def main():
global args, best_prec1, model, writer, best_loss, length, width, height, input_size, scheduler
args = parser.parse_args()
training_continue = args.contine
if '3D' in args.arch:
if 'I3D' in args.arch or 'MFNET3D' in args.arch:
if '112' in args.arch:
scale = 0.5
else:
scale = 1
else:
if '224' in args.arch:
scale = 1
else:
scale = 0.5
elif 'r2plus1d' in args.arch:
scale = 0.5
else:
scale = 1
print('scale: %.1f' % (scale))
input_size = int(224 * scale)
width = int(340 * scale)
height = int(256 * scale)
saveLocation = "./checkpoint/" + args.dataset + "_" + args.arch + "_split" + str(
args.split)
if not os.path.exists(saveLocation):
os.makedirs(saveLocation)
writer = SummaryWriter(saveLocation)
# create model
if args.evaluate:
print("Building validation model ... ")
model = build_model_validate()
optimizer = AdamW(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif training_continue:
model, startEpoch, optimizer, best_prec1 = build_model_continue()
for param_group in optimizer.param_groups:
lr = param_group['lr']
#param_group['lr'] = lr
print(
"Continuing with best precision: %.3f and start epoch %d and lr: %f"
% (best_prec1, startEpoch, lr))
else:
print("Building model with ADAMW... ")
model = build_model()
optimizer = AdamW(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
startEpoch = 0
if HALF:
model.half() # convert to half precision
for layer in model.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.float()
print("Model %s is loaded. " % (args.arch))
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
criterion2 = nn.MSELoss().cuda()
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
patience=5,
verbose=True)
print("Saving everything to directory %s." % (saveLocation))
if args.dataset == 'ucf101':
dataset = './datasets/ucf101_frames'
elif args.dataset == 'hmdb51':
dataset = './datasets/hmdb51_frames'
elif args.dataset == 'smtV2':
dataset = './datasets/smtV2_frames'
elif args.dataset == 'window':
dataset = './datasets/window_frames'
elif args.dataset == 'haa500_basketball':
dataset = './datasets/haa500_basketball_frames'
else:
print("No convenient dataset entered, exiting....")
return 0
cudnn.benchmark = True
modality = args.arch.split('_')[0]
if "3D" in args.arch or 'tsm' in args.arch or 'slowfast' in args.arch or 'r2plus1d' in args.arch:
if '64f' in args.arch:
length = 64
elif '32f' in args.arch:
length = 32
else:
length = 16
else:
length = 1
# Data transforming
if modality == "rgb" or modality == "pose":
is_color = True
scale_ratios = [1.0, 0.875, 0.75, 0.66]
if 'I3D' in args.arch:
if 'resnet' in args.arch:
clip_mean = [0.45, 0.45, 0.45] * args.num_seg * length
clip_std = [0.225, 0.225, 0.225] * args.num_seg * length
else:
clip_mean = [0.5, 0.5, 0.5] * args.num_seg * length
clip_std = [0.5, 0.5, 0.5] * args.num_seg * length
#clip_std = [0.25, 0.25, 0.25] * args.num_seg * length
elif 'MFNET3D' in args.arch:
clip_mean = [0.48627451, 0.45882353, 0.40784314
] * args.num_seg * length
clip_std = [0.234, 0.234, 0.234] * args.num_seg * length
elif "3D" in args.arch:
clip_mean = [114.7748, 107.7354, 99.4750] * args.num_seg * length
clip_std = [1, 1, 1] * args.num_seg * length
elif "r2plus1d" in args.arch:
clip_mean = [0.43216, 0.394666, 0.37645] * args.num_seg * length
clip_std = [0.22803, 0.22145, 0.216989] * args.num_seg * length
elif "rep_flow" in args.arch:
clip_mean = [0.5, 0.5, 0.5] * args.num_seg * length
clip_std = [0.5, 0.5, 0.5] * args.num_seg * length
elif "slowfast" in args.arch:
clip_mean = [0.45, 0.45, 0.45] * args.num_seg * length
clip_std = [0.225, 0.225, 0.225] * args.num_seg * length
else:
