Hi, I am trying to use a CNN with 2D convolutions on 1D data, because I am trying to deploy my model on an FPGA, and Conv1d is not supported by it. This is what my model looks like:
class Network(nn.Module):
def __init__(self, d):
super().__init__()
self.d = d
self.batchnorm = nn.BatchNorm1d(3)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool1d(kernel_size=(4, 1), stride=4)
self.flatten = nn.Flatten()
self.conv1 = nn.Conv2d(
in_channels=3, out_channels=32, kernel_size=(128, 1)
)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(32, 1))
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(16, 1))
self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(32, 1))
self.conv5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(128, 1))
self.fc1 = nn.Linear(59904, 128)
self.fc2 = nn.Linear(128, 1)
def forward(self, x):
print(f"Input shape: {x.shape}")
if self.d == "pc":
x = self.batchnorm(x.type(torch.FloatTensor))
else:
x = self.batchnorm(x)
print(f"Shape after batch normalization: {x.shape}")
x = self.conv1(x)
print(f"Shape after 1st convolution: {x.shape}")
x = self.relu(x)
x = self.maxpool(x)
x = self.conv2(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.conv3(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.conv4(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.conv5(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
And these are the shapes of the prints in the code:
Input shape: torch.Size([2, 3, 155648])
Shape after batch normalization: torch.Size([2, 3, 155648])
However, my error RuntimeError: Given groups=1, weight of size [32, 3, 128, 1], expected input[1, 2, 3, 155648] to have 3 channels, but got 2 channels instead is saying, from what I understand, that the input shape of conv1 has the shape (1, 2, 3, 155648), but I have no idea where the first dimension comes from.
I have also tried to input x[0] to conv1 but it tells me I am inputting something of the shape (3, 155648) and it is expecting something 3D or 4D…
Does anyone have any tips?
