forked from mindspore-Ecosystem/mindspore
318 lines
11 KiB
Python
318 lines
11 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 USPS dataset operations
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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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import mindspore.dataset.vision as vision
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/testSBUDataset"
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WRONG_DIR = "../data/dataset/testMnistData"
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def load_sbu(path):
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"""
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load SBU data
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"""
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images = []
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captions = []
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file1 = os.path.realpath(os.path.join(path, 'SBU_captioned_photo_dataset_urls.txt'))
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file2 = os.path.realpath(os.path.join(path, 'SBU_captioned_photo_dataset_captions.txt'))
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for line1, line2 in zip(open(file1), open(file2)):
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url = line1.rstrip()
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image = url[23:].replace("/", "_")
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filename = os.path.join(path, 'sbu_images', image)
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if os.path.exists(filename):
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caption = line2.rstrip()
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images.append(np.asarray(Image.open(filename).convert('RGB')).astype(np.uint8))
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captions.append(caption)
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return images, captions
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def visualize_dataset(images, captions):
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"""
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Helper function to visualize the dataset samples
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"""
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num_samples = len(images)
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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(images[i].squeeze())
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plt.title(captions[i])
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plt.show()
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def test_sbu_content_check():
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"""
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Feature: SBUDataset
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Description: Test SBUDataset image readings with content check
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test SBUDataset Op with content check")
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=50, shuffle=False)
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images, captions = load_sbu(DATA_DIR)
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num_iter = 0
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# in this example, each dictionary has keys "image" and "caption"
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for i, data in enumerate(dataset.create_dict_iterator(num_epochs=1, output_numpy=True)):
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assert data["image"].shape == images[i].shape
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assert data["caption"] == captions[i]
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num_iter += 1
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assert num_iter == 5
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def test_sbu_case():
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"""
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Feature: SBUDataset
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Description: Test SBUDataset cases
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Expectation: The dataset is processed as expected
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"""
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dataset = ds.SBUDataset(DATA_DIR, decode=True)
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dataset = dataset.map(operations=[vision.Resize((224, 224))], input_columns=["image"])
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repeat_num = 4
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dataset = dataset.repeat(repeat_num)
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batch_size = 2
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dataset = dataset.batch(batch_size, drop_remainder=True)
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num = 0
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num += 1
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# 4 x 5 / 2
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assert num == 10
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dataset = ds.SBUDataset(DATA_DIR, decode=False)
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dataset = dataset.map(operations=[vision.Decode(), vision.Resize((224, 224))], input_columns=["image"])
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repeat_num = 4
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dataset = dataset.repeat(repeat_num)
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batch_size = 2
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dataset = dataset.batch(batch_size, drop_remainder=True)
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num = 0
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num += 1
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# 4 x 5 / 2
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assert num == 10
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def test_sbu_basic():
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"""
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Feature: SBUDataset
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Description: Test SBUDataset basic usage
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test SBUDataset Op")
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# case 1: test loading whole dataset
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dataset = ds.SBUDataset(DATA_DIR, decode=True)
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num_iter = 0
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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assert num_iter == 5
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# case 2: test num_samples
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
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num_iter = 0
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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assert num_iter == 5
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# case 3: test repeat
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
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dataset = dataset.repeat(5)
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num_iter = 0
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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assert num_iter == 25
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# case 4: test batch
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
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assert dataset.get_dataset_size() == 5
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assert dataset.get_batch_size() == 1
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num_iter = 0
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for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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num_iter += 1
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assert num_iter == 5
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# case 5: test get_class_indexing
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
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assert dataset.get_class_indexing() == {}
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# case 6: test get_col_names
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=5)
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assert dataset.get_col_names() == ["image", "caption"]
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def test_sbu_sequential_sampler():
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"""
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Feature: SBUDataset
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Description: Test SBUDataset wtih SequentialSampler
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test SBUDataset 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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dataset_1 = ds.SBUDataset(DATA_DIR, decode=True, sampler=sampler)
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dataset_2 = ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_samples=num_samples)
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num_iter = 0
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for item1, item2 in zip(dataset_1.create_dict_iterator(num_epochs=1, output_numpy=True),
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dataset_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(item1["caption"], item2["caption"])
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num_iter += 1
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assert num_iter == num_samples
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def test_sbu_exception():
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"""
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Feature: SBUDataset
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Description: Test error cases for SBUDataset
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Expectation: Correct error is thrown as expected
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"""
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logger.info("Test error cases for SBUDataset")
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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.SBUDataset(DATA_DIR, decode=True, shuffle=False, sampler=ds.SequentialSampler())
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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.SBUDataset(DATA_DIR, decode=True, sampler=ds.SequentialSampler(), 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.SBUDataset(DATA_DIR, decode=True, 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.SBUDataset(DATA_DIR, decode=True, 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.SBUDataset(DATA_DIR, decode=True, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.SBUDataset(DATA_DIR, decode=True, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.SBUDataset(DATA_DIR, decode=True, 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.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.SBUDataset(DATA_DIR, decode=True, shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.SBUDataset(DATA_DIR, decode=True, 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.SBUDataset(DATA_DIR, decode=True, num_shards=2, shard_id="0")
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def exception_func(item):
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raise Exception("Error occur!")
