easycv.datasets.classification.pipelines package¶

class easycv.datasets.classification.pipelines.MMAutoAugment(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶

Bases: object

Auto augmentation. This data augmentation is proposed in AutoAugment: Learning Augmentation Policies from Data. :param policies: The policies of auto augmentation. Each

policy in policies is a specific augmentation policy, and is composed by several augmentations (dict). When AutoAugment is called, a random policy in policies will be selected to augment images.

Parameters: hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

__init__(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.MMRandAugment(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶

Bases: object

Random augmentation. This data augmentation is proposed in RandAugment: Practical automated data augmentation with a reduced search space. :param policies: The policies of random augmentation. Each

policy in policies is one specific augmentation policy (dict). The policy shall at least have key type, indicating the type of augmentation. For those which have magnitude, (given to the fact they are named differently in different augmentation, ) magnitude_key and magnitude_range shall be the magnitude argument (str) and the range of magnitude (tuple in the format of (val1, val2)), respectively. Note that val1 is not necessarily less than val2.

Parameters

num_policies (int) – Number of policies to select from policies each time.
magnitude_level (int | float) – Magnitude level for all the augmentation selected.
total_level (int | float) – Total level for the magnitude. Defaults to 30.
magnitude_std (Number | str) –
Deviation of magnitude noise applied. - If positive number, magnitude is sampled from normal distribution

(mean=magnitude, std=magnitude_std).
- If 0 or negative number, magnitude remains unchanged.
- If str “inf”, magnitude is sampled from uniform distribution (range=[min, magnitude]).
hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

Note

magnitude_std will introduce some randomness to policy, modified by https://github.com/rwightman/pytorch-image-models. When magnitude_std=0, we calculate the magnitude as follows: .. math:

\text{magnitude} = \frac{\text{magnitude\_level}}
{\text{total\_level}} \times (\text{val2} - \text{val1})
+ \text{val1}

__init__(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.MMRandomErasing(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶

Bases: object

Randomly selects a rectangle region in an image and erase pixels. :param erase_prob: Probability that image will be randomly erased.

Default: 0.5

Parameters

min_area_ratio (float) – Minimum erased area / input image area Default: 0.02
max_area_ratio (float) – Maximum erased area / input image area Default: 0.4
aspect_range (sequence | float) – Aspect ratio range of erased area. if float, it will be converted to (aspect_ratio, 1/aspect_ratio) Default: (3/10, 10/3)
mode (str) – Fill method in erased area, can be: - const (default): All pixels are assign with the same value. - rand: each pixel is assigned with a random value in [0, 255]
fill_color (sequence | Number) – Base color filled in erased area. Defaults to (128, 128, 128).
fill_std (sequence | Number, optional) – If set and mode is ‘rand’, fill erased area with random color from normal distribution (mean=fill_color, std=fill_std); If not set, fill erased area with random color from uniform distribution (0~255). Defaults to None.

Note

See Random Erasing Data Augmentation This paper provided 4 modes: RE-R, RE-M, RE-0, RE-255, and use RE-M as default. The config of these 4 modes are: - RE-R: RandomErasing(mode=’rand’) - RE-M: RandomErasing(mode=’const’, fill_color=(123.67, 116.3, 103.5)) - RE-0: RandomErasing(mode=’const’, fill_color=0) - RE-255: RandomErasing(mode=’const’, fill_color=255)

__init__(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Submodules¶

easycv.datasets.classification.pipelines.auto_augment module¶

easycv.datasets.classification.pipelines.auto_augment.random_negative(value, random_negative_prob)[source]¶: Randomly negate value based on random_negative_prob.

easycv.datasets.classification.pipelines.auto_augment.merge_hparams(policy: dict, hparams: dict)[source]¶

Merge hyperparameters into policy config. Only merge partial hyperparameters required of the policy. :param policy: Original policy config dict. :type policy: dict :param hparams: Hyperparameters need to be merged. :type hparams: dict

Returns: Policy config dict after adding hparams.
Return type: dict

class easycv.datasets.classification.pipelines.auto_augment.MMAutoAugment(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶

Bases: object

Auto augmentation. This data augmentation is proposed in AutoAugment: Learning Augmentation Policies from Data. :param policies: The policies of auto augmentation. Each

policy in policies is a specific augmentation policy, and is composed by several augmentations (dict). When AutoAugment is called, a random policy in policies will be selected to augment images.

