video_filter.py
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import os
import torch.optim
import numpy as np
import torch.nn.parallel
from ops.models import TSN
from ops.transforms import *
from ops.dataset import TSNDataSet
from torch.nn import functional as F
def gen_file_list(frame_save_dir, frame_list_dir):
val_path = os.path.join(frame_list_dir, 'val.txt')
video_names = os.listdir(frame_save_dir)
ucf101_rgb_val_file = open(val_path, 'w')
for video_name in video_names:
images_dir = os.path.join(frame_save_dir, video_name)
ucf101_rgb_val_file.write(video_name)
ucf101_rgb_val_file.write(' ')
ucf101_rgb_val_file.write(str(len(os.listdir(images_dir))))
ucf101_rgb_val_file.write('\n')
ucf101_rgb_val_file.close()
return val_path
def start_filter(config):
arch = config['FIGHTING']['ARCH']
prefix = config['VIDEO']['PREFIX']
modality = config['VIDEO_FILTER']['MODALITY']
test_crop = config['VIDEO_FILTER']['TEST_CROP']
batch_size = config['VIDEO_FILTER']['BATCH_SIZE']
weights_path = config['MODEL']['CLS_VIDEO']
test_segment = config['VIDEO_FILTER']['TEST_SEGMENT']
frame_save_dir = config['VIDEO']['FRAME_SAVE_DIR']
frame_list_dir = config['VIDEO']['FRAME_LIST_DIR']
result_file_name = config['VIDEO_FILTER']['RESULT_FILE']
workers = 8
num_class = 3
shift_div = 8
img_feature_dim = 256
softmax = False
is_shift = True
full_res = False
non_local = False
dense_sample = False
twice_sample = False
val_list = gen_file_list(frame_save_dir, frame_list_dir)
result_file_path = os.path.join(frame_list_dir, result_file_name)
pretrain = 'imagenet'
shift_place = 'blockres'
crop_fusion_type = 'avg'
net = TSN(num_class, test_segment if is_shift else 1, modality,
base_model=arch,
consensus_type=crop_fusion_type,
img_feature_dim=img_feature_dim,
pretrain=pretrain,
is_shift=is_shift, shift_div=shift_div, shift_place=shift_place,
non_local=non_local,
)
checkpoint = torch.load(weights_path)
checkpoint = checkpoint['state_dict']
base_dict = {'.'.join(k.split('.')[1:]): v for k, v in list(checkpoint.items())}
replace_dict = {'base_model.classifier.weight': 'new_fc.weight',
'base_model.classifier.bias': 'new_fc.bias',
}
for k, v in replace_dict.items():
if k in base_dict:
base_dict[v] = base_dict.pop(k)
net.load_state_dict(base_dict)
input_size = net.scale_size if full_res else net.input_size
if test_crop == 1:
cropping = torchvision.transforms.Compose([
GroupScale(net.scale_size),
GroupCenterCrop(input_size),
])
elif test_crop == 3: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupFullResSample(input_size, net.scale_size, flip=False)
])
elif test_crop == 5: # do not flip, so only 5 crops
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size, flip=False)
])
elif test_crop == 10:
cropping = torchvision.transforms.Compose([
GroupOverSample(input_size, net.scale_size)
])
else:
raise ValueError("Only 1, 5, 10 crops are supported while we got {}".format(test_crop))
data_loader = torch.utils.data.DataLoader(
TSNDataSet(frame_save_dir, val_list, num_segments=test_segment,
new_length=1 if modality == "RGB" else 5,
modality=modality,
image_tmpl=prefix,
test_mode=True,
remove_missing=False,
transform=torchvision.transforms.Compose([
cropping,
Stack(roll=(arch in ['BNInception', 'InceptionV3'])),
ToTorchFormatTensor(div=(arch not in ['BNInception', 'InceptionV3'])),
GroupNormalize(net.input_mean, net.input_std),
]), dense_sample=dense_sample, twice_sample=twice_sample),
batch_size=batch_size, shuffle=False,
num_workers=workers, pin_memory=True,
)
net = torch.nn.DataParallel(net.cuda())
net.eval()
data_gen = enumerate(data_loader)
max_num = len(data_loader.dataset)
result_file = open(result_file_path, 'w')
for i, data_pair in data_gen:
directory, data = data_pair
with torch.no_grad():
if i >= max_num:
break
num_crop = test_crop
if dense_sample:
num_crop *= 10 # 10 clips for testing when using dense sample
if twice_sample:
num_crop *= 2
if modality == 'RGB':
length = 3
elif modality == 'Flow':
length = 10
elif modality == 'RGBDiff':
length = 18
else:
raise ValueError("Unknown modality " + modality)
data_in = data.view(-1, length, data.size(2), data.size(3))
if is_shift:
data_in = data_in.view(batch_size * num_crop, test_segment, length, data_in.size(2), data_in.size(3))
rst, feature = net(data_in)
rst = rst.reshape(batch_size, num_crop, -1).mean(1)
if softmax:
# take the softmax to normalize the output to probability
rst = F.softmax(rst, dim=1)
rst = rst.data.cpu().numpy().copy()
if net.module.is_shift:
rst = rst.reshape(batch_size, num_class)
else:
rst = rst.reshape((batch_size, -1, num_class)).mean(axis=1).reshape((batch_size, num_class))
proba = np.squeeze(rst)
proba = np.exp(proba)/sum(np.exp(proba))
result_file.write(str(directory[0]) + ' ')
result_file.write(str(proba[0]) + ',' + str(proba[1]) + ',' + str(proba[2]) + '\n')
result_file.close()
print('video filter end')