See also: https://www.tensorflow.org/tutorials/generative/pix2pix This section describes how to deal with errors that occur when building or executing the environment up to the execution of the above pix2 pix-tensorflow sample code. I don't have any particular knowledge in this area, so it's unclear if the solution described is the optimal solution.
OS: Windows10 home CPU: Ryzen3 3200 GPU: RTX-2060 Editor: Visual Studio Code
・ Ryzen3 3200 ⇒ Error occurred in Tensorflow 2.1.0 ⇒ Tensorflow 2.0.0 (Information that Tensorflow 2.1 works only on the latest CPU. Truth unknown. Source forgotten) -Tensorflow 2.0.0 ⇒ CUDA 10.0, cuDNN7.4.1 ⇒ changed to cuDNN 7.6.0 according to the correspondence table (code execution error) Reference: https://qiita.com/rhene/items/31bf4713b9dbda28bcc1 -Error occurred when securing GPU memory ⇒ Added corresponding code to sample code Reference: https://qiita.com/studio_haneya/items/4dfaf2fb2ac44818e7e0
#Error message
tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,
so try looking to see if a warning log message was printed above. [Op:Conv2D]
#Corresponding code
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
for k in range(len(physical_devices)):
#tf.config.experimental.per_process_gpu_memory_fraction = 0.8
tf.config.experimental.set_memory_growth(physical_devices[k], True)
print('memory growth:', tf.config.experimental.get_memory_growth(physical_devices[k]))
else:
print("Not enough GPU hardware devices available")
-Error when graphviz does not exist when executing code ⇒ graphviz 2.3.8 installation Reference: http://ruby.kyoto-wu.ac.jp/info-com/Softwares/Graphviz/
import tensorflow as tf
import os
import time
from matplotlib import pyplot as plt
from IPython import display
#Correspondence part in GPU error
physical_devices = tf.config.experimental.list_physical_devices('GPU')
if len(physical_devices) > 0:
for k in range(len(physical_devices)):
tf.config.experimental.set_memory_growth(physical_devices[k], True)
print('memory growth:', tf.config.experimental.get_memory_growth(physical_devices[k]))
else:
print("Not enough GPU hardware devices available")
#Below, there is no change from the code on the TensorFlow official website
_URL = 'https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/facades.tar.gz'
path_to_zip = tf.keras.utils.get_file('facades.tar.gz',
origin=_URL,
extract=True)
PATH = os.path.join(os.path.dirname(path_to_zip), 'facades/')
BUFFER_SIZE = 400
BATCH_SIZE = 1
IMG_WIDTH = 256
IMG_HEIGHT = 256
def load(image_file):
image = tf.io.read_file(image_file)
image = tf.image.decode_jpeg(image)
w = tf.shape(image)[1]
w = w // 2
real_image = image[:, :w, :]
input_image = image[:, w:, :]
input_image = tf.cast(input_image, tf.float32)
real_image = tf.cast(real_image, tf.float32)
return input_image, real_image
inp, re = load(PATH+'train/100.jpg')
# casting to int for matplotlib to show the image
plt.figure()
plt.imshow(inp/255.0)
plt.figure()
plt.imshow(re/255.0)
def resize(input_image, real_image, height, width):
input_image = tf.image.resize(input_image, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
real_image = tf.image.resize(real_image, [height, width],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return input_image, real_image
def random_crop(input_image, real_image):
stacked_image = tf.stack([input_image, real_image], axis=0)
cropped_image = tf.image.random_crop(
stacked_image, size=[2, IMG_HEIGHT, IMG_WIDTH, 3])
return cropped_image[0], cropped_image[1]
# normalizing the images to [-1, 1]
def normalize(input_image, real_image):
input_image = (input_image / 127.5) - 1
real_image = (real_image / 127.5) - 1
return input_image, real_image
@tf.function()
def random_jitter(input_image, real_image):
# resizing to 286 x 286 x 3
input_image, real_image = resize(input_image, real_image, 286, 286)
# randomly cropping to 256 x 256 x 3
input_image, real_image = random_crop(input_image, real_image)
if tf.random.uniform(()) > 0.5:
# random mirroring
input_image = tf.image.flip_left_right(input_image)
real_image = tf.image.flip_left_right(real_image)
return input_image, real_image
plt.figure(figsize=(6, 6))
for i in range(4):
rj_inp, rj_re = random_jitter(inp, re)
plt.subplot(2, 2, i+1)
plt.imshow(rj_inp/255.0)
plt.axis('off')
plt.show()
def load_image_train(image_file):
input_image, real_image = load(image_file)
input_image, real_image = random_jitter(input_image, real_image)
input_image, real_image = normalize(input_image, real_image)
return input_image, real_image
def load_image_test(image_file):
input_image, real_image = load(image_file)
input_image, real_image = resize(input_image, real_image,
IMG_HEIGHT, IMG_WIDTH)
input_image, real_image = normalize(input_image, real_image)
return input_image, real_image
train_dataset = tf.data.Dataset.list_files(PATH+'train/*.jpg')
train_dataset = train_dataset.map(load_image_train,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = tf.data.Dataset.list_files(PATH+'test/*.jpg')
