| import torch.nn as nn |
| import torchvision |
|
|
|
|
| class TVDeeplabRes101Encoder(nn.Module): |
| """ |
| FCN-Resnet101 backbone from torchvision deeplabv3 |
| No ASPP is used as we found emperically it hurts performance |
| """ |
|
|
| def __init__(self, use_coco_init, aux_dim_keep=64, use_aspp=False): |
| super().__init__() |
| _model = torchvision.models.segmentation.deeplabv3_resnet101(pretrained=use_coco_init, progress=True, |
| num_classes=21, aux_loss=None) |
| if use_coco_init: |
| print("###### NETWORK: Using ms-coco initialization ######") |
|
|
| _model_list = list(_model.children()) |
| self.aux_dim_keep = aux_dim_keep |
| self.backbone = _model_list[0] |
| self.localconv = nn.Conv2d(2048, 256, kernel_size=1, stride=1, bias=False) |
| self.asppconv = nn.Conv2d(256, 256, kernel_size=1, bias=False) |
|
|
| _aspp = _model_list[1][0] |
| _conv256 = _model_list[1][1] |
| self.aspp_out = nn.Sequential(*[_aspp, _conv256]) |
| self.use_aspp = use_aspp |
|
|
| def forward(self, x_in, low_level): |
| """ |
| Args: |
| low_level: whether returning aggregated low-level features in FCN |
| """ |
| fts = self.backbone(x_in) |
| if self.use_aspp: |
| fts256 = self.aspp_out(fts['out']) |
| high_level_fts = fts256 |
| else: |
| fts2048 = fts['out'] |
| high_level_fts = self.localconv(fts2048) |
|
|
| if low_level: |
| low_level_fts = fts['aux'][:, : self.aux_dim_keep] |
| return high_level_fts, low_level_fts |
| else: |
| return high_level_fts |
|
|
