-
Notifications
You must be signed in to change notification settings - Fork 10
/
Copy pathextract_SlowFast_features_LSVQ.py
168 lines (123 loc) · 6.56 KB
/
extract_SlowFast_features_LSVQ.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
# -*- coding: utf-8 -*-
import argparse
import os
import numpy as np
import torch
from data_loader import VideoDataset_NR_LSVQ_SlowFast_feature
from torchvision import transforms
from pytorchvideo.models.hub import slowfast_r50
import torch.nn as nn
def pack_pathway_output(frames, device):
"""
Prepare output as a list of tensors. Each tensor corresponding to a
unique pathway.
Args:
frames (tensor): frames of images sampled from the video. The
dimension is `channel` x `num frames` x `height` x `width`.
Returns:
frame_list (list): list of tensors with the dimension of
`channel` x `num frames` x `height` x `width`.
"""
fast_pathway = frames
# Perform temporal sampling from the fast pathway.
slow_pathway = torch.index_select(
frames,
2,
torch.linspace(
0, frames.shape[2] - 1, frames.shape[2] // 4
).long(),
)
frame_list = [slow_pathway.to(device), fast_pathway.to(device)]
return frame_list
class slowfast(torch.nn.Module):
def __init__(self):
super(slowfast, self).__init__()
slowfast_pretrained_features = nn.Sequential(*list(slowfast_r50(pretrained=True).children())[0])
self.feature_extraction = torch.nn.Sequential()
self.slow_avg_pool = torch.nn.Sequential()
self.fast_avg_pool = torch.nn.Sequential()
self.adp_avg_pool = torch.nn.Sequential()
for x in range(0,5):
self.feature_extraction.add_module(str(x), slowfast_pretrained_features[x])
self.slow_avg_pool.add_module('slow_avg_pool', slowfast_pretrained_features[5].pool[0])
self.fast_avg_pool.add_module('fast_avg_pool', slowfast_pretrained_features[5].pool[1])
self.adp_avg_pool.add_module('adp_avg_pool', slowfast_pretrained_features[6].output_pool)
def forward(self, x):
with torch.no_grad():
x = self.feature_extraction(x)
slow_feature = self.slow_avg_pool(x[0])
fast_feature = self.fast_avg_pool(x[1])
slow_feature = self.adp_avg_pool(slow_feature)
fast_feature = self.adp_avg_pool(fast_feature)
return slow_feature, fast_feature
def main(config):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = slowfast()
model = model.to(device)
resize = config.resize
## training data
videos_dir = 'LSVQ'
datainfo_train = 'data/LSVQ_whole_train.csv'
datainfo_test = 'data/LSVQ_whole_test.csv'
datainfo_test_1080p = 'data/LSVQ_whole_test_1080p.csv'
transformations_test = transforms.Compose([transforms.Resize([resize, resize]),transforms.ToTensor(),\
transforms.Normalize(mean = [0.45, 0.45, 0.45], std = [0.225, 0.225, 0.225])])
trainset = VideoDataset_NR_LSVQ_SlowFast_feature(videos_dir, datainfo_train, transformations_test, resize)
testset = VideoDataset_NR_LSVQ_SlowFast_feature(videos_dir, datainfo_test, transformations_test, resize)
testset_1080p = VideoDataset_NR_LSVQ_SlowFast_feature(videos_dir, datainfo_test_1080p, transformations_test, resize, is_test_1080p = True)
## dataloader
train_loader = torch.utils.data.DataLoader(trainset, batch_size=1,
shuffle=False, num_workers=config.num_workers)
test_loader = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False, num_workers=config.num_workers)
test_loader_1080p = torch.utils.data.DataLoader(testset_1080p, batch_size=1,
shuffle=False, num_workers=config.num_workers)
# do validation after each epoch
with torch.no_grad():
model.eval()
for i, (video, mos, video_name) in enumerate(train_loader):
video_name = video_name[0]
print(video_name)
if not os.path.exists(config.feature_save_folder + video_name):
os.makedirs(config.feature_save_folder + video_name)
for idx, ele in enumerate(video):
# ele = ele.to(device)
ele = ele.permute(0, 2, 1, 3, 4)
inputs = pack_pathway_output(ele, device)
slow_feature, fast_feature = model(inputs)
np.save(config.feature_save_folder + video_name + '/' + 'feature_' + str(idx) + '_slow_feature', slow_feature.to('cpu').numpy())
np.save(config.feature_save_folder + video_name + '/' + 'feature_' + str(idx) + '_fast_feature', fast_feature.to('cpu').numpy())
for i, (video, mos, video_name) in enumerate(test_loader):
video_name = video_name[0]
print(video_name)
if not os.path.exists(config.feature_save_folder + video_name):
os.makedirs(config.feature_save_folder + video_name)
for idx, ele in enumerate(video):
# ele = ele.to(device)
ele = ele.permute(0, 2, 1, 3, 4)
inputs = pack_pathway_output(ele, device)
slow_feature, fast_feature = model(inputs)
np.save(config.feature_save_folder + video_name + '/' + 'feature_' + str(idx) + '_slow_feature', slow_feature.to('cpu').numpy())
np.save(config.feature_save_folder + video_name + '/' + 'feature_' + str(idx) + '_fast_feature', fast_feature.to('cpu').numpy())
for i, (video, mos, video_name) in enumerate(test_loader_1080p):
video_name = video_name[0]
print(video_name)
if not os.path.exists(config.feature_save_folder + video_name):
os.makedirs(config.feature_save_folder + video_name)
for idx, ele in enumerate(video):
# ele = ele.to(device)
ele = ele.permute(0, 2, 1, 3, 4)
inputs = pack_pathway_output(ele, device)
slow_feature, fast_feature = model(inputs)
np.save(config.feature_save_folder + video_name + '/' + 'feature_' + str(idx) + '_slow_feature', slow_feature.to('cpu').numpy())
np.save(config.feature_save_folder + video_name + '/' + 'feature_' + str(idx) + '_fast_feature', fast_feature.to('cpu').numpy())
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--database', type=str)
parser.add_argument('--model_name', type=str)
parser.add_argument('--num_workers', type=int, default=8)
parser.add_argument('--resize', type=int, default=112)
parser.add_argument('--gpu_ids', type=list, default=None)
parser.add_argument('--feature_save_folder', type=str, default='ckpts')
config = parser.parse_args()
main(config)