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stringlengths 12
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open-mmlab/mmcv | imresize_like | def imresize_like(img, dst_img, return_scale=False, interpolation='bilinear'):
h, w = dst_img.shape[:2]
return imresize(img, (w, h), return_scale, interpolation) | Resize image to the same size of a given image. | def imresize_like(img, dst_img, return_scale=False, interpolation='bilinear'):
"""Resize image to the same size of a given image.
Args:
img (ndarray): The input image.
dst_img (ndarray): The target image.
return_scale (bool): Whether to return `w_scale` and `h_scale`.
interpolation (str): Same as :func:`resize`.
Returns:
tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or
`resized_img`.
"""
h, w = dst_img.shape[:2]
return imresize(img, (w, h), return_scale, interpolation) | mmcv/image/transforms/resize.py |
open-mmlab/mmcv | imrescale | def imrescale(img, scale, return_scale=False, interpolation='bilinear'):
h, w = img.shape[:2]
if isinstance(scale, (float, int)):
if scale <= 0:
raise ValueError(
'Invalid scale {}, must be positive.'.format(scale))
scale_factor = scale
elif isinstance(scale, tuple):
max_long_edge = max(scale)
max_short_edge = min(scale)
scale_factor = min(max_long_edge / max(h, w),
max_short_edge / min(h, w))
else:
raise TypeError(
'Scale must be a number or tuple of int, but got {}'.format(
type(scale)))
new_size = _scale_size((w, h), scale_factor)
rescaled_img = imresize(img, new_size, interpolation=interpolation)
if return_scale:
return rescaled_img, scale_factor
else:
return rescaled_img | Resize image while keeping the aspect ratio. | def imrescale(img, scale, return_scale=False, interpolation='bilinear'):
"""Resize image while keeping the aspect ratio.
Args:
img (ndarray): The input image.
scale (float or tuple[int]): The scaling factor or maximum size.
If it is a float number, then the image will be rescaled by this
factor, else if it is a tuple of 2 integers, then the image will
be rescaled as large as possible within the scale.
return_scale (bool): Whether to return the scaling factor besides the
rescaled image.
interpolation (str): Same as :func:`resize`.
Returns:
ndarray: The rescaled image.
"""
h, w = img.shape[:2]
if isinstance(scale, (float, int)):
if scale <= 0:
raise ValueError(
'Invalid scale {}, must be positive.'.format(scale))
scale_factor = scale
elif isinstance(scale, tuple):
max_long_edge = max(scale)
max_short_edge = min(scale)
scale_factor = min(max_long_edge / max(h, w),
max_short_edge / min(h, w))
else:
raise TypeError(
'Scale must be a number or tuple of int, but got {}'.format(
type(scale)))
new_size = _scale_size((w, h), scale_factor)
rescaled_img = imresize(img, new_size, interpolation=interpolation)
if return_scale:
return rescaled_img, scale_factor
else:
return rescaled_img | mmcv/image/transforms/resize.py |
open-mmlab/mmcv | _register_handler | def _register_handler(handler, file_formats):
if not isinstance(handler, BaseFileHandler):
raise TypeError(
'handler must be a child of BaseFileHandler, not {}'.format(
type(handler)))
if isinstance(file_formats, str):
file_formats = [file_formats]
if not is_list_of(file_formats, str):
raise TypeError('file_formats must be a str or a list of str')
for ext in file_formats:
file_handlers[ext] = handler | Register a handler for some file extensions. | def _register_handler(handler, file_formats):
"""Register a handler for some file extensions.
Args:
handler (:obj:`BaseFileHandler`): Handler to be registered.
file_formats (str or list[str]): File formats to be handled by this
handler.
"""
if not isinstance(handler, BaseFileHandler):
raise TypeError(
'handler must be a child of BaseFileHandler, not {}'.format(
type(handler)))
if isinstance(file_formats, str):
file_formats = [file_formats]
if not is_list_of(file_formats, str):
raise TypeError('file_formats must be a str or a list of str')
for ext in file_formats:
file_handlers[ext] = handler | mmcv/fileio/io.py |
open-mmlab/mmcv | get_priority | def get_priority(priority):
if isinstance(priority, int):
if priority < 0 or priority > 100:
raise ValueError('priority must be between 0 and 100')
return priority
elif isinstance(priority, Priority):
return priority.value
elif isinstance(priority, str):
return Priority[priority.upper()].value
else:
raise TypeError('priority must be an integer or Priority enum value') | Get priority value. | def get_priority(priority):
"""Get priority value.
Args:
priority (int or str or :obj:`Priority`): Priority.
Returns:
int: The priority value.
"""
if isinstance(priority, int):
if priority < 0 or priority > 100:
raise ValueError('priority must be between 0 and 100')
return priority
elif isinstance(priority, Priority):
return priority.value
elif isinstance(priority, str):
return Priority[priority.upper()].value
else:
raise TypeError('priority must be an integer or Priority enum value') | mmcv/runner/priority.py |
open-mmlab/mmcv | dequantize | def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
if not (isinstance(levels, int) and levels > 1):
raise ValueError(
'levels must be a positive integer, but got {}'.format(levels))
if min_val >= max_val:
raise ValueError(
'min_val ({}) must be smaller than max_val ({})'.format(
min_val, max_val))
dequantized_arr = (arr + 0.5).astype(dtype) * (
max_val - min_val) / levels + min_val
return dequantized_arr | Dequantize an array. | def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
"""Dequantize an array.
Args:
arr (ndarray): Input array.
min_val (scalar): Minimum value to be clipped.
max_val (scalar): Maximum value to be clipped.
levels (int): Quantization levels.
dtype (np.type): The type of the dequantized array.
Returns:
tuple: Dequantized array.
"""
if not (isinstance(levels, int) and levels > 1):
raise ValueError(
'levels must be a positive integer, but got {}'.format(levels))
if min_val >= max_val:
raise ValueError(
'min_val ({}) must be smaller than max_val ({})'.format(
min_val, max_val))
dequantized_arr = (arr + 0.5).astype(dtype) * (
max_val - min_val) / levels + min_val
return dequantized_arr | mmcv/arraymisc/quantization.py |
open-mmlab/mmcv | imshow | def imshow(img, win_name='', wait_time=0):
cv2.imshow(win_name, imread(img))
cv2.waitKey(wait_time) | Show an image. | def imshow(img, win_name='', wait_time=0):
"""Show an image.
Args:
img (str or ndarray): The image to be displayed.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
"""
cv2.imshow(win_name, imread(img))
cv2.waitKey(wait_time) | mmcv/visualization/image.py |
open-mmlab/mmcv | imshow_bboxes | def imshow_bboxes(img,
bboxes,
colors='green',
top_k=-1,
thickness=1,
show=True,
win_name='',
wait_time=0,
out_file=None):
img = imread(img)
if isinstance(bboxes, np.ndarray):
bboxes = [bboxes]
if not isinstance(colors, list):
colors = [colors for _ in range(len(bboxes))]
colors = [color_val(c) for c in colors]
assert len(bboxes) == len(colors)
for i, _bboxes in enumerate(bboxes):
_bboxes = _bboxes.astype(np.int32)
if top_k <= 0:
_top_k = _bboxes.shape[0]
else:
_top_k = min(top_k, _bboxes.shape[0])
for j in range(_top_k):
left_top = (_bboxes[j, 0], _bboxes[j, 1])
right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
cv2.rectangle(
img, left_top, right_bottom, colors[i], thickness=thickness)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file) | Draw bboxes on an image. | def imshow_bboxes(img,
bboxes,
colors='green',
top_k=-1,
thickness=1,
show=True,
win_name='',
wait_time=0,
out_file=None):
"""Draw bboxes on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (list or ndarray): A list of ndarray of shape (k, 4).
colors (list[str or tuple or Color]): A list of colors.
top_k (int): Plot the first k bboxes only if set positive.
thickness (int): Thickness of lines.
show (bool): Whether to show the image.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
out_file (str, optional): The filename to write the image.
