Basic segmentation network, no ASPP, no Mscale
| 36 | |
| 37 | |
| 38 | class Basic(nn.Module): |
| 39 | """ |
| 40 | Basic segmentation network, no ASPP, no Mscale |
| 41 | """ |
| 42 | def __init__(self, num_classes, trunk='hrnetv2', criterion=None): |
| 43 | super(Basic, self).__init__() |
| 44 | self.criterion = criterion |
| 45 | self.backbone, _, _, high_level_ch = get_trunk( |
| 46 | trunk_name=trunk, output_stride=8) |
| 47 | self.seg_head = make_seg_head(in_ch=high_level_ch, |
| 48 | out_ch=num_classes) |
| 49 | initialize_weights(self.seg_head) |
| 50 | |
| 51 | def forward(self, inputs): |
| 52 | x = inputs['images'] |
| 53 | _, _, final_features = self.backbone(x) |
| 54 | pred = self.seg_head(final_features) |
| 55 | pred = scale_as(pred, x) |
| 56 | |
| 57 | if self.training: |
| 58 | assert 'gts' in inputs |
| 59 | gts = inputs['gts'] |
| 60 | loss = self.criterion(pred, gts) |
| 61 | return loss |
| 62 | else: |
| 63 | output_dict = {'pred': pred} |
| 64 | return output_dict |
| 65 | |
| 66 | |
| 67 | class ASPP(nn.Module): |