def generate_training_video(run_id, duration_sec=20.0, time_warp=1.5, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M'):
src_result_subdir = misc.locate_result_subdir(run_id)
if mp4 is None:
mp4 = os.path.basename(src_result_subdir) + '-train.mp4'
# Parse log.
times = []
snaps = [] # [(png, kimg, lod), ...]
with open(os.path.join(src_result_subdir, 'log.txt'), 'rt') as log:
for line in log:
k = re.search(r'kimg ([\d\.]+) ', line)
l = re.search(r'lod ([\d\.]+) ', line)
t = re.search(r'time (\d+d)? *(\d+h)? *(\d+m)? *(\d+s)? ', line)
if k and l and t:
k = float(k.group(1))
l = float(l.group(1))
t = [int(t.group(i)[:-1]) if t.group(i) else 0 for i in range(1, 5)]
t = t[0] * 24*60*60 + t[1] * 60*60 + t[2] * 60 + t[3]
png = os.path.join(src_result_subdir, 'fakes%06d.png' % int(np.floor(k)))
if os.path.isfile(png):
times.append(t)
snaps.append((png, k, l))
assert len(times)
# Frame generation func for moviepy.
png_cache = [None, None] # [png, img]
def make_frame(t):
wallclock = ((t / duration_sec) ** time_warp) * times[-1]
png, kimg, lod = snaps[max(bisect.bisect(times, wallclock) - 1, 0)]
if png_cache[0] == png:
img = png_cache[1]
else:
img = scipy.misc.imread(png)
# Check if the img is greyscale. If so, it needs to be converted to RGB, since MoviePy expects it in that format
if img.ndim == 2:
tmpimg = img
img = np.zeros([tmpimg.shape[0], tmpimg.shape[1], 3], tmpimg.dtype)
img[:,:,0] = tmpimg
img[:,:,1] = tmpimg
img[:,:,2] = tmpimg
while img.shape[1] > 1920 or img.shape[0] > 1080:
img = img.astype(np.float32).reshape(img.shape[0]//2, 2, img.shape[1]//2, 2, -1).mean(axis=(1,3))
png_cache[:] = [png, img]
img = misc.draw_text_label(img, 'lod %.2f' % lod, 16, img.shape[0]-4, alignx=0.0, aligny=1.0)
img = misc.draw_text_label(img, misc.format_time(int(np.rint(wallclock))), img.shape[1]//2, img.shape[0]-4, alignx=0.5, aligny=1.0)
img = misc.draw_text_label(img, '%.0f kimg' % kimg, img.shape[1]-16, img.shape[0]-4, alignx=1.0, aligny=1.0)
return img
# Generate video.
import moviepy.editor # pip install moviepy
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def generate_training_video(run_id, duration_sec=20.0, time_warp=1.5, mp4=None, mp4_fps=30, mp4_codec='libx265', mp4_bitrate='16M'):
src_result_subdir = misc.locate_result_subdir(run_id)
if mp4 is None:
mp4 = os.path.basename(src_result_subdir) + '-train.mp4'
# Parse log.
times = []
snaps = [] # [(png, kimg, lod), ...]
with open(os.path.join(src_result_subdir, 'log.txt'), 'rt') as log:
for line in log:
k = re.search(r'kimg ([\d\.]+) ', line)
l = re.search(r'lod ([\d\.]+) ', line)
t = re.search(r'time (\d+d)? *(\d+h)? *(\d+m)? *(\d+s)? ', line)
if k and l and t:
k = float(k.group(1))
l = float(l.group(1))
t = [int(t.group(i)[:-1]) if t.group(i) else 0 for i in range(1, 5)]
t = t[0] * 24*60*60 + t[1] * 60*60 + t[2] * 60 + t[3]
png = os.path.join(src_result_subdir, 'fakes%06d.png' % int(np.floor(k)))
if os.path.isfile(png):
times.append(t)
snaps.append((png, k, l))
assert len(times)
