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runner.py
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# coding=utf-8
"""
Train a Transformer ML Model for Planning
"""
import logging
import os
import random
import shutil
import sys
import pickle
import copy
import torch
from tqdm import tqdm
import copy
import multiprocessing as mp
import datasets
import numpy as np
import evaluate
import transformers
from datasets import Dataset
from datasets.arrow_dataset import _concatenate_map_style_datasets
from functools import partial
from transformers import (
HfArgumentParser,
set_seed,
)
# from transformer4planning.models.model import build_models
from transformer4planning.models.backbone.str_base import build_models
from transformer4planning.utils.args import (
ModelArguments,
DataTrainingArguments,
ConfigArguments,
PlanningTrainingArguments
)
from transformers.trainer_utils import get_last_checkpoint
from transformer4planning.trainer import (PlanningTrainer, CustomCallback)
from torch.utils.data import DataLoader
from transformers.trainer_callback import DefaultFlowCallback
from transformer4planning.trainer import compute_metrics
from datasets import Dataset, Value
# os.environ["WANDB_DISABLED"] = "true"
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
logger = logging.getLogger(__name__)
def load_dataset(root, split='train', dataset_scale=1, agent_type="all", select=False):
datasets = []
index_root_folders = os.path.join(root, split)
indices = os.listdir(index_root_folders)
for index in indices:
index_path = os.path.join(index_root_folders, index)
if os.path.isdir(index_path):
# load training dataset
logger.info("Loading dataset {}".format(index_path))
dataset = Dataset.load_from_disk(index_path)
if dataset is not None:
datasets.append(dataset)
else:
continue
# For nuplan dataset directory structure, each split obtains multi cities directories, so concat is required;
# But for waymo dataset, index directory is just the datset, so load directory directly to build dataset.
if len(datasets) > 0:
dataset = _concatenate_map_style_datasets(datasets)
for each in datasets:
each.cleanup_cache_files()
else:
dataset = Dataset.load_from_disk(index_root_folders)
# add split column
dataset.features.update({'split': Value('string')})
try:
# for some new dataset, split column is already added
if split == 'train_alltype':
dataset = dataset.add_column(name='split', column=['train'] * len(dataset))
else:
dataset = dataset.add_column(name='split', column=[split] * len(dataset))
except:
pass
dataset.set_format(type='torch')
# if "centerline" in dataset.column_names:
# dataset = dataset.filter(lambda example: np.sum(np.array(example["centerline"])) != 0, num_proc=mp.cpu_count())
# if 'halfs_intention' in dataset.column_names and split == 'train':
# dataset = dataset.filter(lambda example: not (int(example["halfs_intention"]) == 4 and random.random() > 0.1), num_proc=mp.cpu_count())
if agent_type != "all":
agent_type_list = agent_type.split()
agent_type_list = [int(t) for t in agent_type_list]
dataset = dataset.filter(lambda example: example["object_type"] in agent_type_list, num_proc=mp.cpu_count())
if select:
samples = int(len(dataset) * float(dataset_scale))
dataset = dataset.select(range(samples))
return dataset
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, ConfigArguments, PlanningTrainingArguments))
model_args, data_args, _, training_args = parser.parse_args_into_dataclasses()
# set default label names
training_args.label_names = ['trajectory_label']
# pre-compute raster channels number
if model_args.raster_channels == 0:
road_types = 20
agent_types = 8
traffic_types = 4
