-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathActionEval_classification.py
executable file
·184 lines (147 loc) · 8.39 KB
/
ActionEval_classification.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
#!/usr/bin/env python
# debug version 1:
# Evaluator
import argparse
import os,sys
project_root = os.path.join(os.path.expanduser('~'), 'Dev/S2N-release')
sys.path.append(project_root)
import pprint as pp
import numpy as np
import torch.nn.utils.clip_grad
import torch
print(torch.__version__)
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.nn.functional as F
from SDN.PointerGRU2Heads_v8 import PointerNetwork
from PtUtils import cuda_model
from Devs_ActionProp.datasets.THUMOS14.single_video_evaluator_loader import SingleVideoLoader
import progressbar
import SDN.helper
from Devs_ActionProp.PropEval.Utils import non_maxima_supression
import pandas as pd
def str2bool(v):
return v.lower() in ('true', '1', 'y', 'yes')
parser = argparse.ArgumentParser(description="Evaluation of Pointer-Net-LSTM-2Heads")
# Data
parser.add_argument('--seq_len', default=360, type=int, help='clip size')
parser.add_argument('--net_outputs', default=15, type=int, help='number of intervals for lstm outputs')
parser.add_argument('--batch_size', default=10, type=int, help='batch size')
# GPU
parser.add_argument("--gpu_id", default='0', type=str)
parser.add_argument('--multiGpu', '-m', action='store_true', help='positivity constraint')
# Network
parser.add_argument('--input_dim', type=int, default=500, help='Number of hidden units')
parser.add_argument('--embedding_dim', type=int, default=500, help='Number of embedding units')
parser.add_argument('--hidden_dim', type=int, default=512, help='Number of hidden units')
parser.add_argument('--eval', type=str, default='/home/zwei/Dev/NetModules/ckpts/THUMOS/ActionExp_v3_l2_loss-NoDepsIn3-assgin0.50-alpha0.1000-dim512-dropout0.5000-seqlen90-L2-HUG', help='check-point file')
parser.add_argument('--fileid', type=int, default=15, help='file id in ckpt file')
user_home_directory = os.path.expanduser('~')
def main():
global args
args = (parser.parse_args())
ckpt_idx = args.fileid
savefile_stem = os.path.basename(args.eval)
proposal_save_file = 'Dev/S2N-release/Devs_ActionProp/PropEval/baselines_results/{:s}-{:04d}-check-02_thumos14_test.csv'.format(savefile_stem, ckpt_idx)
feature_directory = os.path.join(user_home_directory, 'datasets/THUMOS14/features/c3dd-fc7-red500')
ground_truth_file = os.path.join(user_home_directory, 'Dev/NetModules/Devs_ActionProp/action_det_prep/thumos14_tag_test_proposal_list_c3dd.csv')
ground_truth = pd.read_csv(ground_truth_file, sep=' ')
target_video_frms = ground_truth[['video-name', 'video-frames']].drop_duplicates().values
frm_nums = {}
for s_target_videofrms in target_video_frms:
frm_nums[s_target_videofrms[0]] = s_target_videofrms[1]
target_file_names = ground_truth['video-name'].unique()
feature_file_ext = 'npy'
use_cuda = cuda_model.ifUseCuda(args.gpu_id, args.multiGpu)
# Pretty print the run args
pp.pprint(vars(args))
model = PointerNetwork(input_dim=args.input_dim, embedding_dim=args.embedding_dim,
hidden_dim=args.hidden_dim, max_decoding_len=args.net_outputs, output_classes=2)
print("Number of Params\t{:d}".format(sum([p.data.nelement() for p in model.parameters()])))
model = cuda_model.convertModel2Cuda(model, gpu_id=args.gpu_id, multiGpu=args.multiGpu)
model.eval()
if args.eval is not None:
# if os.path.isfile(args.resume):
ckpt_filename = os.path.join(args.eval, 'checkpoint_{:04d}.pth.tar'.format(ckpt_idx))
assert os.path.isfile(ckpt_filename), 'Error: no checkpoint directory found!'
