| 207 | |
| 208 | @classmethod |
| 209 | def from_pretrained(cls, model_name, advprop=False, num_classes=1000, in_channels=3): |
| 210 | model = cls.from_name(model_name, override_params={'num_classes': num_classes}) |
| 211 | load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000), advprop=advprop) |
| 212 | if in_channels != 3: |
| 213 | Conv2d = get_same_padding_conv2d(image_size = model._global_params.image_size) |
| 214 | out_channels = round_filters(32, model._global_params) |
| 215 | model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) |
| 216 | return model |
| 217 | |
| 218 | @classmethod |
| 219 | def get_image_size(cls, model_name): |