past_sample_number = int(2 * 20 / model_args.past_sample_interval) # past_seconds-2, frame_rate-20
if 'auto' not in model_args.model_name:
# will cast into each frame
if model_args.with_traffic_light:
model_args.raster_channels = 1 + road_types + traffic_types + agent_types
else:
model_args.raster_channels = 1 + road_types + agent_types
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# Pass in the directory to load a saved dataset
# See generation.py to process and save a dataset from the NuPlan Dataset
"""
Set saved dataset folder to load a saved dataset
1. Pass None to load from data_args.saved_dataset_folder as the root folder path to load all sub-datasets of each city
2. Pass the folder of an index files to load one sub-dataset of one city
"""
from datasets import disable_caching
disable_caching()
# loop all datasets
logger.info("Loading full set of datasets from {}".format(data_args.saved_dataset_folder))
assert os.path.isdir(data_args.saved_dataset_folder)
if model_args.task == "nuplan" or model_args.task == "waymo": # nuplan datasets are stored in index format
index_root = os.path.join(data_args.saved_dataset_folder, 'index')
elif model_args.task == "train_diffusion_decoder":
index_root = data_args.saved_dataset_folder
root_folders = os.listdir(index_root)
if data_args.use_full_training_set:
if 'train_alltype' in root_folders:
train_dataset = load_dataset(index_root, "train_alltype", data_args.dataset_scale, data_args.agent_type, True)
else:
raise ValueError("No training dataset found in {}, must include at least one city in /train_alltype".format(index_root))
else:
if 'train' in root_folders:
train_dataset = load_dataset(index_root, "train", data_args.dataset_scale, data_args.agent_type, True)
else:
raise ValueError("No training dataset found in {}, must include at least one city in /train".format(index_root))
if model_args.camera_image_encoder is not None:
train_dataset = train_dataset.filter(lambda example: len(example["images_path"]) == 8, num_proc=mp.cpu_count())
if training_args.do_test and 'test' in root_folders:
# TODO: compatible with older training args
test_dataset = load_dataset(index_root, "test", data_args.dataset_scale, data_args.agent_type, False)
else:
logger.warning('Using training set as test set')
test_dataset = train_dataset
if (training_args.do_eval or training_args.do_predict) and 'val' in root_folders:
val_dataset = load_dataset(index_root, "val", data_args.dataset_scale, data_args.agent_type, False)
if model_args.camera_image_encoder is not None:
val_dataset = val_dataset.filter(lambda example: len(example["images_path"]) == 8, num_proc=mp.cpu_count())
else:
logger.warning('Validation set not found, using training set as val set')
val_dataset = test_dataset
# val_dataset = train_dataset
if data_args.do_closed_loop_simulation and 'val1k' in root_folders:
# WIP
val1k_dataset = load_dataset(index_root, "val1k", data_args.dataset_scale, data_args.agent_type, False)
val1k_dataset = val1k_dataset.suffle(seed=training_args.seed)
# clean image fodler
def check_images(each):
if 'images_path' not in each:
logger.error('images_path not found in dataset')
print(each)
raise ValueError('images_path not found in dataset')
return each
def clean_images(each):
global success, fail
for each_image in each['images_path']:
# requires python 3.2+
src_fpath = os.path.join(data_args.camera_images_path, each_image)
if os.path.exists(src_fpath):
try:
# src_fpath = os.path.join(data_args.camera_images_path, each_image)
dest_fpath = os.path.join(training_args.images_cleaning_to_folder, each_image)
os.makedirs(os.path.dirname(dest_fpath), exist_ok=True)
shutil.copy(src_fpath, dest_fpath)
