hhs2bmDL1m2czoaHFANDgwOsX7+eurV1KyLIgiCwYeNGJiYmFhT9B4NBHj16yMaNm+h53L0qkj/iHaGhsRG3212UycJYZpA1hnXEKO4sU0XBKZWRyJQIcgklPGsEAgECgQAPHy6tURYWJrU/8eP/lKbmZsrKyvG43ThdLpwOBza7fVZy5EJE+0kcPHiQ3/jN3wIgHA7nm7wLz0W589H3/72/x927XYvu61z49V//DSxWK+l0WjuPZZstU+k0t2/f5i//4qvLGi8Hg8GAKIqfuoplsSEIAna7A7fbrRFhtwe324XRaMqvEwqFmPJP0dPzGL8/gN8/Vfrc50AiHufatat03e1i06ZNbNiwkYaGBoaHhui628WU79PrLrUkiYWqqhqxTSZIJlNkMmn0Oj06vR6dTkKSdOh0Oi0ZTqdDEEzzXrabC1u2bmXzli2ziHYmk9Fu6TSpdJp0KkUy26iSTCRIJBIYjUbWrFnDzZs38oL6TCbD3a67bN+xg8rKyqJlo4+OjqKqKuUrDAypqanBbrdz6+bNRde9c/s2DQ0N7Ny5i5MnT6zo9QBGvV5UVaW2dk1RCPJkZgQRadXjPAkBAZdUwVhmcSlIycGihBJebNy9e3fJ7jnLOZ4fP37Md7/zHewOO5mMTEVFxYxzkE6SECUJnU43g4gvV6f90vHjeZ33k9i1axd/8dU/n/UelvIaP/fzP8/nP/+FWfuUI/Z/+4tfzJP6J8dfDP/fn/4pJpMZWdbOm6lUOnv+THH16jX++I//aNExCqHT6WhoaMRqtRCLJ0jEY8Tj8aJdQZ4Ler0eg8Ewx82IwWDAaDLidLpwOp3odBp9kWWZYCDA0NAQfr+fgN9PIBAo2S0uE8lEgls3b3Lv7l02btzExk2bePXV1xgdHeXRwweMjY196j7TBQnyN//qG3g8HjyeMjweD2Xl5bhc7ryova+vl5HhYSJZqcRcMJlMWCxWTCYtEMBoMmE0GjEaDOgNhryLRTweR6fTIUlSnmibTEsn2lu3bmPLlq0zKgiqqnLk6DGi0QiZjJwn2qlUklQyS7YTCRKJ+IwDfyEkk8kVh5s0NTUTj8cZGlo8/jmdTnPzxg327N1HY1MTfSuMoU4mk5rdW00NXV13VjRGIYKyD0VdfZDJXCjTVcEnuz+ihBJKWAV6e3v5l//yXwDLs3tc7qR6y+YOjEYjFoslf96ymM0YjSaCwcCc4y3lNc6ePUt1dQ16fZbQ6/R5Yq/T6QiFgnOOsxTyvW3bdvR6/Zz70tjUzB/90R/Ou+1cBL+6pobOJ2wTBUFAr8/us07H0OCgVsyS5ezVY+0KsizLyJnCZTKyIqPX6dEbDDgdDnbu2pUtvCW16nwqteD7lGWZVCpFKBiku/sRAb8fv99PKBSasd3WrVvZuXOnVkhLJknkCmrJJPFYLJ8RUMLcSKfTdHXd4cGD+6xbv56WllYOHT6Coij4JicZHR1ldNTL1NTUigOdPi5YkCAnEglGRkYYGRkBtIOjrKyM2jVrqK1dw44dO9mxYyehUJDh4WFGhkeYnJyY8aElspXe+bB//wHcHg/f/c7fzLuOKIoYjUbMZi15y5gl2y6Xm/r6eoLBAMlEEp1ej16vQ5I0oq0oCpIkYbc78vu/GHL7Xki0Cw98URSRJInDR46SSMQ16UgySTKZyOuz5yLakiRRXVNDX1/vkr9Uvb29rFu/gW3btjE8NLRiyxmv10t7RwcGgzEva1kpgrIPQSi+k0VKTVClry/6uCWUUEIJy4WiKPnf8mLi7bfe4u233lr2dksqEm3ZPOP/JpMJs9mMyWQilUotwQN85vP+qSkURaG+vl4rVGkr5c9f6XSaQCCAlC1sSZKEwWBAkiR0koSUreznil6gnVdzRFiWZRwOR34/czeTycTt27f5gz/4fVJZaWYqlVpywuwv/cf/xLp16+Z8TpblvO5+uaivb+BrX/86sizneYF2n0GWFf7n//wf/I8//dMVjf3aa6+xbt06TY6aSmULeElSySThcISzZ8+saNzVIJPJ8OD+fR49fEh5eTnVNTVUV1XTsXkzm7dsIZVKMTY2xtiol9HR0QULpR9XLMvFQlVVJicnmZyc5NbNm1itVmpr17BmzRo2btxEa2sbqVQK78gIwyPDeEdGFiV1kWiENXV1C16imu/H6qXjLxOPxXj3nXfmPHgEQeCNN99EVVXefustBEGY8aNhMpkxmYwYjMb8pRxtdjyTaEuShF6vRxSEfJLAfA0ohZ9V7j530+l0rF1bT1VlFRk5QzqVlY6kNY22JmHRdNqJeJxYLP7/t3df0W2mZ57g/9/3IUfmTIoixaxcUlVJrmy7qux2t3Noxz5zsTsXO7t7sTN7sTNn93ovd7wz450zfXoc2u62pz1uu9uhVCpJFVVVKlGZJJhzAEAQOXxhLz4AAkmQIkiQIKT/z4eHIgB+eCGrpD8fPO/z4sbHH+HV117H8eMncPPmJ9s+51bm5+dx/MQJNDY2YGqPx29H1DVoUCFALOqhIUktjhpDc9GuR0T0JHtUcepRIpEI/u4XPy9KS5sgCJAkaU9v0e90HT/84b/HwMAATEb9XWqj0ai3bhiNSCQSu349RpMRgiCk+8gFCIIIURSz73K3tR3Z9azz//l/+V/R0ZF/z9deQn13dzf+2z/8OudddRWq+rCV9b/+zd/gP/7H/7DtNVRVxfLyMpaXl3Ebt2AymVFfX4/nnn8eHR0daG3VR7Rm2psA/YenoQcPkEzp7wykknoPfyr9dUtrKxRFQSway7bvRqPRPU3t2g97GvMWiUTg8YzA4xmBwWBAQ0Mjmpqb0NTUjCPt7ZBlGdPTU/CMjGzZ/xoJRyBJEqxWa0Hjf6qrq1FXV4dPbtzY8idLTdNw+/ZtPPfc8zjS3o7JiQlEo9E9jRmyWCz44pe+DK93BYM3b8JitcJi1ivaJrMZZpP+FpLJaFrXo202m6FpWnbuM1BYRbu7pwfHurrWV7RlGamcHu3MxJFEMj11JBZDPBFHJBJBPB5HY2PTngOyBg2r8jIsog0JrXjVlZSWRJXUUPQRckREVFqaph1Y/+puq/SPMj42hqfPn9v2Mbuddf5v/+3/gaPt7TCZ9YOFzGY9Q5hMph1V/7eSTKawvLwESTJk3/3OfNY/xE2FyczXWz1nIhHH9PQUfvD9/4y2I0fg9/sxNjaGqampbNaIxWLo6OzMtvxsZLVa8W/+zb/Je9/c3Bw+/crLO3p9G9f49a9/Hf/D//gvsxkpu49NliHLKfy7f/fvCprtXrQ5yLIsY3Z2BrOzMxAEAVVV1Th69CiOtLejo6MTXq8Xox4Ppqen1jX3R7KzkO0FBdeenl4kk8lHHqYxOzMDv8+HE8dPYHpqas8bCzKbE5xOF3w73N0pCAK++KUvY2lxEe+//172dlEU0z3aNljS/W0Wsxlmizn706/JqAfuzPxlVVVhMBj0inb6P8adBu0j7e1oO3JE/2lSUaCs2wyZgpySc3q0M/1byfTmDP30rmQyiWV5DsfMJ4oakAFAhQqXVAW/slTU6xIRER1WNz/5BDc/2d07xNuZmprEZz796W0fszE/7DSM/+AHP0B1dZXe8mo263vLzGaYLRYoioJ/+t3vsj3rpvSeM1M60/z5X3wRv/3tP8JgMMJkMsJoNGVzzYP793e8ho3hvr6+HvX19dmq/sY9bI2NjRgbHd3RtYF9OihE0zT4fF74fF7cujWI9qNH0dXVhWcvXMDpM2cwPj6GsdFRRCIRhMP6WC+7w4GVlZUdXd9ms6GltRUjw8M7+qn01u1bePnlV9B57Bg8BYwx2kokHIazgANDqqtrYLFYNp17rqrqjivanceO4fz5p/HBB+9vOvRDFMVNPVym9B/WTOuIw+6Ay+1GLBaDAGR3eu8maNf7jqB1uR0xU1Xe8Uq5H5mf5FRFgaxkfpJToKr5q/5uqZoBmYiI6BDb6lTRXJme842tEz/89//PrqvtuTZmlh/+8If44Q9/WND3bGffT9JLpVLwjIzAMzKC+vp6HOvqRm9vH/r6+jE/P4/RUQ80TStoFnJXdzcAbHvYRq6lRf3/yIGB45gYH9/zWz1enxcutxsNDY1YXHz0jtiWlhYoioKFhfldP+f42Bg6O4/hzJmzWJifX/caVFVFJBLZ9mhtk8mEL3/lqxgfG8Pdu3fyPiYzOcRitcKamTpitsBqywneJjNCwipUqIAg6H3Z2M3bP9qmrywJJz5/8kuYd/VBVdXsiL+UnMr2ZgMC4rEYpmemEYtGn7ixM0RERLT/DvSo6aWlJSwtLcFms6Gz8xg6j3WiufklqKqK+oZ63Lt395ETHgwGAzo7j2F2dqagloxbt27h1VdfQ09P757Hnc3MzKCjoxOtbW07CsjNLS17niGoaRpu3PgYn/3sq+gfGNjRLOVcyWQSvvS4t60CsqIoMJstaGxoREVFBewOO+x2x6Y+oqQWhSLLiMoRyHIKqZSc3pwAAPpbGnqfkwhR1Hue9M0MYnpzg77BQd/zqH+GIEATFTiS1TCZTAC2/0nvxMmT2d8XANmxfrkTRzKH1WTaRjIbIfX+bH2mZzQWQ+qQbQwgIiKi0jrQgJwRjUZx585t3Lt3Fy2trTh//mnU1tbh1ddex63Bm1hc3Lpsf/RoB0wmE4aHdlY9zvD7fJidmUFPby88Hs+exp0tzM9D0zTU7uDAELfbDafTiaEHOxuavx2f14uJ8XH09PRiYny84DPTF9LTLMxmMxIJ/fUbDAY0NDaiuakZjU1NsFgsUFUVweAawuEIlpaWEYmEsxXqSDgMyCL+Ve1p+JXt314plFmwIqqG8XfXfg5AHxrvcDjhdDphs9tgtVhRXVMDo9GIQCCQ7V0y5myGlNKtI4UcVpM7cURvDdFneMrp/uxUKqmfpJVI5szWjCMWzczPPny7b+lgFTKTl4iIDr+SBOQMVVUxPTWFxsZGNDU1w2g04qWXX8HCwgJuDd5EIM9Z4N09PfB69f7mQt2+fRuvf+5z6O/vx+DgzT2tPZFIwO6wP/JxzS0t0DQNc3OFH02dz61bg2huacHZp57C1StXCvre+YV5nDh5Ekfa2wENaGpuQm1tHSRJQiKRwMLCAuZ3OJ4vqoaKPnEiqSVQJdVDgAANGlKpFFZX/VhdXX8sdyFhJLMR0mq1wmqzwZpuG8n2Z5sfjvbLDL8XRVHfSZwe4QM8um9pc8je6jTIRPY0yHjs4SE10WgMiQSPQS1XDMdERDtVHn9fljQgZ0TCEZhMJvz2H3+Djs5ODAwcx2uvfw6TkxO4c/t2tpWiqblZP6r5dmHtBRnB4BqmJifR1d2N4eGhPQ2BX1sLoK6uHhaLZdtZk81NzfB5vUU7Az4ej+Pu3Ts4e/YpNDc37zh4O50utLW1QVVVnD37VPo1rGFkZBjzc3Pwer0FnYrjkxdQa2yGrBYvIGtQIQoS7KIbYTVQlGuu2wi5hzPlMydCWq2Z+dkWfdpIdmduZqzf+tnZZrN5zyFbyVPJTqaSSMTTM7PjMcRicUSjkaL9OSMiItoP5VJPOBQBORwJpw/xsGJkeBgT4+PoHxhAd3cPWlvbMDIyjAf376OnpxeRSASzM48+qnkrd+/eQduRIzh+/AQ++ujDXV9ncXEJ9fUNaG9vx9DQUN7HCIKAisrKHW8m3CnPyAg6Oztx+sxZLCwsbDu6zm634/iJEzhypB2apiERj8NoMuEPf/g9wgW2aORalmfRbOpEFLu/Rj6apsItVRctIBfLXofuA8g5McoGi9UCq8UCk9mSPXbdZDRmQ3YmaO8lZGuqCiUdtHNnQWaGtieT+jGs8XgC8XgsfThNtOinhz3u2F5BRPT4ORQBOZI+otDhsCMcDiGVSuHW4CA8IyM4efIU+vsHcOzYMZhMZgzevLmn878jkQhGRz3o6urG0NCDgvt4M6YmJ3Dq1Ck0NDZuGZAdDgckScLa2tqu15uPpmm4+clNvPTyy+jq0qvhG1mtVgwMHEdHZydUVcXw8BCGHjxAXX09PvWp52A2mRHeQ7hdkechQtrLy8hLFES4pRrMpfLPty7nMJIJ2VsdmrMTJpMJNrtdD9oWC8wWa3r+5OZKdqYv22AwwGQy7S5krztuXR/Rl0qf/phM5mx+jMeyA+Kj0Wh2XjgREVE5OhwBOT2ezL5h1Fs0GsUHH7yP4eEhvPDiSwCAuvp6TEyMZzeZ7cb9e/fQ0dGJ4ydO4P333nv0N+QRjUahKAoqKiq3fIy7ogIAsJanl3qvFhcXMD8/j4HjxzExMZHddGixWNDX349jx7oAAKOjHty/dy9b/VxMV5wbmxp31cedsaZ4i3rUdIYKBbWG7Y/xfpJlZkoG9hiyrVZbui9bPwnSlA3Z5mwlO/e4dT1kGyEIhc3M3hyylTyV7CTiCf1wmlg8/nDCSDTKkE1ERCVxKAJyLBaDoihbbnoLhUIwmUxYWV5GfX09Pvf5z+PD69cxP7+7ucKJRAIezwj6+vpx7+5dBIPBXV0nEonA6XRueX+FuyI9EWJ313+UwZuf4PXPfR7HTxzH3Tt30NvXh+7uHoiiiInxcdy7d3fTKLxUKgWfz4umxibcvZN/3NtOBBU/9qPRPqHGUWdoLfp16aFMKF1bC+z6GgaDATa7Hbb0fOzMzOxsX3a6kr0xZO/mYJqNh9DIsrzumPVE+vUk09NF4vF4ul0khmg0wpBNREQFOxQBWdM0RKPRLQ8LaW5ugSRJuHVrEKlUCs9euIgXXnwJHs8IBm/ehKLkP5VtO0MPhtDV1Y2BgePrjn8uhM/rhcvlQkNDQ97RdO4KN8Lh8K7WtxPBYBBjY6M4dqwLR492wGAwYGpqEnfv3EU4vHX7xML8PE6eOv3IDYbbCasBaFCzEyeKJanFUcMK8qEnyzKCa2sI7qF9yGAwwGqzwZbuydZDduYESHP6mPVMu4gRkiRlp5IUGrIBbDrhMdOX/bBdJIlkIo5EtpIdRzQW5YE0RERPoEMRkAG9D9nuyB+Q29raEI1E4PXqLQFv/OmPOHHyJPr6+lFfX4/3338fq35/3u/dSjL5sIp89+5dhEKFV3lnZ2dwtKMDLS2t+QOyu2JPVbpHMRgMcDgcEEURiUQCb/zpTwgGHx1Y5hcWcPLUaTQ0NmJyYmJXz61Bw6q8AotoRUIr3qYuBTKsoh0mwYKkxokMjzNZlhEKBhHawzss+umPVthsVlgy1ex0y4jJZILZlK5kGwyQcsb4ZU6NLHRW9uYDaRRYrVZEo1EEg2tIJvXpIolEnCGbiKiMHZqAHI6E0VK5+a11o9GIhsZGeDwj2dtUVcWtwUEsLCzg2Wcv4LOffRV379zGgwcPCtrANzyUriIfP44PdlFFnpubg6ZpqKmt3XSfJElwOByYnpoq+Lo7Ybfb8fwLL8LlcmF2ZgYtra2w2qw7CsiB1VXEolE0NTbtOiADgFeeQ4f5eFEDMgComoIKqQbL8mxRr0uPH1mWEQ6Htn3H5FFEUYTNZss5Yt0Ks8UCi/nhGD+9kr1+jJ8etvWj1l0uF1wu17bPs7NTH5PrQra+8ZEhm4jooB2agBwJR2CxWGAwGNb9I9DSordXTE9Nb/qe5aUl/P6f/wnnzz+Nk6dOo7GxCe+8+w4SO2wb2NiLvJsqcjKZhCNP5dvlckEURQT2oYJcU1OL555/HqIo4urVK1hZXsbn/+wLOHPmDP74hz/s6IeEhcUFtLS0QhCEXU8FWZSn0WM5t6vv3Y4gCHBL1QzIdCBUVUU4HEY4PU2nUJqmQZKk9KxsffOjJTthJL3xscinPhYasuPxOCJRhmwiop3aNiCfOn1aH+mUTGVHO6WyJ4Il07el9jR2LSMc0f9xstvt68aitbYdQTgcht+f/5CHVCqF9957F/Pzczh3/ml89rOv4uqVt3Y8vm14aAjd3T0YOD6AD95/v+B1r60FUFtbB7PZsu4kNLe7Qr8/UNwRb0c7OnDu3HlEIhG8fe1q9nXeGryJTz33PI52dGB8LP+ItFzz8/Po6OhEdXUNvN6VXa0loHihYT/6q0VUSnX7cF2i/bHuQJpdyvRXH+aQHY3FEI1EGLKJ6LG3bUDu6uqGwfDoIrMsy0glk3pwzgTpdKhOpkP1o0J2JJwe9eZwZAOyyWRCQ0MDhreYM5xrcnISwWAIL7z4Ij7z2Vfx9rWr2Z7l7SQSCXhGRtDT25uuIhf2Vu3S4iLq6upxpP0IRoYfHgjirnBDUZQ9vfWbSxAEnDp9Gr29fVhcWMC7776z7jjomZkZrKws4+TJk5iemnrkP2BLi4vZcW+7Dcj6qLfibdDLSGlx1BuPFP26RIcZQzYR0eGxbfr91S//HqIowmg0Pvww6X+5mowP/6I1mjL3m2AyGWEymWG3O7Lfs5OQnQl7588/jUh/BKlUEiaTWf8L32pB/8BAOmSng3jyYdjOfPj9Przxpz/ixZdexsuvfBrvv//ejk7dGxp6gK5ufaLFBx8UVkWenJzEiZOn0NjYuD4gu9wIBoNFqa4bjUZcuPgpNDU1YXh4GIM3P8l73ZuffIJXX3sd/f0DjzyOO5VKwetdQVNTE+7cvr2rdQUVP6R9OCwkqcU5C5loFw4+ZK/vyT7IkJ2Ix/c0D5+IaDvbV5C7uzE+NoZEIrGnv4geGbLTf+F2d3dDTqUgyymYTGa43W6oqorW1rYdh+xUKgVZlqGqKj71qecQWF2Fz+/bVMXeGLLHx8ZwrKsL9+4VVkWORCJ5DwwxWyxFObJXkiS8/MqnUVFRgY8+vI6xbdon/H4/Jicm0NPbi9FRzyP/kZyfn8fp02dgsVoR38Vak1ocUTUCA4yQkXr0N+xQSkugQqqDCAnqvrRwENFWDipkm0ymvBsfCwnZAPIWC3JPg8x8znwAGjTt4XHsavY+FZqqQU3P3M5+rapQtYcjAlVFfXibkj7OXVHSx7or+q8VBYqiQsn5Wpb18K+oKhRZzo4azMz2JqLDZdvU+dRT53D8+Al4RkYwMjKMZDK5qydRVXVHIbuhoQHhcBjvvH0NZrMZX/zSl/HgwX3cuX17U8g2pUP2w4CdrmCn749GI6isrEJlVRWc6d3lOwnZn/v8nyEej2fbRNa1jWwRsuPxOGw2G4xGI2RZhqZpMBqNu970k+upc+dQVVWFa9euYn5u7pGPv337FlpaW3Hq1OlHzndeWFjA6dNn0NjYiInx8V2tzyvPo9bYBFktXkDW0v9zSVUIKLtr/yCi0tnvkG2z2VBTU4tYLIZUKglRFCEIIkRRhCgIEEQBgiBmg7Z+vwA9cwsQRf12/cuHYTxfKD+oo+0PW9A3GvXf5+XlZchyan3wV9IBX1HSYV9NH0XPoE+Pj20T43/5L/8FL734Io6fOIHunh6MjnrgGRkpSmU0n0g4DEf6NL3W1laIoojpaX16xU5D9kanz5xBb28fZmdmcP36B+mgrYdqk9G4LmS3trahpqYGvnTvstFkhNlshsPhyL6tKElbtxR89WtfB6BXsg0GA8xmMz7zmc9uH7I3tIokk8lsyO7o6EBHRyfu3b27o3AM6EdgDw8NYeD4cYyMDMPny7+5EdCPwI5Go2hsbNp1QF6WZ9Fs6kAUxem1fkiDW6pmQCZ6Qj0qZGuadmDhNXd+tiRJkAwGSKKo93AbpGwFXBIlSJKoH2ojSRBFCZIoQEzfJ0oixEyQlzKBPv1ZFLPB/jAF/SNHCtsPstOgn/m8/YcKVX14kqaWDvibAv82YV9TlZz2nfVVfYZ92s62AXlmZgY/+elPUVdXh8985tPo7e1Db28fpqencffO7aJUSHOFIxHU1dcDANrajmBtbQ1rgcCerjl48yYikQjOnn0Kz5tfwNUrV7YM2VOTk/jzv/giZEXB9S16kR9Wsh+G7IaGRvT192NlZRmLi4vZdpFEIgFZUQoK2RmZkJ1KpVBfX4/Kqpc2bIDM2RS5IWR7PCPo6OzEmbNP4dIbf9r2eRbm59Ha1rbrcW9eeQ7iPvQhi5DglmqKfl0iokJlwtJu30U9LB4V9A3pYC+J+hx/l9uF1dVVCIKYDfp6mNeDvyBK2XAvikI28AuZwL8u1GcC/cOv1wd/ANgc8suhqp/5vN9hf7sWHob94tvRHOTl5WX87d/+HBUVFXjuuU/h5MlTaGpqwvUPPsDcXPFm1UYiYRiNRrhcLtTW1eHe3btFua5nZASJRAIXLlzEp557Hu+8fQ2qqm56XGYuck+PPtEi3wSKfJXslZUV9Pb1wWg04d7duxBFEb29vZicGMf9+/c3XSNfyM7tzbaYLTjW1QVFUbC8vARJMuwqZFutVnz5K19Nvw25ecJIKpWEBg0mkwm9vX3welfyVrK3E1T8+9InLGsp1Bs2HxxDRES7U2jQP8gqfaE2hv2HveyGdVX87ar6DPsHH/adTicADV6vNyfcpz9UVf+c7dHP9O+nsr37+bLbfinooJBAIIDf/e6f8M477+Lb3/5LPP/CC5ifn8MnN24UpZocSV+jvf0oBEHA9HTxTqGbnpqCQZLw9DPP4sLFi3jv3Xfz/oEZyp6uN4DrH3ywo2urqrruwBCjyQQASKby9+U+ql3k+edfgCRJuPzmpS1bJDIh+2Ev9vp+bJPRhGNdXZAkCaFQEEajKSdk69+TG7JPnT6d93lS6XF8D9tE1odsY9ICe9wJWYs//I8h85Ouqv9HhDyj4AwGQ3onvAGyrGQ3V2qa/odfn2TRkndNRET0ZHtcqvrAhrBvMMCQadl5jMN+U1Pzrn+/Nma3/EE/p0c/tz9fezg5J9O7PzQ8svEpAOzyJL1AIIAf/ej/w8WLF/Dcc8/jpZdfwZuX3thzb3I4os9Cdrn1GcLBYOEn221nfHwcBoMRZ596Ck8/k7+NIhGPY9TjQXdPD+7dvbfjOcbBtTXU1NbqR9MajQCAVLLwjWt9ff1obmnBjY8/3rZ/eCc92Ssry3j5lU/D6/XmnSUtiiKMJhOef/4FmM1mfPzRh5tC9sMxfvrrspjNcDqd2VAuiRIqhlywGCVgi/829D+UmT/Q6b66LR6sqPrObiWpoCZZh6MVHQgGgwiHQxzpREREj53HLexng32esF9ZVQVJkhAOhfOEfTH9g0BOj3467Es5ffrZnn1BXBfwtw/6D4N9JvRnvt7ytez2N0FRFLz99jsYH5/AX/3VX+HV117H+++9i+Xl5d1eMltBttls+7YRcGRkGAajASdPnoIsp3Dj4483PebB0AMc6+rCwMAArl/fWRV5aWkJtXV1OHLkSDbYplKF/WGvq6vDiZMnMTU1CY8n/080hVhaWsLCwjz6+wcwPja27mARIB2y43HMzszg9JkzCAaDBf++i6IIe80xWEQ7ZFHfTW6z2eBwOGAw6D8o6H9gtfRbMipUGfofUEHQN6IIYvZ6kihBMkmACbAkHfhU14uQDXowzoTsaDSKwOqqfkCNnIKcevhZllNIyfoM1VT6a1mWsxVq9mJRMR3mt6CJiA7ao/6dnZubK5u/M3cdkDPm5uYw+ksvzjx/Al8+9z0MPriB0elhxLUI4moUCnYeSGRZRiKRgMViyYbl/XD/3j0YDAb09w9AlmXcGhxcd38iHsfoqAfd3T24d+/ujtpHJicncfzECTQ2NSGYnqO8MZBux2g04uKnPoVQKISPPvywoNeznVuDg3jt9c+hr78ft2/lPzxkfn4ep8+cQWNT046Oqc6lqiqW4jNoN/dDNIioqqqGxWJBIpmAd3EB0VgU8Vgc8UQC2ha9Q4Io6sHYIGU/GwwSakwNGAqMIlURhsvlgt1uh9PphKooMJnNcDidev+2ybSjEX6AHmjkVDpE5wbn9GcGbiIiItpzQG61deIzFd9A9FYIDY0N6HQ9C1+jF8lkCiJEJLUEomoIETWIsBpASAkgpKwirkUQU8OIa1HE1QhiahQJLYpIOAyX2w3vPlWQM27fugWDwYi+vn7IKRn37q3fEDj04AGOHetC/8AAPrx+/ZHXC4dDUBQFlZWVMKZbLLbqQc6ns/MYLBYrrl29WtTQFQgEMDU5ie7uni1H9AWDa4hEImhsLDwgA0DYsoqGqiZoLr0/eXp6Cl6vd8dTMTRVhayqkOX1v19Gsw33J0fwyerbj7xGpifbbDbDZDKt+2yxWGCxmGE2m9O3p39tMsFkNuvtJAzctAualn43hIiIHit7Dsgz0THMRschQMDSxBz6+/tgbTDBP7OElJyCCAmSYEClVIMaQyMkwQADDNCgQUVmA5eQ/Z9j2gajS0T92hxcrg6ElFWE1QDiahQxLZIO0xHEtShS2t56Uj+58TGMBgNOnDyJRDKBUY8ne188Hsfo6Ci6u7tx/969HVWRo9Govgku24O8sxYLURTR3dODpcVF+P3+3b2Ybdy5cxutbW04ceIkPvwwf9hfmJ/HkfZ2iKK4412iTqcTJ06eRG/lMRimjZicm8DS8vKWleJCaVBRb9nZJIvdzsneiiAIm4L2TgK30WTS+9BzArch3a+9Ewzc5YXhmIjo8bTngAwA76z8Hn/W9B0sJ+bg8XjQ19eHhsZGzM7OQtUUqJqCFBL5hhlsXpBaD4tSBUeqEl3mUzAIxuyRw1r6AnqUFqFCQVQNI6oEEdbW0tVpP6JqCLF0ZTquRrdt9/jww+swmkw4e/YprAUCWFl5eDDF0IP76OrqQl9fPz766NFtD36fD06nE06XGwCg7rCC2tZ2BDabbcvwuleRSAQejwfd3d0YGhpCMLi26TELC/M41tWFmpqaHfWRV9fU4JVXPg1VVfHu9avQVquxFFss6rrjSgxt9mNFveZOaZq2r4E78+vcwG0ymbOfcx+XG7hzT5Pc6etg4CYiIipMUQLyrcB7+ELzdwHos4THxsfR1dWF+vp6LC4uFHQtWU1BkWTEEEFI3RzkcgkQIMEIh+SGW6iGZDTCIBgACOtm8woQt233CHwSQ7vRjVef+iLefPuPCER9SGgxxONxjI2NobOzE/fu3X3ksamzszM40t6OiooKAIBhB7OKAaC3rxeBQACLC4X9XhXi/r276OjowKlTp/D229c23b+0tARFUdDY1PTIgGwymXDx4qcQiUTwox/9CEpcw8VTXy36mhNqDI3WtqJftxQOOnA//PU+BO6cwJwbnPUgnhO4s7cxcBMRUXkpSkD2JhYxEx1HlakWYXkNoWAQc7OzaGlpRVVVNfz+rceVbZSpum41BiyXBg0ykpC15I6q0/nbPYzQoEEaElFTW4v+I6/oUyg0IKZGkJyJoclcD9eRDgxN30FIDeiV6XRVOrfdY25uDpqmpQdhA9IO+lgbGhtRUVGJD7Y4ua9YkskkHjy4j1OnTqO2tnZdpRzQN0iurCyjsbFp06bFjZ5+5hlYLBb89V//NSLp0XwROQSjYEJKK86YGhESBIioMNXAJJqRVDniLdd+Be6N7SRbBu50yM4N3Q6Hg4GbiIgeC0UJyADw7so/41tH/ieEZb3qu7S0BKvNhuqqaiSTyR3PE87Yj94+Fdu0eyhAbDWI+oZGiIoCr3cFEgwwqEZoXgO6bKdQ4WwCVCHb7qFpmj58O6fdo3GsDiljAnIojnPmOJYs8+vaPWJaGAk1lm336OvtQzQaxfRU8Q5F2crI8DC6urpx6vSZvEdQL8wv4MzZs7DZbFtWy7u7e9DS0oo33ngDc3Nz2dsX4zNosLQiJW8dkAUIMAhGGEQTjKIRRsEEg6j/gKKmDwgRIEAUJCTVBIIpP26tvpe9j/ZPbuAOhQr7bzUfBm4iIipnRQvId9c+hqqpECBCgx5opiYnYbVYUFtbh1gsBkV59D82mfl4oriz9oRiCkfCMAdWUVFRmQ4KQSiajKW1eZgqjIBLhs/nzfu9mXYPxAxwKU4IMQnnTS4kXUmoUIFs/7QIEQKSWgKyMYG6eA2mZ8bxvO2LCCmriGphvW86XZmOqxEktFi2/3ovFEXB3Tt38PQzz6ClpQWzs+uPCZ+fn8eZs2fR2NiEsbHRTd9fWVWFU6dPY2x0FO++++66++aiE+hxnoJRNMIgmCAKIlRNza5bTM86DqfWsJbyI5D0YTW5Al9iCWFlDeFUEBElqH+Wg0WrRFNpPAmBG5oGh9MJn9eLaDSaDt+Zx6X7ufN9HxERHXpFC8gROYj7wRvocp5AIKmHSE3TMD4+joGBAVRVVWFl5dGbv/Tjj7Udj9kqNp/PB7PZjNraWiQSCSSTCaTkFMLhEFwuN1ZXV6Gqyqbvy7R7hBIBGGx6GAzCh6gSyfs8IiQ0uJtgijvgDNXhjK0BUrrdI7d/WoT+g0JMjaT7p9cQVgIIqYEt2j0i24bLiYlx9PT24uSp09mWkIxQKIhwOIzGps0B2Wg04uLFTyEWi+JX/+2/bbru4Oq7aLS2YTXphT+5jEDSh4i8hogcQkQOIiwHEVX2b7Y1Pd4OInCbTKb0hsmHgTv7kSdwW61W2O122O32wsYC5gvcW2yaXLd5ctPnFBRl899FRES0d0VNode9lzDgPrfutkQiAa/Xi9raWqytrSGZ3L5n0mCQoGkaDMbSBGRAw9LiIlpaW/VJHDMzUFUFq/5VOB0uVFS4tx3FFgqFUFlZBUAf37YV0SDC4jTDH1hGUH70aDcJBlhEK+ySE43GdhgEY7Zar0EDtPQRihChagqiWhgRZQ0RdQ0hdS1nukcEC7f8OP3MSfQfPYkH43fXBfKF+Xm0Hz26adzbiZOnYLfb8V//5m/yzlKeiAzhP3j+z0e+DqLD4KAr3BaLJR3Ac8YCmk0wmcybxgLudg73wyp1TrtInrCdd2JJ+jMDN2XwlEh60hU1hQ4HbyGlJmEQjJC1h4c+zM3NoaqqCjU1NZifn9vmCoAkGaGqKoyGnb0Fuh8UVcHi4iKam5tRX1+PhYV5pFJJRMIhuN0VCAQCW84KTqWS0KCle2m3/svFld7It7YW2NmaIEPR5B1tRhQgQoIBTqkCFYZaGAQDJGSme6hAUkPNUA167a9gpX4FSTWOiBpERA1CXFLR7G7Ei/VJLKzOZds9jlQdw+zYPKanZ3a0XqInSbEDtyiK66rVmdCdv8KduX99JdxkdMJoMsFoNO44cKuqur43e1O43nhb/t7tzGcGbiIqV0UNyCktiY99V/BMzafhTTyciasoChYWFtDS0gqbzY5oNH/bAYDsIRMGgwEChKL03u5GIhFPV77r4HK5EQyuwb+6ilaHE2633mqxFUWWYTAYIWxTQbZYrUgmEvvSk6hBfeR0j/haCM3NrVAdCUQCQUiCAVVSPQxJE2p9rXjR3oygsgYVCgRBQONKEwKrARw/+3m9ZSLdS6z3E3vxzsrvEZIDRX8tRE8iVVURj8cRj8eLcr1M4N56DrdlU+Vb/3WmncQGo+th/3YhgXtzZXt91Xpza8mG+3I+7/QQIyKivSp6H8PH/mu4UPvqptuXl5dRW1eHmpoazExHtwy+iWQCFqsFgACDwYCUvPPjmostGFyD3e5ATU0NYtEokskEItEI3BUVCATWoG0xXSERj8Pg2G4jkACz2YJQOLh/i3+EeDyOSCSMispKBINrSKkJfbqHAgQUKwwOA/yr+g85FosFCZMb8+EpBOJrMAgGWA12uIwVOCr2wmWsxEJ8GoOr7z7iWYmoFPYjcG88ZXK7Hu6Nj7PabHCnq9tGozG992Rnr0PvvZZhNlsQiUSQSCSgqSoUVYWqqlBVRf+s6F8rm25THj5WyXyPCiXzGFWFqig5tz/8vuz9qgol/RgiejwVPSBPRoYQTq3BLFqRUB/2qmqahpmZGRzrPAaX271la0EqmYSQnnhgMBpLGpABYHl5CW1tbairr8fc3BwCfj+aW1rhdrsQCATyfk84EoHd4YTZbMl7v9FohCiKSMRLO9vX5/Ohtc2OyqoqeL0P5yJHoxHU1NTCYDBCllMwmc0AgEg0CkCDrKUgKylk/qmVBAPiyvaHqBDR40NVVcRisbz7EXZDkqQtj3a3Wiww54Zukwk2ux3Nzc2IRCJIpVKQJEm/htEIyWCBKErZ20RRhCSKENNfF9vGwKzmBHVFUTfclg7aWwbzDbcpOdda9xglJ/xvDvC5a9F2eKIrEa1X9ICsQcM7K3/A603fxHJ8fb/xWiCAcDiEyqoqhEKhvNMgksmHExiMRgOK9PfvrimKDK93BXV1DaiocCMQCCAWi6KiohJra8G8VeRIOAzUazBusdHQatGDc7GqObuVSiURCgbhculhX07/MJKZgWyz2RAMrsFsMkNRZKSSW0/HSCgl/j+KiMqWoihFDdzbyQTnzIfBYNh0W+7tBoMh+5Hv9kdfR8oGdoMkQTSZ1of3Db8u9sY4TdOyVXElTwU874eiwF1RgUQigbW1wKYAnw3sm34AWP8cuY9fV33fUKVniKfDaF9GRQwG3sXrTd/Ke9/09DT6+vtRVVUJr3fzTOFENoRpMOxwVul+C4VCsNsdqKqqRiQSxarfj6bmFjhdTgTXNh+HraUbSMQtqhVmiwWKqiCVKv2sX7/fD6fTierqaiwt6S0VqVQSqVQSNrtdD8hm8yP/4WIFmYjKgaIoh3rzYG5oflSQz9yWaVPJF9oNhszj1/8633NkKvmSJMFms8FiscBut2cr8Jm1FVsmxG8M7Ru/zttKkw7iW7XY5N62OdRvX4nPXQdD/JNnXwLycnwOi7EpOI2ViMjr+2xjsRhW/X5UVlZi1b8KZUMVWdNUyHIKoiiVdJLFRisrK2htbUV9fT1mZ2cRj8dQWVGJ0Fowbz+1qijZqsDGPjWLxYJEiavHGYoiIxAIoLKyCqurq9kxfNFoFC6XC4Kgb+4JBjf/IJAhAEioh+P1EBGVs0woS6VK2164nZ0G+J1U4jf/evsA//AHAsum6nvuDxf7ITew56/Ap9tntI397XmC98Z2mk335am6b6i8syd+f+3bsOG3l/8ZX2/7l5sCMgAsLCygqqoaLrcbq6ubZwAnk0lYLNZDFZAVRcbKygoaGhr1cL/qR2NjM5xOJ4Khza8xHo/Dbnegwl0Bf85rzATOcPjwHJoRCKzC5XajuroaCwvzAPQ+ZLe7Ak6nE4IgIhzeukIsCCIryERET4jDXoUH1lfitwrv6yvt+dtoNob2vJV8SYJkMOQ8p6g/Lue2fCF+P+ZMbxWc84b5PG0ymwL7Vv3yW7TQPKoan/koB/sWkO+ufYSvC/8y76i2eDyOUDgIt9uNwOrqpvuTySSsNlsJDwvJLxIJI5TuoZ6bmUEiEdenQIRC2DhPbW1Nn4BhczjWBWSLRZ/QkUgcnoqrqqoIrK6iuroGFosV8XgMsVgcmqbCZrMB0KeLbEWEuG5DJhERUSmVQyVeEIQdVeGzIV6SIOYJ9I+uyGcq7en7cjatmszG/JX4fdzYCqSr8Vv0xcupFFRNy98/r+S2xSjrJ9Ns2qy6s3aarexbAg3JAQwHB9Fu78Vayrfp/sWFRXR1dcPhdCK0oQKbTCYhQIAkGSAI4pbj1ErBu7IMq8WK2vp6rPr8aGhshMPhQDi8/nCAWEyf+GDa0EdttugTIeIlnmCx0draGioqKlBdXY25uVlomr5LPTPBwmwyId/0ahEiUloKina4qwlERESHSebo+f04D6GYtqu+77waL8FmtaGpuRmrfr8+fSYd+qXMY9K/NplMEEURgiDot5nNm3vzixjgbw7eynv7vpZo3/P+CQPup2GRrFA1FSktCVlNIqUmEQ5GkIjH4a6oyBuQM4xGw7qvS01VVXi9equFwWhAMplAZWXVpoAM5D+q02K2IJVKIt8Ej1LSNBV+vx+1tXWw2WyIRqOIRqOosdkBZCrfm0mCATH58LSLEBERUfEc9paaQlppNm5m3e7Qo30NyPcCH+E/jf5fcBoqUWmqQZWpHlXmGriN1XCbqpFc0VBbX4OUTUMykYAAAYAARZZhlM1QBRUmg+VQBWRAb7WIRiOoqqqGz6eftme3OxCJrA+KqVQKJpMZVqstXVHWg2ZmjNphEwoGUVFZiaqqaj0gRyJATS1UVYU5XUneSBIMiCoMyERERHTwMgG+2FlxXwOyChX3125seb/FYMW//lf/O2K2BO5+/AA20QG76IZbqoZVehZ2wQ27wQmTZEuPTtNbLQSIECBCRUo/tCL9oUA+sKOpV1ZW0NbWBqvVilQqicqqyk0BORIJw2Qyw+12ZwOyKIqQlcP5dooGDat+H+rqGuCwOxCOhJFKJSFJEswmU97vkUQJ0SQDMhERET0+SroLLi7H8O6ta3jhhRfgvzuLyeDDVouEdQFNTU0YTg3j9sQtWEQbbKIDVsEJm+iATXTCJVXDJVXBJVbCIVXAKVZBgAAVKpBubxAgQoP6MEgjBUVL6Y/ZA1lOYXV1FVVV1QgEVlFRUQmbzY5o9GGnbiCwhsrKqnXtCYIgAurhnacYCoVRUZFAVXU1wpEIotEIXO6KbC/yRpJgQFTe3F5CREREVK5KPibigw8+wMWLF9Hd04OPP/ooe/vaWgBNTU1wOOxQoSCqhhBVQwAWtr2eRbDBKjr0MC3qYdopVqbDdCUcYgWcYiUkwQgNarbeLEJE9hhlpKBocrYqvZXA6iqcTifsdjtkOYXKysp1AVlVlXUHhugtJMh51sNIg9/nQ0NjE1xOJ6KRKNzuShiMRgiCsGlYuiQYEFY2j7kjIiIiKlclD8jRaBT3791D/8AA7ty+jURCn+4QXFuDIAhwOV0FXS+uRRFXolhVlrd9nFEwwSo41oVph+iCW6qBS6qCU6qAXXTDIlihQtMnaQiZIC1CSbd3BFfCqGmoRTgWhN1uz45Jy8g9MCSbLQ9xBRkAItEI4vE4KquqMD09DU1TIQgijEZT9iCRDEkwIJxiQCYiIqLHR8kDMgC8/c47OHnqFI4d68K9e3cB6GPHAMBmt+/Lc6a0JFKaH0F180EluURIOSHakW3zcEvVcEtVcMqVcEScqDLVQ4traHYegS/lgwC9vUOMWGCyGlHrasRa0A9owqGuH2f4/T40NTXD5XIhkUjAYrHCbM4XkCWE5a1P2SMiIiIqN4ciIK+srGBycgJd3d0YGnoARVEQDAahaRoM6Zl4pZpkoUJBRF1DRN06BFrjVnz+819AKqKiylaDD65chxLRYBWdaDW142TdUxAgQQsDZtkGp1oFUTKlNxQKm9o7Mv3SKko3ViUWiyIWi6KyshLB4BosFiucTidCofX9xhqAmJJvQjIRERFReToUARkALl9+C//iX/wLdB47hpHhYSiKgng8DqvVCrvdfuhGveWKxWK4e+8Ozpw5i7C8iooeKz54/30AwO3Zd+B4ToEsy/jDyD/jz3u+iBsf3MCyzwur8LAy7RAr4Jaq4ZQq4ZQq4RDdMAuWTe0dAsR1IVpJh+r94PP50NLSqm8sBOB0Ojc9RoOKhMJT9IiIiOjxcWgC8vT0NGZnZ9Db24dRjweqqiIYXNMDssOB1dXVUi9xWyPDw+jo6ITVYkFb2xHcuX0bkYheWVUUJTuUGgIQV2NYU3xYw+YTBnNJMGTbOiyiXZ/eIbjgNlTDLVbBKVXCLrrhEqvzjMEToEJZF6JlLVXQGLxEIo5IJAyXywVNU2FNHzudS9M0xNXDOdeZiIiIaDcOTUAGgMtvXsb3f/ADdHR2YtTjgd/vR319A5yOzZXLw0bTNNy6NYgXXngRqqqip7cPn9z4GAAQDodRUVGBmtpaANj27O9cCmSE1QDCamDbxwkQYBHsOb3SdlhFJ1xild4nnd506BZrIApSesTdw/YODdq6EJ3b3uHz+dDW1gZZlmEwGGG1WhGLra8Yx1lBJiIiosfIoQrI4xMTWJifR19fP8bHxhBIV40rKitLvLKdmZ+bw/LSEqpratDR0YF7d+8gkUhgfm4OFRUVaGs7AgBQlb3NYN5Ig4aYFkZMCcP/iLZlk2DJ2Wyof3aIFXClNx06pAo4RDdMggUqFGiqBnVNgt3shKAIONbQh6nZCaTUJGRVb+2IK6wgExER0ePjUAVkALj81lv4zne+g/ajR+H36S0ILldho95K6ebgTbz22uvQNA3d3T24c+c2hoeH0Nffj4qKCgD6fORSSWpxJJU41hTvto/Lbe+oStTipQufgUmxwpiwYGJoCBWmariMVQjLQYRSgYNZPBEREdEBOHQB2ePxYGlpCf19/fj97/8ZmqbBlqf39bBa9fsxNTmJ1rY2dHV348GD+0gkEtA0LXtcs1LkCvJ+yG3vWF6bhW1FQldXFwRBwG9C/xnLy9vPmSYiIiIqV2KpF5DPW2+9BYfTiba2NiSTSZjSwbJc3L59CwBgMpnQeewYAOhTOAT9JL2Np9GVg/v37mZ7p5ubm0u8GiIiIqL9cygD8tDQEFZWVtDXP4BoNApBEGCxWku9rB2LRCIYGR6Gpmno6+2DKIrw+XwQ0gHZZiuf15IRj8cxOTkBTdNw8eLFUi+HiIiIaN8cyoAMAFeuXIHL5YIiywDyz+A9zO7duwtZlmGxWtHe3o6x0dHsfY4ymMqRz+DNmwCAmpoamM3mEq+GiIiIaH8c2oB8//59+Hw+2B36UdM1NTUlXlFhUqkU7t65AwAYOH4CCwvz0DQNmqbB4XCUeHW7k0qlsLy8DEEQ8IUvfKHUyyEiIiLaF4c2IGuahqtXr8Jq1TfoVVZWlXhFhfN4RhCPx2C329Hc3AJF0adXOJzlGZAB4KMPr0PTNPT395d6KURERET74tAGZAC4c+cOVldXoWla2bVYAPqBIIM3BwEAp06fRiQSgSAIcDrLZ2zdRuFwGPF4HJIk4eTJk6VeDhEREVHRHeqArGkarl27BkEQ4LDbS72cXZmamkQsGoXT6UQgEAAAWK1WiOKh/q3f1uCg3ov82muvlXglRERERMV36FParVu3oKoqDEZjqZeyK5qm4ZObnwAA3G43NE2DIAiwl2ngB4CpyUkoigKbzQa3213q5RAREREV1aEPyKqqIhAIQBAENLe0ZDe6bfw4zGampxGNRrMn6QHlO8kiY2pyAoIg4Ktf/Wqpl0JERERUVIc+IAPAD3/4Q6ytrWFgYACCIOT92Co4H5YA/cmNjwE8PCTEWcYb9QDgk08+gaZpaG1tzc53JiIiInoclEVAVlUVV69eRVVV9ZanuG0VnIWc0+tKGZ5nZ2ezh54AQGvbkQN53v0iy3K2sv/ySy+VejlERERERVMWARkABgcH9Sry8RMFf+924flR1ediunHj42xAfhx6d69/8D40TcOzFy6UeilERERERVM2AflhFbkKzc0tRb32bsNzoQF6bnYW0WgEmqbBWKabDnMFAgEkk0kYjUa0tBT3/xMiIiKiUimbgAzoVeRAIIDjx48f2HMWu3Xjxsc3st/75S8vw+GQD+y17Id7d+9AEAR85StfKfVSiIiIiIqirAJypopcWVV1KCqWu2ndWFycgtnsgyAIeOnlJL7z3aVSv4w9GRkZgaqqqKysfCyq4kRERERlFZABfS5yILC6q17kg5YvNP9v/3oWvb3/HYKgIBisw8mTEZjNqUM1caNQ8/PzrCITERHRY6PsArKqqrhy5SoqKyvR0tpa6uUU7B9/UwO3ewatrf83rlwJQRSBb3/Hu6O2jcPqg/ffg6Zp6O7uLvVSiIiIiPas7AIyoFeRV1dXD7QXuVgePLDD7zegvT2BwZs2xOMCnnoqBINBBbB12wZweIOzLMuIRMKQJAmnT58u9XKIiIiI9qQsA7Kmabjy1luoqKhEa2tbqZdTsF/9shaCAHz/+yt4441KSBLwzW8tb/s9h2FM3XY+vH4dmqbh1VdfPbDnJCIiItoPZRmQAeD2nTvw+/0YOH687E5yGxx0IhCQ0NUdxztvO5BMCnj22SBEUd3V9Q5D1Xl5eRmyLMNqta47UpuIiIio3JRtQNY0DW+99RYqKirKsor863+oSVeRvbjylhsGA/C1r60U9TkOOjiPejwQBAHf+MY3irF8IiIiopIo24AMAHfv3i3bKvKHH7oRCono64/i0iU3Uingued3X0UuxG7aNXbi1q1BaJqGxsbGfX4FRERERPunrANyporsdrvR2lZ+VeTf/bYGogj85be9eO89F4xGDV/4c19J17TXPmevV5/I8dprr5XoFRARERHtTVkHZECvIvt8PgwMlF8V+dq1CkSjIk6diuAff1MFRQFeeSUAYP+ryIXaabvGu++8DU3T8Mwzz5R4xURERES7U/YBObeK3NZ2pNTLKdgffl8JUQS+8U0fPrzuhNms4bXXV0u9rB3bGJjj8TiSySQEQYDD4Sj18oiIiIgKVvYBGQDu3bsHr9dblr3Ib7xRiXhcwLlzIfzylzVQFOC11/w4jFXknbr0xp8gCALOnz9f6qUQERERFeyxCMiZKrLL5cKRI+VWRRZx6ZI+C/nLX/Fh8KYDVquGl19eK/XCdi0UCmFxYQFPnT1bdj+wEBERET0WARkA7t+/D693pSx7kf/pd1VIJgVcuBDEz39eDVUFPv9npd2st1ejox44nE709PSUeilEREREBXlsArKmabh8+TKcLheOtLeXejkFEnHtqj4L+XOfD+DePRscDhUXLgRKvbBdm5ubQzQaxbPPPlvqpRAREREV5LEJyABw//4DrKwsl2UV+de/roEsA88/H8TPfloLTQO+9KXyrSJrmoaxsVG0t7ejsrKy1MshIiIi2rHHKiAD0KvITifa24+WeikFUVUR773nhsmk4YUXghgassLlVvDUU8FSL23XxsfGoKoqq8hERERUVh67gPzgwRCWl5fRPzBQdlXkX/59LRQF+PRnAvjxj2ugacBXvuot9bJ2LRaLYXZ2FqdOnYLBYCj1coiIiIh25LELyEBOFfloeVWRZVnERx/ps5CfPh/B2JgFlZUyjh8Pl3ppuzY26oHFYsGJEydKvRQiIiKiHXksA/LQ0BCWlpYw0F9+VeS/+0UdFAV4/XMB/OTHdQCAb3xzucSr2r2lpSUEg0E8/fTTpV4KERER0Y48lgEZ0KvIDqcTR492lHopBYnHJdwadMBqVdHXH8P0tBk1NTK6uiKlXtqujY560NjYiIaGhlIvhYiIiOiRHtuAPDw8jMXFRfQPDEAUy+tl/uxndVBV4Atf8OEnP9GryH/57ZUSr2r3JsbHIcsyLly4UOqlEBERET1SeSXHAl2+fBkOhwNHy6wXORo1ZGcht7YkMD9vQkNDEm1tsVIvbVdSqRSmp6fQ398Ps9lc6uUQERERbeuxDsgjIyNlW0X+6U8a9FnIX/bhb39WCwD47veWSryq3fN4PDAajTh79kypl0JERES0rfJKjbvw5ptvwm534GhHefUiB4MGDA9b4XYrcFcoWF42oqUlicbGeKmXtiurfj98Ph/On+dmPSIiIjrcHvuA7PF4sLCwgP7+cqwi10PTgK99zYtf/FyvIn/v++VbRR4d9aCqqgpHjhwp9VKIiIiItlReiXGX9CqyHR0dnaVeSkF8PhPGx/VZyKII+P0GtLcnUF2TLPXSdmV6agqJRIKb9YiIiOhQeyIC8ujoKBbm59E/0F92VeSf/LgeAPDNb63gl39fA0EAvv/9xRKvancURcHkxAS6urpgt9tLvRwiIiKivMorLe7BpTffhM1mR0dneVWRl5bMmJkxo7Y2hXBYQiAgoasrDrc7Veql7croqAeSJOHpp8+XeilEREREeT0xAXlsbAzz83Pl2Yv8U30W8re/s4J/+Ae9ilyuvcihUAiLi4s4e/apsjvlkIiIiJ4M5ZUU9+jSpTdhs9nQ2Xms1EspyMy0FQsLJjQ2JrG4aEQoJKKvLwqHQy710nZl1OOB0+lET09PqZdCREREtMkTFZDHx8cxNzeLvv5+SJJU6uUU5Gc/06vI3/3uMn77j9UQReA73ynPKvLc3Cyi0SieffaZUi+FiIiIaJMnKiADwKU3LpVlFXl8zIaVFSNaW5MY8VgRjYo4eSoCi0Up9dIKpmkaxsfGcORIOyorK0u9HCIiIqJ1nriAPDE5idnZmbKsIv/i53oV+fvfW8bvf18JUQS+9ZfLJV7V7oyNjULTNDz77LOlXgoRERHROk9cQAaAN964BKvVis5j5VVFfvDADr/fgKMdcdz8xIZ4XMC5cyEYDGqpl1awWCyGublZnDx5sux+UCEiIqLH2xMZkKempjAzM4O+vvKrIv/ql7XpKRZeXLpUAUkCvvnN8qwij46Owmq14sSJE6VeChEREVHWExmQAeDSJb2KfOxYV6mXUpDBQSfW1iR0d8fw9jUnkkkBz14IQhTLr4q8tLiIYDCIp59+utRLISIiIsp6YgPy1NQUpqen0dvXV3ZV5F//uiZbRb56xQ2DAfjqV72lXtaujI2OoqmpCfX19aVeChERERGAJzggAzlV5K7yqiJf/8CNUEhEf38Uf/yjG7IMPPf8WllWkScmxiHLMi5cuFDqpRAREREBeMID8vT0NKampsqyF/l3v62BKALf/rYX773rgsmk4Qtf8Jd6WQVLJpOYnp7CwEA/zGZzqZdDRERE9GQHZECvIlssFnR1d5d6KQW5dq0C0aiI02ci+M1vqqAowCufXgVQflXkUc8ojEYTzpw5U+qlEBERETEgz8zMYHJyEr29fTAYDKVeTkH++Ed9FvLXv+HDRx86YTZreO211VIvq2B+vw9+vw/nz58v9VKIiIiIGJCBnCpyV3lVkf/0x0rE4wLOnw/hV7+qgaIAr71efgEZAEY9HlRXV6Otra3USyEiIqInHAMygNnZWUxOTKC3r9yqyCLefLMSkgT8xRd9GBx0wGpV8eJL5ReSp6amkEwmcfHixVIvhYiIiJ5wDMhpb1y6BLPZjO4y60X+3W+rkEwKuHgxiJ//bTVUFfjCF3ylXlbBFEXBxMQEurq6YLfbS70cIiIieoIxIKfNzc1hYnwcPWXXiyzi2lV9FvJrr6/h3j0bHA4Vz14IlHphBRv1eCBJEs6fO1fqpRAREdETjAE5x6U330xXkXtKvZSC/PrXNZBl4MUX1/CTH9dC04Avfan8qsihUBBLi4t46tw5CIJQ6uUQERHRE4oBOcfc3BzGx8fR09sLo9FY6uXsmKqKeO89N0wmDS++FMTwsBVut4IzZ0OlXlrBRkc9cDqdZdfqQkRERI8PBuQNLmV7kcurivzLv6+FogCf/nQAP/lxDTQN+NrXVkq9rILNzs4iFovh2WefKfVSiIiI6AnFgLzB/Pw8xsbG0N3TU1ZVZFkW8fFHTlgsGs6di2B83ILKShkDA+FSL60gmqZhbGwU7e1HUVlZWerlEBER0ROIATmPbBW5p7yqyL/4RR1UFXjt9QB+/F/rAADf/Gb5VZHHx8cBAF/72tdKvBIiIiJ6EjEg57GwsIDR0VH09JRXL3I8LuHWoB02m4re3hhmZsyoqU2hqytS6qXtmKZpiITDCIfDqK2thSRJpV4SERERPWEYkLfwxhtvwGQyoaent9RLKchPf1qvz0L+cx9++hO9ivytvzz8VWRN06BpGgBAEAR8/PFHMJlMOHHiRIlXRkRERE8aBuQtLC0twePxlF0vcjRqwL17NjidKpqaElhYMKGxMYm2tlipl5bXxmCcGe+2tLiIYDCIZ555upTLIyIioicQA/I2Ll26BJPJhN7evlIvpSA//UkDNA348ld8+NlPawEA3/3uUolXtd5WwTjX6KgHjY1NaGhoOOjlERER0ROMAXkbS0tLGBkZQXd3N0wmU6mXs2PBoAEjI/osZJdLwcqKES2tSTQ2xku9tB0F44yJ8XHIsoyLFy4c1PKIiIiIGJAf5dKlSzCWYS/yT35SD00Dvv51L37x83QV+XulqyIXEowzUqkUpqYm0dffD4vFst9LJCIiIgLAgPxIy8vLGBkeRndPT1lVkX1eEyYmLKiskgEAq34Djh5NoLomeaDr2E0wzjXq8cBoNOLs2bP7sTwiIiKiTRiQd+CNS5dgMBjKrhf5Jz+uB6BPsfjlL2sgCMD3DqiKvNdgnLG6ugqv14vz588Xc3lEREREW2JA3oGVlRWMDA+jq7sbJpO51MvZscVFM2ZmzKitTSEYlLC2JqG7Owa3O7Vvz1msYJxr1DOCyspKdHZ07PlaRERERI/CgLxDl958U68i95VXL/JPf6rPQv7Od1fw63+ohiDsTy9yJhhrmla0YJwxPT2NeDyOZ7lZj4iIiA4AA/IOraysYHh4CF1d3TCby6eKPDNtzc5Cnp83IxQS0d8fhc0mF+X6G4OxKBb/j5SqqpgYH0dnZydcLlfRr09ERESUiwG5AJcu6VXknt7yqiL//G/1KvJ3v7eM3/22GqK49yryxlaK/QjGuUZHPRAEgQeHEBER0b5jQC6A1+vF8PAwurrKay7y6KgNKytGtLYmMDRkRTQq4tSpCCwWpeBr7UeP8U5EIhHMz8/jzJmzkCTpQJ6TiIiInkwMyAW6fPkyjEYjunt6Sr2UgvzdL/Qq8vd/sIw//qECogh861vLO/7+UgXjXKMeD2w2G/r7+w/8uYmIiOjJwYBcoOXlZXg8HnR398BoNJZ6OTt2/74dq34DOjri+PhjO+JxAefOh2AwqNt+32EIxhmLiwsIh0J45plnSrYGIiIievwxIO/C5cuXYTKZ0NXdXeqlFORXv6rVZyF/34s336yAJAHf+MZK3scepmCcoWkaRsdG0dLSgtra2lIvh4iIiB5TDMi7sLCwgPHxcfT09MJgMJR6OTt286YTa2sSenpiuHrFhWRSwIWLaxDFh1XkjaPaDkMwzjU+Ng5FUXDxIke+ERER0f5gQN6lN998E2azGce6ukq9lIL8+tfpE/W+v4K3r7lgMABf+ap332YYF1symcD09DQGBo6X1UZJIiIiKh8MyLs0NzeHqakp9PT0ltVUhesfuBEOixgYiCKe+DoURcRzz60d+mCcy+MZgclkwpkzZ0q9FCIiInoMMSDvwZtvvgmr1YrOY8dKvZSCZGYhnzv3Djye1/Ef/t+WUi+pIH6fD36/D08/zZnIREREVHwMyHswPT2N2dkZ9Pb27ftBGcWiaRquXKnA4KAd77zdhKWlp+FwPlfqZRVs1ONBdXU1jhw5UuqlEBER0WOmPFLdIXb5zcuw2Wzo6Ogs9VK2tbHH+Ef/qRmXLs1CURR0dpZXBRwApqamkEgkcPECN+sRERFRcTEg79H4xAQW5ufR199/KKvIj9p8Nz42BkmScPLkqRKsbvcURcHExAS6urvhcDhKvRwiIiJ6jBy+RFeGLr/1Fux2O9rbj5Z6KVk7nUpx8+YnUFW17E4GBICxUQ9EUWQvMhERERUVA3IReDweLC0toa+/v+STIAod16aqKiYnJ2AwGNA/MHAAKyyeUCiEhYUFnD179lBW74mIiKg8MVUUyZUrb8HpdOLIkfaSPP9e5hh/cuMGVFVFX2/fPq1u/4x6PHA4HOjtLb8KOBERER1ODMhF8uDBELzeFfQPDBxoFbkYB3zIsozZmRkYTSZ0dZXX8dnz83OIRCJ49llu1iMiIqLiYEAuoitXrsLlcqG1rW3fn6vYJ9999PFH0DQNx0+cKMLqDo6maRgbHUVbWxuqq6tLvRwiIiJ6DDAgF9G9e/fg9/vR379/vbz7dSR0KpnE/PwczGYzjnZ0FO26B2F8fAyKouACR74RERFRETAgF5Gmabh69SoqKirQ0tJa9GvvRzDO9eH1D6FpWtmNfIvH45idncGJEydgNBpLvRwiIiIqcwzIRXb79m0EAoGiTYQ4iGCckUjEsby0BKvVitbW4gb8/Tbq8cBsNuPUqfIK90RERHT4MCAXmaZpuHbtGqqqqtDU1Lyn6xxUMM51/foH0DQNp8+cPbDnLIbFxSUEAgG0NO/+95yIiIgIYEDeF4ODgwgGgxg4XngVuVTBOCMajcLn9cJms6GhofHAn79QiqIglUpheWUFP/rRj/Dff/ObUi+JiIiIyhwD8j5QVRXvvPMOqqtrdhwySx2Mc12//gEA4Klz50q6jq2oqgpVVRGLxTA/v4Cx8Qmsra0hFouVemlERET0GGBA3ic3btxAOBx+ZC/yYQrGGaFQCKurq3A4HKitrS31crIywTgUDmNqahpT0zOIRKOlXhYRERE9ZhiQ94miKHj33XdQV1eHurq6TfcfxmCc68MPrwMAzp1/usQr0X8vFUWBf3UVY+MTWFhYRCKZLPWyiIiI6DHFgLyPPvroY0QiEQwMHM/edtiDcUZgdRVra2twuVyorKwsyRoUVc32F4+OjcPr9UFRlJKshYiIiJ4cDMj7SJZlfPjhh6hvaEBHZ2dZBONcH3/0IQDg/NPPHNhzZtoootEo5ubm0/3FQWiadmBrICIioicbA/I++/BD/fCN3t7esgnGGV6vF+FwCJWVlXC6XPv6XNn+4lAYk1PTmJ6ZRZT9xURERFQCDMj7LBaL4e2334bL5UZlVVWpl1OwGx9/DAB45pn9qSIrqqr3F/vT/cWLi0iyv5iIiIhKiAH5ALz77rtIJBLo7y/O6XoHaXFxEdFoFNXVNbDb7UW5pqZpUFUVyWQSy0vLen+xj/3FREREdDgwIB+ARCKBjz76CK2trXC73aVeTsFu3vwEgiDg6T1WkTPBOBqLYXZuDuMTk1gLsr+YiIiIDhcG5APy7rvvIpVKlmUVeXZmBrFoFHV19bBYLAV/f6a/eC0YxOTUFGZmZhGN8lAPIiIiOpwYkA9ILBbDjRufoO3IETidzlIvp2C3bg0WXEXOzC/2+fwYHRvH4uISksnUPq6SiIiIaO8YkA/Q22+/DUVR0NffX+qlFGxychKJRByNjU0wmUxbPi63v3hpeRme0TH4/H6oqnqAqyUiIiLaPQbkAxSJRDA4OIj29qNF2/B2kO7euQNBEPKerpftL45GMTur9xcHg6ESrJKIiIhobxiQD9i1a9f0uch95VdF9ng8SCaTaGlpgcFgAJDTX7y2honJKczMziEaY38xERERlS8G5AMWDAZx584ddHR0wGq1lno5BXtw/z5EUcTZp85BURR4fT69v3hpGakU+4uJiIio/DEgl8CVK1cgCAJ6e/tKvZSCaJqGe/fuIplMwmQywTM6Br9/lf3FRERE9FgRAHAIbQl87WtfQ29vL377j79BIpEo9XK2pWkaNE1DNBaDz+dHIpFgKCYiIqLHFivIJfLWW29BkiT09PaWeilbyvQXB9L9xbOzc4jFYgzHRERE9FgzlHoBTyqfz4fh4WF0dXVj6MEDJJPJUi8pK3Pks8/vRyCwxkBMRERETxRWkEvo8uXLMBqN6O7uKfVS1s8vXlpmfzERERE9sVhBLqHl5WWMjo6iq7sbQ0MPIMvyga8h018ci8Xg9fkR44g2IiIiesKxglxily9fhtlsRldX94E+b775xQzHRERERKwgl9z8/DwmJyfR09uLkZHhbP/vflEUFYDG/mIiIiKiLbCCfAhcvnwZFosFnceO7cv11/UXLy+xv5iIiIhoG6wgHwLT09OYmZlBb28fRj2eogXXbH9xPA6f18cjoImIiIh2gBXkQ+Ktt96CzWbD0Y6OPV8r218cDGJyagozM7MMx0REREQ7xAryITE+Po6FhQX09fVjfGwMmlb4AYeKqgKaBv/qKgKBQLrfmIiIiIgKwQryIfLWW2/B4XCgvb29oO9TcuYXj46Nw+fzMxwTERER7RIryIfIyMgIVlaW0dc/gMnJyW2ryJk+5Xg8Dq/Pj2g0elDLJCIiInqssYJ8yFy5chUulwutrW1578/0F4dCYUxOTWN6ZpbhmIiIiKiIWEE+ZO7fvw+/34/+gX5MT09lb1dVFZqmYXU1gNVAYN/nJRMRERE9qVhBPmQ0TcPVq1dRUVGJ5pYWqKqKVCqV7S/2+nwMx0RERET7iAH5ELp9+zYCq6vo7OzE7Nw8xsYnsBYM7mqyBREREREVRgDA1HUI1dfXIxAIIJFIlHopRERERE8UBmQiIiIiohxssSAiIiIiysGATERERESUgwGZiIiIiCgHAzIRERERUQ4GZCIiIiKiHAzIREREREQ5GJCJiIiIiHIwIBMRERER5WBAJiIiIiLKwYBMRERERJSDAZmIiIiIKAcDMhERERFRDgZkIiIiIqIcDMhERERERDkYkImIiIiIcjAgExERERHlYEAmIiIiIsrBgExERERElIMBmYiIiIgoBwMyEREREVEOBmQiIiIiohwMyEREREREORiQiYiIiIhyMCATEREREeVgQCYiIiIiysGATERERESUgwGZiIiIiCgHAzIRERERUQ4GZCIiIiKiHAzIREREREQ5GJCJiIiIiHIwIBMRERER5WBAJiIiIiLKwYBMRERERJSDAZmIiIiIKAcDMhERERFRDgZkIiIiIqIcDMhERERERDkYkImIiIiIcjAgExERERHlYEAmIiIiIsrBgExERERElIMBmYiIiIgoBwMyEREREVEOBmQiIiIiohwMyEREREREORiQiYiIiIhyMCATEREREeVgQCYiIiIiysGATERERESUgwGZiIiIiCgHAzIRERERUQ4GZCIiIiKiHAzIREREREQ5GJCJiIiIiHIwIBMRERER5WBAJiIiIiLKwYBMRERERJSDAZmIiIiIKAcDMhERERFRDgZkIiIiIqIcDMhERERERDkYkImIiIiIcjAgExERERHlYEAmIiIiIsrx/wM+t8Xf2CHyVAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "argoverse2_sensor_dataset_teaser(args)" ] @@ -340,7 +381,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.9" + "version": "3.9.12" } }, "nbformat": 4, From 967cc3918cf818d673ca0bff931143503c10f3a7 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sun, 3 Apr 2022 15:04:26 -0400 Subject: [PATCH 48/81] Add test log. --- src/av2/evaluation/detection/utils.py | 3 +-- .../annotations.feather | Bin 0 -> 642618 bytes .../city_SE3_egovehicle.feather | Bin 0 -> 161634 bytes ...5b0-a2fa-b4cea13a6d76___img_Sim2_city.json | 1 + ...ea13a6d76_ground_height_surface____PIT.npy | Bin 0 -> 1135548 bytes ...0-a2fa-b4cea13a6d76____PIT_city_57819.json | 1 + tests/evaluation/detection/test_eval.py | 22 ++++++++++++++++++ 7 files changed, 25 insertions(+), 2 deletions(-) create mode 100644 tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/annotations.feather create mode 100644 tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/city_SE3_egovehicle.feather create mode 100644 tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/map/adcf7d18-0510-35b0-a2fa-b4cea13a6d76___img_Sim2_city.json create mode 100644 tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/map/adcf7d18-0510-35b0-a2fa-b4cea13a6d76_ground_height_surface____PIT.npy create mode 100644 tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/map/log_map_archive_adcf7d18-0510-35b0-a2fa-b4cea13a6d76____PIT_city_57819.json diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index a7d3bd84..3b52442e 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -13,10 +13,9 @@ import logging from dataclasses import dataclass from pathlib import Path -from typing import Dict, Final, List, Optional, Tuple +from typing import Dict, List, Optional, Tuple import numpy as np -import pandas as pd from joblib import Parallel, delayed from scipy.spatial.distance import cdist diff --git a/tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/annotations.feather b/tests/evaluation/detection/data/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/annotations.feather new file mode 100644 index 0000000000000000000000000000000000000000..4ed097ba2a5a333097f7ade24632fb14ce5833c2 GIT binary patch literal 642618 zcmdqqahPlQA;A6YZd7QrK@g-B>teBLRq3MG+m;5SlWGuz&9>P#OS+qG(>85Wf*@t8 zK}xiPv{F*0L>n4J8*NIoLqnsDHLYL}ga(6P>b{@Mz00Dn=Xsx}f4$k~v-3OW{F0Mz zP9{0CdnfnuSAX^X*L`*%aK&Z&A%Q?7u)BO{;30tr1wJScGIF^t_@C>;7K7K;ZyvN8 z+stl#$iH{nLKow!w`EG8bjRKKJDwE?3@yCi#=Gv$-&wi+srMA0{fxVxb>kN#zVVxG zPTY8P@%D>tcm9vH7mF{r@$NgGai{4wmhXDTozE)Y@r=^#uI{Q&yX($k{`UX3tylMb zd--{#i!0xCclq-kwY%2c&wfVfdH;L$Gwv+kk-wvShaKJh>|*hSHzyvj<9qLT>bKth 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18.62}, {"x": 1518.43, "y": 72.33, "z": 18.62}, {"x": 1519.1, "y": 72.3, "z": 18.63}, {"x": 1519.75, "y": 72.47, "z": 18.64}, {"x": 1520.65, "y": 72.94, "z": 18.66}, {"x": 1521.35, "y": 73.5, "z": 18.68}, {"x": 1522.07, "y": 74.41, "z": 18.71}, {"x": 1531.69, "y": 93.82, "z": 18.13}, {"x": 1544.61, "y": 120.0, "z": 17.14}], "id": 1413627}}} \ No newline at end of file diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 316464d4..2362b17a 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -23,12 +23,16 @@ compute_affinity_matrix, compute_evaluated_dts_mask, compute_evaluated_gts_mask, + compute_objects_in_roi_mask, distance, interpolate_precision, ) from av2.geometry.geometry import wrap_angles from av2.geometry.iou import iou_3d_axis_aligned +from av2.map.map_api import ArgoverseStaticMap +from av2.structures.cuboid import ORDERED_CUBOID_COL_NAMES from av2.utils.constants import PI +from av2.utils.io import read_city_SE3_ego, read_feather from av2.utils.typing import NDArrayBool, NDArrayFloat TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" @@ -304,6 +308,24 @@ def test_compute_evaluated_gts_mask() -> None: gts_mask_: NDArrayBool = np.array([True, False, False, False]) np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore +def test_compute_objects_in_roi_mask() -> None: + log_dir = TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" + annotations_path = log_dir / "annotations.feather" + annotations = read_feather(annotations_path) + + timestamp_to_egoposes = read_city_SE3_ego(log_dir) + params = annotations.loc[:, ORDERED_CUBOID_COL_NAMES] + + avm = ArgoverseStaticMap.from_map_dir(log_dir / "map", build_raster=True) + + + avm.raster_roi_layer.array + breakpoint() + # read_city_SE3_ego() + compute_objects_in_roi_mask(params) + breakpoint() + + # def test_val_identity() -> None: # root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" From ae8aeb55f011027f4302cabc8738ec57b583e053 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sun, 3 Apr 2022 15:06:34 -0400 Subject: [PATCH 49/81] Revert strings. --- tutorials/map_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb index 31a079e7..89bb3b60 100644 --- a/tutorials/map_tutorial.ipynb +++ b/tutorials/map_tutorial.ipynb @@ -27,10 +27,10 @@ "outputs": [], "source": [ "# path to where the logs live\n", - "dataroot = \"/Users/admin/data/datasets/av2/sensor/val/\"\n", + "dataroot = \"/Users/jlambert/Downloads/av2-v7-2022-01-15\"\n", "\n", "# unique log identifier\n", - "log_id = \"36aec72e-5086-376c-b109-295b128e77e1/\"" + "log_id = \"adcf7d18-0510-35b0-a2fa-b4cea13a6d76\"" ] }, { From d8a0ae9ba89f8e4c0df63baf49995a3b31782cdb Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sun, 3 Apr 2022 15:07:34 -0400 Subject: [PATCH 50/81] Remove outputs. --- tutorials/map_tutorial.ipynb | 73 ++++++++---------------------------- 1 file changed, 15 insertions(+), 58 deletions(-) diff --git a/tutorials/map_tutorial.ipynb b/tutorials/map_tutorial.ipynb index 89bb3b60..f68e27b6 100644 --- a/tutorials/map_tutorial.ipynb +++ b/tutorials/map_tutorial.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "f25e1e66", "metadata": {}, "outputs": [], @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "d70c7396", "metadata": {}, "outputs": [], @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "f919b29b", "metadata": {}, "outputs": [], @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "15a8196d", "metadata": {}, "outputs": [], @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "3a02b3b8", "metadata": {}, "outputs": [], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "8d5ae748", "metadata": {}, "outputs": [], @@ -125,28 +125,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "4e27fe09", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "single_log_teaser(args)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "56a98bd9", "metadata": {}, "outputs": [], @@ -188,38 +177,17 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "2bdc3926", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize_raster_layers(args)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "f45e82da", "metadata": {}, "outputs": [], @@ -276,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "4f7dec27", "metadata": {}, "outputs": [], @@ -337,21 +305,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "ac070630", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "argoverse2_sensor_dataset_teaser(args)" ] From 87239c3ea5266a4ff41c87c8b13288776bb612ad Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 4 Apr 2022 14:50:08 -0400 Subject: [PATCH 51/81] Address missing detection edge cases. --- src/av2/evaluation/detection/eval.py | 15 ++++++++++++--- src/av2/evaluation/detection/utils.py | 22 +++++++++++++++------- 2 files changed, 27 insertions(+), 10 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index bdf13cbb..2ef34479 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -129,14 +129,23 @@ def evaluate( log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(log_ids, cfg.dataset_dir) args_list: List[Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]]] = [] - for uuid, sweep_gts in uuid_to_gts.items(): + uuids = sorted(uuid_to_dts.keys() | uuid_to_gts.keys()) + for uuid in uuids: log_id, timestamp_ns, _ = uuid.split(":") args: Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]] - args = uuid_to_dts[uuid], sweep_gts, cfg, None, None + + sweep_dts: NDArrayFloat = np.zeros((0, 10)) + sweep_gts: NDArrayFloat = np.zeros((0, 10)) + if uuid in uuid_to_dts: + sweep_dts = uuid_to_dts[uuid] + if uuid in uuid_to_gts: + sweep_gts = uuid_to_gts[uuid] + + args = sweep_dts, sweep_gts, cfg, None, None if log_id_to_avm is not None and log_id_to_timestamped_poses is not None: avm = log_id_to_avm[log_id] city_SE3_ego = log_id_to_timestamped_poses[log_id][int(timestamp_ns)] - args = uuid_to_dts[uuid], sweep_gts, cfg, avm, city_SE3_ego + args = sweep_dts, sweep_gts, cfg, avm, city_SE3_ego args_list.append(args) logger.info("Starting evaluation ...") diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 3b52442e..52d2fa41 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -132,18 +132,17 @@ def accumulate( dt_results: NDArrayFloat = np.zeros((N, 8)) gt_results: NDArrayFloat = np.zeros((M, 8)) - if is_evaluated_dts.sum() == 0 or is_evaluated_gts.sum() == 0: - return dt_results, gt_results - - # Compute true positives through assigning detections and ground truths. - dts_assignments, gts_assignments = assign(dts[is_evaluated_dts], gts[is_evaluated_gts], cfg) - dt_results[is_evaluated_dts, :-1] = dts_assignments - gt_results[is_evaluated_gts, :-1] = gts_assignments # `is_evaluated` boolean flag is always the last column of the array. dt_results[is_evaluated_dts, -1] = True gt_results[is_evaluated_gts, -1] = True + if is_evaluated_dts.sum() > 0 and is_evaluated_gts.sum() > 0: + # Compute true positives through assigning detections and ground truths. + dts_assignments, gts_assignments = assign(dts[is_evaluated_dts], gts[is_evaluated_gts], cfg) + dt_results[is_evaluated_dts, :-1] = dts_assignments + gt_results[is_evaluated_gts, :-1] = gts_assignments + # Permute the detections according to the original ordering. outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(permutation, return_index=True) # type: ignore _, inverse_permutation = outputs @@ -337,6 +336,9 @@ def compute_objects_in_roi_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ + if len(cuboids_parameters) == 0: + is_within_roi: NDArrayBool = np.zeros((0,), dtype=bool) + return is_within_roi cuboid_list_ego: CuboidList = CuboidList([Cuboid.from_numpy(params) for params in cuboids_parameters]) cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) cuboid_list_vertices_m = cuboid_list_city.vertices_m @@ -366,6 +368,9 @@ def compute_evaluated_dts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ + if len(dts) == 0: + is_evaluated: NDArrayBool = np.zeros((0,), dtype=bool) + return is_evaluated norm: NDArrayFloat = np.linalg.norm(dts[:, :3], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m is_evaluated: NDArrayBool = is_within_radius @@ -390,6 +395,9 @@ def compute_evaluated_gts_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ + if len(gts) == 0: + is_evaluated: NDArrayBool = np.zeros((0,), dtype=bool) + return is_evaluated norm: NDArrayFloat = np.linalg.norm(gts[:, :3], axis=1) # type: ignore is_within_radius: NDArrayBool = norm < cfg.max_range_m is_valid_num_points: NDArrayBool = gts[:, -1] > 0 From 9a2acc6476d6d93258724748da6b2a5e8fc80b25 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 11:44:07 -0400 Subject: [PATCH 52/81] Address jhony comments. --- src/av2/evaluation/detection/eval.py | 6 +-- src/av2/evaluation/detection/utils.py | 5 +-- tests/evaluation/detection/test_eval.py | 54 +++++++++++++++++-------- 3 files changed, 42 insertions(+), 23 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 2ef34479..9818eb32 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -100,7 +100,7 @@ def evaluate( """ uuid_column_names = ["log_id", "timestamp_ns", "category"] - # Sort both the detections and annotations by in lexicographic order for grouping. + # Sort both the detections and annotations by lexicographic order for grouping. dts = dts.sort_values(uuid_column_names) gts = gts.sort_values(uuid_column_names) @@ -122,7 +122,7 @@ def evaluate( log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None - # # Load maps and egoposes if roi-pruning is enabled. + # Load maps and egoposes if roi-pruning is enabled. if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: logger.info("Loading maps and egoposes ...") log_ids = gts["log_id"].unique().tolist() @@ -185,7 +185,7 @@ def summarize_metrics( The summary metrics. """ # Sample recall values in the [0, 1] interval. - recall_interpolated: NDArrayFloat = np.linspace(0, 1, cfg.num_recall_samples) + recall_interpolated: NDArrayFloat = np.linspace(0, 1, cfg.num_recall_samples, endpoint=True) # Initialize the summary metrics. summary = pd.DataFrame( diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 52d2fa41..e1bd88a6 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -68,7 +68,7 @@ class DetectionCfg: filter_metric: FilterMetricType = FilterMetricType.EUCLIDEAN max_num_dts_per_category: int = 100 max_range_m: float = 200.0 - num_recall_samples: int = 101 + num_recall_samples: int = 100 split: str = "val" tp_threshold_m: float = 2.0 @@ -126,7 +126,6 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - # num_interior_pts: NDArrayFloat = gts[..., -1] is_evaluated_dts &= compute_evaluated_dts_mask(dts, cfg) is_evaluated_gts &= compute_evaluated_gts_mask(gts, cfg) @@ -348,7 +347,7 @@ def compute_objects_in_roi_mask( ) is_within_roi = is_within_roi.reshape(-1, 8) is_within_roi = is_within_roi.any(axis=1) - return is_within_roi + return cuboid_list_vertices_m, is_within_roi def compute_evaluated_dts_mask( diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 2362b17a..267cc37c 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -3,18 +3,19 @@ """Detection evaluation unit tests. Only the last two unit tests here use map ROI information. -The rest apply no filtering to objects that have their corners located outside of the ROI). +The rest apply no filtering to objects that have their corners located outside of the ROI. """ import math from pathlib import Path +from time import sleep from typing import Final, List import numpy as np import pandas as pd from scipy.spatial.transform import Rotation -from av2.evaluation.detection.constants import AffinityType, DistanceType, TruePositiveErrorNames +from av2.evaluation.detection.constants import AffinityType, DistanceType from av2.evaluation.detection.eval import evaluate from av2.evaluation.detection.utils import ( DetectionCfg, @@ -32,7 +33,7 @@ from av2.map.map_api import ArgoverseStaticMap from av2.structures.cuboid import ORDERED_CUBOID_COL_NAMES from av2.utils.constants import PI -from av2.utils.io import read_city_SE3_ego, read_feather +from av2.utils.io import read_city_SE3_ego, read_feather, write_img from av2.utils.typing import NDArrayBool, NDArrayFloat TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" @@ -308,23 +309,42 @@ def test_compute_evaluated_gts_mask() -> None: gts_mask_: NDArrayBool = np.array([True, False, False, False]) np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore + def test_compute_objects_in_roi_mask() -> None: log_dir = TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" annotations_path = log_dir / "annotations.feather" annotations = read_feather(annotations_path) - timestamp_to_egoposes = read_city_SE3_ego(log_dir) - params = annotations.loc[:, ORDERED_CUBOID_COL_NAMES] - - avm = ArgoverseStaticMap.from_map_dir(log_dir / "map", build_raster=True) - - - avm.raster_roi_layer.array - breakpoint() - # read_city_SE3_ego() - compute_objects_in_roi_mask(params) - breakpoint() - + root = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" + logs = sorted(root.iterdir()) + from av2.rendering.rasterize import draw_points_xy_in_img + + for log_dir in logs: + if not log_dir.is_dir(): + continue + + annotations_path = log_dir / "annotations.feather" + annotations = read_feather(annotations_path) + + # breakpoint() + timestamp_to_egoposes = read_city_SE3_ego(log_dir) + # params = annotations.loc[:, ORDERED_CUBOID_COL_NAMES] + avm = ArgoverseStaticMap.from_map_dir(log_dir / "map", build_raster=True) + for timestamp_ns, sweep_annotations in annotations.groupby("timestamp_ns"): + vertices, mask = compute_objects_in_roi_mask( + sweep_annotations.loc[:, ORDERED_CUBOID_COL_NAMES].to_numpy(), timestamp_to_egoposes[timestamp_ns], avm + ) + if (~mask).sum() == 0: + continue + roi, sim2 = avm.get_rasterized_roi() + points_arr = sim2.transform_point_cloud(vertices.reshape(-1, 3)[..., :2]).round().astype(int) + points = points_arr.reshape(-1, 8, 2)[~mask].reshape(-1, 2) + roi = roi.astype(np.uint8)[..., None].repeat(3, axis=2) + draw_points_xy_in_img(roi, points, colors=np.full((len(points_arr), 3), fill_value=(0, 0, 1)), diameter=100) + # print((~mask).sum()) + write_img(Path("roi.png"), roi * 255) + sleep(0.05) + # breakpoint() # def test_val_identity() -> None: @@ -345,5 +365,5 @@ def test_compute_objects_in_roi_mask() -> None: # dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) -# if __name__ == "__main__": -# test_val_identity() +if __name__ == "__main__": + test_compute_objects_in_roi_mask() From ba6c3373474bf17ae43648d8041659dc4977078a Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 11:55:40 -0400 Subject: [PATCH 53/81] Update docstring. --- src/av2/evaluation/detection/utils.py | 27 +++++++++++++++++++++------ 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index e1bd88a6..a2511d08 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -102,16 +102,30 @@ def accumulate( ) -> Tuple[NDArrayFloat, NDArrayFloat]: """Accumulate the true / false positives (boolean flags) and true positive errors for each class. + The detections (gts) and ground truth annotations (gts) are expected to be shape (N,11) and (M,11) + respectively. Their _ordered_ columns are shown below: + + dts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, score + gts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, num_interior_pts + + NOTE: The columns for dts and gts only differ by their _last_ column. Score represents the + "confidence" of the detection and `num_interior_pts` are the number of points interior + to the ground truth cuboid at the time of annotation. + Args: - dts: (N,len(AnnotationColumns)) Detections of shape. Must contain all of the columns in AnnotationColumns. - gts: (M,len(AnnotationColumns) + 1) Ground truth labels. Must contain all of the columns in AnnotationColumns - and the `num_interior_pts` column. - cfg: Detection configuration. + dts: (N,11) Detections array. + gts: (M,11) Ground truth annotations array. + cfg: 3D object detection configuration. avm: Argoverse static map for the log. city_SE3_ego: Egovehicle pose in the city reference frame. Returns: - The detection and ground truth cuboids augmented with assigment and evaluation fields. + (N,11+8) Augmented detections + (M,11+8) Augmented ground truth annotations. + NOTE: The eight additional columns consist of the following: + *cfg.affinity_thresholds_m (0.5, 1.0, 2.0, 4.0 by default), ATE, ASE, AOE, is_evaluated. + `is_evaluated` indicates whether the ground truth or detection was filtered and consequently + not considered during the assignment process. """ N, M = len(dts), len(gts) @@ -129,6 +143,7 @@ def accumulate( is_evaluated_dts &= compute_evaluated_dts_mask(dts, cfg) is_evaluated_gts &= compute_evaluated_gts_mask(gts, cfg) + # Initialize results array. dt_results: NDArrayFloat = np.zeros((N, 8)) gt_results: NDArrayFloat = np.zeros((M, 8)) @@ -137,7 +152,7 @@ def accumulate( gt_results[is_evaluated_gts, -1] = True if is_evaluated_dts.sum() > 0 and is_evaluated_gts.sum() > 0: - # Compute true positives through assigning detections and ground truths. + # Compute true positives by assigning detections and ground truths. dts_assignments, gts_assignments = assign(dts[is_evaluated_dts], gts[is_evaluated_gts], cfg) dt_results[is_evaluated_dts, :-1] = dts_assignments gt_results[is_evaluated_gts, :-1] = gts_assignments From aa036aa27c9fc60faf9491adbac4818430f067c6 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 12:18:18 -0400 Subject: [PATCH 54/81] Clean docstrings. --- src/av2/evaluation/detection/utils.py | 114 +++++++++++++++----------- 1 file changed, 64 insertions(+), 50 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index a2511d08..cc57cb78 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -105,8 +105,8 @@ def accumulate( The detections (gts) and ground truth annotations (gts) are expected to be shape (N,11) and (M,11) respectively. Their _ordered_ columns are shown below: - dts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, score - gts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, num_interior_pts + dts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, score. + gts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz, num_interior_pts. NOTE: The columns for dts and gts only differ by their _last_ column. Score represents the "confidence" of the detection and `num_interior_pts` are the number of points interior @@ -120,12 +120,13 @@ def accumulate( city_SE3_ego: Egovehicle pose in the city reference frame. Returns: - (N,11+8) Augmented detections - (M,11+8) Augmented ground truth annotations. - NOTE: The eight additional columns consist of the following: - *cfg.affinity_thresholds_m (0.5, 1.0, 2.0, 4.0 by default), ATE, ASE, AOE, is_evaluated. - `is_evaluated` indicates whether the ground truth or detection was filtered and consequently - not considered during the assignment process. + (N,11+T+E+1) Augmented detections. + (M,11+T+E+1) Augmented ground truth annotations. + NOTE: The $$T+E+1$$ additional columns consist of the following: + $$T$$: cfg.affinity_thresholds_m (0.5, 1.0, 2.0, 4.0 by default). + $$E$$: ATE, ASE, AOE. + 1: `is_evaluated` flag indicating whether the detections or ground truth annotations + are considered during assignment. """ N, M = len(dts), len(gts) @@ -140,43 +141,54 @@ def accumulate( is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) - is_evaluated_dts &= compute_evaluated_dts_mask(dts, cfg) - is_evaluated_gts &= compute_evaluated_gts_mask(gts, cfg) + is_evaluated_dts &= compute_evaluated_dts_mask(dts[..., :3], cfg) + is_evaluated_gts &= compute_evaluated_gts_mask(gts[..., :3], gts[..., -1], cfg) # Initialize results array. - dt_results: NDArrayFloat = np.zeros((N, 8)) - gt_results: NDArrayFloat = np.zeros((M, 8)) + dts_augmented: NDArrayFloat = np.zeros((N, 8)) + gts_augmented: NDArrayFloat = np.zeros((M, 8)) # `is_evaluated` boolean flag is always the last column of the array. - dt_results[is_evaluated_dts, -1] = True - gt_results[is_evaluated_gts, -1] = True + dts_augmented[is_evaluated_dts, -1] = True + gts_augmented[is_evaluated_gts, -1] = True if is_evaluated_dts.sum() > 0 and is_evaluated_gts.sum() > 0: # Compute true positives by assigning detections and ground truths. dts_assignments, gts_assignments = assign(dts[is_evaluated_dts], gts[is_evaluated_gts], cfg) - dt_results[is_evaluated_dts, :-1] = dts_assignments - gt_results[is_evaluated_gts, :-1] = gts_assignments + dts_augmented[is_evaluated_dts, :-1] = dts_assignments + gts_augmented[is_evaluated_gts, :-1] = gts_assignments # Permute the detections according to the original ordering. outputs: Tuple[NDArrayInt, NDArrayInt] = np.unique(permutation, return_index=True) # type: ignore _, inverse_permutation = outputs - dt_results = dt_results[inverse_permutation] - return dt_results, gt_results + dts_augmented = dts_augmented[inverse_permutation] + return dts_augmented, gts_augmented def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDArrayFloat, NDArrayFloat]: """Attempt assignment of each detection to a ground truth label. + The detections (gts) and ground truth annotations (gts) are expected to be shape (N,10) and (M,10) + respectively. Their _ordered_ columns are shown below: + + dts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz. + gts: tx_m, ty_m, tz_m, length_m, width_m, height_m, qw, qx, qy, qz. + + NOTE: The columns for dts and gts only differ by their _last_ column. Score represents the + "confidence" of the detection and `num_interior_pts` are the number of points interior + to the ground truth cuboid at the time of annotation. + Args: - dts: (N,23) Detections of shape. Must contain all columns in AnnotationColumns and the - additional columns: (is_evaluated, *cfg.affinity_thresholds_m, *TruePositiveErrorNames). - gts: (M,23) Ground truth labels. Must contain all columns in AnnotationColumns and the - additional columns: (is_evaluated, *cfg.affinity_thresholds_m, *TruePositiveErrorNames). - cfg: Detection configuration. + dts: (N,10) Detections array. + gts: (M,10) Ground truth annotations array. + cfg: 3D object detection configuration. Returns: - The (N,K+S) confusion table containing the true and false positives augmented with the true positive errors - where K is the number of thresholds and S is the number of true positive error names. + (N,T+E) Detections metrics table. + (M,T+E) Ground truth annotations metrics table. + NOTE: The $$T+E$$ additional columns consist of the following: + $$T$$: cfg.affinity_thresholds_m (0.5, 1.0, 2.0, 4.0 by default). + $$E$$: ATE, ASE, AOE. """ affinity_matrix = compute_affinity_matrix(dts, gts, cfg.affinity_type) @@ -192,10 +204,11 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) # type: ignore idx_gts, idx_dts = assignments - K = len(cfg.affinity_thresholds_m) + 3 - dts_table: NDArrayFloat = np.zeros((len(dts), K)) + T = len(cfg.affinity_thresholds_m) + E = 3 + dts_table: NDArrayFloat = np.zeros((len(dts), T + E)) dts_table[:, 4:] = cfg.metrics_defaults[1:4] - gts_table: NDArrayFloat = np.zeros((len(gts), K)) + gts_table: NDArrayFloat = np.zeros((len(gts), T + E)) gts_table[:, 4:] = cfg.metrics_defaults[1:4] for i, threshold_m in enumerate(cfg.affinity_thresholds_m): is_tp: NDArrayBool = affinities[idx_dts] > -threshold_m @@ -366,56 +379,57 @@ def compute_objects_in_roi_mask( def compute_evaluated_dts_mask( - dts: NDArrayFloat, + xyz_m_ego: NDArrayFloat, cfg: DetectionCfg, ) -> NDArrayBool: """Compute the evaluated cuboids mask. - Valid cuboids meet _two_ conditions: + Valid detections cuboids meet _two_ conditions: 1. The cuboid's centroid (x,y,z) must lie within the maximum range in the detection configuration. - 2. The cuboid must have at _least_ one point in its interior. + 2. The total number of cuboids must not exceed `cfg.max_num_dts_per_category`. Args: - dts: Dataframes containing cuboids. - cfg: Detection configuration object. + xyz_m_ego: (N,3) Center of the detections in the egovehicle frame. + cfg: 3D object detection configuration. Returns: The boolean mask indicating which cuboids will be evaluated. """ - if len(dts) == 0: - is_evaluated: NDArrayBool = np.zeros((0,), dtype=bool) + is_evaluated: NDArrayBool + if len(xyz_m_ego) == 0: + is_evaluated = np.zeros((0,), dtype=bool) return is_evaluated - norm: NDArrayFloat = np.linalg.norm(dts[:, :3], axis=1) # type: ignore - is_within_radius: NDArrayBool = norm < cfg.max_range_m - is_evaluated: NDArrayBool = is_within_radius + norm: NDArrayFloat = np.linalg.norm(xyz_m_ego, axis=1) # type: ignore + is_evaluated: NDArrayBool = norm < cfg.max_range_m is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. return is_evaluated def compute_evaluated_gts_mask( - gts: NDArrayFloat, + xyz_m_ego: NDArrayFloat, + num_interior_pts: NDArrayInt, cfg: DetectionCfg, ) -> NDArrayBool: - """Compute the evaluated cuboids mask. + """Compute the ground truth annotations evaluated cuboids mask. - Valid cuboids meet _two_ conditions: + Valid detections cuboids meet _two_ conditions: 1. The cuboid's centroid (x,y,z) must lie within the maximum range in the detection configuration. - 2. The cuboid must have at _least_ one point in its interior. + 2. The cuboid must have at _least_ one point in each cuboid. Args: - gts: (N,11) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES` + "num_interior_pts". - cfg: Detection configuration object. + xyz_m_ego: (M,3) Center of the ground truth annotations in the egovehicle frame. + num_interior_pts: (M,) Number of points interior to each cuboid. + cfg: 3D object detection configuration. Returns: The boolean mask indicating which cuboids will be evaluated. """ - if len(gts) == 0: - is_evaluated: NDArrayBool = np.zeros((0,), dtype=bool) + is_evaluated: NDArrayBool + if len(xyz_m_ego) == 0: + is_evaluated = np.zeros((0,), dtype=bool) return is_evaluated - norm: NDArrayFloat = np.linalg.norm(gts[:, :3], axis=1) # type: ignore - is_within_radius: NDArrayBool = norm < cfg.max_range_m - is_valid_num_points: NDArrayBool = gts[:, -1] > 0 - is_evaluated: NDArrayBool = is_within_radius & is_valid_num_points + norm: NDArrayFloat = np.linalg.norm(xyz_m_ego, axis=1) # type: ignore + is_evaluated = np.logical_and(norm < cfg.max_range_m, num_interior_pts > 0) return is_evaluated From a6b345d22dc92795fc1d3b18cbdf7e50b42a019d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 12:21:18 -0400 Subject: [PATCH 55/81] Change roi method. --- src/av2/evaluation/detection/utils.