def data_loader(self):
""" load traing and testing dataset """
hps = self.params
image_data_class = ImageData(load_size=hps.image_size,
channels=3,
data_path=hps.data_path,
ids=hps.ids)
image_data_class.preprocess()
train_dataset_num = len(image_data_class.train_images)
test_dataset_num = len(image_data_class.test_images)
print("train dataset ", len(image_data_class.train_images))
print("test dataset ", len(image_data_class.test_images))
train_dataset = tf.data.Dataset.from_tensor_slices(
(image_data_class.train_images, image_data_class.train_angles_r,
image_data_class.train_labels, image_data_class.train_images_t,
image_data_class.train_angles_g))
test_dataset = tf.data.Dataset.from_tensor_slices(
(image_data_class.test_images, image_data_class.test_angles_r,
image_data_class.test_labels, image_data_class.test_images_t,
image_data_class.test_angles_g))
train_dataset = train_dataset.apply(
shuffle_and_repeat(train_dataset_num)).apply(
map_and_batch(image_data_class.image_processing,
hps.batch_size,
num_parallel_batches=8))
valid_dataset = test_dataset.apply(
shuffle_and_repeat(test_dataset_num)).apply(
map_and_batch(image_data_class.image_processing,
hps.batch_size,
num_parallel_batches=8))
test_dataset = test_dataset.apply(
map_and_batch(image_data_class.image_processing,
hps.batch_size,
num_parallel_batches=8))
train_dataset_iterator = train_dataset.make_one_shot_iterator()
valid_dataset = valid_dataset.make_one_shot_iterator()
test_dataset_iterator = test_dataset.make_one_shot_iterator()
return (train_dataset_iterator, valid_dataset, test_dataset_iterator,
train_dataset_num)
def make_dataset(self):
self.filename = 'Dataset/cond_dataset.tfrecord'
self.dataset = tf.data.TFRecordDataset(self.filename, num_parallel_reads=8)
def _parse(example_proto):
feature = {
'label': tf.FixedLenFeature([], tf.int64),
'data': tf.FixedLenFeature([], tf.string)
}
parsed = tf.parse_single_example(example_proto, feature)
data = tf.decode_raw(parsed['data'], tf.uint8)
label = tf.cast(parsed['label'], tf.uint8)
data = tf.py_func(func=np.unpackbits, inp=[data], Tout=tf.uint8)
label = tf.py_func(func=np.unpackbits, inp=[label], Tout=tf.uint8)
data = tf.cast(data, tf.float32)
label = tf.cast(label, tf.float32)
data = tf.reshape(data, [self.channel_num, self.class_num, self.input_length])
label.set_shape([8])
label = label[:self.channel_num]
label = tf.expand_dims(tf.expand_dims(label, axis=-1), axis=-1)
data = data * 2 - 1
return {'data': data, 'label': label}
self.dataset = self.dataset.apply(data.shuffle_and_repeat(buffer_size=16384))
self.dataset = self.dataset.apply(
data.map_and_batch(_parse, batch_size=256, num_parallel_batches=32, drop_remainder=True)
)
self.dataset = self.dataset.prefetch(48)
self.dataset = self.dataset.apply(data.prefetch_to_device('/gpu:0'))
self.iterator = self.dataset.make_one_shot_iterator()
batch = self.iterator.get_next()
return batch
def load_mask(args):
# mask files
with open(args.TRAIN_MASK_FLIST) as f:
fnames = f.read().splitlines()
# TODO: create input dataset (masks)
inputs = tf.data.Dataset.from_tensor_slices(
fnames) # a tf dataset object (op)
if args.NUM_GPUS == 1:
device = '/gpu:0' # to which gpu. prefetch_to_device(device, batch_size)
else:
device = '/cpu:0'
dataset_num = len(fnames)
# TODO: dataset with preprocessing (masks)
Image_Data_Class = ImageData(args=args)
# inputs = inputs.apply(shuffle_and_repeat(dataset_num)).apply(
# map_and_batch(Image_Data_Class.image_processing, args.BATCH_SIZE, num_parallel_batches=16,
# drop_remainder=True)).apply(prefetch_to_device(gpu_device, args.BATCH_SIZE))
inputs = inputs.apply(
shuffle_and_repeat(dataset_num)).map(lambda filename: tf.py_func(
Image_Data_Class.mask_processing2, [filename], [tf.float32]),
num_parallel_calls=3)
inputs = inputs.batch(1, drop_remainder=True).apply(
prefetch_to_device(device, 1))
# inputs = inputs.apply(prefetch_to_device(device))
inputs_iterator = inputs.make_one_shot_iterator() # iterator
masks = inputs_iterator.get_next() # an iteration get a batch of data
return masks
def _get_dataset_from_path(self):
dataset = tf.data.Dataset.list_files(self._train_data_path)
dataset = dataset.apply(contrib_data.shuffle_and_repeat(buffer_size=1000))
dataset = dataset.apply(
contrib_data.parallel_interleave(
tf.data.TFRecordDataset, cycle_length=20, sloppy=True))
return dataset
def make_source_dataset(self, index, num_hosts):
"""See base class."""
if not self.data_dir:
tf.logging.info('Undefined data_dir implies null input')
return tf.data.Dataset.range(1).repeat().map(self._get_null_input)
# Shuffle the filenames to ensure better randomization.
file_pattern = os.path.join(
self.data_dir, 'train-*' if self.is_training else 'validation-*')
# For multi-host training, we want each hosts to always process the same
# subset of files. Each host only sees a subset of the entire dataset,
# allowing us to cache larger datasets in memory.
dataset = tf.data.Dataset.list_files(file_pattern, shuffle=False)
dataset = dataset.shard(num_hosts, index)
if self.is_training and not self.cache:
dataset = dataset.repeat()
def fetch_dataset(filename):
buffer_size = 8 * 1024 * 1024 # 8 MiB per file
dataset = tf.data.TFRecordDataset(filename, buffer_size=buffer_size)
return dataset
# Read the data from disk in parallel
dataset = dataset.apply(
contrib_data.parallel_interleave(
fetch_dataset, cycle_length=64, sloppy=True))
if self.cache:
dataset = dataset.cache().apply(
contrib_data.shuffle_and_repeat(1024 * 16))
else:
dataset = dataset.shuffle(1024)
return dataset
def get_dataset(train, src='data'):
files = get_files(train, src)
pairs = []
for l in files: # for each folder of a character
pairs.extend(itertools.combinations(l, 2)) # take all possible pairs
# sub-sample the pairs
pairs = random.sample(pairs, k=FLAGS.n_samples)
pairs = [pair + (1.0, ) for pair in pairs] # append label=1.0 to all pairs
for _ in range(len(pairs)): # sample an equal number of dissimilar pairs
c1, c2 = random.choices(files, k=2) # choose two random characters
f1, f2 = random.choice(c1), random.choice(c2) # sample from each
pairs.append((f1, f2, 0.0))
f1, f2, labels = map(np.array, zip(*pairs))
shuffle_idxs = np.arange(len(labels))
np.random.shuffle(shuffle_idxs)
f1 = f1[shuffle_idxs]
f2 = f2[shuffle_idxs]
labels = labels[shuffle_idxs]
dataset = tf.data.Dataset.from_tensor_slices((f1, f2, labels)) \
.map(_parse_function, num_parallel_calls=8) \
.apply(shuffle_and_repeat(buffer_size=10_000)) \
.batch(FLAGS.batch_size) \
.prefetch(FLAGS.batch_size)
return dataset
def build_model(self):
""" Graph Input """
# images
Image_Data_Class = ImageData(self.img_size, self.c_dim, self.custom_dataset)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.\
apply(shuffle_and_repeat(self.dataset_num)).\
apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
# noises
self.z = tf.truncated_normal(shape=[self.batch_size, 1, 1, self.z_dim], name='random_z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else:
GP = 0
# get loss for discriminator
self.d_loss = discriminator_loss(self.gan_type, real=real_logits, fake=fake_logits) + GP
# get loss for generator
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate, beta1=self.beta1, beta2=self.beta2).minimize(self.d_loss, var_list=d_vars)
self.opt = MovingAverageOptimizer(tf.train.AdamOptimizer(self.g_learning_rate, beta1=self.beta1, beta2=self.beta2), average_decay=self.moving_decay)
self.g_optim = self.opt.minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z, is_training=False, reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
def build_model(self):
""" Graph Input """
# images
if self.custom_dataset :
Image_Data_Class = ImageData(self.img_size, self.c_dim)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.apply(shuffle_and_repeat(self.dataset_num)).apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).apply(prefetch_to_device(gpu_device, self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
else :
self.inputs = tf.placeholder(tf.float32, [self.batch_size, self.img_size, self.img_size, self.c_dim], name='real_images')
# noises
self.z = tf.placeholder(tf.float32, [self.batch_size, self.z_dim], name='z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan' :
GP = self.gradient_penalty(real=self.inputs, fake=fake_images)
else :
GP = 0
# get loss for discriminator
self.d_loss = discriminator_loss(self.gan_type, real=real_logits, fake=fake_logits) + GP
# get loss for generator
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
""" Training """
# divide trainable variables into a group for D and a group for G
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if 'discriminator' in var.name]
g_vars = [var for var in t_vars if 'generator' in var.name]
# optimizers
self.d_optim = tf.train.AdamOptimizer(self.d_learning_rate, beta1=self.beta1, beta2=self.beta2).minimize(self.d_loss, var_list=d_vars)
self.g_optim = tf.train.AdamOptimizer(self.g_learning_rate, beta1=self.beta1, beta2=self.beta2).minimize(self.g_loss, var_list=g_vars)
"""" Testing """
# for test
self.fake_images = self.generator(self.z, is_training=False, reuse=True)
""" Summary """
self.d_sum = tf.summary.scalar("d_loss", self.d_loss)
self.g_sum = tf.summary.scalar("g_loss", self.g_loss)
def _input_fn():
dataset = tf.data.TFRecordDataset(self.record_path)
dataset = dataset.prefetch(batch_size * 4)
dataset = dataset.map(self.decode_feature, num_parallel_calls=num_parallel_calls)
if training:
dataset = dataset.apply(map_and_batch(self.preprocess_for_train, batch_size=batch_size,
num_parallel_calls=num_parallel_calls))
dataset = dataset.apply(shuffle_and_repeat(batch_size, epochs))
else:
dataset = dataset.apply(map_and_batch(self.preprocess_for_test, batch_size=batch_size,
num_parallel_calls=num_parallel_calls))
dataset = dataset.prefetch(AUTOTUNE)
return dataset
def input_fn(params, sequence_schema, context_schema, part_files):
dataset = Dataset.from_tensor_slices(part_files).shuffle(len(part_files))
dataset = dataset.apply(
parallel_interleave(
lambda file: TFRecordDataset(file, compression_type='GZIP'),
cycle_length=params['cycle_length'],
sloppy=True))
dataset = dataset.map(partial(parse_example, context_schema,
sequence_schema),
num_parallel_calls=cpu_count())
dataset = dataset.apply(
shuffle_and_repeat(params['buffer_size'], count=params['epochs']))
dataset = dataset.batch(params['batch_size'])
return dataset
def load_dataset(opt, train=True):
if train:
data_path = opt.data_path
else:
data_path = opt.val_data_path
image_path_all = []
lm_path_all = []
mask_path_all = []
for dataset in data_path:
image_path = glob.glob(dataset + '/' + '*.png')
image_path.sort()
lm_path_ = [
os.path.join(dataset, 'lm',
f.split('/')[-1].replace('png', 'txt'))
for f in image_path
]
lm_path_.sort()
mask_path = [
os.path.join(dataset, 'mask',
f.split('/')[-1]) for f in image_path
]
mask_path.sort()
# check if landmark binary files exist
check_lm_bin(dataset, lm_path_)
lm_path = [
os.path.join(dataset, 'lm_bin',
f.split('/')[-1].replace('png', 'bin'))
for f in image_path
]
lm_path.sort()
image_path_all += image_path
mask_path_all += mask_path
lm_path_all += lm_path
dataset_num = len(image_path_all)
dataset = tf.data.Dataset.from_tensor_slices(
(image_path_all, lm_path_all, mask_path_all))
dataset = dataset. \
apply(shuffle_and_repeat(dataset_num)). \
apply(map_and_batch(_parse_function, opt.batch_size, num_parallel_batches=4, drop_remainder=True)). \
apply(prefetch_to_device('/gpu:0', None)) # When using dataset.prefetch, use buffer_size=None to let it detect optimal buffer size
inputs_iterator = dataset.make_one_shot_iterator()
return inputs_iterator
def load_op(batch_size, iteration_count):
neighborhood = 0
loader = GRSS2013DataLoader('C:/GoogleDriveBack/PHD/Tez/Source')
data_set = loader.load_data(neighborhood, )
shadow_map, shadow_ratio = loader._load_shadow_map(
neighborhood,
data_set.concrete_data[:, :, 0:data_set.concrete_data.shape[2] - 1])
# normal_data_as_matrix, shadow_data_as_matrix = GRSS2013DataLoader.get_targetbased_shadowed_normal_data(data_set,
# loader,
# shadow_map,
# loader.load_samples(0.1))
normal_data_as_matrix, shadow_data_as_matrix = get_data_from_scene(
data_set, loader, shadow_map)
# normal_data_as_matrix, shadow_data_as_matrix = GRSS2013DataLoader.get_all_shadowed_normal_data(
# data_set,
# loader,
# shadow_map)
normal_data_as_matrix = normal_data_as_matrix[:, :, :,
0:normal_data_as_matrix.