"""
img = imread(img)
if isinstance(bboxes, np.ndarray):
bboxes = [bboxes]
if not isinstance(colors, list):
colors = [colors for _ in range(len(bboxes))]
colors = [color_val(c) for c in colors]
assert len(bboxes) == len(colors)
for i, _bboxes in enumerate(bboxes):
_bboxes = _bboxes.astype(np.int32)
if top_k <= 0:
_top_k = _bboxes.shape[0]
else:
_top_k = min(top_k, _bboxes.shape[0])
for j in range(_top_k):
left_top = (_bboxes[j, 0], _bboxes[j, 1])
right_bottom = (_bboxes[j, 2], _bboxes[j, 3])
cv2.rectangle(
img, left_top, right_bottom, colors[i], thickness=thickness)
if show:
imshow(img, win_name, wait_time)
if out_file is not None:
imwrite(img, out_file) | mmcv/visualization/image.py |
open-mmlab/mmcv | flowread | def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
if isinstance(flow_or_path, np.ndarray):
if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2):
raise ValueError('Invalid flow with shape {}'.format(
flow_or_path.shape))
return flow_or_path
elif not is_str(flow_or_path):
raise TypeError(
'"flow_or_path" must be a filename or numpy array, not {}'.format(
type(flow_or_path)))
if not quantize:
with open(flow_or_path, 'rb') as f:
try:
header = f.read(4).decode('utf-8')
except Exception:
raise IOError('Invalid flow file: {}'.format(flow_or_path))
else:
if header != 'PIEH':
raise IOError(
'Invalid flow file: {}, header does not contain PIEH'.
format(flow_or_path))
w = np.fromfile(f, np.int32, 1).squeeze()
h = np.fromfile(f, np.int32, 1).squeeze()
flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
else:
assert concat_axis in [0, 1]
cat_flow = imread(flow_or_path, flag='unchanged')
if cat_flow.ndim != 2:
raise IOError(
'{} is not a valid quantized flow file, its dimension is {}.'.
format(flow_or_path, cat_flow.ndim))
assert cat_flow.shape[concat_axis] % 2 == 0
dx, dy = np.split(cat_flow, 2, axis=concat_axis)
flow = dequantize_flow(dx, dy, *args, **kwargs)
return flow.astype(np.float32) | Read an optical flow map. | def flowread(flow_or_path, quantize=False, concat_axis=0, *args, **kwargs):
"""Read an optical flow map.
Args:
flow_or_path (ndarray or str): A flow map or filepath.
quantize (bool): whether to read quantized pair, if set to True,
remaining args will be passed to :func:`dequantize_flow`.
concat_axis (int): The axis that dx and dy are concatenated,
can be either 0 or 1. Ignored if quantize is False.
Returns:
ndarray: Optical flow represented as a (h, w, 2) numpy array
"""
if isinstance(flow_or_path, np.ndarray):
if (flow_or_path.ndim != 3) or (flow_or_path.shape[-1] != 2):
raise ValueError('Invalid flow with shape {}'.format(
flow_or_path.shape))
return flow_or_path
elif not is_str(flow_or_path):
raise TypeError(
'"flow_or_path" must be a filename or numpy array, not {}'.format(
type(flow_or_path)))
if not quantize:
with open(flow_or_path, 'rb') as f:
try:
header = f.read(4).decode('utf-8')
except Exception:
raise IOError('Invalid flow file: {}'.format(flow_or_path))
else:
if header != 'PIEH':
raise IOError(
'Invalid flow file: {}, header does not contain PIEH'.
format(flow_or_path))
w = np.fromfile(f, np.int32, 1).squeeze()
h = np.fromfile(f, np.int32, 1).squeeze()
flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
else:
assert concat_axis in [0, 1]
cat_flow = imread(flow_or_path, flag='unchanged')
if cat_flow.ndim != 2:
raise IOError(
'{} is not a valid quantized flow file, its dimension is {}.'.
format(flow_or_path, cat_flow.ndim))
assert cat_flow.shape[concat_axis] % 2 == 0
dx, dy = np.split(cat_flow, 2, axis=concat_axis)
flow = dequantize_flow(dx, dy, *args, **kwargs)
return flow.astype(np.float32) | mmcv/video/optflow.py |
open-mmlab/mmcv | dequantize_flow | def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
assert dx.shape == dy.shape
assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
if denorm:
dx *= dx.shape[1]
dy *= dx.shape[0]
flow = np.dstack((dx, dy))
return flow | Recover from quantized flow. | def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
"""Recover from quantized flow.
Args:
dx (ndarray): Quantized dx.
dy (ndarray): Quantized dy.
max_val (float): Maximum value used when quantizing.
denorm (bool): Whether to multiply flow values with width/height.
Returns:
ndarray: Dequantized flow.
"""
assert dx.shape == dy.shape
assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
if denorm:
dx *= dx.shape[1]
dy *= dx.shape[0]
flow = np.dstack((dx, dy))
return flow | mmcv/video/optflow.py |
open-mmlab/mmcv | load_checkpoint | def load_checkpoint(model,
filename,
map_location=None,
strict=False,
logger=None):
if filename.startswith('modelzoo://'):
import torchvision
model_urls = dict()
for _, name, ispkg in pkgutil.walk_packages(
torchvision.models.__path__):
if not ispkg:
_zoo = import_module('torchvision.models.{}'.format(name))
_urls = getattr(_zoo, 'model_urls')
model_urls.update(_urls)
model_name = filename[11:]
checkpoint = model_zoo.load_url(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_name = filename[13:]
checkpoint = model_zoo.load_url(open_mmlab_model_urls[model_name])
elif filename.startswith(('http://', 'https://')):
checkpoint = model_zoo.load_url(filename)
else:
if not osp.isfile(filename):
raise IOError('{} is not a checkpoint file'.format(filename))
checkpoint = torch.load(filename, map_location=map_location)
if isinstance(checkpoint, OrderedDict):
state_dict = checkpoint
elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
raise RuntimeError(
'No state_dict found in checkpoint file {}'.format(filename))
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for k, v in checkpoint['state_dict'].items()}
if hasattr(model, 'module'):
load_state_dict(model.module, state_dict, strict, logger)
else:
load_state_dict(model, state_dict, strict, logger)
return checkpoint | Load checkpoint from a file or URI. | def load_checkpoint(model,
filename,
map_location=None,
strict=False,
logger=None):
"""Load checkpoint from a file or URI.
