forked from mindspore-Ecosystem/mindspore
368 lines
14 KiB
Python
368 lines
14 KiB
Python
# Copyright 2020-2022 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 Normalize 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.transforms
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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, visualize_image
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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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MNIST_DATA_DIR = "../data/dataset/testMnistData"
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GENERATE_GOLDEN = False
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def normalize_pad_np(image, mean, std):
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"""
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Apply the normalize+pad
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"""
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# DE decodes the image in RGB by default, 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(mean)
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image = image * (1.0 / np.array(std))
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zeros = np.zeros([image.shape[0], image.shape[1], 1], dtype=np.float32)
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output = np.concatenate((image, zeros), axis=2)
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return output
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def test_normalize_pad_op_hwc(plot=False):
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with Decode versus NumPy comparison
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Expectation: Test succeeds. MSE difference is negligible.
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"""
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logger.info("Test NormalizePad with hwc")
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mean = [121.0, 115.0, 100.0]
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std = [70.0, 68.0, 71.0]
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# define map operations
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decode_op = vision.Decode()
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normalize_pad_op = vision.NormalizePad(mean, std, is_hwc=True)
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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(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=normalize_pad_op, 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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data2 = data2.map(operations=decode_op, input_columns=["image"])
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num_iter = 0
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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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image_de_normalized = item1["image"]
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image_original = item2["image"]
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image_np_normalized = normalize_pad_np(image_original, mean, std)
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mse = diff_mse(image_de_normalized, image_np_normalized)
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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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if plot:
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visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
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num_iter += 1
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def test_normalize_pad_op_chw(plot=False):
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with CHW input, Decode(to_pil=True) & ToTensor versus NumPy comparison
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Expectation: Test succeeds. MSE difference is negligible.
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"""
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logger.info("Test NormalizePad with chw")
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mean = [0.475, 0.45, 0.392]
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std = [0.275, 0.267, 0.278]
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# define map operations
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transforms = [
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vision.Decode(True),
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vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.Compose(transforms)
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normalize_pad_op = vision.NormalizePad(mean, std, is_hwc=False)
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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(operations=transform, input_columns=["image"])
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data1 = data1.map(operations=normalize_pad_op, input_columns=["image"])
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transforms2 = [
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vision.Decode(True),
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vision.ToTensor()
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]
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transform2 = mindspore.dataset.transforms.Compose(transforms2)
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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(operations=transform2, input_columns=["image"])
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num_iter = 0
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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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image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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image_np_normalized = (normalize_pad_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8)
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image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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mse = diff_mse(image_de_normalized, image_np_normalized)
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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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if plot:
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visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
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num_iter += 1
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def test_normalize_pad_op_comp_chw():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with CHW input, Decode(to_pil=True) & ToTensor versus Decode(to_pil=False) & HWC2CHW
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comparison.
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Expectation: Test succeeds. MSE difference is negligible.
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"""
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logger.info("Test NormalizePad with CHW input")
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mean = [0.475, 0.45, 0.392]
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std = [0.275, 0.267, 0.278]
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# define map operations
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transforms = [
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vision.Decode(True),
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vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.Compose(transforms)
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normalize_pad_op = vision.NormalizePad(mean, std, is_hwc=False)
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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(operations=transform, input_columns=["image"])
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data1 = data1.map(operations=normalize_pad_op, 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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data2 = data2.map(operations=vision.Decode(), input_columns=["image"])
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data2 = data2.map(operations=vision.HWC2CHW(), input_columns=["image"])
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data2 = data2.map(operations=vision.NormalizePad(mean, std, is_hwc=False), input_columns=["image"])
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num_iter = 0
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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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image_de_normalized = item1["image"]
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image_np_normalized = item2["image"] / 255
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mse = diff_mse(image_de_normalized, image_np_normalized)
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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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def test_decode_normalize_pad_op():
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"""
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Feature: NormalizePad
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Description: Test Decode op followed by NormalizePad op
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Expectation: Passes the md5 check test
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"""
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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_pad_op = vision.NormalizePad([121.0, 115.0, 100.0], [70.0, 68.0, 71.0], "float16")
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# apply map operations on images
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data1 = data1.map(operations=[decode_op, normalize_pad_op], input_columns=["image"])
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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logger.info("Looping inside iterator {}".format(num_iter))
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assert item["image"].dtype == np.float16
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num_iter += 1
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def test_multi_channel_normalize_pad_chw():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad Op with multi-channel CHW input
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Expectation: Test succeeds.
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"""
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mean = [0.475, 0.45, 0.392, 0.5]
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std = [0.275, 0.267, 0.278, 0.3]
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image = np.random.randn(4, 102, 85).astype(np.uint8)
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op = vision.NormalizePad(mean, std, is_hwc=False)
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op(image)
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def test_multi_channel_normalize_pad_hwc():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad Op with multi-channel HWC input
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Expectation: Test succeeds.
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"""
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mean = [0.475, 0.45, 0.392, 0.5]
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std = [0.275, 0.267, 0.278, 0.3]
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image = np.random.randn(102, 85, 4).astype(np.uint8)
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op = vision.NormalizePad(mean, std, is_hwc=True)
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op(image)
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def test_normalize_pad_op_1channel(plot=False):
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"""
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Feature: NormalizePad
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Description: Test NormalizePad Op with single channel input
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Expectation: Test succeeds. MSE difference is negligible.
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"""
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logger.info("Test NormalizePad Single Channel with HWC")
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mean = [121.0]
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std = [70.0]
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normalize_pad_op = vision.NormalizePad(mean, std, is_hwc=True)
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# First dataset
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data2 = ds.MnistDataset(MNIST_DATA_DIR, shuffle=False)
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data1 = data2.map(operations=normalize_pad_op, input_columns=["image"])
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num_iter = 0
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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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image_de_normalized = item1["image"]
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image_original = item2["image"]
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image_np_normalized = normalize_pad_np(image_original, mean, std)
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mse = diff_mse(image_de_normalized, image_np_normalized)
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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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if plot:
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visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
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num_iter += 1
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assert num_iter == 10000
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def test_normalize_pad_exception_unequal_size_1():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with error input: len(mean) != len(std)
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Expectation: ValueError raised
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"""
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logger.info("test_normalize_pad_exception_unequal_size_1")
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try:
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_ = vision.NormalizePad([100, 250, 125], [50, 50, 75, 75])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Length of mean and std must be equal."
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try:
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_ = vision.NormalizePad([100, 250, 125], [50, 50, 75], 1)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype should be string."
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try:
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_ = vision.NormalizePad([100, 250, 125], [50, 50, 75], "")
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype only supports float32 or float16."
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def test_normalize_pad_exception_unequal_size_2():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with error input: len(mean) != len(std)
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Expectation: ValueError raised
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"""
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logger.info("test_normalize_pad_exception_unequal_size_2")
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try:
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_ = vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71, 0.72], is_hwc=False)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "Length of mean and std must be equal."
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try:
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_ = vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71], 1, is_hwc=False)
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except TypeError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype should be string."
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try:
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_ = vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71], "", is_hwc=False)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert str(e) == "dtype only supports float32 or float16."
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def test_normalize_pad_exception_invalid_range():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with error input: value is not in range [0,1]
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Expectation: ValueError raised
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"""
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logger.info("test_normalize_pad_exception_invalid_range")
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try:
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_ = vision.NormalizePad([0.75, 1.25, 0.5], [0.1, 0.18, 1.32], is_hwc=False)
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert "Input mean_value is not within the required interval of [0.0, 1.0]." in str(e)
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def test_normalize_pad_runtime_error():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with error input image
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Expectation: RuntimeError raised
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"""
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logger.info("test_normalize_pad_runtime_error")
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try:
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mean = [0.25, 0.65, 0.39]
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std = [0.75, 0.27, 0.28]
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image = np.random.randn(128, 128, 3, 3).astype(np.uint8)
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_ = vision.NormalizePad(mean, std, is_hwc=True)(image)
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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 "input tensor is not in shape of <H,W> or <H,W,C>" in str(e)
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try:
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mean = [0.25, 0.65, 0.39]
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std = [0.75, 0.27, 0.28]
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image = np.random.randn(3, 10, 10).astype(np.float32)
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_ = vision.NormalizePad(mean, std, dtype="float32", is_hwc=True)(image)
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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 "number of channels does not match the size of mean and std vectors" in str(e)
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def test_normalize_pad_invalid_param():
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"""
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Feature: NormalizePad
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Description: Test NormalizePad with invalid param
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Expectation: TypeError raised
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"""
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logger.info("test_normalize_pad_invalid_param")
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with pytest.raises(TypeError) as error_info:
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_ = vision.NormalizePad([0.75, 1.25, 0.5], [0.1, 0.18, "0.22"])
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assert "Argument std[2] with value 0.22 is not of type [<class 'int'>, <class 'float'>]" in str(error_info.value)
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if __name__ == "__main__":
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test_normalize_pad_op_hwc(plot=True)
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test_normalize_pad_op_chw(plot=True)
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test_normalize_pad_op_comp_chw()
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test_decode_normalize_pad_op()
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test_multi_channel_normalize_pad_chw()
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test_multi_channel_normalize_pad_hwc()
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test_normalize_pad_exception_unequal_size_1()
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test_normalize_pad_exception_unequal_size_2()
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test_normalize_pad_exception_invalid_range()
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test_normalize_pad_op_1channel()
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test_normalize_pad_runtime_error()
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test_normalize_pad_invalid_param()
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