RuntimeError: Given groups=1, weight of size 256 1024 1 1, expected input[32, 512, 15, 15] to have 1024 channels, but got 512 channels instead
This is my code:
class ResNet(nn.Module):
def init(self, block, layers, used_layers):
self.inplanes = 64
super(ResNet, self).init()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=0,  # 3
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    self.feature_size = 128 * block.expansion
    self.used_layers = used_layers
    layer3 = True if 3 in used_layers else False
    layer4 = True if 4 in used_layers else False

    if layer3:
        self.layer3 = self._make_layer(block, 256, layers[2],
                                       stride=1, dilation=2)  # 15x15, 7x7
        self.feature_size = (256 + 128) * block.expansion
    else:
        self.layer3 = lambda x: x  # identity

    if layer4:
        self.layer4 = self._make_layer(block, 512, layers[3],
                                       stride=1, dilation=4)  # 7x7, 3x3
        self.feature_size = 512 * block.expansion
    else:
        self.layer4 = lambda x: x  # identity

    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
            m.weight.data.normal_(0, math.sqrt(2. / n))
        elif isinstance(m, nn.BatchNorm2d):
            m.weight.data.fill_(1)
            m.bias.data.zero_()

    self.smooth1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
    self.smooth2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
    self.latlayer1 = nn.Conv2d(2048, 512, kernel_size=1, stride=1, padding=0)
    #nn.init.xavier_uniform(self.latlayer1.weight)
    #nn.init.constant(self.latlayer1.bias, 0.1)
    self.latlayer2 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0)

def _make_layer(self, block, planes, blocks, stride=1, dilation=1):

    downsample = None
    dd = dilation
    if stride != 1 or self.inplanes != planes * block.expansion:
        if stride == 1 and dilation == 1:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )
        else:
            if dilation > 1:
                dd = dilation // 2
                padding = dd
            else:
                dd = 1
                padding = 0
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=3, stride=stride, bias=False,
                          padding=padding, dilation=dd),
                nn.BatchNorm2d(planes * block.expansion),
            )

    layers = []
    layers.append(block(self.inplanes, planes, stride,
                        downsample, dilation=dilation))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
        layers.append(block(self.inplanes, planes, dilation=dilation))

    return nn.Sequential(*layers)
def _upsample_add(self, x, y):
    '''Upsample and add two feature maps.
    Args:
      x: (Variable) top feature map to be upsampled.
      y: (Variable) lateral feature map.
    Returns:
      (Variable) added feature map.
    Note in PyTorch, when input size is odd, the upsampled feature map
    with `F.upsample(..., scale_factor=2, mode='nearest')`
    maybe not equal to the lateral feature map size.
    e.g.
    original input size: [N,_,15,15] ->
    conv2d feature map size: [N,_,8,8] ->
    upsampled feature map size: [N,_,16,16]
    So we choose bilinear upsample which supports arbitrary output sizes.
    '''
    _,_,H,W = y.size()
    return nn.functional.interpolate(x, size=(H,W), mode='bilinear', align_corners=True) + y

def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x_ = self.relu(x)
    x = self.maxpool(x_)
    # Bottom-up
    p1 = self.layer1(x)
    p2 = self.layer2(p1)
    p3 = self.layer3(p2)
    p4 = self.layer4(p3)
    # Top-down
    p5 = self.latlayer1(p4)
    p6 = self._upsample_add(p5, self.latlayer2(p3))
    p6 = self.smooth1(p6)
    p7 = self._upsample_add(p6, p2)
    p7 = self.smooth2(p7)
    out = [x_, p1, p7, p6, p4]
    out = [out[i] for i in self.used_layers]
    if len(out) == 1:
        return out[0]
    else:
        return out

Thanks for your reply!