How to extract features of an image from a trained model

49

Because of the way Inception_v3 is structured, you would need to do some more manual work to get the layers that you want.
Something on the lines of

class MyInceptionFeatureExtractor(nn.Module):
    def __init__(self, inception, transform_input=False):
        super(MyInceptionFeatureExtractor, self).__init__()
        self.transform_input = transform_input
        self.Conv2d_1a_3x3 = inception.Conv2d_1a_3x3
        self.Conv2d_2a_3x3 = inception.Conv2d_2a_3x3
        self.Conv2d_2b_3x3 = inception.Conv2d_3a_3x3
        self.Conv2d_3b_1x1 = inception.Conv2d_3b_3x3
        self.Conv2d_4a_3x3 = inception.Conv2d_4a_3x3
        self.Mixed_5b = inception.Mixed_5b
        # stop where you want, copy paste from the model def

    def forward(self, x):
        if self.transform_input:
            x = x.clone()
            x[0] = x[0] * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
            x[1] = x[1] * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
            x[2] = x[2] * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
        # 299 x 299 x 3
        x = self.Conv2d_1a_3x3(x)
        # 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)
        # copy paste from model definition, just stopping where you want
        return x

inception = torchvision.models['inception_v3_google']
my_inception = MyInceptionFeatureExtractor(inception)
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