Updated 10/Dec/2021 by Yoshihisa Nitta 
CycleGAN_VIdTIMIT_Train をローカルのWindows上で動作するJupyterで実行する¶
https://nw.tsuda.ac.jp/lec/GoogleColab/pub/html/CycleGAN_VidTIMIT_Train.html
[注意] Google Colab (tensorflow 2.7.0) 上では tf.keras.utils.load_img() であったが、本「生成ディープラーニング」のpython 仮想環境 generative (tensorflow 2.2.0) では tf.keras.preprocessing.image.load_img() である。
In [1]:
import tensorflow as tf
print(tf.__version__)
In [2]:
import numpy as np
np.random.seed(2022)
CycleGAN クラスの定義¶
In [3]:
import tensorflow as tf
import tensorflow_addons as tf_addons
import numpy as np
import matplotlib.pyplot as plt
from collections import deque
import os
import pickle as pkl
import random
import datetime
################################################################################
# Data Loader
################################################################################
class PairDataset():
def __init__(self, paths_A, paths_B, batch_size= 1, target_size = None, unaligned=False):
self.paths_A = np.array(paths_A)
self.paths_B = np.array(paths_B)
self.target_size = target_size
self.batch_size = batch_size
self.unaligned = unaligned
self.lenA = len(paths_A)
self.lenB = len(paths_B)
self.index = 0
def __len__(self):
return max(self.lenA, self.lenB)
def __getitem__(self, index):
if isinstance(index, slice):
start, stop, step = index.indices(self.__len__())
if start == None: start = 0
if stop == None: stop = self.__len__()
if step == None:
if start < stop:
step = 1
elif start > stop:
step = -1
else:
step = 0
return np.array([self.__getitemInt__(i) for i in range(start, stop, step) ])
else:
return self.__getitemInt__(index)
def __getitemInt__(self, index):
path_A = self.paths_A[index % self.lenA]
if self.unaligned:
path_B = self.paths_B[np.random.choice(self.lenB, 1)]
else:
path_B = self.paths_B[index % self.lenB]
img_A = np.array(tf.keras.preprocessing.image.load_img(path_A, target_size = self.target_size))
img_B = np.array(tf.keras.preprocessing.image.load_img(path_B, target_size = self.target_size))
img_A = (img_A.astype('float32') - 127.5) / 127.5
img_B = (img_B.astype('float32') - 127.5) / 127.5
return np.array([img_A, img_B])
def __next__(self):
self.index += 1
return self.__getitem__(self.index-1)
################################################################################
# Layer
################################################################################
class ReflectionPadding2D(tf.keras.layers.Layer):
def __init__(self, padding=(1, 1), **kwargs):
self.padding = tuple(padding)
self.input_spec = [tf.keras.layers.InputSpec(ndim=4)]
super().__init__(**kwargs)
def compute_output_shape(self, s):
'''
If you are using "channels_last" configuration
'''
return (s[0], s[1]+2*self.padding[0], s[2]+2*self.padding[1], s[3])
def call(self, x, mask=None):
w_pad, h_pad = self.padding
return tf.pad(x, [[0, 0], [h_pad, h_pad], [w_pad, w_pad], [0, 0]], 'REFLECT')
################################################################################
# Model
################################################################################
class CycleGAN():
def __init__(
self,
input_dim,
learning_rate,
lambda_validation,
lambda_reconstr,
lambda_id,
generator_type,
gen_n_filters,
disc_n_filters,
buffer_max_length = 50,
epoch = 0,
d_losses = [],
g_losses = []
):
self.input_dim = input_dim
self.learning_rate = learning_rate
self.buffer_max_length = buffer_max_length
self.lambda_validation = lambda_validation
self.lambda_reconstr = lambda_reconstr
self.lambda_id = lambda_id
self.generator_type = generator_type
self.gen_n_filters = gen_n_filters
self.disc_n_filters = disc_n_filters
# Input shape
self.img_rows = input_dim[0]
self.img_cols = input_dim[1]
self.channels = input_dim[2]
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.epoch = epoch
self.d_losses = d_losses
self.g_losses = g_losses
self.buffer_A = deque(maxlen=self.buffer_max_length)
self.buffer_B = deque(maxlen=self.buffer_max_length)
# Calculate output shape of D (PatchGAN)
patch = int(self.img_rows / 2**3)
self.disc_patch = (patch, patch, 1)
