forked from mindspore-Ecosystem/mindspore
137 lines
4.9 KiB
Python
137 lines
4.9 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 RandomChoice op in DE
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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.vision.py_transforms as py_vision
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from mindspore import log as logger
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from util import visualize_list, diff_mse
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def test_random_choice_op(plot=False):
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"""
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Test RandomChoice in python transformations
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"""
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logger.info("test_random_choice_op")
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# define map operations
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transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)]
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transforms1 = [
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py_vision.Decode(),
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py_vision.RandomChoice(transforms_list),
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py_vision.ToTensor()
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]
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transform1 = py_vision.ComposeOp(transforms1)
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transforms2 = [
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py_vision.Decode(),
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py_vision.ToTensor()
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]
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transform2 = py_vision.ComposeOp(transforms2)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(input_columns=["image"], operations=transform1())
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(input_columns=["image"], operations=transform2())
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image_choice = []
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image_original = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_choice.append(image1)
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image_original.append(image2)
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if plot:
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visualize_list(image_original, image_choice)
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def test_random_choice_comp(plot=False):
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"""
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Test RandomChoice and compare with single CenterCrop results
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"""
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logger.info("test_random_choice_comp")
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# define map operations
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transforms_list = [py_vision.CenterCrop(64)]
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transforms1 = [
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py_vision.Decode(),
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py_vision.RandomChoice(transforms_list),
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py_vision.ToTensor()
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]
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transform1 = py_vision.ComposeOp(transforms1)
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transforms2 = [
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py_vision.Decode(),
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py_vision.CenterCrop(64),
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py_vision.ToTensor()
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]
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transform2 = py_vision.ComposeOp(transforms2)
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data1 = data1.map(input_columns=["image"], operations=transform1())
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(input_columns=["image"], operations=transform2())
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image_choice = []
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image_original = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1), data2.create_dict_iterator(num_epochs=1)):
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image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_choice.append(image1)
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image_original.append(image2)
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mse = diff_mse(image1, image2)
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assert mse == 0
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if plot:
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visualize_list(image_original, image_choice)
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def test_random_choice_exception_random_crop_badinput():
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"""
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Test RandomChoice: hit error in RandomCrop with greater crop size,
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expected to raise error
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"""
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logger.info("test_random_choice_exception_random_crop_badinput")
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# define map operations
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# note: crop size[5000, 5000] > image size[4032, 2268]
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transforms_list = [py_vision.RandomCrop(5000)]
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transforms = [
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py_vision.Decode(),
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py_vision.RandomChoice(transforms_list),
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py_vision.ToTensor()
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]
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transform = py_vision.ComposeOp(transforms)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data = data.map(input_columns=["image"], operations=transform())
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try:
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_ = data.create_dict_iterator(num_epochs=1).get_next()
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except RuntimeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Crop size" in str(e)
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if __name__ == '__main__':
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test_random_choice_op(plot=True)
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test_random_choice_comp(plot=True)
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test_random_choice_exception_random_crop_badinput()
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