shape[3] - 1]
shadow_data_as_matrix = shadow_data_as_matrix[:, :, :,
0:shadow_data_as_matrix.
shape[3] - 1]
normal = tf.placeholder(dtype=normal_data_as_matrix.dtype,
shape=normal_data_as_matrix.shape,
name='x')
shadow = tf.placeholder(dtype=shadow_data_as_matrix.dtype,
shape=shadow_data_as_matrix.shape,
name='y')
epoch = int(
(iteration_count * batch_size) / normal_data_as_matrix.shape[0])
data_set = tf.data.Dataset.from_tensor_slices(
(normal,
shadow)).apply(shuffle_and_repeat(buffer_size=10000,
count=epoch)).batch(batch_size)
data_set_itr = data_set.make_initializable_iterator()
return InitializerHook(data_set_itr, normal, shadow, normal_data_as_matrix,
shadow_data_as_matrix)
def __init__(self,
file_pattern,
num_epochs,
batch_size,
image_size,
shuffle_buff_size=5000):
files = tf.data.Dataset.list_files(file_pattern)
dataset = tf.data.TFRecordDataset(files, num_parallel_reads=4)
dataset = dataset.apply(
shuffle_and_repeat(shuffle_buff_size, num_epochs))
dataset = dataset.apply(
map_and_batch(lambda x: self._parse_fn(x, image_size),
batch_size,
num_parallel_batches=4))
self.dataset = dataset
def load_op(batch_size, iteration_count, loader_name, path):
neighborhood = 0
loader = get_class(loader_name + '.' + loader_name)(path)
data_set = loader.load_data(neighborhood, True)
shadow_map, shadow_ratio = loader.load_shadow_map(neighborhood, data_set)
# normal_data_as_matrix, shadow_data_as_matrix = GRSS2013DataLoader.get_targetbased_shadowed_normal_data(data_set,
# loader,
# shadow_map,
# loader.load_samples(0.1))
if FLAGS.use_target_map:
normal_data_as_matrix, shadow_data_as_matrix = get_data_from_scene(
data_set, loader, shadow_map)
else:
normal_data_as_matrix, shadow_data_as_matrix = get_all_shadowed_normal_data(
data_set, loader, shadow_map, multiply_shadowed_data=False)
# normal_data_as_matrix, shadow_data_as_matrix = create_dummy_shadowed_normal_data(data_set, loader)
hsi_channel_len = normal_data_as_matrix.shape[3] - 1
normal_data_as_matrix = normal_data_as_matrix[:, :, :, 0:hsi_channel_len]
shadow_data_as_matrix = shadow_data_as_matrix[:, :, :, 0:hsi_channel_len]
normal = tf.placeholder(dtype=normal_data_as_matrix.dtype,
shape=normal_data_as_matrix.shape,
name='x')
shadow = tf.placeholder(dtype=shadow_data_as_matrix.dtype,
shape=shadow_data_as_matrix.shape,
name='y')
epoch = int(
(iteration_count * batch_size) / normal_data_as_matrix.shape[0])
data_set = tf.data.Dataset.from_tensor_slices(
(normal,
shadow)).apply(shuffle_and_repeat(buffer_size=10000, count=epoch))
data_set = data_set.map(
lambda param_x, param_y_: perform_shadow_augmentation_random(
param_x, param_y_, shadow_ratio[0:hsi_channel_len]),
num_parallel_calls=4)
data_set = data_set.batch(batch_size)
data_set_itr = data_set.make_initializable_iterator()
return InitializerHook(data_set_itr, normal, shadow, normal_data_as_matrix,
shadow_data_as_matrix)
def make_source_dataset(self, index=0, num_hosts=1):
"""See base class."""
if not self.data_dir:
tf.logging.info("Undefined data_dir implies null input")
return tf.data.Dataset.range(1).repeat().map(self._get_null_input)
get_filenames = get_filenames_func()
filenames, _ = get_filenames(self.dataset_split)
dataset = tf.data.Dataset.from_tensor_slices(filenames)
if self.is_training and not self.cache:
if filenames is not None:
dataset = dataset.shuffle(len(filenames))
dataset = dataset.repeat()
def fetch_dataset(filename):
buffer_size = 8 * 1024 * 1024 # 8 MB per file
dataset = tf.data.TFRecordDataset(filename,
buffer_size=buffer_size)
return dataset
cycle_length = 64
shuffle_size = 1024
# Read the data from disk in parallel
if self.is_training:
dataset = dataset.apply(
contrib_data.parallel_interleave(fetch_dataset,
cycle_length=cycle_length,
sloppy=True))
else:
dataset = dataset.apply(
contrib_data.parallel_interleave(fetch_dataset,
cycle_length=1,
sloppy=False))
if self.cache:
dataset = dataset.cache().apply(
contrib_data.shuffle_and_repeat(shuffle_size))
else:
if self.is_training:
dataset = dataset.shuffle(shuffle_size)
return dataset
def training_nn_iterator(data_set, augmentation_info, batch_size, num_epochs, device):
main_cycle_data_set = data_set.apply(shuffle_and_repeat(buffer_size=10000, count=num_epochs))
if augmentation_info.offline_or_online is False:
main_cycle_data_set = add_augmentation_graph(main_cycle_data_set, augmentation_info,
perform_rotation_augmentation_random,
perform_shadow_augmentation_random,
perform_reflection_augmentation_random)
main_cycle_data_set = main_cycle_data_set.batch(batch_size)
# main_cycle_data_set = main_cycle_data_set.prefetch(1000)
if augmentation_info.offline_or_online is True:
main_cycle_data_set = add_augmentation_graph(main_cycle_data_set, augmentation_info,
perform_rotation_augmentation,
perform_shadow_augmentation,
perform_reflection_augmentation)
main_cycle_data_set = main_cycle_data_set.apply(prefetch_to_device(device, 10000))
return main_cycle_data_set.make_initializable_iterator()
def build_model(self):
""" Graph Input """
# images
Image_Data_Class = ImageData(self.img_size, self.c_dim,
self.custom_dataset)
inputs = tf.data.Dataset.from_tensor_slices(self.data)
gpu_device = '/gpu:0'
inputs = inputs.\
apply(shuffle_and_repeat(self.dataset_num)).\
apply(map_and_batch(Image_Data_Class.image_processing, self.batch_size, num_parallel_batches=16, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
inputs_iterator = inputs.make_one_shot_iterator()
self.inputs = inputs_iterator.get_next()
# noises
self.z = tf.random_normal(shape=[self.batch_size, 1, 1, self.z_dim],
name='random_z')
""" Loss Function """
# output of D for real images
real_logits = self.discriminator(self.inputs)
# output of D for fake images
fake_images = self.generator(self.z)
fake_logits = self.discriminator(fake_images, reuse=True)
# get loss for discriminator
self.d_loss = discriminator_loss(self.gan_type,
real=real_logits,
fake=fake_logits)
# get loss for generator
self.g_loss = generator_loss(self.gan_type, fake=fake_logits)
t_vars = tf.trainable_variables()
for var in t_vars:
if 'discriminator' in var.name:
d_vars = [var]
for var in t_vars:
if 'generator' in var.name:
g_vars = [var]
def load_op(batch_size, iteration_count):
hsi_2013_spatial_repeat = 10
hsi_2018_spectral_repeat = 3
hsi_2013_scale_diff = 2.5
neighborhood = 0
band_size = 144
grss2013_data_set = GRSS2013DataLoader(FLAGS.path).load_data(neighborhood, False)
grss2018_data_set = GRSS2018DataLoader(FLAGS.path).load_data(neighborhood, False)
hsi2013_global_minimum, hsi2018_global_minimum, hsi2013_global_maximum, hsi2018_global_maximum = \
extract_common_normalizer(grss2013_data_set.concrete_data[:, :, 0:-1], grss2018_data_set.casi)
hsi_grss2013 = grss2013_data_set.concrete_data[7:347, 256 + 8:1894 + 8, 0:-1]
hsi_grss2018 = grss2018_data_set.casi[0 + 265:-350 + 265, 0:-75, :].astype(numpy.float32)
debug_data = False
if debug_data:
test_match(band_size, hsi_2013_scale_diff,
hsi_2013_spatial_repeat, hsi_2018_spectral_repeat,
hsi_grss2013, hsi_grss2018)
hsi_grss2013 -= hsi2013_global_minimum
hsi_grss2018 -= hsi2018_global_minimum
hsi_grss2013 /= hsi2013_global_maximum.astype(numpy.float32)
hsi_grss2018 /= hsi2018_global_maximum.astype(numpy.float32)
tensor_output_shape = [hsi_2013_spatial_repeat, hsi_2013_spatial_repeat, band_size]
tensor_type_info = (tf.float32, tf.float32)
epoch = int((iteration_count * batch_size) / hsi_grss2013.shape[0])
data_set = tf.data.Dataset.from_generator(
lambda: _matched_data_generator(hsi_grss2013, hsi_grss2018, hsi_2013_spatial_repeat,
hsi_2018_spectral_repeat, hsi_2013_scale_diff,
0, 0, hsi_grss2013.shape[0], hsi_grss2013.shape[1],
ceil(hsi_2013_scale_diff / 2)), tensor_type_info,
(tensor_output_shape, tensor_output_shape))
data_set = data_set.apply(shuffle_and_repeat(buffer_size=10000, count=epoch))
data_set = data_set.batch(batch_size)
data_set_itr = data_set.make_initializable_iterator()
return InitializerHook(data_set_itr)
def load_img_scale_edge(args):
"""
Load image data
"""
# training data: 0, as file list
# image files
with open(args.DATA_FLIST[args.DATASET][0]) as f:
fnames = f.read().splitlines()
# TODO: create input dataset (images and masks)
inputs = tf.data.Dataset.from_tensor_slices(
fnames) # a tf dataset object (op)
if args.NUM_GPUS == 1:
