This is the implementation of an ECCV'18 paper, "Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network" in Tensorflow (link)

For this implementation, I designed new layers and a model by subclassing, which was a good practice of this tensorflow code guide on 'Making new Layers and Models via subclassing'. (link)

• Language: Python3
• Required Packages: numpy, cv2 (for read image input), and tensorflow
• To install the required packages, type the following command:

1. Using tensorflow-gpu==2.0.0

pip3 install numpy opencv-python tensorflow-gpu==2.0.0 

2. Using higher version of tensorflow

pip3 install numpy opencv-python tensorflow 

By subclassing layers and models, we can easily design CARN model simply specifying a few parameters.

• Parameters for CARN model:

1. initial_filter_num: represents the number of filters for the inital convolution layer
2. num_cas_block: represents the number of cascading blocks
3. num_res_block: represents the number of resnet blocks in a cascading block
4. num_conv_block: represents the number of convolution layer in a resnet block
5. kernel_size: represnets the size of kernel in a convolution layer

• In this implementation, tensorflow-gpu==2.0.0 is installed. If higher version of TF used, you can easily enable 'group convolution' in tf.keras.layers.Conv2D by setting 'groups' attribute to the number of groups you want
• You can remove the last Conv2D layer as its functionality already exists in the upsample layer.
• How to create the CARN model

#Set the model parameters initial_filter_num = 16 num_cas_block = 1 num_res_block = 2 num_conv_block = 2 kernel_size = 3 #Load your dataset input = cv2.imread('output.jpg').astype(np.float32) input_small = cv2.resize(input, None, fx = 1/4, fy = 1/4) / 255. #Set the training parameters epochs = 10 optimizer = tf.keras.optimizers.Adam(learning_rate = 1e-4) #Set the model save path save_path = 'model' if not os.path.exists(save_path): os.makedirs(save_path) model = CasResNet(initial_filter_num, num_cas_block, num_res_block, num_conv_block, kernel_size) optimizer = tf.keras.optimizers.Adam(learning_rate = 1e-4) model.compile(optimizer, loss = tf.keras.losses.MeanSquaredError()) model.fit(input_small[np.newaxis, ...], input[np.newaxis, ...], epochs = epochs) model.build_graph().summary() tf.keras.utils.plot_model(model.build_graph(), show_shapes=True, dpi=256) model.save(save_path) ''' model = tf.keras.models.load_model(save_path) result = model(input_small[np.newaxis, ...]) print(result) ''' 

• Model Structure