def optimizers_config(self, mixed_precision=False, learning_rate=2e-4):
self.G_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
self.D_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
if mixed_precision:
self.G_optimizer = self.G_optimizer.get_mixed_precision()
self.D_optimizer = self.D_optimizer.get_mixed_precision()
return [self.G_optimizer, self.D_optimizer]
def optimizers_config(self, mixed_precision=False, learning_rate=2e-4):
self.G_optimizer = Adam(2e-4)
self.D_optimizer = Adam(2e-4)
if mixed_precision:
self.G_optimizer = self.G_optimizer.get_mixed_precision()
self.D_optimizer = self.D_optimizer.get_mixed_precision()
return [self.G_optimizer, self.D_optimizer]
class CycleGAN(tf.keras.Model):
"""
模型只负责给定训练集和测试(验证)集后的操作
"""
def __init__(self,
train_set,
test_set,
loss_name="WGAN-GP",
mixed_precision=False,
learning_rate=2e-4,
tmp_path=None,
out_path=None):
super(CycleGAN, self).__init__()
#接收数据集和相关参数
self.train_set = train_set
self.test_set = test_set
self.tmp_path = tmp_path
self.out_path = out_path
#定义模型
self.G = networks.Generator(name="G_X2Y")
self.F = networks.Generator(name="G_Y2X")
if loss_name in ["WGAN-SN", "WGAN-GP-SN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=False,
sn=True)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=False,
sn=True)
self.loss_name = loss_name[:-3]
elif loss_name in ["WGAN", "WGAN-GP"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=False,
sn=False)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=False,
sn=False)
self.loss_name = loss_name
elif loss_name in ["Vanilla", "LSGAN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=True,
sn=False)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=True,
sn=False)
self.loss_name = loss_name
else:
raise ValueError("Do not support the loss " + loss_name)
self.model_list = [self.G, self.F, self.Dy, self.Dx]
#定义损失函数 优化器 记录等
self.gan_loss = GanLoss(self.loss_name)
self.optimizers_list = self.optimizers_config(
mixed_precision=mixed_precision, learning_rate=learning_rate)
self.mixed_precision = mixed_precision
self.matrics_list = self.matrics_config()
self.checkpoint_config()
self.get_seed()
def build(self, X_shape, Y_shape):
"""
input_shape必须切片 因为在底层会被当做各层的输出shape而被改动
"""
self.G.build(input_shape=X_shape[:]) #G X->Y
self.Dy.build(input_shape=Y_shape[:]) #Dy Y or != Y
self.F.build(input_shape=Y_shape[:]) #F Y->X
self.Dx.build(input_shape=X_shape[:]) #Dx X or != X
self.built = True
def optimizers_config(self, mixed_precision=False, learning_rate=2e-4):
self.G_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
self.Dy_optimizer = Adam(learning_rate=4e-4, beta_1=0.0, beta_2=0.9)
self.F_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
self.Dx_optimizer = Adam(learning_rate=4e-4, beta_1=0.0, beta_2=0.9)
if mixed_precision:
self.G_optimizer = self.G_optimizer.get_mixed_precision()
self.Dy_optimizer = self.Dy_optimizer.get_mixed_precision()
self.F_optimizer = self.F_optimizer.get_mixed_precision()
self.Dx_optimizer = self.Dx_optimizer.get_mixed_precision()
return [
self.G_optimizer, self.Dy_optimizer, self.F_optimizer,
self.Dx_optimizer
]
def matrics_config(self):
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_logdir = self.tmp_path + "/logs/" + current_time
self.train_summary_writer = tf.summary.create_file_writer(train_logdir)
self.m_psnr_X2Y = tf.keras.metrics.Mean('psnr_y', dtype=tf.float32)
self.m_psnr_Y2X = tf.keras.metrics.Mean('psnr_x', dtype=tf.float32)
self.m_ssim_X2Y = tf.keras.metrics.Mean('ssim_y', dtype=tf.float32)
self.m_ssim_Y2X = tf.keras.metrics.Mean('ssim_x', dtype=tf.float32)
return [
self.m_psnr_X2Y, self.m_psnr_Y2X, self.m_ssim_X2Y, self.m_ssim_Y2X
]
# return None
def checkpoint_config(self):
self.ckpt = tf.train.Checkpoint(step=tf.Variable(1),
optimizer=self.optimizers_list,
model=self.model_list,
dataset=self.train_set)
self.manager = tf.train.CheckpointManager(self.ckpt,
self.tmp_path + '/tf_ckpts',
max_to_keep=3)
def pix_gradient(self, x):
x = tf.reshape(x, shape=[1, 64, 64,
1]) #在各batch和通道上进行像素梯度 对2D单通道而言其实没必要reshape
dx, dy = tf.image.image_gradients(x)
return dx, dy
@tf.function(input_signature=[
tf.TensorSpec(shape=global_input_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_input_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=[4], dtype=tf.int32),
tf.TensorSpec(shape=[1], dtype=tf.uint32)
])
def train_step_D(self, trainX, trainY, maskX, maskY, wgp_shape, step):
with tf.GradientTape(persistent=True) as D_tape:
GeneratedY = self.G(trainX)
GeneratedY = tf.multiply(GeneratedY, maskY)
Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
GeneratedX = tf.multiply(GeneratedX, maskX)
Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
e = tf.random.uniform(shape=wgp_shape, minval=0.0, maxval=1.0)
mid_Y = e * trainY + (1 - e) * GeneratedY
with tf.GradientTape() as gradient_penaltyY:
gradient_penaltyY.watch(mid_Y)
inner_loss = self.Dy(mid_Y)
penalty = gradient_penaltyY.gradient(inner_loss, mid_Y)
penalty_normY = 10.0 * tf.math.square(
tf.norm(tf.reshape(penalty, shape=[wgp_shape[0], -1]),
ord=2,
axis=-1) - 1)
e = tf.random.uniform(shape=wgp_shape, minval=0.0, maxval=1.0)
mid_X = e * trainX + (1 - e) * GeneratedX
with tf.GradientTape() as gradient_penaltyX:
gradient_penaltyX.watch(mid_X)
inner_loss = self.Dx(mid_X)
penalty = gradient_penaltyX.gradient(inner_loss, mid_X)
penalty_normX = 10.0 * tf.math.square(
tf.norm(tf.reshape(penalty, shape=[wgp_shape[0], -1]),
ord=2,
axis=-1) - 1)
Dy_loss = self.gan_loss.DiscriminatorLoss(
Dy_real_out, Dy_fake_out) + tf.reduce_mean(penalty_normY)
Dx_loss = self.gan_loss.DiscriminatorLoss(
Dx_real_out, Dx_fake_out) + tf.reduce_mean(penalty_normX)
if self.mixed_precision:
scaled_Dy_loss = self.Dy_optimizer.get_scaled_loss(Dy_loss)
scaled_Dx_loss = self.Dx_optimizer.get_scaled_loss(Dx_loss)
if self.mixed_precision:
scaled_gradients_of_Dy = D_tape.gradient(
scaled_Dy_loss, self.Dy.trainable_variables)
scaled_gradients_of_Dx = D_tape.gradient(
scaled_Dx_loss, self.Dx.trainable_variables)
gradients_of_Dy = self.Dy_optimizer.get_unscaled_gradients(
scaled_gradients_of_Dy)
gradients_of_Dx = self.Dx_optimizer.get_unscaled_gradients(
scaled_gradients_of_Dx)
else:
gradients_of_Dy = D_tape.gradient(Dy_loss,
self.Dy.trainable_variables)
gradients_of_Dx = D_tape.gradient(Dx_loss,
self.Dx.trainable_variables)
self.Dy_optimizer.apply_gradients(
zip(gradients_of_Dy, self.Dy.trainable_variables))
self.Dx_optimizer.apply_gradients(
zip(gradients_of_Dx, self.Dx.trainable_variables))
return Dy_loss, Dx_loss
@tf.function(input_signature=[
tf.TensorSpec(shape=global_input_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_input_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=[4], dtype=tf.int32),
tf.TensorSpec(shape=[1], dtype=tf.uint32)
])
def train_step_G(self, trainX, trainY, maskX, maskY, wgp_shape, step):
with tf.GradientTape(persistent=True) as G_tape:
GeneratedY = self.G(trainX)
GeneratedY = tf.multiply(GeneratedY, maskY)
# Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
GeneratedX = tf.multiply(GeneratedX, maskX)
# Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
cycle_consistent_loss_X2Y = tf.reduce_mean(
tf.abs(self.F(GeneratedY) - trainX))
cycle_consistent_loss_Y2X = tf.reduce_mean(
tf.abs(self.G(GeneratedX) - trainY))
cycle_consistent = cycle_consistent_loss_X2Y + cycle_consistent_loss_Y2X