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index cc57cb78..e5ffffe3 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -51,7 +51,7 @@ class DetectionCfg: affinity_thresholds_m: Affinity thresholds for determining a true positive (in meters). affinity_type: Type of affinity function to be used for calculating average precision. categories: Detection classes for evaluation. - eval_only_roi_instances: Only use dets and ground truth that lie within region of interest during eval. + eval_only_roi_instances: Only use detections and ground truth annotations that lie within region of interest during eval. filter_metric: Detection metric to use for filtering of both detections and ground truth annotations. max_range_m: Max distance (under a specific metric in meters) for a detection or ground truth cuboid to be considered for evaluation. @@ -138,8 +138,8 @@ def accumulate( is_evaluated_dts: NDArrayBool = np.ones(N, dtype=bool) is_evaluated_gts: NDArrayBool = np.ones(M, dtype=bool) if avm is not None and city_SE3_ego is not None: - is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) - is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) + is_evaluated_dts &= compute_objects_in_roi(dts, city_SE3_ego, avm) + is_evaluated_gts &= compute_objects_in_roi(gts, city_SE3_ego, avm) is_evaluated_dts &= compute_evaluated_dts_mask(dts[..., :3], cfg) is_evaluated_gts &= compute_evaluated_gts_mask(gts[..., :3], gts[..., -1], cfg) @@ -350,9 +350,9 @@ def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDAr raise NotImplementedError("This distance metric is not implemented!") -def compute_objects_in_roi_mask( +def compute_objects_in_roi( cuboids_parameters: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap -) -> NDArrayBool: +) -> Tuple[NDArrayFloat, NDArrayBool]: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. Args: @@ -363,8 +363,9 @@ def compute_objects_in_roi_mask( Returns: The boolean mask indicating which cuboids will be evaluated. """ + is_within_roi: NDArrayBool if len(cuboids_parameters) == 0: - is_within_roi: NDArrayBool = np.zeros((0,), dtype=bool) + is_within_roi = np.zeros((0,), dtype=bool) return is_within_roi cuboid_list_ego: CuboidList = CuboidList([Cuboid.from_numpy(params) for params in cuboids_parameters]) cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) From e72514731289eadf492a137ccabb55ade7a46c25 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 12:24:41 -0400 Subject: [PATCH 56/81] Clean up roi method. --- src/av2/evaluation/detection/utils.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index e5ffffe3..e90b2c40 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -351,32 +351,34 @@ def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDAr def compute_objects_in_roi( - cuboids_parameters: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap + cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap ) -> Tuple[NDArrayFloat, NDArrayBool]: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. Args: - cuboids_parameters: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. + cuboids_ego: (N,10) Array of cuboid parameters corresponding to `ORDERED_CUBOID_COL_NAMES`. city_SE3_ego: Egovehicle pose in the city reference frame. avm: Argoverse map object. Returns: - The boolean mask indicating which cuboids will be evaluated. + (N,8,3) Cuboid vertices in _city_ coordinates. + (N,) Boolean mask indicating which cuboids will be evaluated. """ is_within_roi: NDArrayBool - if len(cuboids_parameters) == 0: + if len(cuboids_ego) == 0: is_within_roi = np.zeros((0,), dtype=bool) - return is_within_roi - cuboid_list_ego: CuboidList = CuboidList([Cuboid.from_numpy(params) for params in cuboids_parameters]) + cuboid_list_vertices_m_city: NDArrayFloat = np.zeros((len(cuboids_ego), 8, 3)) + return cuboid_list_vertices_m_city, is_within_roi + cuboid_list_ego: CuboidList = CuboidList([Cuboid.from_numpy(params) for params in cuboids_ego]) cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) - cuboid_list_vertices_m = cuboid_list_city.vertices_m + cuboid_list_vertices_m_city = cuboid_list_city.vertices_m is_within_roi = avm.get_raster_layer_points_boolean( - cuboid_list_vertices_m.reshape(-1, 3)[..., :2], RasterLayerType.ROI + cuboid_list_vertices_m_city.reshape(-1, 3)[..., :2], RasterLayerType.ROI ) is_within_roi = is_within_roi.reshape(-1, 8) is_within_roi = is_within_roi.any(axis=1) - return cuboid_list_vertices_m, is_within_roi + return cuboid_list_vertices_m_city, is_within_roi def compute_evaluated_dts_mask( From ce896f065392183b205b708246c653df258ef24d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 12:28:15 -0400 Subject: [PATCH 57/81] Update roi returns. --- src/av2/evaluation/detection/utils.py | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index e90b2c40..7621020a 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -138,8 +138,8 @@ def accumulate( is_evaluated_dts: NDArrayBool = np.ones(N, dtype=bool) is_evaluated_gts: NDArrayBool = np.ones(M, dtype=bool) if avm is not None and city_SE3_ego is not None: - is_evaluated_dts &= compute_objects_in_roi(dts, city_SE3_ego, avm) - is_evaluated_gts &= compute_objects_in_roi(gts, city_SE3_ego, avm) + is_evaluated_dts &= compute_objects_in_roi_mask(dts, city_SE3_ego, avm) + is_evaluated_gts &= compute_objects_in_roi_mask(gts, city_SE3_ego, avm) is_evaluated_dts &= compute_evaluated_dts_mask(dts[..., :3], cfg) is_evaluated_gts &= compute_evaluated_gts_mask(gts[..., :3], gts[..., -1], cfg) @@ -269,8 +269,8 @@ def compute_affinity_matrix(dts: NDArrayFloat, gts: NDArrayFloat, metric: Affini """Calculate the affinity matrix between detections and ground truth annotations. Args: - dts: (N,) Detections. - gts: (M,) Ground truth annotations. + dts: (N,K) Detections. + gts: (M,K) Ground truth annotations. metric: Affinity metric type. Returns: @@ -350,9 +350,7 @@ def distance(dts: NDArrayFloat, gts: NDArrayFloat, metric: DistanceType) -> NDAr raise NotImplementedError("This distance metric is not implemented!") -def compute_objects_in_roi( - cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap -) -> Tuple[NDArrayFloat, NDArrayBool]: +def compute_objects_in_roi_mask(cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap) -> NDArrayBool: """Compute the evaluated cuboids mask based off whether _any_ of their vertices fall into the ROI. Args: @@ -361,14 +359,12 @@ def compute_objects_in_roi( avm: Argoverse map object. Returns: - (N,8,3) Cuboid vertices in _city_ coordinates. (N,) Boolean mask indicating which cuboids will be evaluated. """ is_within_roi: NDArrayBool if len(cuboids_ego) == 0: is_within_roi = np.zeros((0,), dtype=bool) - cuboid_list_vertices_m_city: NDArrayFloat = np.zeros((len(cuboids_ego), 8, 3)) - return cuboid_list_vertices_m_city, is_within_roi + return is_within_roi cuboid_list_ego: CuboidList = CuboidList([Cuboid.from_numpy(params) for params in cuboids_ego]) cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego) cuboid_list_vertices_m_city = cuboid_list_city.vertices_m @@ -378,7 +374,7 @@ def compute_objects_in_roi( ) is_within_roi = is_within_roi.reshape(-1, 8) is_within_roi = is_within_roi.any(axis=1) - return cuboid_list_vertices_m_city, is_within_roi + return is_within_roi def compute_evaluated_dts_mask( From aeea6171c88c214fedee58c321b953fdb81540f3 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 12:37:54 -0400 Subject: [PATCH 58/81] Autoflake.: --- noxfile.py | 1 - src/av2/evaluation/detection/utils.py | 33 +++++------ src/av2/utils/typing.py | 2 +- tests/evaluation/detection/test_eval.py | 79 +++++++++++-------------- 4 files changed, 53 insertions(+), 62 deletions(-) diff --git a/noxfile.py b/noxfile.py index 644a1619..b1ed5d00 100644 --- a/noxfile.py +++ b/noxfile.py @@ -3,7 +3,6 @@ """Test automation using `nox`.""" from pathlib import Path -from sys import platform from typing import Dict, Final, List, Union import nox diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 7621020a..16bb6b8b 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -129,6 +129,7 @@ def accumulate( are considered during assignment. """ N, M = len(dts), len(gts) + T, E = len(cfg.affinity_thresholds_m), 3 # Sort the detections by score in _descending_ order. scores: NDArrayFloat = dts[..., -1] @@ -145,8 +146,8 @@ def accumulate( is_evaluated_gts &= compute_evaluated_gts_mask(gts[..., :3], gts[..., -1], cfg) # Initialize results array. - dts_augmented: NDArrayFloat = np.zeros((N, 8)) - gts_augmented: NDArrayFloat = np.zeros((M, 8)) + dts_augmented: NDArrayFloat = np.zeros((N, T + E + 1)) + gts_augmented: NDArrayFloat = np.zeros((M, T + E + 1)) # `is_evaluated` boolean flag is always the last column of the array. dts_augmented[is_evaluated_dts, -1] = True @@ -190,7 +191,7 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA $$T$$: cfg.affinity_thresholds_m (0.5, 1.0, 2.0, 4.0 by default). $$E$$: ATE, ASE, AOE. """ - affinity_matrix = compute_affinity_matrix(dts, gts, cfg.affinity_type) + affinity_matrix = compute_affinity_matrix(dts[..., :2], gts[..., :2], cfg.affinity_type) # Get the GT label for each max-affinity GT label, detection pair. idx_gts = affinity_matrix.argmax(axis=1)[None] @@ -202,19 +203,18 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA # Find the indices of the _first_ detection assigned to each GT. assignments: Tuple[NDArrayInt, NDArrayInt] = np.unique(idx_gts, return_index=True) # type: ignore - idx_gts, idx_dts = assignments - T = len(cfg.affinity_thresholds_m) - E = 3 - dts_table: NDArrayFloat = np.zeros((len(dts), T + E)) - dts_table[:, 4:] = cfg.metrics_defaults[1:4] - gts_table: NDArrayFloat = np.zeros((len(gts), T + E)) - gts_table[:, 4:] = cfg.metrics_defaults[1:4] + + T, E = len(cfg.affinity_thresholds_m), 3 + dts_metrics: NDArrayFloat = np.zeros((len(dts), T + E)) + dts_metrics[:, 4:] = cfg.metrics_defaults[1:4] + gts_metrics: NDArrayFloat = np.zeros((len(gts), T + E)) + gts_metrics[:, 4:] = cfg.metrics_defaults[1:4] for i, threshold_m in enumerate(cfg.affinity_thresholds_m): is_tp: NDArrayBool = affinities[idx_dts] > -threshold_m - dts_table[idx_dts[is_tp], i] = True - gts_table[idx_gts, i] = True + dts_metrics[idx_dts[is_tp], i] = True + gts_metrics[idx_gts, i] = True if threshold_m != cfg.tp_threshold_m: continue # Skip if threshold isn't the true positive threshold. @@ -230,9 +230,8 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA translation_errors = distance(tps_dts[:, :3], tps_gts[:, :3], DistanceType.TRANSLATION) scale_errors = distance(tps_dts[:, 3:6], tps_gts[:, 3:6], DistanceType.SCALE) orientation_errors = distance(tps_dts[:, 6:10], tps_gts[:, 6:10], DistanceType.ORIENTATION) - - dts_table[idx_tps_dts, 4:] = np.stack((translation_errors, scale_errors, orientation_errors), axis=-1) - return dts_table, gts_table + dts_metrics[idx_tps_dts, 4:] = np.stack((translation_errors, scale_errors, orientation_errors), axis=-1) + return dts_metrics, gts_metrics def interpolate_precision(precision: NDArrayFloat, interpolation_method: InterpType = InterpType.ALL) -> NDArrayFloat: @@ -280,8 +279,8 @@ def compute_affinity_matrix(dts: NDArrayFloat, gts: NDArrayFloat, metric: Affini NotImplementedError: If the affinity metric is not implemented. """ if metric == AffinityType.CENTER: - dts_xy_m = dts[:, :2] - gts_xy_m = gts[:, :2] + dts_xy_m = dts + gts_xy_m = gts affinities: NDArrayFloat = -cdist(dts_xy_m, gts_xy_m) else: raise NotImplementedError("This affinity metric is not implemented!") diff --git a/src/av2/utils/typing.py b/src/av2/utils/typing.py index e3b501ef..096567f9 100644 --- a/src/av2/utils/typing.py +++ b/src/av2/utils/typing.py @@ -4,7 +4,7 @@ from __future__ import annotations -from typing import Any +from typing import Any # noqa import numpy as np import numpy.typing as npt diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 267cc37c..a72e4ba4 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -8,7 +8,6 @@ import math from pathlib import Path -from time import sleep from typing import Final, List import numpy as np @@ -24,16 +23,12 @@ compute_affinity_matrix, compute_evaluated_dts_mask, compute_evaluated_gts_mask, - compute_objects_in_roi_mask, distance, interpolate_precision, ) from av2.geometry.geometry import wrap_angles from av2.geometry.iou import iou_3d_axis_aligned -from av2.map.map_api import ArgoverseStaticMap -from av2.structures.cuboid import ORDERED_CUBOID_COL_NAMES from av2.utils.constants import