forked from mindspore-Ecosystem/mindspore
325 lines
12 KiB
Python
325 lines
12 KiB
Python
# Copyright 2021-2022 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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Test PhotoTour dataset operator
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"""
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import pytest
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from PIL import Image
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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/testPhotoTourData"
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NAME = 'liberty'
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LEN = 100
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def load_photo_tour_dataset(path, name):
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"""
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Feature: load_photo_tour_dataset.
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Description: Load photo tour.
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Expectation: Get data of photo tour dataset.
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"""
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def pil2array(img: Image.Image):
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"""
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Convert PIL image type to numpy 2D array
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"""
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return np.array(img.getdata(), dtype=np.uint8).reshape((64, 64, 1))
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def find_files(data_dir: str, image_ext_: str):
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"""
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Return a list with the file names of the images containing the patches
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"""
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files = []
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# find those files with the specified extension
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for file_dir in os.listdir(data_dir):
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if file_dir.endswith(image_ext_):
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files.append(os.path.join(data_dir, file_dir))
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return sorted(files) # sort files in ascend order to keep relations
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patches = []
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list_files = find_files(os.path.realpath(os.path.join(path, name)), 'bmp')
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idx = 0
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for fpath in list_files:
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img = Image.open(fpath)
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for y in range(0, 1024, 64):
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for x in range(0, 1024, 64):
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patch = img.crop((x, y, x + 64, y + 64))
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patches.append(pil2array(patch))
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idx += 1
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if idx > LEN:
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break
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if idx > LEN:
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break
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matches_path = os.path.join(os.path.realpath(os.path.join(path, name)), 'm50_100000_100000_0.txt')
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matches = []
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with open(matches_path, 'r') as f:
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for line in f.readlines():
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line_split = line.split()
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matches.append([int(line_split[0]), int(line_split[3]),
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int(line_split[1] == line_split[4])])
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return patches, matches
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def visualize_dataset(images1, images2, matches):
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"""
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Feature: visualize_dataset.
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Description: Visualize photo tour dataset.
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Expectation: Plot images.
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"""
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num_samples = len(images1)
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for i in range(num_samples):
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plt.subplot(1, num_samples, i + 1)
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plt.imshow(images1[i].squeeze(), cmap=plt.cm.gray)
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plt.title(matches[i])
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num_samples = len(images2)
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for i in range(num_samples):
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plt.subplot(2, num_samples, i + 1)
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plt.imshow(images2[i].squeeze(), cmap=plt.cm.gray)
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plt.title(matches[i])
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plt.show()
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def test_photo_tour_content_check():
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"""
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Feature: test_photo_tour_content_check.
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Description: Validate PhotoTourDataset image readings.
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Expectation: Get correct number of data and correct content.
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"""
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logger.info("Test PhotoTourDataset Op with content check")
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data1 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=10, shuffle=False)
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images, matches = load_photo_tour_dataset(DATA_DIR, NAME)
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num_iter = 0
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# in this example, each dictionary has keys "image1" "image2" and "matches"
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for i, data in enumerate(data1.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(data["image1"], images[matches[i][0]])
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np.testing.assert_array_equal(data["image2"], images[matches[i][1]])
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np.testing.assert_array_equal(data["matches"], matches[i][2])
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num_iter += 1
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assert num_iter == 10
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def test_photo_tour_basic():
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"""
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Feature: test_photo_tour_basic.
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Description: Test basic usage of PhotoTourDataset.
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Expectation: Get correct number of data.
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"""
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logger.info("Test PhotoTourDataset Op")
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# case 1: test loading whole dataset
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data1 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test')
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num_iter1 = 0
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for _ in data1.create_dict_iterator(num_epochs=1):
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num_iter1 += 1
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assert num_iter1 == 16
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# case 2: test num_samples
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data2 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=10)
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num_iter2 = 0
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for _ in data2.create_dict_iterator(num_epochs=1):
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num_iter2 += 1
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assert num_iter2 == 10
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# case 3: test repeat
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data3 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=5)
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data3 = data3.repeat(5)
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num_iter3 = 0
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for _ in data3.create_dict_iterator(num_epochs=1):
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num_iter3 += 1
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assert num_iter3 == 25
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# case 4: test batch with drop_remainder=False
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data4 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=10)
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assert data4.get_dataset_size() == 10
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assert data4.get_batch_size() == 1
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data4 = data4.batch(batch_size=7) # drop_remainder is default to be False
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assert data4.get_dataset_size() == 2
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assert data4.get_batch_size() == 7
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num_iter4 = 0
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for _ in data4.create_dict_iterator(num_epochs=1):
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num_iter4 += 1
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assert num_iter4 == 2
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# case 5: test batch with drop_remainder=True
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data5 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=10)
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assert data5.get_dataset_size() == 10
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assert data5.get_batch_size() == 1
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data5 = data5.batch(batch_size=7, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert data5.get_dataset_size() == 1
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assert data5.get_batch_size() == 7
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num_iter5 = 0
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for _ in data5.create_dict_iterator(num_epochs=1):
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num_iter5 += 1
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assert num_iter5 == 1
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# case 6: test get_col_names
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data6 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=10)
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assert data6.get_col_names() == ['image1', 'image2', 'matches']
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def test_photo_tour_pk_sampler():
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"""
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Feature: test_photo_tour_pk_sampler.
