Using convolutional neural networks to predict the Coding Units (CUs) depths in HEVC intra-prediction mode, in order to reduce the time of the encoding process in HEVC.
I need help, I am trying to reimplement the code on the cpu but I am getting this error: "RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU."
I tried to add map_loction =torch.device('cpu') after model.load_state_dict(torch.load(paft.format(LOAD_DIR)) as shown below but I am still getting the same error
model.load_state_dict(torch.load(paft.format(LOAD_DIR)),map_location=torch.device('cpu'))
Are the vectors depicting 4x4 blocks in the 16x16 matrix from Left to right, top to bottom?
Also, can you please provide example text files that you used in your pipeline that was read by the HM encoder.
Hello there,
I am recently studying on your code, the outcome of the CNN oriented network is fansinating, but I am wondering if you could show me some clues about the ResNet architecture. Is there any reference? I am currently facing the problem of training loss non-convergence, and I don't know why. Could you please give me more details about your network architecture?