My input images are (210,128,128,3) size.
Giving errors–RuntimeError: Given groups=1, weight[32, 3, 5, 5], so expected input[210, 128, 128, 3] to have 3 channels, but got 128 channels instead
(TRIED OTHER  answers but nothing works)
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(3, 32, 5, padding=4)
self.conv2 = nn.Conv2d(32, 96, 5)
# max pooling layer
self.pool = nn.MaxPool2d(2, 2)
    self.fc1 = nn.Linear(29*29*96, 500)
    # linear layer (500 -> 10)
    self.fc2 = nn.Linear(500, 10)
    # dropout layer (p=0.25)
    self.dropout = nn.Dropout(0.25)

def forward(self, x):
    # add sequence of convolutional and max pooling layers
    x = self.pool(F.relu(self.conv1(x)))
    x = self.pool(F.relu(self.conv2(x)))
    # flatten image input
    x = x.view(-1, 29*29*96)
    # add dropout layer
    x = self.dropout(x)
    # add 1st hidden layer, with relu activation function
    x = F.relu(self.fc1(x))
    # add dropout layer
    x = self.dropout(x)
    # add 2nd hidden layer, with relu activation function
    x = (self.fc2(x))
    return x

create a complete CNN
model = Net()