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This tutorial demonstrates how to build and train a conditional generative adversarial network (cGAN) called pix2pix that learns a mapping from input images to output images, as described in Image-to-image translation with conditional adversarial networks by Isola et al. (2017). pix2pix is not application specific—it can be applied to a wide range of tasks, including synthesizing photos from label maps, generating colorized photos from black and white images, turning Google Maps photos into aerial images, and even transforming sketches into photos.
In this example, your network will generate images of building facades using the CMP Facade Database provided by the Center for Machine Perception at the Czech Technical University in Prague. To keep it short, you will use a preprocessed copy of this dataset created by the pix2pix authors.
In the pix2pix cGAN, you condition on input images and generate corresponding output images. cGANs were first proposed in Conditional Generative Adversarial Nets (Mirza and Osindero, 2014)
The architecture of your network will contain:
- A generator with a U-Net-based architecture.
- A discriminator represented by a convolutional PatchGAN classifier (proposed in the pix2pix paper).
Note that each epoch can take around 15 seconds on a single V100 GPU.
Below are some examples of the output generated by the pix2pix cGAN after training for 200 epochs on the facades dataset (80k steps).
Import TensorFlow and other libraries
import tensorflow as tf
import os
import pathlib
import time
import datetime
from matplotlib import pyplot as plt
from IPython import display
2024-08-16 05:11:33.561479: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2024-08-16 05:11:33.582597: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2024-08-16 05:11:33.589027: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
Load the dataset
Download the CMP Facade Database data (30MB). Additional datasets are available in the same format here. In Colab you can select other datasets from the drop-down menu. Note that some of the other datasets are significantly larger (edges2handbags is 8GB in size).
dataset_name = "facades"
_URL = f'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/{dataset_name}.tar.gz'
path_to_zip = tf.keras.utils.get_file(
fname=f"{dataset_name}.tar.gz",
origin=_URL,
extract=True)
path_to_zip = pathlib.Path(path_to_zip)
PATH = path_to_zip.parent/dataset_name
Downloading data from http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/facades.tar.gz 30168306/30168306 ━━━━━━━━━━━━━━━━━━━━ 8s 0us/step
list(PATH.parent.iterdir())
[PosixPath('/home/kbuilder/.keras/datasets/flower_photos.tar'),
PosixPath('/home/kbuilder/.keras/datasets/fashion-mnist'),
PosixPath('/home/kbuilder/.keras/datasets/mnist.npz'),
PosixPath('/home/kbuilder/.keras/datasets/HIGGS.csv.gz'),
PosixPath('/home/kbuilder/.keras/datasets/facades.tar.gz'),
PosixPath('/home/kbuilder/.keras/datasets/jena_climate_2009_2016.csv'),
PosixPath('/home/kbuilder/.keras/datasets/iris_test.csv'),
PosixPath('/home/kbuilder/.keras/datasets/cats_and_dogs_filtered'),
PosixPath('/home/kbuilder/.keras/datasets/jena_climate_2009_2016.csv.zip'),
PosixPath('/home/kbuilder/.keras/datasets/facades'),
PosixPath('/home/kbuilder/.keras/datasets/cifar-10-batches-py'),
PosixPath('/home/kbuilder/.keras/datasets/kandinsky5.jpg'),
PosixPath('/home/kbuilder/.keras/datasets/iris_training.csv'),
PosixPath('/home/kbuilder/.keras/datasets/cats_and_dogs.zip'),
PosixPath('/home/kbuilder/.keras/datasets/Red_sunflower'),
PosixPath('/home/kbuilder/.keras/datasets/cifar-10-batches-py.tar.gz'),
PosixPath('/home/kbuilder/.keras/datasets/YellowLabradorLooking_new.jpg'),
PosixPath('/home/kbuilder/.keras/datasets/flower_photos')]
Each original image is of size 256 x 512 containing two 256 x 256 images:
sample_image = tf.io.read_file(str(PATH / 'train/1.jpg'))
sample_image = tf.io.decode_jpeg(sample_image)
print(sample_image.shape)
(256, 512, 3) WARNING: All log messages before absl::InitializeLog() is called are written to STDERR I0000 00:00:1723785104.657821 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.661724 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.665422 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.669063 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.681592 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.685097 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.688447 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.691826 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.695256 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.698866 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.702269 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785104.705685 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.940631 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.942787 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.944821 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.946901 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.948931 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.950912 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.952829 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.954858 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.956710 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.958713 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.960654 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785105.962741 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.001208 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.003283 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.005256 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.007288 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.009146 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.011252 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.013222 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.015226 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.017081 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.019604 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.021954 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355 I0000 00:00:1723785106.024390 166285 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
plt.figure()
plt.imshow(sample_image)
<matplotlib.image.AxesImage at 0x7f1607efcfd0>
You need to separate real building facade images from the architecture label images—all of which will be of size 256 x 256.