Args:
model (Module): Module to load checkpoint.
filename (str): Either a filepath or URL or modelzoo://xxxxxxx.
map_location (str): Same as :func:`torch.load`.
strict (bool): Whether to allow different params for the model and
checkpoint.
logger (:mod:`logging.Logger` or None): The logger for error message.
Returns:
dict or OrderedDict: The loaded checkpoint.
"""
# load checkpoint from modelzoo or file or url
if filename.startswith('modelzoo://'):
import torchvision
model_urls = dict()
for _, name, ispkg in pkgutil.walk_packages(
torchvision.models.__path__):
if not ispkg:
_zoo = import_module('torchvision.models.{}'.format(name))
_urls = getattr(_zoo, 'model_urls')
model_urls.update(_urls)
model_name = filename[11:]
checkpoint = model_zoo.load_url(model_urls[model_name])
elif filename.startswith('open-mmlab://'):
model_name = filename[13:]
checkpoint = model_zoo.load_url(open_mmlab_model_urls[model_name])
elif filename.startswith(('http://', 'https://')):
checkpoint = model_zoo.load_url(filename)
else:
if not osp.isfile(filename):
raise IOError('{} is not a checkpoint file'.format(filename))
checkpoint = torch.load(filename, map_location=map_location)
# get state_dict from checkpoint
if isinstance(checkpoint, OrderedDict):
state_dict = checkpoint
elif isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
raise RuntimeError(
'No state_dict found in checkpoint file {}'.format(filename))
# strip prefix of state_dict
if list(state_dict.keys())[0].startswith('module.'):
state_dict = {k[7:]: v for k, v in checkpoint['state_dict'].items()}
# load state_dict
if hasattr(model, 'module'):
load_state_dict(model.module, state_dict, strict, logger)
else:
load_state_dict(model, state_dict, strict, logger)
return checkpoint | mmcv/runner/checkpoint.py |
open-mmlab/mmcv | weights_to_cpu | def weights_to_cpu(state_dict):
state_dict_cpu = OrderedDict()
for key, val in state_dict.items():
state_dict_cpu[key] = val.cpu()
return state_dict_cpu | Copy a model state_dict to cpu. | def weights_to_cpu(state_dict):
"""Copy a model state_dict to cpu.
Args:
state_dict (OrderedDict): Model weights on GPU.
Returns:
OrderedDict: Model weights on GPU.
"""
state_dict_cpu = OrderedDict()
for key, val in state_dict.items():
state_dict_cpu[key] = val.cpu()
return state_dict_cpu | mmcv/runner/checkpoint.py |
open-mmlab/mmcv | save_checkpoint | def save_checkpoint(model, filename, optimizer=None, meta=None):
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError('meta must be a dict or None, but got {}'.format(
type(meta)))
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
mmcv.mkdir_or_exist(osp.dirname(filename))
if hasattr(model, 'module'):
model = model.module
checkpoint = {
'meta': meta,
'state_dict': weights_to_cpu(model.state_dict())
}
if optimizer is not None:
checkpoint['optimizer'] = optimizer.state_dict()
torch.save(checkpoint, filename) | Save checkpoint to file. The checkpoint will have 3 | def save_checkpoint(model, filename, optimizer=None, meta=None):
"""Save checkpoint to file.
The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
``optimizer``. By default ``meta`` will contain version and time info.
Args:
model (Module): Module whose params are to be saved.
filename (str): Checkpoint filename.
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
meta (dict, optional): Metadata to be saved in checkpoint.
"""
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError('meta must be a dict or None, but got {}'.format(
type(meta)))
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
mmcv.mkdir_or_exist(osp.dirname(filename))
if hasattr(model, 'module'):
model = model.module
checkpoint = {
'meta': meta,
'state_dict': weights_to_cpu(model.state_dict())
}
if optimizer is not None:
checkpoint['optimizer'] = optimizer.state_dict()
torch.save(checkpoint, filename) | mmcv/runner/checkpoint.py |
open-mmlab/mmcv | Runner.init_optimizer | def init_optimizer(self, optimizer):
if isinstance(optimizer, dict):
optimizer = obj_from_dict(
optimizer, torch.optim, dict(params=self.model.parameters()))
elif not isinstance(optimizer, torch.optim.Optimizer):
raise TypeError(
'optimizer must be either an Optimizer object or a dict, '
'but got {}'.format(type(optimizer)))
return optimizer | Init the optimizer. | def init_optimizer(self, optimizer):
"""Init the optimizer.
Args:
optimizer (dict or :obj:`~torch.optim.Optimizer`): Either an
optimizer object or a dict used for constructing the optimizer.
Returns:
:obj:`~torch.optim.Optimizer`: An optimizer object.
Examples:
>>> optimizer = dict(type='SGD', lr=0.01, momentum=0.9)
>>> type(runner.init_optimizer(optimizer))
<class 'torch.optim.sgd.SGD'>
"""
if isinstance(optimizer, dict):
optimizer = obj_from_dict(
optimizer, torch.optim, dict(params=self.model.parameters()))
elif not isinstance(optimizer, torch.optim.Optimizer):
raise TypeError(
'optimizer must be either an Optimizer object or a dict, '
'but got {}'.format(type(optimizer)))
return optimizer | mmcv/runner/runner.py |
open-mmlab/mmcv | Runner.init_logger | def init_logger(self, log_dir=None, level=logging.INFO):
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s', level=level)
logger = logging.getLogger(__name__)
if log_dir and self.rank == 0:
filename = '{}.log'.format(self.timestamp)
log_file = osp.join(log_dir, filename)
self._add_file_handler(logger, log_file, level=level)
return logger | Init the logger. | def init_logger(self, log_dir=None, level=logging.INFO):
"""Init the logger.
Args:
log_dir(str, optional): Log file directory. If not specified, no
log file will be used.
level (int or str): See the built-in python logging module.
Returns:
:obj:`~logging.Logger`: Python logger.
"""
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s', level=level)
logger = logging.getLogger(__name__)
if log_dir and self.rank == 0:
filename = '{}.log'.format(self.timestamp)
log_file = osp.join(log_dir, filename)
self._add_file_handler(logger, log_file, level=level)
return logger | mmcv/runner/runner.py |
open-mmlab/mmcv | Runner.current_lr | def current_lr(self):
if self.optimizer is None:
raise RuntimeError(
'lr is not applicable because optimizer does not exist.')
return [group['lr'] for group in self.optimizer.param_groups] | Get current learning rates. | def current_lr(self):
"""Get current learning rates.
Returns:
list: Current learning rate of all param groups.
"""
if self.optimizer is None:
raise RuntimeError(
'lr is not applicable because optimizer does not exist.')
return [group['lr'] for group in self.optimizer.param_groups] | mmcv/runner/runner.py |
open-mmlab/mmcv | Runner.register_hook | def register_hook(self, hook, priority='NORMAL'):
assert isinstance(hook, Hook)
if hasattr(hook, 'priority'):
raise ValueError('"priority" is a reserved attribute for hooks')
priority = get_priority(priority)
hook.priority = priority
inserted = False
for i in range(len(self._hooks) - 1, -1, -1):
if priority >= self._hooks[i].priority:
self._hooks.insert(i + 1, hook)
inserted = True
break
if not inserted:
self._hooks.insert(0, hook) | Register a hook into the hook list. | def register_hook(self, hook, priority='NORMAL'):
"""Register a hook into the hook list.
