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utils.py
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utils.py
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import torch
import torch.nn as nn
import numpy as np
from scipy.interpolate import interp1d
import os
import sys
import random
import config
def upgrade_resolution(arr, scale):
x = np.arange(0, arr.shape[0])
f = interp1d(x, arr, kind='linear', axis=0, fill_value='extrapolate')
scale_x = np.arange(0, arr.shape[0], 1 / scale)
up_scale = f(scale_x)
return up_scale
def get_proposal_oic(tList, wtcam, final_score, c_pred, scale, v_len, sampling_frames, num_segments, _lambda=0.25, gamma=0.2):
t_factor = (16 * v_len) / (scale * num_segments * sampling_frames)
temp = []
for i in range(len(tList)):
c_temp = []
temp_list = np.array(tList[i])[0]
if temp_list.any():
grouped_temp_list = grouping(temp_list)
for j in range(len(grouped_temp_list)):
if len(grouped_temp_list[j]) < 2:
continue
inner_score = np.mean(wtcam[grouped_temp_list[j], i, 0])
len_proposal = len(grouped_temp_list[j])
outer_s = max(0, int(grouped_temp_list[j][0] - _lambda * len_proposal))
outer_e = min(int(wtcam.shape[0] - 1), int(grouped_temp_list[j][-1] + _lambda * len_proposal))
outer_temp_list = list(range(outer_s, int(grouped_temp_list[j][0]))) + list(range(int(grouped_temp_list[j][-1] + 1), outer_e + 1))
if len(outer_temp_list) == 0:
outer_score = 0
else:
outer_score = np.mean(wtcam[outer_temp_list, i, 0])
c_score = inner_score - outer_score + gamma * final_score[c_pred[i]]
t_start = grouped_temp_list[j][0] * t_factor
t_end = (grouped_temp_list[j][-1] + 1) * t_factor
c_temp.append([c_pred[i], c_score, t_start, t_end])
temp.append(c_temp)
return temp
def result2json(result):
result_file = []
for i in range(len(result)):
for j in range(len(result[i])):
line = {'label': config.class_dict[result[i][j][0]], 'score': result[i][j][1],
'segment': [result[i][j][2], result[i][j][3]]}
result_file.append(line)
return result_file
def grouping(arr):
return np.split(arr, np.where(np.diff(arr) != 1)[0] + 1)
def save_best_record_thumos(test_info, file_path):
fo = open(file_path, "w")
fo.write("Step: {}\n".format(test_info["step"][-1]))
fo.write("Test_acc: {:.4f}\n".format(test_info["test_acc"][-1]))
fo.write("average_mAP: {:.4f}\n".format(test_info["average_mAP"][-1]))
tIoU_thresh = np.linspace(0.1, 0.9, 9)
for i in range(len(tIoU_thresh)):
fo.write("mAP@{:.1f}: {:.4f}\n".format(tIoU_thresh[i], test_info["mAP@{:.1f}".format(tIoU_thresh[i])][-1]))
fo.close()
def minmax_norm(act_map, min_val=None, max_val=None):
if min_val is None or max_val is None:
relu = nn.ReLU()
max_val = relu(torch.max(act_map, dim=1)[0])
min_val = relu(torch.min(act_map, dim=1)[0])
delta = max_val - min_val
delta[delta <= 0] = 1
ret = (act_map - min_val) / delta
ret[ret > 1] = 1
ret[ret < 0] = 0
return ret
def nms(proposals, thresh):
proposals = np.array(proposals)
x1 = proposals[:, 2]
x2 = proposals[:, 3]
scores = proposals[:, 1]
areas = x2 - x1 + 1
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(proposals[i].tolist())
xx1 = np.maximum(x1[i], x1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
inter = np.maximum(0.0, xx2 - xx1 + 1)
iou = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(iou < thresh)[0]
order = order[inds + 1]
return keep
def set_seed(seed):
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed_all(seed)
random.seed(seed)
torch.backends.cudnn.deterministic=True
torch.backends.cudnn.benchmark=False
def save_config(config, file_path):
fo = open(file_path, "w")
fo.write("Configurtaions:\n")
fo.write(str(config))
fo.close()