forked from mindspore-Ecosystem/mindspore
290 lines
12 KiB
Python
290 lines
12 KiB
Python
# Copyright 2020 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 numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as vision
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DATA_DIR = "../data/dataset/testCOCO/train/"
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DATA_DIR_2 = "../data/dataset/testCOCO/train"
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ANNOTATION_FILE = "../data/dataset/testCOCO/annotations/train.json"
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KEYPOINT_FILE = "../data/dataset/testCOCO/annotations/key_point.json"
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PANOPTIC_FILE = "../data/dataset/testCOCO/annotations/panoptic.json"
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INVALID_FILE = "../data/dataset/testCOCO/annotations/invalid.json"
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LACKOFIMAGE_FILE = "../data/dataset/testCOCO/annotations/lack_of_images.json"
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INVALID_CATEGORY_ID_FILE = "../data/dataset/testCOCO/annotations/invalid_category_id.json"
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def test_coco_detection():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection",
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decode=True, shuffle=False)
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num_iter = 0
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image_shape = []
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bbox = []
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category_id = []
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for data in data1.create_dict_iterator(num_epochs=1):
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image_shape.append(data["image"].shape)
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bbox.append(data["bbox"])
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category_id.append(data["category_id"])
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num_iter += 1
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assert num_iter == 6
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assert image_shape[0] == (2268, 4032, 3)
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assert image_shape[1] == (561, 595, 3)
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assert image_shape[2] == (607, 585, 3)
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assert image_shape[3] == (642, 675, 3)
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assert image_shape[4] == (2268, 4032, 3)
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assert image_shape[5] == (2268, 4032, 3)
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np.testing.assert_array_equal(np.array([[10., 10., 10., 10.], [70., 70., 70., 70.]]), bbox[0])
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np.testing.assert_array_equal(np.array([[20., 20., 20., 20.], [80., 80., 80.0, 80.]]), bbox[1])
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np.testing.assert_array_equal(np.array([[30.0, 30.0, 30.0, 30.]]), bbox[2])
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np.testing.assert_array_equal(np.array([[40., 40., 40., 40.]]), bbox[3])
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np.testing.assert_array_equal(np.array([[50., 50., 50., 50.]]), bbox[4])
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np.testing.assert_array_equal(np.array([[60., 60., 60., 60.]]), bbox[5])
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np.testing.assert_array_equal(np.array([[1], [7]]), category_id[0])
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np.testing.assert_array_equal(np.array([[2], [8]]), category_id[1])
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np.testing.assert_array_equal(np.array([[3]]), category_id[2])
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np.testing.assert_array_equal(np.array([[4]]), category_id[3])
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np.testing.assert_array_equal(np.array([[5]]), category_id[4])
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np.testing.assert_array_equal(np.array([[6]]), category_id[5])
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def test_coco_stuff():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Stuff",
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decode=True, shuffle=False)
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num_iter = 0
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image_shape = []
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segmentation = []
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iscrowd = []
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for data in data1.create_dict_iterator(num_epochs=1):
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image_shape.append(data["image"].shape)
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segmentation.append(data["segmentation"])
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iscrowd.append(data["iscrowd"])
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num_iter += 1
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assert num_iter == 6
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assert image_shape[0] == (2268, 4032, 3)
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assert image_shape[1] == (561, 595, 3)
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assert image_shape[2] == (607, 585, 3)
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assert image_shape[3] == (642, 675, 3)
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assert image_shape[4] == (2268, 4032, 3)
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assert image_shape[5] == (2268, 4032, 3)
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np.testing.assert_array_equal(np.array([[10., 12., 13., 14., 15., 16., 17., 18., 19., 20.],
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[70., 72., 73., 74., 75., -1., -1., -1., -1., -1.]]),
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segmentation[0])
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np.testing.assert_array_equal(np.array([[0], [0]]), iscrowd[0])
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np.testing.assert_array_equal(np.array([[20.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0],
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[10.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, -1.0]]),
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segmentation[1])
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np.testing.assert_array_equal(np.array([[0], [1]]), iscrowd[1])
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np.testing.assert_array_equal(np.array([[40., 42., 43., 44., 45., 46., 47., 48., 49., 40., 41., 42.]]),
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segmentation[2])
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np.testing.assert_array_equal(np.array([[0]]), iscrowd[2])
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np.testing.assert_array_equal(np.array([[50., 52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63.]]),
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segmentation[3])
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np.testing.assert_array_equal(np.array([[0]]), iscrowd[3])
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np.testing.assert_array_equal(np.array([[60., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71., 72., 73., 74.]]),
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segmentation[4])
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np.testing.assert_array_equal(np.array([[0]]), iscrowd[4])
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np.testing.assert_array_equal(np.array([[60., 62., 63., 64., 65., 66., 67.], [68., 69., 70., 71., 72., 73., 74.]]),
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segmentation[5])
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np.testing.assert_array_equal(np.array([[0]]), iscrowd[5])
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def test_coco_keypoint():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=KEYPOINT_FILE, task="Keypoint",
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decode=True, shuffle=False)
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num_iter = 0
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image_shape = []
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keypoints = []
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num_keypoints = []
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for data in data1.create_dict_iterator(num_epochs=1):
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image_shape.append(data["image"].shape)
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keypoints.append(data["keypoints"])
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num_keypoints.append(data["num_keypoints"])
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num_iter += 1
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assert num_iter == 2
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assert image_shape[0] == (2268, 4032, 3)
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assert image_shape[1] == (561, 595, 3)
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np.testing.assert_array_equal(np.array([[368., 61., 1., 369., 52., 2., 0., 0., 0., 382., 48., 2., 0., 0., 0., 368.,
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84., 2., 435., 81., 2., 362., 125., 2., 446., 125., 2., 360., 153., 2., 0.,
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0., 0., 397., 167., 1., 439., 166., 1., 369., 193., 2., 461., 234., 2.,
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361., 246., 2., 474., 287., 2.]]), keypoints[0])
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np.testing.assert_array_equal(np.array([[14]]), num_keypoints[0])
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np.testing.assert_array_equal(np.array([[244., 139., 2., 0., 0., 0., 226., 118., 2., 0., 0., 0., 154., 159., 2.,
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143., 261., 2., 135., 312., 2., 271., 423., 2., 184., 530., 2., 261., 280.,
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2., 347., 592., 2., 0., 0., 0., 123., 596., 2., 0., 0., 0., 0., 0., 0., 0.,
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0., 0., 0., 0., 0.]]),
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keypoints[1])
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np.testing.assert_array_equal(np.array([[10]]), num_keypoints[1])
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def test_coco_panoptic():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic", decode=True, shuffle=False)
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num_iter = 0
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image_shape = []
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bbox = []
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category_id = []
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iscrowd = []
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area = []
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for data in data1.create_dict_iterator(num_epochs=1):
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image_shape.append(data["image"].shape)
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bbox.append(data["bbox"])
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category_id.append(data["category_id"])
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iscrowd.append(data["iscrowd"])
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area.append(data["area"])
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num_iter += 1
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assert num_iter == 2
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assert image_shape[0] == (2268, 4032, 3)
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np.testing.assert_array_equal(np.array([[472, 173, 36, 48], [340, 22, 154, 301], [486, 183, 30, 35]]), bbox[0])
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np.testing.assert_array_equal(np.array([[1], [1], [2]]), category_id[0])
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np.testing.assert_array_equal(np.array([[0], [0], [0]]), iscrowd[0])
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np.testing.assert_array_equal(np.array([[705], [14062], [626]]), area[0])
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assert image_shape[1] == (642, 675, 3)
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np.testing.assert_array_equal(np.array([[103, 133, 229, 422], [243, 175, 93, 164]]), bbox[1])
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np.testing.assert_array_equal(np.array([[1], [3]]), category_id[1])
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np.testing.assert_array_equal(np.array([[0], [0]]), iscrowd[1])
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np.testing.assert_array_equal(np.array([[43102], [6079]]), area[1])
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def test_coco_detection_classindex():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True)
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class_index = data1.get_class_indexing()
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assert class_index == {'person': [1], 'bicycle': [2], 'car': [3], 'cat': [4], 'dog': [5], 'monkey': [6],
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'bag': [7], 'orange': [8]}
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num_iter = 0
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for _ in data1.__iter__():
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num_iter += 1
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assert num_iter == 6
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def test_coco_panootic_classindex():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=PANOPTIC_FILE, task="Panoptic", decode=True)
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class_index = data1.get_class_indexing()
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assert class_index == {'person': [1, 1], 'bicycle': [2, 1], 'car': [3, 1]}
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num_iter = 0
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for _ in data1.__iter__():
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num_iter += 1
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assert num_iter == 2
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def test_coco_case_0():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True)
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data1 = data1.shuffle(10)
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data1 = data1.batch(3, pad_info={})
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num_iter = 0
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for _ in data1.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 2
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def test_coco_case_1():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True)
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sizes = [0.5, 0.5]
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randomize = False
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dataset1, dataset2 = data1.split(sizes=sizes, randomize=randomize)
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num_iter = 0
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for _ in dataset1.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 3
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num_iter = 0
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for _ in dataset2.create_dict_iterator(num_epochs=1):
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num_iter += 1
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assert num_iter == 3
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def test_coco_case_2():
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Detection", decode=True)
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resize_op = vision.Resize((224, 224))
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data1 = data1.map(input_columns=["image"], operations=resize_op)
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data1 = data1.repeat(4)
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num_iter = 0
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for _ in data1.__iter__():
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num_iter += 1
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assert num_iter == 24
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def test_coco_case_3():
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data1 = ds.CocoDataset(DATA_DIR_2, annotation_file=ANNOTATION_FILE, task="Detection", decode=True)
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resize_op = vision.Resize((224, 224))
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data1 = data1.map(input_columns=["image"], operations=resize_op)
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data1 = data1.repeat(4)
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num_iter = 0
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for _ in data1.__iter__():
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num_iter += 1
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assert num_iter == 24
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def test_coco_case_exception():
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try:
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data1 = ds.CocoDataset("path_not_exist/", annotation_file=ANNOTATION_FILE, task="Detection")
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for _ in data1.__iter__():
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pass
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assert False
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except ValueError as e:
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assert "does not exist or permission denied" in str(e)
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try:
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data1 = ds.CocoDataset(DATA_DIR, annotation_file="./file_not_exist", task="Detection")
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for _ in data1.__iter__():
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pass
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assert False
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except ValueError as e:
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assert "does not exist or permission denied" in str(e)
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try:
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=ANNOTATION_FILE, task="Invalid task")
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for _ in data1.__iter__():
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pass
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assert False
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except ValueError as e:
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assert "Invalid task type" in str(e)
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try:
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=LACKOFIMAGE_FILE, task="Detection")
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for _ in data1.__iter__():
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pass
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assert False
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except RuntimeError as e:
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assert "Invalid node found in json" in str(e)
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try:
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_CATEGORY_ID_FILE, task="Detection")
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for _ in data1.__iter__():
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pass
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assert False
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except RuntimeError as e:
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assert "category_id can't find in categories" in str(e)
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try:
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_FILE, task="Detection")
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for _ in data1.__iter__():
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pass
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assert False
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except RuntimeError as e:
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assert "json.exception.parse_error" in str(e)
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try:
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sampler = ds.PKSampler(3)
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data1 = ds.CocoDataset(DATA_DIR, annotation_file=INVALID_FILE, task="Detection", sampler=sampler)
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for _ in data1.__iter__():
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pass
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assert False
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except ValueError as e:
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assert "CocoDataset doesn't support PKSampler" in str(e)
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if __name__ == '__main__':
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test_coco_detection()
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test_coco_stuff()
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test_coco_keypoint()
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test_coco_panoptic()
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test_coco_detection_classindex()
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test_coco_panootic_classindex()
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test_coco_case_0()
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test_coco_case_1()
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test_coco_case_2()
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test_coco_case_3()
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test_coco_case_exception()
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