clip_mean = [0.485, 0.456, 0.406] * args.num_seg * length
clip_std = [0.229, 0.224, 0.225] * args.num_seg * length
elif modality == "pose":
is_color = True
scale_ratios = [1.0, 0.875, 0.75, 0.66]
clip_mean = [0.485, 0.456, 0.406] * args.num_seg
clip_std = [0.229, 0.224, 0.225] * args.num_seg
elif modality == "flow":
is_color = False
scale_ratios = [1.0, 0.875, 0.75, 0.66]
if 'I3D' in args.arch:
clip_mean = [0.5, 0.5] * args.num_seg * length
clip_std = [0.5, 0.5] * args.num_seg * length
elif "3D" in args.arch:
clip_mean = [127.5, 127.5] * args.num_seg * length
clip_std = [1, 1] * args.num_seg * length
else:
clip_mean = [0.5, 0.5] * args.num_seg * length
clip_std = [0.226, 0.226] * args.num_seg * length
elif modality == "both":
is_color = True
scale_ratios = [1.0, 0.875, 0.75, 0.66]
clip_mean = [0.485, 0.456, 0.406, 0.5, 0.5] * args.num_seg * length
clip_std = [0.229, 0.224, 0.225, 0.226, 0.226] * args.num_seg * length
else:
print("No such modality. Only rgb and flow supported.")
normalize = video_transforms.Normalize(mean=clip_mean, std=clip_std)
if "3D" in args.arch and not ('I3D' in args.arch):
train_transform = video_transforms.Compose([
video_transforms.MultiScaleCrop((input_size, input_size),
scale_ratios),
video_transforms.RandomHorizontalFlip(),
video_transforms.ToTensor2(),
normalize,
])
val_transform = video_transforms.Compose([
video_transforms.CenterCrop((input_size)),
video_transforms.ToTensor2(),
normalize,
])
else:
train_transform = video_transforms.Compose([
video_transforms.MultiScaleCrop((input_size, input_size),
scale_ratios),
video_transforms.RandomHorizontalFlip(),
video_transforms.ToTensor(),
normalize,
])
val_transform = video_transforms.Compose([
video_transforms.CenterCrop((input_size)),
video_transforms.ToTensor(),
normalize,
])
# data loading
train_setting_file = "train_%s_split%d.txt" % (modality, args.split)
train_split_file = os.path.join(args.settings, args.dataset,
train_setting_file)
val_setting_file = "val_%s_split%d.txt" % (modality, args.split)
val_split_file = os.path.join(args.settings, args.dataset,
val_setting_file)
if not os.path.exists(train_split_file) or not os.path.exists(
val_split_file):
print(
"No split file exists in %s directory. Preprocess the dataset first"
% (args.settings))
train_dataset = datasets.__dict__[args.dataset](
root=dataset,
source=train_split_file,
phase="train",
modality=modality,
is_color=is_color,
new_length=length,
new_width=width,
new_height=height,
video_transform=train_transform,
num_segments=args.num_seg)
val_dataset = datasets.__dict__[args.dataset](
root=dataset,
source=val_split_file,
phase="val",
modality=modality,
is_color=is_color,
new_length=length,
new_width=width,
new_height=height,
video_transform=val_transform,
num_segments=args.num_seg)
print('{} samples found, {} train samples and {} test samples.'.format(
len(val_dataset) + len(train_dataset), len(train_dataset),
len(val_dataset)))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
if args.evaluate:
prec1, prec3, _ = validate(val_loader, model, criterion, criterion2,
modality)
return
for epoch in range(startEpoch, args.epochs):
# if learning_rate_index > max_learning_rate_decay_count:
# break
# adjust_learning_rate(optimizer, epoch)
train(train_loader, model, criterion, criterion2, optimizer, epoch,
modality)
# evaluate on validation set
prec1 = 0.0
lossClassification = 0
if (epoch + 1) % args.save_freq == 0:
prec1, prec3, lossClassification = validate(
val_loader, model, criterion, criterion2, modality)
writer.add_scalar('data/top1_validation', prec1, epoch)
writer.add_scalar('data/top3_validation', prec3, epoch)