Args:
hook (:obj:`Hook`): The hook to be registered.
priority (int or str or :obj:`Priority`): Hook priority.
Lower value means higher priority.
"""
assert isinstance(hook, Hook)
if hasattr(hook, 'priority'):
raise ValueError('"priority" is a reserved attribute for hooks')
priority = get_priority(priority)
hook.priority = priority
# insert the hook to a sorted list
inserted = False
for i in range(len(self._hooks) - 1, -1, -1):
if priority >= self._hooks[i].priority:
self._hooks.insert(i + 1, hook)
inserted = True
break
if not inserted:
self._hooks.insert(0, hook) | mmcv/runner/runner.py |
open-mmlab/mmcv | Runner.run | def run(self, data_loaders, workflow, max_epochs, **kwargs):
assert isinstance(data_loaders, list)
assert mmcv.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(
'runner 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):
if mode == 'train' and self.epoch >= max_epochs:
return
epoch_runner(data_loaders[i], **kwargs)
time.sleep(1)
self.call_hook('after_run') | Start running. | 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 mmcv.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): # self.train()
if not hasattr(self, mode):
raise ValueError(
'runner has no method named "{}" to run an epoch'.
format(mode))
epoch_runner = getattr(self, mode)
elif callable(mode): # custom train()
epoch_runner = mode
else:
raise TypeError('mode in workflow must be a str or '
'callable function, not {}'.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') | mmcv/runner/runner.py |
open-mmlab/mmcv | Runner.register_training_hooks | def register_training_hooks(self,
lr_config,
optimizer_config=None,
checkpoint_config=None,
log_config=None):
if optimizer_config is None:
optimizer_config = {}
if checkpoint_config is None:
checkpoint_config = {}
self.register_lr_hooks(lr_config)
self.register_hook(self.build_hook(optimizer_config, OptimizerHook))
self.register_hook(self.build_hook(checkpoint_config, CheckpointHook))
self.register_hook(IterTimerHook())
if log_config is not None:
self.register_logger_hooks(log_config) | Register default hooks for training. Default hooks | def register_training_hooks(self,
lr_config,
optimizer_config=None,
checkpoint_config=None,
log_config=None):
"""Register default hooks for training.
Default hooks include:
- LrUpdaterHook
- OptimizerStepperHook
- CheckpointSaverHook
- IterTimerHook
- LoggerHook(s)
"""
if optimizer_config is None:
optimizer_config = {}
if checkpoint_config is None:
checkpoint_config = {}
self.register_lr_hooks(lr_config)
self.register_hook(self.build_hook(optimizer_config, OptimizerHook))
self.register_hook(self.build_hook(checkpoint_config, CheckpointHook))
self.register_hook(IterTimerHook())
if log_config is not None:
self.register_logger_hooks(log_config) | mmcv/runner/runner.py |
open-mmlab/mmcv | convert_video | def convert_video(in_file, out_file, print_cmd=False, pre_options='',
**kwargs):
options = []
for k, v in kwargs.items():
if isinstance(v, bool):
if v:
options.append('-{}'.format(k))
elif k == 'log_level':
assert v in [
'quiet', 'panic', 'fatal', 'error', 'warning', 'info',
'verbose', 'debug', 'trace'
]
options.append('-loglevel {}'.format(v))
else:
options.append('-{} {}'.format(k, v))
cmd = 'ffmpeg -y {} -i {} {} {}'.format(pre_options, in_file,
' '.join(options), out_file)
if print_cmd:
print(cmd)
subprocess.call(cmd, shell=True) | Convert a video with ffmpeg. This provides a general api to ffmpeg, the executed command | def convert_video(in_file, out_file, print_cmd=False, pre_options='',
**kwargs):
"""Convert a video with ffmpeg.
This provides a general api to ffmpeg, the executed command is::
`ffmpeg -y <pre_options> -i <in_file> <options> <out_file>`
Options(kwargs) are mapped to ffmpeg commands with the following rules:
- key=val: "-key val"
- key=True: "-key"
- key=False: ""
Args:
in_file (str): Input video filename.
out_file (str): Output video filename.
pre_options (str): Options appears before "-i <in_file>".
print_cmd (bool): Whether to print the final ffmpeg command.
"""
options = []
for k, v in kwargs.items():
if isinstance(v, bool):
if v:
options.append('-{}'.format(k))
elif k == 'log_level':
assert v in [
'quiet', 'panic', 'fatal', 'error', 'warning', 'info',
'verbose', 'debug', 'trace'
]
options.append('-loglevel {}'.format(v))
else:
options.append('-{} {}'.format(k, v))
cmd = 'ffmpeg -y {} -i {} {} {}'.format(pre_options, in_file,
' '.join(options), out_file)
if print_cmd:
print(cmd)
subprocess.call(cmd, shell=True) | mmcv/video/processing.py |
open-mmlab/mmcv | resize_video | def resize_video(in_file,
out_file,
size=None,
ratio=None,
keep_ar=False,
log_level='info',
print_cmd=False,
**kwargs):
if size is None and ratio is None:
raise ValueError('expected size or ratio must be specified')
elif size is not None and ratio is not None:
raise ValueError('size and ratio cannot be specified at the same time')
options = {'log_level': log_level}
if size:
if not keep_ar:
options['vf'] = 'scale={}:{}'.format(size[0], size[1])
else:
options['vf'] = ('scale=w={}:h={}:force_original_aspect_ratio'
'=decrease'.format(size[0], size[1]))
else:
if not isinstance(ratio, tuple):
ratio = (ratio, ratio)
options['vf'] = 'scale="trunc(iw*{}):trunc(ih*{})"'.format(
ratio[0], ratio[1])
convert_video(in_file, out_file, print_cmd, **options) | Resize a video. | def resize_video(in_file,
out_file,
size=None,
ratio=None,
keep_ar=False,
log_level='info',
print_cmd=False,
**kwargs):
"""Resize a video.
Args:
in_file (str): Input video filename.
out_file (str): Output video filename.
size (tuple): Expected size (w, h), eg, (320, 240) or (320, -1).
ratio (tuple or float): Expected resize ratio, (2, 0.5) means
(w*2, h*0.5).
keep_ar (bool): Whether to keep original aspect ratio.
log_level (str): Logging level of ffmpeg.
print_cmd (bool): Whether to print the final ffmpeg command.
"""
if size is None and ratio is None:
raise ValueError('expected size or ratio must be specified')
elif size is not None and ratio is not None:
raise ValueError('size and ratio cannot be specified at the same time')
options = {'log_level': log_level}
if size:
if not keep_ar:
options['vf'] = 'scale={}:{}'.format(size[0], size[1])
else:
options['vf'] = ('scale=w={}:h={}:force_original_aspect_ratio'
'=decrease'.format(size[0], size[1]))
else:
if not isinstance(ratio, tuple):
ratio = (ratio, ratio)
options['vf'] = 'scale="trunc(iw*{}):trunc(ih*{})"'.format(
ratio[0], ratio[1])
convert_video(in_file, out_file, print_cmd, **options) | mmcv/video/processing.py |
open-mmlab/mmcv | cut_video | def cut_video(in_file,
out_file,
start=None,
end=None,
vcodec=None,
acodec=None,
log_level='info',
print_cmd=False,
**kwargs):
options = {'log_level': log_level}
if vcodec is None:
options['vcodec'] = 'copy'
if acodec is None:
options['acodec'] = 'copy'
if start:
options['ss'] = start
else:
start = 0
if end:
options['t'] = end - start
convert_video(in_file, out_file, print_cmd, **options) | Cut a clip from a video. | def cut_video(in_file,
out_file,
start=None,
end=None,
vcodec=None,
acodec=None,
log_level='info',
print_cmd=False,
**kwargs):
"""Cut a clip from a video.
