def load(self, dataset_dir):
import glob
abs_dirname = os.path.join(submit.get_path_from_template(dataset_dir), '*')
fnames = sorted(glob.glob(abs_dirname))
if len(fnames) == 0:
print('\nERROR: No files found using the following glob pattern:', abs_dirname, '\n')
sys.exit(1)
images = []
for fname in fnames:
try:
if config.is_image_npy():
im = np.load(fname)
else:
if config.get_nb_channels() == 1:
im = PIL.Image.open(fname).convert('L')
else:
im = PIL.Image.open(fname).convert('RGB')
arr = np.array(im, dtype=np.float32)
# If only one channel, we can have arr.shape = (256, 256) instead of (1, 256, 256) or (256, 256, 1)
if len(arr.shape) == 2:
reshaped = np.array([arr / 255.0 - 0.5])
elif config.is_image_npy():
reshaped = arr / 255.0 - 0.5
else:
reshaped = arr.transpose([2, 0, 1]) / 255.0 - 0.5
images.append(reshaped)
except OSError as e:
print('Skipping file', fname, 'due to error: ', e)
self.images = images
def train(args):
if args:
n2n = args.noise2noise if 'noise2noise' in args else True
train_config.noise2noise = n2n
if 'long_train' in args and args.long_train:
train_config.iteration_count = 500000
train_config.eval_interval = 100
train_config.ramp_down_perc = 0.5
else:
print('running with defaults in train_config')
noise = 'gaussian'
if 'noise' in args:
if args.noise not in corruption_types:
error('Unknown noise type', args.noise)
else:
noise = args.noise
train_config.noise = corruption_types[noise]
if train_config.noise2noise:
submit_config.run_desc += "-n2n"
else:
submit_config.run_desc += "-n2c"
if 'train_tfrecords' in args and args.train_tfrecords is not None:
train_config.train_tfrecords = submit.get_path_from_template(
args.train_tfrecords)
print(train_config)
dnnlib.submission.submit.submit_run(submit_config, **train_config)
def train(args):
if args:
if 'long_train' in args and args.long_train:
train_config.iteration_count = 500000
train_config.eval_interval = 5000
train_config.ramp_down_perc = 0.5
else:
print ('running with defaults in train_config')
noise = 'gaussian'
train_config.train_tfrecords = submit.get_path_from_template(args.tfrecords)
print (train_config.train_tfrecords)
dnnlib.submission.submit.submit_run(submit_config, **train_config)
def train(in_args):
""" Read the arguments given to train and lauch the training
:param in_args:
:return:
"""
# Reading 'noise2noise" and 'long-run' args
if in_args:
n2n = in_args.noise2noise if 'noise2noise' in in_args else True
train_config.noise2noise = n2n
if 'long_train' in in_args and in_args.long_train:
train_config.iteration_count = 500000
train_config.eval_interval = 1000
train_config.ramp_down_perc = 0.5
else:
print('running with defaults in train_config')
# Reading 'noise' argument
noise = 'gaussian'
if 'noise' in in_args:
if in_args.noise in corruption_types:
noise = in_args.noise
else:
error('Unknown noise type', in_args.noise)
train_config.noise = corruption_types[noise]
# Reading type of training : noise 2 noise or noise 2 clean
# NB : default == noise 2 noise
if train_config.noise2noise:
submit_config.run_desc += "-n2n"
else:
submit_config.run_desc += "-n2c"
# Reading the 'tfrecords' directory argument
if 'train_tfrecords' in in_args and in_args.train_tfrecords is not None:
train_config.train_tfrecords = submit.get_path_from_template(in_args.train_tfrecords)
# Reading the validation directory
val_dir = 'default'
if 'val_dir' in in_args:
if in_args.val_dir not in val_datasets:
error('Unknown validation directory', in_args.val_dir)
else:
val_dir = in_args.val_dir
train_config.validation_config = val_datasets[val_dir]
# Finally, printing the config and launching the training
print(train_config)
dnnlib.submission.submit.submit_run(submit_config, **train_config)
def load(self, dataset_dir):
import glob
fnames = sorted(
glob.glob(
os.path.join(submit.get_path_from_template(dataset_dir), '*')))
images = []
for fname in fnames:
try:
im = PIL.Image.open(fname).convert('RGB')
arr = np.array(im, dtype=np.float32)
reshaped = arr.transpose([2, 0, 1]) / 255.0 - 0.5
images.append(reshaped)
except OSError as e:
print('Skipping file', fname, 'due to error: ', e)
self.images = images
def load(self, dataset_dir):
import glob
abs_dirname = os.path.join(submit.get_path_from_template(dataset_dir),
'*')
fnames = sorted(glob.glob(abs_dirname))
if len(fnames) == 0:
print('\nERROR: No files found using the following glob pattern:',
abs_dirname, '\n')
sys.exit(1)
images = []
for fname in fnames:
try:
im = PIL.Image.open(fname).convert('RGB')
arr = np.array(im, dtype=np.float32)
reshaped = arr.transpose([2, 0, 1]) / 255.0 - 0.5
images.append(reshaped)
except OSError as e:
print('Skipping file', fname, 'due to error: ', e)
self.images = images
def load(self, dataset_dir):
import glob
abs_dirname = os.path.join(submit.get_path_from_template(dataset_dir),
'*')
fnames = sorted(glob.glob(abs_dirname))
if len(fnames) == 0:
print('\nERROR: No files found using the following glob pattern:',
abs_dirname, '\n')
sys.exit(1)
images = []
for fname in fnames:
try:
#im = PIL.Image.open(fname).convert('RGB')
#arr = np.array(im, dtype=np.float32)
#arr = np.mean(im, axis=0)
#reshaped = arr.transpose([2, 0, 1]) / 255.0 - 0.5
try:
im = np.load(fname)
except:
im = PIL.Image.open(fname).convert('RGB')
if len(np.array(im).shape) == 3:
im = np.mean(im, axis=-1)
arr = np.array(im, dtype=np.float32)
arr = arr[0:256, 0:256]
arr = np.expand_dims(arr, axis=2)
reshaped = arr.transpose([2, 0, 1])
reshaped = (reshaped - reshaped.min()) / (reshaped.max() -
reshaped.min()) - 0.5
images.append(reshaped)
except OSError as e:
print('Skipping file', fname, 'due to error: ', e)
self.images = images
def load_snapshot(fname):
fname = os.path.join(submit.get_path_from_template(fname))
with open(fname, "rb") as f:
return pickle.load(f)
