Python load_pretrained_weights Examples

Example #1

File: efficientNet.py Project: xr0927/project4--Garbage-sorting

 def from_pretrained(cls, model_name, num_classes=1000):
     model = EfficientNet.from_name(
         model_name, override_params={'num_classes': num_classes})
     load_pretrained_weights(model,
                             model_name,
                             load_fc=(num_classes == 1000))
     return model

Example #2

File: efficientnet.py Project: youwh-PIRI/EfficientDet.Pytorch

 def from_pretrained(cls, model_name, num_classes=1000, in_channels = 3):
     model = cls.from_name(model_name, override_params={'num_classes': num_classes})
     load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000))
     if in_channels != 3:
         Conv2d = get_same_padding_conv2d(image_size = model._global_params.image_size)
         out_channels = round_filters(32, model._global_params)
         model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)
     return model

Example #3

    def from_pretrained(cls, model_name, num_classes=1000):
        model = cls.from_name(model_name,
                              override_params={'num_classes': num_classes})

        print(type(model))
        load_pretrained_weights(model,
                                model_name,
                                load_fc=(num_classes == 1000))

        return model