close_cams_file = '/home/yogesh/kara/data/panoptic/closecams.list'
RANDOM_ALL = True
BATCH_SIZE = 32
CHANNELS = 3
FRAMES = 8
SKIP_LEN = 2
HEIGHT = 112
WIDTH = 112
PRECROP = False
CLOSE_VIEWS = True
VIEW_DIST_MAX = 10
trainset = PanopticDataset(root_dir=data_root_dir, data_file=train_split,
resize_height=HEIGHT, resize_width=WIDTH,
clip_len=FRAMES, skip_len=SKIP_LEN,
random_all=RANDOM_ALL, close_views=CLOSE_VIEWS,
close_cams_file=close_cams_file, precrop=PRECROP)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
testset = PanopticDataset(root_dir=data_root_dir, data_file=train_split,
resize_height=HEIGHT, resize_width=WIDTH,
clip_len=FRAMES, skip_len=SKIP_LEN,
random_all=RANDOM_ALL, close_views=CLOSE_VIEWS,
close_cams_file=close_cams_file, precrop=PRECROP)
testloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
print('TRAINING...')
for batch_idx, (vp_diff, vid1, vid2) in enumerate(trainloader):
print('{} {} {} {}'.format(batch_idx, vp_diff.size(), vid1.size(), vid2.size()))
clip_len=FRAMES, skip_len=SKIP_LEN,
random_all=RANDOM_ALL, precrop=PRECROP)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
elif DATASET.lower() == 'panoptic':
data_root_dir, test_split, weights_path, output_video_dir = panoptic_config()
# generator
model = FullNetwork(vp_value_count=3, output_shape=(BATCH_SIZE, CHANNELS, FRAMES, HEIGHT, WIDTH))
model.load_state_dict(torch.load(weights_path))
model = model.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
criterion = nn.MSELoss()
testset = PanopticDataset(root_dir=data_root_dir, data_file=test_split,
resize_height=HEIGHT, resize_width=WIDTH,
clip_len=FRAMES, skip_len=SKIP_LEN,
random_all=RANDOM_ALL, precrop=PRECROP)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
else:
print('This network has only been set up to run on the NTU and panoptic datasets.')
print_params()
print(model)
test_model()
random_all=RANDOM_ALL, precrop=PRECROP)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
elif DATASET.lower() == 'panoptic':
data_root_dir, test_split, weights_path, output_video_dir = panoptic_config()
# generator
model = FullNetwork(vp_value_count=3, output_shape=(BATCH_SIZE, CHANNELS, FRAMES, HEIGHT, WIDTH))
model.load_state_dict(torch.load(weights_path))
model = model.to(device)
if device == 'cuda':
net = torch.nn.DataParallel(model)
cudnn.benchmark = True
criterion = nn.MSELoss()
testset = PanopticDataset(root_dir=data_root_dir, data_file=test_split,
resize_height=HEIGHT, resize_width=WIDTH,
clip_len=FRAMES, skip_len=SKIP_LEN,
random_all=RANDOM_ALL, close_views=CLOSE_VIEWS, view_dist_max=VIEW_DIST_MAX,
precrop=PRECROP)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2)
else:
print('This network has only been set up to run on the NTU and panoptic datasets.')
print_params()
print(model)
test_model()