PI -from av2.utils.io import read_city_SE3_ego, read_feather, write_img from av2.utils.typing import NDArrayBool, NDArrayFloat TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" @@ -277,7 +272,6 @@ def test_orientation_error() -> None: def test_compute_evaluated_dts_mask() -> None: """Unit test for computing valid detections cuboids.""" - columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS dts: NDArrayFloat = np.array( [ [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0], # In bounds with at least 1 point. @@ -294,7 +288,6 @@ def test_compute_evaluated_dts_mask() -> None: def test_compute_evaluated_gts_mask() -> None: """Unit test for computing valid ground truth cuboids.""" - # columns = TRANSLATION_COLS + DIMS_COLS + QUAT_COLS + ["num_interior_pts"] gts: NDArrayFloat = np.array( [ [5.0, 5.0, 5.0, 1.0, 0.0, 0.0, 0.0, 3.0, 4.0, 0.0, 5], # In bounds with at least 1 point. @@ -304,47 +297,47 @@ def test_compute_evaluated_gts_mask() -> None: ], ) detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + gts_xyz_ego = gts[..., :3] + num_interior_pts = gts[..., -1] - gts_mask = compute_evaluated_gts_mask(gts, detection_cfg) + gts_mask = compute_evaluated_gts_mask(gts_xyz_ego, num_interior_pts, detection_cfg) gts_mask_: NDArrayBool = np.array([True, False, False, False]) np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -def test_compute_objects_in_roi_mask() -> None: - log_dir = TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" - annotations_path = log_dir / "annotations.feather" - annotations = read_feather(annotations_path) - - root = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" - logs = sorted(root.iterdir()) - from av2.rendering.rasterize import draw_points_xy_in_img - - for log_dir in logs: - if not log_dir.is_dir(): - continue - - annotations_path = log_dir / "annotations.feather" - annotations = read_feather(annotations_path) - - # breakpoint() - timestamp_to_egoposes = read_city_SE3_ego(log_dir) - # params = annotations.loc[:, ORDERED_CUBOID_COL_NAMES] - avm = ArgoverseStaticMap.from_map_dir(log_dir / "map", build_raster=True) - for timestamp_ns, sweep_annotations in annotations.groupby("timestamp_ns"): - vertices, mask = compute_objects_in_roi_mask( - sweep_annotations.loc[:, ORDERED_CUBOID_COL_NAMES].to_numpy(), timestamp_to_egoposes[timestamp_ns], avm - ) - if (~mask).sum() == 0: - continue - roi, sim2 = avm.get_rasterized_roi() - points_arr = sim2.transform_point_cloud(vertices.reshape(-1, 3)[..., :2]).round().astype(int) - points = points_arr.reshape(-1, 8, 2)[~mask].reshape(-1, 2) - roi = roi.astype(np.uint8)[..., None].repeat(3, axis=2) - draw_points_xy_in_img(roi, points, colors=np.full((len(points_arr), 3), fill_value=(0, 0, 1)), diameter=100) - # print((~mask).sum()) - write_img(Path("roi.png"), roi * 255) - sleep(0.05) - # breakpoint() +# def test_compute_objects_in_roi_mask() -> None: +# log_dir = TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" +# annotations_path = log_dir / "annotations.feather" +# annotations = read_feather(annotations_path) + +# root = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" +# logs = sorted(root.iterdir()) +# from av2.rendering.rasterize import draw_points_xy_in_img + +# for log_dir in logs: +# if not log_dir.is_dir(): +# continue + +# annotations_path = log_dir / "annotations.feather" +# annotations = read_feather(annotations_path) + +# timestamp_to_egoposes = read_city_SE3_ego(log_dir) +# # params = annotations.loc[:, ORDERED_CUBOID_COL_NAMES] +# avm = ArgoverseStaticMap.from_map_dir(log_dir / "map", build_raster=True) +# for timestamp_ns, sweep_annotations in annotations.groupby("timestamp_ns"): +# vertices, mask = compute_objects_in_roi_mask( +# sweep_annotations.loc[:, ORDERED_CUBOID_COL_NAMES].to_numpy(), timestamp_to_egoposes[timestamp_ns], avm +# ) +# if (~mask).sum() == 0: +# continue +# roi, sim2 = avm.get_rasterized_roi() +# points_arr = sim2.transform_point_cloud(vertices.reshape(-1, 3)[..., :2]).round().astype(int) +# points = points_arr.reshape(-1, 8, 2)[~mask].reshape(-1, 2) +# roi = roi.astype(np.uint8)[..., None].repeat(3, axis=2) +# draw_points_xy_in_img(roi, points, colors=np.full((len(points_arr), 3), fill_value=(0, 0, 1)), diameter=100) +# write_img(Path("roi.png"), roi * 255) +# sleep(0.05) +# # breakpoint() # def test_val_identity() -> None: From da897c1ae0615281dd22facce40c6a37fdd86424 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 13:28:39 -0400 Subject: [PATCH 59/81] Fix lint. --- src/av2/evaluation/detection/utils.py | 5 +++-- tests/evaluation/detection/test_eval.py | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 16bb6b8b..fa437671 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -383,7 +383,8 @@ def compute_evaluated_dts_mask( """Compute the evaluated cuboids mask. Valid detections cuboids meet _two_ conditions: - 1. The cuboid's centroid (x,y,z) must lie within the maximum range in the detection configuration. + 1. The cuboid's centroid (x,y,z) must lie within the maximum range + defined in the detection configuration. 2. The total number of cuboids must not exceed `cfg.max_num_dts_per_category`. Args: @@ -398,7 +399,7 @@ def compute_evaluated_dts_mask( is_evaluated = np.zeros((0,), dtype=bool) return is_evaluated norm: NDArrayFloat = np.linalg.norm(xyz_m_ego, axis=1) # type: ignore - is_evaluated: NDArrayBool = norm < cfg.max_range_m + is_evaluated = norm < cfg.max_range_m is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. return is_evaluated diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index a72e4ba4..9c319e04 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -358,5 +358,5 @@ def test_compute_evaluated_gts_mask() -> None: # dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) -if __name__ == "__main__": - test_compute_objects_in_roi_mask() +# if __name__ == "__main__": +# test_compute_objects_in_roi_mask() From c459c26832c2142eab08c461306a07fa6a2853f0 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 5 Apr 2022 13:50:16 -0400 Subject: [PATCH 60/81] Fix lint. --- src/av2/evaluation/detection/utils.py | 3 ++- tests/evaluation/detection/test_eval.py | 35 ------------------------- 2 files changed, 2 insertions(+), 36 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index fa437671..1345381b 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -51,7 +51,8 @@ class DetectionCfg: affinity_thresholds_m: Affinity thresholds for determining a true positive (in meters). affinity_type: Type of affinity function to be used for calculating average precision. categories: Detection classes for evaluation. - eval_only_roi_instances: Only use detections and ground truth annotations that lie within region of interest during eval. + eval_only_roi_instances: Only use detections and ground truth annotations that lie + within region of interest during eval. filter_metric: Detection metric to use for filtering of both detections and ground truth annotations. max_range_m: Max distance (under a specific metric in meters) for a detection or ground truth cuboid to be considered for evaluation. diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 9c319e04..43b30601 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -305,41 +305,6 @@ def test_compute_evaluated_gts_mask() -> None: np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -# def test_compute_objects_in_roi_mask() -> None: -# log_dir = TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" -# annotations_path = log_dir / "annotations.feather" -# annotations = read_feather(annotations_path) - -# root = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" -# logs = sorted(root.iterdir()) -# from av2.rendering.rasterize import draw_points_xy_in_img - -# for log_dir in logs: -# if not log_dir.is_dir(): -# continue - -# annotations_path = log_dir / "annotations.feather" -# annotations = read_feather(annotations_path) - -# timestamp_to_egoposes = read_city_SE3_ego(log_dir) -# # params = annotations.loc[:, ORDERED_CUBOID_COL_NAMES] -# avm = ArgoverseStaticMap.from_map_dir(log_dir / "map", build_raster=True) -# for timestamp_ns, sweep_annotations in annotations.groupby("timestamp_ns"): -# vertices, mask = compute_objects_in_roi_mask( -# sweep_annotations.loc[:, ORDERED_CUBOID_COL_NAMES].to_numpy(), timestamp_to_egoposes[timestamp_ns], avm -# ) -# if (~mask).sum() == 0: -# continue -# roi, sim2 = avm.get_rasterized_roi() -# points_arr = sim2.transform_point_cloud(vertices.reshape(-1, 3)[..., :2]).round().astype(int) -# points = points_arr.reshape(-1, 8, 2)[~mask].reshape(-1, 2) -# roi = roi.astype(np.uint8)[..., None].repeat(3, axis=2) -# draw_points_xy_in_img(roi, points, colors=np.full((len(points_arr), 3), fill_value=(0, 0, 1)), diameter=100) -# write_img(Path("roi.png"), roi * 255) -# sleep(0.05) -# # breakpoint() - - # def test_val_identity() -> None: # root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" # paths = sorted(root_dir.glob("*/annotations.feather")) From ca5d644b68d756c33437b7b3e21523c0d3a41d0d Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 7 Apr 2022 19:13:06 +0000 Subject: [PATCH 61/81] Update detection limiting logic. --- src/av2/evaluation/detection/utils.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 1345381b..6d964d43 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -192,7 +192,7 @@ def assign(dts: NDArrayFloat, gts: NDArrayFloat, cfg: DetectionCfg) -> Tuple[NDA $$T$$: cfg.affinity_thresholds_m (0.5, 1.0, 2.0, 4.0 by default). $$E$$: ATE, ASE, AOE. """ - affinity_matrix = compute_affinity_matrix(dts[..., :2], gts[..., :2], cfg.affinity_type) + affinity_matrix = compute_affinity_matrix(dts[..., :3], gts[..., :3], cfg.affinity_type) # Get the GT label for each max-affinity GT label, detection pair. idx_gts = affinity_matrix.argmax(axis=1)[None] @@ -374,7 +374,7 @@ def compute_objects_in_roi_mask(cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, av ) is_within_roi = is_within_roi.reshape(-1, 8) is_within_roi = is_within_roi.any(axis=1) - return is_within_roi + return cuboid_list_vertices_m_city, is_within_roi def compute_evaluated_dts_mask( @@ -401,7 +401,10 @@ def compute_evaluated_dts_mask( return is_evaluated norm: NDArrayFloat = np.linalg.norm(xyz_m_ego, axis=1) # type: ignore is_evaluated = norm < cfg.max_range_m - is_evaluated[cfg.max_num_dts_per_category :] = False # Limit the number of detections. + + cumsum: NDArrayInt = np.cumsum(is_evaluated) + max_idx: int = np.where(cumsum > cfg.max_num_dts_per_category)[0][0] + is_evaluated[max_idx:] = False # Limit the number of detections. return is_evaluated From 375aaba60d3406e77d25488e9f03124279912c82 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 7 Apr 2022 19:32:30 +0000 Subject: [PATCH 62/81] Fix indexing. --- src/av2/evaluation/detection/utils.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 6d964d43..479a161d 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -403,8 +403,10 @@ def compute_evaluated_dts_mask( is_evaluated = norm < cfg.max_range_m cumsum: NDArrayInt = np.cumsum(is_evaluated) - max_idx: int = np.where(cumsum > cfg.max_num_dts_per_category)[0][0] - is_evaluated[max_idx:] = False # Limit the number of detections. + max_idx_arr: NDArrayInt = np.where(cumsum > cfg.max_num_dts_per_category)[0] + if len(max_idx_arr) > 0: + max_idx = max_idx_arr[0] + is_evaluated[max_idx:] = False # type: ignore return is_evaluated From 5113202dad15e5b238a04107637c4b101d1d51ac Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 7 Apr 2022 19:40:26 +0000 Subject: [PATCH 63/81] Fix tuple return. --- src/av2/evaluation/detection/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 479a161d..20004c00 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -374,7 +374,7 @@ def compute_objects_in_roi_mask(cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, av ) is_within_roi = is_within_roi.reshape(-1, 8) is_within_roi = is_within_roi.any(axis=1) - return cuboid_list_vertices_m_city, is_within_roi + return is_within_roi def compute_evaluated_dts_mask( From 5a4c76ddde1cbd143eb9f77ea9585e167c45188b Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Thu, 7 Apr 2022 19:59:04 +0000 Subject: [PATCH 64/81] Update CI. --- .github/workflows/ci.yml | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4517f107..13f2f83e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -48,24 +48,3 @@ jobs: --wheel --outdir dist/ . - - - name: Publish package to Test PyPI. - if: matrix.os == 'ubuntu-latest' - uses: pypa/gh-action-pypi-publish@master - with: - user: __token__ - password: ${{ secrets.TEST_PYPI_API_TOKEN }} - repository_url: https://test.pypi.org/legacy/ - print_hash: true - skip_existing: true - verbose: true - - - name: Publish package to PyPI. - if: matrix.os == 'ubuntu-latest' && github.event_name == 'push' && startsWith(github.ref, 'refs/tags') - uses: pypa/gh-action-pypi-publish@master - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} - print_hash: true - skip_existing: true - verbose: true From 07d3fbf0fc58225035a1145e419652ca826c0097 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 9 Apr 2022 13:55:13 +0000 Subject: [PATCH 65/81] Add ROI unit tests. --- tests/evaluation/detection/test_eval.py | 81 ++++++++++++++---- .../annotations.feather | Bin 0 -> 642618 bytes ...5b0-a2fa-b4cea13a6d76___img_Sim2_city.json | 1 + ...ea13a6d76_ground_height_surface____PIT.npy | Bin 0 -> 1135548 bytes ...0-a2fa-b4cea13a6d76____PIT_city_57819.json | 1 + 5 files changed, 66 insertions(+), 17 deletions(-) create mode 100644 tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/annotations.feather create mode 100644 tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/map/adcf7d18-0510-35b0-a2fa-b4cea13a6d76___img_Sim2_city.json create mode 100644 tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/map/adcf7d18-0510-35b0-a2fa-b4cea13a6d76_ground_height_surface____PIT.npy create mode 100644 tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/map/log_map_archive_adcf7d18-0510-35b0-a2fa-b4cea13a6d76____PIT_city_57819.json diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 43b30601..6423b7bd 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -15,7 +15,7 @@ from scipy.spatial.transform import Rotation from av2.evaluation.detection.constants import AffinityType, DistanceType -from av2.evaluation.detection.eval import evaluate +from av2.evaluation.detection.eval import ORDERED_CUBOID_COL_NAMES, evaluate from