device = '/gpu:0' # to which gpu. prefetch_to_device(device, batch_size)
# gpu_device = '/gpu:{}'.format(args.GPU_ID)
else:
device = '/cpu:0'
dataset_num = len(fnames)
# TODO: dataset with preprocessing (images and masks)
Image_Data_Class = ImageData(args=args)
# inputs = inputs.apply(shuffle_and_repeat(dataset_num)).apply(
# map_and_batch(Image_Data_Class.image_processing, args.BATCH_SIZE, num_parallel_batches=16,
# drop_remainder=True)).apply(prefetch_to_device(gpu_device, args.BATCH_SIZE))
inputs = inputs.apply(shuffle_and_repeat(dataset_num)).map(
lambda filename: tf.py_func(
Image_Data_Class.image_edge_scale_processing, [filename],
[tf.float32, tf.float32, tf.float32, tf.float32]),
num_parallel_calls=3)
inputs = inputs.batch(args.BATCH_SIZE * args.NUM_GPUS,
drop_remainder=True).apply(
prefetch_to_device(
device, args.BATCH_SIZE * args.NUM_GPUS))
inputs_iterator = inputs.make_one_shot_iterator() # iterator
images_edges = inputs_iterator.get_next(
) # an iteration get a batch of data
return images_edges
def create_dataset(role):
filenames = filenames_dict[role]
labels = labels_dict[role]
num_images = len(filenames)
# Read the images
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
dataset = dataset.map(read_image, num_parallel_calls=2)
# Shuffle and batch the data
dataset = dataset.apply(
shuffle_and_repeat(buffer_size=num_images,
count=num_epochs,
seed=seed_train))
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(num_images // batch_size)
# iterator = dataset.make_one_shot_iterator()
# images, labels = iterator.get_next()
# images = {"x": images} # Put into a dict, since this is expected by the model functions
return dataset
def load_op(batch_size, iteration_count, loader, data_set, shadow_map,
shadow_ratio, reg_support_rate):
if FLAGS.use_target_map:
normal_data_as_matrix, shadow_data_as_matrix = get_data_from_scene(
data_set, loader, shadow_map)
else:
normal_data_as_matrix, shadow_data_as_matrix = get_all_shadowed_normal_data(
data_set, loader, shadow_map, multiply_shadowed_data=False)
# normal_data_as_matrix, shadow_data_as_matrix = create_dummy_shadowed_normal_data(data_set, loader)
hsi_channel_len = normal_data_as_matrix.shape[3] - 1
normal_data_as_matrix = normal_data_as_matrix[:, :, :, 0:hsi_channel_len]
shadow_data_as_matrix = shadow_data_as_matrix[:, :, :, 0:hsi_channel_len]
normal_holder = tf.placeholder(dtype=normal_data_as_matrix.dtype,
shape=normal_data_as_matrix.shape,
name='x')
shadow_holder = tf.placeholder(dtype=shadow_data_as_matrix.dtype,
shape=shadow_data_as_matrix.shape,
name='y')
epoch = int(
(iteration_count * batch_size) / normal_data_as_matrix.shape[0])
data_set = tf.data.Dataset.from_tensor_slices(
(normal_holder, shadow_holder)).apply(
shuffle_and_repeat(buffer_size=10000, count=epoch))
data_set = data_set.map(
lambda param_x, param_y_: perform_shadow_augmentation_random(
param_x, param_y_, shadow_ratio[0:hsi_channel_len],
reg_support_rate),
num_parallel_calls=4)
data_set = data_set.batch(batch_size)
data_set_itr = data_set.make_initializable_iterator()
return InitializerHook(data_set_itr, normal_holder, shadow_holder,
normal_data_as_matrix, shadow_data_as_matrix)
def build_model(self):
if self.phase == 'train':
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.domain_A = trainA_iterator.get_next()
self.domain_B = trainB_iterator.get_next()
""" Define Generator, Discriminator """
x_ab, cam_ab = self.generate_a2b(self.domain_A) # real a
x_ba, cam_ba = self.generate_b2a(self.domain_B) # real b
x_aba, _ = self.generate_b2a(x_ab, reuse=True) # real b
x_bab, _ = self.generate_a2b(x_ba, reuse=True) # real a
x_aa, cam_aa = self.generate_b2a(self.domain_A,
reuse=True) # fake b
x_bb, cam_bb = self.generate_a2b(self.domain_B,
reuse=True) # fake a
real_A_logit, real_A_cam_logit, real_B_logit, real_B_cam_logit = self.discriminate_real(
self.domain_A, self.domain_B)
fake_A_logit, fake_A_cam_logit, fake_B_logit, fake_B_cam_logit = self.discriminate_fake(
x_ba, x_ab)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP_A, GP_CAM_A = self.gradient_panalty(real=self.domain_A,
fake=x_ba,
scope="discriminator_A")
GP_B, GP_CAM_B = self.gradient_panalty(real=self.domain_B,
fake=x_ab,
scope="discriminator_B")
else:
GP_A, GP_CAM_A = 0, 0
GP_B, GP_CAM_B = 0, 0
G_ad_loss_A = (generator_loss(self.gan_type, fake_A_logit) +
generator_loss(self.gan_type, fake_A_cam_logit))
G_ad_loss_B = (generator_loss(self.gan_type, fake_B_logit) +
generator_loss(self.gan_type, fake_B_cam_logit))
D_ad_loss_A = (
discriminator_loss(self.gan_type, real_A_logit, fake_A_logit) +
discriminator_loss(self.gan_type, real_A_cam_logit,
fake_A_cam_logit) + GP_A + GP_CAM_A)
D_ad_loss_B = (
discriminator_loss(self.gan_type, real_B_logit, fake_B_logit) +
discriminator_loss(self.gan_type, real_B_cam_logit,
fake_B_cam_logit) + GP_B + GP_CAM_B)
reconstruction_A = L1_loss(x_aba, self.domain_A) # reconstruction
reconstruction_B = L1_loss(x_bab, self.domain_B) # reconstruction
identity_A = L1_loss(x_aa, self.domain_A)
identity_B = L1_loss(x_bb, self.domain_B)
cam_A = cam_loss(source=cam_ba, non_source=cam_aa)
cam_B = cam_loss(source=cam_ab, non_source=cam_bb)
Generator_A_gan = self.adv_weight * G_ad_loss_A
Generator_A_cycle = self.cycle_weight * reconstruction_B
Generator_A_identity = self.identity_weight * identity_A
Generator_A_cam = self.cam_weight * cam_A
Generator_B_gan = self.adv_weight * G_ad_loss_B
Generator_B_cycle = self.cycle_weight * reconstruction_A
Generator_B_identity = self.identity_weight * identity_B
Generator_B_cam = self.cam_weight * cam_B
Generator_A_loss = Generator_A_gan + Generator_A_cycle + Generator_A_identity + Generator_A_cam
Generator_B_loss = Generator_B_gan + Generator_B_cycle + Generator_B_identity + Generator_B_cam
Discriminator_A_loss = self.adv_weight * D_ad_loss_A
Discriminator_B_loss = self.adv_weight * D_ad_loss_B
self.Generator_loss = Generator_A_loss + Generator_B_loss + regularization_loss(
'generator')
self.Discriminator_loss = Discriminator_A_loss + Discriminator_B_loss + regularization_loss(
'discriminator')
""" Result Image """
self.fake_A = x_ba
self.fake_B = x_ab
self.real_A = self.domain_A
self.real_B = self.domain_B
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
self.G_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Generator_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.Discriminator_loss,
var_list=D_vars)
"""" Summary """
self.all_G_loss = tf.summary.scalar("Generator_loss",
self.Generator_loss)
self.all_D_loss = tf.summary.scalar("Discriminator_loss",
self.Discriminator_loss)
self.G_A_loss = tf.summary.scalar("G_A_loss", Generator_A_loss)
self.G_A_gan = tf.summary.scalar("G_A_gan", Generator_A_gan)
self.G_A_cycle = tf.summary.scalar("G_A_cycle", Generator_A_cycle)
self.G_A_identity = tf.summary.scalar("G_A_identity",
Generator_A_identity)
self.G_A_cam = tf.summary.scalar("G_A_cam", Generator_A_cam)
self.G_B_loss = tf.summary.scalar("G_B_loss", Generator_B_loss)
self.G_B_gan = tf.summary.scalar("G_B_gan", Generator_B_gan)
self.G_B_cycle = tf.summary.scalar("G_B_cycle", Generator_B_cycle)
self.G_B_identity = tf.summary.scalar("G_B_identity",
Generator_B_identity)
self.G_B_cam = tf.summary.scalar("G_B_cam", Generator_B_cam)
self.D_A_loss = tf.summary.scalar("D_A_loss", Discriminator_A_loss)
self.D_B_loss = tf.summary.scalar("D_B_loss", Discriminator_B_loss)
self.rho_var = []
for var in tf.trainable_variables():
if 'rho' in var.name:
self.rho_var.append(tf.summary.histogram(var.name, var))
self.rho_var.append(
tf.summary.scalar(var.name + "_min",
tf.reduce_min(var)))
self.rho_var.append(
tf.summary.scalar(var.name + "_max",
tf.reduce_max(var)))
self.rho_var.append(
tf.summary.scalar(var.name + "_mean",
tf.reduce_mean(var)))
g_summary_list = [
self.G_A_loss, self.G_A_gan, self.G_A_cycle, self.G_A_identity,
self.G_A_cam, self.G_B_loss, self.G_B_gan, self.G_B_cycle,
self.G_B_identity, self.G_B_cam, self.all_G_loss
]
g_summary_list.extend(self.rho_var)
d_summary_list = [self.D_A_loss, self.D_B_loss, self.all_D_loss]
self.G_loss = tf.summary.merge(g_summary_list)
self.D_loss = tf.summary.merge(d_summary_list)
else:
""" Test """
self.test_domain_A = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_A')
self.test_domain_B = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_domain_B')