# print('Copied ', src_fpath, ' to ', dest_fpath)
# success += 1
except:
logger.warning('Failed to copy ' + src_fpath, ' to ' + dest_fpath)
# fail += 1
else:
logger.warning('Image not found: ' + src_fpath)
def save_smaller_images(each):
import PIL
for each_image in each['images_path']:
src_fpath = os.path.join(data_args.camera_images_path, each_image)
if os.path.exists(src_fpath):
dest_fpath = os.path.join(training_args.images_cleaning_to_folder, each_image)
os.makedirs(os.path.dirname(dest_fpath), exist_ok=True)
img = PIL.Image.open(src_fpath)
img = img.resize((1080 // 4, 1920 // 4))
img.save(dest_fpath)
else:
logger.warning('Image not found: ' + src_fpath)
if training_args.images_cleaning_to_folder is not None:
if data_args.camera_images_path is None:
raise ValueError("Must provide camera_images_path to clean images")
logger.info(f'Cleaning images from: {data_args.camera_images_path} to folder: {training_args.images_cleaning_to_folder}')
logger.info('checking if any invalid folders')
for each_folder in os.listdir(data_args.camera_images_path):
if not os.path.isdir(os.path.join(data_args.camera_images_path, each_folder)):
logger.error('invalid folder: ' + each_folder)
raise ValueError('invalid folder: ' + each_folder)
if len(os.listdir(os.path.join(data_args.camera_images_path, each_folder))) != 8:
logger.error(f'invalid folder: {each_folder}, with: {os.listdir(os.path.join(data_args.camera_images_path, each_folder))}')
raise ValueError('invalid folder: ', each_folder)
logger.info('Cleaning training/val set')
if not os.path.isdir(training_args.images_cleaning_to_folder):
os.mkdir(training_args.images_cleaning_to_folder)
datasets_list = [val_dataset]
for dataset in datasets_list:
success = 0
fail = 0
logger.info('Checking training/val set')
dataset = dataset.map(check_images, num_proc=120)
logger.info('Moving Files')
# dataset = dataset.map(clean_images, num_proc=120)
dataset.map(save_smaller_images, num_proc=120)
logger.info('Success: ' + str(success) + ' Fail: ' + str(fail))
# Val: Success: 15218 Fail: 127560
logger.info('Image clean finished')
exit()
if model_args.task == "nuplan":
all_maps_dic = {}
map_folder = os.path.join(data_args.saved_dataset_folder, 'map')
for each_map in os.listdir(map_folder):
if each_map.endswith('.pkl'):
map_path = os.path.join(map_folder, each_map)
with open(map_path, 'rb') as f:
map_dic = pickle.load(f)
map_name = each_map.split('.')[0]
all_maps_dic[map_name] = map_dic
# loop split info and update for test set
logger.info('TrainingSet: '+ str(train_dataset) + '\nValidationSet' + str(val_dataset) + '\nTestingSet' + str(test_dataset))
dataset_dict = dict(
train=train_dataset.shuffle(seed=training_args.seed),
validation=val_dataset.shuffle(seed=training_args.seed),
test=test_dataset.shuffle(seed=training_args.seed),
)
# Load a model's pretrained weights from a path or from hugging face's model base
model = build_models(model_args)
# clf_metrics = dict(
# accuracy=evaluate.load("accuracy"),
# f1=evaluate.load("f1"),
# precision=evaluate.load("precision"),
# recall=evaluate.load("recall")
# )
# if 'auto' in model_args.model_name and model_args.k == -1: # for the case action label as token
# model.clf_metrics = clf_metrics
if training_args.do_train:
import multiprocessing
if 'OMP_NUM_THREADS' not in os.environ:
# os.environ["OMP_NUM_THREADS"] = str(int(multiprocessing.cpu_count() / training_args.dataloader_num_workers))
os.environ["OMP_NUM_THREADS"] = str(int(multiprocessing.cpu_count() / 8))
train_dataset = dataset_dict["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
if training_args.do_eval:
eval_dataset = dataset_dict["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
