forked from mindspore-Ecosystem/mindspore
150 lines
4.9 KiB
Python
150 lines
4.9 KiB
Python
# Copyright 2019 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 mindspore.dataset.transforms.vision.c_transforms as vision
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import numpy as np
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import matplotlib.pyplot as plt
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import mindspore.dataset as ds
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from mindspore import log as logger
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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 normalize_np(image):
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"""
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Apply the normalization
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"""
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# DE decodes the image in RGB by deafult, hence
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# the values here are in RGB
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image = np.array(image, np.float32)
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image = image - np.array([121.0, 115.0, 100.0])
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image = image * (1.0 / np.array([70.0, 68.0, 71.0]))
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return image
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def get_normalized(image_id):
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"""
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Reads the image using DE ops and then normalizes using Numpy
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"""
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], 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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num_iter = 0
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for item in data1.create_dict_iterator():
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image = item["image"]
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if num_iter == image_id:
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return normalize_np(image)
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num_iter += 1
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def test_normalize_op():
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"""
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Test Normalize
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"""
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logger.info("Test Normalize")
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# define map operations
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decode_op = vision.Decode()
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normalize_op = vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
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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=decode_op)
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data1 = data1.map(input_columns=["image"], operations=normalize_op)
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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=decode_op)
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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image_de_normalized = item1["image"]
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image_np_normalized = normalize_np(item2["image"])
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diff = image_de_normalized - image_np_normalized
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mse = np.sum(np.power(diff, 2))
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logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
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assert mse < 0.01
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# Uncomment these blocks to see visual results
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# plt.subplot(131)
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# plt.imshow(image_de_normalized)
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# plt.title("DE normalize image")
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#
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# plt.subplot(132)
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# plt.imshow(image_np_normalized)
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# plt.title("Numpy normalized image")
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#
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# plt.subplot(133)
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# plt.imshow(diff)
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# plt.title("Difference image, mse : {}".format(mse))
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#
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# plt.show()
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num_iter += 1
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def test_decode_op():
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logger.info("Test Decode")
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
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shuffle=False)
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# define map operations
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decode_op = vision.Decode()
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# apply map operations on images
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data1 = data1.map(input_columns=["image"], operations=decode_op)
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num_iter = 0
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image = None
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for item in data1.create_dict_iterator():
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logger.info("Looping inside iterator {}".format(num_iter))
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image = item["image"]
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# plt.subplot(131)
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# plt.imshow(image)
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# plt.title("DE image")
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# plt.show()
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num_iter += 1
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def test_decode_normalize_op():
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logger.info("Test [Decode, Normalize] in one Map")
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image", "label"], num_parallel_workers=1,
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shuffle=False)
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# define map operations
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decode_op = vision.Decode()
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normalize_op = vision.Normalize([121.0, 115.0, 100.0], [70.0, 68.0, 71.0])
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# apply map operations on images
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data1 = data1.map(input_columns=["image"], operations=[decode_op, normalize_op])
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num_iter = 0
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image = None
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for item in data1.create_dict_iterator():
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logger.info("Looping inside iterator {}".format(num_iter))
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image = item["image"]
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# plt.subplot(131)
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# plt.imshow(image)
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# plt.title("DE image")
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# plt.show()
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num_iter += 1
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if __name__ == "__main__":
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test_decode_op()
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test_decode_normalize_op()
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test_normalize_op()
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