self.test_fake_B, _ = self.generate_a2b(self.test_domain_A)
self.test_fake_A, _ = self.generate_b2a(self.test_domain_B)
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_Data_Class = ImageData(self.img_size, self.img_ch,
self.augment_flag)
trainA = tf.data.Dataset.from_tensor_slices(self.trainA_dataset)
trainB = tf.data.Dataset.from_tensor_slices(self.trainB_dataset)
gpu_device = '/gpu:0'
trainA = trainA.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, self.batch_size))
trainB = trainB.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(Image_Data_Class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device, self.batch_size))
trainA_iterator = trainA.make_one_shot_iterator()
trainB_iterator = trainB.make_one_shot_iterator()
self.identity_A = trainA_iterator.get_next()
self.shape_A = trainA_iterator.get_next()
self.other_A = trainA_iterator.get_next()
self.shape_B = trainB_iterator.get_next()
self.test_identity_A = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_identity_A')
self.test_shape_B = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='test_shape_B')
""" Define Generator, Discriminator """
self.fake_same = self.generator(x_identity=self.identity_A,
x_shape=self.shape_A)
self.fake_diff = self.generator(x_identity=self.identity_A,
x_shape=self.shape_B,
reuse=True)
fake_diff_shape = self.generator(x_identity=self.shape_B,
x_shape=self.fake_diff,
reuse=True)
fake_diff_identity = self.generator(x_identity=self.fake_diff,
x_shape=self.shape_B,
reuse=True)
real_logit = self.discriminator(x_identity=self.identity_A,
x=self.other_A)
fake_logit = self.discriminator(x_identity=self.identity_A,
x=self.fake_diff,
reuse=True)
""" Define Loss """
g_identity_loss = self.adv_weight * generator_loss(
self.gan_type, fake_logit) * 64
g_shape_loss_same = self.L1_weight * L1_loss(self.fake_same,
self.shape_A)
g_shape_loss_diff_shape = self.L1_weight * L1_loss(
fake_diff_shape, self.shape_B) + self.L1_weight * L1_loss(
self.fake_diff, self.shape_B)
g_shape_loss_diff_identity = self.L1_weight * L1_loss(
fake_diff_identity, self.fake_diff)
self.Generator_loss = g_identity_loss + g_shape_loss_same + g_shape_loss_diff_shape + g_shape_loss_diff_identity
self.Discriminator_loss = self.adv_weight * discriminator_loss(
self.gan_type, real=real_logit, fake=fake_logit)
""" Result Image """
self.test_fake = self.generator(x_identity=self.test_identity_A,
x_shape=self.test_shape_B,
reuse=True)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
self.G_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(self.Generator_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(
self.lr, beta1=0.5, beta2=0.999).minimize(self.Discriminator_loss,
var_list=D_vars)
"""" Summary """
self.G_loss = tf.summary.scalar("Generator_loss", self.Generator_loss)
self.D_loss = tf.summary.scalar("Discriminator_loss",
self.Discriminator_loss)
self.G_identity = tf.summary.scalar("G_identity", g_identity_loss)
self.G_shape_loss_same = tf.summary.scalar("G_shape_loss_same",
g_shape_loss_same)
self.G_shape_loss_diff_shape = tf.summary.scalar(
"G_shape_loss_diff_shape", g_shape_loss_diff_shape)
self.G_shape_loss_diff_identity = tf.summary.scalar(
"G_shape_loss_diff_identity", g_shape_loss_diff_identity)
self.G_loss_merge = tf.summary.merge([
self.G_loss, self.G_identity, self.G_shape_loss_same,
self.G_shape_loss_diff_shape, self.G_shape_loss_diff_identity
])
self.D_loss_merge = tf.summary.merge([self.D_loss])
def _input_fn(params=None):
"""Input function compatible with `Experiment` object.
Pipeline proceeds as:
-> generation examples from json or tfrecords
-> limit to first N examples (if limit is not None)
-> encode any _tokens fields as bytes
-> cache the results to avoid recomputing
-> Lookup tokens in GLOVE embeddings
-> Shuffle & repeat
Args:
params: Params passed to the estimator. Contains 'batch_size'.
Returns:
A tuple of feature tensors and target tensors.
Raises:
ValueError: If filtering by length is set during eval mode.
"""
if not is_training:
assert not is_tpu
tf.logging.info('Data pipeline given params:\n%s' % params)
if params:
if is_training:
batch_size = params['train_batch_size']
else:
batch_size = params['eval_batch_size']
if use_generator:
tf.logging.info('Building generator data pipeline.')
if tokenizer == 'word':
tf.logging.info('Using word split encoder.')
tokenizer_fn = word_tokenize
elif tokenizer == 'nltk':
tf.logging.info('Using NLTK encoder.')
tokenizer_fn = build_nltk_tokenizer()
elif tokenizer == 'subword':
tokenizer_fn = build_subword_tokenizer(vocab_path=vocab_path)
else:
raise ValueError('Unknown tokenizer %s' % tokenizer)
ds = build_generator_pipeline(data_path=data_path,
split=split,
tokenizer_fn=tokenizer_fn,
sort_by_length=sort_by_length,
is_subword=tokenizer == 'subword')
else:
tf.logging.info('Loading TFRecords from %s' % data_path)
filenames = tf.gfile.Glob(os.path.join(data_path, '%s_*' % split))
tf.logging.info(filenames)
ds = build_tfrecord_pipeline(filenames=filenames)
if max_length:
if not is_training:
raise ValueError(
'Unable to filter or resample examples at eval time.')
if resample_too_long:
tf.logging.info('Resampling with max length %s', max_length)
def _resample(x):
return resample_example(x, max_length=max_length)
ds = ds.map(_resample, num_parallel_calls=16)
else:
# Filter out examples over our max length to avoid an error downstream.
tf.logging.info('Filtering out examples over max length %s',
max_length)
def _not_too_long(x):
return tf.greater_equal(tf.to_int32(max_length),
tf.to_int32(x['context_length']))
ds = ds.filter(_not_too_long)
if limit:
# Take the first N examples
ds = ds.take(limit)
if include_bytes:
tokens_to_bytes = lambda x: _tokens_to_bytes(x, bytes_per_word)
ds = ds.map(tokens_to_bytes, num_parallel_calls=16)
if cache:
# Cache dataset to avoid hitting the python generator after first epoch
ds = ds.cache()
# Subset that we should actually pass back to the caller
# This is required to filter out tf.string fields which are not TPU
# compatible
# Specifically: id, context, question, context_tokens and question_tokens
# are all string fields that will be removed.
shapes, _ = get_shapes_and_types(is_tpu=is_tpu,
max_length=max_length,
include_bytes=include_bytes,
bytes_per_word=bytes_per_word,
include_ids=include_ids)
if do_embedding:
# Embed tokens with pretrained word vectors
# Add in shape info before batching
shapes['context_vecs'] = [max_length if is_tpu else None, 300]
shapes['question_vecs'] = [max_length if is_tpu else None, 300]
def lookup(words):
# Do embedding lookups on a tensor of words
# We use a py_func so we can check for both upper and lowercase.
# TODO(ddohan): Revert to embedding_lookup for TPU support
def embed_words(words):
def embed_word(word):
utf_word = word.decode('utf-8')
for key in [
word,
word.lower(), utf_word,
utf_word.lower(), 'UNK'
]:
if key in embeddings:
return embeddings[key]
emb = [embed_word(word) for word in words]
emb = np.array(emb, dtype=np.float32)
return emb
embedded = tf.py_func(embed_words,
inp=[words],
Tout=[tf.float32],
stateful=False)
embedded = tf.reshape(embedded, [-1, 300])
return embedded
def lookup_fields(d):
d['context_vecs'] = lookup(d['context_tokens'])
d['question_vecs'] = lookup(d['question_tokens'])
return d
ds = ds.map(lookup_fields, num_parallel_calls=16)
repeats = num_repeats if num_repeats else None
if shuffle and repeats != 1:
tf.logging.info('Shuffle and repeat size: %s' %
shuffle_buffer_size)
ds = ds.apply(
contrib_data.shuffle_and_repeat(
buffer_size=shuffle_buffer_size, count=repeats))
elif repeats != 1:
tf.logging.info('Repeating')
ds = ds.repeat(count=repeats)
elif shuffle:
tf.logging.info('Shuffle size: %s' % shuffle_buffer_size)
ds = ds.shuffle(buffer_size=shuffle_buffer_size)
def filter_fields(example):
out = {}
for k in shapes:
out[k] = example[k]
return out
ds = ds.map(filter_fields, num_parallel_calls=16)
if is_training:
ds = ds.padded_batch(batch_size,
padded_shapes=shapes,
drop_remainder=True)
else:
# Never want to ignore values at eval time
ds = ds.padded_batch(batch_size, padded_shapes=shapes)
ds = ds.prefetch(
tf.data.experimental.AUTOTUNE) # Buffer a few batches ahead
if do_embedding:
iterator = ds.make_initializable_iterator()