if training_args.do_predict:
predict_dataset = dataset_dict["test"]
if data_args.max_predict_samples is not None:
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
predict_dataset = predict_dataset.select(range(max_predict_samples))
# Initialize our Trainer
if model_args.task == "nuplan":
if model_args.encoder_type == "raster":
from transformer4planning.preprocess.nuplan_rasterize import nuplan_rasterize_collate_func
collate_fn = partial(nuplan_rasterize_collate_func,
dic_path=data_args.saved_dataset_folder,
all_maps_dic=all_maps_dic,
**model_args.__dict__)
elif model_args.encoder_type == "vector":
from nuplan.common.maps.nuplan_map.map_factory import get_maps_api
from transformer4planning.preprocess.pdm_vectorize import nuplan_vector_collate_func
map_api = dict()
for map in ['sg-one-north', 'us-ma-boston', 'us-nv-las-vegas-strip', 'us-pa-pittsburgh-hazelwood']:
map_api[map] = get_maps_api(map_root=data_args.nuplan_map_path,
map_version="nuplan-maps-v1.0",
map_name=map)
collate_fn = partial(nuplan_vector_collate_func,
dic_path=data_args.saved_dataset_folder,
map_api=map_api,
use_centerline=model_args.use_centerline)
elif model_args.task == "waymo":
from transformer4planning.preprocess.waymo_vectorize import waymo_collate_func
if model_args.encoder_type == "vector":
collate_fn = partial(waymo_collate_func,
dic_path=data_args.saved_dataset_folder)
elif model_args.encoder_type == "raster":
raise NotImplementedError
from transformer4planning.trainer import compute_metrics_waymo
elif model_args.task == "train_diffusion_decoder":
from torch.utils.data._utils.collate import default_collate
def feat_collate_func(batch, predict_yaw):
excepted_keys = ['label', 'hidden_state']
result = dict()
for key in excepted_keys:
list_of_dvalues = []
for d in batch:
if key in excepted_keys:
if key == "label" and not predict_yaw:
d[key] = d[key][:, :2]
list_of_dvalues.append(d[key])
result[key] = default_collate(list_of_dvalues)
return result
collate_fn = partial(feat_collate_func, predict_yaw=model_args.predict_yaw)
else:
raise AttributeError("task must be nuplan or waymo or train_diffusion_decoder")
trainer = PlanningTrainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
callbacks=[CustomCallback,],
data_collator=collate_fn,
compute_metrics=compute_metrics_waymo if model_args.task == "waymo" else compute_metrics
)
trainer.pop_callback(DefaultFlowCallback)
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
# Training
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.save_state()
# Evaluation
results = {}
if training_args.do_eval:
if not training_args.do_train and training_args.resume_from_checkpoint is not None:
assert 'pretrain' in model_args.model_name, 'resume_from_checkpoint is only for training, use pretrain model to load for eval only'
result = trainer.evaluate(eval_dataset=eval_dataset, metric_key_prefix="eval")
logger.info("***** Final Eval results *****")
logger.info(f" {result}")
# hyperparams = {"model": model_args.model_name, "dataset": data_args.saved_dataset_folder, "seed": training_args.seed}
# evaluate.save("./results/", ** result, ** hyperparams)
# logger.info(f" fde: {trainer.fde} ade: {trainer.ade}")
if data_args.do_closed_loop_simulation:
"""
Will save closed loop simulation results
"""
logger.info("*** Closed Loop Simulation ***")
if training_args.do_predict:
# Currently only supports single GPU predict outputs
"""
Will save prediction results, and dagger results if dagger is enabled
"""
# TODO: fit new online process pipeline to save dagger and prediction results
logger.info("*** Predict ***")
with torch.no_grad():
dagger_results = {