if step >= 0: #先不进行像素梯度和重建损失的使用
cycle_l = 10.0
else:
cycle_l = 10.0
G_loss = self.gan_loss.GeneratorLoss(
Dy_fake_out) + cycle_l * (cycle_consistent)
F_loss = self.gan_loss.GeneratorLoss(
Dx_fake_out) + cycle_l * (cycle_consistent)
if self.mixed_precision:
scaled_G_loss = self.G_optimizer.get_scaled_loss(G_loss)
scaled_F_loss = self.F_optimizer.get_scaled_loss(F_loss)
if self.mixed_precision:
scaled_gradients_of_G = G_tape.gradient(scaled_G_loss,
self.G.trainable_variables)
scaled_gradients_of_F = G_tape.gradient(scaled_F_loss,
self.F.trainable_variables)
gradients_of_G = self.G_optimizer.get_unscaled_gradients(
scaled_gradients_of_G)
gradients_of_F = self.F_optimizer.get_unscaled_gradients(
scaled_gradients_of_F)
else:
gradients_of_G = G_tape.gradient(G_loss,
self.G.trainable_variables)
gradients_of_F = G_tape.gradient(F_loss,
self.F.trainable_variables)
self.G_optimizer.apply_gradients(
zip(gradients_of_G, self.G.trainable_variables))
self.F_optimizer.apply_gradients(
zip(gradients_of_F, self.F.trainable_variables))
return G_loss, F_loss
@tf.function(input_signature=[
tf.TensorSpec(shape=global_input_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_input_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=[4], dtype=tf.int32),
tf.TensorSpec(shape=[1], dtype=tf.uint32)
])
def train_step(self, trainX, trainY, maskX, maskY, wgp_shape, step):
with tf.GradientTape(persistent=True) as cycle_type:
GeneratedY = self.G(trainX)
GeneratedY = tf.multiply(GeneratedY, maskY)
Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
GeneratedX = tf.multiply(GeneratedX, maskX)
Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
cycle_consistent_loss_X2Y = tf.reduce_mean(
tf.abs(self.F(GeneratedY) - trainX))
cycle_consistent_loss_Y2X = tf.reduce_mean(
tf.abs(self.G(GeneratedX) - trainY))
cycle_consistent = cycle_consistent_loss_X2Y + cycle_consistent_loss_Y2X
if step >= 0: #先不进行像素梯度和重建损失的使用
cycle_l = 10.0
else:
cycle_l = 10.0
Dy_loss = self.gan_loss.DiscriminatorLoss(Dy_real_out, Dy_fake_out)
Dx_loss = self.gan_loss.DiscriminatorLoss(Dx_real_out, Dx_fake_out)
G_loss = self.gan_loss.GeneratorLoss(
Dy_fake_out) + cycle_l * (cycle_consistent)
F_loss = self.gan_loss.GeneratorLoss(
Dx_fake_out) + cycle_l * (cycle_consistent)
gradients_of_Dy = cycle_type.gradient(Dy_loss,
self.Dy.trainable_variables)
gradients_of_Dx = cycle_type.gradient(Dx_loss,
self.Dx.trainable_variables)
gradients_of_G = cycle_type.gradient(G_loss,
self.G.trainable_variables)
gradients_of_F = cycle_type.gradient(F_loss,
self.F.trainable_variables)
self.Dy_optimizer.apply_gradients(
zip(gradients_of_Dy, self.Dy.trainable_variables))
self.Dx_optimizer.apply_gradients(
zip(gradients_of_Dx, self.Dx.trainable_variables))
self.G_optimizer.apply_gradients(
zip(gradients_of_G, self.G.trainable_variables))
self.F_optimizer.apply_gradients(
zip(gradients_of_F, self.F.trainable_variables))
return G_loss, Dy_loss, F_loss, Dx_loss
def train(self, epoches):
self.ckpt.restore(self.manager.latest_checkpoint)
for _ in range(epoches):
start = time.time()
for trainX, trainY, maskX, maskY in self.train_set:
self.ckpt.step.assign_add(1)
step = int(self.ckpt.step)
if self.loss_name in ["WGAN", "WGAN-GP"]:
for __ in range(1):
Dy_loss, Dx_loss = self.train_step_D(
trainX, trainY, maskX, maskY,
tf.constant([trainY.shape[0], 1, 1, 1],
shape=[4],
dtype=tf.int32),
tf.constant(step, shape=[1], dtype=tf.uint32))
for __ in range(1):
G_loss, F_loss = self.train_step_G(
trainX, trainY, maskX, maskY,
tf.constant([trainY.shape[0], 1, 1, 1],
shape=[4],
dtype=tf.int32),
tf.constant(step, shape=[1], dtype=tf.uint32))
elif self.loss_name in ["Vanilla", "LSGAN"]:
G_loss, Dy_loss, F_loss, Dx_loss = self.train_step(
trainX, trainY, maskX, maskY,
tf.constant([trainY.shape[0], 1, 1, 1],
shape=[4],
dtype=tf.int32),
tf.constant(step, shape=[1], dtype=tf.uint32))
else:
raise ValueError("Inner Error")
if step % 100 == 0:
save_path = self.manager.save()
print("Saved checkpoint for step {}: {}".format(
step, save_path))
self.G.save_weights(self.tmp_path +
'/weights_saved/G.ckpt')
self.Dy.save_weights(self.tmp_path +
'/weights_saved/Dy.ckpt')
self.F.save_weights(self.tmp_path +
'/weights_saved/F.ckpt')
self.Dx.save_weights(self.tmp_path +
'/weights_saved/Dx.ckpt')
self.wirte_summary(step=step,
seed=self.seed,
G=self.G,
F=self.F,
G_loss=G_loss,
Dy_loss=Dy_loss,
F_loss=F_loss,
Dx_loss=Dx_loss,
out_path=self.out_path)
print('Time to next 100 step {} is {} sec'.format(
step,
time.time() - start))
start = time.time()
def get_seed(self):
seed_get = iter(self.test_set)
testX, testY, maskX, maskY = next(seed_get)
print(testX.shape, testY.dtype, maskX.dtype, maskY.shape)
plt.imshow(testX[0, :, :, 0], cmap='gray')
plt.show()
plt.imshow(testY[0, :, :, 0], cmap='gray')
plt.show()
plt.imshow(maskX[0, :, :, 0], cmap='gray')
plt.show()
plt.imshow(maskY[0, :, :, 0], cmap='gray')
plt.show()
self.seed = testX, testY, maskX, maskY
def wirte_summary(self, step, seed, G, F, G_loss, Dy_loss, F_loss, Dx_loss,
out_path):
testX, testY, maskX, maskY = seed
GeneratedY = G(testX)
GeneratedY = tf.multiply(GeneratedY, maskX)
GeneratedX = F(testY)
GeneratedX = tf.multiply(GeneratedX,
maskY) #测试时mask正好相反 因为只知道原来模态和原来模态的mask
plt.figure(figsize=(5, 5)) #图片大一点才可以承载像素
plt.subplot(2, 2, 1)
plt.title('real X')
plt.imshow(testX[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.title('fake Y')
plt.imshow(GeneratedY[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.title('fake X')
plt.imshow(GeneratedX[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 4)
plt.title('real Y')
plt.imshow(testY[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.savefig(out_path + '/image_at_{}.png'.format(step))
plt.close()
img = Image.open(out_path + '/image_at_{}.png'.format(step))
img = tf.reshape(np.array(img), shape=(1, 500, 500, 4))
with self.train_summary_writer.as_default():
##########################
self.m_psnr_X2Y(tf.image.psnr(GeneratedY, testY, 1.0, name=None))
self.m_psnr_Y2X(tf.image.psnr(GeneratedX, testX, 1.0, name=None))
self.m_ssim_X2Y(
tf.image.ssim(GeneratedY,
testY,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
self.m_ssim_Y2X(
tf.image.ssim(GeneratedX,
testX,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
tf.summary.scalar('G_loss', G_loss, step=step)
tf.summary.scalar('Dy_loss', Dy_loss, step=step)
tf.summary.scalar('F_loss', F_loss, step=step)
tf.summary.scalar('Dx_loss', Dx_loss, step=step)
tf.summary.scalar('test_psnr_y',
self.m_psnr_X2Y.result(),
step=step)
tf.summary.scalar('test_psnr_x',
self.m_psnr_Y2X.result(),
step=step)
tf.summary.scalar('test_ssim_y',
self.m_ssim_X2Y.result(),
step=step)
tf.summary.scalar('test_ssim_x',
self.m_ssim_Y2X.result(),
step=step)
tf.summary.image("img", img, step=step)
##########################
self.m_psnr_X2Y.reset_states()
self.m_psnr_Y2X.reset_states()
self.m_ssim_X2Y.reset_states()
self.m_ssim_Y2X.reset_states()
def test(self):
self.ckpt.restore(self.manager.latest_checkpoint)
step = 0
black_board_X = np.zeros(shape=[240, 240], dtype=np.float32)
black_board_Y = np.zeros(shape=[240, 240], dtype=np.float32)
black_board_rX = np.zeros(shape=[240, 240], dtype=np.float32)
black_board_rY = np.zeros(shape=[240, 240], dtype=np.float32)
for i, (testX, testY, maskX, maskY) in enumerate(self.test_set):
GeneratedY = self.G(testX)
GeneratedY = tf.multiply(GeneratedY, maskX)
GeneratedX = self.F(testY)
GeneratedX = tf.multiply(GeneratedX,
maskY) #测试时mask正好相反 因为只知道原来模态和原来模态的mask
if (i + 1) % 4 == 1:
black_board_Y[48:175 + 1,
22:149 + 1] += GeneratedY.numpy()[0, :, :, 0]
black_board_X[48:175 + 1,
22:149 + 1] += GeneratedX.numpy()[0, :, :, 0]
black_board_rY[48:175 + 1,
22:149 + 1] += testY.numpy()[0, :, :, 0]
black_board_rX[48:175 + 1,
22:149 + 1] += testX.numpy()[0, :, :, 0]
elif (i + 1) % 4 == 2:
black_board_Y[48:175 + 1,
90:217 + 1] += GeneratedY.numpy()[0, :, :, 0]
black_board_X[48:175 + 1,
90:217 + 1] += GeneratedX.numpy()[0, :, :, 0]
black_board_rY[48:175 + 1,
90:217 + 1] += testY.numpy()[0, :, :, 0]
black_board_rX[48:175 + 1,
90:217 + 1] += testX.numpy()[0, :, :, 0]
elif (i + 1) % 4 == 3:
black_board_Y[64:191 + 1,
22:149 + 1] += GeneratedY.numpy()[0, :, :, 0]
black_board_X[64:191 + 1,
22:149 + 1] += GeneratedX.numpy()[0, :, :, 0]
black_board_rY[64:191 + 1,
22:149 + 1] += testY.numpy()[0, :, :, 0]
black_board_rX[64:191 + 1,
22:149 + 1] += testX.numpy()[0, :, :, 0]
elif (i + 1) % 4 == 0:
black_board_Y[64:191 + 1,
90:217 + 1] += GeneratedY.numpy()[0, :, :, 0]
black_board_X[64:191 + 1,
90:217 + 1] += GeneratedX.numpy()[0, :, :, 0]
black_board_rY[64:191 + 1,
90:217 + 1] += testY.numpy()[0, :, :, 0]
black_board_rX[64:191 + 1,
90:217 + 1] += testX.numpy()[0, :, :, 0]
#norm
black_board_Y[64:175 +
1, :] = black_board_Y[64:175 + 1, :] / 2.0
black_board_Y[:,
90:149 + 1] = black_board_Y[:, 90:149 + 1] / 2.0
black_board_X[64:175 +
1, :] = black_board_X[64:175 + 1, :] / 2.0
black_board_X[:,
90:149 + 1] = black_board_X[:, 90:149 + 1] / 2.0
black_board_rY[64:175 +
1, :] = black_board_rY[64:175 + 1, :] / 2.0
black_board_rY[:, 90:149 +
1] = black_board_rY[:, 90:149 + 1] / 2.0
black_board_rX[64:175 +
1, :] = black_board_rX[64:175 + 1, :] / 2.0
black_board_rX[:, 90:149 +
1] = black_board_rX[:, 90:149 + 1] / 2.0
else:
raise ValueError("inner error")
out_path = self.out_path
if (i + 1) % 4 == 0:
step += 1
plt.figure(figsize=(10, 10)) #图片大一点才可以承载像素
plt.subplot(2, 2, 1)
plt.title('real X')
plt.imshow(black_board_rX, cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.title('fake Y')
plt.imshow(black_board_Y, cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.title('fake X')
plt.imshow(black_board_X, cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 4)
plt.title('real Y')
plt.imshow(black_board_rY, cmap='gray')
plt.axis('off')
plt.savefig(out_path + '/test/image_at_{}.png'.format(step))
plt.close()
img = Image.open(out_path +
'/test/image_at_{}.png'.format(step))
img = tf.reshape(np.array(img), shape=(1, 1000, 1000, 4))
with self.train_summary_writer.as_default():
##########################
black_board_Y = tf.reshape(tf.constant(black_board_Y,
dtype=tf.float32),
shape=[1, 240, 240, 1])
black_board_X = tf.reshape(tf.constant(black_board_X,
dtype=tf.float32),
shape=[1, 240, 240, 1])
black_board_rY = tf.reshape(tf.constant(black_board_rY,
dtype=tf.float32),
shape=[1, 240, 240, 1])
black_board_rX = tf.reshape(tf.constant(black_board_rX,
dtype=tf.float32),
shape=[1, 240, 240, 1])
self.m_psnr_X2Y(
tf.image.psnr(black_board_Y,
black_board_rY,
1.0,
name=None))
self.m_psnr_Y2X(
tf.image.psnr(black_board_X,
black_board_rX,
1.0,
name=None))
self.m_ssim_X2Y(
tf.image.ssim(black_board_Y,
black_board_rY,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
self.m_ssim_Y2X(
tf.image.ssim(black_board_X,
black_board_rX,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
tf.summary.scalar('test_psnr_y',
self.m_psnr_X2Y.result(),
step=step)
tf.summary.scalar('test_psnr_x',
self.m_psnr_Y2X.result(),
step=step)
tf.summary.scalar('test_ssim_y',
self.m_ssim_X2Y.result(),
step=step)
tf.summary.scalar('test_ssim_x',
self.m_ssim_Y2X.result(),
step=step)
tf.summary.image("img", img, step=step)
##########################
self.m_psnr_X2Y.reset_states()
self.m_psnr_Y2X.reset_states()
self.m_ssim_X2Y.reset_states()
self.m_ssim_Y2X.reset_states()
black_board_X = np.zeros(shape=[240, 240], dtype=np.float32)
black_board_Y = np.zeros(shape=[240, 240], dtype=np.float32)
black_board_rX = np.zeros(shape=[240, 240], dtype=np.float32)
black_board_rY = np.zeros(shape=[240, 240], dtype=np.float32)
class DCGAN(tf.keras.Model):
"""
模型只负责给定训练集和测试(验证)集后的操作
"""
def __init__(self,
train_set,
test_set,
loss_name="Vanilla",
mixed_precision=False,
learning_rate=2e-4,
tmp_path=None,
out_path=None):
super(DCGAN, self).__init__()
#接收数据集和相关参数
self.train_set = train_set
self.test_set = test_set
self.tmp_path = tmp_path
self.out_path = out_path
#定义模型
self.G = networks.Generator(name="G")
if loss_name in ["WGAN-SN", "WGAN-GP-SN"]:
self.D = networks.Discriminator(name="If_is_real",
use_sigmoid=False,
sn=True)
self.loss_name = loss_name[:-3]
elif loss_name in ["WGAN", "WGAN-GP"]:
self.D = networks.Discriminator(name="If_is_real",
use_sigmoid=False,
sn=False)
self.loss_name = loss_name
elif loss_name in ["Vanilla", "LSGAN"]:
self.D = networks.Discriminator(name="If_is_real",
use_sigmoid=True,
sn=False)
self.loss_name = loss_name
else:
raise ValueError("Do not support the loss " + loss_name)
self.model_list = [self.G, self.D]
#定义损失函数 优化器 记录等
self.gan_loss = GanLoss(self.loss_name)
self.optimizers_list = self.optimizers_config(
mixed_precision=mixed_precision, learning_rate=learning_rate)
self.mixed_precision = mixed_precision
self.matrics_list = self.matrics_config()
self.checkpoint_config()
self.get_seed()
def build(self, input_shape_G, input_shape_D):
"""
input_shape必须切片 因为在底层会被当做各层的输出shape而被改动
"""
self.G.build(input_shape=input_shape_G[:]) #G X->Y
self.D.build(input_shape=input_shape_D[:]) #D Y or not Y
self.built = True
def optimizers_config(self, mixed_precision=False, learning_rate=2e-4):
self.G_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
self.D_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
if mixed_precision:
self.G_optimizer = self.G_optimizer.get_mixed_precision()
self.D_optimizer = self.D_optimizer.get_mixed_precision()
return [self.G_optimizer, self.D_optimizer]
def matrics_config(self):
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_logdir = self.tmp_path + "/logs/" + current_time
self.train_summary_writer = tf.summary.create_file_writer(train_logdir)
return []
# return None
def checkpoint_config(self):
self.ckpt = tf.train.Checkpoint(step=tf.Variable(1),
optimizer=self.optimizers_list,
model=self.model_list,
dataset=self.train_set)
self.manager = tf.train.CheckpointManager(self.ckpt,
self.tmp_path + '/tf_ckpts',
max_to_keep=3)
def pix_gradient(self, x):
x = tf.reshape(x, shape=[1, 64, 64,
1]) #在各batch和通道上进行像素梯度 对2D单通道而言其实没必要reshape
dx, dy = tf.image.image_gradients(x)
return dx, dy
@tf.function(input_signature=[tf.TensorSpec(shape=global_input_X_shape,dtype=tf.float32),\
tf.TensorSpec(shape=global_input_Y_shape,dtype=tf.float32),\
tf.TensorSpec(shape=[4],dtype=tf.int32),\
tf.TensorSpec(shape=[1],dtype=tf.uint32)])
def train_step_D(self, trainX, trainY, y_shape, step):
with tf.GradientTape(persistent=True) as D_tape:
GeneratedY = self.G(trainX)
D_real_out = self.D(trainY)
D_fake_out = self.D(GeneratedY)
e = tf.random.uniform(shape=y_shape, minval=0.0, maxval=1.0)
mid_Y = e * trainY + (1 - e) * GeneratedY
with tf.GradientTape() as GP:
GP.watch(mid_Y)
inner_loss = self.D(mid_Y)
penalty = GP.gradient(inner_loss, mid_Y)