# Must be initialized when the graph is initialized and before the
# dataset tensors are evaluated.
# Run `tf.tables_initializer()` before getting first batch
tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS,
iterator.initializer)
else:
iterator = ds.make_one_shot_iterator()
batch = iterator.get_next()
if legacy_rename:
batch = do_renames(batch)
return batch, batch
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
img_class = Image_data(self.img_height, self.img_width, self.img_ch,
self.segmap_ch, self.dataset_path,
self.augment_flag)
img_class.preprocess()
self.dataset_num = len(img_class.image)
self.test_dataset_num = len(img_class.segmap_test)
img_and_segmap = tf.data.Dataset.from_tensor_slices(
(img_class.image, img_class.segmap))
segmap_test = tf.data.Dataset.from_tensor_slices(img_class.segmap_test)
gpu_device = '/gpu:0'
img_and_segmap = img_and_segmap.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_class.image_processing,
self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
segmap_test = segmap_test.apply(shuffle_and_repeat(
self.dataset_num)).apply(
map_and_batch(img_class.test_image_processing,
batch_size=self.batch_size,
num_parallel_batches=16,
drop_remainder=True)).apply(
prefetch_to_device(gpu_device,
self.batch_size))
img_and_segmap_iterator = img_and_segmap.make_one_shot_iterator()
segmap_test_iterator = segmap_test.make_one_shot_iterator()
self.real_x, self.real_x_segmap, self.real_x_segmap_onehot = img_and_segmap_iterator.get_next(
)
self.real_x_segmap_test, self.real_x_segmap_test_onehot = segmap_test_iterator.get_next(
)
""" Define Generator, Discriminator """
fake_x, x_mean, x_var = self.image_translate(
segmap_img=self.real_x_segmap_onehot, x_img=self.real_x)
real_logit, fake_logit = self.image_discriminate(
segmap_img=self.real_x_segmap_onehot,
real_img=self.real_x,
fake_img=fake_x)
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_penalty(real=self.real_x,
segmap=self.real_x_segmap_onehot,
fake=fake_x)
else:
GP = 0
""" Define Loss """
g_adv_loss = self.adv_weight * generator_loss(self.gan_type,
fake_logit)
g_kl_loss = self.kl_weight * kl_loss(x_mean, x_var)
g_vgg_loss = self.vgg_weight * VGGLoss()(self.real_x, fake_x)
g_feature_loss = self.feature_weight * feature_loss(
real_logit, fake_logit)
g_reg_loss = regularization_loss('generator') + regularization_loss(
'encoder')
d_adv_loss = self.adv_weight * (
discriminator_loss(self.gan_type, real_logit, fake_logit) + GP)
d_reg_loss = regularization_loss('discriminator')
self.g_loss = g_adv_loss + g_kl_loss + g_vgg_loss + g_feature_loss + g_reg_loss
self.d_loss = d_adv_loss + d_reg_loss
""" Result Image """
self.fake_x = fake_x
self.random_fake_x, _, _ = self.image_translate(
segmap_img=self.real_x_segmap_onehot,
random_style=True,
reuse=True)
""" Test """
self.test_segmap_image = tf.placeholder(tf.float32, [
1, self.img_height, self.img_width,
len(img_class.color_value_dict)
])
self.random_test_fake_x, _, _ = self.image_translate(
segmap_img=self.test_segmap_image, random_style=True, reuse=True)
self.test_guide_image = tf.placeholder(
tf.float32, [1, self.img_height, self.img_width, self.img_ch])
self.guide_test_fake_x, _, _ = self.image_translate(
segmap_img=self.test_segmap_image,
x_img=self.test_guide_image,
reuse=True)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [
var for var in t_vars
if 'encoder' in var.name or 'generator' in var.name
]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
if self.TTUR:
beta1 = 0.0
beta2 = 0.9
g_lr = self.lr / 2
d_lr = self.lr * 2
else:
beta1 = self.beta1
beta2 = self.beta2
g_lr = self.lr
d_lr = self.lr
self.G_optim = tf.train.AdamOptimizer(
g_lr, beta1=beta1, beta2=beta2).minimize(self.g_loss,
var_list=G_vars)
self.D_optim = tf.train.AdamOptimizer(
d_lr, beta1=beta1, beta2=beta2).minimize(self.d_loss,
var_list=D_vars)
"""" Summary """
self.summary_g_loss = tf.summary.scalar("g_loss", self.g_loss)
self.summary_d_loss = tf.summary.scalar("d_loss", self.d_loss)
self.summary_g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.summary_g_kl_loss = tf.summary.scalar("g_kl_loss", g_kl_loss)
self.summary_g_vgg_loss = tf.summary.scalar("g_vgg_loss", g_vgg_loss)
self.summary_g_feature_loss = tf.summary.scalar(
"g_feature_loss", g_feature_loss)
g_summary_list = [
self.summary_g_loss, self.summary_g_adv_loss,
self.summary_g_kl_loss, self.summary_g_vgg_loss,
self.summary_g_feature_loss
]
d_summary_list = [self.summary_d_loss]
self.G_loss = tf.summary.merge(g_summary_list)
self.D_loss = tf.summary.merge(d_summary_list)
def _initialize_tf_dataset(file_names,
augment,
over_sample,
shuffle,
target_size,
normalize,
nb_workers=8,
batch_size=64,
shuffle_buffer_size=3000,
input_type="img",
nr_epochs=1):
import tensorflow as tf
from tensorflow.contrib.data import map_and_batch
from tensorflow.contrib.data import shuffle_and_repeat
if not type(target_size) is list:
target_size = list(target_size)
with tf.name_scope('input_pipeline'):
dataset = tf.data.TFRecordDataset(file_names)
if shuffle:
dataset = dataset.apply(
shuffle_and_repeat(shuffle_buffer_size, nr_epochs))
def _decode_and_augment_image(example_proto):
keys_to_features = {
'label': tf.FixedLenFeature([], tf.int64),
'shape': tf.FixedLenFeature([], tf.string),
'image': tf.FixedLenFeature([], tf.string),
}
tfrecord_features = tf.parse_single_example(example_proto,
keys_to_features)
image = tf.decode_raw(tfrecord_features['image'], tf.uint8)
shape = tf.decode_raw(tfrecord_features['shape'], tf.int64)
if input_type == ".jpeg":
image = tf.reshape(image, target_size + [3])
else:
image = tf.reshape(image, target_size)
label = tfrecord_features['label']
if augment:
image = tf.image.random_flip_left_right(image)
image = tf.image.random_flip_up_down(image)
degrees = tf.random_uniform((), minval=-180, maxval=180)
image = tf.contrib.image.rotate(image, degrees)
width_shift = tf.random_uniform((), minval=0, maxval=0.05)
height_shift = tf.random_uniform((), minval=0, maxval=0.05)
horizontal_pad = tf.cast(
tf.ceil(width_shift * target_size[0]), tf.int32)
vertical_pad = tf.cast(tf.ceil(height_shift * target_size[1]), tf.int32)
padding = tf.stack([
horizontal_pad, horizontal_pad, vertical_pad, vertical_pad,
tf.constant(0),
tf.constant(0)
])
padding = tf.reshape(padding, (3, 2))
image = tf.pad(image, padding)
image = tf.random_crop(image, target_size + [3])
zoom = tf.random_uniform((), minval=-0.1, maxval=0.1)
new_dim = tf.cast(tf.ceil((1 - zoom) * target_size[0]), dtype=tf.int32)
image = tf.image.resize_image_with_crop_or_pad(image, new_dim, new_dim)
image = tf.image.resize_images(
image, target_size, method=tf.image.ResizeMethod.BILINEAR)
if normalize:
std = tf.constant(
np.array([70.53946096, 51.71475228, 43.03428563]), dtype=tf.float32)
std = tf.expand_dims(tf.expand_dims(std, axis=0), axis=0)
mean = tf.constant(
np.array([108.64628601, 75.86886597, 54.34005736]),
dtype=tf.float32)
mean = tf.expand_dims(tf.expand_dims(mean, axis=0), axis=0)
image = (tf.cast(image, dtype=tf.float32) - mean) / std
label = tf.reshape(label, [1])
if input_type == ".jpeg":
image = tf.reshape(image, target_size + [3])
else:
image = tf.reshape(image, target_size)
return {'shape': shape, 'image': image}, label
dataset = dataset \
.apply(map_and_batch(_decode_and_augment_image, batch_size=batch_size, num_parallel_batches=nb_workers,
drop_remainder=True)) \
.prefetch(nb_workers)
# def _augment_images(example)
return dataset
merged_summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=3)
dataset = tf.data.TFRecordDataset('Dataset/dataset_1.tfrecord')
def _parse(example_proto):
feature = {'roll' : tf.FixedLenFeature([], tf.string)}
parsed = tf.parse_single_example(example_proto, feature)
data = tf.decode_raw(parsed['roll'], tf.uint8)
data = tf.py_func(func=np.unpackbits, inp=[data], Tout=tf.uint8)
data = tf.cast(data, tf.float32)
data = tf.reshape(data, [CLASS_NUM, 600])
data = data * 2 - 1
return data
dataset = dataset.apply(data.shuffle_and_repeat(buffer_size=60000, count=3))
dataset = dataset.apply(data.map_and_batch(_parse, batch_size=batch_size, num_parallel_batches=2))
iterator = dataset.prefetch(batch_size).make_one_shot_iterator()
real_input_next_element = iterator.get_next()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if os.path.exists(model_path):
load_path = saver.restore(sess, model_path)
print("Model restored from file: %s" % load_path)
for i in range(total_batch):
tfdata = sess.run(real_input_next_element)
reshape_tfdata = tfdata.reshape([-1, CLASS_NUM, 600]) #
cuted_tfdata = reshape_tfdata[:, :, :INPUT_LENGTH]
def build_model(self, A):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_data_class = ImageData(load_size=self.img_size, channels=self.img_ch, data_path=self.dataset_path, selected_attrs=self.selected_attrs, augment_flag=self.augment_flag)
Image_data_class.preprocess()
train_dataset_num = len(Image_data_class.train_dataset)
test_dataset_num = len(Image_data_class.test_dataset)
train_dataset = tf.data.Dataset.from_tensor_slices((Image_data_class.train_dataset, Image_data_class.train_dataset_label, Image_data_class.train_dataset_fix_label))
