CSN-Group-Project/main.py at master · fabian1409/CSN-Group-Project · GitHub

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import os
import time
import random
import numpy as np
import scipy, multiprocessing
from tensorflow import keras
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import (Input, Conv2d, BatchNorm2d, Elementwise, SubpixelConv2d, Flatten, Dense)
from tensorlayer.models import Model
from PIL import Image

def get_G(input_shape):
    w_init = tf.random_normal_initializer(stddev=0.02)
    g_init = tf.random_normal_initializer(1., 0.02)

    nin = Input(input_shape)
    n = Conv2d(64, (3, 3), (1, 1), act=tf.nn.relu, padding='SAME', W_init=w_init)(nin)
    temp = n

    # B residual blocks
    for i in range(16):
        nn = Conv2d(64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
        nn = BatchNorm2d(act=tf.nn.relu, gamma_init=g_init)(nn)
        nn = Conv2d(64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(nn)
        nn = BatchNorm2d(gamma_init=g_init)(nn)
        nn = Elementwise(tf.add)([n, nn])
        n = nn

    n = Conv2d(64, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(gamma_init=g_init)(n)
    n = Elementwise(tf.add)([n, temp])
    # B residual blacks end

    n = Conv2d(256, (3, 3), (1, 1), padding='SAME', W_init=w_init)(n)
    n = SubpixelConv2d(scale=2, n_out_channels=None, act=tf.nn.relu)(n)

    n = Conv2d(256, (3, 3), (1, 1), act=None, padding='SAME', W_init=w_init)(n)
    n = SubpixelConv2d(scale=2, n_out_channels=None, act=tf.nn.relu)(n)

    nn = Conv2d(3, (1, 1), (1, 1), act=tf.nn.tanh, padding='SAME', W_init=w_init)(n)
    G = Model(inputs=nin, outputs=nn, name="generator")
    return G

def get_D(input_shape):
    w_init = tf.random_normal_initializer(stddev=0.02)
    gamma_init = tf.random_normal_initializer(1., 0.02)
    df_dim = 64
    lrelu = lambda x: tl.act.lrelu(x, 0.2)

    nin = Input(input_shape)
    n = Conv2d(df_dim, (4, 4), (2, 2), act=lrelu, padding='SAME', W_init=w_init)(nin)

    n = Conv2d(df_dim * 2, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 4, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 8, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 16, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 32, (4, 4), (2, 2), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 16, (1, 1), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 8, (1, 1), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
    nn = BatchNorm2d(gamma_init=gamma_init)(n)

    n = Conv2d(df_dim * 2, (1, 1), (1, 1), padding='SAME', W_init=w_init, b_init=None)(nn)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 2, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(act=lrelu, gamma_init=gamma_init)(n)
    n = Conv2d(df_dim * 8, (3, 3), (1, 1), padding='SAME', W_init=w_init, b_init=None)(n)
    n = BatchNorm2d(gamma_init=gamma_init)(n)
    n = Elementwise(combine_fn=tf.add, act=lrelu)([n, nn])

    n = Flatten()(n)
    no = Dense(n_units=1, W_init=w_init)(n)
    D = Model(inputs=nin, outputs=no, name="discriminator")
    return D

def cropImages():
    # Opens a image in RGB mode
    im_hr = Image.open(r"./samples/valid_gen.png")
    im_lr = Image.open(r"./samples/valid_lr.png")

    # Setting the points for cropped image
    left = 650
    top = 120
    right = 1150
    bottom = 500

    # Cropped image of above dimension
    # (It will not change original image)
    im1 = im_hr.crop((left, top, right, bottom))
    im2 = im_lr.crop((left/4, top/4, right/4, bottom/4))

    # Shows the image in image viewer
    im1.save("./samples/valid_gen_crop.png")
    im2.save("./samples/valid_lr_crop.png")