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model.py
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model.py
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import tensorflow as tf
import os
import matplotlib.pyplot as plt
import cv2
import numpy as np
class myModel():
def __init__(self):
self.OUTPUT_CHANNELS = 3
self.IMG_WIDTH = 256
self.IMG_HEIGHT = 256
self.generator = self.Generator()
self.discriminator = self.Discriminator()
self.generator_optimizer = tf.keras.optimizers.Adam(2e-4,beta_1= 0.5)
self.discriminator_optimizer = tf.keras.optimizers.Adam(2e-4,beta_1 = 0.5)
def downsample(self, filters,size,apply_batchnorm = True):
initializer = tf.random_normal_initializer(0.,0.02)
result = tf.keras.Sequential()
result.add(tf.keras.layers.Conv2D(filters,size,strides = 2, padding='same',
kernel_initializer = initializer,use_bias =False))
if apply_batchnorm:
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.LeakyReLU())
return result
def upsample(self,filters,size,apply_dropout=False):
initializer = tf.random_normal_initializer(0.,0.02)
result = tf.keras.Sequential()
result.add(tf.keras.layers.Conv2DTranspose(filters, size, strides=2,padding = 'same',
kernel_initializer = initializer, use_bias = False)
)
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
def Generator(self):
inputs = tf.keras.layers.Input(shape=[self.IMG_WIDTH,self.IMG_HEIGHT,self.OUTPUT_CHANNELS]) # (bs, 256,256, 3)
down_stack = [
self.downsample( 64, 4, apply_batchnorm = False), # (bs, 128,128, 64)
self.downsample(128,4), # (bs, 64, 64, 128)
self.downsample(256,4), # (bs, 32, 32, 256)
self.downsample(512,4), # (bs, 16, 16, 512)
self.downsample(512,4), # (bs, 8, 8, 512)
self.downsample(512,4), # (bs, 4, 4, 512)
self.downsample(512,4), # (bs, 2, 2, 512)
self.downsample(512,4), # (bs, 1, 1, 512)
]
up_stack = [
self.upsample(512,4, apply_dropout = True), # (bs, 2,2, 1024)
self.upsample(512,4, apply_dropout = True), # (bs, 4,4, 1024)
self.upsample(512,4, apply_dropout = True), # (bs, 8,8, 1024)
self.upsample(512,4), # (bs, 16,16, 1024)
self.upsample(256,4), # (bs, 32, 32, 512)
self.upsample(128,4), # (bs,64, 64, 256)
self.upsample(64,4), # (bs, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0.,0.02)
last = tf.keras.layers.Conv2DTranspose(self.OUTPUT_CHANNELS, 4,
strides = 2,
padding = 'same',
kernel_initializer = initializer,
activation = 'tanh') #(bs, 256,256, 3)
x = inputs
#Downsampling through the model
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1])
#Upsampling and establishing the skip connections
for up,skip in zip(up_stack,skips):
x = up(x)
x = tf.keras.layers.Concatenate()([x,skip])
x = last(x)
return tf.keras.Model(inputs=inputs,outputs= x)
def Discriminator(self):
initializer = tf.random_normal_initializer(0., 0.02)
inp = tf.keras.layers.Input(shape = [256,256,3], name = 'input_image')
tar = tf.keras.layers.Input(shape = [256,256,3], name = 'target_image')
x = tf.keras.layers.concatenate([inp,tar]) # (bs, 256,256, channels*2)
down1 = self.downsample(64,4, False)(x) # (bs, 128,128,64)
down2 = self.downsample(128,4)(down1) # (bs, 64,64, 128)
down3 = self.downsample(256,4)(down2) # (bs, 32,32,256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (bs, 34,34, 256)
conv = tf.keras.layers.Conv2D(512, 4, strides = 1,
kernel_initializer = initializer,
use_bias = False)(zero_pad1) #(bs,31,31,512)
batchnorm1 = tf.keras.layers.BatchNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) #(bs, 33,33,512)
last = tf.keras.layers.Conv2D(1,4, strides=1,
kernel_initializer = initializer)(zero_pad2)# (bs, 30, 30, 1)
return tf.keras.Model(inputs = [inp,tar],outputs= last)
def loadModel(self,model_path):
checkpoint = tf.train.Checkpoint(generator_optimizer=self.generator_optimizer,
discriminator_optimizer=self.discriminator_optimizer,
generator=self.generator,
discriminator=self.discriminator)
latest = tf.train.latest_checkpoint(model_path)
checkpoint.restore((latest))
print("[+] Model is successfully loaded [+]")
def model_predict(self):
img_path = "captured.png"
image = tf.io.read_file(img_path)
image = tf.image.decode_jpeg(image)
image = tf.cast(image, tf.float32)
image = tf.image.resize(image, [256,256],
method = tf.image.ResizeMethod.NEAREST_NEIGHBOR)
image = (image/127.5)-1
data_input = tf.expand_dims(image,0)
output = self.generator(data_input, training=True)
output_image = output[0]* 0.5 + 0.5
return output_image