Hello l got this issue for a week l tried many strategies but it still doesn´t work.
my code many thnaks 
class DoubleConv(nn.Module):
def __init__(self, in_channels, out_channels):
super(DoubleConv, self).__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=2),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=2),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
def forward(self, out):
x=self.double_conv(out)
return x
In[5]:
class DownBlock(nn.Module):
def init(self, in_channels, out_channels):
super(DownBlock, self).__init__()
self.double_conv = DoubleConv(in_channels, out_channels)
self.down_sample = nn.MaxPool2d(2)
def forward(self, x):
down_out = self.down_sample(x)
skip_out = self.double_conv(down_out)
return (down_out, skip_out)
In[6]:
class UpBlock (nn.Module):
“”“Upscaling then double conv”“”
def __init__(self, in_channels, out_channels, up_sample_mode):
super(UpBlock, self).__init__()
if up_sample_mode == 'conv_transpose':
self.up_sample = nn.ConvTranspose2d(in_channels-out_channels, in_channels-out_channels, kernel_size=2, stride=2)
elif up_sample_mode == 'bilinear':
self.up_sample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
raise ValueError("Unsupported `up_sample_mode` (can take one of `conv_transpose` or `bilinear`)")
self.double_conv = DoubleConv(in_channels, out_channels)
def forward(self, down_input, skip_input):
x = self.up_sample(down_input)
x = torch.cat([x, skip_input], dim=1)
return self.double_conv(x)
In[9]:
class Siamese_UNet_Assement(nn.Module):
def init(self, out_classes, up_sample_mode=‘conv_transpose’):
super(Siamese_UNet_Assement, self).__init__()
self.up_sample_mode = up_sample_mode
# Downsampling Path
self.down_conv1 = DownBlock(3, 64)
self.down_conv2 = DownBlock(64, 128)
self.down_conv3 = DownBlock(128, 256)
self.down_conv4 = DownBlock(256, 512)
# Bottleneck
self.double_conv = DoubleConv(512, 1024)
# Upsampling Path
self.up_conv4 = UpBlock(1024, 512, self.up_sample_mode)
self.up_conv3 = UpBlock(512, 256, self.up_sample_mode)
self.up_conv2 = UpBlock(256, 128, self.up_sample_mode)
self.up_conv1 = UpBlock(128 , 64, self.up_sample_mode)
# Final Convolution
self.conv_last = nn.Conv2d(64, out_classes, kernel_size=1)
def forward(self, input1,input2):
# Unet1
# Encoder
enc1_1, skip1_out1 = self.down_conv1(input1)
enc1_2, skip2_out1 = self.down_conv2(enc1_1)
enc1_3, skip3_out1 = self.down_conv3(enc1_2)
enc1_4, skip4_out1 = self.down_conv4(enc1_3)
bottleneck_1 = self.double_conv(enc1_4)
#Decoder
dec4_1= self.up_conv4(bottleneck_1, skip4_out1)
dec3_1_= self.up_conv3(dec4_1, skip3_out1)
dec2_1= self.up_conv2(dec3_1_, skip2_out1)
dec1_1 = self.up_conv1(dec2_1, skip1_out1)
dec1_1 = self.conv_last(dec1_1)
# Unet2
# Encoder
enc2_1, skip1_out2 = self.down_conv1(input2)
enc2_2, skip2_out2 = self.down_conv2(enc2_1)
enc2_3, skip3_out2 = self.down_conv3(enc2_2)
enc2_4, skip4_out2 = self.down_conv4(enc2_3)
bottleneck_2 = self.double_conv(enc2_4)
#Decoder
dec4_2= self.up_conv4(bottleneck_2 , skip4_out2)
dec3_2_= self.up_conv3(dec4_2, skip3_out2)
dec2_2= self.up_conv2(dec3_2_, skip2_out2)
dec1_2 = self.up_conv1(dec2_2, skip1_out2)
dec1_2 = self.conv_last(dec1_2)
# Siamese
output = torch.cat((dec1_1,dec1_2), 1)
return output = torch.cat((dec1_1,dec1_2), 1)
RuntimeError: Given groups=1, weight of size [128, 64, 3, 3], expected input[2, 3, 64, 64] to have 64 channels, but got 3 channels instead