def load_dataset(class_name='dataset.TFRecordDataset', verbose=False, **kwargs):
adjusted_kwargs = dict(kwargs)
if verbose:
print('Streaming data using %s...' % class_name)
dataset = tfutil.import_obj(class_name)(**adjusted_kwargs)
if verbose:
print('Dataset shape =', np.int32(dataset.shape).tolist())
print('Dynamic range =', dataset.dynamic_range)
print('Label size =', dataset.label_size)
return dataset
def load_dataset(class_name='dataset.TFRecordDataset', data_dir=None, verbose=False, **kwargs):
adjusted_kwargs = dict(kwargs)
if 'tfrecord_dir' in adjusted_kwargs and data_dir is not None:
adjusted_kwargs['tfrecord_dir'] = os.path.join(data_dir, adjusted_kwargs['tfrecord_dir'])
if verbose:
print('Streaming data using %s...' % class_name)
dataset = tfutil.import_obj(class_name)(**adjusted_kwargs)
if verbose:
print('Dataset shape =', np.int32(dataset.shape).tolist())
print('Dynamic range =', dataset.dynamic_range)
print('Label size =', dataset.label_size)
return dataset
def load_dataset(class_name='dataset.TFRecordDataset', data_dir=None, verbose=False, **kwargs):
adjusted_kwargs = dict(kwargs)
if 'tfrecord_dir' in adjusted_kwargs and data_dir is not None:
adjusted_kwargs['tfrecord_dir'] = os.path.join(data_dir, adjusted_kwargs['tfrecord_dir'])
if verbose:
print('Streaming data using %s...' % class_name)
dataset = tfutil.import_obj(class_name)(**adjusted_kwargs)
if verbose:
print('Dataset shape =', np.int32(dataset.shape).tolist())
print('Dynamic range =', dataset.dynamic_range)
print('Label size =', dataset.label_size)
return dataset
def evaluate_metrics(run_id,
log,
metrics,
num_images,
real_passes,
minibatch_size=None):
metric_class_names = {
'swd': 'metrics.sliced_wasserstein.API',
'fid': 'metrics.frechet_inception_distance.API',
'is': 'metrics.inception_score.API',
'msssim': 'metrics.ms_ssim.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
assert len(snapshot_pkls) >= 1
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
if minibatch_size is None:
minibatch_size = np.clip(8192 // dataset_obj.shape[1], 4, 256)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(num_images=num_images,
image_shape=image_shape,
image_dtype=np.uint8,
minibatch_size=minibatch_size)
tfutil.init_uninited_vars()
mode = 'warmup'
obj.begin(mode)
for idx in range(10):
obj.feed(
mode,
np.random.randint(0,
256,
size=[minibatch_size] + image_shape,
dtype=np.uint8))
obj.end(mode)
metric_objs.append(obj)
# Print table header.
print()
print('%-10s%-12s' % ('Snapshot', 'Time_eval'), end='')
for obj in metric_objs:
for name, fmt in zip(obj.get_metric_names(),
obj.get_metric_formatting()):
print('%-*s' % (len(fmt % 0), name), end='')
print()
print('%-10s%-12s' % ('---', '---'), end='')
for obj in metric_objs:
for fmt in obj.get_metric_formatting():
print('%-*s' % (len(fmt % 0), '---'), end='')
print()
# Feed in reals.
for title, mode in [('Reals', 'reals'), ('Reals2', 'fakes')][:real_passes]:
print('%-10s' % title, end='')
time_begin = time.time()
labels = np.zeros([num_images, dataset_obj.label_size],
dtype=np.float32)
[obj.begin(mode) for obj in metric_objs]
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
images, labels[begin:end] = dataset_obj.get_minibatch_np(end -
begin)
if mirror_augment:
images = misc.apply_mirror_augment(images)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