self.weight_init = tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.02)
self.compile_models()
def compile_models(self):
# Build and compile the discriminators
self.d_A = self.build_discriminator()
self.d_B = self.build_discriminator()
self.d_A.compile(
loss='mse',
optimizer=tf.keras.optimizers.Adam(self.learning_rate, 0.5),
metrics=['accuracy']
)
self.d_B.compile(
loss='mse',
optimizer=tf.keras.optimizers.Adam(self.learning_rate, 0.5),
metrics=['accuracy']
)
# Build the generators
if self.generator_type == 'unet':
self.g_AB = self.build_generator_unet()
self.g_BA = self.build_generator_unet()
else:
self.g_AB = self.build_generator_resnet()
self.g_BA = self.build_generator_resnet()
# For the combined model we will only train the generators
self.d_A.trainable = False
self.d_B.trainable = False
# Input images from both domains
img_A = tf.keras.layers.Input(shape=self.img_shape)
img_B = tf.keras.layers.Input(shape=self.img_shape)
# Translate images to the other domain
fake_B = self.g_AB(img_A)
fake_A = self.g_BA(img_B)
# translate images back to original domain
reconstr_A = self.g_BA(fake_B)
reconstr_B = self.g_AB(fake_A)
# Identity mapping of images
img_A_id = self.g_BA(img_A) # [self memo] ??? translate *A* from domainB to domainA
img_B_id = self.g_AB(img_B) # [self memo] ??? translate *B* from domainA to domainB
# Discriminators determines validity of traslated images
valid_A = self.d_A(fake_A)
valid_B = self.d_B(fake_B)
# Combined model trains generators to fool discriminators
self.combined = tf.keras.models.Model(
inputs=[img_A, img_B],
outputs=[valid_A, valid_B, reconstr_A, reconstr_B, img_A_id, img_B_id]
)
self.combined.compile(
loss=['mse', 'mse', 'mae', 'mae', 'mae', 'mae'], # Mean Squared Error, Mean Absolute Error
loss_weights=[ self.lambda_validation, self.lambda_validation,
self.lambda_reconstr, self.lambda_reconstr,
self.lambda_id, self.lambda_id ],
optimizer=tf.keras.optimizers.Adam(0.0002, 0.5)
)
self.d_A.trainable = True
self.d_B.trainable = True
def build_generator_unet(self):
def downsample(layer_input, filters, f_size=4):
d = tf.keras.layers.Conv2D(
filters,
kernel_size=f_size,
strides=2,
padding='same',
kernel_initializer = self.weight_init # [self memo] added by nitta
)(layer_input)
d = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(d)
d = tf.keras.layers.Activation('relu')(d)
return d
def upsample(layer_input, skip_input, filters, f_size=4, dropout_rate=0):
u = tf.keras.layers.UpSampling2D(size=2)(layer_input)
u = tf.keras.layers.Conv2D(
filters,
kernel_size=f_size,
strides=1,
padding='same',
kernel_initializer = self.weight_init # [self memo] added by nitta
)(u)
u = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(u)
u = tf.keras.layers.Activation('relu')(u)
if dropout_rate:
u = tf.keras.layers.Dropout(dropout_rate)(u)
u = tf.keras.layers.Concatenate()([u, skip_input])
return u
# Image input
img = tf.keras.layers.Input(shape=self.img_shape)
# Downsampling
d1 = downsample(img, self.gen_n_filters)
d2 = downsample(d1, self.gen_n_filters*2)
d3 = downsample(d2, self.gen_n_filters*4)
d4 = downsample(d3, self.gen_n_filters*8)
# Upsampling
u1 = upsample(d4, d3, self.gen_n_filters*4)
u2 = upsample(u1, d2, self.gen_n_filters*2)
u3 = upsample(u2, d1, self.gen_n_filters)
u4 = tf.keras.layers.UpSampling2D(size=2)(u3)
output_img = tf.keras.layers.Conv2D(
self.channels,
kernel_size=4,
strides=1,
padding='same',
activation='tanh',
kernel_initializer = self.weight_init # [self memo] added by nitta
)(u4)
return tf.keras.models.Model(img, output_img)
def build_generator_resnet(self):
def conv7s1(layer_input, filters, final):
y = ReflectionPadding2D(padding=(3,3))(layer_input)
y = tf.keras.layers.Conv2D(
filters,
kernel_size=(7,7),
strides=1,
padding='valid',
kernel_initializer=self.weight_init
)(y)
if final:
y = tf.keras.layers.Activation('tanh')(y)
else:
y = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(y)
y = tf.keras.layers.Activation('relu')(y)
return y
def downsample(layer_input, filters):
y = tf.keras.layers.Conv2D(
filters,
kernel_size=(3,3),
strides=2,
padding='same',
kernel_initializer = self.weight_init