# Frame generation func for moviepy.
png_cache = [None, None] # [png, img]
def make_frame(t):
wallclock = ((t / duration_sec) ** time_warp) * times[-1]
png, kimg, lod = snaps[max(bisect.bisect(times, wallclock) - 1, 0)]
if png_cache[0] == png:
img = png_cache[1]
else:
img = scipy.misc.imread(png)
while img.shape[1] > 1920 or img.shape[0] > 1080:
img = img.astype(np.float32).reshape(img.shape[0]//2, 2, img.shape[1]//2, 2, -1).mean(axis=(1,3))
png_cache[:] = [png, img]
img = misc.draw_text_label(img, 'lod %.2f' % lod, 16, img.shape[0]-4, alignx=0.0, aligny=1.0)
img = misc.draw_text_label(img, misc.format_time(int(np.rint(wallclock))), img.shape[1]//2, img.shape[0]-4, alignx=0.5, aligny=1.0)
img = misc.draw_text_label(img, '%.0f kimg' % kimg, img.shape[1]-16, img.shape[0]-4, alignx=1.0, aligny=1.0)
return img
# Generate video.
import moviepy.editor # pip install moviepy
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
moviepy.editor.VideoClip(make_frame, duration=duration_sec).write_videofile(os.path.join(result_subdir, mp4), fps=mp4_fps, codec='libx264', bitrate=mp4_bitrate)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
def anomaly_detection_encoder(run_id,
test_data_folder,
log,
test_batch_size=10,
start_at_batch=0,
end_at_batch=10):
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=True)
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
print('# snapshot_pkls: ' + str(len(snapshot_pkls)))
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
#Load network from specific run
G, D, Gs, E = misc.load_pkl(snapshot_pkls[-1])
# Take off the requirement of the generator having labels
Ga = tfutil.Network('G_anomaly',
num_channels=Gs.output_shapes[0][1],
resolution=Gs.output_shapes[0][2],
label_size=dataset_obj.label_size,
**config.G_anomaly)
Ga.copy_vars_from(Gs)
print("Initializing Anomaly detector")
anoGAN = tfutil.AnomalyDetectorEncoder(config,
Ga,
E,
test_data_folder,
test_batch_size=test_batch_size)
print('# AnoGAN test data names: ' + str(len(anoGAN.test_data_names)))
for batch in range(anoGAN.filename_batches.__len__()):
if batch < start_at_batch:
continue
test_data = anoGAN.preprocess_img(anoGAN.filename_batches[batch])
test_input = test_data
test_name = anoGAN.filename_batches[batch]
anoGAN.find_closest_match(test_input, test_name)
print(f'Batch {batch} complete..')
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 generate_training_video(
run_id,
duration_sec=20.0,
time_warp=1.5,
mp4=None,
mp4_fps=30,
mp4_codec="libx265",
mp4_bitrate="16M",
):
src_result_subdir = misc.locate_result_subdir(run_id)
if mp4 is None:
mp4 = os.path.basename(src_result_subdir) + "-train.mp4"
# Parse log.
times = []
snaps = [] # [(png, kimg, lod), ...]
with open(os.path.join(src_result_subdir, "log.txt"), "rt") as log:
for line in log:
k = re.search(r"kimg ([\d\.]+) ", line)
l = re.search(r"lod ([\d\.]+) ", line)
t = re.search(r"time (\d+d)? *(\d+h)? *(\d+m)? *(\d+s)? ", line)
if k and l and t:
k = float(k.group(1))
l = float(l.group(1))
t = [
int(t.group(i)[:-1]) if t.group(i) else 0
for i in range(1, 5)
]
t = t[0] * 24 * 60 * 60 + t[1] * 60 * 60 + t[2] * 60 + t[3]
png = os.path.join(src_result_subdir,
"fakes%06d.png" % int(np.floor(k)))
if os.path.isfile(png):
times.append(t)
snaps.append((png, k, l))
assert len(times)
