Hello, I am met with the following error in my ConvNet implementation.
The error comes up when the trying to execute the self.blocks line in the forward method of the ConvNet.
After investigation I realized my tensor would actually turn to None type after going through self.input_net in ConvNet forward.
Also if helpful, I added the architecture of the model I am trying to implement.
VGG Architecture Implemented
TypeError: conv2d() received an invalid combination of arguments - got (NoneType, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
* (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
didn't match because some of the arguments have invalid types: (!NoneType!, !Parameter!, !Parameter!, !tuple!, !tuple!, !tuple!, int)
* (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
didn't match because some of the arguments have invalid types: (!NoneType!, !Parameter!, !Parameter!, !tuple!, !tuple!, !tuple!, int)
class PreActResnetBlock(nn.Module):
def __init__(self, c_in, c_out, kernel=3, stride=1, padding=1):
"""
Inputs:
c_in: number of input feeatures
c_out: numberof output features
kernel: convolution kernel size
stride: convolution stride
padding: convolution padding
"""
super().__init__()
self.net = nn.Sequential(
nn.BatchNorm2d(c_in),
nn.ReLU(),
nn.Conv2d(c_in, c_out, kernel_size=kernel, padding=padding, stride=stride, bias=False)
)
def forward(self, x):
out = self.net(x)
out += x
class VGGBlock(nn.Module):
def __init__(self, c_in, c_out, last_block=False):
"""
Inputs:
last_block: if True add a convolution at the beggining of the block
c_in: number of input features to the convolution
c_out: number of output features to the convolution
"""
super().__init__()
layers = []
if not last_block:
layers.append(nn.Conv2d(c_in, c_out, kernel_size=1, padding=0, stride=1))
layers.extend([nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
PreActResnetBlock(c_out, c_out),
PreActResnetBlock(c_out, c_out)])
self.net = nn.Sequential(*layers)
def forward(self, x):
x = self.net(x)
return x
class ConvNet(nn.Module):
"""
This class implements a Convolutional Neural Network in PyTorch.
It handles the different layers and parameters of the model.
Once initialized an ConvNet object can perform forward.
"""
def __init__(self, n_channels, n_classes):
"""
Initializes ConvNet object.
Args:
n_channels: number of input channels
n_classes: number of classes of the classification problem
"""
super().__init__()
self.hparams = SimpleNamespace(n_channels = n_channels,
n_classes = n_classes)
self._create_network()
#self._init_params()
def _create_network(self):
hidden_dims = [64, 128, 256, 512]
# Stemm to scale up the channel size
c_out = hidden_dims[0]
self.input_net = nn.Sequential(
nn.Conv2d(self.hparams.n_channels, c_out, kernel_size=3, padding=1, bias=False),
PreActResnetBlock(c_out, c_out)
)
#VGGBlocks
blocks = []
for i in range(4):
if i == 3:
c_in = c_out = hidden_dims[-1]
blocks.append(VGGBlock(c_in, c_out, last_block=True))
break
c_in = hidden_dims[i]
c_out = hidden_dims[i+1]
blocks.append(VGGBlock(c_in, c_out))
self.blocks = nn.Sequential(*blocks)
# Mapping for classification head to target
self.output_net = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.Linear(c_out, self.hparams.n_classes)
)
def forward(self, x):
"""
Performs forward pass of the input. Here an input tensor x is transformed through
several layer transformations.
Args:
x: input to the network
Returns:
out: outputs of the network
"""
############
# My input x turns to None type after going through self.input_net for some reason
# and I can't figure out why.
x = self.input_net(x)
x = self.blocks(x)
out = self.output_net(x)
return out