def random_sample(img_scales):
assert torchie.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`]);
workflow (list[tuple]): A list of (phase, epochs) to specify the running order and epochs;
max_epochs (int);
"""
assert isinstance(data_loaders, list)
assert torchie.is_list_of(workflow, tuple)
assert len(data_loaders) == len(workflow)
self._max_epochs = max_epochs
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") # for summarywriter
while self.epoch < max_epochs:
for i, flow in enumerate(workflow):
mode, epochs = flow
if isinstance(mode, str):
if not hasattr(self, mode):
raise ValueError(
"Trainer has no method named '{}' to run an epoch".
format(mode))
epoch_runner = getattr(
self, mode) # todo: get self.train() or self.val()
elif callable(mode):
epoch_runner = mode
else:
raise TypeError(
"mode in workflow must be a str or callable function not '{}'"
.format(type(mode)))
for _ in range(
epochs
): # todo: epoches=1 for val mode; epoches=5 for train mode
if mode == "train" and self.epoch > max_epochs:
return
# todo: modified by zhengwu, to eval in last epoch
elif mode == "train" and self.epoch == max_epochs:
epoch_runner = getattr(self, "val")
epoch_runner(data_loaders[1], **kwargs)
return
elif mode == "val":
epoch_runner(data_loaders[i], **kwargs)
else:
epoch_runner = getattr(self, "train")
epoch_runner(data_loaders[i], self.epoch, **kwargs)
if 55 <= self.epoch <= 59:
epoch_runner = getattr(self, "val")
epoch_runner(data_loaders[1], **kwargs)
self.call_hook("after_run")
def run(self, data_loaders, workflow, max_epochs, **kwargs):
""" Start running.
Args:
data_loaders (list[:obj:`DataLoader`])
workflow (list[tuple]): A list of (phase, epochs) to specify the
running order and epochs.
max_epochs (int)
"""
assert isinstance(data_loaders, list)
assert torchie.is_list_of(workflow, tuple)
assert len(data_loaders) == len(workflow)
self._max_epochs = max_epochs
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):
if not hasattr(self, mode):
raise ValueError(
"Trainer has no method named '{}' to run an epoch".
format(mode))
epoch_runner = getattr(self, mode)
elif callable(mode):
epoch_runner = mode
else:
raise TypeError("mode in workflow must be a str or "
"callable function not '{}'".format(
type(mode)))
for _ in range(epochs):
# mode='val'
# self.epoch=1
if mode == "train" and self.epoch >= max_epochs:
return
elif mode == "val":
epoch_runner(data_loaders[i], **kwargs)
else:
epoch_runner(data_loaders[i], self.epoch, **kwargs)
# time.sleep(1)
self.call_hook("after_run")
def __init__(
self, img_scale=None, multiscale_mode="range", ratio_range=None, keep_ratio=True
):
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 torchie.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_select(img_scales):
assert torchie.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
def __init__(self, transforms, img_scale, flip=False):
self.transforms = Compose(transforms)
self.img_scale = img_scale if isinstance(img_scale, list) else [img_scale]
assert torchie.is_list_of(self.img_scale, tuple)
self.flip = flip