Args:
in_file (str): Input video filename.
out_file (str): Output video filename.
start (None or float): Start time (in seconds).
end (None or float): End time (in seconds).
vcodec (None or str): Output video codec, None for unchanged.
acodec (None or str): Output audio codec, None for unchanged.
log_level (str): Logging level of ffmpeg.
print_cmd (bool): Whether to print the final ffmpeg command.
"""
options = {'log_level': log_level}
if vcodec is None:
options['vcodec'] = 'copy'
if acodec is None:
options['acodec'] = 'copy'
if start:
options['ss'] = start
else:
start = 0
if end:
options['t'] = end - start
convert_video(in_file, out_file, print_cmd, **options) | mmcv/video/processing.py |
open-mmlab/mmcv | concat_video | def concat_video(video_list,
out_file,
vcodec=None,
acodec=None,
log_level='info',
print_cmd=False,
**kwargs):
_, tmp_filename = tempfile.mkstemp(suffix='.txt', text=True)
with open(tmp_filename, 'w') as f:
for filename in video_list:
f.write('file {}\n'.format(osp.abspath(filename)))
options = {'log_level': log_level}
if vcodec is None:
options['vcodec'] = 'copy'
if acodec is None:
options['acodec'] = 'copy'
convert_video(
tmp_filename,
out_file,
print_cmd,
pre_options='-f concat -safe 0',
**options)
os.remove(tmp_filename) | Concatenate multiple videos into a single one. | def concat_video(video_list,
out_file,
vcodec=None,
acodec=None,
log_level='info',
print_cmd=False,
**kwargs):
"""Concatenate multiple videos into a single one.
Args:
video_list (list): A list of video filenames
out_file (str): Output video filename
vcodec (None or str): Output video codec, None for unchanged
acodec (None or str): Output audio codec, None for unchanged
log_level (str): Logging level of ffmpeg.
print_cmd (bool): Whether to print the final ffmpeg command.
"""
_, tmp_filename = tempfile.mkstemp(suffix='.txt', text=True)
with open(tmp_filename, 'w') as f:
for filename in video_list:
f.write('file {}\n'.format(osp.abspath(filename)))
options = {'log_level': log_level}
if vcodec is None:
options['vcodec'] = 'copy'
if acodec is None:
options['acodec'] = 'copy'
convert_video(
tmp_filename,
out_file,
print_cmd,
pre_options='-f concat -safe 0',
**options)
os.remove(tmp_filename) | mmcv/video/processing.py |
open-mmlab/mmcv | list_from_file | def list_from_file(filename, prefix='', offset=0, max_num=0):
cnt = 0
item_list = []
with open(filename, 'r') as f:
for _ in range(offset):
f.readline()
for line in f:
if max_num > 0 and cnt >= max_num:
break
item_list.append(prefix + line.rstrip('\n'))
cnt += 1
return item_list | Load a text file and parse the content as a list of strings. | def list_from_file(filename, prefix='', offset=0, max_num=0):
"""Load a text file and parse the content as a list of strings.
Args:
filename (str): Filename.
prefix (str): The prefix to be inserted to the begining of each item.
offset (int): The offset of lines.
max_num (int): The maximum number of lines to be read,
zeros and negatives mean no limitation.
Returns:
list[str]: A list of strings.
"""
cnt = 0
item_list = []
with open(filename, 'r') as f:
for _ in range(offset):
f.readline()
for line in f:
if max_num > 0 and cnt >= max_num:
break
item_list.append(prefix + line.rstrip('\n'))
cnt += 1
return item_list | mmcv/fileio/parse.py |
open-mmlab/mmcv | conv3x3 | def conv3x3(in_planes, out_planes, dilation=1):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
padding=dilation,
dilation=dilation) | 3x3 convolution with padding | def conv3x3(in_planes, out_planes, dilation=1):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
padding=dilation,
dilation=dilation) | mmcv/cnn/vgg.py |
open-mmlab/mmcv | imread | def imread(img_or_path, flag='color'):
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
flag = imread_flags[flag] if is_str(flag) else flag
check_file_exist(img_or_path,
'img file does not exist: {}'.format(img_or_path))
return cv2.imread(img_or_path, flag)
else:
raise TypeError('"img" must be a numpy array or a filename') | Read an image. | def imread(img_or_path, flag='color'):
"""Read an image.
Args:
img_or_path (ndarray or str): Either a numpy array or image path.
If it is a numpy array (loaded image), then it will be returned
as is.
flag (str): Flags specifying the color type of a loaded image,
candidates are `color`, `grayscale` and `unchanged`.
Returns:
ndarray: Loaded image array.
"""
if isinstance(img_or_path, np.ndarray):
return img_or_path
elif is_str(img_or_path):
flag = imread_flags[flag] if is_str(flag) else flag
check_file_exist(img_or_path,
'img file does not exist: {}'.format(img_or_path))
return cv2.imread(img_or_path, flag)
else:
raise TypeError('"img" must be a numpy array or a filename') | mmcv/image/io.py |
open-mmlab/mmcv | imfrombytes | def imfrombytes(content, flag='color'):
img_np = np.frombuffer(content, np.uint8)
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imdecode(img_np, flag)
return img | Read an image from bytes. | def imfrombytes(content, flag='color'):
"""Read an image from bytes.
Args:
content (bytes): Image bytes got from files or other streams.
flag (str): Same as :func:`imread`.
Returns:
ndarray: Loaded image array.
"""
img_np = np.frombuffer(content, np.uint8)
flag = imread_flags[flag] if is_str(flag) else flag
img = cv2.imdecode(img_np, flag)
return img | mmcv/image/io.py |
open-mmlab/mmcv | imwrite | def imwrite(img, file_path, params=None, auto_mkdir=True):
if auto_mkdir:
dir_name = osp.abspath(osp.dirname(file_path))
mkdir_or_exist(dir_name)
return cv2.imwrite(file_path, img, params) | Write image to file | def imwrite(img, file_path, params=None, auto_mkdir=True):
"""Write image to file
Args:
img (ndarray): Image array to be written.
file_path (str): Image file path.
params (None or list): Same as opencv's :func:`imwrite` interface.
auto_mkdir (bool): If the parent folder of `file_path` does not exist,
whether to create it automatically.
Returns:
bool: Successful or not.
"""
if auto_mkdir:
dir_name = osp.abspath(osp.dirname(file_path))
mkdir_or_exist(dir_name)
return cv2.imwrite(file_path, img, params) | mmcv/image/io.py |
open-mmlab/mmcv | bgr2gray | def bgr2gray(img, keepdim=False):
out_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if keepdim:
out_img = out_img[..., None]
return out_img | Convert a BGR image to grayscale image. | def bgr2gray(img, keepdim=False):
"""Convert a BGR image to grayscale image.
