I'm following the tutorial on tensorflows webpage using cyclegan. It works fine running the code through colab but when I am downloading the jupiter code and converting it using jupyter nbconvert:
jupyter nbconvert — to script cyclegan.ipynb --to python
I am running the code with python cyclegan.py but are getting an error:
File "C:\Users\myname\Desktop\PROJECT\GanTutorial\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 549, in rename_v2 compat.path_to_bytes(src), compat.path_to_bytes(dst), overwrite) UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc5 in position 167: invalid continuation byte
I can't get rid of this error. Does anyone successfully run the example outside google colab?
UPDATE After trying to use the trained data on some of my own files I got this errormessage:
' ' tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected 'tf.Tensor(False, shape=(), dtype=bool)' to be true. Summarized data: b'No files matched pattern:
here is my complete code:
#!/usr/bin/env python
import subprocess
subprocess.run(["pip", "install", "git+https://github.com/tensorflow/examples.git"])
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_examples.models.pix2pix import pix2pix
import os
import time
import matplotlib.pyplot as plt
from IPython.display import clear_output
AUTOTUNE = tf.data.AUTOTUNE
GPUS = tf.config.experimental.list_physical_devices('GPU')
if GPUS:
try:
for GPU in GPUS:
tf.config.experimental.set_memory_growth(GPU, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(GPUS), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as RE:
print(RE)
dataset, metadata = tfds.load('cycle_gan/horse2zebra',
with_info=True, as_supervised=True)
train_horses, train_zebras = dataset['trainA'], dataset['trainB']
test_horses, test_zebras = dataset['testA'], dataset['testB']
BUFFER_SIZE = 256
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256
def random_crop(image):
cropped_image = tf.image.random_crop(
image, 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, label):
image = random_jitter(image)
image = normalize(image)
return image
def preprocess_image_test(image, label):
image = normalize(image)
return image
train_horses = train_horses.map(
preprocess_image_train, num_parallel_calls=AUTOTUNE).cache().shuffle(
BUFFER_SIZE).batch(1)
train_zebras = train_zebras.map(
preprocess_image_train, num_parallel_calls=AUTOTUNE).cache().shuffle(
BUFFER_SIZE).batch(1)
test_horses = test_horses.map(
preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(
BUFFER_SIZE).batch(1)
test_zebras = test_zebras.map(
preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(
BUFFER_SIZE).batch(1)
sample_horse = next(iter(train_horses))
sample_zebra = next(iter(train_zebras))
plt.subplot(121)
plt.title('Horse')
plt.imshow(sample_horse[0] * 0.5 + 0.5)
plt.subplot(122)
plt.title('Horse with random jitter')
plt.imshow(random_jitter(sample_horse[0]) * 0.5 + 0.5)
plt.subplot(121)
plt.title('Zebra')
plt.imshow(sample_zebra[0] * 0.5 + 0.5)
plt.subplot(122)
plt.title('Zebra with random jitter')
plt.imshow(random_jitter(sample_zebra[0]) * 0.5 + 0.5)
OUTPUT_CHANNELS = 3
generator_g = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
generator_f = pix2pix.unet_generator(OUTPUT_CHANNELS, norm_type='instancenorm')
discriminator_x = pix2pix.discriminator(norm_type='instancenorm', target=False)
discriminator_y = pix2pix.discriminator(norm_type='instancenorm', target=False)
to_zebra = generator_g(sample_horse)
to_horse = generator_f(sample_zebra)
plt.figure(figsize=(8, 8))
contrast = 8
imgs = [sample_horse, to_zebra, sample_zebra, to_horse]
title = ['Horse', 'To Zebra', 'Zebra', 'To Horse']
for i in range(len(imgs)):
plt.subplot(2, 2, i+1)
plt.title(title[i])
if i % 2 == 0:
plt.imshow(imgs[i][0] * 0.5 + 0.5)
else:
plt.imshow(imgs[i][0] * 0.5 * contrast + 0.5)
plt.show()
plt.figure(figsize=(8, 8))
plt.subplot(121)
plt.title('Is a real zebra?')
plt.imshow(discriminator_y(sample_zebra)[0, ..., -1], cmap='RdBu_r')
plt.subplot(122)
plt.title('Is a real horse?')
plt.imshow(discriminator_x(sample_horse)[0, ..., -1], cmap='RdBu_r')
plt.show()
LAMBDA = 10
loss_obj = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(real, generated):
real_loss = loss_obj(tf.ones_like(real), real)
generated_loss = loss_obj(tf.zeros_like(generated), generated)
total_disc_loss = real_loss + generated_loss
return total_disc_loss * 0.5
def generator_loss(generated):
return loss_obj(tf.ones_like(generated), generated)
def calc_cycle_loss(real_image, cycled_image):
loss1 = tf.reduce_mean(tf.abs(real_image - cycled_image))
return LAMBDA * loss1
def identity_loss(real_image, same_image):
loss = tf.reduce_mean(tf.abs(real_image - same_image))
return LAMBDA * 0.5 * loss
generator_g_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
generator_f_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_x_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_y_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
checkpoint_path = "./checkpoints/train"
ckpt = tf.train.Checkpoint(generator_g=generator_g,
generator_f=generator_f,
discriminator_x=discriminator_x,
discriminator_y=discriminator_y,
generator_g_optimizer=generator_g_optimizer,
generator_f_optimizer=generator_f_optimizer,
discriminator_x_optimizer=discriminator_x_optimizer,
discriminator_y_optimizer=discriminator_y_optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)
# if a checkpoint exists, restore the latest checkpoint.
if ckpt_manager.latest_checkpoint:
ckpt.restore(ckpt_manager.latest_checkpoint)
print ('Latest checkpoint restored!!')
# ## Training
#
EPOCHS = 4
def generate_images(model, test_input):
prediction = model(test_input)
plt.figure(figsize=(12, 12))
display_list = [test_input[0], prediction[0]]
title = ['Input Image', 'Predicted Image']
for i in range(2):
plt.subplot(1, 2, i+1)
plt.title(title[i])
# getting the pixel values between [0, 1] to plot it.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
@tf.function
def train_step(real_x, real_y):
# persistent is set to True because the tape is used more than
# once to calculate the gradients.
with tf.GradientTape(persistent=True) as tape:
# Generator G translates X -> Y
# Generator F translates Y -> X.
fake_y = generator_g(real_x, training=True)
cycled_x = generator_f(fake_y, training=True)
fake_x = generator_f(real_y, training=True)
cycled_y = generator_g(fake_x, training=True)
# same_x and same_y are used for identity loss.
same_x = generator_f(real_x, training=True)
same_y = generator_g(real_y, training=True)
disc_real_x = discriminator_x(real_x, training=True)
disc_real_y = discriminator_y(real_y, training=True)
disc_fake_x = discriminator_x(fake_x, training=True)
disc_fake_y = discriminator_y(fake_y, training=True)
# calculate the loss
gen_g_loss = generator_loss(disc_fake_y)
gen_f_loss = generator_loss(disc_fake_x)
total_cycle_loss = calc_cycle_loss(real_x, cycled_x) + calc_cycle_loss(real_y, cycled_y)
# Total generator loss = adversarial loss + cycle loss
total_gen_g_loss = gen_g_loss + total_cycle_loss + identity_loss(real_y, same_y)
total_gen_f_loss = gen_f_loss + total_cycle_loss + identity_loss(real_x, same_x)
disc_x_loss = discriminator_loss(disc_real_x, disc_fake_x)
disc_y_loss = discriminator_loss(disc_real_y, disc_fake_y)
# Calculate the gradients for generator and discriminator
generator_g_gradients = tape.gradient(total_gen_g_loss,
generator_g.trainable_variables)
generator_f_gradients = tape.gradient(total_gen_f_loss,
generator_f.trainable_variables)
discriminator_x_gradients = tape.gradient(disc_x_loss,
discriminator_x.trainable_variables)
discriminator_y_gradients = tape.gradient(disc_y_loss,
discriminator_y.trainable_variables)
# Apply the gradients to the optimizer
generator_g_optimizer.apply_gradients(zip(generator_g_gradients,
generator_g.trainable_variables))
generator_f_optimizer.apply_gradients(zip(generator_f_gradients,
generator_f.trainable_variables))
discriminator_x_optimizer.apply_gradients(zip(discriminator_x_gradients,
discriminator_x.trainable_variables))
discriminator_y_optimizer.apply_gradients(zip(discriminator_y_gradients,
discriminator_y.trainable_variables))
for epoch in range(EPOCHS):
start = time.time()
n = 0
for image_x, image_y in tf.data.Dataset.zip((train_horses, train_zebras)):
train_step(image_x, image_y)
if n % 10 == 0:
print ('.', end='')
n += 1
clear_output(wait=True)
# Using a consistent image (sample_horse) so that the progress of the model
# is clearly visible.
generate_images(generator_g, sample_horse)
if (epoch + 1) % 5 == 0:
ckpt_save_path = ckpt_manager.save()
print ('Saving checkpoint for epoch {} at {}'.format(epoch+1,
ckpt_save_path))
print ('Time taken for epoch {} is {} sec\n'.format(epoch + 1,
time.time()-start))
# ## Generate using test dataset
# Run the trained model on the test dataset
# for inp in test_horses.take(5):
# generate_images(generator_g, inp)
import subprocess
subprocess.run(["pip", "install", "git+https://github.com/tensorflow/examples.git"])
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow_examples.models.pix2pix import pix2pix
from glob import glob
import os
image_path_list = glob('/content/horse/*.jpg')
horse_img = tf.data.Dataset.list_files(image_path_list)
for i in horse_img:
print(i)
tf.Tensor(b'/content/horse/horse4.jpg', shape=(), dtype=string)
tf.Tensor(b'/content/horse/horse3.jpg', shape=(), dtype=string)
tf.Tensor(b'/content/horse/horse2.jpg', shape=(), dtype=string)
tf.Tensor(b'/content/horse/horse1.jpg', shape=(), dtype=string)
def normalize(image):
image = tf.cast(image, tf.float32)
image = (image / 127.5) - 1
return image
def load_images(path):
image = tf.io.read_file(path)
image = tf.io.decode_image(image, expand_animations = False)
return image
def preprocess_image_test(image):
image = tf.image.resize(image, [256, 256])
image = normalize(image)
return image
horse_img = horse_img.map(load_images)
horse_img = horse_img.map(
preprocess_image_test, num_parallel_calls=AUTOTUNE).cache().shuffle(
BUFFER_SIZE).batch(1)
for i in horse_img:
print(i.shape)
(1, 256, 256, 3)
(1, 256, 256, 3)
(1, 256, 256, 3)
(1, 256, 256, 3)
for inp in horse_img.take(4):
generate_images(generator_g, inp)
from Unable to run cyclegan example from tensorflow outside google colab
No comments:
Post a Comment