1432199ddd553325de0983f0793f400d5db7f7a8,pretrainedmodels/models/torchvision_models.py,,inceptionv3,#Any#Any#,233

Before Change

        return x
        
    // Modify methods
    setattr(model.__class__, "features", features)
    setattr(model.__class__, "logits", logits)
    setattr(model.__class__, "forward", forward)  
    return model

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

After Change

    rInception v3 model architecture from
    `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.
    
    model = models.inception_v3(pretrained=False)
    if pretrained is not None:
        settings = pretrained_settings["inceptionv3"][pretrained]
        model = load_pretrained(model, num_classes, settings)

    // Modify attributs
    model.last_linear = model.fc
    del model.fc

    def features(self, input):
        // 299 x 299 x 3
        x = self.Conv2d_1a_3x3(input) // 149 x 149 x 32
        x = self.Conv2d_2a_3x3(x) // 147 x 147 x 32
        x = self.Conv2d_2b_3x3(x) // 147 x 147 x 64
        x = F.max_pool2d(x, kernel_size=3, stride=2) // 73 x 73 x 64
        x = self.Conv2d_3b_1x1(x) // 73 x 73 x 80
        x = self.Conv2d_4a_3x3(x) // 71 x 71 x 192
        x = F.max_pool2d(x, kernel_size=3, stride=2) // 35 x 35 x 192
        x = self.Mixed_5b(x) // 35 x 35 x 256
        x = self.Mixed_5c(x) // 35 x 35 x 288
        x = self.Mixed_5d(x) // 35 x 35 x 288
        x = self.Mixed_6a(x) // 17 x 17 x 768
        x = self.Mixed_6b(x) // 17 x 17 x 768
        x = self.Mixed_6c(x) // 17 x 17 x 768
        x = self.Mixed_6d(x) // 17 x 17 x 768
        x = self.Mixed_6e(x) // 17 x 17 x 768
        if self.training and self.aux_logits:
            self._out_aux = self.AuxLogits(x) // 17 x 17 x 768
        x = self.Mixed_7a(x) // 8 x 8 x 1280
        x = self.Mixed_7b(x) // 8 x 8 x 2048
        x = self.Mixed_7c(x) // 8 x 8 x 2048
        return x

    def logits(self, features):
        x = F.avg_pool2d(features, kernel_size=8) // 1 x 1 x 2048
        x = F.dropout(x, training=self.training) // 1 x 1 x 2048
        x = x.view(x.size(0), -1) // 2048
        x = self.last_linear(x) // 1000 (num_classes)
        if self.training and self.aux_logits:
            aux = self._out_aux
            self._out_aux = None
            return x, aux
        return x

    def forward(self, input):
        x = self.features(input)
        x = self.logits(x)
        return x
        
    // Modify methods
    model.features = types.MethodType(features, model)
    model.logits = types.MethodType(logits, model)
    model.forward = types.MethodType(forward, model)  
    return model

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

Instances