Args:
img (ndarray): The input image.
keepdim (bool): If False (by default), then return the grayscale image
with 2 dims, otherwise 3 dims.
Returns:
ndarray: The converted grayscale image.
"""
out_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if keepdim:
out_img = out_img[..., None]
return out_img | mmcv/image/transforms/colorspace.py |
open-mmlab/mmcv | gray2bgr | def gray2bgr(img):
img = img[..., None] if img.ndim == 2 else img
out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
return out_img | Convert a grayscale image to BGR image. | def gray2bgr(img):
"""Convert a grayscale image to BGR image.
Args:
img (ndarray or str): The input image.
Returns:
ndarray: The converted BGR image.
"""
img = img[..., None] if img.ndim == 2 else img
out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
return out_img | mmcv/image/transforms/colorspace.py |
open-mmlab/mmcv | iter_cast | def iter_cast(inputs, dst_type, return_type=None):
if not isinstance(inputs, collections_abc.Iterable):
raise TypeError('inputs must be an iterable object')
if not isinstance(dst_type, type):
raise TypeError('"dst_type" must be a valid type')
out_iterable = six.moves.map(dst_type, inputs)
if return_type is None:
return out_iterable
else:
return return_type(out_iterable) | Cast elements of an iterable object into some type. | def iter_cast(inputs, dst_type, return_type=None):
"""Cast elements of an iterable object into some type.
Args:
inputs (Iterable): The input object.
dst_type (type): Destination type.
return_type (type, optional): If specified, the output object will be
converted to this type, otherwise an iterator.
Returns:
iterator or specified type: The converted object.
"""
if not isinstance(inputs, collections_abc.Iterable):
raise TypeError('inputs must be an iterable object')
if not isinstance(dst_type, type):
raise TypeError('"dst_type" must be a valid type')
out_iterable = six.moves.map(dst_type, inputs)
if return_type is None:
return out_iterable
else:
return return_type(out_iterable) | mmcv/utils/misc.py |
open-mmlab/mmcv | is_seq_of | def is_seq_of(seq, expected_type, seq_type=None):
if seq_type is None:
exp_seq_type = collections_abc.Sequence
else:
assert isinstance(seq_type, type)
exp_seq_type = seq_type
if not isinstance(seq, exp_seq_type):
return False
for item in seq:
if not isinstance(item, expected_type):
return False
return True | Check whether it is a sequence of some type. | def is_seq_of(seq, expected_type, seq_type=None):
"""Check whether it is a sequence of some type.
Args:
seq (Sequence): The sequence to be checked.
expected_type (type): Expected type of sequence items.
seq_type (type, optional): Expected sequence type.
Returns:
bool: Whether the sequence is valid.
"""
if seq_type is None:
exp_seq_type = collections_abc.Sequence
else:
assert isinstance(seq_type, type)
exp_seq_type = seq_type
if not isinstance(seq, exp_seq_type):
return False
for item in seq:
if not isinstance(item, expected_type):
return False
return True | mmcv/utils/misc.py |
open-mmlab/mmcv | slice_list | def slice_list(in_list, lens):
if not isinstance(lens, list):
raise TypeError('"indices" must be a list of integers')
elif sum(lens) != len(in_list):
raise ValueError(
'sum of lens and list length does not match: {} != {}'.format(
sum(lens), len(in_list)))
out_list = []
idx = 0
for i in range(len(lens)):
out_list.append(in_list[idx:idx + lens[i]])
idx += lens[i]
return out_list | Slice a list into several sub lists by a list of given length. | def slice_list(in_list, lens):
"""Slice a list into several sub lists by a list of given length.
Args:
in_list (list): The list to be sliced.
lens(int or list): The expected length of each out list.
Returns:
list: A list of sliced list.
"""
if not isinstance(lens, list):
raise TypeError('"indices" must be a list of integers')
elif sum(lens) != len(in_list):
raise ValueError(
'sum of lens and list length does not match: {} != {}'.format(
sum(lens), len(in_list)))
out_list = []
idx = 0
for i in range(len(lens)):
out_list.append(in_list[idx:idx + lens[i]])
idx += lens[i]
return out_list | mmcv/utils/misc.py |
open-mmlab/mmcv | check_prerequisites | def check_prerequisites(
prerequisites,
checker,
msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
'found, please install them first.'):
def wrap(func):
@functools.wraps(func)
def wrapped_func(*args, **kwargs):
requirements = [prerequisites] if isinstance(
prerequisites, str) else prerequisites
missing = []
for item in requirements:
if not checker(item):
missing.append(item)
if missing:
print(msg_tmpl.format(', '.join(missing), func.__name__))
raise RuntimeError('Prerequisites not meet.')
else:
return func(*args, **kwargs)
return wrapped_func
return wrap | A decorator factory to check if prerequisites are satisfied. | def check_prerequisites(
prerequisites,
checker,
msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
'found, please install them first.'):
"""A decorator factory to check if prerequisites are satisfied.
Args:
prerequisites (str of list[str]): Prerequisites to be checked.
checker (callable): The checker method that returns True if a
prerequisite is meet, False otherwise.
msg_tmpl (str): The message template with two variables.
Returns:
decorator: A specific decorator.
"""
def wrap(func):
@functools.wraps(func)
def wrapped_func(*args, **kwargs):
requirements = [prerequisites] if isinstance(
prerequisites, str) else prerequisites
missing = []
for item in requirements:
if not checker(item):
missing.append(item)
if missing:
print(msg_tmpl.format(', '.join(missing), func.__name__))
raise RuntimeError('Prerequisites not meet.')
else:
return func(*args, **kwargs)
return wrapped_func
return wrap | mmcv/utils/misc.py |
open-mmlab/mmcv | LogBuffer.average | def average(self, n=0):
assert n >= 0
for key in self.val_history:
values = np.array(self.val_history[key][-n:])
nums = np.array(self.n_history[key][-n:])
avg = np.sum(values * nums) / np.sum(nums)
self.output[key] = avg
self.ready = True | Average latest n values or all values | def average(self, n=0):
"""Average latest n values or all values"""
assert n >= 0
for key in self.val_history:
values = np.array(self.val_history[key][-n:])
nums = np.array(self.n_history[key][-n:])
avg = np.sum(values * nums) / np.sum(nums)
self.output[key] = avg
self.ready = True | mmcv/runner/log_buffer.py |
open-mmlab/mmcv | scatter | def scatter(input, devices, streams=None):
if streams is None:
streams = [None] * len(devices)
if isinstance(input, list):
chunk_size = (len(input) - 1) // len(devices) + 1
outputs = [
scatter(input[i], [devices[i // chunk_size]],
[streams[i // chunk_size]]) for i in range(len(input))
]
return outputs
elif isinstance(input, torch.Tensor):
output = input.contiguous()
stream = streams[0] if output.numel() > 0 else None
with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
output = output.cuda(devices[0], non_blocking=True)
return output
else:
raise Exception('Unknown type {}.'.format(type(input))) | Scatters tensor across multiple GPUs. | def scatter(input, devices, streams=None):
"""Scatters tensor across multiple GPUs.
