Hello,
I have image sequences for 300 persons, each person has 8 images and each image has 6 channels, i pass it to my network as [8,6,128,64]. My network is given below:
class MyModel(nn.Module):
    
    def __init__(self):
        super(MyModel, self).__init__()
        self.CNNLayer1 = nn.Sequential(
                nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1, padding=4),
                nn.Tanh(),
                nn.MaxPool2d(kernel_size=2, stride=2)
                )      
        self.CNNLayer2 = nn.Sequential(
                nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=4),
                nn.Tanh(),
                nn.MaxPool2d(kernel_size=2, stride=2)
                )
        self.CNNLayer3 = nn.Sequential(
                nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=4),
                nn.Tanh(),
                nn.MaxPool2d(kernel_size=2, stride=2)
                )
        self.DropoutLayer = nn.Dropout(0.6)
        self.FullyConnected = nn.Linear(1, 8, 32*19*11)
        
        
        #here i have to put RNN and then convert the sequence to a [1,8, channels*width*height] matrix
        #after RNN here i want to get a [1,128] feature vector for the whole sequence, i.e. the 8 images
        #should have one vector
    def forward(self, inp):
        #print(inp.shape) #prints torch.Size([1, 8, 6, 128, 64])[batchsize, sequenceLength, channels, H, W]
        out = self.CNNLayer1(inp[0])
        out = self.CNNLayer2(out)
        out = self.CNNLayer3(out)
        out = self.DropoutLayer(out)
        print(out.shape)#prints torch.Size([ 8, 32, 19, 11]) [batchsize, sequenceLength, channels, H, W]
        out = self.FullyConnected(out)
        return out

this error arises:
RuntimeError: size mismatch, m1: [4864 x 11], m2: [1 x 8] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:266