https://discuss.pytorch.org/t/mat1-and-mat2-shapes-cannot-be-multiplied-256x320-and-640x8/164265

cnn_sample.py

from turtle import forward
import torch
import torch.nn as nn, torch.nn.functional as F

class CNN(nn.Module):
    def __init__(self) -> None:
        super().__init__()

        self.conv1 = nn.Conv2d(in_channels=1, out_channels=8, kernel_size=6, stride=1, padding=2)
        self.RL1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(2)

        self.conv2 = nn.Conv2d(in_channels=8, out_channels=16, kernel_size=5, stride=1, padding=2)
        self.RL2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(2)        

        self.conv3 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=4, stride=1, padding=2)
        self.RL3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(2)

        self.conv4 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=2)
        self.RL4 = nn.ReLU()
        self.pool4 = nn.MaxPool2d(2)        

        self.flatten = nn.Flatten()
        self.fc1 = nn.Linear(64*5*2, 8)        

    def forward(self, x):
        x = self.conv1(x)
        x = self.RL1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.RL2(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = self.RL3(x)
        x = self.pool3(x)

        x = self.conv4(x)
        x = self.RL4(x)
        x = self.pool4(x)      

        x = self.flatten(x)      
        x = self.fc1(x)
        x = x.log_softmax(dim=1)
        x = x.unsqueeze(1)
        return x

if __name__ == '__main__':
    d = torch.randn(256, 1, 64, 16)
    m = CNN()
    o = m(d)
    print(o.shape)