model_stage1_color.py 文件源码

def build_model(self, img_batch, pose_code):        

        img_size = self.options['img_size']
        pose_code_size = self.options['pose_code_size']                        
        filter_size = self.options['filter_size']        
        batch_size = img_batch.shape[0]

        # image encoding        
        l_in = InputLayer(shape = [None, img_size[0], img_size[1], img_size[2]], input_var=img_batch)
        l_in_dimshuffle = DimshuffleLayer(l_in, (0,3,1,2))

        l_conv1_1 = Conv2DLayer(l_in_dimshuffle, num_filters=64, filter_size=filter_size, W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))        
        l_conv1_2 = Conv2DLayer(l_conv1_1, num_filters=64, filter_size=filter_size, W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))
        l_pool1 = MaxPool2DLayer(l_conv1_2, pool_size=(2,2)) 

        # pose encoding
        l_in_2 = InputLayer(shape=(None, pose_code_size), input_var=pose_code)     
        l_pose_1 = DenseLayer(l_in_2, num_units=512, W=HeNormal(),nonlinearity=rectify)
        l_pose_2 = DenseLayer(l_pose_1, num_units=pose_code_size*l_pool1.output_shape[2]*l_pool1.output_shape[3], W=HeNormal(),nonlinearity=rectify)
        l_pose_reshape = ReshapeLayer(l_pose_2, shape=(batch_size, pose_code_size, l_pool1.output_shape[2], l_pool1.output_shape[3])) 

        # deeper fusion
        l_concat = ConcatLayer([l_pool1, l_pose_reshape], axis=1)
        l_pose_conv_1 = Conv2DLayer(l_concat, num_filters=128, filter_size=filter_size, W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2)) 
        l_pose_conv_2 = Conv2DLayer(l_pose_conv_1, num_filters=128, filter_size=filter_size, W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))

        l_pool2 = MaxPool2DLayer(l_pose_conv_2, pool_size=(2,2))         
        l_conv_3 = Conv2DLayer(l_pool2, num_filters=128, filter_size=(1,1), W=HeNormal()) 
        l_unpool1 = Unpool2DLayer(l_conv_3, ds = (2,2))

        # image decoding
        l_deconv_conv1_1 = Conv2DLayer(l_unpool1, num_filters=128, filter_size=filter_size, nonlinearity=rectify,W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))
        l_deconv_conv1_2 = Conv2DLayer(l_deconv_conv1_1, num_filters=64, filter_size=filter_size, nonlinearity=rectify,W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))

        l_unpool2 = Unpool2DLayer(l_deconv_conv1_2, ds = (2,2))
        l_deconv_conv2_1 = Conv2DLayer(l_unpool2, num_filters=64, filter_size=filter_size, nonlinearity=None, W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))
        l_deconv_conv2_2 = Conv2DLayer(l_deconv_conv2_1, num_filters=img_size[2], filter_size=filter_size, nonlinearity=None, W=HeNormal(), pad=(filter_size[0]//2, filter_size[1]//2))  

        return l_deconv_conv2_2, l_pose_reshape