test_dataset = tf.data.Dataset.from_tensor_slices((Image_data_class.test_dataset, Image_data_class.test_dataset_label, Image_data_class.test_dataset_fix_label))
gpu_device = '/gpu:0'
train_dataset = train_dataset.\
apply(shuffle_and_repeat(train_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
test_dataset = test_dataset.\
apply(shuffle_and_repeat(test_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
train_dataset_iterator = train_dataset.make_one_shot_iterator()
test_dataset_iterator = test_dataset.make_one_shot_iterator()
self.x_real, label_org, label_fix_list = train_dataset_iterator.get_next() # Input image / Original domain labels
label_trg = tf.random_shuffle(label_org) # Target domain labels
label_fix_list = tf.transpose(label_fix_list, perm=[1, 0, 2])
self.x_test, test_label_org, test_label_fix_list = test_dataset_iterator.get_next() # Input image / Original domain labels
test_label_fix_list = tf.transpose(test_label_fix_list, perm=[1, 0, 2])
self.custom_image = tf.placeholder(tf.float32, [1, self.img_size, self.img_size, self.img_ch], name='custom_image') # Custom Image
custom_label_fix_list = tf.transpose(create_labels(self.custom_label, self.selected_attrs), perm=[1, 0, 2])
""" Define Generator, Discriminator """
# binary label transformation
dist = tf_contrib.distributions.Categorical(probs=[0.25, 0.5, 0.25])
hat_c = tf.cast(dist.sample([self.batch_size, self.c_dim]) - 1, dtype ='float32')
x_fake, w_fake = self.generator(self.x_real, hat_c) # real a
x_recon, w_recon = self.generator(x_fake, -hat_c, reuse=True) # real b
real_logit, real_cls = self.discriminator(self.x_real)
fake_logit, fake_cls = self.discriminator(x_fake, reuse=True)
# warp cycle
A_fake = tf_contrib.image.dense_image_warp(A, w_fake)
A_cycle = tf_contrib.image.dense_image_warp(A_fake, w_recon)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan' :
GP = self.gradient_panalty(real=self.x_real, fake=x_fake)
else :
GP = 0
g_adv_loss = generator_loss(loss_func=self.gan_type, fake=fake_logit)
g_cls_loss = binary_label_loss(hat_c, fake_cls, self.c_dim)
warp_cycle_loss = tf.reduce_mean((A_cycle - A)** 2, axis=[1,2,3])
g_rec_loss = tf.reduce_mean(warp_cycle_loss)
smooth_loss = tf.reduce_mean(total_variation(w_fake))
d_adv_loss = discriminator_loss(loss_func=self.gan_type, real=real_logit, fake=fake_logit)
d_cls_loss = classification_loss(logit=real_cls, label=label_org)
self.d_loss = self.adv_weight * d_adv_loss + self.gp_weight * GP + self.cls_weight * d_cls_loss
self.g_loss = self.adv_weight * g_adv_loss + self.cls_weight * g_cls_loss + self.rec_weight * g_rec_loss + self.smooth_weight * smooth_loss
""" Result Image """
self.x_fake_list = tf.map_fn(lambda x : self.generator(self.x_real, x, reuse=True)[0], label_fix_list, dtype=tf.float32)
""" Test Image """
self.x_test_fake_list = tf.map_fn(lambda x : self.generator(self.x_test, x, reuse=True)[0], test_label_fix_list, dtype=tf.float32)
self.custom_fake_image = tf.map_fn(lambda x : self.generator(self.custom_image, x, reuse=True)[0], custom_label_fix_list, dtype=tf.float32)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
self.g_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.g_loss, var_list=G_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.d_loss, var_list=D_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("Generator_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("Discriminator_loss", self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_cls_loss = tf.summary.scalar("g_cls_loss", g_cls_loss)
self.g_rec_loss = tf.summary.scalar("g_rec_loss", g_rec_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
self.d_cls_loss = tf.summary.scalar("d_cls_loss", d_cls_loss)
self.g_summary_loss = tf.summary.merge([self.Generator_loss, self.g_adv_loss, self.g_cls_loss, self.g_rec_loss])
self.d_summary_loss = tf.summary.merge([self.Discriminator_loss, self.d_adv_loss, self.d_cls_loss])
def build_model(self):
self.ema = tf.train.ExponentialMovingAverage(decay=self.ema_decay)
if self.phase == 'train' :
""" Input Image"""
img_class = Image_data(self.img_height, self.img_width, self.img_ch, self.dataset_path, self.label_list,
self.augment_flag)
img_class.preprocess()
dataset_num = len(img_class.image)
print("Dataset number : ", dataset_num)
self.lr = tf.placeholder(tf.float32, name='learning_rate')
self.ds_weight_placeholder = tf.placeholder(tf.float32, name='ds_weight')
img_and_label = tf.data.Dataset.from_tensor_slices((img_class.image, img_class.label))
gpu_device = '/gpu:0'
img_and_label = img_and_label.apply(shuffle_and_repeat(dataset_num)).apply(
map_and_batch(img_class.image_processing, self.batch_size * self.gpu_num, num_parallel_batches=16,
drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))
img_and_label_iterator = img_and_label.make_one_shot_iterator()
self.x_real, label_org = img_and_label_iterator.get_next() # [bs, 256, 256, 3], [bs, 1]
# label_trg = tf.random_shuffle(label_org) # Target domain labels
label_trg = tf.random_uniform(shape=tf.shape(label_org), minval=0, maxval=self.c_dim, dtype=tf.int32) # Target domain labels
""" split """
x_real_gpu_split = tf.split(self.x_real, num_or_size_splits=self.gpu_num, axis=0)
label_org_gpu_split = tf.split(label_org, num_or_size_splits=self.gpu_num, axis=0)
label_trg_gpu_split = tf.split(label_trg, num_or_size_splits=self.gpu_num, axis=0)
g_adv_loss_per_gpu = []
g_sty_recon_loss_per_gpu = []
g_sty_diverse_loss_per_gpu = []
g_cyc_loss_per_gpu = []
g_loss_per_gpu = []
d_adv_loss_per_gpu = []
d_loss_per_gpu = []
for gpu_id in range(self.gpu_num):
with tf.device(tf.DeviceSpec(device_type="GPU", device_index=gpu_id)):
with tf.variable_scope(tf.get_variable_scope(), reuse=(gpu_id > 0)):
x_real_split = tf.split(x_real_gpu_split[gpu_id], num_or_size_splits=self.batch_size, axis=0)
label_org_split = tf.split(label_org_gpu_split[gpu_id], num_or_size_splits=self.batch_size, axis=0)
label_trg_split = tf.split(label_trg_gpu_split[gpu_id], num_or_size_splits=self.batch_size, axis=0)
g_adv_loss = None
g_sty_recon_loss = None
g_sty_diverse_loss = None
g_cyc_loss = None
d_adv_loss = None
d_simple_gp = None
d_gp = None
for each_bs in range(self.batch_size) :
""" Define Generator, Discriminator """
x_real_each = x_real_split[each_bs] # [1, 256, 256, 3]
label_org_each = tf.squeeze(label_org_split[each_bs], axis=[0, 1]) # [1, 1] -> []
label_trg_each = tf.squeeze(label_trg_split[each_bs], axis=[0, 1])
random_style_code = tf.random_normal(shape=[1, self.style_dim])
random_style_code_1 = tf.random_normal(shape=[1, self.style_dim])
random_style_code_2 = tf.random_normal(shape=[1, self.style_dim])
random_style = tf.gather(self.mapping_network(random_style_code), label_trg_each)
random_style_1 = tf.gather(self.mapping_network(random_style_code_1), label_trg_each)
random_style_2 = tf.gather(self.mapping_network(random_style_code_2), label_trg_each)
x_fake = self.generator(x_real_each, random_style) # for adversarial objective
x_fake_1 = self.generator(x_real_each, random_style_1) # for style diversification
x_fake_2 = self.generator(x_real_each, random_style_2) # for style diversification
x_real_each_style = tf.gather(self.style_encoder(x_real_each), label_org_each) # for cycle consistency
x_fake_style = tf.gather(self.style_encoder(x_fake), label_trg_each) # for style reconstruction
x_cycle = self.generator(x_fake, x_real_each_style) # for cycle consistency
real_logit = tf.gather(self.discriminator(x_real_each), label_org_each)
fake_logit = tf.gather(self.discriminator(x_fake), label_trg_each)
""" Define loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_panalty(real=x_real_each, fake=x_fake, real_label=label_org_each)
else:
GP = tf.constant([0], tf.float32)
if each_bs == 0 :
g_adv_loss = self.adv_weight * generator_loss(self.gan_type, fake_logit)
g_sty_recon_loss = self.sty_weight * L1_loss(random_style, x_fake_style)
g_sty_diverse_loss = self.ds_weight_placeholder * L1_loss(x_fake_1, x_fake_2)
g_cyc_loss = self.cyc_weight * L1_loss(x_real_each, x_cycle)
d_adv_loss = self.adv_weight * discriminator_loss(self.gan_type, real_logit, fake_logit)
d_simple_gp = self.adv_weight * simple_gp(real_logit, fake_logit, x_real_each, x_fake, r1_gamma=self.r1_weight, r2_gamma=0.0)
d_gp = self.adv_weight * GP
else :
g_adv_loss = tf.concat([g_adv_loss, self.adv_weight * generator_loss(self.gan_type, fake_logit)], axis=0)
g_sty_recon_loss = tf.concat([g_sty_recon_loss, self.sty_weight * L1_loss(random_style, x_fake_style)], axis=0)
g_sty_diverse_loss = tf.concat([g_sty_diverse_loss, self.ds_weight_placeholder * L1_loss(x_fake_1, x_fake_2)], axis=0)
g_cyc_loss = tf.concat([g_cyc_loss, self.cyc_weight * L1_loss(x_real_each, x_cycle)], axis=0)
d_adv_loss = tf.concat([d_adv_loss, self.adv_weight * discriminator_loss(self.gan_type, real_logit, fake_logit)], axis=0)
d_simple_gp = tf.concat([d_simple_gp, self.adv_weight * simple_gp(real_logit, fake_logit, x_real_each, x_fake, r1_gamma=self.r1_weight, r2_gamma=0.0)], axis=0)
d_gp = tf.concat([d_gp, self.adv_weight * GP], axis=0)
g_adv_loss = tf.reduce_mean(g_adv_loss)
