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normalize.py
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normalize.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 Any, Dict, Iterable, List, Sequence, Tuple, Union
import numpy as np
from albumentations.augmentations.transforms import Normalize as NormalizeAlb
from fastestimator.op.numpyop.univariate.univariate import ImageOnlyAlbumentation
from fastestimator.util.traceability_util import traceable
@traceable()
class Normalize(ImageOnlyAlbumentation):
"""Divide pixel values by a maximum value, subtract mean per channel and divide by std per channel.
Args:
inputs: Key(s) of images to be modified.
outputs: Key(s) into which to write the modified images.
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".
mean: Mean values to subtract.
std: The divisor.
max_pixel_value: Maximum possible pixel value.
Image types:
uint8, float32
"""
def __init__(self,
inputs: Union[str, Sequence[str]],
outputs: Union[str, Sequence[str]],
mode: Union[None, str, Iterable[str]] = None,
ds_id: Union[None, str, Iterable[str]] = None,
mean: Union[float, Tuple[float, ...]] = (0.485, 0.456, 0.406),
std: Union[float, Tuple[float, ...]] = (0.229, 0.224, 0.225),
max_pixel_value: float = 255.0):
super().__init__(NormalizeAlb(mean=mean, std=std, max_pixel_value=max_pixel_value, always_apply=True),
inputs=inputs,
outputs=outputs,
mode=mode,
ds_id=ds_id)
def forward(self, data: List[np.ndarray], state: Dict[str, Any]) -> List[np.ndarray]:
results = super().forward(data, state)
# Albumentation library casts the result to float64 iff the image is HxWx3, but we want consistent output
return [result.astype(np.float32) for result in results]