writer.add_scalar('data/classification_loss_validation',
lossClassification, epoch)
scheduler.step(lossClassification)
# remember best prec@1 and save checkpoint
is_best = prec1 >= best_prec1
best_prec1 = max(prec1, best_prec1)
# best_in_existing_learning_rate = max(prec1, best_in_existing_learning_rate)
#
# if best_in_existing_learning_rate > prec1 + 1:
# learning_rate_index = learning_rate_index
# best_in_existing_learning_rate = 0
if (epoch + 1) % args.save_freq == 0:
checkpoint_name = "%03d_%s" % (epoch + 1, "checkpoint.pth.tar")
if is_best:
print("Model works well")
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
}, is_best, checkpoint_name, saveLocation)
checkpoint_name = "%03d_%s" % (epoch + 1, "checkpoint.pth.tar")
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
'best_loss': best_loss,
'optimizer': optimizer.state_dict(),
}, is_best, checkpoint_name, saveLocation)
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
def main(args):
global best_prec1, best_loss
input_size = int(224 * args.scale)
width = int(340 * args.scale)
height = int(256 * args.scale)
if not os.path.exists(args.savelocation):
os.makedirs(args.savelocation)
now = time.time()
savelocation = os.path.join(args.savelocation, str(now))
os.makedirs(savelocation)
logging.basicConfig(filename=os.path.join(savelocation, "log.log"),
level=logging.INFO)
model = build_model(args.arch, args.pre, args.num_seg, args.resume)
optimizer = AdamW(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss().cuda()
criterion2 = nn.MSELoss().cuda()
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
'min',
patience=5,
verbose=True)
# if args.dataset=='sign':
# dataset="/data/AUTSL/train_img_c"
# elif args.dataset=="signd":
# dataset="/data/AUTSL/train_img_c"
# elif args.dataset=="customd":
# dataset="/data/AUTSL/train_img_c"
# else:
# print("no dataset")
# return 0
cudnn.benchmark = True
length = 64
scale_ratios = [1.0, 0.875, 0.75, 0.66]
clip_mean = [0.43216, 0.394666, 0.37645] * args.num_seg * length
clip_std = [0.22803, 0.22145, 0.216989] * args.num_seg * length
normalize = video_transforms.Normalize(mean=clip_mean, std=clip_std)
train_transform = video_transforms.Compose([
video_transforms.CenterCrop(input_size),
video_transforms.ToTensor2(),
normalize,
])
val_transform = video_transforms.Compose([
video_transforms.CenterCrop((input_size)),
video_transforms.ToTensor2(),
normalize,
])
# test_transform = video_transforms.Compose([
# video_transforms.CenterCrop((input_size)),
# video_transforms.ToTensor2(),
# normalize,
# ])
# test_file = os.path.join(args.datasetpath, args.testlist)
if not os.path.exists(args.trainlist) or not os.path.exists(args.vallist):
print(
"No split file exists in %s directory. Preprocess the dataset first"
% (args.datasetpath))
train_dataset = datasets.__dict__[args.dataset](
root=args.datasetpath,
source=args.trainlist,
phase="train",
modality="rgb",
is_color=True,
new_length=length,
new_width=width,
new_height=height,
video_transform=train_transform,
num_segments=args.num_seg)
val_dataset = datasets.__dict__[args.dataset](
root=args.datasetpath,
source=args.vallist,
phase="val",
modality="rgb",
is_color=True,
new_length=length,
new_width=width,
new_height=height,
video_transform=val_transform,
num_segments=args.num_seg)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
best_prec1 = 0
for epoch in range(0, args.epochs):
train(length, input_size, train_loader, model, criterion, criterion2,
optimizer, epoch)
if (epoch + 1) % args.save_freq == 0:
is_best = False
prec1, prec3, lossClassification = validate(
