def test_inception_score():
print("========== Test Inception score ==========")
(x_train, y_train), (_, _) = demo.load_cifar10()
x_train = x_train[:100].astype(np.float32).reshape([-1, 32, 32, 3]) * 255.0
score = InceptionScore().score(x_train)
print("Inception score: {:.4f}+-{:.4f}".format(score[0], score[1]))
def test_cganv1_cifar10(show_figure=False, block_figure_on_end=False):
print("========== Test CGANv1 on CIFAR10 data ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"CGANv1 - Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['x_samples'],
'title': "CGANv1 - Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = CGANv1(
model_name="CGANv1_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=64, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=2, # set to 32 for a full run
num_dis_feature_maps=2, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=1, # set to 100 for a full run
random_state=random_seed(),
verbose=1)
model.fit(x_train, y_train)
def test_frechet_inception_distance():
print("========== Test Frechet Inception Distance (FID) ==========")
(x_train, y_train), (_, _) = demo.load_cifar10()
x_train = x_train.astype(np.float32).reshape([-1, 32, 32, 3]) * 255.0
score = FID(data="cifar10").score(x_train[:100])
print("Case #0: FID = {:.4f}".format(score))
score = FID(data=x_train[:200]).score(x_train[:100])
print("Case #1: FID = {:.4f}".format(score))
def test_wgan_cifar10(show_figure=False, block_figure_on_end=False):
print("========== Test WGAN on CIFAR10 data ==========")
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/WGAN/CIFAR10")
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[os.path.join(root_dir, "loss/loss_{epoch:04d}.png"),
os.path.join(root_dir, "loss/loss_{epoch:04d}.pdf")],
monitor=[{'metrics': ['d_loss', 'g_loss'],
'type': 'line',
'labels': ["discriminator loss", "generator loss"],
'title': "Losses",
'xlabel': "epoch",
'ylabel': "loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(root_dir, "samples/samples_{epoch:04d}.png"),
monitor=[{'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = WGAN(model_name="WGAN_CIFAR10",
num_z=10, # set to 100 for a full run
z_prior=Uniform1D(low=-1.0, high=1.0),
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=4, # set to 100 for a full run
log_path=os.path.join(root_dir, "logs"),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
def test_inception_metric_list():
print("========== Test Frechet Inception Distance (FID) ==========")
(x_train, y_train), (_, _) = demo.load_cifar10()
x_train = x_train.astype(np.float32).reshape([-1, 32, 32, 3]) * 255.0
scores = InceptionMetricList(
[InceptionScore(),
FID(data="cifar10"),
FID(data=x_train[:200])]).score(x_train[:100])
for (i, s) in enumerate(scores):
if isinstance(s, tuple):
print("Case #{}: score = {:.4f}+-{:.4f}".format(i, s[0], s[1]))
else:
print("Case #{}: score = {:.4f}".format(i, s))
def test_image_saver_callback():
np.random.seed(random_seed())
(x_train, y_train), (_, _) = demo.load_mnist()
(cifar10_train, _), (_, _) = demo.load_cifar10()
imgsaver1 = ImageSaver(freq=1,
filepath=os.path.join(
model_dir(), "male/callbacks/imagesaver/"
"mnist/mnist_{epoch:04d}.png"),
monitor={
'metrics': 'x_data',
'img_size': (28, 28, 1),
'tile_shape': (10, 10),
'images': x_train[:100].reshape([-1, 28, 28, 1])
})
imgsaver2 = ImageSaver(freq=1,
filepath=os.path.join(
model_dir(), "male/callbacks/imagesaver/"
"cifar10/cifar10_{epoch:04d}.png"),
monitor={
'metrics':
'x_data',
'img_size': (32, 32, 3),
'tile_shape': (10, 10),
'images':
cifar10_train[:100].reshape([-1, 32, 32, 3])
})
optz = SGD(learning_rate=0.001)
clf = GLM(model_name="imagesaver_callback",
link='softmax',
loss='softmax',
optimizer=optz,
num_epochs=4,
batch_size=100,
task='classification',
callbacks=[imgsaver1, imgsaver2],
random_state=random_seed(),
verbose=1)
clf.fit(x_train, y_train)
def test_dcgan_image_saver():
print("========== Test DCGAN with Image Saver ==========")
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_mnist()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 28, 28, 1
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/DCGAN/imagesaver/mnist")
imgsaver = ImageSaver(freq=1,
filepath=os.path.join(root_dir,
"mnist_{epoch:04d}.png"),
monitor={
'metrics': 'x_samples',
'num_samples': 100,
'tile_shape': (10, 10),
})
model = DCGAN(
model_name="DCGAN_MNIST",
num_z=10, # set to 100 for a full run
img_size=(28, 28, 1),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[imgsaver],
num_epochs=4, # set to 100 for a full run
