(num_classes=1000, pretrained=False, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m3)
| 121 | |
| 122 | @register_model |
| 123 | def EfficientViT_M3(num_classes=1000, pretrained=False, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m3): |
| 124 | model = EfficientViT(num_classes=num_classes, distillation=distillation, **model_cfg) |
| 125 | if pretrained: |
| 126 | pretrained = _checkpoint_url_format.format(pretrained) |
| 127 | checkpoint = torch.hub.load_state_dict_from_url( |
| 128 | pretrained, map_location='cpu') |
| 129 | d = checkpoint['model'] |
| 130 | D = model.state_dict() |
| 131 | for k in d.keys(): |
| 132 | if D[k].shape != d[k].shape: |
| 133 | d[k] = d[k][:, :, None, None] |
| 134 | model.load_state_dict(d) |
| 135 | if fuse: |
| 136 | replace_batchnorm(model) |
| 137 | return model |
| 138 | |
| 139 | @register_model |
| 140 | def EfficientViT_M4(num_classes=1000, pretrained=False, distillation=False, fuse=False, pretrained_cfg=None, model_cfg=EfficientViT_m4): |
nothing calls this directly
no test coverage detected