# Evaluate each network snapshot.
for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
prefix = 'network-snapshot-'
postfix = '.pkl'
snapshot_name = os.path.basename(snapshot_pkl)
assert snapshot_name.startswith(prefix) and snapshot_name.endswith(
postfix)
snapshot_kimg = int(snapshot_name[len(prefix):-len(postfix)])
print('%-10d' % snapshot_kimg, end='')
mode = 'fakes'
[obj.begin(mode) for obj in metric_objs]
time_begin = time.time()
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(snapshot_pkl)
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
latents = misc.random_latents(end - begin, Gs)
images = Gs.run(latents,
labels[begin:end],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
print()
def evaluate_metrics_swd_distributions_training_trad_prog(
run_id,
network_dir_conv,
network_dir_prog,
log,
metrics,
num_images_per_group,
num_groups,
real_passes,
minibatch_size=None):
metric_class_names = {
'swd_distri_training_trad_prog':
'metrics.swd_distributions_training_trad_prog.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(image_shape=image_shape, image_dtype=np.uint8)
tfutil.init_uninited_vars()
metric_objs.append(obj)
mode = 'fakes'
[obj.begin(mode) for obj in metric_objs]
images_real, labels = dataset_obj.get_minibatch_np(
num_groups * num_images_per_group)
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(network_dir_conv)
#G, D, Gs = pickle.load(file)
latents = misc.random_latents(num_groups * num_images_per_group,
Gs)
images = images_real
for k in range(
10
): # because Gs can not generate lots of (>3000 around) images at one time. Make sure /10 = int
nn = int(num_groups * num_images_per_group / 10)
images_fake = Gs.run(latents[k * nn:(k + 1) * nn],
labels[k * nn:(k + 1) * nn],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
images = np.concatenate((images, images_fake), axis=0)
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(network_dir_prog)
# G, D, Gs = pickle.load(file)
latents = misc.random_latents(num_groups * num_images_per_group,
Gs)
for k in range(
10
): # because Gs can not generate lots of (>3000 around) images at one time. Make sure /10 = int
nn = int(num_groups * num_images_per_group / 10)
images_fake = Gs.run(latents[k * nn:(k + 1) * nn],
labels[k * nn:(k + 1) * nn],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
images = np.concatenate((images, images_fake), axis=0)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[
obj.feed(mode, images, num_images_per_group, num_groups,
result_subdir) for obj in metric_objs
]
def evaluate_metrics_swd_distributions(run_id,
log,
metrics,
num_images_per_group,
num_groups,
real_passes,
minibatch_size=None):
metric_class_names = {
'swd_distri': 'metrics.swd_distributions.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
assert len(snapshot_pkls) >= 1
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(image_shape=image_shape, image_dtype=np.uint8)
tfutil.init_uninited_vars()
metric_objs.append(obj)
# Evaluate each network snapshot.
for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
prefix = 'network-snapshot-'
postfix = '.pkl'
snapshot_name = os.path.basename(snapshot_pkl)
assert snapshot_name.startswith(prefix) and snapshot_name.endswith(
postfix)
snapshot_kimg = int(snapshot_name[len(prefix):-len(postfix)])
print('%-10d' % snapshot_kimg, end='')
mode = 'fakes'
[obj.begin(mode) for obj in metric_objs]
images_real, labels = dataset_obj.get_minibatch_np(
num_groups * num_images_per_group)
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
G, D, Gs = misc.load_pkl(snapshot_pkl)
latents = misc.random_latents(num_groups * num_images_per_group,
Gs)
images = images_real
for k in range(
10
): # because Gs can not generate lots of (>3000 around) images at one time. Make sure /10 = int
nn = int(num_groups * num_images_per_group / 10)
images_fake = Gs.run(latents[k * nn:(k + 1) * nn],
labels[k * nn:(k + 1) * nn],
num_gpus=config.num_gpus,
out_mul=127.5,
out_add=127.5,
out_dtype=np.uint8)
images = np.concatenate((images, images_fake), axis=0)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[
obj.feed(mode, images, num_images_per_group, num_groups,
snapshot_kimg, result_subdir) for obj in metric_objs
]
def evaluate_metrics(run_id, log, metrics, num_images, real_passes, minibatch_size=None):