)(layer_input)
y = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(y)
y = tf.keras.layers.Activation('relu')(y)
return y
def residual(layer_input, filters):
shortcut = layer_input
y = ReflectionPadding2D(padding=(1,1))(layer_input)
y = tf.keras.layers.Conv2D(
filters,
kernel_size=(3,3),
strides=1,
padding='valid',
kernel_initializer=self.weight_init
)(y)
y = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(y)
y = tf.keras.layers.Activation('relu')(y)
y = ReflectionPadding2D(padding=(1,1))(y)
y = tf.keras.layers.Conv2D(
filters,
kernel_size=(3,3),
strides=1,
padding='valid',
kernel_initializer=self.weight_init
)(y)
y = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(y)
return tf.keras.layers.add([shortcut, y])
def upsample(layer_input, filters):
y = tf.keras.layers.Conv2DTranspose(
filters,
kernel_size=(3,3),
strides=2,
padding='same',
kernel_initializer=self.weight_init
)(layer_input)
y = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(y)
y = tf.keras.layers.Activation('relu')(y)
return y
# Image input
img = tf.keras.layers.Input(shape=self.img_shape)
y = img
y = conv7s1(y, self.gen_n_filters, False)
y = downsample(y, self.gen_n_filters * 2)
y = downsample(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = residual(y, self.gen_n_filters * 4)
y = upsample(y, self.gen_n_filters * 2)
y = upsample(y, self.gen_n_filters)
y = conv7s1(y, 3, True)
output = y
return tf.keras.models.Model(img, output)
def build_discriminator(self):
def conv4(layer_input, filters, stride=2, norm=True):
y = tf.keras.layers.Conv2D(
filters,
kernel_size=(4,4),
strides=stride,
padding='same',
kernel_initializer = self.weight_init
)(layer_input)
if norm:
y = tf_addons.layers.InstanceNormalization(axis=-1, center=False, scale=False)(y)
y = tf.keras.layers.LeakyReLU(0.2)(y)
return y
img = tf.keras.layers.Input(shape=self.img_shape)
y = conv4(img, self.disc_n_filters, stride=2, norm=False)
y = conv4(y, self.disc_n_filters*2, stride=2)
y = conv4(y, self.disc_n_filters*4, stride=2)
y = conv4(y, self.disc_n_filters*8, stride=1)
output = tf.keras.layers.Conv2D(
1,
kernel_size=4,
strides=1,
padding='same',
kernel_initializer=self.weight_init
)(y)
return tf.keras.models.Model(img, output)
def train_discriminators(self, imgs_A, imgs_B, valid, fake):
# Translate images to opposite domain
fake_B = self.g_AB.predict(imgs_A)
fake_A = self.g_BA.predict(imgs_B)
self.buffer_B.append(fake_B)
self.buffer_A.append(fake_A)
fake_A_rnd = random.sample(self.buffer_A, min(len(self.buffer_A), len(imgs_A))) # random sampling without replacement
fake_B_rnd = random.sample(self.buffer_B, min(len(self.buffer_B), len(imgs_B)))
# Train the discriminators (original images=real / translated = fake)
dA_loss_real = self.d_A.train_on_batch(imgs_A, valid)
dA_loss_fake = self.d_A.train_on_batch(fake_A_rnd, fake)
dA_loss = 0.5 * np.add(dA_loss_real, dA_loss_fake)
dB_loss_real = self.d_B.train_on_batch(imgs_B, valid)
dB_loss_fake = self.d_B.train_on_batch(fake_B_rnd, fake)
dB_loss = 0.5 * np.add(dB_loss_real, dB_loss_fake)
# Total discriminator loss
d_loss_total = 0.5 * np.add(dA_loss, dB_loss)
return (
d_loss_total[0],
dA_loss[0], dA_loss_real[0], dA_loss_fake[0],
dB_loss[0], dB_loss_real[0], dB_loss_fake[0],
d_loss_total[1],
dA_loss[1], dA_loss_real[1], dA_loss_fake[1],
dB_loss[1], dB_loss_real[1], dB_loss_fake[1]
)
def train_generators(self, imgs_A, imgs_B, valid):
# Train the generators
return self.combined.train_on_batch(
[imgs_A, imgs_B],
[valid, valid, imgs_A, imgs_B, imgs_A, imgs_B]
)
def train(self, data_loader, epochs, batch_size=1, run_folder='./run', print_step_interval=100, save_epoch_interval=100):
start_time = datetime.datetime.now()
# Adversarial loss ground truthes
valid = np.ones((batch_size,) + self.disc_patch)
fake = np.zeros((batch_size,) + self.disc_patch)
steps = len(data_loader) // batch_size
for epoch in range(self.epoch, epochs):
step_d_losses = []
step_g_losses = []
for step in range(steps):
start = step * batch_size
end = start + batch_size
pairs = data_loader[start:end] # ((a,b), (a, b), ....)
imgs_A, imgs_B = [], []
for img_A, img_B in pairs:
imgs_A.append(img_A)
imgs_B.append(img_B)
imgs_A = np.array(imgs_A)
imgs_B = np.array(imgs_B)
step_d_loss = self.train_discriminators(imgs_A, imgs_B, valid, fake)
step_g_loss = self.train_generators(imgs_A, imgs_B, valid)
step_d_losses.append(step_d_loss)
step_g_losses.append(step_g_loss)
elapsed_time = datetime.datetime.now() - start_time
if (step+1) % print_step_interval == 0:
print(f'Epoch {epoch+1}/{epochs} {step+1}/{steps} [D loss: {step_d_loss[0]:.3f} acc: {step_d_loss[7]:.3f}][G loss: {step_g_loss[0]:.3f} adv: {np.sum(step_g_loss[1:3]):.3f} recon: {np.sum(step_g_loss[3:5]):.3f} id: {np.sum(step_g_loss[5:7]):.3f} time: {elapsed_time:}')
d_loss = np.mean(step_d_losses, axis=0)
g_loss = np.mean(step_g_losses, axis=0)
elapsed_time = datetime.datetime.now() - start_time
elapsed_time = datetime.datetime.now() - start_time
print(f'Epoch {epoch+1}/{epochs} [D loss: {d_loss[0]:.3f} acc: {d_loss[7]:.3f}][G loss: {g_loss[0]:.3f} adv: {np.sum(g_loss[1:3]):.3f} recon: {np.sum(g_loss[3:5]):.3f} id: {np.sum(g_loss[5:7]):.3f} time: {elapsed_time:}')
self.d_losses.append(d_loss)
self.g_losses.append(g_loss)
self.epoch += 1
if (self.epoch) % save_epoch_interval == 0:
self.save(run_folder, self.epoch)
self.save(run_folder)
self.save(run_folder, self.epoch)
self.save(run_folder)
def save(self, folder, epoch=None):
self.save_params(folder, epoch)
self.save_weights(folder,epoch)
@staticmethod
def load(folder, epoch=None):
params = CycleGAN.load_params(folder, epoch)
gan = CycleGAN(*params)
gan.load_weights(folder, epoch)
return gan
def save_weights(self, run_folder, epoch=None):
if epoch is None:
self.save_model_weights(self.combined, os.path.join(run_folder, 'weights/combined-weights.h5'))
self.save_model_weights(self.d_A, os.path.join(run_folder, 'weights/d_A-weights.h5'))
self.save_model_weights(self.d_B, os.path.join(run_folder, 'weights/d_B-weights.h5'))
self.save_model_weights(self.g_AB, os.path.join(run_folder, 'weights/g_AB-weights.h5'))
self.save_model_weights(self.g_BA, os.path.join(run_folder, 'weights/g_BA-weights.h5'))
else:
self.save_model_weights(self.combined, os.path.join(run_folder, f'weights/combined-weights_{epoch}.h5'))
self.save_model_weights(self.d_A, os.path.join(run_folder, f'weights/d_A-weights_{epoch}.h5'))
self.save_model_weights(self.d_B, os.path.join(run_folder, f'weights/d_B-weights_{epoch}.h5'))
self.save_model_weights(self.g_AB, os.path.join(run_folder, f'weights/g_AB-weights_{epoch}.h5'))
self.save_model_weights(self.g_BA, os.path.join(run_folder, f'weights/g_BA-weights_{epoch}.h5'))
def load_weights(self, run_folder, epoch=None):
if epoch is None:
self.load_model_weights(self.combined, os.path.join(run_folder, 'weights/combined-weights.h5'))
self.load_model_weights(self.d_A, os.path.join(run_folder, 'weights/d_A-weights.h5'))
self.load_model_weights(self.d_B, os.path.join(run_folder, 'weights/d_B-weights.h5'))
self.load_model_weights(self.g_AB, os.path.join(run_folder, 'weights/g_AB-weights.h5'))
self.load_model_weights(self.g_BA, os.path.join(run_folder, 'weights/g_BA-weights.h5'))
else:
self.load_model_weights(self.combined, os.path.join(run_folder, f'weights/combined-weights_{epoch}.h5'))
self.load_model_weights(self.d_A, os.path.join(run_folder, f'weights/d_A-weights_{epoch}.h5'))
self.load_model_weights(self.d_B, os.path.join(run_folder, f'weights/d_B-weights_{epoch}.h5'))
self.load_model_weights(self.g_AB, os.path.join(run_folder, f'weights/g_AB-weights_{epoch}.h5'))
self.load_model_weights(self.g_BA, os.path.join(run_folder, f'weights/g_BA-weights_{epoch}.h5'))
def save_model_weights(self, model, filepath):
dpath, fname = os.path.split(filepath)
if dpath != '' and not os.path.exists(dpath):
os.makedirs(dpath)
model.save_weights(filepath)
def load_model_weights(self, model, filepath):
model.load_weights(filepath)
def save_params(self, folder, epoch=None):
if epoch is None:
filepath = os.path.join(folder, 'params.pkl')
else:
filepath = os.path.join(folder, f'params_{epoch}.pkl')
dpath, fname = os.path.split(filepath)
if dpath != '' and not os.path.exists(dpath):
os.makedirs(dpath)
with open(filepath, 'wb') as f:
pkl.dump([
self.input_dim,
self.learning_rate,
self.lambda_validation,
self.lambda_reconstr,
self.lambda_id,
self.generator_type,
self.gen_n_filters,
self.disc_n_filters,
self.buffer_max_length,
self.epoch,
self.d_losses,
self.g_losses
], f)
@staticmethod
def load_params(folder, epoch=None):
if epoch is None:
filepath = os.path.join(folder, 'params.pkl')
else:
filepath = os.path.join(folder, f'params_{epoch}.pkl')
with open(filepath, 'rb') as f:
params = pkl.load(f)
return params
def generate_image(self, img_A, img_B):
gen_A = self.generate_image_from_A(img_A)
gen_B = self.generate_image_from_B(img_B)
return np.concatenate([gen_A, gen_B], axis=0)
def generate_image_from_A(self, img_A):
fake_B = self.g_AB.predict(img_A) # Translate images to the other domain
reconstr_A = self.g_BA.predict(fake_B) # Translate back to original domain
id_A = self.g_BA.predict(img_A) # ID the images
return np.concatenate([img_A, fake_B, reconstr_A, id_A])
def generate_image_from_B(self, img_B):
fake_A = self.g_BA.predict(img_B)
reconstr_B = self.g_AB.predict(fake_A)
id_B = self.g_AB.predict(img_B)
return np.concatenate([img_B, fake_A, reconstr_B, id_B])
@staticmethod
def showImages(imgs, trans, recon, idimg, w=2.8, h=2.8, filepath=None):
N = len(imgs)
M = len(imgs[0])
titles = ['Original', 'Translated', 'Reconstructed', 'ID']
fig, ax = plt.subplots(N, M, figsize=(w*M, h*N))
for i in range(N):
for j in range(M):
ax[i][j].imshow(imgs[i][j])
ax[i][j].set_title(title[j])
ax[i][j].axis('off')
if not filepath is None:
dpath, fname = os.path.split(filepath)
if dpath != '' and not os.path.exists(dpath):
os.makedirs(dpath)
fig.savefig(filepath, dpi=600)
plt.close()
else:
plt.show()
def showLoss(self, xlim=[], ylim=[]):
print('loss AB')
self.showLossAB(xlim, ylim)
print('loss BA')
self.showLossBA(xlim, ylim)
def showLossAB(self, xlim=[], ylim=[]):
g = np.array(self.g_losses)
g_loss = g[:, 0]
g_adv = g[:, 1]
g_recon = g[:, 3]
g_id = g[:, 5]
CycleGAN.plot_history(
[g_loss, g_adv, g_recon, g_id],
['g_loss', 'AB discrim', 'AB cycle', 'AB id'],
xlim,
ylim)
def showLossBA(self, xlim=[], ylim=[]):
g = np.array(self.g_losses)