g_sty_recon_loss = tf.reduce_mean(g_sty_recon_loss)
g_sty_diverse_loss = tf.reduce_mean(g_sty_diverse_loss)
g_cyc_loss = tf.reduce_mean(g_cyc_loss)
d_adv_loss = tf.reduce_mean(d_adv_loss)
d_simple_gp = tf.reduce_mean(tf.reduce_sum(d_simple_gp, axis=[1, 2, 3]))
d_gp = tf.reduce_mean(d_gp)
g_loss = g_adv_loss + g_sty_recon_loss - g_sty_diverse_loss + g_cyc_loss
d_loss = d_adv_loss + d_simple_gp + d_gp
g_adv_loss_per_gpu.append(g_adv_loss)
g_sty_recon_loss_per_gpu.append(g_sty_recon_loss)
g_sty_diverse_loss_per_gpu.append(g_sty_diverse_loss)
g_cyc_loss_per_gpu.append(g_cyc_loss)
d_adv_loss_per_gpu.append(d_adv_loss)
g_loss_per_gpu.append(g_loss)
d_loss_per_gpu.append(d_loss)
g_adv_loss = tf.reduce_mean(g_adv_loss_per_gpu)
g_sty_recon_loss = tf.reduce_mean(g_sty_recon_loss_per_gpu)
g_sty_diverse_loss = tf.reduce_mean(g_sty_diverse_loss_per_gpu)
g_cyc_loss = tf.reduce_mean(g_cyc_loss_per_gpu)
self.g_loss = tf.reduce_mean(g_loss_per_gpu)
d_adv_loss = tf.reduce_mean(d_adv_loss_per_gpu)
self.d_loss = tf.reduce_mean(d_loss_per_gpu)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
E_vars = [var for var in t_vars if 'encoder' in var.name]
F_vars = [var for var in t_vars if 'mapping' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
if self.gpu_num == 1 :
prev_g_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=G_vars)
prev_e_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=E_vars)
prev_f_optimizer = tf.train.AdamOptimizer(self.lr * 0.01, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=F_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.d_loss, var_list=D_vars)
else :
prev_g_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss, var_list=G_vars,
colocate_gradients_with_ops=True)
prev_e_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.g_loss,
var_list=E_vars,
colocate_gradients_with_ops=True)
prev_f_optimizer = tf.train.AdamOptimizer(self.lr * 0.01, beta1=0, beta2=0.99).minimize(self.g_loss,
var_list=F_vars,
colocate_gradients_with_ops=True)
self.d_optimizer = tf.train.AdamOptimizer(self.lr, beta1=0, beta2=0.99).minimize(self.d_loss,
var_list=D_vars,
colocate_gradients_with_ops=True)
with tf.control_dependencies([prev_g_optimizer, prev_e_optimizer, prev_f_optimizer]):
self.g_optimizer = self.ema.apply(G_vars)
self.e_optimizer = self.ema.apply(E_vars)
self.f_optimizer = self.ema.apply(F_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("g_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("d_loss", self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_sty_recon_loss = tf.summary.scalar("g_sty_recon_loss", g_sty_recon_loss)
self.g_sty_diverse_loss = tf.summary.scalar("g_sty_diverse_loss", g_sty_diverse_loss)
self.g_cyc_loss = tf.summary.scalar("g_cyc_loss", g_cyc_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
g_summary_list = [self.Generator_loss, self.g_adv_loss, self.g_sty_recon_loss, self.g_sty_diverse_loss, self.g_cyc_loss]
d_summary_list = [self.Discriminator_loss, self.d_adv_loss]
self.g_summary_loss = tf.summary.merge(g_summary_list)
self.d_summary_loss = tf.summary.merge(d_summary_list)
""" Result Image """
def return_g_images(generator, image, code):
x = generator(image, code)
return x
self.x_fake_list = []
first_x_real = tf.expand_dims(self.x_real[0], axis=0)
label_fix_list = tf.constant([idx for idx in range(self.c_dim)])
for _ in range(self.num_style):
random_style_code = tf.truncated_normal(shape=[1, self.style_dim])
self.x_fake_list.append(tf.map_fn(
lambda c: return_g_images(self.generator,
first_x_real,
tf.gather(self.mapping_network(random_style_code), c)),
label_fix_list, dtype=tf.float32))
elif self.phase == 'refer_test':
""" Test """
def return_g_images(generator, image, code):
x = generator(image, code)
return x
self.custom_image = tf.placeholder(tf.float32, [1, self.img_height, self.img_width, self.img_ch], name='custom_image')
self.refer_image = tf.placeholder(tf.float32, [1, self.img_height, self.img_width, self.img_ch], name='refer_image')
label_fix_list = tf.constant([idx for idx in range(self.c_dim)])
self.refer_fake_image = tf.map_fn(
lambda c : return_g_images(self.generator,
self.custom_image,
tf.gather(self.style_encoder(self.refer_image), c)),
label_fix_list, dtype=tf.float32)
else :
""" Test """
def return_g_images(generator, image, code):
x = generator(image, code)
return x
self.custom_image = tf.placeholder(tf.float32, [1, self.img_height, self.img_width, self.img_ch], name='custom_image')
label_fix_list = tf.constant([idx for idx in range(self.c_dim)])
random_style_code = tf.truncated_normal(shape=[1, self.style_dim])
self.custom_fake_image = tf.map_fn(
lambda c : return_g_images(self.generator,
self.custom_image,
tf.gather(self.mapping_network(random_style_code), c)),
label_fix_list, dtype=tf.float32)
def build_model(self):
self.lr = tf.placeholder(tf.float32, name='learning_rate')
""" Input Image"""
Image_data_class = ImageData(load_size=self.img_size,
channels=self.img_ch,
data_path=self.dataset_path,
dataset_name=self.dataset_name,
selected_attrs=self.selected_attrs,
augment_flag=self.augment_flag)
Image_data_class.preprocess()
train_dataset_num = len(Image_data_class.train_dataset)
test_dataset_num = len(Image_data_class.test_dataset)
train_dataset = tf.data.Dataset.from_tensor_slices(
(Image_data_class.train_dataset,
Image_data_class.train_dataset_label,
Image_data_class.train_dataset_fix_label))
test_dataset = tf.data.Dataset.from_tensor_slices(
(Image_data_class.test_dataset,
Image_data_class.test_dataset_label,
Image_data_class.test_dataset_fix_label))
gpu_device = '/gpu:0'
train_dataset = train_dataset.\
apply(shuffle_and_repeat(train_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
test_dataset = test_dataset.\
apply(shuffle_and_repeat(test_dataset_num)).\
apply(map_and_batch(Image_data_class.image_processing, self.batch_size, num_parallel_batches=8, drop_remainder=True)).\
apply(prefetch_to_device(gpu_device, self.batch_size))
train_dataset_iterator = train_dataset.make_one_shot_iterator()
test_dataset_iterator = test_dataset.make_one_shot_iterator()
self.x_real, label_org, label_fix_list = train_dataset_iterator.get_next(
) # Input image / Original domain labels
label_trg = tf.random_shuffle(label_org) # Target domain labels
label_fix_list = tf.transpose(label_fix_list, perm=[1, 0, 2])
self.label_org = label_org
self.label_trg = label_trg
self.label_fix_list = label_fix_list
self.x_test, test_label_org, test_label_fix_list = test_dataset_iterator.get_next(
) # Input image / Original domain labels
test_label_fix_list = tf.transpose(test_label_fix_list, perm=[1, 0, 2])
self.custom_image = tf.placeholder(
tf.float32, [1, self.img_size, self.img_size, self.img_ch],
name='custom_image') # Custom Image
custom_label_fix_list = tf.transpose(create_labels(
self.custom_label, self.selected_attrs, self.dataset_name),
perm=[1, 0, 2])
""" Define Generator, Discriminator """
x_fake = self.generator(self.x_real, label_trg) # real a
x_recon = self.generator(x_fake, label_org, reuse=True) # real b
real_logit, real_cls = self.discriminator(self.x_real)
fake_logit, fake_cls = self.discriminator(x_fake, reuse=True)
""" Define Loss """
if self.gan_type.__contains__('wgan') or self.gan_type == 'dragan':
GP = self.gradient_panalty(real=self.x_real, fake=x_fake)
else:
GP = 0
g_adv_loss = generator_loss(loss_func=self.gan_type, fake=fake_logit)
g_cls_loss = classification_loss(logit=fake_cls, label=label_trg)
g_rec_loss = L1_loss(self.x_real, x_recon)
d_adv_loss = discriminator_loss(
loss_func=self.gan_type, real=real_logit, fake=fake_logit) + GP
d_cls_loss = classification_loss(logit=real_cls, label=label_org)
self.d_loss = self.adv_weight * d_adv_loss + self.cls_weight * d_cls_loss
self.g_loss = self.adv_weight * g_adv_loss + self.cls_weight * g_cls_loss + self.rec_weight * g_rec_loss
""" Result Image """
self.x_fake_list = tf.map_fn(
lambda x: self.generator(self.x_real, x, reuse=True),
label_fix_list,
dtype=tf.float32)
""" Test Image """
self.x_test_fake_list = tf.map_fn(
lambda x: self.generator(self.x_test, x, reuse=True),
test_label_fix_list,
dtype=tf.float32)
self.custom_fake_image = tf.map_fn(
lambda x: self.generator(self.custom_image, x, reuse=True),
custom_label_fix_list,
dtype=tf.float32)
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
self.g_optimizer = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.g_loss,
var_list=G_vars)
self.d_optimizer = tf.train.AdamOptimizer(self.lr,
beta1=0.5,
beta2=0.999).minimize(
self.d_loss,
var_list=D_vars)
"""" Summary """
self.Generator_loss = tf.summary.scalar("Generator_loss", self.g_loss)
self.Discriminator_loss = tf.summary.scalar("Discriminator_loss",
self.d_loss)
self.g_adv_loss = tf.summary.scalar("g_adv_loss", g_adv_loss)
self.g_cls_loss = tf.summary.scalar("g_cls_loss", g_cls_loss)
self.g_rec_loss = tf.summary.scalar("g_rec_loss", g_rec_loss)
self.d_adv_loss = tf.summary.scalar("d_adv_loss", d_adv_loss)
self.d_cls_loss = tf.summary.scalar("d_cls_loss", d_cls_loss)
self.g_summary_loss = tf.summary.merge([
self.Generator_loss, self.g_adv_loss, self.g_cls_loss,
self.g_rec_loss
])
