How exactly should look the second parametr? #88
Description
Hello,
I wanted to use hiddenlayer, but I am not sure about the second parameter (torch.zeros([1, 1, 512, 512]).to(device)), how exactly it should look? I think that the last 3 number are channels and size of image, but what exactly is the first number? So far I have implemented it like this:
summary(net, (1, 512, 512))
# Build HiddenLayer graph
hl_graph = hl.build_graph(net, torch.zeros([1, 1, 512, 512]).to(device))
# Use a different color theme
hl_graph.theme = hl.graph.THEMES["blue"].copy() # Two options: basic and blue
hl_graph.save(path=os.path.join(dirname, outputDir) , format="png")But I'm getting this error
Unsupported: ONNX export of Pad in opset 9. The sizes of the padding must be constant. Please try opset version 11.
The output from summary seems to work ok:
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 512, 512] 640
BatchNorm2d-2 [-1, 64, 512, 512] 128
ReLU-3 [-1, 64, 512, 512] 0
Conv2d-4 [-1, 64, 512, 512] 36,928
BatchNorm2d-5 [-1, 64, 512, 512] 128
ReLU-6 [-1, 64, 512, 512] 0
DoubleConv-7 [-1, 64, 512, 512] 0
MaxPool2d-8 [-1, 64, 256, 256] 0
Conv2d-9 [-1, 128, 256, 256] 73,856
BatchNorm2d-10 [-1, 128, 256, 256] 256
ReLU-11 [-1, 128, 256, 256] 0
Conv2d-12 [-1, 128, 256, 256] 147,584
BatchNorm2d-13 [-1, 128, 256, 256] 256
ReLU-14 [-1, 128, 256, 256] 0
DoubleConv-15 [-1, 128, 256, 256] 0
Down-16 [-1, 128, 256, 256] 0
MaxPool2d-17 [-1, 128, 128, 128] 0
Conv2d-18 [-1, 256, 128, 128] 295,168
BatchNorm2d-19 [-1, 256, 128, 128] 512
ReLU-20 [-1, 256, 128, 128] 0
Conv2d-21 [-1, 256, 128, 128] 590,080
BatchNorm2d-22 [-1, 256, 128, 128] 512
ReLU-23 [-1, 256, 128, 128] 0
DoubleConv-24 [-1, 256, 128, 128] 0
Down-25 [-1, 256, 128, 128] 0
MaxPool2d-26 [-1, 256, 64, 64] 0
Conv2d-27 [-1, 512, 64, 64] 1,180,160
BatchNorm2d-28 [-1, 512, 64, 64] 1,024
ReLU-29 [-1, 512, 64, 64] 0
Conv2d-30 [-1, 512, 64, 64] 2,359,808
BatchNorm2d-31 [-1, 512, 64, 64] 1,024
ReLU-32 [-1, 512, 64, 64] 0
DoubleConv-33 [-1, 512, 64, 64] 0
Down-34 [-1, 512, 64, 64] 0
MaxPool2d-35 [-1, 512, 32, 32] 0
Conv2d-36 [-1, 512, 32, 32] 2,359,808
BatchNorm2d-37 [-1, 512, 32, 32] 1,024
ReLU-38 [-1, 512, 32, 32] 0
Conv2d-39 [-1, 512, 32, 32] 2,359,808
BatchNorm2d-40 [-1, 512, 32, 32] 1,024
ReLU-41 [-1, 512, 32, 32] 0
DoubleConv-42 [-1, 512, 32, 32] 0
Down-43 [-1, 512, 32, 32] 0
Upsample-44 [-1, 512, 64, 64] 0
Conv2d-45 [-1, 512, 64, 64] 4,719,104
BatchNorm2d-46 [-1, 512, 64, 64] 1,024
ReLU-47 [-1, 512, 64, 64] 0
Conv2d-48 [-1, 256, 64, 64] 1,179,904
BatchNorm2d-49 [-1, 256, 64, 64] 512
ReLU-50 [-1, 256, 64, 64] 0
DoubleConv-51 [-1, 256, 64, 64] 0
Up-52 [-1, 256, 64, 64] 0
Upsample-53 [-1, 256, 128, 128] 0
Conv2d-54 [-1, 256, 128, 128] 1,179,904
BatchNorm2d-55 [-1, 256, 128, 128] 512
ReLU-56 [-1, 256, 128, 128] 0
Conv2d-57 [-1, 128, 128, 128] 295,040
BatchNorm2d-58 [-1, 128, 128, 128] 256
ReLU-59 [-1, 128, 128, 128] 0
DoubleConv-60 [-1, 128, 128, 128] 0
Up-61 [-1, 128, 128, 128] 0
Upsample-62 [-1, 128, 256, 256] 0
Conv2d-63 [-1, 128, 256, 256] 295,040
BatchNorm2d-64 [-1, 128, 256, 256] 256
ReLU-65 [-1, 128, 256, 256] 0
Conv2d-66 [-1, 64, 256, 256] 73,792
BatchNorm2d-67 [-1, 64, 256, 256] 128
ReLU-68 [-1, 64, 256, 256] 0
DoubleConv-69 [-1, 64, 256, 256] 0
Up-70 [-1, 64, 256, 256] 0
Upsample-71 [-1, 64, 512, 512] 0
Conv2d-72 [-1, 64, 512, 512] 73,792
BatchNorm2d-73 [-1, 64, 512, 512] 128
ReLU-74 [-1, 64, 512, 512] 0
Conv2d-75 [-1, 64, 512, 512] 36,928
BatchNorm2d-76 [-1, 64, 512, 512] 128
ReLU-77 [-1, 64, 512, 512] 0
DoubleConv-78 [-1, 64, 512, 512] 0
Up-79 [-1, 64, 512, 512] 0
Conv2d-80 [-1, 1, 512, 512] 65
OutConv-81 [-1, 1, 512, 512] 0
================================================================
Total params: 17,266,241
Trainable params: 17,266,241
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 1.00
Forward/backward pass size (MB): 3768.00
Params size (MB): 65.87
Estimated Total Size (MB): 3834.87
----------------------------------------------------------------