random_state=random_seed(),
log_path=os.path.join(root_dir, "logs"),
verbose=1)
model.fit(x_train)
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/DCGAN/imagesaver/cifar10")
imgsaver = ImageSaver(freq=1,
filepath=os.path.join(root_dir,
"cifar10_{epoch:04d}.png"),
monitor={
'metrics': 'x_samples',
'num_samples': 100,
'tile_shape': (10, 10),
})
model = DCGAN(
model_name="DCGAN_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[imgsaver],
num_epochs=4, # set to 100 for a full run
random_state=random_seed(),
log_path=os.path.join(root_dir, "logs"),
verbose=1)
model.fit(x_train)
def test_dcgan_cifar10_inception_metric(show_figure=False,
block_figure_on_end=False):
print(
"========== Test DCGAN with Inception Score and FID on CIFAR10 data =========="
)
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/DCGAN/cifar10")
checkpoints_is = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_is/the_best_is.ckpt"),
mode='max',
monitor='inception_score',
verbose=1,
save_best_only=True)
checkpoints_fid = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_fid/the_best_fid.ckpt"),
mode='min',
monitor='FID',
verbose=1,
save_best_only=True)
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir,
"loss/loss_{epoch:04d}.png"),
os.path.join(root_dir,
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
inception_score_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.png"),
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['inception_score'],
'type': 'line',
'labels': ["Inception Score"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
fid_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.png"),
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['FID'],
'type': 'line',
'labels': ["FID"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(
root_dir, "samples/samples_{epoch:04d}.png"),
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = DCGAN(
model_name="DCGAN_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=[
'd_loss', 'g_loss', 'inception_score', 'inception_score_std', 'FID'
],
callbacks=[
loss_display, inception_score_display, fid_display, sample_display,
checkpoints_is, checkpoints_fid
],
num_epochs=4, # set to 100 for a full run
inception_metrics=[InceptionScore(),
FID(data="cifar10")],
inception_metrics_freq=1,
# summary_freq=1, # uncomment this for a full run
log_path=os.path.join(root_dir, "logs"),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
filepath = os.path.join(root_dir, "checkpoints_fid/the_best_fid.ckpt")
print("Reloading the latest model at: {}".format(filepath))
model1 = TensorFlowModel.load_model(filepath)
model1.inception_metrics = InceptionMetricList(
[InceptionScore(), FID(data="cifar10")])
model1.num_epochs = 6
model1.fit(x_train)
print("Done!")
def test_wgan_gp_cifar10_fid(show_figure=False, block_figure_on_end=False):
print("========== Test WGAN-GP with FID on CIFAR10 data ==========")
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/WGAN-GP/CIFAR10")
checkpoints = ModelCheckpoint(
os.path.join(root_dir, "checkpoints/{epoch:04d}_{FID:.6f}.ckpt"),
mode='min',
monitor='FID',
verbose=1,
save_best_only=True)
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[os.path.join(root_dir, "loss/loss_{epoch:04d}.png"),
os.path.join(root_dir, "loss/loss_{epoch:04d}.pdf")],
monitor=[{'metrics': ['d_loss', 'g_loss'],
'type': 'line',
'labels': ["discriminator loss", "generator loss"],
'title': "Losses",
'xlabel': "epoch",
'ylabel': "loss",
},
])
fid_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.png"),
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.pdf")],
monitor=[{'metrics': ['FID'],
'type': 'line',
'labels': ["FID"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(root_dir, "samples/"
"samples_{epoch:04d}.png"),
monitor=[{'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = WGAN_GP(model_name="WGAN_GP_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss', 'FID', 'FID_100points'],
callbacks=[loss_display, fid_display,
sample_display, checkpoints],
num_epochs=4, # set to 100 for a full run
inception_metrics=[FID(data="cifar10"),
FID(name="FID_100points", data=x_train[:100])],
inception_metrics_freq=1,
# summary_freq=1, # uncomment this for a full run
log_path=os.path.join(root_dir, "logs"),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
def test_gank_image_saver():
print("========== Test GANK-Logit with Image Saver ==========")
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_mnist()
x_train = x_train.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 28, 28, 1]) / 0.5 - 1.