# print("penalty",penalty.shape)
# penalty_norm = 10.0*tf.math.square(tf.maximum(tf.norm(penalty,ord='euclidean'),1.0)-1) #
penalty_norm = 10.0 * tf.math.square(
tf.norm(tf.reshape(penalty, [y_shape[0], -1]), axis=-1, ord=2)
- 1) #这是按照算法愿意
# print("penalty_norm",penalty_norm.shape)
D_loss = self.gan_loss.DiscriminatorLoss(
D_real_out, D_fake_out) + tf.reduce_mean(penalty_norm)
if self.mixed_precision:
scaled_D_loss = self.D_optimizer.get_scaled_loss(D_loss)
if self.mixed_precision:
scaled_gradients_of_D = D_tape.gradient(scaled_D_loss,
self.D.trainable_variables)
gradients_of_D = self.D_optimizer.get_unscaled_gradients(
scaled_gradients_of_D)
else:
gradients_of_D = D_tape.gradient(D_loss,
self.D.trainable_variables)
self.D_optimizer.apply_gradients(
zip(gradients_of_D, self.D.trainable_variables))
return D_loss
@tf.function(input_signature=[tf.TensorSpec(shape=global_input_X_shape,dtype=tf.float32),\
tf.TensorSpec(shape=global_input_Y_shape,dtype=tf.float32),\
tf.TensorSpec(shape=[4],dtype=tf.int32),\
tf.TensorSpec(shape=[1],dtype=tf.uint32)])
def train_step_G(self, trainX, trainY, y_shape, step):
with tf.GradientTape(persistent=True) as G_tape:
GeneratedY = self.G(trainX)
# Dy_real_out = self.Dy(trainY)
D_fake_out = self.D(GeneratedY)
G_loss = self.gan_loss.GeneratorLoss(D_fake_out)
if self.mixed_precision:
scaled_G_loss = self.G_optimizer.get_scaled_loss(G_loss)
if self.mixed_precision:
scaled_gradients_of_G = G_tape.gradient(scaled_G_loss,
self.G.trainable_variables)
gradients_of_G = self.G_optimizer.get_unscaled_gradients(
scaled_gradients_of_G)
else:
gradients_of_G = G_tape.gradient(G_loss,
self.G.trainable_variables)
self.G_optimizer.apply_gradients(
zip(gradients_of_G, self.G.trainable_variables))
return G_loss
@tf.function(input_signature=[tf.TensorSpec(shape=global_input_X_shape,dtype=tf.float32),\
tf.TensorSpec(shape=global_input_Y_shape,dtype=tf.float32),\
tf.TensorSpec(shape=[4],dtype=tf.int32),\
tf.TensorSpec(shape=[1],dtype=tf.uint32)])
def train_step(self, trainX, trainY, y_shape, step):
with tf.GradientTape(persistent=True) as gan_type:
GeneratedY = self.G(trainX)
D_real_out = self.D(trainY)
D_fake_out = self.D(GeneratedY)
D_loss = self.gan_loss.DiscriminatorLoss(D_real_out, D_fake_out)
G_loss = self.gan_loss.GeneratorLoss(D_fake_out)
if self.mixed_precision:
scaled_D_loss = self.D_optimizer.get_scaled_loss(D_loss)
scaled_G_loss = self.G_optimizer.get_scaled_loss(G_loss)
if self.mixed_precision:
scaled_gradients_of_D = gan_type.gradient(
scaled_D_loss, self.D.trainable_variables)
scaled_gradients_of_G = gan_type.gradient(
scaled_G_loss, self.G.trainable_variables)
gradients_of_D = self.D_optimizer.get_unscaled_gradients(
scaled_gradients_of_D)
gradients_of_G = self.G_optimizer.get_unscaled_gradients(
scaled_gradients_of_G)
else:
gradients_of_D = gan_type.gradient(D_loss,
self.D.trainable_variables)
gradients_of_G = gan_type.gradient(G_loss,
self.G.trainable_variables)
self.D_optimizer.apply_gradients(
zip(gradients_of_D, self.D.trainable_variables))
self.G_optimizer.apply_gradients(
zip(gradients_of_G, self.G.trainable_variables))
return D_loss, G_loss
def train(self, epoches):
self.ckpt.restore(self.manager.latest_checkpoint)
for _ in range(epoches):
start = time.time()
for trainX, trainY in self.train_set:
self.ckpt.step.assign_add(1)
step = int(self.ckpt.step)
if self.loss_name in ["WGAN", "WGAN-GP"]:
for __ in range(5):
D_loss = self.train_step_D(
trainX, trainY,
tf.constant([trainY.shape[0], 1, 1, 1],
shape=[4],
dtype=tf.int32),
tf.constant(step, shape=[1], dtype=tf.uint32))
for __ in range(1):
G_loss = self.train_step_G(
trainX, trainY,
tf.constant([trainY.shape[0], 1, 1, 1],
shape=[4],
dtype=tf.int32),
tf.constant(step, shape=[1], dtype=tf.uint32))
elif self.loss_name in ["Vanilla", "LSGAN"]:
D_loss, G_loss = self.train_step(
trainX, trainY,
tf.constant([trainY.shape[0], 1, 1, 1],
shape=[4],
dtype=tf.int32),
tf.constant(step, shape=[1], dtype=tf.uint32))
else:
raise ValueError("Inner Error")
if step % 100 == 0:
save_path = self.manager.save()
print("Saved checkpoint for step {}: {}".format(
step, save_path))
self.G.save_weights(self.tmp_path +
'/weights_saved/G.ckpt')
self.D.save_weights(self.tmp_path +
'/weights_saved/D.ckpt')
self.wirte_summary(step=step,
seed=self.seed,
G=self.G,
G_loss=G_loss,
D_loss=D_loss,
out_path=self.out_path)
print('Time to next 100 step {} is {} sec'.format(
step,
time.time() - start))
start = time.time()
def test(self, take_nums):
out_path = self.out_path + "/test"
import os
if not os.path.exists(out_path):
os.makedirs(out_path)
self.ckpt.restore(self.manager.latest_checkpoint)
seed_get = iter(self.test_set)
for take in range(take_nums):
plt.figure(figsize=(10, 10)) #图片大一点才可以承载像素
for i in range(100):
single_seed = next(seed_get)
GeneratedY = self.G(single_seed, training=False)
plt.subplot(10, 10, (i + 1))
plt.imshow(GeneratedY[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.savefig(out_path + '/image_at_{}.png'.format(take))
plt.close()
def get_seed(self):
self.seed = []
seed_get = iter(self.test_set)
for _ in range(100):
seed = next(seed_get)
self.seed.append(seed)
def wirte_summary(self, step, seed, G, G_loss, D_loss, out_path):
plt.figure(figsize=(10, 10)) #图片大一点才可以承载像素
for i, single_seed in enumerate(seed):
GeneratedY = G(single_seed, training=False)
plt.subplot(10, 10, (i + 1))
plt.imshow(GeneratedY[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.savefig(out_path + '/image_at_{}.png'.format(step))
plt.close()
img = Image.open(out_path + '/image_at_{}.png'.format(step))
img = tf.reshape(np.array(img), shape=(1, 1000, 1000, 4))
with self.train_summary_writer.as_default():
##########################
tf.summary.scalar('G_loss', G_loss, step=step)
tf.summary.scalar('D_loss', D_loss, step=step)
tf.summary.image("img", img, step=step)
class CycleGAN(tf.keras.Model):
"""
模型只负责给定训练集和测试(验证)集后的操作
"""
def __init__(self,
train_set,
test_set,
loss_name="WGAN-GP",
mixed_precision=False,
learning_rate=2e-4,
tmp_path=None,
out_path=None):
super(CycleGAN, self).__init__()
#接收数据集和相关参数
self.train_set = train_set
self.test_set = test_set
self.tmp_path = tmp_path
self.out_path = out_path
#定义模型
self.G = networks.Generator(name="G_X2Y")
self.F = networks.Generator(name="G_Y2X")
if loss_name in ["WGAN-SN", "WGAN-GP-SN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=False,
sn=True)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=False,
sn=True)
self.loss_name = loss_name[:-3]
elif loss_name in ["WGAN", "WGAN-GP"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=False,
sn=False)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=False,
sn=False)
self.loss_name = loss_name
elif loss_name in ["Vanilla-SN", "LSGAN-SN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=True,
sn=True)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=True,
sn=True)
self.loss_name = loss_name[:-3]
elif loss_name in ["Vanilla", "LSGAN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",
use_sigmoid=True,
sn=False)
self.Dx = networks.Discriminator(name="If_is_real_X",
use_sigmoid=True,
sn=False)
self.loss_name = loss_name
else:
raise ValueError("Do not support the loss " + loss_name)
self.vgg = vgg16()
self.model_list = [self.G, self.F, self.Dy, self.Dx]
#定义损失函数 优化器 记录等
self.gan_loss = GanLoss(self.loss_name)
self.optimizers_list = self.optimizers_config(