# Frame generation func for moviepy.
png_cache = [None, None] # [png, img]
def make_frame(t):
wallclock = ((t / duration_sec)**time_warp) * times[-1]
png, kimg, lod = snaps[max(bisect.bisect(times, wallclock) - 1, 0)]
if png_cache[0] == png:
img = png_cache[1]
else:
img = scipy.misc.imread(png)
while img.shape[1] > 1920 or img.shape[0] > 1080:
img = (img.astype(np.float32).reshape(img.shape[0] // 2, 2,
img.shape[1] // 2, 2,
-1).mean(axis=(1, 3)))
png_cache[:] = [png, img]
img = misc.draw_text_label(img,
"lod %.2f" % lod,
16,
img.shape[0] - 4,
alignx=0.0,
aligny=1.0)
img = misc.draw_text_label(
img,
misc.format_time(int(np.rint(wallclock))),
img.shape[1] // 2,
img.shape[0] - 4,
alignx=0.5,
aligny=1.0,
)
img = misc.draw_text_label(
img,
"%.0f kimg" % kimg,
img.shape[1] - 16,
img.shape[0] - 4,
alignx=1.0,
aligny=1.0,
)
return img
# Generate video.
import moviepy.editor # pip install moviepy
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
moviepy.editor.VideoClip(make_frame,
duration=duration_sec).write_videofile(
os.path.join(result_subdir, mp4),
fps=mp4_fps,
codec="libx264",
bitrate=mp4_bitrate,
)
open(os.path.join(result_subdir, "_done.txt"), "wt").close()
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 anomaly_detection_encoder(run_id,
log,
test_data_folder,
test_batch_size=64,
n_samples=1000):
result_subdir = misc.locate_result_subdir(run_id)
snapshot_pkls = misc.list_network_pkls(result_subdir, include_final=False)
print('# snapshot_pkls: ' + str(len(snapshot_pkls)))
for idx in range(0, n_samples, test_batch_size):
with tf.Graph().as_default(), tfutil.create_session(
config.tf_config).as_default():
#Load network from specific run
G, D, Gs, E = misc.load_pkl(snapshot_pkls[-1])
print(snapshot_pkls[-1])
dataset_obj, mirror_augment = misc.load_dataset_for_previous_run(
result_subdir, verbose=True, shuffle_mb=0)
Ga = tfutil.Network('G_anomaly',
num_channels=G.output_shapes[0][1],
resolution=G.output_shapes[0][2],
label_size=dataset_obj.label_size,
**config.G_anomaly)
Ga.copy_vars_from(Gs)
Da_Gout = tfutil.Network('D_anomaly_Gout',
num_channels=G.output_shapes[0][1],
resolution=G.output_shapes[0][2],
label_size=dataset_obj.label_size,
images_in=Ga.output_templates[0],
**config.D_anomaly_Gout)
image_dims = [
G.output_shapes[0][1], G.output_shapes[0][2],
G.output_shapes[0][3]
]
Da_test = tfutil.Network('D_anomaly_test',
num_channels=G.output_shapes[0][1],
resolution=G.output_shapes[0][2],
label_size=dataset_obj.label_size,
**config.D_anomaly_test)
Da_Gout.copy_vars_from(D)
Da_test.copy_vars_from(D)
Da_Gout.print_layers()
Da_test.print_layers()
E.print_layers()
print("Initializing Anomaly detector")
anoGAN = tfutil.AnomalyDetectorEncoder(config, Ga, Da_Gout,
Da_test, E,
test_data_folder)
print('# AnoGAN test data names: ' +
str(len(anoGAN.test_data_names)))
assert len(anoGAN.test_data_names) > 0
test_input = anoGAN.test_data[idx:idx + test_batch_size]
test_name = anoGAN.test_data_names[idx:idx + test_batch_size]
anoGAN.find_closest_match(test_input, test_name)
tf.reset_default_graph()
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()
