Zheng Wei

@linbinskn Same question.

@87Candy I have not encountered this error. `self._build_state_embedding()` function is used to build the ten-dimensional feature vector as the paper section 3.1 shows, you can check it.

@87Candy The error may be caused by `data = torch.zeros(1, 3, H, W).cuda()` in `measure_model` function. You can change the batch size.

@alan303138 See https://github.com/mit-han-lab/haq/blob/8228d126c800446fdc1cc263555d1e5aed7d9dfd/lib/env/linear_quantize_env.py#L115

@alan303138 1. Modify the strategy = [[8,-1], [8,8], [8,8], [8,8],...., [8,8]] and use run_linear_quantize_finetune.sh to obtain a W8A8 quantized mobilnetv2 model. https://github.com/mit-han-lab/haq/blob/8228d126c800446fdc1cc263555d1e5aed7d9dfd/finetune.py#L316 2. Modify the path variable to the W8A8...

@alan303138 1. The QConv2d inherits from the QModule base class. https://github.com/mit-han-lab/haq/blob/8228d126c800446fdc1cc263555d1e5aed7d9dfd/lib/utils/quantize_utils.py#L363 The construction function of QConv2d initializes the w_bit=-1, which will first initialize the self._w_bit = w_bit in QModule, i.e.,...