mixed_precision=mixed_precision, learning_rate=learning_rate)
self.mixed_precision = mixed_precision
self.matrics_list = self.matrics_config()
self.checkpoint_config()
self.get_seed()
def build(self, X_shape, Y_shape):
"""
input_shape必须切片 因为在底层会被当做各层的输出shape而被改动
"""
self.G.build(input_shape=X_shape[:]) #G X->Y
self.Dy.build(input_shape=Y_shape[:]) #Dy Y or != Y
self.F.build(input_shape=Y_shape[:]) #F Y->X
self.Dx.build(input_shape=X_shape[:]) #Dx X or != X
self.built = True
def optimizers_config(self, mixed_precision=False, learning_rate=2e-4):
self.G_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
self.Dy_optimizer = Adam(learning_rate=4e-4, beta_1=0.0, beta_2=0.9)
self.F_optimizer = Adam(learning_rate=1e-4, beta_1=0.0, beta_2=0.9)
self.Dx_optimizer = Adam(learning_rate=4e-4, beta_1=0.0, beta_2=0.9)
# self.G_optimizer = Adam(learning_rate=2e-4)
# self.Dy_optimizer = Adam(learning_rate=2e-4)
# self.F_optimizer = Adam(learning_rate=2e-4)
# self.Dx_optimizer = Adam(learning_rate=2e-4)
if mixed_precision:
self.G_optimizer = self.G_optimizer.get_mixed_precision()
self.Dy_optimizer = self.Dy_optimizer.get_mixed_precision()
self.F_optimizer = self.F_optimizer.get_mixed_precision()
self.Dx_optimizer = self.Dx_optimizer.get_mixed_precision()
return [
self.G_optimizer, self.Dy_optimizer, self.F_optimizer,
self.Dx_optimizer
]
def matrics_config(self):
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_logdir = self.tmp_path + "/logs/" + current_time
self.train_summary_writer = tf.summary.create_file_writer(train_logdir)
self.m_psnr_X2Y = tf.keras.metrics.Mean('psnr_y', dtype=tf.float32)
self.m_psnr_Y2X = tf.keras.metrics.Mean('psnr_x', dtype=tf.float32)
self.m_ssim_X2Y = tf.keras.metrics.Mean('ssim_y', dtype=tf.float32)
self.m_ssim_Y2X = tf.keras.metrics.Mean('ssim_x', dtype=tf.float32)
return [
self.m_psnr_X2Y, self.m_psnr_Y2X, self.m_ssim_X2Y, self.m_ssim_Y2X
]
# return None
def checkpoint_config(self):
self.ckpt = tf.train.Checkpoint(step=tf.Variable(1),
optimizer=self.optimizers_list,
model=self.model_list,
dataset=self.train_set)
self.manager = tf.train.CheckpointManager(self.ckpt,
self.tmp_path + '/tf_ckpts',
max_to_keep=3)
def pix_gradient(self, x):
x = tf.reshape(x, shape=[1, 64, 64,
1]) #在各batch和通道上进行像素梯度 对2D单通道而言其实没必要reshape
dx, dy = tf.image.image_gradients(x)
return dx, dy
@tf.function(input_signature=[
tf.TensorSpec(shape=global_input_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_input_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_Y_shape, dtype=tf.float32)
])
def train_step_D(self, trainX, trainY, maskX, maskY):
with tf.GradientTape(persistent=True) as D_tape:
GeneratedY = self.G(trainX)
GeneratedY = tf.multiply(GeneratedY, maskY)
Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
GeneratedX = tf.multiply(GeneratedX, maskX)
Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
e = tf.random.uniform(shape=self.wgp_shape, minval=0.0, maxval=1.0)
mid_Y = e * trainY + (1 - e) * GeneratedY
with tf.GradientTape() as gradient_penaltyY:
gradient_penaltyY.watch(mid_Y)
inner_loss = self.Dy(mid_Y)
penalty = gradient_penaltyY.gradient(inner_loss, mid_Y)
penalty_normY = 10.0 * tf.math.square(
tf.norm(tf.reshape(penalty, shape=[self.wgp_shape[0], -1]),
ord=2,
axis=-1) - 1)
e = tf.random.uniform(shape=self.wgp_shape, minval=0.0, maxval=1.0)
mid_X = e * trainX + (1 - e) * GeneratedX
with tf.GradientTape() as gradient_penaltyX:
gradient_penaltyX.watch(mid_X)
inner_loss = self.Dx(mid_X)
penalty = gradient_penaltyX.gradient(inner_loss, mid_X)
penalty_normX = 10.0 * tf.math.square(
tf.norm(tf.reshape(penalty, shape=[self.wgp_shape[0], -1]),
ord=2,
axis=-1) - 1)
Dy_loss = self.gan_loss.DiscriminatorLoss(
Dy_real_out, Dy_fake_out) + tf.reduce_mean(penalty_normY)
Dx_loss = self.gan_loss.DiscriminatorLoss(
Dx_real_out, Dx_fake_out) + tf.reduce_mean(penalty_normX)
if self.mixed_precision:
scaled_Dy_loss = self.Dy_optimizer.get_scaled_loss(Dy_loss)
scaled_Dx_loss = self.Dx_optimizer.get_scaled_loss(Dx_loss)
if self.mixed_precision:
scaled_gradients_of_Dy = D_tape.gradient(
scaled_Dy_loss, self.Dy.trainable_variables)
scaled_gradients_of_Dx = D_tape.gradient(
scaled_Dx_loss, self.Dx.trainable_variables)
gradients_of_Dy = self.Dy_optimizer.get_unscaled_gradients(
scaled_gradients_of_Dy)
gradients_of_Dx = self.Dx_optimizer.get_unscaled_gradients(
scaled_gradients_of_Dx)
else:
gradients_of_Dy = D_tape.gradient(Dy_loss,
self.Dy.trainable_variables)
gradients_of_Dx = D_tape.gradient(Dx_loss,
self.Dx.trainable_variables)
self.Dy_optimizer.apply_gradients(
zip(gradients_of_Dy, self.Dy.trainable_variables))
self.Dx_optimizer.apply_gradients(
zip(gradients_of_Dx, self.Dx.trainable_variables))
return Dy_loss, Dx_loss
@tf.function(input_signature=[
tf.TensorSpec(shape=global_input_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_input_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_Y_shape, dtype=tf.float32)
])
def train_step_G(self, trainX, trainY, maskX, maskY):
with tf.GradientTape(persistent=True) as G_tape:
GeneratedY = self.G(trainX)
GeneratedY = tf.multiply(GeneratedY, maskY)
# Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
GeneratedX = tf.multiply(GeneratedX, maskX)
# Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
cycle_consistent_loss_X2Y = tf.reduce_mean(
tf.abs(self.F(GeneratedY) - trainX))
cycle_consistent_loss_Y2X = tf.reduce_mean(
tf.abs(self.G(GeneratedX) - trainY))
cycle_consistent = cycle_consistent_loss_X2Y + cycle_consistent_loss_Y2X
fake_Y_perceptual = self.vgg(GeneratedY)
real_Y_perceptual = self.vgg(trainY)
fake_X_perceptual = self.vgg(GeneratedX)
real_X_perceptual = self.vgg(trainX)
reconstruction_loss_X2Y = 0
reconstruction_loss_Y2X = 0
for i in range(7):
reconstruction_loss_X2Y += 0.14 * tf.reduce_mean(
tf.abs(fake_Y_perceptual[i] - real_Y_perceptual[i]))
reconstruction_loss_Y2X += 0.14 * tf.reduce_mean(
tf.abs(fake_X_perceptual[i] - real_X_perceptual[i]))
# reconstruction_loss_X2Y = tf.reduce_mean(tf.abs(GeneratedY-trainY))
# reconstruction_loss_Y2X = tf.reduce_mean(tf.abs(GeneratedX-trainX))
G_loss = self.gan_loss.GeneratorLoss(
Dy_fake_out
) + 10.0 * cycle_consistent + reconstruction_loss_X2Y
F_loss = self.gan_loss.GeneratorLoss(
Dx_fake_out
) + 10.0 * cycle_consistent + reconstruction_loss_Y2X
if self.mixed_precision:
scaled_G_loss = self.G_optimizer.get_scaled_loss(G_loss)
scaled_F_loss = self.F_optimizer.get_scaled_loss(F_loss)
if self.mixed_precision:
scaled_gradients_of_G = G_tape.gradient(scaled_G_loss,
self.G.trainable_variables)
scaled_gradients_of_F = G_tape.gradient(scaled_F_loss,
self.F.trainable_variables)
gradients_of_G = self.G_optimizer.get_unscaled_gradients(
scaled_gradients_of_G)
gradients_of_F = self.F_optimizer.get_unscaled_gradients(
scaled_gradients_of_F)
else:
gradients_of_G = G_tape.gradient(G_loss,
self.G.trainable_variables)
gradients_of_F = G_tape.gradient(F_loss,
self.F.trainable_variables)
self.G_optimizer.apply_gradients(
zip(gradients_of_G, self.G.trainable_variables))
self.F_optimizer.apply_gradients(
zip(gradients_of_F, self.F.trainable_variables))
return G_loss, F_loss
@tf.function(input_signature=[
tf.TensorSpec(shape=global_input_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_input_Y_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_X_shape, dtype=tf.float32),
tf.TensorSpec(shape=global_mask_Y_shape, dtype=tf.float32)
