forked from mindspore-Ecosystem/mindspore
122 lines
4.1 KiB
Python
122 lines
4.1 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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Testing FiveCrop in DE
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"""
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import pytest
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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 vision
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from mindspore import log as logger
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from util import visualize_list, save_and_check_md5
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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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GENERATE_GOLDEN = False
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def test_five_crop_op(plot=False):
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"""
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Test FiveCrop
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"""
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logger.info("test_five_crop")
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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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transforms_1 = [
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vision.Decode(),
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vision.ToTensor(),
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]
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transform_1 = vision.ComposeOp(transforms_1)
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data1 = data1.map(input_columns=["image"], operations=transform_1())
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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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transforms_2 = [
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vision.Decode(),
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vision.FiveCrop(200),
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lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 5 images
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]
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transform_2 = vision.ComposeOp(transforms_2)
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data2 = data2.map(input_columns=["image"], operations=transform_2())
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num_iter = 0
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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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num_iter += 1
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image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_2 = item2["image"]
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logger.info("shape of image_1: {}".format(image_1.shape))
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logger.info("shape of image_2: {}".format(image_2.shape))
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logger.info("dtype of image_1: {}".format(image_1.dtype))
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logger.info("dtype of image_2: {}".format(image_2.dtype))
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if plot:
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visualize_list(np.array([image_1]*5), (image_2 * 255).astype(np.uint8).transpose(0, 2, 3, 1))
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# The output data should be of a 4D tensor shape, a stack of 5 images.
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assert len(image_2.shape) == 4
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assert image_2.shape[0] == 5
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def test_five_crop_error_msg():
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"""
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Test FiveCrop error message.
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"""
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logger.info("test_five_crop_error_msg")
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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vision.Decode(),
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vision.FiveCrop(200),
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vision.ToTensor()
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]
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transform = vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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with pytest.raises(RuntimeError) as info:
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for _ in data:
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pass
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error_msg = "TypeError: img should be PIL Image or Numpy array. Got <class 'tuple'>"
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# error msg comes from ToTensor()
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assert error_msg in str(info.value)
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def test_five_crop_md5():
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"""
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Test FiveCrop with md5 check
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"""
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logger.info("test_five_crop_md5")
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# First dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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vision.Decode(),
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vision.FiveCrop(100),
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lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 5 images
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]
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transform = vision.ComposeOp(transforms)
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data = data.map(input_columns=["image"], operations=transform())
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# Compare with expected md5 from images
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filename = "five_crop_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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if __name__ == "__main__":
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test_five_crop_op(plot=True)
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test_five_crop_error_msg()
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test_five_crop_md5()
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