Open wanboyang opened 5 years ago
Layer (type) Output Shape Param #
the loss of network can not calculate because mismatch of input and output
hello,Mr GongDong,Is there open source code for this article?
when I use the hyper_parameters of network in your paper, the network product feature like:
Layer (type) Output Shape Param #
================================================================
Conv3d-1 [8, 96, 7, 119, 159] 2,592 BatchNorm3d-2 [8, 96, 7, 119, 159] 192 ReLU-3 [8, 96, 7, 119, 159] 0 Conv3d-4 [8, 128, 3, 59, 79] 331,776 BatchNorm3d-5 [8, 128, 3, 59, 79] 256 ReLU-6 [8, 128, 3, 59, 79] 0 Conv3d-7 [8, 256, 1, 29, 39] 884,736 BatchNorm3d-8 [8, 256, 1, 29, 39] 512 ReLU-9 [8, 256, 1, 29, 39] 0 ConvTranspose3d-10 [8, 256, 3, 59, 79] 1,769,472 BatchNorm3d-11 [8, 256, 3, 59, 79] 512 ReLU-12 [8, 256, 3, 59, 79] 0 ConvTranspose3d-13 [8, 128, 7, 119, 159] 884,736 BatchNorm3d-14 [8, 128, 7, 119, 159] 256 ReLU-15 [8, 128, 7, 119, 159] 0 ConvTranspose3d-16 [8, 1, 15, 239, 319] 3,456 BatchNorm3d-17 [8, 1, 15, 239, 319] 2 the loss of network can not calculate because mismatch of input and output
I got the same issue when testing: 'operands could not be broadcast together with shapes (16,1,240,368) (16,1,240,360) ' It seems that after the encode-decode operations the width was wrongly extended to 368. I troubleshoot this and find it is actually a typical Conv Operation Mistake --- Since the width of input image 360 is not fully divisble by 16 (i.e. four times of Conv Op with stride 2) So after that the width will be 22.5 which will later be rounded up to 23 in Pytorch. Then after decoding process which deconv four times on the latent features map, the final height will be 23 X 16 = 368 as it turns out to be.
The code is incomplete and not bug-free. Sad.
I have read your excellent paper. I have some question about the content “Accordingly, the input of the network is a cuboid constructed by stacking 16 neighbor frames in grayscale. The structures of encoder and decoder are designed as: Conv3(3, 2, 96)- Conv3(3, 2, 128)-Conv3(3, 2, 256)-Conv3(3, 2, 256) and Dconv3(3, 2, 256)-Dconv3(3, 2, 256)-Dconv3(3, 2, 128)- Dconv3(3, 2, 1), where Conv3 and Dconv3 denote 3D convolution and deconvolution, respectively. A BN and a ReLU activation follow each layer (except the last one)” in section4.2.