])
def train_step(self, trainX, trainY, maskX, maskY):
with tf.GradientTape(persistent=True) as cycle_type:
GeneratedY = self.G(trainX)
GeneratedY = tf.multiply(GeneratedY, maskY)
Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
GeneratedX = tf.multiply(GeneratedX, maskX)
Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
cycle_consistent_loss_X2Y = tf.reduce_mean(
tf.abs(self.F(GeneratedY) - trainX))
cycle_consistent_loss_Y2X = tf.reduce_mean(
tf.abs(self.G(GeneratedX) - trainY))
cycle_consistent = cycle_consistent_loss_X2Y + cycle_consistent_loss_Y2X
fake_Y_perceptual = self.vgg(GeneratedY)
real_Y_perceptual = self.vgg(trainY)
fake_X_perceptual = self.vgg(GeneratedX)
real_X_perceptual = self.vgg(trainX)
reconstruction_loss_X2Y = 0
reconstruction_loss_Y2X = 0
for i in range(7):
reconstruction_loss_X2Y += 0.14 * tf.reduce_mean(
tf.abs(fake_Y_perceptual[i] - real_Y_perceptual[i]))
reconstruction_loss_Y2X += 0.14 * tf.reduce_mean(
tf.abs(fake_X_perceptual[i] - real_X_perceptual[i]))
# reconstruction_loss_X2Y = tf.reduce_mean(tf.abs(GeneratedY-trainY))
# reconstruction_loss_Y2X = tf.reduce_mean(tf.abs(GeneratedX-trainX))
Dy_loss = self.gan_loss.DiscriminatorLoss(Dy_real_out, Dy_fake_out)
Dx_loss = self.gan_loss.DiscriminatorLoss(Dx_real_out, Dx_fake_out)
G_loss = self.gan_loss.GeneratorLoss(Dy_fake_out) + 10.0 * (
cycle_consistent) + reconstruction_loss_X2Y
F_loss = self.gan_loss.GeneratorLoss(Dx_fake_out) + 10.0 * (
cycle_consistent) + reconstruction_loss_Y2X
gradients_of_Dy = cycle_type.gradient(Dy_loss,
self.Dy.trainable_variables)
gradients_of_Dx = cycle_type.gradient(Dx_loss,
self.Dx.trainable_variables)
gradients_of_G = cycle_type.gradient(G_loss,
self.G.trainable_variables)
gradients_of_F = cycle_type.gradient(F_loss,
self.F.trainable_variables)
self.Dy_optimizer.apply_gradients(
zip(gradients_of_Dy, self.Dy.trainable_variables))
self.Dx_optimizer.apply_gradients(
zip(gradients_of_Dx, self.Dx.trainable_variables))
self.G_optimizer.apply_gradients(
zip(gradients_of_G, self.G.trainable_variables))
self.F_optimizer.apply_gradients(
zip(gradients_of_F, self.F.trainable_variables))
return G_loss, Dy_loss, F_loss, Dx_loss
def train(self, epoches):
self.ckpt.restore(self.manager.latest_checkpoint)
my_step = int(self.ckpt.step)
stop_flag = 0
for _ in range(epoches):
start = time.time()
for trainX, trainY, maskX, maskY in self.train_set:
my_step += 1
self.ckpt.step.assign_add(1)
step = int(self.ckpt.step)
###必要的超参数、变化的学习率都定义在这里
self.l = 10.0 * (1.0 / (step * 0.1 + 1.0))
self.wgp_shape = [trainY.shape[0], 1, 1, 1, 1] #3D 多一个通道
###
if self.loss_name in ["WGAN", "WGAN-GP"]:
for __ in range(1):
Dy_loss, Dx_loss = self.train_step_D(
trainX, trainY, maskX, maskY)
for __ in range(1):
G_loss, F_loss = self.train_step_G(
trainX, trainY, maskX, maskY)
elif self.loss_name in ["Vanilla", "LSGAN"]:
G_loss, Dy_loss, F_loss, Dx_loss = self.train_step(
trainX, trainY, maskX, maskY)
else:
raise ValueError("Inner Error")
if step % 100 == 0:
save_path = self.manager.save()
print("Saved checkpoint for step {}: {}".format(
step, save_path))
self.G.save_weights(self.tmp_path +
'/weights_saved/G.ckpt')
self.Dy.save_weights(self.tmp_path +
'/weights_saved/Dy.ckpt')
self.F.save_weights(self.tmp_path +
'/weights_saved/F.ckpt')
self.Dx.save_weights(self.tmp_path +
'/weights_saved/Dx.ckpt')
self.wirte_summary(step=step,
seed=self.seed,
G=self.G,
F=self.F,
G_loss=G_loss,
Dy_loss=Dy_loss,
F_loss=F_loss,
Dx_loss=Dx_loss,
out_path=self.out_path)
print('Time to next 100 step {} is {} sec'.format(
step,
time.time() - start))
start = time.time()
if step == 80200:
stop_flag = 1
break
if stop_flag == 1:
break
def get_seed(self):
seed_get = iter(self.test_set)
testX, testY, maskX, maskY = next(seed_get)
print(testX.shape, testY.dtype, maskX.dtype, maskY.shape)
plt.imshow(testX[0, :, :, 1, 0], cmap='gray')
plt.show()
plt.imshow(testY[0, :, :, 1, 0], cmap='gray')
plt.show()
plt.imshow(maskX[0, :, :, 1, 0], cmap='gray')
plt.show()
plt.imshow(maskY[0, :, :, 1, 0], cmap='gray')
plt.show()
self.seed = testX, testY, maskX, maskY
def wirte_summary(self, step, seed, G, F, G_loss, Dy_loss, F_loss, Dx_loss,
out_path):
testX, testY, maskX, maskY = seed
GeneratedY = G(testX)
GeneratedY = tf.multiply(GeneratedY, maskX)
GeneratedX = F(testY)
GeneratedX = tf.multiply(GeneratedX, maskY)
testX = testX[:, :, :, 1, :]
testY = testY[:, :, :, 1, :]
maskX = maskX[:, :, :, 1, :]
maskY = maskY[:, :, :, 1, :]
GeneratedY = GeneratedY[:, :, :, 1, :]
GeneratedX = GeneratedX[:, :, :, 1, :]
plt.figure(figsize=(5, 5)) #图片大一点才可以承载像素
plt.subplot(2, 2, 1)
plt.title('real X')
plt.imshow(testX[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 2)
plt.title('fake Y')
plt.imshow(GeneratedY[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 3)
plt.title('fake X')
plt.imshow(GeneratedX[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.subplot(2, 2, 4)
plt.title('real Y')
plt.imshow(testY[0, :, :, 0], cmap='gray')
plt.axis('off')
plt.savefig(out_path + '/image_at_{}.png'.format(step))
plt.close()
img = Image.open(out_path + '/image_at_{}.png'.format(step))
img = tf.reshape(np.array(img), shape=(1, 500, 500, 4))
with self.train_summary_writer.as_default():
##########################
self.m_psnr_X2Y(tf.image.psnr(GeneratedY, testY, 1.0, name=None))
self.m_psnr_Y2X(tf.image.psnr(GeneratedX, testX, 1.0, name=None))
self.m_ssim_X2Y(
tf.image.ssim(GeneratedY,
testY,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
self.m_ssim_Y2X(
tf.image.ssim(GeneratedX,
testX,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
tf.summary.scalar('G_loss', G_loss, step=step)
tf.summary.scalar('Dy_loss', Dy_loss, step=step)
tf.summary.scalar('F_loss', F_loss, step=step)
tf.summary.scalar('Dx_loss', Dx_loss, step=step)
tf.summary.scalar('test_psnr_y',
self.m_psnr_X2Y.result(),
step=step)
tf.summary.scalar('test_psnr_x',
self.m_psnr_Y2X.result(),
step=step)
tf.summary.scalar('test_ssim_y',
self.m_ssim_X2Y.result(),
step=step)
tf.summary.scalar('test_ssim_x',
self.m_ssim_Y2X.result(),
step=step)
tf.summary.image("img", img, step=step)
##########################
self.m_psnr_X2Y.reset_states()
self.m_psnr_Y2X.reset_states()
self.m_ssim_X2Y.reset_states()
self.m_ssim_Y2X.reset_states()
def test(self):
buf = self.manager.latest_checkpoint
buf = buf[:-3]
for index, temp_point in enumerate(["800", "801", "802"]):
self.ckpt.restore(buf + temp_point)
step = 0
out_path = self.out_path + "/test"
if not os.path.exists(out_path):
os.makedirs(out_path)
result_buf = []
for i, (testX, testY, maskX, maskY) in enumerate(self.test_set):
GeneratedY = self.G(testX)
GeneratedY = tf.multiply(GeneratedY, maskX)
GeneratedX = self.F(testY)
GeneratedX = tf.multiply(
GeneratedX, maskY) #测试时mask正好相反 因为只知道原来模态和原来模态的mask
testX = testX[:, :, :, 1, :]
testY = testY[:, :, :, 1, :]
maskX = maskX[:, :, :, 1, :]
maskY = maskY[:, :, :, 1, :]
GeneratedY = GeneratedY[:, :, :, 1, :]
GeneratedX = GeneratedX[:, :, :, 1, :]
ABS_Y = tf.abs(GeneratedY - testY)
ABS_X = tf.abs(GeneratedX - testX)
black_board_rX = testX[0, :, :, 0]
black_board_Y = GeneratedY[0, :, :, 0]
black_board_absY = ABS_Y[0, :, :, 0]
black_board_X = GeneratedX[0, :, :, 0]
black_board_rY = testY[0, :, :, 0]
black_board_absX = ABS_X[0, :, :, 0]
step = i + 1
plt.figure(figsize=(10, 10)) #图片大一点才可以承载像素
plt.subplot(3, 2, 1)
plt.title('real X')
plt.imshow(black_board_rX, cmap='gray')
plt.axis('off')
plt.subplot(3, 2, 2)
plt.title('fake Y')
plt.imshow(black_board_Y, cmap='gray')
plt.axis('off')
plt.subplot(3, 2, 3)
plt.title('ABS Y')
plt.imshow(black_board_absY, cmap='hot')
plt.axis('off')
plt.subplot(3, 2, 4)
plt.title('ABS X')
plt.imshow(black_board_absX, cmap='hot')
plt.axis('off')
plt.subplot(3, 2, 5)
plt.title('fake X')
plt.imshow(black_board_X, cmap='gray')
plt.axis('off')
plt.subplot(3, 2, 6)
plt.title('real Y')
plt.imshow(black_board_rY, cmap='gray')
plt.axis('off')
plt.savefig(out_path + '/image_at_{}.png'.format(step))
plt.close()
img = Image.open(out_path + '/image_at_{}.png'.format(step))
img = tf.reshape(np.array(img), shape=(1, 1000, 1000, 4))
if (i + 1) == 1:
np.save(out_path + "/out_Y.npy", black_board_Y)
np.save(out_path + "/out_X.npy", black_board_X)
with self.train_summary_writer.as_default():
##########################
black_board_Y = tf.reshape(tf.constant(black_board_Y,
dtype=tf.float32),
shape=[1, 128, 128, 1])
black_board_X = tf.reshape(tf.constant(black_board_X,
dtype=tf.float32),
shape=[1, 128, 128, 1])
black_board_rY = tf.reshape(tf.constant(black_board_rY,
dtype=tf.float32),
shape=[1, 128, 128, 1])
black_board_rX = tf.reshape(tf.constant(black_board_rX,
dtype=tf.float32),
shape=[1, 128, 128, 1])
self.m_psnr_X2Y(
tf.image.psnr(black_board_Y,
black_board_rY,
1.0,
name=None))
self.m_psnr_Y2X(
tf.image.psnr(black_board_X,
black_board_rX,
1.0,
name=None))
self.m_ssim_X2Y(
tf.image.ssim(black_board_Y,
black_board_rY,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
self.m_ssim_Y2X(
tf.image.ssim(black_board_X,
black_board_rX,
1,
filter_size=11,
filter_sigma=1.5,
k1=0.01,
k2=0.03))
tf.summary.scalar('test_psnr_y',
self.m_psnr_X2Y.result(),
step=step)
tf.summary.scalar('test_psnr_x',
self.m_psnr_Y2X.result(),
step=step)
tf.summary.scalar('test_ssim_y',
self.m_ssim_X2Y.result(),
step=step)
tf.summary.scalar('test_ssim_x',
self.m_ssim_Y2X.result(),
step=step)
tf.summary.image("img", img, step=step)
dx1, dy1 = tf.image.image_gradients(black_board_Y)
dx2, dy2 = tf.image.image_gradients(black_board_rY)
dx_mean = tf.reduce_mean(tf.math.abs(dx1 - dx2))
dy_mean = tf.reduce_mean(tf.math.abs(dy1 - dy2))
IG_y = dy_mean + dx_mean
tf.summary.scalar('IG_y', IG_y, step=step)
dx1, dy1 = tf.image.image_gradients(black_board_X)
dx2, dy2 = tf.image.image_gradients(black_board_rX)
dx_mean = tf.reduce_mean(tf.math.abs(dx1 - dx2))
dy_mean = tf.reduce_mean(tf.math.abs(dy1 - dy2))
IG_x = dy_mean + dx_mean
tf.summary.scalar('IG_x', IG_x, step=step)
tf.summary.image("img", img, step=step)
result_buf.append([
i + 1,
self.m_psnr_X2Y.result().numpy(),
self.m_psnr_Y2X.result().numpy(),
self.m_ssim_X2Y.result().numpy(),
self.m_ssim_Y2X.result().numpy(),
IG_y.numpy(),
IG_x.numpy()
])
##########################
self.m_psnr_X2Y.reset_states()
self.m_psnr_Y2X.reset_states()
self.m_ssim_X2Y.reset_states()
self.m_ssim_Y2X.reset_states()
import csv
headers = [
'Instance', 'test_psnr_y', 'test_psnr_x', 'test_ssim_y',
'test_ssim_x', 'IG_y', 'IG_x'
]
rows = result_buf
with open(out_path + '/result' + str(index) + '.csv', 'w') as f:
f_csv = csv.writer(f)
f_csv.writerow(headers)
f_csv.writerows(rows)
class CycleGAN(tf.keras.Model):
"""
模型只负责给定训练集和测试(验证)集后的操作
"""
def __init__(self,
train_set,
test_set,
loss_name="WGAN-GP",
mixed_precision=False,
learning_rate=2e-4,
tmp_path=None,
out_path=None):
super(CycleGAN,self).__init__()
#接收数据集和相关参数
self.train_set = train_set
self.test_set = test_set
self.tmp_path = tmp_path
self.out_path = out_path
#定义模型
self.G = networks.Generator(name="G_X2Y")
self.F = networks.Generator(name="G_Y2X")
if loss_name in ["WGAN-SN","WGAN-GP-SN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",use_sigmoid=False,sn=True)
self.Dx = networks.Discriminator(name="If_is_real_X",use_sigmoid=False,sn=True)
self.loss_name = loss_name[:-3]
elif loss_name in ["WGAN","WGAN-GP"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",use_sigmoid=False,sn=False)
self.Dx = networks.Discriminator(name="If_is_real_X",use_sigmoid=False,sn=False)
self.loss_name = loss_name
elif loss_name in ["Vanilla","LSGAN"]:
self.Dy = networks.Discriminator(name="If_is_real_Y",use_sigmoid=True,sn=False)
self.Dx = networks.Discriminator(name="If_is_real_X",use_sigmoid=True,sn=False)
self.loss_name = loss_name
else:
raise ValueError("Do not support the loss "+loss_name)
self.model_list=[self.G,self.F,self.Dy,self.Dx]
#定义损失函数 优化器 记录等
self.gan_loss = GanLoss(self.loss_name)
self.optimizers_list = self.optimizers_config(mixed_precision=mixed_precision,learning_rate=learning_rate)
self.mixed_precision = mixed_precision
self.matrics_list = self.matrics_config()
self.checkpoint_config()
self.get_seed()
def build(self,X_shape,Y_shape):
"""
input_shape必须切片 因为在底层会被当做各层的输出shape而被改动
"""
self.G.build(input_shape=X_shape[:])#G X->Y
self.Dy.build(input_shape=Y_shape[:])#Dy Y or != Y
self.F.build(input_shape=Y_shape[:])#F Y->X
self.Dx.build(input_shape=X_shape[:])#Dx X or != X
self.built = True
def optimizers_config(self,mixed_precision=False,learning_rate=2e-4):
self.G_optimizer = Adam(2e-4)
self.Dy_optimizer = Adam(2e-4)
self.F_optimizer = Adam(2e-4)
self.Dx_optimizer = Adam(2e-4)
if mixed_precision:
self.G_optimizer=self.G_optimizer.get_mixed_precision()
self.Dy_optimizer=self.Dy_optimizer.get_mixed_precision()
self.F_optimizer=self.F_optimizer.get_mixed_precision()
self.Dx_optimizer=self.Dx_optimizer.get_mixed_precision()
return [self.G_optimizer,self.Dy_optimizer,self.F_optimizer,self.Dx_optimizer]
def matrics_config(self):
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_logdir = self.tmp_path+"/logs/" + current_time
self.train_summary_writer = tf.summary.create_file_writer(train_logdir)
return []
def checkpoint_config(self):
self.ckpt = tf.train.Checkpoint(step=tf.Variable(1),optimizer=self.optimizers_list,model=self.model_list,dataset=self.train_set)
self.manager = tf.train.CheckpointManager(self.ckpt,self.tmp_path+'/tf_ckpts', max_to_keep=3)
@tf.function(input_signature=[tf.TensorSpec(shape=global_input_X_shape,dtype=tf.float32),tf.TensorSpec(shape=global_input_Y_shape,dtype=tf.float32),tf.TensorSpec(shape=[1],dtype=tf.uint32)])
def train_step_D(self,trainX,trainY,step):
with tf.GradientTape(persistent=True) as D_tape:
GeneratedY = self.G(trainX)
Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
e = tf.random.uniform((trainY.shape[0],1,1,1),0.0,1.0)
mid_Y = e*trainY+(1-e)*GeneratedY
with tf.GradientTape() as gradient_penaltyY:
gradient_penaltyY.watch(mid_Y)
inner_loss = self.Dy(mid_Y)
penalty = gradient_penaltyY.gradient(inner_loss,mid_Y)
penalty_normY = 10.0*tf.math.square(tf.maximum(tf.norm(penalty,ord='euclidean'),1.0)-1)# 这是我自己认为的 因为只有梯度大于1的才需要优化哇
e = tf.random.uniform((trainX.shape[0],1,1,1),0.0,1.0)
mid_X = e*trainX+(1-e)*GeneratedX
with tf.GradientTape() as gradient_penaltyX:
gradient_penaltyX.watch(mid_X)
inner_loss = self.Dx(mid_X)
penalty = gradient_penaltyX.gradient(inner_loss,mid_X)
penalty_normX = 10.0*tf.math.square(tf.maximum(tf.norm(penalty,ord='euclidean'),1.0)-1)
Dy_loss = self.gan_loss.DiscriminatorLoss(Dy_real_out,Dy_fake_out)+tf.reduce_mean(penalty_normY)
Dx_loss = self.gan_loss.DiscriminatorLoss(Dx_real_out,Dx_fake_out)+tf.reduce_mean(penalty_normX)
if self.mixed_precision:
scaled_Dy_loss = self.Dy_optimizer.get_scaled_loss(Dy_loss)