Define a function that loads image files and outputs two image tensors:
def load(image_file):
# Read and decode an image file to a uint8 tensor
image = tf.io.read_file(image_file)
image = tf.io.decode_jpeg(image)
# Split each image tensor into two tensors:
# - one with a real building facade image
# - one with an architecture label image
w = tf.shape(image)[1]
w = w // 2
input_image = image[:, w:, :]
real_image = image[:, :w, :]
# Convert both images to float32 tensors
input_image = tf.cast(input_image, tf.float32)
real_image = tf.cast(real_image, tf.float32)
return input_image, real_image
Plot a sample of the input (architecture label image) and real (building facade photo) images:
inp, re = load(str(PATH / 'train/100.jpg'))
# Casting to int for matplotlib to display the images
plt.figure()
plt.imshow(inp / 255.0)
plt.figure()
plt.imshow(re / 255.0)
<matplotlib.image.AxesImage at 0x7f15f031bdf0>
As described in the pix2pix paper, you need to apply random jittering and mirroring to preprocess the training set.
Define several functions that:
- Resize each
256 x 256image to a larger height and width—286 x 286. - Randomly crop it back to
256 x 256. - Randomly flip the image horizontally i.e., left to right (random mirroring).
- Normalize the images to the
[-1, 1]range.
# The facade training set consist of 400 images
BUFFER_SIZE = 400
# The batch size of 1 produced better results for the U-Net in the original pix2pix experiment
BATCH_SIZE = 1
# Each image is 256x256 in size
IMG_WIDTH = 256
IMG_HEIGHT = 256
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 286x286
input_image, real_image = resize(input_image, real_image, 286, 286)
# Random cropping back to 256x256
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
You can inspect some of the preprocessed output:
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()
Having checked that the loading and preprocessing works, let's define a couple of helper functions that load and preprocess the training and test sets:
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
Build an input pipeline with tf.data
train_dataset = tf.data.Dataset.list_files(str(PATH / 'train/*.jpg'))
train_dataset = train_dataset.map(load_image_train,
num_parallel_calls=tf.data.AUTOTUNE)
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE)
try:
test_dataset = tf.data.Dataset.list_files(str(PATH / 'test/*.jpg'))
except tf.errors.InvalidArgumentError:
test_dataset = tf.data.Dataset.list_files(str(PATH / 'val/*.jpg'))
test_dataset = test_dataset.map(load_image_test)
test_dataset = test_dataset.batch(BATCH_SIZE)
Build the generator
The generator of your pix2pix cGAN is a modified U-Net. A U-Net consists of an encoder (downsampler) and decoder (upsampler). (You can find out more about it in the Image segmentation tutorial and on the U-Net project website.)
- Each block in the encoder is: Convolution -> Batch normalization -> Leaky ReLU
- Each block in the decoder is: Transposed convolution -> Batch normalization -> Dropout (applied to the first 3 blocks) -> ReLU
- There are skip connections between the encoder and decoder (as in the U-Net).