'file_name':[],
'frame_id':[],
'rank':[],
'ADE':[],
'FDE':[],
'y_bias':[]
}
prediction_results = {
'file_names': [],
'current_frame': [],
'next_step_action': [],
'predicted_trajectory': [],
}
test_dataloader = DataLoader(
dataset=predict_dataset,
batch_size=training_args.per_device_eval_batch_size,
num_workers=training_args.per_device_eval_batch_size,
collate_fn=collate_fn,
pin_memory=True,
drop_last=True
)
if model_args.predict_trajectory:
end_bias_x = []
end_bias_y = []
all_bias_x = []
all_bias_y = []
losses = []
loss_fn = torch.nn.MSELoss(reduction="mean")
for itr, input in enumerate(tqdm(test_dataloader)):
# move batch to device
for each_key in input:
if isinstance(input[each_key], type(torch.tensor(0))):
input[each_key] = input[each_key].to("cuda")
eval_batch_size = training_args.per_device_eval_batch_size
if model_args.autoregressive or model_args.use_key_points is not None:
# Todo: add autoregressive predict
traj_pred = model.generate(**input)
else:
output = model(**copy.deepcopy(input))
traj_pred = output.logits
try:
file_name = input['file_name']
current_frame_idx = input['frame_id']
except:
file_name = ["null"] * eval_batch_size
current_frame_idx = -1 * torch.ones(eval_batch_size)
prediction_results['file_names'].extend(file_name)
prediction_results['current_frame'].extend(current_frame_idx.cpu().numpy())
if data_args.dagger:
dagger_results['file_name'].extend(file_name)
dagger_results['frame_id'].extend(list(current_frame_idx.cpu().numpy()))
if model_args.predict_trajectory:
if model_args.autoregressive:# trajectory label as token case
trajectory_label = model.compute_normalized_points(input["trajectory"][:, 10:, :])
traj_pred = model.compute_normalized_points(traj_pred)
else:
if 'mmtransformer' in model_args.model_name and model_args.task == 'waymo':
trajectory_label = input["trajectory_label"][:, :, :2]
trajectory_label = torch.where(trajectory_label != -1, trajectory_label, traj_pred)
else:
trajectory_label = input["trajectory_label"][:, 1::2, :]
loss = loss_fn(trajectory_label[:, :, :2], traj_pred[:, -trajectory_label.shape[1]:, :2])
end_trajectory_label = trajectory_label[:, -1, :]
end_point = traj_pred[:, -1, :]
end_bias_x.append(end_trajectory_label[:, 0] - end_point[:, 0])
end_bias_y.append(end_trajectory_label[:, 1] - end_point[:, 1])
all_bias_x.append(trajectory_label[:, :, 0] - traj_pred[:, -trajectory_label.shape[1]:, 0])
all_bias_y.append(trajectory_label[:, :, 1] - traj_pred[:, -trajectory_label.shape[1]:, 1])
losses.append(loss)
if model_args.predict_trajectory:
end_bias_x = torch.stack(end_bias_x, 0).cpu().numpy()
end_bias_y = torch.stack(end_bias_y, 0).cpu().numpy()
all_bias_x = torch.stack(all_bias_x, 0).reshape(-1).cpu().numpy()
all_bias_y = torch.stack(all_bias_y, 0).reshape(-1).cpu().numpy()
final_loss = torch.mean(torch.stack(losses, 0)).item()
print('Mean L2 loss: ', final_loss)
print('End point x offset: ', np.average(np.abs(end_bias_x)))
print('End point y offset: ', np.average(np.abs(end_bias_y)))
distance_error = np.sqrt(np.abs(all_bias_x)**2 + np.abs(all_bias_y)**2).reshape(-1, 80)
final_distance_error = np.sqrt(np.abs(end_bias_x)**2 + np.abs(end_bias_y)**2)
if data_args.dagger:
dagger_results['ADE'].extend(list(np.average(distance_error, axis=1).reshape(-1)))
dagger_results['FDE'].extend(list(final_distance_error.reshape(-1)))
dagger_results['y_bias'].extend(list(np.average(all_bias_y.reshape(-1, 80), axis=1).reshape(-1)))
print('ADE', np.average(distance_error))
print('FDE', np.average(final_distance_error))
# print(dagger_results)
def compute_dagger_dict(dic):
tuple_list = list()
fde_result_list = dict()
y_bias_result_list = dict()
