Hi,
I am trying to implement a 1D CNN network for 1D signal processing. I managed to implement a simple network taking some input and giving me an output after processing in a conv1D layer followed by a fully connected relu output layer.
However, I wanted to apply MaxPool1d and I get in trouble with the size of its output, necessary to calculate the input size of the fully connected output layer. It seems that if stride = kernel_size, then for odd input length, my implementation of the formula provided in the docs does not allow to calculate properly the output size of MaxPool1d.
The following code illustrates this:
import numpy as np
import torch
# Some fake X input of dimension (100 samples, 1 channel, 1999 length)
X = torch.FloatTensor(np.ones((100,1,1999)))
# init parameters
input_channels = 1
# First conv layer parameters
out_channels_conv1 = 10 # number of kernels
Conv1_padding = 1
Conv1_dilation = 1
Conv1_kernel_size = 3
Conv1_stride = 1
# MasPool parameters
MaxPool_kernel_size = 3
MaxPool_stride = 3
MaxPool_padding = 0
MaxPool_dilation = 1
input_size = X.shape[2]
# first conv layer
conv1 = torch.nn.Conv1d(input_channels, out_channels_conv1, Conv1_kernel_size, stride=Conv1_stride, padding = padding)
out1 = conv1(X)
# calculating the output size of Conv1 with the formula from Pytorch docs
L_out = ((input_size + 2*Conv1_padding - Conv1_dilation*(Conv1_kernel_size-1) -1)/Conv1_stride +1)
if int(L_out) == out1.shape[2]:
print("Length at output of conv1 equals calculated length so everything looks good.\n Now pushing output of conv1 in MaxPool1d...")
# Now pushing data through MaxPool1d
MP1 = torch.nn.MaxPool1d(MaxPool_kernel_size,stride=MaxPool_stride)
out2 = MP1(out1)
print("Observed length is {}".format(out2.shape[2]))
Lout2 = (L_out+2*MaxPool_padding-MaxPool_dilation*(MaxPool_kernel_size-1)-1)/MaxPool_stride+1
print("Calculated length with Pytorch doc is {}".format(Lout2))
Output highlights that observed length is 666 while calculated length with the formula from Pytorch doc is 666.33…
Did I miss something? Should stride always be smaller than kernel_size? Or is there something wrong with my calculation?