metric_class_names = {
'swd': 'metrics.sliced_wasserstein.API',
'fid': 'metrics.frechet_inception_distance.API',
'is': 'metrics.inception_score.API',
'msssim': 'metrics.ms_ssim.API',
}
# Locate training run and initialize logging.
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
assert len(snapshot_pkls) >= 1
log_file = os.path.join(result_subdir, log)
print('Logging output to', log_file)
misc.set_output_log_file(log_file)
# Initialize dataset and select minibatch size.
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(result_subdir, verbose=True, shuffle_mb=0)
if minibatch_size is None:
minibatch_size = np.clip(8192 // dataset_obj.shape[1], 4, 256)
# Initialize metrics.
metric_objs = []
for name in metrics:
class_name = metric_class_names.get(name, name)
print('Initializing %s...' % class_name)
class_def = tfutil.import_obj(class_name)
image_shape = [3] + dataset_obj.shape[1:]
obj = class_def(num_images=num_images, image_shape=image_shape, image_dtype=np.uint8, minibatch_size=minibatch_size)
tfutil.init_uninited_vars()
mode = 'warmup'
obj.begin(mode)
for idx in range(10):
obj.feed(mode, np.random.randint(0, 256, size=[minibatch_size]+image_shape, dtype=np.uint8))
obj.end(mode)
metric_objs.append(obj)
# Print table header.
print()
print('%-10s%-12s' % ('Snapshot', 'Time_eval'), end='')
for obj in metric_objs:
for name, fmt in zip(obj.get_metric_names(), obj.get_metric_formatting()):
print('%-*s' % (len(fmt % 0), name), end='')
print()
print('%-10s%-12s' % ('---', '---'), end='')
for obj in metric_objs:
for fmt in obj.get_metric_formatting():
print('%-*s' % (len(fmt % 0), '---'), end='')
print()
# Feed in reals.
for title, mode in [('Reals', 'reals'), ('Reals2', 'fakes')][:real_passes]:
print('%-10s' % title, end='')
time_begin = time.time()
labels = np.zeros([num_images, dataset_obj.label_size], dtype=np.float32)
[obj.begin(mode) for obj in metric_objs]
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
images, labels[begin:end] = dataset_obj.get_minibatch_np(end - begin)
if mirror_augment:
images = misc.apply_mirror_augment(images)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
# Evaluate each network snapshot.
for snapshot_idx, snapshot_pkl in enumerate(reversed(snapshot_pkls)):
prefix = 'network-snapshot-'; postfix = '.pkl'
snapshot_name = os.path.basename(snapshot_pkl)
assert snapshot_name.startswith(prefix) and snapshot_name.endswith(postfix)
snapshot_kimg = int(snapshot_name[len(prefix) : -len(postfix)])
print('%-10d' % snapshot_kimg, end='')
mode ='fakes'
[obj.begin(mode) for obj in metric_objs]
time_begin = time.time()
with tf.Graph().as_default(), tfutil.create_session(config.tf_config).as_default():
G, D, Gs = misc.load_pkl(snapshot_pkl)
for begin in range(0, num_images, minibatch_size):
end = min(begin + minibatch_size, num_images)
latents = misc.random_latents(end - begin, Gs)
images = Gs.run(latents, labels[begin:end], num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_dtype=np.uint8)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
[obj.feed(mode, images) for obj in metric_objs]
results = [obj.end(mode) for obj in metric_objs]
print('%-12s' % misc.format_time(time.time() - time_begin), end='')
for obj, vals in zip(metric_objs, results):
for val, fmt in zip(vals, obj.get_metric_formatting()):
print(fmt % val, end='')
print()
print()