"""
if streams is None:
streams = [None] * len(devices)
if isinstance(input, list):
chunk_size = (len(input) - 1) // len(devices) + 1
outputs = [
scatter(input[i], [devices[i // chunk_size]],
[streams[i // chunk_size]]) for i in range(len(input))
]
return outputs
elif isinstance(input, torch.Tensor):
output = input.contiguous()
# TODO: copy to a pinned buffer first (if copying from CPU)
stream = streams[0] if output.numel() > 0 else None
with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
output = output.cuda(devices[0], non_blocking=True)
return output
else:
raise Exception('Unknown type {}.'.format(type(input))) | mmcv/parallel/_functions.py |
open-mmlab/mmcv | color_val | def color_val(color):
if is_str(color):
return Color[color].value
elif isinstance(color, Color):
return color.value
elif isinstance(color, tuple):
assert len(color) == 3
for channel in color:
assert channel >= 0 and channel <= 255
return color
elif isinstance(color, int):
assert color >= 0 and color <= 255
return color, color, color
elif isinstance(color, np.ndarray):
assert color.ndim == 1 and color.size == 3
assert np.all((color >= 0) & (color <= 255))
color = color.astype(np.uint8)
return tuple(color)
else:
raise TypeError('Invalid type for color: {}'.format(type(color))) | Convert various input to color tuples. | def color_val(color):
"""Convert various input to color tuples.
Args:
color (:obj:`Color`/str/tuple/int/ndarray): Color inputs
Returns:
tuple[int]: A tuple of 3 integers indicating BGR channels.
"""
if is_str(color):
return Color[color].value
elif isinstance(color, Color):
return color.value
elif isinstance(color, tuple):
assert len(color) == 3
for channel in color:
assert channel >= 0 and channel <= 255
return color
elif isinstance(color, int):
assert color >= 0 and color <= 255
return color, color, color
elif isinstance(color, np.ndarray):
assert color.ndim == 1 and color.size == 3
assert np.all((color >= 0) & (color <= 255))
color = color.astype(np.uint8)
return tuple(color)
else:
raise TypeError('Invalid type for color: {}'.format(type(color))) | mmcv/visualization/color.py |
open-mmlab/mmcv | check_time | def check_time(timer_id):
if timer_id not in _g_timers:
_g_timers[timer_id] = Timer()
return 0
else:
return _g_timers[timer_id].since_last_check() | Add check points in a single line. This method is suitable for running a task on a list of items. A timer will be registered when the method is called for the first time. | def check_time(timer_id):
"""Add check points in a single line.
This method is suitable for running a task on a list of items. A timer will
be registered when the method is called for the first time.
:Example:
>>> import time
>>> import mmcv
>>> for i in range(1, 6):
>>> # simulate a code block
>>> time.sleep(i)
>>> mmcv.check_time('task1')
2.000
3.000
4.000
5.000
Args:
timer_id (str): Timer identifier.
"""
if timer_id not in _g_timers:
_g_timers[timer_id] = Timer()
return 0
else:
return _g_timers[timer_id].since_last_check() | mmcv/utils/timer.py |
open-mmlab/mmcv | Timer.start | def start(self):
if not self._is_running:
self._t_start = time()
self._is_running = True
self._t_last = time() | Start the timer. | def start(self):
"""Start the timer."""
if not self._is_running:
self._t_start = time()
self._is_running = True
self._t_last = time() | mmcv/utils/timer.py |
open-mmlab/mmcv | Timer.since_start | def since_start(self):
if not self._is_running:
raise TimerError('timer is not running')
self._t_last = time()
return self._t_last - self._t_start | Total time since the timer is started. | def since_start(self):
"""Total time since the timer is started.
Returns (float): Time in seconds.
"""
if not self._is_running:
raise TimerError('timer is not running')
self._t_last = time()
return self._t_last - self._t_start | mmcv/utils/timer.py |
open-mmlab/mmcv | Timer.since_last_check | def since_last_check(self):
if not self._is_running:
raise TimerError('timer is not running')
dur = time() - self._t_last
self._t_last = time()
return dur | Time since the last checking. Either :func:`since_start` or :func:`since_last_check` is a checking operation. | def since_last_check(self):
"""Time since the last checking.
Either :func:`since_start` or :func:`since_last_check` is a checking
operation.
Returns (float): Time in seconds.
"""
if not self._is_running:
raise TimerError('timer is not running')
dur = time() - self._t_last
self._t_last = time()
return dur | mmcv/utils/timer.py |
open-mmlab/mmcv | flowshow | def flowshow(flow, win_name='', wait_time=0):
flow = flowread(flow)
flow_img = flow2rgb(flow)
imshow(rgb2bgr(flow_img), win_name, wait_time) | Show optical flow. | def flowshow(flow, win_name='', wait_time=0):
"""Show optical flow.
Args:
flow (ndarray or str): The optical flow to be displayed.
win_name (str): The window name.
wait_time (int): Value of waitKey param.
"""
flow = flowread(flow)
flow_img = flow2rgb(flow)
imshow(rgb2bgr(flow_img), win_name, wait_time) | mmcv/visualization/optflow.py |
open-mmlab/mmcv | flow2rgb | def flow2rgb(flow, color_wheel=None, unknown_thr=1e6):
assert flow.ndim == 3 and flow.shape[-1] == 2
if color_wheel is None:
color_wheel = make_color_wheel()
assert color_wheel.ndim == 2 and color_wheel.shape[1] == 3
num_bins = color_wheel.shape[0]
dx = flow[:, :, 0].copy()
dy = flow[:, :, 1].copy()
ignore_inds = (np.isnan(dx) | np.isnan(dy) | (np.abs(dx) > unknown_thr) |
(np.abs(dy) > unknown_thr))
dx[ignore_inds] = 0
dy[ignore_inds] = 0
rad = np.sqrt(dx**2 + dy**2)
if np.any(rad > np.finfo(float).eps):
max_rad = np.max(rad)
dx /= max_rad
dy /= max_rad
[h, w] = dx.shape
rad = np.sqrt(dx**2 + dy**2)
angle = np.arctan2(-dy, -dx) / np.pi
bin_real = (angle + 1) / 2 * (num_bins - 1)
bin_left = np.floor(bin_real).astype(int)
bin_right = (bin_left + 1) % num_bins
w = (bin_real - bin_left.astype(np.float32))[..., None]
flow_img = (
1 - w) * color_wheel[bin_left, :] + w * color_wheel[bin_right, :]
small_ind = rad <= 1
flow_img[small_ind] = 1 - rad[small_ind, None] * (1 - flow_img[small_ind])
flow_img[np.logical_not(small_ind)] *= 0.75
flow_img[ignore_inds, :] = 0
return flow_img | Convert flow map to RGB image. | def flow2rgb(flow, color_wheel=None, unknown_thr=1e6):
"""Convert flow map to RGB image.
Args:
flow (ndarray): Array of optical flow.
color_wheel (ndarray or None): Color wheel used to map flow field to
RGB colorspace. Default color wheel will be used if not specified.
unknown_thr (str): Values above this threshold will be marked as
unknown and thus ignored.
Returns:
ndarray: RGB image that can be visualized.
