Initialize one adaptation layer for every extraction point. Args: hypercolumn_layers: The list of the hypercolumn layer names. output_dim: The output channel dimension.
(self, hypercolumn_layers: List[str], output_dim: int = 128)
| 44 | """ |
| 45 | |
| 46 | def __init__(self, hypercolumn_layers: List[str], output_dim: int = 128): |
| 47 | """Initialize one adaptation layer for every extraction point. |
| 48 | |
| 49 | Args: |
| 50 | hypercolumn_layers: The list of the hypercolumn layer names. |
| 51 | output_dim: The output channel dimension. |
| 52 | """ |
| 53 | super(AdaptLayers, self).__init__() |
| 54 | self.layers = [] |
| 55 | channel_sizes = [vgg16_layers[name] for name in hypercolumn_layers] |
| 56 | for i, l in enumerate(channel_sizes): |
| 57 | layer = nn.Sequential( |
| 58 | nn.Conv2d(l, 64, kernel_size=1, stride=1, padding=0), |
| 59 | nn.ReLU(), |
| 60 | nn.Conv2d(64, output_dim, kernel_size=5, stride=1, padding=2), |
| 61 | nn.BatchNorm2d(output_dim), |
| 62 | ) |
| 63 | self.layers.append(layer) |
| 64 | self.add_module("adapt_layer_{}".format(i), layer) # ex: adapt_layer_0 |
| 65 | |
| 66 | def forward(self, features: List[torch.tensor]): |
| 67 | """Apply adaptation layers. # here is list of three levels of features |