self.d_summary_loss = tf.summary.merge(
[self.Discriminator_loss, self.d_adv_loss, self.d_cls_loss])
def build_model(self):
""" Graph """
if self.phase == 'train':
self.d_loss_per_res = {}
self.g_loss_per_res = {}
self.generator_optim = {}
self.discriminator_optim = {}
self.alpha_summary_per_res = {}
self.d_summary_per_res = {}
self.g_summary_per_res = {}
self.train_fake_images = {}
for res in self.resolutions[self.resolutions.index(self.start_res
):]:
g_loss_per_gpu = []
d_loss_per_gpu = []
train_fake_images_per_gpu = []
batch_size = self.batch_sizes.get(res, self.batch_size_base)
global_step = tf.get_variable(
'global_step_{}'.format(res),
shape=[],
dtype=tf.float32,
initializer=tf.initializers.zeros(),
trainable=False,
aggregation=tf.VariableAggregation.ONLY_FIRST_TOWER)
alpha_const, zero_constant = get_alpha_const(
self.iteration // 2, batch_size * self.gpu_num,
global_step)
# smooth transition variable
do_train_trans = self.train_with_trans[res]
alpha = tf.get_variable(
'alpha_{}'.format(res),
shape=[],
dtype=tf.float32,
initializer=tf.initializers.ones()
if do_train_trans else tf.initializers.zeros(),
trainable=False,
aggregation=tf.VariableAggregation.ONLY_FIRST_TOWER)
if do_train_trans:
alpha_assign_op = tf.assign(alpha, alpha_const)
else:
alpha_assign_op = tf.assign(alpha, zero_constant)
with tf.control_dependencies([alpha_assign_op]):
for gpu_id in range(self.gpu_num):
with tf.device(
tf.DeviceSpec(device_type="GPU",
device_index=gpu_id)):
with tf.variable_scope(tf.get_variable_scope(),
reuse=(gpu_id > 0)):
# images
gpu_device = '/gpu:{}'.format(gpu_id)
image_class = ImageData(res)
inputs = tf.data.Dataset.from_tensor_slices(
self.dataset)
inputs = inputs. \
apply(shuffle_and_repeat(self.dataset_num)). \
apply(map_and_batch(image_class.image_processing, batch_size, num_parallel_batches=16, drop_remainder=True)). \
apply(prefetch_to_device(gpu_device, None))
# When using dataset.prefetch, use buffer_size=None to let it detect optimal buffer size
inputs_iterator = inputs.make_one_shot_iterator(
)
real_img = inputs_iterator.get_next()
z = tf.random_normal(
shape=[batch_size, self.z_dim])
fake_img = self.generator(z, alpha, res)
real_img = smooth_crossfade(real_img, alpha)
real_logit = self.discriminator(
real_img, alpha, res)
fake_logit = self.discriminator(
fake_img, alpha, res)
# compute loss
d_loss, g_loss = compute_loss(
real_img, real_logit, fake_logit)
d_loss_per_gpu.append(d_loss)
g_loss_per_gpu.append(g_loss)
train_fake_images_per_gpu.append(fake_img)
print("Create graph for {} resolution".format(res))
# prepare appropriate training vars
d_vars, g_vars = filter_trainable_variables(res)
d_loss = tf.reduce_mean(d_loss_per_gpu)
g_loss = tf.reduce_mean(g_loss_per_gpu)
d_lr = self.d_learning_rates.get(res, self.learning_rate_base)
g_lr = self.g_learning_rates.get(res, self.learning_rate_base)
if self.gpu_num == 1:
colocate_grad = False
else:
colocate_grad = True
d_optim = tf.train.AdamOptimizer(
d_lr, beta1=0.0, beta2=0.99, epsilon=1e-8).minimize(
d_loss,
var_list=d_vars,
colocate_gradients_with_ops=colocate_grad)
g_optim = tf.train.AdamOptimizer(
g_lr, beta1=0.0, beta2=0.99, epsilon=1e-8).minimize(
g_loss,
var_list=g_vars,
global_step=global_step,
colocate_gradients_with_ops=colocate_grad)
self.discriminator_optim[res] = d_optim
self.generator_optim[res] = g_optim
self.d_loss_per_res[res] = d_loss
self.g_loss_per_res[res] = g_loss
self.train_fake_images[res] = tf.concat(
train_fake_images_per_gpu, axis=0)
""" Summary """
self.alpha_summary_per_res[res] = tf.summary.scalar(
"alpha_{}".format(res), alpha)
self.d_summary_per_res[res] = tf.summary.scalar(
"d_loss_{}".format(res), self.d_loss_per_res[res])
self.g_summary_per_res[res] = tf.summary.scalar(
"g_loss_{}".format(res), self.g_loss_per_res[res])
else:
"""" Testing """
test_z = tf.random_normal(shape=[self.batch_size, self.z_dim])
alpha = tf.constant(0.0, dtype=tf.float32, shape=[])
self.fake_images = self.generator(test_z,
alpha=alpha,
target_img_size=self.img_size,
is_training=False)
def build_model(self):
""" Input Image"""
img_data_class = Image_data(self.img_height, self.img_width, self.img_ch, self.dataset_path, self.augment_flag)
train_captions, train_images, test_captions, test_images, idx_to_word, word_to_idx = img_data_class.preprocess()
"""
train_captions: (8855, 10, 18), test_captions: (2933, 10, 18)
train_images: (8855,), test_images: (2933,)
idx_to_word : 5450 5450
"""
if self.phase == 'train' :
self.lr = tf.placeholder(tf.float32, name='learning_rate')
self.dataset_num = len(train_images)
img_and_caption = tf.data.Dataset.from_tensor_slices((train_images, train_captions))
gpu_device = '/gpu:0'
img_and_caption = img_and_caption.apply(shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing, batch_size=self.batch_size, num_parallel_batches=16,
drop_remainder=True)).apply(prefetch_to_device(gpu_device, None))
img_and_caption_iterator = img_and_caption.make_one_shot_iterator()
real_img_256, caption = img_and_caption_iterator.get_next()
target_sentence_index = tf.random_uniform(shape=[], minval=0, maxval=10, dtype=tf.int32)
caption = tf.gather(caption, target_sentence_index, axis=1)
word_emb, sent_emb, mask = self.rnn_encoder(caption, n_words=len(idx_to_word),
embed_dim=self.embed_dim, drop_rate=0.5, n_hidden=128, n_layers=1,
bidirectional=True, rnn_type='lstm')
noise = tf.random_normal(shape=[self.batch_size, self.z_dim], mean=0.0, stddev=1.0)
fake_imgs, _, mu, logvar = self.generator(noise, sent_emb, word_emb, mask)
real_img_64, real_img_128 = resize(real_img_256, target_size=[64, 64]), resize(real_img_256, target_size=[128, 128])
fake_img_64, fake_img_128, fake_img_256 = fake_imgs[0], fake_imgs[1], fake_imgs[2]
uncond_real_logits, cond_real_logits = self.discriminator([real_img_64, real_img_128, real_img_256], sent_emb)
_, cond_wrong_logits = self.discriminator([real_img_64[:(self.batch_size - 1)], real_img_128[:(self.batch_size - 1)], real_img_256[:(self.batch_size - 1)]], sent_emb[1:self.batch_size])
uncond_fake_logits, cond_fake_logits = self.discriminator([fake_img_64, fake_img_128, fake_img_256], sent_emb)
self.g_adv_loss, self.d_adv_loss = 0, 0
for i in range(3):
self.g_adv_loss += self.adv_weight * (generator_loss(self.gan_type, uncond_fake_logits[i]) + generator_loss(self.gan_type, cond_fake_logits[i]))
uncond_real_loss, uncond_fake_loss = discriminator_loss(self.gan_type, uncond_real_logits[i], uncond_fake_logits[i])
cond_real_loss, cond_fake_loss = discriminator_loss(self.gan_type, cond_real_logits[i], cond_fake_logits[i])
_, cond_wrong_loss = discriminator_loss(self.gan_type, None, cond_wrong_logits[i])
self.d_adv_loss += self.adv_weight * (((uncond_real_loss + cond_real_loss) / 2) + (uncond_fake_loss + cond_fake_loss + cond_wrong_loss) / 3)
self.g_kl_loss = self.kl_weight * kl_loss(mu, logvar)
self.g_loss = self.g_adv_loss + self.g_kl_loss
self.d_loss = self.d_adv_loss
self.real_img = real_img_256
self.fake_img = fake_img_256
""" Training """
t_vars = tf.trainable_variables()
G_vars = [var for var in t_vars if 'generator' in var.name]
D_vars = [var for var in t_vars if 'discriminator' in var.name]
self.g_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.g_loss, var_list=G_vars)
self.d_optim = tf.train.AdamOptimizer(self.lr, beta1=0.5, beta2=0.999).minimize(self.d_loss,var_list=D_vars)
"""" Summary """
self.summary_g_loss = tf.summary.scalar("g_loss", self.g_loss)
self.summary_d_loss = tf.summary.scalar("d_loss", self.d_loss)
self.summary_g_adv_loss = tf.summary.scalar("g_adv_loss", self.g_adv_loss)
self.summary_g_kl_loss = tf.summary.scalar("g_kl_loss", self.g_kl_loss)
self.summary_d_adv_loss = tf.summary.scalar("d_adv_loss", self.d_adv_loss)
g_summary_list = [self.summary_g_loss,
self.summary_g_adv_loss, self.summary_g_kl_loss]
d_summary_list = [self.summary_d_loss,
self.summary_d_adv_loss]
self.summary_merge_g_loss = tf.summary.merge(g_summary_list)
self.summary_merge_d_loss = tf.summary.merge(d_summary_list)
else :
""" Test """
self.dataset_num = len(test_captions)
gpu_device = '/gpu:0'
img_and_caption = tf.data.Dataset.from_tensor_slices((test_images, test_captions))
img_and_caption = img_and_caption.apply(
shuffle_and_repeat(self.dataset_num)).apply(
map_and_batch(img_data_class.image_processing, batch_size=self.batch_size, num_parallel_batches=16, drop_remainder=True)).apply(
prefetch_to_device(gpu_device, None))
img_and_caption_iterator = img_and_caption.make_one_shot_iterator()
real_img_256, caption = img_and_caption_iterator.get_next()
target_sentence_index = tf.random_uniform(shape=[], minval=0, maxval=10, dtype=tf.int32)
caption = tf.gather(caption, target_sentence_index, axis=1)
word_emb, sent_emb, mask = self.rnn_encoder(caption, n_words=len(idx_to_word),
embed_dim=self.embed_dim, drop_rate=0.5, n_hidden=128,
n_layers=1,
bidirectional=True, rnn_type='lstm',
is_training=False)
noise = tf.random_normal(shape=[self.batch_size, self.z_dim], mean=0.0, stddev=1.0)
fake_imgs, _, _, _ = self.generator(noise, sent_emb, word_emb, mask, is_training=False)
self.test_real_img = real_img_256
self.test_fake_img = fake_imgs[2]