| 114 | """ |
| 115 | |
| 116 | def __init__( |
| 117 | self, out_channels: int = 1, upsample_mode: str = "bilinear", pretrained: bool = True, progress: bool = True |
| 118 | ): |
| 119 | super().__init__() |
| 120 | |
| 121 | conv2d_type: type[nn.Conv2d] = Conv[Conv.CONV, 2] |
| 122 | |
| 123 | self.upsample_mode = upsample_mode |
| 124 | self.conv2d_type = conv2d_type |
| 125 | self.out_channels = out_channels |
| 126 | resnet = models.resnet50( |
| 127 | progress=progress, weights=models.ResNet50_Weights.IMAGENET1K_V1 if pretrained else None |
| 128 | ) |
| 129 | |
| 130 | self.conv1 = resnet.conv1 |
| 131 | self.bn0 = resnet.bn1 |
| 132 | self.relu = resnet.relu |
| 133 | self.maxpool = resnet.maxpool |
| 134 | |
| 135 | self.layer1 = resnet.layer1 |
| 136 | self.layer2 = resnet.layer2 |
| 137 | self.layer3 = resnet.layer3 |
| 138 | self.layer4 = resnet.layer4 |
| 139 | |
| 140 | self.gcn1 = GCN(2048, self.out_channels) |
| 141 | self.gcn2 = GCN(1024, self.out_channels) |
| 142 | self.gcn3 = GCN(512, self.out_channels) |
| 143 | self.gcn4 = GCN(64, self.out_channels) |
| 144 | self.gcn5 = GCN(64, self.out_channels) |
| 145 | |
| 146 | self.refine1 = Refine(self.out_channels) |
| 147 | self.refine2 = Refine(self.out_channels) |
| 148 | self.refine3 = Refine(self.out_channels) |
| 149 | self.refine4 = Refine(self.out_channels) |
| 150 | self.refine5 = Refine(self.out_channels) |
| 151 | self.refine6 = Refine(self.out_channels) |
| 152 | self.refine7 = Refine(self.out_channels) |
| 153 | self.refine8 = Refine(self.out_channels) |
| 154 | self.refine9 = Refine(self.out_channels) |
| 155 | self.refine10 = Refine(self.out_channels) |
| 156 | self.transformer = self.conv2d_type(in_channels=256, out_channels=64, kernel_size=1) |
| 157 | |
| 158 | if self.upsample_mode == "transpose": |
| 159 | self.up_conv = UpSample(spatial_dims=2, in_channels=self.out_channels, scale_factor=2, mode="deconv") |
| 160 | |
| 161 | def forward(self, x: torch.Tensor): |
| 162 | """ |