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error_msg_8 = "The corresponding data file is"
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with pytest.raises(RuntimeError, match=error_msg_8):
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dataset = ds.SBUDataset(DATA_DIR, decode=True)
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dataset = dataset.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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for _ in dataset.__iter__():
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pass
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with pytest.raises(RuntimeError, match=error_msg_8):
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dataset = ds.SBUDataset(DATA_DIR, decode=True)
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dataset = dataset.map(operations=vision.Decode(), input_columns=["image"], num_parallel_workers=1)
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for _ in dataset.__iter__():
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pass
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error_msg_9 = "does not exist or permission denied"
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with pytest.raises(ValueError, match=error_msg_9):
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dataset = ds.SBUDataset(WRONG_DIR, decode=True)
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for _ in dataset.__iter__():
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pass
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error_msg_10 = "Argument decode with value"
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with pytest.raises(TypeError, match=error_msg_10):
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dataset = ds.SBUDataset(DATA_DIR, decode="not_bool")
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for _ in dataset.__iter__():
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pass
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def test_sbu_visualize(plot=False):
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"""
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Feature: SBUDataset
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Description: Test SBUDataset visualized results
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test SBUDataset visualization")
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dataset = ds.SBUDataset(DATA_DIR, decode=True, num_samples=10, shuffle=False)
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num_iter = 0
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image_list, caption_list = [], []
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for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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image = item["image"]
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caption = item["caption"]
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image_list.append(image)
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caption_list.append("caption {}".format(caption))
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assert isinstance(image, np.ndarray)
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assert image.dtype == np.uint8
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assert caption.dtype.type == np.str_
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num_iter += 1
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assert num_iter == 5
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if plot:
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visualize_dataset(image_list, caption_list)
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def test_sbu_decode():
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"""
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Feature: SBUDataset
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Description: Test SBUDataset image readings with decode flag
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Expectation: The dataset is processed as expected
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"""
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logger.info("Test SBUDataset decode flag")
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sampler = ds.SequentialSampler(num_samples=50)
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dataset = ds.SBUDataset(dataset_dir=DATA_DIR, decode=False, sampler=sampler)
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dataset_1 = dataset.map(operations=[vision.Decode()], input_columns=["image"])
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dataset_2 = ds.SBUDataset(dataset_dir=DATA_DIR, decode=True, sampler=sampler)
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num_iter = 0
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for item1, item2 in zip(dataset_1.create_dict_iterator(num_epochs=1, output_numpy=True),
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dataset_2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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np.testing.assert_array_equal(item1["caption"], item2["caption"])
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num_iter += 1
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assert num_iter == 5
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if __name__ == '__main__':
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test_sbu_content_check()
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test_sbu_basic()
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test_sbu_case()
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test_sbu_sequential_sampler()
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test_sbu_exception()
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test_sbu_visualize(plot=True)
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test_sbu_decode()
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