av2.evaluation.detection.utils import ( DetectionCfg, accumulate, @@ -23,12 +23,15 @@ compute_affinity_matrix, compute_evaluated_dts_mask, compute_evaluated_gts_mask, + compute_objects_in_roi_mask, distance, interpolate_precision, ) from av2.geometry.geometry import wrap_angles from av2.geometry.iou import iou_3d_axis_aligned +from av2.map.map_api import ArgoverseStaticMap from av2.utils.constants import PI +from av2.utils.io import read_city_SE3_ego, read_feather, write_img from av2.utils.typing import NDArrayBool, NDArrayFloat TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" @@ -286,6 +289,17 @@ def test_compute_evaluated_dts_mask() -> None: np.testing.assert_array_equal(dts_mask, dts_mask_) # type: ignore +def test_compute_evaluated_dts_mask_2() -> None: + """Randomly generate detections and ensure that they never exceed the maximum detection limit.""" + detection_cfg = DetectionCfg(categories=("REGULAR_VEHICLE",), eval_only_roi_instances=False) + for i in range(1000): + dts: NDArrayFloat = np.random.randint(0, 250, size=(detection_cfg.max_num_dts_per_category + i, 10)).astype( + float + ) + dts_mask = compute_evaluated_dts_mask(dts, detection_cfg) + assert dts_mask.sum() <= detection_cfg.max_num_dts_per_category + + def test_compute_evaluated_gts_mask() -> None: """Unit test for computing valid ground truth cuboids.""" gts: NDArrayFloat = np.array( @@ -305,23 +319,56 @@ def test_compute_evaluated_gts_mask() -> None: np.testing.assert_array_equal(gts_mask, gts_mask_) # type: ignore -# def test_val_identity() -> None: -# root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" -# paths = sorted(root_dir.glob("*/annotations.feather")) +def test_compute_objects_in_roi_mask() -> None: + """Test filtering ground truth annotations by the ROI. + + Three annotations are tested. The first and third are partially in the ROI --- these + are both considered VALID since they have at least on of their cuboid vertices within + the ROI. + + The second annotations is _FULLY_ outside of the ROI, thus it is filtered. + """ + map_dir = TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" / "map" + + timestamp_ns = 315973157959879000 + track_uuids = [ + "f53639ef-794e-420e-bb2a-d0cde0203b3a", # Two vertices within ROI. + "6c198de2-cb7d-4c09-96aa-52547d9bbe37", # Completely outside of ROI. + "a7c8f6a2-26b6-4610-9eb3-294799f9846c", # Two vertices within ROI. + ] + avm = ArgoverseStaticMap.from_map_dir(map_dir, build_raster=True) + annotations = read_feather(TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76" / "annotations.feather") + timestamped_city_SE3_egoposes = read_city_SE3_ego(TEST_DATA_DIR / "adcf7d18-0510-35b0-a2fa-b4cea13a6d76") + + selected_cuboids_mask = np.logical_and( + annotations.timestamp_ns == timestamp_ns, annotations["track_uuid"].isin(track_uuids) + ) + sweep_annotations = annotations.loc[selected_cuboids_mask] + + mask = compute_objects_in_roi_mask( + sweep_annotations.loc[:, ORDERED_CUBOID_COL_NAMES].to_numpy(), timestamped_city_SE3_egoposes[timestamp_ns], avm + ) + mask_: NDArrayBool = np.array([True, False, True]) + np.testing.assert_array_equal(mask, mask_) # type: ignore + + +def test_val_identity() -> None: + root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" + paths = sorted(root_dir.glob("*/annotations.feather")) -# annotations = [] -# for p in paths: -# df = pd.read_feather(p) -# df["log_id"] = p.parent.stem -# annotations.append(df) -# annotations = pd.concat(annotations).reset_index(drop=True) -# dts = annotations.copy() -# dts["score"] = 1.0 -# annotations.loc[:, "num_interior_pts"] = 1 + annotations = [] + for p in paths: + df = pd.read_feather(p) + df["log_id"] = p.parent.stem + annotations.append(df) + annotations = pd.concat(annotations).reset_index(drop=True) + dts = annotations.copy() + dts["score"] = 1.0 + annotations.loc[:, "num_interior_pts"] = 1 -# detection_cfg = DetectionCfg(eval_only_roi_instances=True, max_num_dts_per_category=1000, dataset_dir=root_dir) -# dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) + detection_cfg = DetectionCfg(eval_only_roi_instances=True, max_num_dts_per_category=1000, dataset_dir=root_dir) + dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) -# if __name__ == "__main__": -# test_compute_objects_in_roi_mask() +if __name__ == "__main__": + test_val_identity() diff --git a/tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/annotations.feather b/tests/test_data/sensor_dataset_logs/adcf7d18-0510-35b0-a2fa-b4cea13a6d76/annotations.feather new file mode 100644 index 0000000000000000000000000000000000000000..4ed097ba2a5a333097f7ade24632fb14ce5833c2 GIT binary patch literal 642618 zcmdqqahPlQA;A6YZd7QrK@g-B>teBLRq3MG+m;5SlWGuz&9>P#OS+qG(>85Wf*@t8 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18.62}, {"x": 1518.43, "y": 72.33, "z": 18.62}, {"x": 1519.1, "y": 72.3, "z": 18.63}, {"x": 1519.75, "y": 72.47, "z": 18.64}, {"x": 1520.65, "y": 72.94, "z": 18.66}, {"x": 1521.35, "y": 73.5, "z": 18.68}, {"x": 1522.07, "y": 74.41, "z": 18.71}, {"x": 1531.69, "y": 93.82, "z": 18.13}, {"x": 1544.61, "y": 120.0, "z": 17.14}], "id": 1413627}}} \ No newline at end of file From 0cd98f9b321201e8d535d71830496bb4ea43f8de Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 9 Apr 2022 14:06:05 +0000 Subject: [PATCH 66/81] Remove val identity. --- tests/evaluation/detection/test_eval.py | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 6423b7bd..0f25b211 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -350,25 +350,3 @@ def test_compute_objects_in_roi_mask() -> None: ) mask_: NDArrayBool = np.array([True, False, True]) np.testing.assert_array_equal(mask, mask_) # type: ignore - - -def test_val_identity() -> None: - root_dir = Path.home() / "data" / "datasets" / "av2" / "sensor" / "val" - paths = sorted(root_dir.glob("*/annotations.feather")) - - annotations = [] - for p in paths: - df = pd.read_feather(p) - df["log_id"] = p.parent.stem - annotations.append(df) - annotations = pd.concat(annotations).reset_index(drop=True) - dts = annotations.copy() - dts["score"] = 1.0 - annotations.loc[:, "num_interior_pts"] = 1 - - detection_cfg = DetectionCfg(eval_only_roi_instances=True, max_num_dts_per_category=1000, dataset_dir=root_dir) - dts_, gts_, metrics_ = evaluate(dts, annotations, detection_cfg) - - -if __name__ == "__main__": - test_val_identity() From 0ef221bfe09b50846fa4707fe24fea8e6f6c5453 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 9 Apr 2022 14:45:21 +0000 Subject: [PATCH 67/81] Fix import. --- tests/evaluation/detection/test_eval.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index 0f25b211..f5a0ac1b 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -15,7 +15,7 @@ from scipy.spatial.transform import Rotation from av2.evaluation.detection.constants import AffinityType, DistanceType -from av2.evaluation.detection.eval import ORDERED_CUBOID_COL_NAMES, evaluate +from av2.evaluation.detection.eval import evaluate from av2.evaluation.detection.utils import ( DetectionCfg, accumulate, @@ -30,6 +30,7 @@ from av2.geometry.geometry import wrap_angles from av2.geometry.iou import iou_3d_axis_aligned from av2.map.map_api import ArgoverseStaticMap +from av2.structures.cuboid import ORDERED_CUBOID_COL_NAMES from av2.utils.constants import PI from av2.utils.io import read_city_SE3_ego, read_feather, write_img from av2.utils.typing import NDArrayBool, NDArrayFloat From 0b00475e7b47eca36c4931200a834231b4142830 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 9 Apr 2022 14:45:32 +0000 Subject: [PATCH 68/81] Remove unused import. --- tests/evaluation/detection/test_eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/evaluation/detection/test_eval.py b/tests/evaluation/detection/test_eval.py index f5a0ac1b..58c9ece8 100644 --- a/tests/evaluation/detection/test_eval.py +++ b/tests/evaluation/detection/test_eval.py @@ -32,7 +32,7 @@ from av2.map.map_api import ArgoverseStaticMap from av2.structures.cuboid import ORDERED_CUBOID_COL_NAMES from av2.utils.constants import PI -from av2.utils.io import read_city_SE3_ego, read_feather, write_img +from av2.utils.io import read_city_SE3_ego, read_feather from av2.utils.typing import NDArrayBool, NDArrayFloat TEST_DATA_DIR: Final[Path] = Path(__file__).parent.resolve() / "data" From 82da97f87d912caee1c86f2243321686f080e532 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Sat, 9 Apr 2022 16:23:52 +0000 Subject: [PATCH 69/81] Update column names. --- src/av2/evaluation/detection/eval.py | 35 ++++++++++++++++------------ 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 9818eb32..16d4ba3b 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -73,6 +73,13 @@ from av2.utils.typing import NDArrayBool, NDArrayFloat TP_ERROR_COLUMNS: Final[Tuple[str, ...]] = tuple(x.value for x in TruePositiveErrorNames) +DTS_COLUMN_NAMES: Final[Tuple[str, ...]] = tuple(ORDERED_CUBOID_COL_NAMES) + ("score",) +GTS_COLUMN_NAMES: Final[Tuple[str, ...]] = tuple(ORDERED_CUBOID_COL_NAMES) + ("num_interior_pts",) +UUID_COLUMN_NAMES: Final[Tuple[str, ...]] = ( + "log_id", + "timestamp_ns", + "category", +) logger = logging.getLogger(__name__) @@ -81,6 +88,7 @@ def evaluate( dts: pd.DataFrame, gts: pd.DataFrame, cfg: DetectionCfg, + n_jobs: int = 8, ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: """Evaluate a set of detections against the ground truth annotations. @@ -90,6 +98,7 @@ def evaluate( dts: (N,15) Table of detections. gts: (M,15) Table of ground truth annotations. cfg: Detection configuration. + n_jobs: Number of jobs running concurrently during evaluation. Returns: (C+1,K) Table of evaluation metrics where C is the number of classes. Plus a row for their means. @@ -98,19 +107,15 @@ def evaluate( Raises: RuntimeError: If accumulation fails. """ - uuid_column_names = ["log_id", "timestamp_ns", "category"] - # Sort both the detections and annotations by lexicographic order for grouping. - dts = dts.sort_values(uuid_column_names) - gts = gts.sort_values(uuid_column_names) + dts = dts.sort_values(list(UUID_COLUMN_NAMES)) + gts = gts.sort_values(list(UUID_COLUMN_NAMES)) - dts_cols = ORDERED_CUBOID_COL_NAMES + ["score"] - gts_cols = ORDERED_CUBOID_COL_NAMES + ["num_interior_pts"] - dts_npy: NDArrayFloat = dts.loc[:, dts_cols].to_numpy() - gts_npy: NDArrayFloat = gts.loc[:, gts_cols].to_numpy() + dts_npy: NDArrayFloat = dts.loc[:, DTS_COLUMN_NAMES].to_numpy() + gts_npy: NDArrayFloat = gts.loc[:, GTS_COLUMN_NAMES].to_numpy() - dts_uuids: List[str] = dts[uuid_column_names].to_numpy().astype(str).tolist() - gts_uuids: List[str] = gts[uuid_column_names].to_numpy().astype(str).tolist() + dts_uuids: List[str] = dts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() + gts_uuids: List[str] = gts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() # We merge the unique identifier -- the tuple of ("log_id", "timestamp_ns", "category") # into a single string to optimize the subsequent grouping operation. @@ -125,7 +130,7 @@ def evaluate( # Load maps and egoposes if roi-pruning is enabled. if cfg.eval_only_roi_instances and cfg.dataset_dir is not None: logger.info("Loading maps and egoposes ...") - log_ids = gts["log_id"].unique().tolist() + log_ids: List[str] = gts.loc[:, "log_id"].unique().tolist() log_id_to_avm, log_id_to_timestamped_poses = load_mapped_avm_and_egoposes(log_ids, cfg.dataset_dir) args_list: List[Tuple[NDArrayFloat, NDArrayFloat, DetectionCfg, Optional[ArgoverseStaticMap], Optional[SE3]]] = [] @@ -149,18 +154,18 @@ def evaluate( args_list.append(args) logger.info("Starting evaluation ...") - outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel(n_jobs=-1, verbose=1)( + outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel(n_jobs=n_jobs, verbose=1)( delayed(accumulate)(*args) for args in args_list ) if outputs is None: raise RuntimeError("Accumulation has failed! Please check the integrity of your detections and annotations.") dts_list, gts_list = zip(*outputs) - COLS = cfg.affinity_thresholds_m + TP_ERROR_COLUMNS + ("is_evaluated",) + METRIC_COLUMN_NAMES = cfg.affinity_thresholds_m + TP_ERROR_COLUMNS + ("is_evaluated",) dts_metrics: NDArrayFloat = np.concatenate(dts_list) # type: ignore gts_metrics: NDArrayFloat = np.concatenate(gts_list) # type: ignore - dts.loc[:, COLS] = dts_metrics - gts.loc[:, COLS] = gts_metrics + dts.loc[:, METRIC_COLUMN_NAMES] = dts_metrics + gts.loc[:, METRIC_COLUMN_NAMES] = gts_metrics # Compute summary metrics. metrics = summarize_metrics(dts, gts, cfg) From 548ea9382990698adfeddee733627a53df062a70 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 11 Apr 2022 15:07:02 -0400 Subject: [PATCH 70/81] Update eval.py --- src/av2/evaluation/detection/eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 16d4ba3b..3ae29cf2 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -7,7 +7,7 @@ Precision/Recall 1. Average Precision: Standard VOC-style average precision calculation - except a true positive requires a bird's eye view center distance of less + except a true positive requires a 3D Euclidean center distance of less than a predefined threshold. True Positive Errors From 39465a6ebf14fe0185effae1e78424ea787a9f15 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 11 Apr 2022 16:19:45 -0400 Subject: [PATCH 71/81] Update detection eval. --- src/av2/evaluation/detection/eval.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 3ae29cf2..d78cf4fe 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -7,7 +7,7 @@ Precision/Recall 1. Average Precision: Standard VOC-style average precision calculation - except a true positive requires a 3D Euclidean center distance of less + except a true positive requires a bird's eye view center distance of less than a predefined threshold. True Positive Errors @@ -95,8 +95,8 @@ def evaluate( Each sweep is processed independently, computing assignment between detections and ground truth annotations. Args: - dts: (N,15) Table of detections. - gts: (M,15) Table of ground truth annotations. + dts: (N,14) Table of detections. + gts: (M,14) Table of ground truth annotations. cfg: Detection configuration. n_jobs: Number of jobs running concurrently during evaluation. @@ -111,11 +111,11 @@ def evaluate( dts = dts.sort_values(list(UUID_COLUMN_NAMES)) gts = gts.sort_values(list(UUID_COLUMN_NAMES)) - dts_npy: NDArrayFloat = dts.loc[:, DTS_COLUMN_NAMES].to_numpy() - gts_npy: NDArrayFloat = gts.loc[:, GTS_COLUMN_NAMES].to_numpy() + dts_npy: NDArrayFloat = dts[DTS_COLUMN_NAMES].to_numpy() + gts_npy: NDArrayFloat = gts[GTS_COLUMN_NAMES].to_numpy() - dts_uuids: List[str] = dts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() - gts_uuids: List[str] = gts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() + dts_uuids: List[str] = dts[UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() + gts_uuids: List[str] = gts[UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() # We merge the unique identifier -- the tuple of ("log_id", "timestamp_ns", "category") # into a single string to optimize the subsequent grouping operation. From 40ac0f18c4bc2196875f01955fef992ba4373750 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 11 Apr 2022 16:22:50 -0400 Subject: [PATCH 72/81] Update docstrings. --- src/av2/evaluation/detection/eval.