def __init__(self,
img_scale=None,
max_stage=3,
multiscale_mode='range',
ratio_range=None,
keep_ratio=True):
self.max_stage = max_stage
if img_scale is None:
self.img_scale = None
else:
if isinstance(img_scale, list):
self.img_scale = img_scale
else:
self.img_scale = [img_scale]
assert commons.is_list_of(self.img_scale, tuple)
if ratio_range is not None:
# mode 1: given a scale and a range of image ratio
assert len(self.img_scale) == 1
else:
# mode 2: given multiple scales or a range of scales
assert multiscale_mode in ['value', 'range']
self.multiscale_mode = multiscale_mode
self.ratio_range = ratio_range
self.keep_ratio = keep_ratio
def random_sample(img_scales):
assert commons.is_list_of(img_scales, tuple) and len(img_scales) == 2
img_scale_long = [max(s) for s in img_scales]
img_scale_short = [min(s) for s in img_scales]
long_edge = np.random.randint(min(img_scale_long),
max(img_scale_long) + 1)
short_edge = np.random.randint(min(img_scale_short),
max(img_scale_short) + 1)
img_scale = (long_edge, short_edge)
return img_scale, None
def run(self, data_loaders, workflow, max_epochs, **kwargs):
"""Start running.
Args:
data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
and validation.
workflow (list[tuple]): A list of (phase, epochs) to specify the
running order and epochs. E.g, [('train', 2), ('val', 1)] means
running 2 epochs for training and 1 epoch for validation,
iteratively.
max_epochs (int): Total training epochs.
"""
assert isinstance(data_loaders, list)
assert commons.is_list_of(workflow, tuple)
assert len(data_loaders) == len(workflow)
self._max_epochs = max_epochs
for i, flow in enumerate(workflow):
mode, epochs = flow
if mode == 'train':
self._max_iters = self._max_epochs * len(data_loaders[i])
break
work_dir = self.work_dir if self.work_dir is not None else 'NONE'
self.logger.info('Start running, host: %s, work_dir: %s',
get_host_info(), work_dir)
self.logger.info('workflow: %s, max: %d epochs', workflow, max_epochs)
self.call_hook('before_run')
while self.epoch < max_epochs:
for i, flow in enumerate(workflow):
mode, epochs = flow
if isinstance(mode, str): # self.train()
if not hasattr(self, mode):
raise ValueError(
f'runner has no method named "{mode}" to run an '
'epoch')
epoch_runner = getattr(self, mode)
else:
raise TypeError(
'mode in workflow must be a str, but got {}'.format(
type(mode)))
for _ in range(epochs):
if mode == 'train' and self.epoch >= max_epochs:
return
epoch_runner(data_loaders[i], **kwargs)
time.sleep(1) # wait for some hooks like loggers to finish
self.call_hook('after_run')
def run(self, data_loaders, workflow, max_iters, **kwargs):
"""Start running.
Args:
data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
and validation.
workflow (list[tuple]): A list of (phase, iters) to specify the
running order and iterations. E.g, [('train', 10000),
('val', 1000)] means running 10000 iterations for training and
1000 iterations for validation, iteratively.
max_iters (int): Total training iterations.
"""
assert isinstance(data_loaders, list)
assert commons.is_list_of(workflow, tuple)
assert len(data_loaders) == len(workflow)
self._max_iters = max_iters
work_dir = self.work_dir if self.work_dir is not None else 'NONE'
self.logger.info('Start running, host: %s, work_dir: %s',
get_host_info(), work_dir)
self.logger.info('workflow: %s, max: %d iters', workflow, max_iters)
self.call_hook('before_run')
iter_loaders = [IterLoader(x) for x in data_loaders]
self.call_hook('before_epoch')
while self.iter < max_iters:
for i, flow in enumerate(workflow):
self._inner_iter = 0
mode, iters = flow
if not isinstance(mode, str) or not hasattr(self, mode):
raise ValueError(
'runner has no method named "{}" to run a workflow'.
format(mode))
iter_runner = getattr(self, mode)
for _ in range(iters):
if mode == 'train' and self.iter >= max_iters:
return
iter_runner(iter_loaders[i], **kwargs)
time.sleep(1) # wait for some hooks like loggers to finish
self.call_hook('after_epoch')
self.call_hook('after_run')
def random_select(img_scales):
assert commons.is_list_of(img_scales, tuple)
scale_idx = np.random.randint(len(img_scales))
img_scale = img_scales[scale_idx]
return img_scale, scale_idx