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49 lines
1.9 KiB
49 lines
1.9 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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random augment class
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"""
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import numpy as np
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import mindspore.dataset.vision.py_transforms as P
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from src import transform_utils
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IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
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IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
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class RandAugment:
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# config_str belongs to str
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# hparams belongs to dict
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def __init__(self, config_str="rand-m9-mstd0.5", hparams=None):
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hparams = hparams if hparams is not None else {}
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self.config_str = config_str
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self.hparams = hparams
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def __call__(self, imgs, labels, batchInfo):
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# assert the imgs object are pil_images
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ret_imgs = []
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ret_labels = []
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py_to_pil_op = P.ToPIL()
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to_tensor = P.ToTensor()
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normalize_op = P.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
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rand_augment_ops = transform_utils.rand_augment_transform(self.config_str, self.hparams)
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for i, image in enumerate(imgs):
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img_pil = py_to_pil_op(image)
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img_pil = rand_augment_ops(img_pil)
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img_array = to_tensor(img_pil)
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img_array = normalize_op(img_array)
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ret_imgs.append(img_array)
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ret_labels.append(labels[i])
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return np.array(ret_imgs), np.array(ret_labels)
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