220 lines
8.8 KiB
Python
220 lines
8.8 KiB
Python
# Copyright 2021 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 math
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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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import mindspore.dataset as ds
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from mindspore import log as logger
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import mindspore.dataset.vision.c_transforms as c_vision
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DATASET_DIR = "../data/dataset/testSBData/sbd"
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def visualize_dataset(images, labels, task):
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"""
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Helper function to visualize the dataset samples
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"""
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image_num = len(images)
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subplot_rows = 1 if task == "Segmentation" else 4
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for i in range(image_num):
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plt.imshow(images[i])
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plt.title('Original')
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plt.savefig('./sbd_original_{}.jpg'.format(str(i)))
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if task == "Segmentation":
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plt.imshow(labels[i])
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plt.title(task)
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plt.savefig('./sbd_segmentation_{}.jpg'.format(str(i)))
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else:
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b_num = labels[i].shape[0]
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for j in range(b_num):
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plt.subplot(subplot_rows, math.ceil(b_num / subplot_rows), j + 1)
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plt.imshow(labels[i][j])
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plt.savefig('./sbd_boundaries_{}.jpg'.format(str(i)))
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plt.close()
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def test_sbd_basic01(plot=False):
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"""
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Validate SBDataset with different usage
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"""
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task = 'Segmentation' # Boundaries, Segmentation
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data = ds.SBDataset(DATASET_DIR, task=task, usage='all', shuffle=False, decode=True)
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count = 0
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images_list = []
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task_list = []
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for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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images_list.append(item['image'])
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task_list.append(item['task'])
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count = count + 1
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assert count == 6
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if plot:
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visualize_dataset(images_list, task_list, task)
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data2 = ds.SBDataset(DATASET_DIR, task=task, usage='train', shuffle=False, decode=False)
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count = 0
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for item in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
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count = count + 1
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assert count == 4
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data3 = ds.SBDataset(DATASET_DIR, task=task, usage='val', shuffle=False, decode=False)
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count = 0
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for item in data3.create_dict_iterator(num_epochs=1, output_numpy=True):
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count = count + 1
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assert count == 2
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def test_sbd_basic02():
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"""
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Validate SBDataset with repeat and batch operation
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"""
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# Boundaries, Segmentation
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# case 1: test num_samples
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data1 = ds.SBDataset(DATASET_DIR, task='Boundaries', usage='train', num_samples=3, shuffle=False)
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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 == 3
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# case 2: test repeat
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data2 = ds.SBDataset(DATASET_DIR, task='Boundaries', usage='train', num_samples=4, shuffle=False)
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data2 = data2.repeat(5)
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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 == 20
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# case 3: test batch with drop_remainder=False
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data3 = ds.SBDataset(DATASET_DIR, task='Segmentation', usage='train', shuffle=False, decode=True)
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resize_op = c_vision.Resize((100, 100))
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data3 = data3.map(operations=resize_op, input_columns=["image"], num_parallel_workers=1)
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data3 = data3.map(operations=resize_op, input_columns=["task"], num_parallel_workers=1)
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assert data3.get_dataset_size() == 4
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assert data3.get_batch_size() == 1
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data3 = data3.batch(batch_size=3) # drop_remainder is default to be False
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assert data3.get_dataset_size() == 2
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assert data3.get_batch_size() == 3
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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 == 2
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# case 4: test batch with drop_remainder=True
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data4 = ds.SBDataset(DATASET_DIR, task='Segmentation', usage='train', shuffle=False, decode=True)
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resize_op = c_vision.Resize((100, 100))
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data4 = data4.map(operations=resize_op, input_columns=["image"], num_parallel_workers=1)
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data4 = data4.map(operations=resize_op, input_columns=["task"], num_parallel_workers=1)
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assert data4.get_dataset_size() == 4
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assert data4.get_batch_size() == 1
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data4 = data4.batch(batch_size=3, drop_remainder=True) # the rest of incomplete batch will be dropped
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assert data4.get_dataset_size() == 1
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assert data4.get_batch_size() == 3
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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 == 1
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def test_sbd_sequential_sampler():
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"""
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Test SBDataset with SequentialSampler
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"""
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logger.info("Test SBDataset 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.SBDataset(DATASET_DIR, task='Segmentation', usage='all', sampler=sampler)
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data2 = ds.SBDataset(DATASET_DIR, task='Segmentation', usage='all', shuffle=False, num_samples=num_samples)
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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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np.testing.assert_array_equal(item1["task"], item2["task"])
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num_iter += 1
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assert num_iter == num_samples
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def test_sbd_exception():
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"""
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Validate SBDataset with error parameters
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"""
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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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', 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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', num_shards=2, shard_id=0,
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sampler=ds.PKSampler(3))
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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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', 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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', 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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.SBDataset(DATASET_DIR, task='Segmentation', usage='train', num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_5):
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ds.SBDataset(DATASET_DIR, task='Segmentation', usage='train', 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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', shuffle=False, num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.SBDataset(DATASET_DIR, task='Segmentation', usage='train', shuffle=False, num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.SBDataset(DATASET_DIR, task='Segmentation', usage='train', 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.SBDataset(DATASET_DIR, task='Segmentation', usage='train', num_shards=2, shard_id="0")
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def test_sbd_usage():
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"""
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Validate SBDataset image readings
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"""
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def test_config(usage):
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try:
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data = ds.SBDataset(DATASET_DIR, task='Segmentation', usage=usage)
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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("train") == 4
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assert test_config("train_noval") == 4
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assert test_config("val") == 2
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assert test_config("all") == 6
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assert "usage is not within the valid set of ['train', 'val', 'train_noval', 'all']" in test_config("invalid")
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assert "Argument usage with value ['list'] is not of type [<class 'str'>]" in test_config(["list"])
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if __name__ == "__main__":
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test_sbd_basic01()
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test_sbd_basic02()
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test_sbd_sequential_sampler()
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test_sbd_exception()
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test_sbd_usage()
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