enhance.py 文件源码

def setup_perceptual(self, input):

        offset = np.array([103.939, 116.779, 123.680], dtype=np.float32).reshape((1,3,1,1))
        self.network['percept'] = lasagne.layers.NonlinearityLayer(input, lambda x: ((x+0.5)*255.0) - offset)

        self.network['mse'] = self.network['percept']
        self.network['conv1_1'] = ConvLayer(self.network['percept'], 64, 3, pad=1)
        self.network['conv1_2'] = ConvLayer(self.network['conv1_1'], 64, 3, pad=1)
        self.network['pool1']   = PoolLayer(self.network['conv1_2'], 2, mode='max')
        self.network['conv2_1'] = ConvLayer(self.network['pool1'],   128, 3, pad=1)
        self.network['conv2_2'] = ConvLayer(self.network['conv2_1'], 128, 3, pad=1)
        self.network['pool2']   = PoolLayer(self.network['conv2_2'], 2, mode='max')
        self.network['conv3_1'] = ConvLayer(self.network['pool2'],   256, 3, pad=1)
        self.network['conv3_2'] = ConvLayer(self.network['conv3_1'], 256, 3, pad=1)
        self.network['conv3_3'] = ConvLayer(self.network['conv3_2'], 256, 3, pad=1)
        self.network['conv3_4'] = ConvLayer(self.network['conv3_3'], 256, 3, pad=1)
        self.network['pool3']   = PoolLayer(self.network['conv3_4'], 2, mode='max')
        self.network['conv4_1'] = ConvLayer(self.network['pool3'],   512, 3, pad=1)
        self.network['conv4_2'] = ConvLayer(self.network['conv4_1'], 512, 3, pad=1)
        self.network['conv4_3'] = ConvLayer(self.network['conv4_2'], 512, 3, pad=1)
        self.network['conv4_4'] = ConvLayer(self.network['conv4_3'], 512, 3, pad=1)
        self.network['pool4']   = PoolLayer(self.network['conv4_4'], 2, mode='max')
        self.network['conv5_1'] = ConvLayer(self.network['pool4'],   512, 3, pad=1)
        self.network['conv5_2'] = ConvLayer(self.network['conv5_1'], 512, 3, pad=1)
        self.network['conv5_3'] = ConvLayer(self.network['conv5_2'], 512, 3, pad=1)
        self.network['conv5_4'] = ConvLayer(self.network['conv5_3'], 512, 3, pad=1)