def test_transforms():
img = np.random.randn(224, 224, 3).astype(np.uint8)
test_transform = build_transform(cfg, is_train=False)
start = time.time()
res = test_transform(img)
end = time.time()
print(res.shape)
print('process time: {}'.format(end - start))
train_transform = build_transform(cfg, is_train=True)
start = time.time()
res = train_transform(img)
end = time.time()
print(res.shape)
print('process time: {}'.format(end - start))
def test_jester_rgbdiff():
cfg.merge_from_file('configs/tsn_r50_jester_rgbdiff_224x3_seg.yaml')
transform = build_transform(cfg, is_train=True)
dataset = build_dataset(cfg, transform=transform, is_train=True)
image, target = dataset.__getitem__(20)
print(image.shape)
print(target)
assert image.shape == (3, 15, 224, 224)
def test_hmdb51_rgb():
cfg.merge_from_file('configs/tsn_r50_hmdb51_rgb_224x3_seg.yaml')
cfg.DATASETS.NUM_CLIPS = 8
transform = build_transform(cfg, is_train=True)
dataset = build_dataset(cfg, transform=transform, is_train=True)
image, target = dataset.__getitem__(20)
print(image.shape)
print(target)
assert image.shape == (3, 8, 224, 224)
def main():
is_train = True
transform = build_transform(cfg, is_train=is_train)
dataset = build_dataset(cfg, transform=transform, is_train=is_train)
sampler = SequentialSampler(dataset)
cfg.SAMPLER.MULTIGRID.DEFAULT_S = cfg.TRANSFORM.TRAIN.TRAIN_CROP_SIZE
sampler = ShortCycleBatchSampler(sampler, cfg.DATALOADER.TRAIN_BATCH_SIZE, False, cfg)
print('batch_size:', cfg.DATALOADER.TRAIN_BATCH_SIZE)
for i, idxs in enumerate(sampler):
print(idxs)
print(len(idxs))
if i > 3:
break
print(len(sampler))
def __init__(self, cfg):
"""
Args:
cfg (CfgNode): configs. Details can be found in
tsn/config/defaults.py
gpu_id (Optional[int]): GPU id.
"""
if cfg.NUM_GPUS > 0:
device = get_device(local_rank=get_local_rank())
else:
device = get_device()
# Build the video model and print model statistics.
self.model = build_model(cfg, device)
self.model.eval()
self.transform = build_transform(cfg, is_train=False)
self.cfg = cfg
self.device = device
def main():
global frame_queue, camera, frame, results, threshold, sample_length, \
data, test_transform, model, device, average_size, label, result_queue, \
frame_interval
args = parse_test_args()
cfg = load_test_config(args)
average_size = 1
threshold = 0.5
np.random.seed(cfg.RNG_SEED)
torch.manual_seed(cfg.RNG_SEED)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
device = get_device(local_rank=get_local_rank())
model = build_model(cfg, device)
model.eval()
camera = cv2.VideoCapture(cfg.VISUALIZATION.INPUT_VIDEO)
with open(cfg.VISUALIZATION.LABEL_FILE_PATH, 'r') as f:
label = [line.strip().split(' ')[1] for line in f]
# prepare test pipeline from non-camera pipeline
test_transform = build_transform(cfg, is_train=False)
sample_length = cfg.DATASETS.CLIP_LEN * cfg.DATASETS.NUM_CLIPS * cfg.DATASETS.FRAME_INTERVAL
frame_interval = cfg.DATASETS.FRAME_INTERVAL
assert sample_length > 0
try:
frame_queue = deque(maxlen=sample_length)
result_queue = deque(maxlen=1)
pw = Thread(target=show_results, args=(), daemon=True)
pr = Thread(target=inference, args=(), daemon=True)
pw.start()
pr.start()
while True:
if not pw.is_alive():
exit(0)
except KeyboardInterrupt:
pass
