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import os
import gradio as gr
import tensorflow as tf
import numpy as np
BUFFER_SIZE = 1000
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256
def random_crop(image):
cropped_image = tf.image.random_crop(
image[0], size=[IMG_HEIGHT, IMG_WIDTH, 3])
return cropped_image
# normalizing the images to [-1, 1]
def normalize(image):
image = tf.cast(image, tf.float32)
image = (image / 127.5) - 1
return image
def random_jitter(image):
# resizing to 286 x 286 x 3
image = tf.image.resize(image, [286, 286],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
# randomly cropping to 256 x 256 x 3
image = random_crop(image)
# random mirroring
image = tf.image.random_flip_left_right(image)
return image
def preprocess_image_train(image):
image = random_jitter(image)
image = normalize(image)
return image
def preprocess_image_test(image):
image = tf.image.resize(image, [286, 286],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
image = random_crop(image)
image = normalize(image)
return image
OUTPUT_CHANNELS=3
def downsample(filters, size, strides=2, apply_batchnorm=True, leaky_relu=None, padding=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
if padding:
result.add(
tf.keras.layers.Conv2D(filters, size, strides=strides, padding='same',
kernel_initializer=initializer, use_bias=False))
else:
result.add(
tf.keras.layers.Conv2D(filters, size, strides=strides, padding='valid',
kernel_initializer=initializer, use_bias=False))
if apply_batchnorm:
result.add(tf.keras.layers.BatchNormalization())
if leaky_relu is True:
result.add(tf.keras.layers.LeakyReLU())
elif leaky_relu is False:
result.add(tf.keras.layers.ReLU())
return result
def upsample(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))
result.add(tf.keras.layers.BatchNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
def resnet_block():
result = tf.keras.models.Sequential()
result.add(downsample(256,3,strides=1,apply_batchnorm=True,leaky_relu=False))
result.add(downsample(256,3,strides=1,apply_batchnorm=True,leaky_relu=None))
return result
def Generator():
inputs = tf.keras.layers.Input(shape=[256, 256, 3])
down_stack = [
downsample(64,7,strides=1,apply_batchnorm=False,leaky_relu=True),
downsample(128,3,strides=2,apply_batchnorm=True,leaky_relu=True),
downsample(256,3,strides=2,apply_batchnorm=True,leaky_relu=True),
]
resnet_stack = [
resnet_block()
]*9
up_stack = [
upsample(128,3),
upsample(64,3),
]
final_layer = downsample(3,7,strides=1,apply_batchnorm=False)
x = inputs
for down in down_stack:
x = down(x)
for res in resnet_stack:
x = x + res(x)
for up in up_stack:
x = up(x)
x = final_layer(x)
return tf.keras.Model(inputs=inputs, outputs=x)
def Discriminator():
initializer = tf.random_normal_initializer(0., 0.02)
inputs = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')
x = inputs
x = downsample(64,4,strides=2,apply_batchnorm=False,leaky_relu=True)(x)
x = downsample(128,4,strides=2,apply_batchnorm=False,leaky_relu=True)(x)
x = downsample(256,4,strides=2,apply_batchnorm=False,leaky_relu=True)(x)
x = tf.keras.layers.ZeroPadding2D()(x)
x = downsample(512,4,strides=1,apply_batchnorm=False,leaky_relu=True,padding=False)(x)
x = tf.keras.layers.ZeroPadding2D()(x)
x = downsample(1,4,strides=1,apply_batchnorm=False,leaky_relu=True,padding=False)(x)
return tf.keras.Model(inputs=inputs,outputs=x)
generator_m2f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_f2m_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_m_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_m2f = Generator()
generator_f2m = Generator()
discriminator_f = Discriminator()
discriminator_m = Discriminator()
checkpoint_dir = "/AnimizerGAN/checkpoint_dir"
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_m2f=generator_m2f,
generator_f2m=generator_f2m,
discriminator_m=discriminator_m,
discriminator_f=discriminator_f,
generator_m2f_optimizer=generator_m2f_optimizer,
generator_f2m_optimizer=generator_f2m_optimizer,
discriminator_m_optimizer=discriminator_m_optimizer,
discriminator_f_optimizer=discriminator_f_optimizer)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
def animize(img):
img = preprocess_image_test(np.reshape(img,(1,np.shape(img)[0],np.shape(img)[1],np.shape(img)[2])))
img = tf.expand_dims(img,0)
prediction = generator_f2m(img)
# converting to -1 to 1
prediction = (prediction - np.min(prediction)) / (np.max(prediction) - np.min(prediction))
prediction = prediction * 2 - 1
prediction = (prediction[0]*0.5+0.5).numpy()
# out = tf.image.resize(image, [128, 128],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return prediction
interface = gr.Interface(fn=animize, inputs="image", outputs="image")
interface.launch(share=True) |