MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
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Bug found in avg_pool2d when convert mxnet 2 pytorch(may be) #909
when I convert mxnet inceptionBN to pytorch, I found the high precision error between origin mxnet model and converted pytorch model. and then I found that the generated pytorch model file has the line avg_pool_3a_pool = F.avg_pool2d(pool2, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False). I change the count_include_pad to True and the precision is correct. Is it the bug or I just take the wrong convert steps?
when I convert mxnet inceptionBN to pytorch, I found the high precision error between origin mxnet model and converted pytorch model. and then I found that the generated pytorch model file has the line
avg_pool_3a_pool = F.avg_pool2d(pool2, kernel_size=(3, 3), stride=(1, 1), padding=(1,), ceil_mode=False, count_include_pad=False)
. I change the count_include_pad to True and the precision is correct. Is it the bug or I just take the wrong convert steps?