imgsaver = ImageSaver(freq=1,
filepath=os.path.join(
model_dir(), "male/GANK/imagesaver/"
"mnist/mnist_{epoch:04d}.png"),
monitor={
'metrics': 'x_samples',
'num_samples': 100,
'tile_shape': (10, 10),
})
model = GANK(
model_name="GANK_MNIST",
num_random_features=50, # set to 1000 for a full run
gamma_init=0.01,
loss='logit',
num_z=10, # set to 100 for a full run
img_size=(28, 28, 1),
batch_size=32, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[imgsaver],
num_epochs=4, # set to 100 for a full run
random_state=random_seed(),
verbose=1)
model.fit(x_train)
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
imgsaver = ImageSaver(freq=1,
filepath=os.path.join(
model_dir(), "male/GANK/imagesaver/"
"cifar10/cifar10_{epoch:04d}.png"),
monitor={
'metrics': 'x_samples',
'num_samples': 100,
'tile_shape': (10, 10),
})
model = GANK(
model_name="GANK_CIFAR10",
num_random_features=50, # set 1000 for a full run
gamma_init=0.01,
loss='logit',
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=32, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[imgsaver],
num_epochs=4, # set to 500 for a full run
random_state=random_seed(),
verbose=1)
model.fit(x_train)
def test_gank_logit_cifar10_inception_score(show_figure=False,
block_figure_on_end=False):
print(
"========== Test GANK-Logit with Inception Score on CIFAR10 data =========="
)
np.random.seed(random_seed())
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
filepath = os.path.join(
model_dir(), "male/GANK/Logit/cifar10/checkpoints/"
"{epoch:04d}_{inception_score:.6f}.ckpt")
checkpoint = ModelCheckpoint(filepath,
mode='max',
monitor='inception_score',
verbose=0,
save_best_only=True)
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(
model_dir(), "male/GANK/Logit/cifar10/"
"loss/loss_{epoch:04d}.png"),
os.path.join(
model_dir(), "male/GANK/Logit/cifar10/"
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
inception_score_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(
model_dir(), "male/GANK/Logit/cifar10/inception_score/"
"inception_score_{epoch:04d}.png"),
os.path.join(
model_dir(), "male/GANK/Logit/cifar10/inception_score/"
"inception_score_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['inception_score'],
'type': 'line',
'labels': ["Inception Score"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(
model_dir(),
"male/GANK/Logit/cifar10/samples/"
"samples_{epoch:04d}.png"),
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = GANK(
model_name="GANK-Logit_CIFAR10",
num_random_features=50, # set 1000 for a full run
gamma_init=0.01,
loss='logit',
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=32, # set to 64 for a full run
num_conv_layers=3, # set to 3 for a full run
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss', 'inception_score'],
# callbacks=[loss_display, inception_score_display, sample_display, checkpoint],
callbacks=[checkpoint],
num_epochs=4, # set to 500 for a full run
inception_score_freq=1,
random_state=random_seed(),
verbose=1)
model.fit(x_train)
def test_wgan_gp_resnet_cifar10(show_figure=False, block_figure_on_end=False):
print("========== Test WGAN-GP-ResNet on CIFAR10 data ==========")
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
root_dir = os.path.join(model_dir(), "male/WGAN-GP-ResNet/CIFAR10")
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir,
"loss/loss_{epoch:04d}.png"),
os.path.join(root_dir,
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = WGAN_GP_ResNet(
model_name="WGAN_GP_ResNet_CIFAR10",
num_z=10, # set to 100 for a full run
img_size=(32, 32, 3),
batch_size=16, # set to 64 for a full run
g_blocks=('up', 'up', 'up'),
d_blocks=('down', 'down', None, None),
num_gen_feature_maps=4, # set to 32 for a full run
num_dis_feature_maps=4, # set to 32 for a full run
metrics=['d_loss', 'g_loss'],
callbacks=[loss_display, sample_display],
num_epochs=2, # set to 100 for a full run
random_state=random_seed(),
log_path=os.path.join(root_dir, "logs"),
verbose=1)
model.fit(x_train)
print("Saving model...")
save_file_path = model.save(os.path.join(root_dir, "checkpoints/ckpt"))
print("Reloading model...")