checkpoint = torch.load(ckpt_filename, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'], strict=True)
train_iou = checkpoint['IoU']
print("=> loading checkpoint '{}', current iou: {:.04f}".format(ckpt_filename, train_iou))
predict_results = {}
overlap=0.9
seq_length = 360
sample_rate = 4
for video_idx, s_target_filename in enumerate(target_file_names):
if not os.path.exists(os.path.join(feature_directory, '{:s}.{:s}'.format(s_target_filename, feature_file_ext))):
print ('{:s} Not found'.format(s_target_filename))
continue
s_feature_path = os.path.join(feature_directory, '{:s}.{:s}'.format(s_target_filename, feature_file_ext))
singlevideo_data = SingleVideoLoader(feature_path=s_feature_path, seq_length=seq_length, overlap=overlap, sample_rate=sample_rate)
n_video_len = singlevideo_data.n_features
n_video_clips = len(singlevideo_data.video_clips)
singlevideo_dataset = DataLoader(singlevideo_data, batch_size=args.batch_size, shuffle=False, num_workers=4)
predict_proposals = []
for batch_idx, data in enumerate(singlevideo_dataset):
clip_feature = Variable(data[0], requires_grad=False)
clip_start_positions = Variable(data[1], requires_grad=False)
clip_end_positions = Variable(data[2], requires_grad=False)
if use_cuda:
clip_feature = clip_feature.cuda()
clip_start_positions = clip_start_positions.cuda()
clip_end_positions = clip_end_positions.cuda()
clip_start_positions = clip_start_positions.repeat(1, args.net_outputs)
clip_end_positions = clip_end_positions.repeat(1, args.net_outputs)
head_pointer_probs, head_positions, tail_pointer_probs, tail_positions, cls_scores, _ = model(clip_feature)
# cls_scores = F.sigmoid(cls_scores)
cls_scores = F.softmax(cls_scores, dim=2)
head_positions, tail_positions = SDN.helper.switch_positions(head_positions, tail_positions)
head_positions = (head_positions*sample_rate + clip_start_positions)
tail_positions = (tail_positions*sample_rate + clip_start_positions)
# cls_scores = cls_scores.contiguous().view(-1)
cls_scores = cls_scores[:,:,1].contiguous().view(-1)
head_positions = head_positions.contiguous().view(-1)
tail_positions = tail_positions.contiguous().view(-1)
outputs = torch.stack([head_positions.float(), tail_positions.float(), cls_scores], dim=-1)
outputs = outputs.data.cpu().numpy()
for output_idx, s_output in enumerate(outputs):
if s_output[0] == s_output[1]:
s_output[0] -= sample_rate/2
s_output[1] += sample_rate/2
s_output[0] = max(0, s_output[0])
s_output[1] = min(n_video_len, s_output[1])
outputs[output_idx] = s_output
predict_proposals.append(outputs)
predict_proposals = np.concatenate(predict_proposals, axis=0)
sorted_idx = np.argsort(predict_proposals[:,-1])[::-1]
predict_proposals = predict_proposals[sorted_idx]
n_proposals = len(predict_proposals)
pred_positions = predict_proposals[:, :2]
pred_scores = predict_proposals[:,-1]
nms_positions, nms_scores = non_maxima_supression(pred_positions, pred_scores, overlap=0.99)
nms_predictions = np.concatenate((nms_positions, np.expand_dims(nms_scores, -1)), axis=-1)
predict_results[s_target_filename] = predict_proposals
print("[{:d} | {:d}]{:s}\t {:d} Frames\t {:d} Clips\t{:d} Proposals, Non-repeat:{:d}".
format(video_idx, len(target_file_names), s_target_filename, n_video_len, n_video_clips, n_proposals, nms_predictions.shape[0]))
data_frame = pkl_frame2dataframe(predict_results, frm_nums)
results = pd.DataFrame(data_frame, columns=['f-end', 'f-init', 'score', 'video-frames', 'video-name'])
results.to_csv(os.path.join(user_home_directory, proposal_save_file),
sep=' ', index=False)
def pkl_frame2dataframe(dt_results, frm_nums):
data_frame = []
print("Saving to cvs files.")
pbar = progressbar.ProgressBar(max_value=len(dt_results))
for i, _key in enumerate(dt_results):
pbar.update(i)
# fps = movie_fps[_key]
frm_num = frm_nums[_key]
for line in dt_results[_key]:
start = int(line[0])
end = int(line[1])
if start == end:
continue
score = float(line[2])
data_frame.append([end, start, score, frm_num, _key])
return data_frame
if __name__ == '__main__':
main()