Parameters: hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

__init__(policies=[[{'type': 'Posterize', 'bits': 4, 'prob': 0.4}, {'type': 'Rotate', 'angle': 30.0, 'prob': 0.6}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 5, 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}], [{'type': 'Solarize', 'thr': 170.66666666666666, 'prob': 0.6}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Posterize', 'bits': 6, 'prob': 0.8}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'Rotate', 'angle': 10.0, 'prob': 0.2}, {'type': 'Solarize', 'thr': 28.444444444444443, 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.6}, {'type': 'Posterize', 'bits': 5, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}], [{'type': 'Rotate', 'angle': 30.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.0}, {'type': 'Equalize', 'prob': 0.8}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Rotate', 'angle': 26.666666666666668, 'prob': 0.8}, {'type': 'ColorTransform', 'magnitude': 0.2, 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.8, 'prob': 0.8}, {'type': 'Solarize', 'thr': 56.888888888888886, 'prob': 0.8}], [{'type': 'Sharpness', 'magnitude': 0.7, 'prob': 0.4}, {'type': 'Invert', 'prob': 0.6}], [{'type': 'Shear', 'magnitude': 0.16666666666666666, 'prob': 0.6, 'direction': 'horizontal'}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.0, 'prob': 0.4}, {'type': 'Equalize', 'prob': 0.6}], [{'type': 'Equalize', 'prob': 0.4}, {'type': 'Solarize', 'thr': 142.22222222222223, 'prob': 0.2}], [{'type': 'Solarize', 'thr': 113.77777777777777, 'prob': 0.6}, {'type': 'AutoContrast', 'prob': 0.6}], [{'type': 'Invert', 'prob': 0.6}, {'type': 'Equalize', 'prob': 1.0}], [{'type': 'ColorTransform', 'magnitude': 0.4, 'prob': 0.6}, {'type': 'Contrast', 'magnitude': 0.8, 'prob': 1.0}], [{'type': 'Equalize', 'prob': 0.8}, {'type': 'Equalize', 'prob': 0.6}]], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.MMRandAugment(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶

Bases: object

Random augmentation. This data augmentation is proposed in RandAugment: Practical automated data augmentation with a reduced search space. :param policies: The policies of random augmentation. Each

policy in policies is one specific augmentation policy (dict). The policy shall at least have key type, indicating the type of augmentation. For those which have magnitude, (given to the fact they are named differently in different augmentation, ) magnitude_key and magnitude_range shall be the magnitude argument (str) and the range of magnitude (tuple in the format of (val1, val2)), respectively. Note that val1 is not necessarily less than val2.

Parameters

num_policies (int) – Number of policies to select from policies each time.
magnitude_level (int | float) – Magnitude level for all the augmentation selected.
total_level (int | float) – Total level for the magnitude. Defaults to 30.
magnitude_std (Number | str) –
Deviation of magnitude noise applied. - If positive number, magnitude is sampled from normal distribution

(mean=magnitude, std=magnitude_std).
- If 0 or negative number, magnitude remains unchanged.
- If str “inf”, magnitude is sampled from uniform distribution (range=[min, magnitude]).
hparams (dict) – Configs of hyperparameters. Hyperparameters will be used in policies that require these arguments if these arguments are not set in policy dicts. Defaults to use _HPARAMS_DEFAULT.

Note

magnitude_std will introduce some randomness to policy, modified by https://github.com/rwightman/pytorch-image-models. When magnitude_std=0, we calculate the magnitude as follows: .. math:

\text{magnitude} = \frac{\text{magnitude\_level}}
{\text{total\_level}} \times (\text{val2} - \text{val1})
+ \text{val1}

__init__(num_policies, magnitude_level, magnitude_std=0.0, total_level=30, policies=[{'type': 'AutoContrast'}, {'type': 'Equalize'}, {'type': 'Invert'}, {'type': 'Rotate', 'magnitude_key': 'angle', 'magnitude_range': (0, 30)}, {'type': 'Posterize', 'magnitude_key': 'bits', 'magnitude_range': (4, 0)}, {'type': 'Solarize', 'magnitude_key': 'thr', 'magnitude_range': (256, 0)}, {'type': 'SolarizeAdd', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 110)}, {'type': 'ColorTransform', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Contrast', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Brightness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Sharpness', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.9)}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'horizontal'}, {'type': 'Shear', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.3), 'direction': 'vertical'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'horizontal'}, {'type': 'Translate', 'magnitude_key': 'magnitude', 'magnitude_range': (0, 0.45), 'direction': 'vertical'}], hparams={'pad_val': 128})[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Shear(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='bicubic')[source]¶

Bases: object

Shear images. :param magnitude: The magnitude used for shear. :type magnitude: int | float :param pad_val: Pixel pad_val value for constant fill.