scaled_Dx_loss = self.Dx_optimizer.get_scaled_loss(Dx_loss)
if self.mixed_precision:
scaled_gradients_of_Dy=D_tape.gradient(scaled_Dy_loss,self.Dy.trainable_variables)
scaled_gradients_of_Dx=D_tape.gradient(scaled_Dx_loss,self.Dx.trainable_variables)
gradients_of_Dy = self.Dy_optimizer.get_unscaled_gradients(scaled_gradients_of_Dy)
gradients_of_Dx = self.Dx_optimizer.get_unscaled_gradients(scaled_gradients_of_Dx)
else:
gradients_of_Dy = D_tape.gradient(Dy_loss,self.Dy.trainable_variables)
gradients_of_Dx = D_tape.gradient(Dx_loss,self.Dx.trainable_variables)
self.Dy_optimizer.apply_gradients(zip(gradients_of_Dy,self.Dy.trainable_variables))
self.Dx_optimizer.apply_gradients(zip(gradients_of_Dx,self.Dx.trainable_variables))
return Dy_loss,Dx_loss
@tf.function(input_signature=[tf.TensorSpec(shape=global_input_X_shape,dtype=tf.float32),tf.TensorSpec(shape=global_input_Y_shape,dtype=tf.float32),tf.TensorSpec(shape=[1],dtype=tf.uint32)])
def train_step_G(self,trainX,trainY,step):
with tf.GradientTape(persistent=True) as G_tape:
GeneratedY = self.G(trainX)
# Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
# Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
cycle_consistent_loss_X2Y = tf.reduce_mean(tf.abs(self.F(GeneratedY)-trainX))
cycle_consistent_loss_Y2X = tf.reduce_mean(tf.abs(self.G(GeneratedX)-trainY))
cycle_consistent = cycle_consistent_loss_X2Y+cycle_consistent_loss_Y2X
if step>=0:#先不进行像素梯度和重建损失的使用
cycle_l = 10.0
else:
cycle_l = 10.0
G_loss = self.gan_loss.GeneratorLoss(Dy_fake_out)+cycle_l*(cycle_consistent)
F_loss = self.gan_loss.GeneratorLoss(Dx_fake_out)+cycle_l*(cycle_consistent)
if self.mixed_precision:
scaled_G_loss = self.G_optimizer.get_scaled_loss(G_loss)
scaled_F_loss = self.F_optimizer.get_scaled_loss(F_loss)
if self.mixed_precision:
scaled_gradients_of_G=G_tape.gradient(scaled_G_loss,self.G.trainable_variables)
scaled_gradients_of_F=G_tape.gradient(scaled_F_loss,self.F.trainable_variables)
gradients_of_G = self.G_optimizer.get_unscaled_gradients(scaled_gradients_of_G)
gradients_of_F = self.F_optimizer.get_unscaled_gradients(scaled_gradients_of_F)
else:
gradients_of_G = G_tape.gradient(G_loss,self.G.trainable_variables)
gradients_of_F = G_tape.gradient(F_loss,self.F.trainable_variables)
self.G_optimizer.apply_gradients(zip(gradients_of_G,self.G.trainable_variables))
self.F_optimizer.apply_gradients(zip(gradients_of_F,self.F.trainable_variables))
return G_loss,F_loss
@tf.function(input_signature=[tf.TensorSpec(shape=global_input_X_shape,dtype=tf.float32),tf.TensorSpec(shape=global_input_Y_shape,dtype=tf.float32),tf.TensorSpec(shape=[1],dtype=tf.uint32)])
def train_step(self,trainX,trainY,step):
with tf.GradientTape(persistent=True) as cycle_type:
GeneratedY = self.G(trainX)
Dy_real_out = self.Dy(trainY)
Dy_fake_out = self.Dy(GeneratedY)
GeneratedX = self.F(trainY)
Dx_real_out = self.Dx(trainX)
Dx_fake_out = self.Dx(GeneratedX)
cycle_consistent_loss_X2Y = tf.reduce_mean(tf.abs(self.F(GeneratedY)-trainX))
cycle_consistent_loss_Y2X = tf.reduce_mean(tf.abs(self.G(GeneratedX)-trainY))
cycle_consistent = cycle_consistent_loss_X2Y+cycle_consistent_loss_Y2X
if step>=0:#先不进行像素梯度和重建损失的使用
cycle_l = 10.0
else:
cycle_l = 10.0
Dy_loss = self.gan_loss.DiscriminatorLoss(Dy_real_out,Dy_fake_out)
Dx_loss = self.gan_loss.DiscriminatorLoss(Dx_real_out,Dx_fake_out)
G_loss = self.gan_loss.GeneratorLoss(Dy_fake_out)+cycle_l*(cycle_consistent)
F_loss = self.gan_loss.GeneratorLoss(Dx_fake_out)+cycle_l*(cycle_consistent)
gradients_of_Dy = cycle_type.gradient(Dy_loss,self.Dy.trainable_variables)
gradients_of_Dx = cycle_type.gradient(Dx_loss,self.Dx.trainable_variables)
gradients_of_G = cycle_type.gradient(G_loss,self.G.trainable_variables)
gradients_of_F = cycle_type.gradient(F_loss,self.F.trainable_variables)
self.Dy_optimizer.apply_gradients(zip(gradients_of_Dy,self.Dy.trainable_variables))
self.Dx_optimizer.apply_gradients(zip(gradients_of_Dx,self.Dx.trainable_variables))
self.G_optimizer.apply_gradients(zip(gradients_of_G,self.G.trainable_variables))
self.F_optimizer.apply_gradients(zip(gradients_of_F,self.F.trainable_variables))
return G_loss,Dy_loss,F_loss,Dx_loss
def train(self,epoches):
self.ckpt.restore(self.manager.latest_checkpoint)
for _ in range(epoches):
start = time.time()
for trainX,trainY in self.train_set:
self.ckpt.step.assign_add(1)
step = int(self.ckpt.step)
if self.loss_name in ["WGAN","WGAN-GP"]:
for __ in range(1):
Dy_loss,Dx_loss = self.train_step_D(trainX,trainY,tf.constant(step,shape=[1],dtype=tf.uint32))
for __ in range(1):
G_loss,F_loss = self.train_step_G(trainX,trainY,tf.constant(step,shape=[1],dtype=tf.uint32))
elif self.loss_name in ["Vanilla","LSGAN"]:
G_loss,Dy_loss,F_loss,Dx_loss = self.train_step(trainX,trainY,tf.constant(step,shape=[1],dtype=tf.uint32))
else:
raise ValueError("Inner Error")
if step % 100 == 0:
save_path = self.manager.save()
print("Saved checkpoint for step {}: {}".format(step,save_path))
self.G.save_weights(self.tmp_path+'/weights_saved/G.ckpt')
self.Dy.save_weights(self.tmp_path+'/weights_saved/Dy.ckpt')
self.F.save_weights(self.tmp_path+'/weights_saved/F.ckpt')
self.Dx.save_weights(self.tmp_path+'/weights_saved/Dx.ckpt')
self.wirte_summary(step=step,
seed=self.seed,
G=self.G,
F=self.F,
G_loss=G_loss,
Dy_loss=Dy_loss,
F_loss=F_loss,
Dx_loss=Dx_loss,
out_path=self.out_path)
print ('Time to next 100 step {} is {} sec'.format(step,time.time()-start))
start = time.time()
def test(self,take_nums):
self.ckpt.restore(self.manager.latest_checkpoint)
for i,(testX,testY) in enumerate(self.test_set):
GeneratedY = self.G(testX)
GeneratedX = self.F(testY)
plt.figure(figsize=(5,5))#图片大一点才可以承载像素
plt.subplot(2,2,1)
plt.title('real X')
plt.imshow(testX[0,:,:,:])
plt.axis('off')
plt.subplot(2,2,2)
plt.title('fake Y')
plt.imshow(GeneratedY[0,:,:,:])
plt.axis('off')
plt.subplot(2,2,3)
plt.title('fake X')
plt.imshow(GeneratedX[0,:,:,:])
plt.axis('off')
plt.subplot(2,2,4)
plt.title('real Y')
plt.imshow(testY[0,:,:,:])
plt.axis('off')
plt.savefig(self.out_path+'/test/{}.png'.format(i))
plt.close()
img = Image.open(self.out_path+'/test/{}.png'.format(i))
img = tf.reshape(np.array(img),shape=(1,500,500,4))
def get_seed(self):
seed_get = iter(self.test_set)
seed = next(seed_get)
print(seed[0].shape,seed[1].dtype)
plt.imshow(seed[0][0,:,:,:])
plt.show()
plt.imshow(seed[1][0,:,:,:])
plt.show()
self.seed = seed
def wirte_summary(self,step,seed,G,F,G_loss,Dy_loss,F_loss,Dx_loss,out_path):
testX,testY= seed
GeneratedY = G(testX)
GeneratedX = F(testY)
plt.figure(figsize=(5,5))#图片大一点才可以承载像素
plt.subplot(2,2,1)
plt.title('real X')
plt.imshow(testX[0,:,:,:])
plt.axis('off')
plt.subplot(2,2,2)
plt.title('fake Y')
plt.imshow(GeneratedY[0,:,:,:])
plt.axis('off')
plt.subplot(2,2,3)
plt.title('fake X')
plt.imshow(GeneratedX[0,:,:,:])
plt.axis('off')
plt.subplot(2,2,4)
plt.title('real Y')
plt.imshow(testY[0,:,:,:])
plt.axis('off')
plt.savefig(out_path+'/image_at_{}.png'.format(step))
plt.close()
img = Image.open(out_path+'/image_at_{}.png'.format(step))
img = tf.reshape(np.array(img),shape=(1,500,500,4))
with self.train_summary_writer.as_default():
tf.summary.scalar('G_loss',G_loss,step=step)
tf.summary.scalar('Dy_loss',Dy_loss,step=step)
tf.summary.scalar('F_loss',F_loss,step=step)
tf.summary.scalar('Dx_loss',Dx_loss,step=step)
tf.summary.image("img",img,step=step)