length, input_size, val_loader, model, criterion, criterion2)
scheduler.step(lossClassification)
if prec1 >= best_prec1:
is_best = True
best_prec1 = prec1
checkpoint_name = "%03d_%s" % (epoch + 1, "checkpoint.pth.tar")
text = "save checkpoint {}".format(checkpoint_name)
print(text)
logging.info(text)
save_checkpoint(
{
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": model.state_dict(),
"prec1": prec1,
"optimizer": optimizer.state_dict()
}, is_best, checkpoint_name, savelocation)
def VideoSpatialPrediction3D(vid_name,
net,
num_categories,
architecture_name,
start_frame=0,
num_frames=0,
length=16,
extension='img_{0:05d}.jpg',
ten_crop=False):
if num_frames == 0:
imglist = os.listdir(vid_name)
newImageList = []
if 'rgb' in architecture_name or 'pose' in architecture_name:
for item in imglist:
if 'img' in item:
newImageList.append(item)
elif 'flow' in architecture_name:
for item in imglist:
if 'flow_x' in item:
newImageList.append(item)
duration = len(newImageList)
else:
duration = num_frames
if 'rgb' in architecture_name or 'pose' in architecture_name:
if 'I3D' in architecture_name:
if not 'resnet' in architecture_name:
clip_mean = [0.5, 0.5, 0.5]
clip_std = [0.5, 0.5, 0.5]
else:
clip_mean = [0.45, 0.45, 0.45]
clip_std = [0.225, 0.225, 0.225]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
if '112' in architecture_name:
scale = 0.5
else:
scale = 1
elif 'MFNET3D' in architecture_name:
clip_mean = [0.48627451, 0.45882353, 0.40784314]
clip_std = [0.234, 0.234, 0.234]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose(
[video_transforms.ToTensor(), normalize])
if '112' in architecture_name:
scale = 0.5
else:
scale = 1
elif 'tsm' in architecture_name:
clip_mean = [0.485, 0.456, 0.406]
clip_std = [0.229, 0.224, 0.225]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose(
[video_transforms.ToTensor(), normalize])
scale = 1
elif "r2plus1d" in architecture_name:
clip_mean = [0.43216, 0.394666, 0.37645]
clip_std = [0.22803, 0.22145, 0.216989]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose(
[video_transforms.ToTensor(), normalize])
scale = 0.5
elif 'rep_flow' in architecture_name:
clip_mean = [0.5, 0.5, 0.5]
clip_std = [0.5, 0.5, 0.5]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 1
elif "slowfast" in architecture_name:
clip_mean = [0.45, 0.45, 0.45]
clip_std = [0.225, 0.225, 0.225]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 1
else:
scale = 0.5
clip_mean = [114.7748, 107.7354, 99.4750]
clip_std = [1, 1, 1]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor2(),
normalize,
])
elif 'flow' in architecture_name:
if 'I3D' in architecture_name:
clip_mean = [0.5] * 2
clip_std = [0.5] * 2
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 1
elif "3D" in architecture_name:
scale = 0.5
clip_mean = [127.5, 127.5]
clip_std = [1, 1]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor2(),
normalize,
])
elif "r2plus1d" in architecture_name:
clip_mean = [0.5] * 2
clip_std = [0.226] * 2
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 0.5
if '224' in architecture_name:
scale = 1
if '112' in architecture_name:
scale = 0.5
# selection
#step = int(math.floor((duration-1)/(num_samples-1)))
dims2 = (224, 224, 3, duration)
imageSize = int(224 * scale)
dims = (int(256 * scale), int(340 * scale), 3, duration)
#dims = (int(256 * scale),int(256 * scale),3,duration)
duration = duration - 1
offsets = []
offsetMainIndexes = list(range(1, duration - length, length))
if len(offsetMainIndexes) == 0:
offsets = list(range(1, duration + 2)) * int(
np.floor(length / (duration + 1))) + list(
range(1, length % (duration + 1) + 1))
else:
shift = int((duration - (offsetMainIndexes[-1] + length)) / 2)
for mainOffsetValue in offsetMainIndexes:
for lengthID in range(1, length + 1):
offsets.append(lengthID + mainOffsetValue + shift)
# offsetMainIndexes = list(range(0,duration,length))
# for mainOffsetValue in offsetMainIndexes:
# for lengthID in range(1, length+1):
# loaded_frame_index = lengthID + mainOffsetValue
# moded_loaded_frame_index = loaded_frame_index % (duration + 1)
# if moded_loaded_frame_index == 0:
# moded_loaded_frame_index = (duration + 1)
# offsets.append(moded_loaded_frame_index)
imageList = []
imageList1 = []
imageList2 = []
imageList3 = []
imageList4 = []
imageList5 = []
imageList6 = []
imageList7 = []
imageList8 = []
imageList9 = []
imageList10 = []
imageList11 = []
imageList12 = []
interpolation = cv2.INTER_LINEAR
for index in offsets:
if 'rgb' in architecture_name or 'pose' in architecture_name:
img_file = os.path.join(vid_name, extension.format(index))
img = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)
img = cv2.resize(img, dims[1::-1], interpolation)
#img2 = cv2.resize(img, dims2[1::-1],interpolation)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_flip = img[:, ::-1, :].copy()
elif 'flow' in architecture_name:
flow_x_file = os.path.join(vid_name, extension.format('x', index))
flow_y_file = os.path.join(vid_name, extension.format('y', index))
img_x = cv2.imread(flow_x_file, cv2.IMREAD_GRAYSCALE)
img_y = cv2.imread(flow_y_file, cv2.IMREAD_GRAYSCALE)
img_x = np.expand_dims(img_x, -1)
img_y = np.expand_dims(img_y, -1)
img = np.concatenate((img_x, img_y), 2)
img = cv2.resize(img, dims[1::-1], interpolation)
img_flip = img[:, ::-1, :].copy()
#img_flip2 = img2[:,::-1,:].copy()
#imageList1.append(img[int(16 * scale):int(16 * scale + imageSize), int(16 * scale) : int(16 * scale + imageSize), :])
imageList1.append(img[int(16 * scale):int(16 * scale + imageSize),
int(58 * scale):int(58 * scale + imageSize), :])
imageList2.append(img[:imageSize, :imageSize, :])
imageList3.append(img[:imageSize, -imageSize:, :])
imageList4.append(img[-imageSize:, :imageSize, :])
imageList5.append(img[-imageSize:, -imageSize:, :])
imageList6.append(img_flip[int(16 * scale):int(16 * scale + imageSize),
int(58 * scale):int(58 * scale +
imageSize), :])
imageList7.append(img_flip[:imageSize, :imageSize, :])
imageList8.append(img_flip[:imageSize, -imageSize:, :])
imageList9.append(img_flip[-imageSize:, :imageSize, :])
imageList10.append(img_flip[-imageSize:, -imageSize:, :])
# imageList11.append(img2)
# imageList12.append(img_flip2)
if ten_crop:
imageList = imageList1 + imageList2 + imageList3 + imageList4 + imageList5 + imageList6 + imageList7 + imageList8 + imageList9 + imageList10
else:
imageList = imageList1
#imageList=imageList11+imageList12
rgb_list = []
for i in range(len(imageList)):
cur_img = imageList[i]
cur_img_tensor = val_transform(cur_img)
rgb_list.append(np.expand_dims(cur_img_tensor.numpy(), 0))
input_data = np.concatenate(rgb_list, axis=0)
if 'rgb' in architecture_name or 'pose' in architecture_name:
input_data = input_data.reshape(-1, length, 3, imageSize, imageSize)
elif 'flow' in architecture_name:
input_data = input_data.reshape(-1, length, 2, imageSize, imageSize)
batch_size = 10
result = np.zeros([input_data.shape[0], num_categories])
num_batches = int(math.ceil(float(input_data.shape[0]) / batch_size))
with torch.no_grad():
for bb in range(num_batches):
span = range(batch_size * bb,
min(input_data.shape[0], batch_size * (bb + 1)))
input_data_batched = input_data[span, :, :, :, :]
imgDataTensor = torch.from_numpy(input_data_batched).type(
torch.FloatTensor).cuda()
if 'rgb' in architecture_name or 'pose' in architecture_name:
imgDataTensor = imgDataTensor.view(-1, length, 3, imageSize,
imageSize).transpose(1, 2)
elif 'flow' in architecture_name:
imgDataTensor = imgDataTensor.view(-1, length, 2, imageSize,
imageSize).transpose(1, 2)
if 'bert' in architecture_name or 'pooling' in architecture_name or 'NLB' in architecture_name \
or 'lstm' in architecture_name or 'adamw' in architecture_name:
output, input_vectors, sequenceOut, maskSample = net(
imgDataTensor)
else:
output = net(imgDataTensor)
#span = range(sample_size*bb, min(int(input_data.shape[0]/length),sample_size*(bb+1)))
result[span, :] = output.data.cpu().numpy()
mean_result = np.mean(result, 0)
prediction = np.argmax(mean_result)
top3 = mean_result.argsort()[::-1][:3]
top5 = mean_result.argsort()[::-1][:5]
return prediction, mean_result, top3
def VideoSpatialPrediction3D_bert(
vid_name,
net,
num_categories,
architecture_name,
start_frame=0,
num_frames=0,
num_seg=4,
length = 16,
extension = 'img_{0:05d}.jpg',
ten_crop = False
):
if num_frames == 0:
imglist = os.listdir(vid_name)
newImageList=[]
if 'rgb' in architecture_name or 'pose' in architecture_name:
for item in imglist:
if 'img' in item:
newImageList.append(item)
elif 'flow' in architecture_name:
for item in imglist:
if 'flow_x' in item:
newImageList.append(item)
duration = len(newImageList)
else:
duration = num_frames
if 'rgb' in architecture_name:
if 'I3D' in architecture_name:
if not 'resnet' in architecture_name:
clip_mean = [0.5, 0.5, 0.5]
clip_std = [0.5, 0.5, 0.5]
else:
clip_mean = [0.45, 0.45, 0.45]
clip_std = [0.225, 0.225, 0.225]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
if '112' in architecture_name:
scale = 0.5
else:
scale = 1
elif 'MFNET3D' in architecture_name:
clip_mean = [0.48627451, 0.45882353, 0.40784314]
clip_std = [0.234, 0.234, 0.234]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize])
if '112' in architecture_name:
scale = 0.5
else:
scale = 1
elif 'tsm' in architecture_name:
clip_mean = [0.485, 0.456, 0.406]
clip_std = [0.229, 0.224, 0.225]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize])
scale = 1
elif "r2plus1d" in architecture_name:
clip_mean = [0.43216, 0.394666, 0.37645]
clip_std = [0.22803, 0.22145, 0.216989]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize])
scale = 0.5
elif 'rep_flow' in architecture_name:
clip_mean = [0.5, 0.5, 0.5]
clip_std = [0.5, 0.5, 0.5]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 1
elif "slowfast" in architecture_name:
clip_mean = [0.45, 0.45, 0.45]
clip_std = [0.225, 0.225, 0.225]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 1
else:
scale = 0.5
clip_mean = [114.7748, 107.7354, 99.4750]
clip_std = [1, 1, 1]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor2(),
normalize,
])
elif 'flow' in architecture_name:
if 'I3D' in architecture_name:
clip_mean = [0.5] * 2
clip_std = [0.5] * 2
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor(),
normalize,
])
scale = 1
else:
scale = 0.5
clip_mean = [127.5, 127.5]
clip_std = [1, 1]
normalize = video_transforms.Normalize(mean=clip_mean,
std=clip_std)
val_transform = video_transforms.Compose([
video_transforms.ToTensor2(),
normalize,
])
# selection
#step = int(math.floor((duration-1)/(num_samples-1)))
if '224' in architecture_name:
scale = 1
if '112' in architecture_name:
scale = 0.5
imageSize=int(224 * scale)
dims = (int(256 * scale),int(340 * scale),3,duration)
duration = duration - 1
average_duration = int(duration / num_seg)
offsetMainIndexes = []
offsets = []
for seg_id in range(num_seg):
if average_duration >= length:
offsetMainIndexes.append(int((average_duration - length + 1)/2 + seg_id * average_duration))
elif duration >=length:
average_part_length = int(np.floor((duration-length)/num_seg))
offsetMainIndexes.append(int((average_part_length*(seg_id) + average_part_length*(seg_id+1))/2))
else:
increase = int(duration / num_seg)
offsetMainIndexes.append(0 + seg_id * increase)
for mainOffsetValue in offsetMainIndexes:
for lengthID in range(1, length+1):
loaded_frame_index = lengthID + mainOffsetValue
moded_loaded_frame_index = loaded_frame_index % (duration + 1)
if moded_loaded_frame_index == 0:
moded_loaded_frame_index = (duration + 1)
offsets.append(moded_loaded_frame_index)
imageList=[]
imageList1=[]
imageList2=[]
imageList3=[]
imageList4=[]
imageList5=[]
imageList6=[]
imageList7=[]
imageList8=[]
imageList9=[]
imageList10=[]
imageList11=[]
imageList12=[]
interpolation = cv2.INTER_LINEAR
for index in offsets:
if 'rgb' in architecture_name or 'pose' in architecture_name:
img_file = os.path.join(vid_name, extension.format(index))
img = cv2.imread(img_file, cv2.IMREAD_UNCHANGED)
img = cv2.resize(img, dims[1::-1],interpolation)
#img2 = cv2.resize(img, dims2[1::-1],interpolation)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_flip = img[:,::-1,:].copy()
elif 'flow' in architecture_name:
flow_x_file = os.path.join(vid_name, extension.format('x',index))
flow_y_file = os.path.join(vid_name, extension.format('y',index))
img_x = cv2.imread(flow_x_file, cv2.IMREAD_GRAYSCALE)
img_y = cv2.imread(flow_y_file, cv2.IMREAD_GRAYSCALE)
img_x = np.expand_dims(img_x,-1)
img_y = np.expand_dims(img_y,-1)
img = np.concatenate((img_x,img_y),2)
img = cv2.resize(img, dims[1::-1],interpolation)
img_flip = img[:,::-1,:].copy()
#img_flip2 = img2[:,::-1,:].copy()
imageList1.append(img[int(16 * scale):int(16 * scale + imageSize), int(58 * scale) : int(58 * scale + imageSize), :])
imageList2.append(img[:imageSize, :imageSize, :])
imageList3.append(img[:imageSize, -imageSize:, :])
imageList4.append(img[-imageSize:, :imageSize, :])
imageList5.append(img[-imageSize:, -imageSize:, :])
imageList6.append(img_flip[int(16 * scale):int(16 * scale + imageSize), int(58 * scale) : int(58 * scale + imageSize), :])
imageList7.append(img_flip[:imageSize, :imageSize, :])
imageList8.append(img_flip[:imageSize, -imageSize:, :])
imageList9.append(img_flip[-imageSize:, :imageSize, :])
imageList10.append(img_flip[-imageSize:, -imageSize:, :])
# imageList11.append(img2)
# imageList12.append(img_flip2)
if ten_crop:
imageList=imageList1+imageList2+imageList3+imageList4+imageList5+imageList6+imageList7+imageList8+imageList9+imageList10
else:
imageList=imageList1
#imageList=imageList11+imageList12
rgb_list=[]
for i in range(len(imageList)):
cur_img = imageList[i]
cur_img_tensor = val_transform(cur_img)
rgb_list.append(np.expand_dims(cur_img_tensor.numpy(), 0))
input_data=np.concatenate(rgb_list,axis=0)
with torch.no_grad():
imgDataTensor = torch.from_numpy(input_data).type(torch.FloatTensor).cuda()
if 'rgb' in architecture_name or 'pose' in architecture_name:
imgDataTensor = imgDataTensor.view(-1,length,3,imageSize,imageSize).transpose(1,2)
elif 'flow' in architecture_name:
imgDataTensor = imgDataTensor.view(-1,length,2,imageSize,imageSize).transpose(1,2)
if 'bert' in architecture_name or 'pooling' in architecture_name:
output, input_vectors, sequenceOut, maskSample = net(imgDataTensor)
else:
output = net(imgDataTensor)
# outputSoftmax=soft(output)
result = output.data.cpu().numpy()
mean_result=np.mean(result,0)
prediction=np.argmax(mean_result)
top3 = mean_result.argsort()[::-1][:3]
return prediction, mean_result, top3