If a sequence of length 3, it is used to pad_val R, G, B channels respectively. Defaults to 128.

Parameters

prob (float) – The probability for performing Shear therefore should be in range [0, 1]. Defaults to 0.5.
direction (str) – The shearing direction. Options are ‘horizontal’ and ‘vertical’. Defaults to ‘horizontal’.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str) – Interpolation method. Options are ‘nearest’, ‘bilinear’, ‘bicubic’, ‘area’, ‘lanczos’. Defaults to ‘bicubic’.

__init__(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='bicubic')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Translate(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='nearest')[source]¶

Bases: object

Translate images. :param magnitude: The magnitude used for translate. Note that

the offset is calculated by magnitude * size in the corresponding direction. With a magnitude of 1, the whole image will be moved out of the range.

Parameters

pad_val (int, Sequence[int]) – Pixel pad_val value for constant fill. If a sequence of length 3, it is used to pad_val R, G, B channels respectively. Defaults to 128.
prob (float) – The probability for performing translate therefore should be in range [0, 1]. Defaults to 0.5.
direction (str) – The translating direction. Options are ‘horizontal’ and ‘vertical’. Defaults to ‘horizontal’.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str) – Interpolation method. Options are ‘nearest’, ‘bilinear’, ‘bicubic’, ‘area’, ‘lanczos’. Defaults to ‘nearest’.

__init__(magnitude, pad_val=128, prob=0.5, direction='horizontal', random_negative_prob=0.5, interpolation='nearest')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Rotate(angle, center=None, scale=1.0, pad_val=128, prob=0.5, random_negative_prob=0.5, interpolation='nearest')[source]¶

Bases: object

Rotate images. :param angle: The angle used for rotate. Positive values stand for

clockwise rotation.

Parameters

center (tuple[float], optional) – Center point (w, h) of the rotation in the source image. If None, the center of the image will be used. Defaults to None.
scale (float) – Isotropic scale factor. Defaults to 1.0.
pad_val (int, Sequence[int]) – Pixel pad_val value for constant fill. If a sequence of length 3, it is used to pad_val R, G, B channels respectively. Defaults to 128.
prob (float) – The probability for performing Rotate therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the angle negative, which should be in range [0,1]. Defaults to 0.5.
interpolation (str) – Interpolation method. Options are ‘nearest’, ‘bilinear’, ‘bicubic’, ‘area’, ‘lanczos’. Defaults to ‘nearest’.

__init__(angle, center=None, scale=1.0, pad_val=128, prob=0.5, random_negative_prob=0.5, interpolation='nearest')[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.AutoContrast(prob=0.5)[source]¶

Bases: object

Auto adjust image contrast. :param prob: The probability for performing invert therefore should

be in range [0, 1]. Defaults to 0.5.

__init__(prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Invert(prob=0.5)[source]¶

Bases: object

Invert images. :param prob: The probability for performing invert therefore should

be in range [0, 1]. Defaults to 0.5.

__init__(prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Equalize(prob=0.5)[source]¶

Bases: object

Equalize the image histogram. :param prob: The probability for performing invert therefore should

be in range [0, 1]. Defaults to 0.5.

__init__(prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Solarize(thr, prob=0.5)[source]¶

Bases: object

Solarize images (invert all pixel values above a threshold). :param thr: The threshold above which the pixels value will be

inverted.

Parameters: prob (float) – The probability for solarizing therefore should be in range [0, 1]. Defaults to 0.5.

__init__(thr, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.SolarizeAdd(magnitude, thr=128, prob=0.5)[source]¶

Bases: object

SolarizeAdd images (add a certain value to pixels below a threshold). :param magnitude: The value to be added to pixels below the thr. :type magnitude: int | float :param thr: The threshold below which the pixels value will be

adjusted.