g_loss = g[:, 0]
g_adv = g[:, 2]
g_recon = g[:, 4]
g_id = g[:, 6]
CycleGAN.plot_history(
[g_loss, g_adv, g_recon, g_id],
['g_loss', 'BA discrim', 'BA cycle', 'BA id'],
xlim,
ylim)
@staticmethod
def plot_history(vals, labels, xlim=[], ylim=[]):
colors = ['red', 'blue', 'green', 'orange', 'black', 'pink']
n = len(vals)
fig, ax = plt.subplots(1, 1, figsize=(12,6))
for i in range(n):
ax.plot(vals[i], c=colors[i], label=labels[i])
ax.legend(loc='upper right')
ax.set_xlabel('epochs')
# ax.set_ylabel('loss')
if xlim != []:
ax.set_xlim(xlim[0], xlim[1])
if ylim != []:
ax.set_ylim(ylim[0], ylim[1])
plt.show()
Preparing VidTIMIT dataset¶
Official WWW of VidTIMIT dataset: http://conradsanderson.id.au/vidtimit/
zip files of 2 persons of VidTIMIT dataset:
https://zenodo.org/record/158963/files/fadg0.zip
https://zenodo.org/record/158963/files/faks0.zip
zip files mirrored on my Google Drive:
https://drive.google.com/uc?id=1_Fv4p9MDNphMZMnLpEvtCtnwXgN8N5Cj
https://drive.google.com/uc?id=1Y8j7ThPVqB0gbx4hb9aMEp9Ptr9wFuoz
VidTIMIT データセットを用意する¶
VidTIMIT データセットの公式ページ: http://conradsanderson.id.au/vidtimit/
VidTIMIT の2名の顔写真の zip ファイル:
https://zenodo.org/record/158963/files/fadg0.zip
https://zenodo.org/record/158963/files/faks0.zip
自分の Google Drive 上にミラーした顔写真:
https://drive.google.com/uc?id=1_Fv4p9MDNphMZMnLpEvtCtnwXgN8N5Cj
https://drive.google.com/uc?id=1Y8j7ThPVqB0gbx4hb9aMEp9Ptr9wFuoz
[注意] 上記のfadg0.zip, faks0.zip をブラウザを使って手動でダウンロードして、以下のフォルダに解凍したものとする。
In [4]:
VidTIMIT_fnames = [ 'fadg0', 'faks0']
data_dir = 'D:\\data\\torch_book1\\ch06'
In [5]:
!dir {data_dir}
In [6]:
IMAGE_SIZE = 128
In [7]:
import os
import glob
imgA_paths = glob.glob(os.path.join(data_dir, VidTIMIT_fnames[0], 'video/*/[0-9]*'))
imgB_paths = glob.glob(os.path.join(data_dir, VidTIMIT_fnames[1], 'video/*/[0-9]*'))
In [8]:
import numpy as np
validation_split = 0.05
nA, nB = len(imgA_paths), len(imgB_paths)
splitA = int(nA * (1 - validation_split))
splitB = int(nB * (1 - validation_split))
np.random.shuffle(imgA_paths)
np.random.shuffle(imgB_paths)
train_imgA_paths = imgA_paths[:splitA]
test_imgA_paths = imgA_paths[splitA:]
train_imgB_paths = imgB_paths[:splitB]
test_imgB_paths = imgB_paths[splitB:]
In [9]:
print(nA, nB)
In [10]:
# Image: [-1, 1] --> [0, 1]
def M1P1_ZeroP1(imgs):
imgs = (imgs + 1) * 0.5
return np.clip(imgs, 0, 1)
# Image: [0, 1] --> [-1, 1]
def ZeroP1_M1P1(imgs):
return imgs * 2 - 1
In [11]:
pair_flow = PairDataset(train_imgA_paths, train_imgB_paths, target_size=(IMAGE_SIZE, IMAGE_SIZE))
test_pair_flow = PairDataset(test_imgA_paths, test_imgB_paths, target_size=(IMAGE_SIZE, IMAGE_SIZE))
画像データをチェックする¶
In [12]:
# Display images
# 画像を表示する。
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def showImages(imgs, rows=-1, cols=-1, w=2, h=2):
N = len(imgs)
if rows < 0: rows = 1
if cols < 0: cols = (N + rows -1) // rows
fig, ax = plt.subplots(rows, cols, figsize=(w*cols, h*rows))
idx = 0
for row in range(rows):
for col in range(cols) :
if rows == 1 and cols == 1:
axis = ax
elif rows == 1:
axis = ax[col]
elif cols == 1:
axis = ax[row]
else:
axis = ax[row][col]
if idx < N:
axis.imshow(imgs[idx])
axis.axis('off')
idx += 1
plt.show()
In [13]:
# Display images with the file paths.
# 画像のpathの配列を受け取って、画像を表示する。
%matplotlib inline
import tensorflow as tf
def showImagesByPath(fnames,rows=-1, cols=-1, w=2, h=2):
imgs = [ tf.keras.preprocessing.image.load_img(fname) for fname in fnames] # if not exist, try 'tf.keras.utils.load_img'
showImages(imgs, rows, cols, w, h)
In [14]:
a, b = next(pair_flow)
print(a.shape, b.shape)
showImages([M1P1_ZeroP1(a), M1P1_ZeroP1(b)])
ニューラルネットワーク・モデルを定義する¶
In [16]:
gan = CycleGAN(
input_dim = (IMAGE_SIZE, IMAGE_SIZE, 3),
learning_rate = 0.0002,
buffer_max_length = 50,
lambda_validation = 1,
lambda_reconstr = 10,
lambda_id = 2,
generator_type = 'unet',
gen_n_filters = 32,
disc_n_filters = 32
)
訓練する¶
In [17]:
save_path = 'run'
In [18]:
gan.train(
pair_flow,
epochs=1,
batch_size=1,
run_folder = save_path
)
In [19]:
! dir {os.path.join(save_path, 'weights')}
In [20]:
gan.train(
pair_flow,
epochs=3,
batch_size=1,
run_folder = save_path,
print_step_interval=500
)
画像を生成する¶
In [21]:
# Display generated and cycle images.
# 生成画像とサイクル画像を表示する。
test_pairs = test_pair_flow[:5]
test_imgsA = test_pairs[:,0]
test_imgsB = test_pairs[:,1]
imgsAB = gan.generate_image_from_A(test_imgsA)
imgsBA = gan.generate_image_from_B(test_imgsB)
print('A-->B-->A, ID')
showImages(M1P1_ZeroP1(imgsAB), 4)
print('B-->A-->B, ID')
showImages(M1P1_ZeroP1(imgsBA), 4)
学習過程のlossと精度を確認する¶
In [22]:
# Display the graph of losses in training
%matplotlib inline
gan.showLoss()
セーブしたファイルをロードして、さらに学習を進める¶
In [23]:
# Load the saved parameters and weights.
# 保存してある学習結果をロードする。
gan_work = CycleGAN.load(save_path)
# Display the epoch count of the model.
# training のepoch回数を表示する。
print(gan_work.epoch)
In [24]:
# Training in addition
# 追加で training する。
gan_work.train(
pair_flow,
epochs=5,
batch_size=1,
run_folder = save_path,
print_step_interval=500,
save_epoch_interval = 5
)
In [25]:
# Display generated and cycle images.
# 生成画像とサイクル画像を表示する。
test_pairs = test_pair_flow[:5]
test_imgsA = test_pairs[:,0]
test_imgsB = test_pairs[:,1]
imgsAB = gan.generate_image_from_A(test_imgsA)
imgsBA = gan.generate_image_from_B(test_imgsB)
print('A-->B-->A, ID')
showImages(M1P1_ZeroP1(imgsAB), 4)
print('B-->A-->B, ID')
showImages(M1P1_ZeroP1(imgsBA), 4)
In [26]:
# Display the graph of losses in training
%matplotlib inline
gan_work.showLoss()
In [ ]: