Creating a Simple 1D CNN in PyTorch with Multiple Channels
You are forgetting the "minibatch dimension", each "1D" sample has indeed two dimensions: the number of channels (7 in your example) and length (10 in your case). However, pytorch expects as input not a single sample, but rather a minibatch of B
samples stacked together along the "minibatch dimension".
So a "1D" CNN in pytorch expects a 3D tensor as input: B
xC
xT
. If you only have one signal, you can add a singleton dimension:
out = model(torch.tensor(X)[None, ...])
Joseph Konan
Updated on June 21, 2022Comments
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Joseph Konan almost 2 years
The dimensionality of the PyTorch inputs are not what the model expects, and I am not sure why.
To my understanding...
in_channels
is first the number of 1D inputs we would like to pass to the model, and is the previous out_channel for all subsequent layers.out_channels
is the desired number of kernels (filters).kernel_size
is the number of parameters per filter.Therefore, we would expect, as data passed to forward, a dataset with 7 1D channels (i.e. a 2D input).
However, the following code throws an error that is not consistent with what I expect, where this code:
import numpy import torch X = numpy.random.uniform(-10, 10, 70).reshape(-1, 7) # Y = np.random.randint(0, 9, 10).reshape(-1, 1) class Simple1DCNN(torch.nn.Module): def __init__(self): super(Simple1DCNN, self).__init__() self.layer1 = torch.nn.Conv1d(in_channels=7, out_channels=20, kernel_size=5, stride=2) self.act1 = torch.nn.ReLU() self.layer2 = torch.nn.Conv1d(in_channels=20, out_channels=10, kernel_size=1) def forward(self, x): x = self.layer1(x) x = self.act1(x) x = self.layer2(x) log_probs = torch.nn.functional.log_softmax(x, dim=1) return log_probs model = Simple1DCNN() print(model(torch.tensor(X)).size)
Throws the following error:
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-5-eca5856a2314> in <module>() 21 22 model = Simple1DCNN() ---> 23 print(model(torch.tensor(X)).size) ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 487 result = self._slow_forward(*input, **kwargs) 488 else: --> 489 result = self.forward(*input, **kwargs) 490 for hook in self._forward_hooks.values(): 491 hook_result = hook(self, input, result) <ipython-input-5-eca5856a2314> in forward(self, x) 12 self.layer2 = torch.nn.Conv1d(in_channels=20, out_channels=10, kernel_size=1) 13 def forward(self, x): ---> 14 x = self.layer1(x) 15 x = self.act1(x) 16 x = self.layer2(x) ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 487 result = self._slow_forward(*input, **kwargs) 488 else: --> 489 result = self.forward(*input, **kwargs) 490 for hook in self._forward_hooks.values(): 491 hook_result = hook(self, input, result) ~/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input) 185 def forward(self, input): 186 return F.conv1d(input, self.weight, self.bias, self.stride, --> 187 self.padding, self.dilation, self.groups) 188 189 RuntimeError: Expected 3-dimensional input for 3-dimensional weight [20, 7, 5], but got 2-dimensional input of size [10, 7] instead
Edit: See below for solution, motivated by Shai.
import numpy import torch X = numpy.random.uniform(-10, 10, 70).reshape(1, 7, -1) # Y = np.random.randint(0, 9, 10).reshape(1, 1, -1) class Simple1DCNN(torch.nn.Module): def __init__(self): super(Simple1DCNN, self).__init__() self.layer1 = torch.nn.Conv1d(in_channels=7, out_channels=20, kernel_size=5, stride=2) self.act1 = torch.nn.ReLU() self.layer2 = torch.nn.Conv1d(in_channels=20, out_channels=10, kernel_size=1) def forward(self, x): x = self.layer1(x) x = self.act1(x) x = self.layer2(x) log_probs = torch.nn.functional.log_softmax(x, dim=1) return log_probs model = Simple1DCNN().double() print(model(torch.tensor(X)).shape)