model1 = TensorFlowModel.load_model(save_file_path)
model1.num_epochs = 4
model1.fit(x_train)
print("Done!")
def test_wgan_gp_resnet_cifar10_inception_metric(show_figure=False,
block_figure_on_end=False):
print(
"========== Test WGAN-GP-ResNet with Inception Score and FID on CIFAR10 data =========="
)
np.random.seed(random_seed())
num_data = 128
(x_train, y_train), (x_test, y_test) = demo.load_cifar10()
x_train = x_train[:num_data].astype(np.float32).reshape([-1, 32, 32, 3
]) / 0.5 - 1.
x_test = x_test.astype(np.float32).reshape([-1, 32, 32, 3]) / 0.5 - 1.
# uncomment for full run
'''
import pickle
from male.configs import data_dir
tmp = pickle.load(open(os.path.join(data_dir(), "cifar10/cifar10_train.pkl"), "rb"))
x_train = tmp['data'].astype(np.float32).reshape(
[-1, 32, 32, 3]) / 127.5 - 1.
'''
root_dir = os.path.join(model_dir(), "male/WGAN-GP-ResNet/CIFAR10")
checkpoints_is = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_is/the_best_is.ckpt"),
mode='max',
monitor='inception_score',
verbose=1,
save_best_only=True)
checkpoints_fid = ModelCheckpoint(os.path.join(
root_dir, "checkpoints_fid/the_best_fid.ckpt"),
mode='min',
monitor='FID',
verbose=1,
save_best_only=True)
loss_display = Display(layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir,
"loss/loss_{epoch:04d}.png"),
os.path.join(root_dir,
"loss/loss_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['d_loss', 'g_loss'],
'type':
'line',
'labels':
["discriminator loss", "generator loss"],
'title':
"Losses",
'xlabel':
"epoch",
'ylabel':
"loss",
},
])
inception_score_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.png"),
os.path.join(root_dir, "inception_score/"
"inception_score_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['inception_score'],
'type': 'line',
'labels': ["Inception Score"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
fid_display = Display(
layout=(1, 1),
dpi='auto',
show=show_figure,
block_on_end=block_figure_on_end,
filepath=[
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.png"),
os.path.join(root_dir, "FID/"
"FID_{epoch:04d}.pdf")
],
monitor=[
{
'metrics': ['FID'],
'type': 'line',
'labels': ["FID"],
'title': "Scores",
'xlabel': "epoch",
'ylabel': "score",
},
],
)
sample_display = Display(layout=(1, 1),
dpi='auto',
figsize=(10, 10),
freq=1,
show=show_figure,
block_on_end=block_figure_on_end,
filepath=os.path.join(
root_dir, "samples/samples_{epoch:04d}.png"),
monitor=[
{
'metrics': ['x_samples'],
'title': "Generated data",
'type': 'img',
'num_samples': 100,
'tile_shape': (10, 10),
},
])
model = WGAN_GP_ResNet(
model_name="WGAN_GP_ResNet_CIFAR10",
num_z=8, # set to 128 for a full run
img_size=(32, 32, 3),
batch_size=64, # set to 64 for a full run
g_blocks=('up', 'up', 'up'),
d_blocks=('down', 'down', None, None),
num_gen_feature_maps=8, # set to 128 for a full run
num_dis_feature_maps=8, # set to 128 for a full run
metrics=[
'd_loss', 'g_loss', 'inception_score', 'inception_score_std', 'FID'
],
callbacks=[
loss_display, inception_score_display, fid_display, sample_display,
checkpoints_is, checkpoints_fid
],
num_epochs=2, # set to 500 for a full run
inception_metrics=[InceptionScore(),
FID(data='cifar10')],
inception_metrics_freq=1,
num_inception_samples=100, # set to 50000 for a full run
# summary_freq=1, # uncomment this for a full run
log_path=os.path.join(root_dir, 'logs'),
random_state=random_seed(),
verbose=1)
model.fit(x_train)
filepath = os.path.join(root_dir, 'checkpoints_fid/the_best_fid.ckpt')
print('Reloading the latest model at: {}'.format(filepath))
model1 = TensorFlowModel.load_model(filepath)
model1.inception_metrics = InceptionMetricList(
[InceptionScore(), FID(data='cifar10')])
model1.num_epochs = 4
model1.fit(x_train)
print('Done!')
