def set_model(args, n_data):
# set the model
if args.model == 'c3d':
model = C3D(with_classifier=False)
elif args.model == 'r3d':
model = R3DNet(layer_sizes=(1, 1, 1, 1), with_classifier=False)
elif args.model == 'r21d':
model = R2Plus1DNet(layer_sizes=(1, 1, 1, 1), with_classifier=False)
if args.intra_neg:
contrast = NCEAverage(args.feat_dim, n_data, args.nce_k, args.nce_t,
args.nce_m, args.softmax)
else:
contrast = NCEAverage_ori(args.feat_dim, n_data, args.nce_k,
args.nce_t, args.nce_m, args.softmax)
criterion_1 = NCESoftmaxLoss() if args.softmax else NCECriterion(n_data)
criterion_2 = NCESoftmaxLoss() if args.softmax else NCECriterion(n_data)
# GPU mode
model = model.cuda()
contrast = contrast.cuda()
criterion_1 = criterion_1.cuda()
criterion_2 = criterion_2.cuda()
cudnn.benchmark = True
return model, contrast, criterion_1, criterion_2
torch.cuda.manual_seed_all(args.seed)
########### model ##############
if args.dataset == 'ucf101':
class_num = 101
elif args.dataset == 'hmdb51':
class_num = 51
elif args.dataset == 'K400':
class_num = 400
if args.model == 'c3d':
model = C3D(with_classifier=True, num_classes=class_num).cuda()
elif args.model == 'r3d':
model = R3DNet(layer_sizes=(1,1,1,1), with_classifier=True, num_classes=class_num).cuda()
elif args.model == 'r21d':
model = R2Plus1DNet(layer_sizes=(1,1,1,1), with_classifier=True, num_classes=class_num).cuda()
elif args.model == 's3d':
model = S3D(num_classes=class_num, space_to_depth=False, with_classifier=True).cuda()
if args.ckpt:
pretrained_weights = torch.load(args.ckpt)['model']
model.load_state_dict({k.replace('module.base_network.',''):v for k,v in pretrained_weights.items()},strict=False)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model, device_ids=[0]).cuda()
if args.desp:
exp_name = 'K400_TCG_split1_finetuned_loss_{}_cl{}_{}_{}'.format(args.model, args.cl, args.desp, time.strftime('%m%d%H%M'))
else:
exp_name = 'K400_TCG_split1_finetuned_loss_{}_cl{}_{}'.format(args.model, args.cl, time.strftime('%m%d%H%M'))
log_dir = os.path.join(args.log, exp_name)
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
if args.seed:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
########### model ##############
if args.model == 'c3d':
base = C3D(with_classifier=False).to(device)
elif args.model == 'r3d':
base = R3DNet(layer_sizes=(3, 4, 6, 3),
with_classifier=False).to(device)
elif args.model == 'r21d':
base = R2Plus1DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False).to(device)
elif args.model == 'i3d':
base = InceptionI3d(final_endpoint='Logits',
with_classifier=False).to(device)
elif args.model == 'r3d50':
base = resnet50(sample_size=112,
sample_duration=16,
with_classifier=False,
return_conv=False).to(device)
elif args.model == 's3d':
base = S3D(gating=True, with_classifier=False,
return_conv=False).to(device)
tcg = TCG_triple_R3D_R21D_FCA2(base_network=base,
feature_size=512,
tuple_len=args.tl).cuda()
def extract_feature(args):
"""Extract and save features for train split, several clips per video."""
torch.backends.cudnn.benchmark = True
# Force the pytorch to create context on the specific device
#os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
########### model ##############
if args.model == 'c3d':
model = C3D(with_classifier=False, return_conv=True).to(device)
elif args.model == 'r3d':
model = R3DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False,
return_conv=True).to(device)
elif args.model == 'r21d':
model = R2Plus1DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False,
return_conv=True).to(device)
elif args.model == 's3d':
model = S3D(space_to_depth=False,
with_classifier=False,
return_conv=True).to(device)
if args.ckpt:
pretrained_weights = torch.load(args.ckpt)['model']
model.load_state_dict(
{
k.replace('module.base_network.', ''): v
for k, v in pretrained_weights.items()
},
strict=False)
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model, device_ids=[0, 1]).cuda()
model.eval()
torch.set_grad_enabled(False)
### Exract for train split ###
train_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
if args.dataset == 'ucf101':
train_dataset = UCF101ClipRetrievalDataset('data/ucf101', 16, 10, True,
train_transforms)
elif args.dataset == 'hmdb51':
train_dataset = HMDB51ClipRetrievalDataset('data/hmdb51', 16, 10, True,
train_transforms)
elif args.dataset == 'K400':
train_dataset = K400ClipRetrievalDataset('data/K400', 16, 10, True,
train_transforms)
train_dataloader = DataLoader(train_dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
features = []
classes = []
for data in tqdm(train_dataloader):
sampled_clips, idxs = data
clips = sampled_clips.reshape((-1, 3, 16, 112, 112))
inputs = clips.to(device)
# forward
outputs = model(inputs)
# print(outputs.shape)
# exit()
features.append(outputs.cpu().numpy().tolist())
classes.append(idxs.cpu().numpy().tolist())
features = np.array(features).reshape(-1, 10, outputs.shape[1])
classes = np.array(classes).reshape(-1, 10)
np.save(os.path.join(args.feature_dir, 'train_feature.npy'), features)
np.save(os.path.join(args.feature_dir, 'train_class.npy'), classes)
### Exract for test split ###
test_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
if args.dataset == 'ucf101':
test_dataset = UCF101ClipRetrievalDataset('data/ucf101', 16, 10, False,
test_transforms)
elif args.dataset == 'hmdb51':
test_dataset = HMDB51ClipRetrievalDataset('data/hmdb51', 16, 10, False,
test_transforms)
elif args.dataset == 'K400':
test_dataset = K400ClipRetrievalDataset('data/K400', 16, 10, False,
test_transforms)
test_dataloader = DataLoader(test_dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
features = []
classes = []
for data in tqdm(test_dataloader):
sampled_clips, idxs = data
clips = sampled_clips.reshape((-1, 3, 16, 112, 112))
inputs = clips.to(device)
# forward
outputs = model(inputs)
features.append(outputs.cpu().numpy().tolist())
classes.append(idxs.cpu().numpy().tolist())
features = np.array(features).reshape(-1, 10, outputs.shape[1])
classes = np.array(classes).reshape(-1, 10)
np.save(os.path.join(args.feature_dir, 'test_feature.npy'), features)
np.save(os.path.join(args.feature_dir, 'test_class.npy'), classes)
def extract_feature(args):
"""Extract and save features for train split, several clips per video."""
torch.backends.cudnn.benchmark = True
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
########### model ##############
if args.model == 'c3d':
model = C3D(with_classifier=False, return_conv=True).to(device)
elif args.model == 'r3d':
model = R3DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False,
return_conv=True).to(device)
elif args.model == 'r21d':
model = R2Plus1DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False,
return_conv=True).to(device)
#model = nn.DataParallel(model)
if args.ckpt:
pretrained_weights = torch.load(args.ckpt)['model']
model.load_state_dict(pretrained_weights, strict=True)
model.eval()
torch.set_grad_enabled(False)
### Exract for train split ###
train_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
if args.dataset == 'ucf101':
train_dataset = UCF101ClipRetrievalDataset('data/ucf101', 16, 10, True,
train_transforms)
elif args.dataset == 'hmdb51':
train_dataset = HMDB51ClipRetrievalDataset('data/hmdb51', 16, 10, True,
train_transforms)
train_dataloader = DataLoader(train_dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
features = []
classes = []
for data in tqdm(train_dataloader):
sampled_clips, u_clips, v_clips, idxs = data
if args.modality == 'u':
input_clips = u_clips
elif args.modality == 'v':
input_clips = v_clips
else: # rgb and res
input_clips = sampled_clips
clips = input_clips.reshape((-1, 3, 16, 112, 112))
inputs = clips.to(device)
if args.modality == 'res':
outputs = model(diff(inputs))
else:
outputs = model(inputs)
if args.merge:
rgb_clips = sampled_clips.reshape((-1, 3, 16, 112, 112)).to(device)
outputs_rgb = model(rgb_clips)
outputs = torch.cat((outputs_rgb, outputs), 1)
features.append(outputs.cpu().numpy().tolist())
classes.append(idxs.cpu().numpy().tolist())
features = np.array(features).reshape(-1, 10, outputs.shape[1])
classes = np.array(classes).reshape(-1, 10)
np.save(os.path.join(args.feature_dir, 'train_feature.npy'), features)
np.save(os.path.join(args.feature_dir, 'train_class.npy'), classes)
## Exract for test split ###
test_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
if args.dataset == 'ucf101':
test_dataset = UCF101ClipRetrievalDataset('data/ucf101', 16, 10, False,
test_transforms)
elif args.dataset == 'hmdb51':
test_dataset = HMDB51ClipRetrievalDataset('data/hmdb51', 16, 10, False,
test_transforms)
test_dataloader = DataLoader(test_dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
features = []
classes = []
for data in tqdm(test_dataloader):
sampled_clips, u_clips, v_clips, idxs = data
if args.modality == 'u':
input_clips = u_clips
elif args.modality == 'v':
input_clips = v_clips
else: # rgb and res
input_clips = sampled_clips
clips = input_clips.reshape((-1, 3, 16, 112, 112))
inputs = clips.to(device)
# forward
if args.modality == 'res':
outputs = model(diff(inputs))
else:
outputs = model(inputs)
if args.merge:
rgb_clips = sampled_clips.reshape((-1, 3, 16, 112, 112)).to(device)
outputs_rgb = model(rgb_clips)
outputs = torch.cat((outputs_rgb, outputs), 1)
features.append(outputs.cpu().numpy().tolist())
classes.append(idxs.cpu().numpy().tolist())
features = np.array(features).reshape(-1, 10, outputs.shape[1])
classes = np.array(classes).reshape(-1, 10)
np.save(os.path.join(args.feature_dir, 'test_feature.npy'), features)
np.save(os.path.join(args.feature_dir, 'test_class.npy'), classes)
print('Saving features to ...', args.feature_dir)
def extract_feature(args):
"""Extract and save features for train split, several clips per video."""
torch.backends.cudnn.benchmark = True
# Force the pytorch to create context on the specific device
#os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
########### model ##############
if args.model == 'c3d':
model = C3D(with_classifier=False, return_conv=True).to(device)
elif args.model == 'r3d':
model = R3DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False,
return_conv=True).to(device)
elif args.model == 'r21d':
model = R2Plus1DNet(layer_sizes=(1, 1, 1, 1),
with_classifier=False,
return_conv=True).to(device)
if args.ckpt:
pretrained_weights = load_pretrained_weights(args.ckpt)
model.load_state_dict(pretrained_weights, strict=True)
model.eval()
torch.set_grad_enabled(False)
### Exract for train split ###
train_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
if args.dataset == 'ucf101':
train_dataset = UCF101ClipRetrievalDataset('data', 16, 10, True,
train_transforms)
#elif args.dataset == 'hmdb51':
# train_dataset = HMDB51ClipRetrievalDataset('data/hmdb51', 16, 10, True, train_transforms)
train_dataloader = DataLoader(train_dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
features = []
classes = []
for data in tqdm(train_dataloader):
sampled_clips, idxs = data
clips = sampled_clips.reshape((-1, 3, 16, 112, 112))
inputs = clips.to(device)
if args.modality == 'res':
outputs = model(diff(inputs))
else:
outputs = model(inputs)
# forward
#outputs = model(inputs)
# print(outputs.shape)
# exit()
features.append(outputs.cpu().numpy().tolist())
classes.append(idxs.cpu().numpy().tolist())
features = np.array(features).reshape(-1, 10, outputs.shape[1])
classes = np.array(classes).reshape(-1, 10)
np.save(os.path.join(args.feature_dir, 'train_feature.npy'), features)
np.save(os.path.join(args.feature_dir, 'train_class.npy'), classes)
### Exract for test split ###
test_transforms = transforms.Compose([
transforms.Resize((128, 171)),
transforms.CenterCrop(112),
transforms.ToTensor()
])
if args.dataset == 'ucf101':
test_dataset = UCF101ClipRetrievalDataset('data', 16, 10, False,
test_transforms)
#elif args.dataset == 'hmdb51':
# test_dataset = HMDB51ClipRetrievalDataset('data/hmdb51', 16, 10, False, test_transforms)
test_dataloader = DataLoader(test_dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
drop_last=True)
features = []
classes = []
for data in tqdm(test_dataloader):
sampled_clips, idxs = data
clips = sampled_clips.reshape((-1, 3, 16, 112, 112))
inputs = clips.to(device)
# forward
if args.modality == 'res':
shift_inputs = torch.roll(inputs, 1, 2)
outputs = model(((shift_inputs - inputs) + 1) / 2)
else:
outputs = model(inputs)
features.append(outputs.cpu().numpy().tolist())
classes.append(idxs.cpu().numpy().tolist())
features = np.array(features).reshape(-1, 10, outputs.shape[1])
classes = np.array(classes).reshape(-1, 10)
np.save(os.path.join(args.feature_dir, 'test_feature.npy'), features)
np.save(os.path.join(args.feature_dir, 'test_class.npy'), classes)
print('Saving features to ...', args.feature_dir)