(self, input1, input2)
| 62 | return output |
| 63 | |
| 64 | def forward(self, input1, input2): |
| 65 | # get two images' features |
| 66 | output1 = self.forward_once(input1) |
| 67 | output2 = self.forward_once(input2) |
| 68 | |
| 69 | # concatenate both images' features |
| 70 | output = torch.cat((output1, output2), 1) |
| 71 | |
| 72 | # pass the concatenation to the linear layers |
| 73 | output = self.fc(output) |
| 74 | |
| 75 | # pass the out of the linear layers to sigmoid layer |
| 76 | output = self.sigmoid(output) |
| 77 | |
| 78 | return output |
| 79 | |
| 80 | class APP_MATCHER(Dataset): |
| 81 | def __init__(self, root, train, download=False): |
nothing calls this directly
no test coverage detected