(num_classes=1000, pretrained=False, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m1)
| 87 | |
| 88 | @register_model |
| 89 | def EfficientViT_M1(num_classes=1000, pretrained=False, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m1): |
| 90 | model = EfficientViT(num_classes=num_classes, distillation=distillation, **model_cfg) |
| 91 | if pretrained: |
| 92 | pretrained = _checkpoint_url_format.format(pretrained) |
| 93 | checkpoint = torch.hub.load_state_dict_from_url( |
| 94 | pretrained, map_location='cpu') |
| 95 | d = checkpoint['model'] |
| 96 | D = model.state_dict() |
| 97 | for k in d.keys(): |
| 98 | if D[k].shape != d[k].shape: |
| 99 | d[k] = d[k][:, :, None, None] |
| 100 | model.load_state_dict(d) |
| 101 | if fuse: |
| 102 | replace_batchnorm(model) |
| 103 | return model |
| 104 | |
| 105 | @register_model |
| 106 | def EfficientViT_M2(num_classes=1000, pretrained=False, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m2): |
nothing calls this directly
no test coverage detected