Define the downsampler (encoder):
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)
(1, 128, 128, 3) W0000 00:00:1723785108.147399 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.182976 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.221808 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.230772 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.231959 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.235100 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.238282 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.239508 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.240794 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.242041 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.243196 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.244345 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.245526 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.246742 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
Define the upsampler (decoder):
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)
(1, 256, 256, 3) W0000 00:00:1723785108.377660 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.387068 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.388344 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.389669 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.455281 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.457890 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.484726 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785108.495839 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced
Define the generator with the downsampler and the upsampler:
def Generator():
inputs = tf.keras.layers.Input(shape=[256, 256, 3])
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (batch_size, 128, 128, 64)
downsample(128, 4), # (batch_size, 64, 64, 128)
downsample(256, 4), # (batch_size, 32, 32, 256)
downsample(512, 4), # (batch_size, 16, 16, 512)
downsample(512, 4), # (batch_size, 8, 8, 512)
downsample(512, 4), # (batch_size, 4, 4, 512)
downsample(512, 4), # (batch_size, 2, 2, 512)
downsample(512, 4), # (batch_size, 1, 1, 512)
]
up_stack = [
upsample(512, 4, apply_dropout=True), # (batch_size, 2, 2, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 8, 8, 1024)
upsample(512, 4), # (batch_size, 16, 16, 1024)
upsample(256, 4), # (batch_size, 32, 32, 512)
upsample(128, 4), # (batch_size, 64, 64, 256)
upsample(64, 4), # (batch_size, 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') # (batch_size, 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)
Visualize the generator model architecture:
generator = Generator()
tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)
Test the generator:
gen_output = generator(inp[tf.newaxis, ...], training=False)
plt.imshow(gen_output[0, ...])
W0000 00:00:1723785109.096026 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.097619 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.098841 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.100040 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.101229 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.102429 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.103610 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.104793 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.106177 166285 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gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.781394 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.810376 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.811802 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.813150 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.814509 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.823004 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.825707 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.843703 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.844529 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.845327 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.846141 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.848246 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.850171 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.853400 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced W0000 00:00:1723785109.860196 166285 gpu_timer.cc:114] Skipping the delay kernel, measurement accuracy will be reduced Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0]. <matplotlib.image.AxesImage at 0x7f15c0139eb0>
Define the generator loss
GANs learn a loss that adapts to the data, while cGANs learn a structured loss that penalizes a possible structure that differs from the network output and the target image, as described in the pix2pix paper.
- The generator loss is a sigmoid cross-entropy loss of the generated images and an array of ones.
- The pix2pix paper also mentions the L1 loss, which is a MAE (mean absolute error) between the generated image and the target image.
- This allows the generated image to become structurally similar to the target image.
- The formula to calculate the total generator loss is
gan_loss + LAMBDA * l1_loss, whereLAMBDA = 100. This value was decided by the authors of the paper.
LAMBDA = 100
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
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
The training procedure for the generator is as follows:
Build the discriminator
The discriminator in the pix2pix cGAN is a convolutional PatchGAN classifier—it tries to classify if each image patch is real or not real, as described in the pix2pix paper.
- Each block in the discriminator is: Convolution -> Batch normalization -> Leaky ReLU.
- The shape of the output after the last layer is
(batch_size, 30, 30, 1). - Each
30 x 30image patch of the output classifies a70 x 70portion of the input image. - The discriminator receives 2 inputs:
- The input image and the target image, which it should classify as real.
- The input image and the generated image (the output of the generator), which it should classify as fake.
- Use
tf.concat([inp, tar], axis=-1)to concatenate these 2 inputs together.
Let's define the discriminator:
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]) # (batch_size, 256, 256, channels*2)
down1 = downsample(64, 4, False)(x) # (batch_size, 128, 128, 64)
down2 = downsample(128, 4)(down1) # (batch_size, 64, 64, 128)
down3 = downsample(256, 4)(down2) # (batch_size, 32, 32, 256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (batch_size, 34, 34, 256)
conv = tf.keras.layers.Conv2D(512, 4, strides=1,
kernel_initializer=initializer,
use_bias=False)(zero_pad1) # (batch_size, 31, 31, 512)
batchnorm1 = tf.keras.layers.BatchNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (batch_size, 33, 33, 512)
last = tf.keras.layers.Conv2D(1, 4, strides=1,
kernel_initializer=initializer)(zero_pad2) # (batch_size, 30, 30, 1)
return tf.keras.Model(inputs=[inp, tar], outputs=last)
Visualize the discriminator model architecture:
discriminator = Discriminator()
tf.keras.utils.plot_model(discriminator, show_shapes=True, dpi=64)
Test the discriminator:
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()
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will be reduced <matplotlib.colorbar.Colorbar at 0x7f16f11c2eb0>
Define the discriminator loss
- The
discriminator_lossfunction takes 2 inputs: real images and generated images. real_lossis a sigmoid cross-entropy loss of the real images and an array of ones(since these are the real images).generated_lossis a sigmoid cross-entropy loss of the generated images and an array of zeros (since these are the fake images).- The
total_lossis the sum ofreal_lossandgenerated_loss.