for filename, id, ade, fde, y_bias in zip(dic["file_name"], dic["frame_id"], dic["ADE"], dic["FDE"], dic["y_bias"]):
if filename == "null":
continue
tuple_list.append((filename, id, ade, fde, abs(y_bias)))
fde_sorted_list = sorted(tuple_list, key=lambda x:x[3], reverse=True)
for idx, tp in enumerate(fde_sorted_list):
if tp[0] in fde_result_list.keys():
fde_result_list[tp[0]]["frame_id"].append(tp[1])
fde_result_list[tp[0]]["ade"].append(tp[2])
fde_result_list[tp[0]]["fde"].append(tp[3])
fde_result_list[tp[0]]["y_bias"].append(tp[4])
fde_result_list[tp[0]]["rank"].append((idx+1)/len(fde_sorted_list))
else:
fde_result_list[tp[0]] = dict(
frame_id=[tp[1]], ade=[tp[2]], fde=[tp[3]], y_bias=[tp[4]], rank=[(idx+1)/len(fde_sorted_list)]
)
y_bias_sorted_list = sorted(tuple_list, key=lambda x:x[-1], reverse=True)
for idx, tp in enumerate(y_bias_sorted_list):
if tp[0] in y_bias_result_list.keys():
y_bias_result_list[tp[0]]["frame_id"].append(tp[1])
y_bias_result_list[tp[0]]["ade"].append(tp[2])
y_bias_result_list[tp[0]]["fde"].append(tp[3])
y_bias_result_list[tp[0]]["y_bias"].append(tp[4])
y_bias_result_list[tp[0]]["rank"].append((idx+1)/len(y_bias_sorted_list))
else:
y_bias_result_list[tp[0]] = dict(
frame_id=[tp[1]], ade=[tp[2]], fde=[tp[3]], y_bias=[tp[4]], rank=[(idx+1)/len(y_bias_sorted_list)]
)
return fde_result_list, y_bias_result_list
def draw_histogram_graph(data, title, savepath):
import matplotlib.pyplot as plt
plt.hist(data, bins=range(20), edgecolor='black')
plt.title(title)
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.savefig(os.path.join(savepath, "{}.png".format(title)))
if data_args.dagger:
draw_histogram_graph(dagger_results["FDE"], title="FDE-distributions", savepath=training_args.output_dir)
draw_histogram_graph(dagger_results["ADE"], title="ADE-distributions", savepath=training_args.output_dir)
draw_histogram_graph(dagger_results["y_bias"], title="ybias-distribution", savepath=training_args.output_dir)
fde_dagger_dic, y_bias_dagger_dic = compute_dagger_dict(dagger_results)
if training_args.output_dir is not None:
# save results
output_file_path = os.path.join(training_args.output_dir, 'generated_predictions.pickle')
with open(output_file_path, 'wb') as handle:
pickle.dump(prediction_results, handle, protocol=pickle.HIGHEST_PROTOCOL)
if data_args.dagger:
dagger_result_path = os.path.join(training_args.output_dir, "fde_dagger.pkl")
with open(dagger_result_path, 'wb') as handle:
pickle.dump(fde_dagger_dic, handle)
dagger_result_path = os.path.join(training_args.output_dir, "ybias_dagger.pkl")
with open(dagger_result_path, 'wb') as handle:
pickle.dump(y_bias_dagger_dic, handle)
print("dagger results save to {}".format(dagger_result_path))
# predict_results = trainer.predict(predict_dataset, metric_key_prefix="predict")
# metrics = predict_results.metrics
# max_predict_samples = (
# data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
# )
# metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))
# trainer.log_metrics("predict", metrics)
# trainer.save_metrics("predict", metrics)
# if trainer.is_world_process_zero():
# if training_args.predict_with_generate:
# predictions = tokenizer.batch_decode(
# predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
# )
# predictions = [pred.strip() for pred in predictions]
# output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt")
# with open(output_prediction_file, "w") as writer:
# writer.write("\n".join(predictions))
kwargs = {"finetuned_from": model_args.model_pretrain_name_or_path, "tasks": "NuPlanPlanning"}
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
return results
if __name__ == "__main__":
main()