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Description: Test usage of PhotoTourDataset with PKSampler.
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Expectation: Get correct number of data.
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"""
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logger.info("Test PhotoTourDataset Op with PKSampler")
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golden = [0, 0, 0, 1, 1, 1]
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sampler = ds.PKSampler(3)
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data = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', sampler=sampler)
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num_iter = 0
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matches_list = []
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for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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matches_list.append(item["matches"])
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num_iter += 1
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np.testing.assert_array_equal(golden, matches_list)
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assert num_iter == 6
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def test_photo_tour_sequential_sampler():
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"""
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Feature: test_photo_tour_sequential_sampler.
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Description: Test usage of PhotoTourDataset with SequentialSampler.
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Expectation: Get correct number of data.
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"""
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logger.info("Test PhotoTourDataset Op with SequentialSampler")
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num_samples = 5
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sampler = ds.SequentialSampler(num_samples=num_samples)
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data1 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', sampler=sampler)
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data2 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', shuffle=False, num_samples=num_samples)
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matches_list1, matches_list2 = [], []
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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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matches_list1.append(item1["matches"].asnumpy())
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matches_list2.append(item2["matches"].asnumpy())
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num_iter += 1
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np.testing.assert_array_equal(matches_list1, matches_list2)
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assert num_iter == num_samples
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def test_photo_tour_exception():
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"""
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Feature: test_photo_tour_exception.
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Description: Test error cases for PhotoTourDataset.
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Expectation: Raise exception.
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"""
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logger.info("Test error cases for PhotoTourDataset")
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error_msg_1 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_1):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', shuffle=False, sampler=ds.PKSampler(3))
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error_msg_2 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_2):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
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error_msg_3 = "num_shards is specified and currently requires shard_id as well"
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with pytest.raises(RuntimeError, match=error_msg_3):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_shards=10)
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error_msg_4 = "shard_id is specified but num_shards is not"
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with pytest.raises(RuntimeError, match=error_msg_4):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', shard_id=0)
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error_msg_5 = "Input shard_id is not within the required interval"
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with pytest.raises(ValueError, match=error_msg_5):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_shards=2, shard_id=5)
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error_msg_6 = "num_parallel_workers exceeds"
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with pytest.raises(ValueError, match=error_msg_6):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', shuffle=False, num_parallel_workers=-2)
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error_msg_7 = "Argument shard_id"
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with pytest.raises(TypeError, match=error_msg_7):
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ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_shards=2, shard_id="0")
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def test_photo_tour_visualize(plot=False):
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"""
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Feature: test_photo_tour_visualize.
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Description: Visualize PhotoTourDataset results.
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Expectation: Get correct number of data and plot them.
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"""
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logger.info("Test PhotoTourDataset visualization")
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data1 = ds.PhotoTourDataset(DATA_DIR, NAME, 'test', num_samples=10, shuffle=False)
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num_iter = 0
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image_list1, image_list2, matches_list = [], [], []
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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image1 = item["image1"]
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image2 = item["image2"]
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matches = item["matches"]
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image_list1.append(image1)
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image_list2.append(image2)
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matches_list.append("matches {}".format(matches))
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assert isinstance(image1, np.ndarray)
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assert isinstance(image2, np.ndarray)
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assert image1.shape == (64, 64, 1)
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assert image1.dtype == np.uint8
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assert image2.shape == (64, 64, 1)
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assert image2.dtype == np.uint8
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assert matches.dtype == np.uint32
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num_iter += 1
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assert num_iter == 10
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if plot:
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visualize_dataset(image_list1, image_list2, matches_list)
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def test_photo_tour_usage():
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"""
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Feature: test_photo_tour_usage.
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Description: Validate PhotoTourDataset image readings.
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Expectation: Get correct number of data.
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"""
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logger.info("Test PhotoTourDataset usage flag")
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def test_config(photo_tour_path, name, usage):
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try:
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data = ds.PhotoTourDataset(photo_tour_path, name, usage, shuffle=False)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_rows += 1
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except (ValueError, TypeError, RuntimeError) as e:
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return str(e)
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return num_rows
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assert test_config(DATA_DIR, NAME, "test") == 16
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assert test_config(DATA_DIR, NAME, "train") == LEN
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assert "usage is not within the valid set of ['train', 'test']" in test_config(DATA_DIR, NAME, "invalid")
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assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(DATA_DIR, NAME, ["list"])
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if __name__ == '__main__':
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test_photo_tour_content_check()
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test_photo_tour_basic()
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test_photo_tour_pk_sampler()
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test_photo_tour_sequential_sampler()
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test_photo_tour_exception()
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test_photo_tour_visualize(plot=True)
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test_photo_tour_usage()
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