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mask_dropout.py
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mask_dropout.py
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# Copyright 2019 The FastEstimator Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from typing import Iterable, Optional, Tuple, Union
from albumentations.augmentations.dropout import MaskDropout as MaskDropoutAlb
from fastestimator.op.numpyop.multivariate.multivariate import MultiVariateAlbumentation
from fastestimator.util.traceability_util import traceable
@traceable()
class MaskDropout(MultiVariateAlbumentation):
"""Zero out objects from an image + mask pair.
An image & mask augmentation that zero out mask and image regions corresponding to randomly chosen object instance
from mask. The mask must be single-channel image, with zero values treated as background. The image can be any
number of channels.
Args:
mode: What mode(s) to execute this Op in. For example, "train", "eval", "test", or "infer". To execute
regardless of mode, pass None. To execute in all modes except for a particular one, you can pass an argument
like "!infer" or "!train".
ds_id: What dataset id(s) to execute this Op in. To execute regardless of ds_id, pass None. To execute in all
ds_ids except for a particular one, you can pass an argument like "!ds1".
image_in: The key of an image to be modified.
mask_in: The key of a mask to be modified (with the same random factors as the image).
masks_in: The key of masks to be modified (with the same random factors as the image).
image_out: The key to write the modified image (defaults to `image_in` if None).
mask_out: The key to write the modified mask (defaults to `mask_in` if None).
masks_out: The key to write the modified masks (defaults to `masks_in` if None).
max_objects: Maximum number of labels that can be zeroed out. Can be tuple, in this case it's [min, max]
image_fill_value: Fill value to use when filling image.
Can be 'inpaint' to apply in-painting (works only for 3-channel images)
mask_fill_value: Fill value to use when filling mask.
Image types:
uint8, float32
"""
def __init__(self,
max_objects: Union[int, Tuple[int, int]] = 1,
image_fill_value: Union[int, float, str] = 0,
mask_fill_value: Union[int, float] = 0,
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None,
image_in: Optional[str] = None,
mask_in: Optional[str] = None,
masks_in: Optional[str] = None,
image_out: Optional[str] = None,
mask_out: Optional[str] = None,
masks_out: Optional[str] = None):
super().__init__(
MaskDropoutAlb(max_objects=max_objects,
image_fill_value=image_fill_value,
mask_fill_value=mask_fill_value,
always_apply=True),
image_in=image_in,
mask_in=mask_in,
masks_in=masks_in,
bbox_in=None,
keypoints_in=None,
image_out=image_out,
mask_out=mask_out,
masks_out=masks_out,
bbox_out=None,
keypoints_out=None,
bbox_params=None,
keypoint_params=None,
mode=mode,
ds_id=ds_id)