# RuntimeError: Given groups=1, weight of size \[128, 1, 5\], expected input\[2, 32, 187\] to have 1 channels, but got 32 channels instead **URL:** \ **Category:** vision\ **Created:** [March 26, 2021, 11:15am UTC](https://discuss.pytorch.org/t/runtimeerror-given-groups-1-weight-of-size-128-1-5-expected-input-2-32-187-to-have-1-channels-but-got-32-channels-instead/116068 "2021-03-26T11:15:59Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![hs99](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/hs99/32/36177_2.png) [@hs99](https://discuss.pytorch.org/u/hs99)\ **Post date:** [March 26, 2021, 11:15am UTC](https://discuss.pytorch.org/t/runtimeerror-given-groups-1-weight-of-size-128-1-5-expected-input-2-32-187-to-have-1-channels-but-got-32-channels-instead/116068/1 "2021-03-26T11:15:59Z")
My CNN model is as follows: class ConvNormPool(nn.Module): “”“Conv Skip-connection module”"" def **init** ( self, input\_size, hidden\_size, kernel\_size, norm\_type=‘bachnorm’ ): super(). **init** () ``` self.kernel_size = kernel_size self.conv_1 = nn.Conv1d( in_channels=input_size, out_channels=hidden_size, kernel_size=kernel_size ) self.conv_2 = nn.Conv1d( in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size ) self.conv_3 = nn.Conv1d( in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size ) self.swish_1 = Swish() self.swish_2 = Swish() self.swish_3 = Swish() if norm_type == 'group': self.normalization_1 = nn.GroupNorm( num_groups=8, num_channels=hidden_size ) self.normalization_2 = nn.GroupNorm( num_groups=8, num_channels=hidden_size ) self.normalization_3 = nn.GroupNorm( num_groups=8, num_channels=hidden_size ) else: self.normalization_1 = nn.BatchNorm1d(num_features=hidden_size) self.normalization_2 = nn.BatchNorm1d(num_features=hidden_size) self.normalization_3 = nn.BatchNorm1d(num_features=hidden_size) self.pool = nn.MaxPool1d(kernel_size=2) def forward(self, input): #print("INPUT SHAPE SKIP") #print(input.shape) conv1 = self.conv_1(input) x = self.normalization_1(conv1) x = self.swish_1(x) x = F.pad(x, pad=(self.kernel_size - 1, 0)) x = self.conv_2(x) x = self.normalization_2(x) x = self.swish_2(x) x = F.pad(x, pad=(self.kernel_size - 1, 0)) conv3 = self.conv_3(x) x = self.normalization_3(conv1+conv3) x = self.swish_3(x) x = F.pad(x, pad=(self.kernel_size - 1, 0)) x = self.pool(x) return x ``` * * * class CNN(nn.Module): def **init** ( self, input\_size = 1, hid\_size = 256, kernel\_size = 5, num\_classes = 5, ): ``` super(). __init__ () self.conv1 = ConvNormPool( input_size=input_size, hidden_size=hid_size, kernel_size=kernel_size, ) self.conv2 = ConvNormPool( input_size=hid_size, hidden_size=hid_size//2, kernel_size=kernel_size, ) self.conv3 = ConvNormPool( input_size=hid_size//2, hidden_size=hid_size//4, kernel_size=kernel_size, ) self.avgpool = nn.AdaptiveAvgPool1d((1)) self.fc = nn.Linear(in_features=hid_size//4, out_features=num_classes) ***self.softmax = nn.LogSoftmax(dim=1)*** def forward(self, input): #print("INPUT SHAPE is ") #print(input.shape) #print("INPUT SHAPE is ") #print(input.shape) #input = input.unsqueeze(1) #input = input.unsqueeze(0) x = self.conv1(input) x = self.conv2(x) x = self.conv3(x) x = self.avgpool(x) # print(x.shape) # num_features * num_channels log_ x = x.view(-1, x.size(1) * x.size(2)) x = F.softmax(self.fc(x), dim=1) #x = F.softmax(self.fc(x), dim=1) x = F.log_softmax(self.fc(x), dim=1) return x ``` m1=CNN(m1=CNN(num\_classes=5, hid\_size=128).to(device) n I call summary(m1,(32,187)) It gives error 🙂 RuntimeError: Given groups=1, weight of size [128, 1, 5], expected input[2, 32, 187] to have 1 channels, but got 32 channels instead How Can I remove this?? However when I use: summary(m1, (1,187),32): I get summary as: # Layer (type) Output Shape Param # ``` Conv1d-1 [32, 128, 183] 768 BatchNorm1d-2 [32, 128, 183] 256 Swish-3 [32, 128, 183] 0 Conv1d-4 [32, 128, 183] 82,048 BatchNorm1d-5 [32, 128, 183] 256 Swish-6 [32, 128, 183] 0 Conv1d-7 [32, 128, 183] 82,048 BatchNorm1d-8 [32, 128, 183] 256 Swish-9 [32, 128, 183] 0 MaxPool1d-10 [32, 128, 93] 0 ConvNormPool-11 [32, 128, 93] 0 Conv1d-12 [32, 64, 89] 41,024 BatchNorm1d-13 [32, 64, 89] 128 Swish-14 [32, 64, 89] 0 Conv1d-15 [32, 64, 89] 20,544 BatchNorm1d-16 [32, 64, 89] 128 Swish-17 [32, 64, 89] 0 Conv1d-18 [32, 64, 89] 20,544 BatchNorm1d-19 [32, 64, 89] 128 Swish-20 [32, 64, 89] 0 MaxPool1d-21 [32, 64, 46] 0 ConvNormPool-22 [32, 64, 46] 0 Conv1d-23 [32, 32, 42] 10,272 BatchNorm1d-24 [32, 32, 42] 64 Swish-25 [32, 32, 42] 0 Conv1d-26 [32, 32, 42] 5,152 BatchNorm1d-27 [32, 32, 42] 64 Swish-28 [32, 32, 42] 0 Conv1d-29 [32, 32, 42] 5,152 BatchNorm1d-30 [32, 32, 42] 64 Swish-31 [32, 32, 42] 0 MaxPool1d-32 [32, 32, 23] 0 ConvNormPool-33 [32, 32, 23] 0 ``` # AdaptiveAvgPool1d-34 [32, 32, 1] 0 Linear-35 [32, 5] 165 ## Total params: 269,061 Trainable params: 269,061 Non-trainable params: 0 ## Input size (MB): 0.02 Forward/backward pass size (MB): 74.56 Params size (MB): 1.03 Estimated Total Size (MB): 75.61 IS MY MODEL CORRECT?? HAVE I PUT ALL LAYERS IN PLACE? How do I corect the 1st error? ALSO, _**self.softmax = nn.LogSoftmax(dim=1)**_ needs to be inserted??I am using CROSSENTROPYLOSS ---
**Author:** ![Dwight\_Foster](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/dwight_foster/32/18182_2.png) [@Dwight\_Foster](https://discuss.pytorch.org/u/Dwight_Foster)\ **Post date:** [March 26, 2021, 12:55pm UTC](https://discuss.pytorch.org/t/runtimeerror-given-groups-1-weight-of-size-128-1-5-expected-input-2-32-187-to-have-1-channels-but-got-32-channels-instead/116068/2 "2021-03-26T12:55:56Z")
Just change your input size to 32.