forked from mindspore-Ecosystem/mindspore
140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
# Copyright 2019-2022 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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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms as data_trans
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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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DATA_DIR_2 = "../data/dataset/testImageNetData2/train"
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def test_mix_up_single():
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"""
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Feature: MixUp Op
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Description: Test Python op, single batch mix up scenario
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Expectation: Dataset pipeline runs successfully and results are verified
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"""
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logger.info("Test single batch mix up op")
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resize_height = 224
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resize_width = 224
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# Create dataset and define map operations
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ds1 = ds.ImageFolderDataset(DATA_DIR_2)
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num_classes = 10
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decode_op = vision.Decode()
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resize_op = vision.Resize((resize_height, resize_width), vision.Inter.LINEAR)
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one_hot_encode = data_trans.OneHot(num_classes) # num_classes is input argument
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ds1 = ds1.map(operations=decode_op, input_columns=["image"])
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ds1 = ds1.map(operations=resize_op, input_columns=["image"])
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ds1 = ds1.map(operations=one_hot_encode, input_columns=["label"])
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# apply batch operations
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batch_size = 3
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ds1 = ds1.batch(batch_size, drop_remainder=True)
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ds2 = ds1
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alpha = 0.2
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transforms = [vision.MixUp(batch_size=batch_size, alpha=alpha, is_single=True)
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]
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ds1 = ds1.map(operations=transforms, input_columns=["image", "label"])
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for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1, output_numpy=True),
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ds2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image1 = data1["image"]
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label = data1["label"]
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logger.info("label is {}".format(label))
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image2 = data2["image"]
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label2 = data2["label"]
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logger.info("label2 is {}".format(label2))
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lam = np.abs(label - label2)
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for index in range(batch_size - 1):
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if np.square(lam[index]).mean() != 0:
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lam_value = 1 - np.sum(lam[index]) / 2
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img_golden = lam_value * image2[index] + (1 - lam_value) * image2[index + 1]
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assert image1[index].all() == img_golden.all()
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logger.info("====test single batch mixup ok====")
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def test_mix_up_multi():
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"""
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Feature: MixUp Op
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Description: Test Python op, multiple batch mix up scenario
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Expectation: Dataset pipeline runs successfully and results are verified
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"""
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logger.info("Test several batch mix up op")
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resize_height = 224
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resize_width = 224
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# Create dataset and define map operations
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ds1 = ds.ImageFolderDataset(DATA_DIR_2)
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num_classes = 3
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decode_op = vision.Decode()
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resize_op = vision.Resize((resize_height, resize_width), vision.Inter.LINEAR)
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one_hot_encode = data_trans.OneHot(num_classes) # num_classes is input argument
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ds1 = ds1.map(operations=decode_op, input_columns=["image"])
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ds1 = ds1.map(operations=resize_op, input_columns=["image"])
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ds1 = ds1.map(operations=one_hot_encode, input_columns=["label"])
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# apply batch operations
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batch_size = 3
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ds1 = ds1.batch(batch_size, drop_remainder=True)
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ds2 = ds1
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alpha = 0.2
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transforms = [vision.MixUp(batch_size=batch_size, alpha=alpha, is_single=False)
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]
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ds1 = ds1.map(operations=transforms, input_columns=["image", "label"])
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num_iter = 0
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batch1_image1 = 0
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for data1, data2 in zip(ds1.create_dict_iterator(num_epochs=1, output_numpy=True),
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ds2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image1 = data1["image"]
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label1 = data1["label"]
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logger.info("label: {}".format(label1))
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image2 = data2["image"]
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label2 = data2["label"]
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logger.info("label2: {}".format(label2))
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if num_iter == 0:
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batch1_image1 = image1
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if num_iter == 1:
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lam = np.abs(label2 - label1)
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logger.info("lam value in multi: {}".format(lam))
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for index in range(batch_size):
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if np.square(lam[index]).mean() != 0:
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lam_value = 1 - np.sum(lam[index]) / 2
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img_golden = lam_value * image2[index] + (1 - lam_value) * batch1_image1[index]
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assert image1[index].all() == img_golden.all()
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logger.info("====test several batch mixup ok====")
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break
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num_iter += 1
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if __name__ == "__main__":
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test_mix_up_single()
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test_mix_up_multi()
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