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
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
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
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 compute_fid(Gs,
minibatch_size,
dataset_obj,
iter_number,
lod=0,
num_images=10000,
printing=True):
# Initialize metrics.
from metrics.frechet_inception_distance import API as class_def
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)
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)
# Print table header
if printing:
print(flush=True)
print('%-10s%-12s' % ('KIMG', 'Time_eval'), end='', flush=True)
print('%-12s' % ('FID'), end='', flush=True)
print(flush=True)
print('%-10s%-12s%-12s' % ('---', '---', '---'), end='', flush=True)
print(flush=True)
# Feed in reals.
print('%-10s' % "Reals", end='', flush=True)
time_begin = time.time()
labels = np.zeros([num_images, dataset_obj.label_size], dtype=np.float32)
obj.begin(mode)
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,
lod=lod)
if images.shape[1] == 1:
images = np.tile(images, [1, 3, 1, 1]) # grayscale => RGB
obj.feed(mode, images)
results = obj.end(mode)
if printing:
print('%-12s' % misc.format_time(time.time() - time_begin),
end='',
flush=True)
print(results[0], end='', flush=True)
print(flush=True)
# Evaluate each network snapshot.
if printing:
print('%-10d' % iter_number, end='', flush=True)
mode = 'fakes'
obj.begin(mode)
time_begin = time.time()
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)
results = obj.end(mode)
if printing:
print('%-12s' % misc.format_time(time.time() - time_begin),
end='',
flush=True)
print(results[0], end='', flush=True)
print(flush=True)
return results[0]
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()