144 lines
4.9 KiB
Python
144 lines
4.9 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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"""
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Testing Decode op in DE
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"""
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import cv2
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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 as vision
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from mindspore import log as logger
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from util import 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_decode_op():
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"""
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Feature: Decode Op
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Description: Test C++ implementation
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Expectation: Dataset pipeline runs successfully and results are verified
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"""
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logger.info("test_decode_op")
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# Serialize and Load dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Decode with rgb format set to True
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data1 = data1.map(operations=[vision.Decode()], input_columns=["image"])
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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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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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actual = item1["image"]
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expected = cv2.imdecode(item2["image"], cv2.IMREAD_COLOR)
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expected = cv2.cvtColor(expected, cv2.COLOR_BGR2RGB)
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assert actual.shape == expected.shape
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mse = diff_mse(actual, expected)
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assert mse == 0
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def test_decode_op_support_format():
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"""
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Feature: Decode Op
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Description: Test support format of decode op
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Expectation: decode image successfully
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"""
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c_decode = vision.Decode(to_pil=False)
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p_decode = vision.Decode(to_pil=True)
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# jpeg: Opencv[√] Pillow[√]
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jpg_image = np.fromfile("../data/dataset/testFormats/apple.jpg", np.uint8)
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c_decode(jpg_image)
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p_decode(jpg_image)
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# bmp: Opencv[√] Pillow[√]
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bmp_image = np.fromfile("../data/dataset/testFormats/apple.bmp", np.uint8)
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c_decode(bmp_image)
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p_decode(bmp_image)
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# png: Opencv[√] Pillow[√]
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png_image = np.fromfile("../data/dataset/testFormats/apple.png", np.uint8)
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c_decode(png_image)
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p_decode(png_image)
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# tiff: Opencv[√] Pillow[√]
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tiff_image = np.fromfile("../data/dataset/testFormats/apple.tiff", np.uint8)
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c_decode(tiff_image)
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p_decode(tiff_image)
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# gif: Opencv[×] Pillow[√]
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gif_image = np.fromfile("../data/dataset/testFormats/apple.gif", np.uint8)
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with pytest.raises(RuntimeError):
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c_decode(gif_image)
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p_decode(gif_image)
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# webp: Opencv[×] Pillow[√]
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webp_image = np.fromfile("../data/dataset/testFormats/apple.webp", np.uint8)
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with pytest.raises(RuntimeError):
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c_decode(webp_image)
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p_decode(webp_image)
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class ImageDataset:
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"""Custom class to generate and read image dataset"""
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def __init__(self, data_path, data_type="numpy"):
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self.data = [data_path]
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self.label = np.random.sample((1, 1))
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self.data_type = data_type
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def __getitem__(self, index):
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# use file open and read method
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f = open(self.data[index], 'rb')
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img_bytes = f.read()
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f.close()
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if self.data_type == "numpy":
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img_bytes = np.frombuffer(img_bytes, dtype=np.uint8)
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# Return bytes directly
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return (img_bytes, self.label[index])
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def __len__(self):
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return len(self.data)
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def test_read_image_decode_op():
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"""
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Feature: Decode Op
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Description: Test Python implementation
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Expectation: Dataset pipeline runs successfully and results are verified
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"""
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data_path = "../data/dataset/testPK/data/class1/0.jpg"
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dataset1 = ds.GeneratorDataset(ImageDataset(data_path, data_type="numpy"), ["data", "label"])
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dataset2 = ds.GeneratorDataset(ImageDataset(data_path, data_type="bytes"), ["data", "label"])
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decode_op = vision.Decode(to_pil=True)
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to_tensor = vision.ToTensor(output_type=np.int32)
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dataset1 = dataset1.map(operations=[decode_op, to_tensor], input_columns=["data"])
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dataset2 = dataset2.map(operations=[decode_op, to_tensor], input_columns=["data"])
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for item1, item2 in zip(dataset1, dataset2):
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assert np.count_nonzero(item1[0].asnumpy() - item2[0].asnumpy()) == 0
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if __name__ == "__main__":
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test_decode_op()
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test_decode_op_support_format()
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test_read_image_decode_op()
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