forked from huawei/mindspore2022
281 lines
12 KiB
Python
281 lines
12 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 os
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import json
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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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import mindspore.dataset.vision.c_transforms as c_vision
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DATASET_DIR = "../data/dataset/testCityscapesData/cityscapes"
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DATASET_DIR_TASK_JSON = "../data/dataset/testCityscapesData/cityscapes/testTaskJson"
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def test_cityscapes_basic(plot=False):
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"""
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Validate CityscapesDataset basic read.
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"""
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task = "color" # instance semantic polygon color
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quality_mode = "fine" # fine coarse
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usage = "train" # quality_mode=fine 'train', 'test', 'val', 'all' else 'train', 'train_extra', 'val', 'all'
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data = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task,
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decode=True, shuffle=False)
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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 == 5
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if plot:
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visualize_dataset(images_list, task_list, task)
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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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if task == "polygon":
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return
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image_num = len(images)
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for i in range(image_num):
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plt.subplot(121)
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plt.imshow(images[i])
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plt.title('Original')
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plt.subplot(122)
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plt.imshow(labels[i])
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plt.title(task)
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plt.savefig('./cityscapes_{}_{}.jpg'.format(task, str(i)))
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def test_cityscapes_polygon():
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"""
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Validate CityscapesDataset with task of polygon.
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"""
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usage = "train"
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quality_mode = "fine"
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task = "polygon"
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data = ds.CityscapesDataset(DATASET_DIR_TASK_JSON, usage=usage, quality_mode=quality_mode, task=task)
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count = 0
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json_file = os.path.join(DATASET_DIR_TASK_JSON, "gtFine/train/aa/aa_000000_gtFine_polygons.json")
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with open(json_file, "r") as f:
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expected = json.load(f)
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for item in data.create_dict_iterator(num_epochs=1, output_numpy=True):
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task_dict = json.loads(str(item['task'], encoding="utf-8"))
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assert task_dict == expected
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count = count + 1
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assert count == 1
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def test_cityscapes_basic_func():
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"""
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Validate CityscapesDataset with repeat, batch and getter operation.
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"""
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# case 1: test num_samples
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usage = "train"
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quality_mode = "fine"
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task = "color"
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data1 = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_samples=4)
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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 == 4
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# case 2: test repeat
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data2 = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_samples=5)
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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 == 25
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# case 3: test batch with drop_remainder=False
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data3 = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, 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() == 5
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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.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, 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() == 5
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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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# case 5: test get_col_names
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data5 = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, decode=True)
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assert data5.get_col_names() == ["image", "task"]
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def test_cityscapes_sequential_sampler():
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"""
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Test CityscapesDataset with SequentialSampler.
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"""
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task = "color"
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quality_mode = "fine"
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usage = "train"
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num_samples = 5
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sampler = ds.SequentialSampler(num_samples=num_samples)
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data1 = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, sampler=sampler)
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data2 = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task,
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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_cityscapes_exception():
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"""
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Validate CityscapesDataset with error parameters.
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"""
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task = "color"
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quality_mode = "fine"
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usage = "train"
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error_msg_1 = "does not exist or is not a directory or permission denied!"
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with pytest.raises(ValueError, match=error_msg_1):
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ds.CityscapesDataset("NoExistsDir", usage=usage, quality_mode=quality_mode, task=task)
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error_msg_2 = "sampler and shuffle cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_2):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, shuffle=False,
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sampler=ds.PKSampler(3))
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error_msg_3 = "sampler and sharding cannot be specified at the same time"
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with pytest.raises(RuntimeError, match=error_msg_3):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_shards=2,
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shard_id=0, sampler=ds.PKSampler(3))
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error_msg_4 = "num_shards is specified and currently requires shard_id as well"
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with pytest.raises(RuntimeError, match=error_msg_4):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_shards=10)
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error_msg_5 = "shard_id is specified but num_shards is not"
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with pytest.raises(RuntimeError, match=error_msg_5):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, shard_id=0)
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error_msg_6 = "Input shard_id is not within the required interval"
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with pytest.raises(ValueError, match=error_msg_6):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_shards=5, shard_id=-1)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_shards=5, shard_id=5)
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with pytest.raises(ValueError, match=error_msg_6):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, num_shards=2, shard_id=5)
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error_msg_7 = "num_parallel_workers exceeds"
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with pytest.raises(ValueError, match=error_msg_7):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, shuffle=False,
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num_parallel_workers=0)
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with pytest.raises(ValueError, match=error_msg_7):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, shuffle=False,
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num_parallel_workers=256)
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with pytest.raises(ValueError, match=error_msg_7):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, shuffle=False,
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num_parallel_workers=-2)
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error_msg_8 = "Argument shard_id"
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with pytest.raises(TypeError, match=error_msg_8):
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ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task, 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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try:
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data = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task)
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data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1):
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num_rows += 1
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assert False
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except RuntimeError as e:
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assert "map operation: [PyFunc] failed. The corresponding data files:" in str(e)
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try:
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data = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task)
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data = data.map(operations=exception_func, input_columns=["image"], num_parallel_workers=1)
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num_rows = 0
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for _ in data.create_dict_iterator(num_epochs=1):
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num_rows += 1
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assert False
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except RuntimeError as e:
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assert "map operation: [PyFunc] failed. The corresponding data files:" in str(e)
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def test_cityscapes_param():
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"""
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Validate CityscapesDataset with basic parameters like usage, quality_mode and task.
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"""
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def test_config(usage="train", quality_mode="fine", task="color"):
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try:
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data = ds.CityscapesDataset(DATASET_DIR, usage=usage, quality_mode=quality_mode, task=task)
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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(usage="train") == 5
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assert test_config(usage="test") == 1
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assert test_config(usage="val") == 1
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assert test_config(usage="all") == 7
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assert "usage is not within the valid set of ['train', 'test', 'val', 'all']" \
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in test_config("invalid", "fine", "instance")
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assert "Argument usage with value ['list'] is not of type [<class 'str'>]" \
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in test_config(["list"], "fine", "instance")
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assert "quality_mode is not within the valid set of ['fine', 'coarse']" \
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in test_config("train", "invalid", "instance")
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assert "Argument quality_mode with value ['list'] is not of type [<class 'str'>]" \
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in test_config("train", ["list"], "instance")
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assert "task is not within the valid set of ['instance', 'semantic', 'polygon', 'color']." \
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in test_config("train", "fine", "invalid")
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assert "Argument task with value ['list'] is not of type [<class 'str'>], but got <class 'list'>." \
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in test_config("train", "fine", ["list"])
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if __name__ == "__main__":
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test_cityscapes_basic()
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test_cityscapes_polygon()
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test_cityscapes_basic_func()
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test_cityscapes_sequential_sampler()
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test_cityscapes_exception()
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test_cityscapes_param()
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