"""
assert flow.ndim == 3 and flow.shape[-1] == 2
if color_wheel is None:
color_wheel = make_color_wheel()
assert color_wheel.ndim == 2 and color_wheel.shape[1] == 3
num_bins = color_wheel.shape[0]
dx = flow[:, :, 0].copy()
dy = flow[:, :, 1].copy()
ignore_inds = (np.isnan(dx) | np.isnan(dy) | (np.abs(dx) > unknown_thr) |
(np.abs(dy) > unknown_thr))
dx[ignore_inds] = 0
dy[ignore_inds] = 0
rad = np.sqrt(dx**2 + dy**2)
if np.any(rad > np.finfo(float).eps):
max_rad = np.max(rad)
dx /= max_rad
dy /= max_rad
[h, w] = dx.shape
rad = np.sqrt(dx**2 + dy**2)
angle = np.arctan2(-dy, -dx) / np.pi
bin_real = (angle + 1) / 2 * (num_bins - 1)
bin_left = np.floor(bin_real).astype(int)
bin_right = (bin_left + 1) % num_bins
w = (bin_real - bin_left.astype(np.float32))[..., None]
flow_img = (
1 - w) * color_wheel[bin_left, :] + w * color_wheel[bin_right, :]
small_ind = rad <= 1
flow_img[small_ind] = 1 - rad[small_ind, None] * (1 - flow_img[small_ind])
flow_img[np.logical_not(small_ind)] *= 0.75
flow_img[ignore_inds, :] = 0
return flow_img | mmcv/visualization/optflow.py |
open-mmlab/mmcv | make_color_wheel | def make_color_wheel(bins=None):
if bins is None:
bins = [15, 6, 4, 11, 13, 6]
assert len(bins) == 6
RY, YG, GC, CB, BM, MR = tuple(bins)
ry = [1, np.arange(RY) / RY, 0]
yg = [1 - np.arange(YG) / YG, 1, 0]
gc = [0, 1, np.arange(GC) / GC]
cb = [0, 1 - np.arange(CB) / CB, 1]
bm = [np.arange(BM) / BM, 0, 1]
mr = [1, 0, 1 - np.arange(MR) / MR]
num_bins = RY + YG + GC + CB + BM + MR
color_wheel = np.zeros((3, num_bins), dtype=np.float32)
col = 0
for i, color in enumerate([ry, yg, gc, cb, bm, mr]):
for j in range(3):
color_wheel[j, col:col + bins[i]] = color[j]
col += bins[i]
return color_wheel.T | Build a color wheel. | def make_color_wheel(bins=None):
"""Build a color wheel.
Args:
bins(list or tuple, optional): Specify the number of bins for each
color range, corresponding to six ranges: red -> yellow,
yellow -> green, green -> cyan, cyan -> blue, blue -> magenta,
magenta -> red. [15, 6, 4, 11, 13, 6] is used for default
(see Middlebury).
Returns:
ndarray: Color wheel of shape (total_bins, 3).
"""
if bins is None:
bins = [15, 6, 4, 11, 13, 6]
assert len(bins) == 6
RY, YG, GC, CB, BM, MR = tuple(bins)
ry = [1, np.arange(RY) / RY, 0]
yg = [1 - np.arange(YG) / YG, 1, 0]
gc = [0, 1, np.arange(GC) / GC]
cb = [0, 1 - np.arange(CB) / CB, 1]
bm = [np.arange(BM) / BM, 0, 1]
mr = [1, 0, 1 - np.arange(MR) / MR]
num_bins = RY + YG + GC + CB + BM + MR
color_wheel = np.zeros((3, num_bins), dtype=np.float32)
col = 0
for i, color in enumerate([ry, yg, gc, cb, bm, mr]):
for j in range(3):
color_wheel[j, col:col + bins[i]] = color[j]
col += bins[i]
return color_wheel.T | mmcv/visualization/optflow.py |
open-mmlab/mmcv | accuracy | def accuracy(output, target, topk=(1, )):
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res | Computes the precision@k for the specified values of k | def accuracy(output, target, topk=(1, )):
"""Computes the precision@k for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res | examples/train_cifar10.py |
open-mmlab/mmcv | scatter | def scatter(inputs, target_gpus, dim=0):
def scatter_map(obj):
if isinstance(obj, torch.Tensor):
return OrigScatter.apply(target_gpus, None, dim, obj)
if isinstance(obj, DataContainer):
if obj.cpu_only:
return obj.data
else:
return Scatter.forward(target_gpus, obj.data)
if isinstance(obj, tuple) and len(obj) > 0:
return list(zip(*map(scatter_map, obj)))
if isinstance(obj, list) and len(obj) > 0:
out = list(map(list, zip(*map(scatter_map, obj))))
return out
if isinstance(obj, dict) and len(obj) > 0:
out = list(map(type(obj), zip(*map(scatter_map, obj.items()))))
return out
return [obj for targets in target_gpus]
try:
return scatter_map(inputs)
finally:
scatter_map = None | Scatter inputs to target gpus. The only difference from original :func:`scatter` is to add support for | def scatter(inputs, target_gpus, dim=0):
"""Scatter inputs to target gpus.
The only difference from original :func:`scatter` is to add support for
:type:`~mmcv.parallel.DataContainer`.
"""
def scatter_map(obj):
if isinstance(obj, torch.Tensor):
return OrigScatter.apply(target_gpus, None, dim, obj)
if isinstance(obj, DataContainer):
if obj.cpu_only:
return obj.data
else:
return Scatter.forward(target_gpus, obj.data)
if isinstance(obj, tuple) and len(obj) > 0:
return list(zip(*map(scatter_map, obj)))
if isinstance(obj, list) and len(obj) > 0:
out = list(map(list, zip(*map(scatter_map, obj))))
return out
if isinstance(obj, dict) and len(obj) > 0:
out = list(map(type(obj), zip(*map(scatter_map, obj.items()))))
return out
return [obj for targets in target_gpus]
# After scatter_map is called, a scatter_map cell will exist. This cell
# has a reference to the actual function scatter_map, which has references
# to a closure that has a reference to the scatter_map cell (because the
# fn is recursive). To avoid this reference cycle, we set the function to
# None, clearing the cell
try:
return scatter_map(inputs)
finally:
scatter_map = None | mmcv/parallel/scatter_gather.py |
open-mmlab/mmcv | scatter_kwargs | def scatter_kwargs(inputs, kwargs, target_gpus, dim=0):
inputs = scatter(inputs, target_gpus, dim) if inputs else []
kwargs = scatter(kwargs, target_gpus, dim) if kwargs else []
if len(inputs) < len(kwargs):
inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
elif len(kwargs) < len(inputs):
kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
inputs = tuple(inputs)
kwargs = tuple(kwargs)
return inputs, kwargs | Scatter with support for kwargs dictionary | def scatter_kwargs(inputs, kwargs, target_gpus, dim=0):
"""Scatter with support for kwargs dictionary"""
inputs = scatter(inputs, target_gpus, dim) if inputs else []
kwargs = scatter(kwargs, target_gpus, dim) if kwargs else []
if len(inputs) < len(kwargs):
inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
elif len(kwargs) < len(inputs):
kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
inputs = tuple(inputs)
kwargs = tuple(kwargs)
return inputs, kwargs | mmcv/parallel/scatter_gather.py |