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lb = vn_data.transform.Letterbox(hyper_params.network_size) it = tf.ToTensor() img_tf = vn_data.transform.Compose([lb, it]) s1 = time.time() data = img_tf(img).cuda() e1 = time.time() data = torch.unsqueeze(data, 0) print('preprocessing per img consume: {}'.format(e1 - s1))
I find it cost 0.02s on K80, and the code in speed.py have not include preprocessing(padding-rescale & .cuda()). how to solve it?
The text was updated successfully, but these errors were encountered:
lb = vn_data.transform.Letterbox(hyper_params.network_size) it = tf.ToTensor() img_tf = vn_data.transform.Compose([lb, it]) s1 = time.time() data = img_tf(img).cuda() e1 = time.time() data = torch.unsqueeze(data, 0) print('preprocessing per img consume: {}'.format(e1 - s1))
I find it cost 0.02s on K80, and the code in speed.py have not include preprocessing(padding-rescale & .cuda()). how to solve it?
The text was updated successfully, but these errors were encountered: