Python mmcv.is_list_of() Examples

The following are 30 code examples of mmcv.is_list_of(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mmcv , or try the search function .
Example #1
Source File: transforms.py    From mmdetection with Apache License 2.0 6 votes vote down vote up 
def random_select(img_scales):
        """Randomly select an img_scale from given candidates.

        Args:
            img_scales (list[tuple]): Images scales for selection.

        Returns:
            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``,
                where ``img_scale`` is the selected image scale and
                ``scale_idx`` is the selected index in the given candidates.
        """

        assert mmcv.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
Example #2
Source File: transforms.py    From mmdetection with Apache License 2.0 6 votes vote down vote up 
def random_sample(img_scales):
        """Randomly sample an img_scale when ``multiscale_mode=='range'``.

        Args:
            img_scales (list[tuple]): Images scale range for sampling.
                There must be two tuples in img_scales, which specify the lower
                and uper bound of image scales.

        Returns:
            (tuple, None): Returns a tuple ``(img_scale, None)``, where
                ``img_scale`` is sampled scale and None is just a placeholder
                to be consistent with :func:`random_select`.
        """

        assert mmcv.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
Example #3
Source File: transforms.py    From mmdetection with Apache License 2.0 6 votes vote down vote up 
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 mmcv.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
Example #4
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 6 votes vote down vote up 
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 mmcv.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
Example #5
Source File: transforms.py    From ttfnet with Apache License 2.0 6 votes vote down vote up 
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 mmcv.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
Example #6
Source File: transforms.py    From kaggle-kuzushiji-recognition with MIT License 6 votes vote down vote up 
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 mmcv.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
Example #7
Source File: transforms.py    From IoU-Uniform-R-CNN with Apache License 2.0 6 votes vote down vote up 
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 mmcv.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
Example #8
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 6 votes vote down vote up 
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 mmcv.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
Example #9
Source File: transforms.py    From RDSNet with Apache License 2.0 6 votes vote down vote up 
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 mmcv.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
Example #10
Source File: fna_search_runner.py    From FNA with Apache License 2.0 5 votes vote down vote up 
def run_step_alter(self, data_loaders, workflow, max_epochs, arch_update_epoch, **kwargs):
        """Start running. Arch and weight optimization alternates by step.

        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)

        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', 'train')

        while self.epoch < max_epochs:
            self.search_stage = 0 if self.epoch<self.cfg.arch_update_epoch else 1
            self.train_step_alter(data_loaders)

        time.sleep(1)  # wait for some hooks like loggers to finish
        self.call_hook('after_run', 'train')
Example #11
Source File: test_aug.py    From ttfnet with Apache License 2.0 5 votes vote down vote up 
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 mmcv.is_list_of(self.img_scale, tuple)
        self.flip = flip
Example #12
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
def random_select(img_scales):
        assert mmcv.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
Example #13
Source File: transforms.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
def random_sample(img_scales):
        assert mmcv.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
Example #14
Source File: test_aug.py    From Cascade-RPN with Apache License 2.0 5 votes vote down vote up 
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 mmcv.is_list_of(self.img_scale, tuple)
        self.flip = flip
Example #15
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
def random_select(img_scales):
        assert mmcv.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
Example #16
Source File: transforms.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
def random_sample(img_scales):
        assert mmcv.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
Example #17
Source File: test_aug.py    From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 5 votes vote down vote up 
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 mmcv.is_list_of(self.img_scale, tuple)
        self.flip = flip
Example #18
Source File: transforms.py    From ttfnet with Apache License 2.0 5 votes vote down vote up 
def random_select(img_scales):
        assert mmcv.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
Example #19
Source File: transforms.py    From ttfnet with Apache License 2.0 5 votes vote down vote up 
def random_sample(img_scales):
        assert mmcv.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
Example #20
Source File: test_aug.py    From IoU-Uniform-R-CNN with Apache License 2.0 5 votes vote down vote up 
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 mmcv.is_list_of(self.img_scale, tuple)
        self.flip = flip
Example #21
Source File: transforms.py    From IoU-Uniform-R-CNN with Apache License 2.0 5 votes vote down vote up 
def random_sample(img_scales):
        assert mmcv.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
Example #22
Source File: transforms.py    From IoU-Uniform-R-CNN with Apache License 2.0 5 votes vote down vote up 
def random_select(img_scales):
        assert mmcv.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
Example #23
Source File: test_aug.py    From RDSNet with Apache License 2.0 5 votes vote down vote up 
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 mmcv.is_list_of(self.img_scale, tuple)
        self.flip = flip
Example #24
Source File: transforms.py    From RDSNet with Apache License 2.0 5 votes vote down vote up 
def random_sample(img_scales):
        assert mmcv.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
Example #25
Source File: transforms.py    From RDSNet with Apache License 2.0 5 votes vote down vote up 
def random_select(img_scales):
        assert mmcv.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
Example #26
Source File: test_aug.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
def __init__(self, transforms, img_scale, max_shape=(4800, 3200)):
        self.transforms = Compose(transforms)
        self.img_scale = img_scale if isinstance(img_scale,
                                                 list) else [img_scale]
        assert mmcv.is_list_of(self.img_scale, float)
        self.max_size = max_shape[0] * max_shape[1]
Example #27
Source File: test_aug.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
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 mmcv.is_list_of(self.img_scale, tuple)
        self.flip = flip
Example #28
Source File: transforms.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
def random_sample(img_scales):
        assert mmcv.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
Example #29
Source File: transforms.py    From kaggle-kuzushiji-recognition with MIT License 5 votes vote down vote up 
def random_select(img_scales):
        assert mmcv.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
Example #30
Source File: test_misc.py    From mmcv with Apache License 2.0 5 votes vote down vote up 
def test_is_seq_of():
    assert mmcv.is_seq_of([1.0, 2.0, 3.0], float)
    assert mmcv.is_seq_of([(1, ), (2, ), (3, )], tuple)
    assert mmcv.is_seq_of((1.0, 2.0, 3.0), float)
    assert mmcv.is_list_of([1.0, 2.0, 3.0], float)
    assert not mmcv.is_seq_of((1.0, 2.0, 3.0), float, seq_type=list)
    assert not mmcv.is_tuple_of([1.0, 2.0, 3.0], float)
    assert not mmcv.is_seq_of([1.0, 2, 3], int)
    assert not mmcv.is_seq_of((1.0, 2, 3), int)