test_dataset = test_dataset.map(load_image_test)
test_dataset = test_dataset.batch(BATCH_SIZE)
OUTPUT_CHANNELS = 3
def downsample(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
down_model = downsample(3, 4)
down_result = down_model(tf.expand_dims(inp, 0))
print (down_result.shape)
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
up_model = upsample(3, 4)
up_result = up_model(down_result)
print (up_result.shape)
def Generator():
inputs = tf.keras.layers.Input(shape=[256,256,3])
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (bs, 128, 128, 64)
downsample(128, 4), # (bs, 64, 64, 128)
downsample(256, 4), # (bs, 32, 32, 256)
downsample(512, 4), # (bs, 16, 16, 512)
downsample(512, 4), # (bs, 8, 8, 512)
downsample(512, 4), # (bs, 4, 4, 512)
downsample(512, 4), # (bs, 2, 2, 512)
downsample(512, 4), # (bs, 1, 1, 512)
]
up_stack = [
upsample(512, 4, apply_dropout=True), # (bs, 2, 2, 1024)
upsample(512, 4, apply_dropout=True), # (bs, 4, 4, 1024)
upsample(512, 4, apply_dropout=True), # (bs, 8, 8, 1024)
upsample(512, 4), # (bs, 16, 16, 1024)
upsample(256, 4), # (bs, 32, 32, 512)
upsample(128, 4), # (bs, 64, 64, 256)
upsample(64, 4), # (bs, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(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)
generator = Generator()
tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)
gen_output = generator(inp[tf.newaxis,...], training=False)
plt.imshow(gen_output[0,...])
LAMBDA = 100
def generator_loss(disc_generated_output, gen_output, target):
gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)
# mean absolute error
l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
total_gen_loss = gan_loss + (LAMBDA * l1_loss)
return total_gen_loss, gan_loss, l1_loss
def Discriminator():
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 = downsample(64, 4, False)(x) # (bs, 128, 128, 64)
down2 = downsample(128, 4)(down1) # (bs, 64, 64, 128)
down3 = 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)
discriminator = Discriminator()
tf.keras.utils.plot_model(discriminator, show_shapes=True, dpi=64)
disc_out = discriminator([inp[tf.newaxis,...], gen_output], training=False)
plt.imshow(disc_out[0,...,-1], vmin=-20, vmax=20, cmap='RdBu_r')
plt.colorbar()
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(disc_real_output, disc_generated_output):
real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)
generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)
total_disc_loss = real_loss + generated_loss
return total_disc_loss
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
def generate_images(model, test_input, tar):
prediction = model(test_input, training=True)
plt.figure(figsize=(15,15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, 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()
for example_input, example_target in test_dataset.take(1):
generate_images(generator, example_input, example_target)
EPOCHS = 150
import datetime
log_dir="logs/"
summary_writer = tf.summary.create_file_writer(
log_dir + "fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
@tf.function
def train_step(input_image, target, epoch):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
gen_output = generator(input_image, training=True)
disc_real_output = discriminator([input_image, target], training=True)
disc_generated_output = discriminator([input_image, gen_output], training=True)
gen_total_loss, gen_gan_loss, gen_l1_loss = generator_loss(disc_generated_output, gen_output, target)
disc_loss = discriminator_loss(disc_real_output, disc_generated_output)
generator_gradients = gen_tape.gradient(gen_total_loss,
generator.trainable_variables)
discriminator_gradients = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(generator_gradients,
generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(discriminator_gradients,
discriminator.trainable_variables))
with summary_writer.as_default():
tf.summary.scalar('gen_total_loss', gen_total_loss, step=epoch)
tf.summary.scalar('gen_gan_loss', gen_gan_loss, step=epoch)
tf.summary.scalar('gen_l1_loss', gen_l1_loss, step=epoch)
tf.summary.scalar('disc_loss', disc_loss, step=epoch)
def fit(train_ds, epochs, test_ds):
for epoch in range(epochs):
start = time.time()
display.clear_output(wait=True)
for example_input, example_target in test_ds.take(1):
generate_images(generator, example_input, example_target)
print("Epoch: ", epoch)
# Train
for n, (input_image, target) in train_ds.enumerate():
print('.', end='')
if (n+1) % 100 == 0:
print()
train_step(input_image, target, epoch)
print()
# saving (checkpoint) the model every 20 epochs
if (epoch + 1) % 20 == 0:
checkpoint.save(file_prefix = checkpoint_prefix)
print ('Time taken for epoch {} is {} sec\n'.format(epoch + 1,
time.time()-start))
checkpoint.save(file_prefix = checkpoint_prefix)
# %load_ext tensorboard
# %tensorboard --logdir {log_dir}
fit(train_dataset, EPOCHS, test_dataset)
Recommended Posts