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 3ae29cf2..d78cf4fe 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -7,7 +7,7 @@ Precision/Recall 1. Average Precision: Standard VOC-style average precision calculation - except a true positive requires a 3D Euclidean center distance of less + except a true positive requires a bird's eye view center distance of less than a predefined threshold. True Positive Errors @@ -95,8 +95,8 @@ def evaluate( Each sweep is processed independently, computing assignment between detections and ground truth annotations. Args: - dts: (N,15) Table of detections. - gts: (M,15) Table of ground truth annotations. + dts: (N,14) Table of detections. + gts: (M,14) Table of ground truth annotations. cfg: Detection configuration. n_jobs: Number of jobs running concurrently during evaluation. @@ -111,11 +111,11 @@ def evaluate( dts = dts.sort_values(list(UUID_COLUMN_NAMES)) gts = gts.sort_values(list(UUID_COLUMN_NAMES)) - dts_npy: NDArrayFloat = dts.loc[:, DTS_COLUMN_NAMES].to_numpy() - gts_npy: NDArrayFloat = gts.loc[:, GTS_COLUMN_NAMES].to_numpy() + dts_npy: NDArrayFloat = dts[DTS_COLUMN_NAMES].to_numpy() + gts_npy: NDArrayFloat = gts[GTS_COLUMN_NAMES].to_numpy() - dts_uuids: List[str] = dts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() - gts_uuids: List[str] = gts.loc[:, UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() + dts_uuids: List[str] = dts[UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() + gts_uuids: List[str] = gts[UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() # We merge the unique identifier -- the tuple of ("log_id", "timestamp_ns", "category") # into a single string to optimize the subsequent grouping operation. From 960d29d27d206d2364687aade220261e1f1ef2c0 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Mon, 11 Apr 2022 16:24:55 -0400 Subject: [PATCH 73/81] Revert docstring. --- src/av2/evaluation/detection/eval.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index d78cf4fe..6897aeb6 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -7,7 +7,7 @@ Precision/Recall 1. Average Precision: Standard VOC-style average precision calculation - except a true positive requires a bird's eye view center distance of less + except a true positive requires a 3D Euclidean center distance of less than a predefined threshold. True Positive Errors From 75e07bfab3e02cccb6e5a5ff1692e94c7ef5b5cb Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 11:55:25 -0400 Subject: [PATCH 74/81] Fix formatting. --- src/av2/evaluation/detection/eval.py | 12 ++++++------ src/av2/geometry/utm.py | 4 ++-- tests/geometry/test_utm.py | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index 6897aeb6..a1050e9c 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -52,11 +52,11 @@ e.g. AP, ATE, ASE, AOE, CDS by default. """ import logging +from multiprocessing import get_context from typing import Dict, Final, List, Optional, Tuple import numpy as np import pandas as pd -from joblib import Parallel, delayed from av2.evaluation.detection.constants import NUM_DECIMALS, MetricNames, TruePositiveErrorNames from av2.evaluation.detection.utils import ( @@ -95,8 +95,8 @@ def evaluate( Each sweep is processed independently, computing assignment between detections and ground truth annotations. Args: - dts: (N,14) Table of detections. - gts: (M,14) Table of ground truth annotations. + dts: (N,15) Table of detections. + gts: (M,15) Table of ground truth annotations. cfg: Detection configuration. n_jobs: Number of jobs running concurrently during evaluation. @@ -154,9 +154,9 @@ def evaluate( args_list.append(args) logger.info("Starting evaluation ...") - outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = Parallel(n_jobs=n_jobs, verbose=1)( - delayed(accumulate)(*args) for args in args_list - ) + with get_context("spawn").Pool(processes=n_jobs) as p: + outputs: Optional[List[Tuple[NDArrayFloat, NDArrayFloat]]] = p.starmap(accumulate, args_list) + if outputs is None: raise RuntimeError("Accumulation has failed! Please check the integrity of your detections and annotations.") dts_list, gts_list = zip(*outputs) diff --git a/src/av2/geometry/utm.py b/src/av2/geometry/utm.py index 035dd681..16f54404 100644 --- a/src/av2/geometry/utm.py +++ b/src/av2/geometry/utm.py @@ -7,7 +7,7 @@ WGS84: https://en.wikipedia.org/wiki/World_Geodetic_System """ -from enum import unique, Enum +from enum import Enum, unique from typing import Dict, Final, Tuple, Union import numpy as np @@ -83,7 +83,7 @@ def convert_city_coords_to_utm(points_city: Union[NDArrayFloat, NDArrayInt], cit latitude, longitude = CITY_ORIGIN_LATLONG_DICT[city_name] # get (easting, northing) of origin origin_utm = convert_gps_to_utm(latitude=latitude, longitude=longitude, city_name=city_name) - + points_utm: NDArrayFloat = points_city.astype(float) + np.array(origin_utm, dtype=float) return points_utm diff --git a/tests/geometry/test_utm.py b/tests/geometry/test_utm.py index 283783a2..a503d06c 100644 --- a/tests/geometry/test_utm.py +++ b/tests/geometry/test_utm.py @@ -5,7 +5,7 @@ import numpy as np import av2.geometry.utm as geo_utils -from av2.geometry.utm import CityName, CITY_ORIGIN_LATLONG_DICT +from av2.geometry.utm import CITY_ORIGIN_LATLONG_DICT, CityName from av2.utils.typing import NDArrayFloat From 28fb345afa5e5f93dd709531c4d72798ffb3d0e6 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 15:05:51 -0400 Subject: [PATCH 75/81] Small fixes. --- src/av2/evaluation/detection/eval.py | 23 +++++++++++++++-------- src/av2/evaluation/detection/utils.py | 2 -- tests/geometry/test_utm.py | 2 +- 3 files changed, 16 insertions(+), 11 deletions(-) diff --git a/src/av2/evaluation/detection/eval.py b/src/av2/evaluation/detection/eval.py index a1050e9c..7d95ff36 100644 --- a/src/av2/evaluation/detection/eval.py +++ b/src/av2/evaluation/detection/eval.py @@ -95,7 +95,7 @@ def evaluate( Each sweep is processed independently, computing assignment between detections and ground truth annotations. Args: - dts: (N,15) Table of detections. + dts: (N,14) Table of detections. gts: (M,15) Table of ground truth annotations. cfg: Detection configuration. n_jobs: Number of jobs running concurrently during evaluation. @@ -106,23 +106,30 @@ def evaluate( Raises: RuntimeError: If accumulation fails. + ValueError: If ROI pruning is enabled but a dataset directory is not specified. """ + if cfg.eval_only_roi_instances and cfg.dataset_dir is None: + raise ValueError( + "ROI pruning has been enabled, but the dataset directory has not be specified. " + "Please set `dataset_directory` to the split root, e.g. av2/sensor/val." + ) + # Sort both the detections and annotations by lexicographic order for grouping. dts = dts.sort_values(list(UUID_COLUMN_NAMES)) gts = gts.sort_values(list(UUID_COLUMN_NAMES)) - dts_npy: NDArrayFloat = dts[DTS_COLUMN_NAMES].to_numpy() - gts_npy: NDArrayFloat = gts[GTS_COLUMN_NAMES].to_numpy() + dts_npy: NDArrayFloat = dts[list(DTS_COLUMN_NAMES)].to_numpy() + gts_npy: NDArrayFloat = gts[list(GTS_COLUMN_NAMES)].to_numpy() - dts_uuids: List[str] = dts[UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() - gts_uuids: List[str] = gts[UUID_COLUMN_NAMES].to_numpy().astype(str).tolist() + dts_uuids: List[str] = dts[list(UUID_COLUMN_NAMES)].to_numpy().tolist() + gts_uuids: List[str] = gts[list(UUID_COLUMN_NAMES)].to_numpy().tolist() # We merge the unique identifier -- the tuple of ("log_id", "timestamp_ns", "category") # into a single string to optimize the subsequent grouping operation. # `groupby_mapping` produces a mapping from the uuid to the group of detections / annotations # which fall into that group. - uuid_to_dts = groupby([":".join(x) for x in dts_uuids], dts_npy) - uuid_to_gts = groupby([":".join(x) for x in gts_uuids], gts_npy) + uuid_to_dts = groupby([":".join(map(str, x)) for x in dts_uuids], dts_npy) + uuid_to_gts = groupby([":".join(map(str, x)) for x in gts_uuids], gts_npy) log_id_to_avm: Optional[Dict[str, ArgoverseStaticMap]] = None log_id_to_timestamped_poses: Optional[Dict[str, TimestampedCitySE3EgoPoses]] = None @@ -182,7 +189,7 @@ def summarize_metrics( """Calculate and print the 3D object detection metrics. Args: - dts: (N,15) Table of detections. + dts: (N,14) Table of detections. gts: (M,15) Table of ground truth annotations. cfg: Detection configuration. diff --git a/src/av2/evaluation/detection/utils.py b/src/av2/evaluation/detection/utils.py index 20004c00..0489c3fe 100644 --- a/src/av2/evaluation/detection/utils.py +++ b/src/av2/evaluation/detection/utils.py @@ -57,7 +57,6 @@ class DetectionCfg: max_range_m: Max distance (under a specific metric in meters) for a detection or ground truth cuboid to be considered for evaluation. num_recall_samples: Number of recall points to sample uniformly in [0, 1]. - splits: Tuple of split names to evaluate. tp_threshold_m: Center distance threshold for the true positive metrics (in meters). """ @@ -70,7 +69,6 @@ class DetectionCfg: max_num_dts_per_category: int = 100 max_range_m: float = 200.0 num_recall_samples: int = 100 - split: str = "val" tp_threshold_m: float = 2.0 @property diff --git a/tests/geometry/test_utm.py b/tests/geometry/test_utm.py index a503d06c..45ed9960 100644 --- a/tests/geometry/test_utm.py +++ b/tests/geometry/test_utm.py @@ -5,7 +5,7 @@ import numpy as np import av2.geometry.utm as geo_utils -from av2.geometry.utm import CITY_ORIGIN_LATLONG_DICT, CityName +from av2.geometry.utm import CityName from av2.utils.typing import NDArrayFloat From 503caa9ea4555f9c94c3e5d4c3bc0ba8f0c23426 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 16:11:49 -0400 Subject: [PATCH 76/81] Update ci.yml --- .github/workflows/ci.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 13f2f83e..cc72b978 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -33,6 +33,10 @@ jobs: run: | # Cache the environments (-r), fail on missing python interpreters (--error-on-missing-interpreters), set dependency resolver backend. python -m nox -r --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }} + - name: Free disk space. + run: | + mamba clean -ya + - name: Install pypa/build. run: >- python -m From 942809b368ef62f4d0ace485cab8c0f03fa636bc Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 17:15:48 -0400 Subject: [PATCH 77/81] Update ci.yml --- .github/workflows/ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index cc72b978..09f6abb3 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -45,6 +45,7 @@ jobs: --user - name: Build a binary wheel and a source tarball. + if: matrix.os != 'windows-latest' || matrix.venv_backend != 'mamba' run: >- python -m build From c549ac2c104d4633b4c8621526d1ecf426a31d10 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 18:01:11 -0400 Subject: [PATCH 78/81] Update ci.yml --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 09f6abb3..91c12197 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,7 +31,7 @@ jobs: - name: Run nox with ${{ matrix.venv_backend }}. run: | # Cache the environments (-r), fail on missing python interpreters (--error-on-missing-interpreters), set dependency resolver backend. - python -m nox -r --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }} + python -m nox --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }} - name: Free disk space. run: | From b9d82cdbff92eab58e0b13f976912bfac29e54c4 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 18:43:18 -0400 Subject: [PATCH 79/81] Update ci.yml --- .github/workflows/ci.yml | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 91c12197..37182705 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,7 +12,7 @@ jobs: python_version: ["3.8", "3.9", "3.10"] venv_backend: - ["virtualenv", "mamba"] # Testing two different resolvers (pip, mamba). + ["virtualenv"] # Testing two different resolvers (pip, mamba). defaults: run: shell: bash -l {0} @@ -33,10 +33,6 @@ jobs: run: | # Cache the environments (-r), fail on missing python interpreters (--error-on-missing-interpreters), set dependency resolver backend. python -m nox --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }} - - name: Free disk space. - run: | - mamba clean -ya - - name: Install pypa/build. run: >- python -m @@ -45,7 +41,6 @@ jobs: --user - name: Build a binary wheel and a source tarball. - if: matrix.os != 'windows-latest' || matrix.venv_backend != 'mamba' run: >- python -m build From 473208961c49d054ff0c58396dce6baef8e2eb66 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 19:03:46 -0400 Subject: [PATCH 80/81] Update ci.yml --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 37182705..b9f795eb 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -31,7 +31,7 @@ jobs: - name: Run nox with ${{ matrix.venv_backend }}. run: | # Cache the environments (-r), fail on missing python interpreters (--error-on-missing-interpreters), set dependency resolver backend. - python -m nox --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }} + python -m nox -r --error-on-missing-interpreters --python ${{ matrix.python_version }} --default-venv-backend ${{ matrix.venv_backend }} - name: Install pypa/build. run: >- From bda0724331fc1ce7e5836edb06a54a50bcaabd25 Mon Sep 17 00:00:00 2001 From: Benjamin Wilson Date: Tue, 26 Apr 2022 19:04:04 -0400 Subject: [PATCH 81/81] Update ci.yml --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b9f795eb..ff39f09b 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -12,7 +12,7 @@ jobs: python_version: ["3.8", "3.9", "3.10"] venv_backend: - ["virtualenv"] # Testing two different resolvers (pip, mamba). + ["virtualenv"] defaults: run: shell: bash -l {0}