Parameters: prob (float) – The probability for solarizing therefore should be in range [0, 1]. Defaults to 0.5.

__init__(magnitude, thr=128, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Posterize(bits, prob=0.5)[source]¶

Bases: object

Posterize images (reduce the number of bits for each color channel). :param bits: Number of bits for each pixel in the output img,

which should be less or equal to 8.

Parameters: prob (float) – The probability for posterizing therefore should be in range [0, 1]. Defaults to 0.5.

__init__(bits, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Contrast(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images contrast. :param magnitude: The magnitude used for adjusting contrast. A

positive magnitude would enhance the contrast and a negative magnitude would make the image grayer. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing contrast adjusting therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.ColorTransform(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images color balance. :param magnitude: The magnitude used for color transform. A

positive magnitude would enhance the color and a negative magnitude would make the image grayer. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing ColorTransform therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Brightness(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images brightness. :param magnitude: The magnitude used for adjusting brightness. A

positive magnitude would enhance the brightness and a negative magnitude would make the image darker. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing contrast adjusting therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Sharpness(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶

Bases: object

Adjust images sharpness. :param magnitude: The magnitude used for adjusting sharpness. A

positive magnitude would enhance the sharpness and a negative magnitude would make the image bulr. A magnitude=0 gives the origin img.

Parameters

prob (float) – The probability for performing contrast adjusting therefore should be in range [0, 1]. Defaults to 0.5.
random_negative_prob (float) – The probability that turns the magnitude negative, which should be in range [0,1]. Defaults to 0.5.

__init__(magnitude, prob=0.5, random_negative_prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.Cutout(shape, pad_val=128, prob=0.5)[source]¶

Bases: object

Cutout images. :param shape: Expected cutout shape (h, w).

If given as a single value, the value will be used for both h and w.

Parameters

pad_val (int, Sequence[int]) – Pixel pad_val value for constant fill. If it is a sequence, it must have the same length with the image channels. Defaults to 128.
prob (float) – The probability for performing cutout therefore should be in range [0, 1]. Defaults to 0.5.

__init__(shape, pad_val=128, prob=0.5)[source]¶: Initialize self. See help(type(self)) for accurate signature.

class easycv.datasets.classification.pipelines.auto_augment.PILGaussianBlur(prob=0.1, radius_min=0.1, radius_max=2.0)[source]¶

Bases: object

__init__(prob=0.1, radius_min=0.1, radius_max=2.0)[source]¶: Initialize self. See help(type(self)) for accurate signature.

easycv.datasets.classification.pipelines.transform module¶

class easycv.datasets.classification.pipelines.transform.MMRandomErasing(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶

Bases: object

Randomly selects a rectangle region in an image and erase pixels. :param erase_prob: Probability that image will be randomly erased.

Default: 0.5

Parameters

min_area_ratio (float) – Minimum erased area / input image area Default: 0.02
max_area_ratio (float) – Maximum erased area / input image area Default: 0.4
aspect_range (sequence | float) – Aspect ratio range of erased area. if float, it will be converted to (aspect_ratio, 1/aspect_ratio) Default: (3/10, 10/3)
mode (str) – Fill method in erased area, can be: - const (default): All pixels are assign with the same value. - rand: each pixel is assigned with a random value in [0, 255]
fill_color (sequence | Number) – Base color filled in erased area. Defaults to (128, 128, 128).
fill_std (sequence | Number, optional) – If set and mode is ‘rand’, fill erased area with random color from normal distribution (mean=fill_color, std=fill_std); If not set, fill erased area with random color from uniform distribution (0~255). Defaults to None.

Note

See Random Erasing Data Augmentation This paper provided 4 modes: RE-R, RE-M, RE-0, RE-255, and use RE-M as default. The config of these 4 modes are: - RE-R: RandomErasing(mode=’rand’) - RE-M: RandomErasing(mode=’const’, fill_color=(123.67, 116.3, 103.5)) - RE-0: RandomErasing(mode=’const’, fill_color=0) - RE-255: RandomErasing(mode=’const’, fill_color=255)

__init__(erase_prob=0.5, min_area_ratio=0.02, max_area_ratio=0.4, aspect_range=(0.3, 3.3333333333333335), mode='const', fill_color=(128, 128, 128), fill_std=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.