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
The training procedure for the discriminator is shown below.
To learn more about the architecture and the hyperparameters you can refer to the pix2pix paper.
Define the optimizers and a checkpoint-saver
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)
Generate images
Write a function to plot some images during training.
- Pass images from the test set to the generator.
- The generator will then translate the input image into the output.
- The last step is to plot the predictions and voila!
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 in the [0, 1] range to plot.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
Test the function:
for example_input, example_target in test_dataset.take(1):
generate_images(generator, example_input, example_target)
Training
- For each example input generates an output.
- The discriminator receives the
input_imageand the generated image as the first input. The second input is theinput_imageand thetarget_image. - Next, calculate the generator and the discriminator loss.
- Then, calculate the gradients of loss with respect to both the generator and the discriminator variables(inputs) and apply those to the optimizer.
- Finally, log the losses to TensorBoard.
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, step):
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=step//1000)
tf.summary.scalar('gen_gan_loss', gen_gan_loss, step=step//1000)
tf.summary.scalar('gen_l1_loss', gen_l1_loss, step=step//1000)
tf.summary.scalar('disc_loss', disc_loss, step=step//1000)
The actual training loop. Since this tutorial can run of more than one dataset, and the datasets vary greatly in size the training loop is setup to work in steps instead of epochs.
- Iterates over the number of steps.
- Every 10 steps print a dot (
.). - Every 1k steps: clear the display and run
generate_imagesto show the progress. - Every 5k steps: save a checkpoint.
def fit(train_ds, test_ds, steps):
example_input, example_target = next(iter(test_ds.take(1)))
start = time.time()
for step, (input_image, target) in train_ds.repeat().take(steps).enumerate():
if (step) % 1000 == 0:
display.clear_output(wait=True)
if step != 0:
print(f'Time taken for 1000 steps: {time.time()-start:.2f} sec\n')
start = time.time()
generate_images(generator, example_input, example_target)
print(f"Step: {step//1000}k")
train_step(input_image, target, step)
# Training step
if (step+1) % 10 == 0:
print('.', end='', flush=True)
# Save (checkpoint) the model every 5k steps
if (step + 1) % 5000 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
This training loop saves logs that you can view in TensorBoard to monitor the training progress.
If you work on a local machine, you would launch a separate TensorBoard process. When working in a notebook, launch the viewer before starting the training to monitor with TensorBoard.
Launch the TensorBoard viewer (Sorry, this doesn't display on tensorflow.org):
%load_ext tensorboard
%tensorboard --logdir {log_dir}
You can view the results of a previous run of this notebook on TensorBoard.dev.
Finally, run the training loop:
fit(train_dataset, test_dataset, steps=40000)
Time taken for 1000 steps: 118.14 sec
Step: 39k ....................................................................................................
Interpreting the logs is more subtle when training a GAN (or a cGAN like pix2pix) compared to a simple classification or regression model. Things to look for:
- Check that neither the generator nor the discriminator model has "won". If either the
gen_gan_lossor thedisc_lossgets very low, it's an indicator that this model is dominating the other, and you are not successfully training the combined model. - The value
log(2) = 0.69is a good reference point for these losses, as it indicates a perplexity of 2 - the discriminator is, on average, equally uncertain about the two options. - For the
disc_loss, a value below0.69means the discriminator is doing better than random on the combined set of real and generated images. - For the
gen_gan_loss, a value below0.69means the generator is doing better than random at fooling the discriminator. - As training progresses, the
gen_l1_lossshould go down.
Restore the latest checkpoint and test the network
ls {checkpoint_dir}
checkpoint ckpt-5.data-00000-of-00001 ckpt-1.data-00000-of-00001 ckpt-5.index ckpt-1.index ckpt-6.data-00000-of-00001 ckpt-2.data-00000-of-00001 ckpt-6.index ckpt-2.index ckpt-7.data-00000-of-00001 ckpt-3.data-00000-of-00001 ckpt-7.index ckpt-3.index ckpt-8.data-00000-of-00001 ckpt-4.data-00000-of-00001 ckpt-8.index ckpt-4.index
# Restoring the latest checkpoint in checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7f15f00fed90>
Generate some images using the test set
# Run the trained model on a few examples from the test set
for inp, tar in test_dataset.take(5):
generate_images(generator, inp, tar)
