FPA tensor size unmatch

[sparkfax]

I run another dataset, image size(3, 320, 640) and get this error: in FPA forward(self, x) 131 print(x2_2.shape) 132 print(x3_upsample.shape) --> 133 x2_merge = self.relu(x2_2 + x3_upsample) 134 x2_upsample = self.relu(self.bn_upsample_2(self.conv_upsample_2(x2_merge))) 135 x1_merge = self.relu(x1_2 + x2_upsample)

RuntimeError: The size of tensor a (5) must match the size of tensor b (6) at non-singleton dimension 2

    print: FPA channels=2048

    # Branch 1
    x1_1 = self.conv7x7_1(x)
    x1_1 = self.bn1_1(x1_1)
    x1_1 = self.relu(x1_1)    #([16, 512, 10, 20])
    x1_2 = self.conv7x7_2(x1_1)
    x1_2 = self.bn1_2(x1_2)     #([16, 512, 10, 20])

    # Branch 2
    x2_1 = self.conv5x5_1(x1_1)
    x2_1 = self.bn2_1(x2_1)
    x2_1 = self.relu(x2_1)            #([16, 512, 5, 10])
    x2_2 = self.conv5x5_2(x2_1)
    x2_2 = self.bn2_2(x2_2)          **#([16, 512, 5, 10])**

    # Branch 3
    x3_1 = self.conv3x3_1(x2_1)
    x3_1 = self.bn3_1(x3_1)
    x3_1 = self.relu(x3_1)           #([16, 512, 3, 5])
    x3_2 = self.conv3x3_2(x3_1)
    x3_2 = self.bn3_2(x3_2)         #([16, 512, 3, 5])

    # Merge branch 1 and 2,  x3_upsample: #**([16, 512, 6, 10])**    
    x3_upsample = self.relu(self.bn_upsample_3(self.conv_upsample_3(x3_2)))   
    x2_merge = self.relu(x2_2 + x3_upsample)
    x2_upsample = self.relu(self.bn_upsample_2(self.conv_upsample_2(x2_merge)))
    x1_merge = self.relu(x1_2 + x2_upsample)

class Attention_Model(nn.Module):  def init(self, in_features=256, num_class=4):   super(Attention_Model, self).init()   self.convnet = ResNet50(pretrained=True)   self.pan = PAN(self.convnet.blocks[::-1])   self.mask_classifier = Mask_Classifier(in_features=256, num_class=(num_class+1))   def forward(self, imgs):   fms_blob, z = self.convnet(imgs)   out_ss = self.pan(fms_blob[::-1])   mask_pred = self.mask_classifier(out_ss)
  return mask_pred

PS: if image resize to (3,512,512), the error not show