416 lines
14 KiB
Python
416 lines
14 KiB
Python
# 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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Testing the MixUpBatch op in DE
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"""
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import numpy as np
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.vision.c_transforms as vision
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import mindspore.dataset.transforms.c_transforms as data_trans
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from mindspore import log as logger
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from util import save_and_check_md5, diff_mse, visualize_list, config_get_set_seed, \
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config_get_set_num_parallel_workers
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DATA_DIR = "../data/dataset/testCifar10Data"
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DATA_DIR2 = "../data/dataset/testImageNetData2/train/"
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DATA_DIR3 = "../data/dataset/testCelebAData/"
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GENERATE_GOLDEN = False
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def test_mixup_batch_success1(plot=False):
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"""
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Test MixUpBatch op with specified alpha parameter
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"""
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logger.info("test_mixup_batch_success1")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5, drop_remainder=True)
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images_original = None
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch(2)
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data1 = data1.batch(5, drop_remainder=True)
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data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
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images_mixup = None
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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if plot:
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visualize_list(images_original, images_mixup)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_mixup[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_mixup_batch_success2(plot=False):
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"""
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Test MixUpBatch op with specified alpha parameter on ImageFolderDataset
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"""
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logger.info("test_mixup_batch_success2")
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# Original Images
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ds_original = ds.ImageFolderDataset(dataset_dir=DATA_DIR2, shuffle=False)
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decode_op = vision.Decode()
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ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
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ds_original = ds_original.batch(4, pad_info={}, drop_remainder=True)
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images_original = None
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.ImageFolderDataset(dataset_dir=DATA_DIR2, shuffle=False)
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decode_op = vision.Decode()
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data1 = data1.map(input_columns=["image"], operations=[decode_op])
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch(2.0)
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data1 = data1.batch(4, pad_info={}, drop_remainder=True)
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data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
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images_mixup = None
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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if plot:
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visualize_list(images_original, images_mixup)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_mixup[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_mixup_batch_success3(plot=False):
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"""
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Test MixUpBatch op without specified alpha parameter.
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Alpha parameter will be selected by default in this case
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"""
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logger.info("test_mixup_batch_success3")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5, drop_remainder=True)
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images_original = None
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch()
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data1 = data1.batch(5, drop_remainder=True)
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data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
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images_mixup = np.array([])
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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if plot:
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visualize_list(images_original, images_mixup)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_mixup[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_mixup_batch_success4(plot=False):
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"""
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Test MixUpBatch op on a dataset where OneHot returns a 2D vector.
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Alpha parameter will be selected by default in this case
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"""
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logger.info("test_mixup_batch_success4")
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# Original Images
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ds_original = ds.CelebADataset(DATA_DIR3, shuffle=False)
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decode_op = vision.Decode()
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ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
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ds_original = ds_original.batch(2, drop_remainder=True)
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images_original = None
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.CelebADataset(DATA_DIR3, shuffle=False)
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decode_op = vision.Decode()
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data1 = data1.map(input_columns=["image"], operations=[decode_op])
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one_hot_op = data_trans.OneHot(num_classes=100)
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data1 = data1.map(input_columns=["attr"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch()
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data1 = data1.batch(2, drop_remainder=True)
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data1 = data1.map(input_columns=["image", "attr"], operations=mixup_batch_op)
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images_mixup = np.array([])
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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if plot:
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visualize_list(images_original, images_mixup)
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num_samples = images_original.shape[0]
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mse = np.zeros(num_samples)
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for i in range(num_samples):
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mse[i] = diff_mse(images_mixup[i], images_original[i])
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logger.info("MSE= {}".format(str(np.mean(mse))))
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def test_mixup_batch_md5():
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"""
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Test MixUpBatch with MD5:
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"""
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logger.info("test_mixup_batch_md5")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# MixUp Images
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data = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data = data.map(input_columns=["label"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch()
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data = data.batch(5, drop_remainder=True)
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data = data.map(input_columns=["image", "label"], operations=mixup_batch_op)
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filename = "mixup_batch_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_mixup_batch_fail1():
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"""
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Test MixUpBatch Fail 1
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We expect this to fail because the images and labels are not batched
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"""
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logger.info("test_mixup_batch_fail1")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5)
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images_original = np.array([])
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch(0.1)
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with pytest.raises(RuntimeError) as error:
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data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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error_message = "You must make sure images are HWC or CHW and batched"
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assert error_message in str(error.value)
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def test_mixup_batch_fail2():
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"""
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Test MixUpBatch Fail 2
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We expect this to fail because alpha is negative
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"""
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logger.info("test_mixup_batch_fail2")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5)
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images_original = np.array([])
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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with pytest.raises(ValueError) as error:
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vision.MixUpBatch(-1)
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error_message = "Input is not within the required interval"
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assert error_message in str(error.value)
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def test_mixup_batch_fail3():
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"""
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Test MixUpBatch op
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We expect this to fail because label column is not passed to mixup_batch
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"""
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logger.info("test_mixup_batch_fail3")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5, drop_remainder=True)
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images_original = None
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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mixup_batch_op = vision.MixUpBatch()
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data1 = data1.batch(5, drop_remainder=True)
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data1 = data1.map(input_columns=["image"], operations=mixup_batch_op)
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with pytest.raises(RuntimeError) as error:
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images_mixup = np.array([])
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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error_message = "Both images and labels columns are required"
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assert error_message in str(error.value)
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def test_mixup_batch_fail4():
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"""
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Test MixUpBatch Fail 2
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We expect this to fail because alpha is zero
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"""
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logger.info("test_mixup_batch_fail4")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5)
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images_original = np.array([])
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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one_hot_op = data_trans.OneHot(num_classes=10)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op)
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with pytest.raises(ValueError) as error:
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vision.MixUpBatch(0.0)
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error_message = "Input is not within the required interval"
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assert error_message in str(error.value)
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def test_mixup_batch_fail5():
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"""
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Test MixUpBatch Fail 5
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We expect this to fail because labels are not OntHot encoded
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"""
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logger.info("test_mixup_batch_fail5")
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# Original Images
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ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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ds_original = ds_original.batch(5)
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images_original = np.array([])
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for idx, (image, _) in enumerate(ds_original):
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if idx == 0:
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images_original = image
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else:
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images_original = np.append(images_original, image, axis=0)
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# MixUp Images
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data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
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mixup_batch_op = vision.MixUpBatch()
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data1 = data1.batch(5, drop_remainder=True)
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data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
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with pytest.raises(RuntimeError) as error:
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images_mixup = np.array([])
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for idx, (image, _) in enumerate(data1):
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if idx == 0:
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images_mixup = image
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else:
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images_mixup = np.append(images_mixup, image, axis=0)
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error_message = "MixUpBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC"
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assert error_message in str(error.value)
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if __name__ == "__main__":
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test_mixup_batch_success1(plot=True)
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test_mixup_batch_success2(plot=True)
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test_mixup_batch_success3(plot=True)
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test_mixup_batch_success4(plot=True)
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test_mixup_batch_md5()
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test_mixup_batch_fail1()
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test_mixup_batch_fail2()
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test_mixup_batch_fail3()
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test_mixup_batch_fail4()
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test_mixup_batch_fail5()
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