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258 lines
9.3 KiB
258 lines
9.3 KiB
3 years ago
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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import inspect
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import copy
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import numpy as np
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import paddlers.transforms as T
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from testing_utils import CpuCommonTest
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from data import build_input_from_file
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WHITE_LIST = []
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def _add_op_tests(cls):
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"""
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Automatically patch testing functions for transform operators.
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"""
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for op_name in T.operators.__all__:
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op_class = getattr(T.operators, op_name)
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if isinstance(op_class, type) and issubclass(op_class,
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T.operators.Transform):
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if op_class is T.DecodeImg or op_class in WHITE_LIST or op_name in WHITE_LIST:
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continue
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attr_name = 'test_' + op_name
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if hasattr(cls, attr_name):
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continue
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# If the operator cannot be initialized with default parameters, skip it
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for key, param in inspect.signature(
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op_class.__init__).parameters.items():
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if key == 'self':
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continue
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if param.default is param.empty:
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break
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else:
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filter_ = OP2FILTER.get(op_name, None)
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setattr(
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cls, attr_name, make_test_func(
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op_class, filter_=filter_))
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return cls
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def make_test_func(op_class,
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*args,
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in_hook=None,
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out_hook=None,
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filter_=None,
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**kwargs):
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def _test_func(self):
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op = op_class(*args, **kwargs)
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decoder = T.DecodeImg()
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inputs = map(decoder, copy.deepcopy(self.inputs))
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for i, input_ in enumerate(inputs):
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if filter_ is not None:
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input_ = filter_(input_)
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with self.subTest(i=i):
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for sample in input_:
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if in_hook:
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sample = in_hook(sample)
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sample = op(sample)
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if out_hook:
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sample = out_hook(sample)
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return _test_func
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class _InputFilter(object):
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def __init__(self, conds):
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self.conds = conds
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def __call__(self, samples):
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for sample in samples:
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for cond in self.conds:
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if cond(sample):
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yield sample
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def __or__(self, filter):
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return _InputFilter(self.conds + filter.conds)
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def __and__(self, filter):
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return _InputFilter(
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[cond for cond in self.conds if cond in filter.conds])
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def get_sample(self, input):
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return input[0]
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def _is_optical(sample):
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return sample['image'].shape[2] == 3
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def _is_sar(sample):
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return sample['image'].shape[2] == 1
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def _is_multispectral(sample):
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return sample['image'].shape[2] > 3
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def _is_mt(sample):
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return 'image2' in sample
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_filter_only_optical = _InputFilter([_is_optical])
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_filter_only_sar = _InputFilter([_is_sar])
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_filter_only_multispectral = _InputFilter([_is_multispectral])
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_filter_no_multispectral = _filter_only_optical | _filter_only_sar
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_filter_no_sar = _filter_only_optical | _filter_only_multispectral
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_filter_no_optical = _filter_only_sar | _filter_only_multispectral
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_filter_only_mt = _InputFilter([_is_mt])
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OP2FILTER = {
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'RandomSwap': _filter_only_mt,
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'SelectBand': _filter_no_sar,
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'Dehaze': _filter_only_optical,
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'Normalize': _filter_only_optical,
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'RandomDistort': _filter_only_optical
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}
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@_add_op_tests
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class TestTransform(CpuCommonTest):
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def setUp(self):
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self.inputs = [
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build_input_from_file(
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'data/ssst/test_optical_clas.txt',
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prefix='./data/ssst'), build_input_from_file(
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'data/ssst/test_sar_clas.txt',
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prefix='./data/ssst'), build_input_from_file(
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'data/ssst/test_multispectral_clas.txt',
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prefix='./data/ssst'), build_input_from_file(
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'data/ssst/test_optical_seg.txt',
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prefix='./data/ssst'), build_input_from_file(
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'data/ssst/test_sar_seg.txt',
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prefix='./data/ssst'), build_input_from_file(
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'data/ssst/test_multispectral_seg.txt',
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prefix='./data/ssst'),
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build_input_from_file(
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'data/ssst/test_optical_det.txt',
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prefix='./data/ssst',
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label_list='data/ssst/labels_det.txt'), build_input_from_file(
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'data/ssst/test_sar_det.txt',
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prefix='./data/ssst',
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label_list='data/ssst/labels_det.txt'),
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build_input_from_file(
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'data/ssst/test_multispectral_det.txt',
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prefix='./data/ssst',
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label_list='data/ssst/labels_det.txt'), build_input_from_file(
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'data/ssmt/test_mixed_binary.txt',
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prefix='./data/ssmt'), build_input_from_file(
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'data/ssmt/test_mixed_multiclass.txt',
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prefix='./data/ssmt'), build_input_from_file(
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'data/ssmt/test_mixed_multitask.txt',
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prefix='./data/ssmt')
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]
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def test_DecodeImg(self):
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decoder = T.DecodeImg(to_rgb=True)
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for i, input in enumerate(self.inputs):
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with self.subTest(i=i):
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for sample in input:
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sample = decoder(sample)
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# Check type
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self.assertIsInstance(sample['image'], np.ndarray)
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if 'mask' in sample:
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self.assertIsInstance(sample['mask'], np.ndarray)
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if 'aux_masks' in sample:
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for aux_mask in sample['aux_masks']:
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self.assertIsInstance(aux_mask, np.ndarray)
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# TODO: Check dtype
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def test_Resize(self):
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TARGET_SIZE = (128, 128)
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def _in_hook(sample):
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self.image_shape = sample['image'].shape
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if 'mask' in sample:
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self.mask_shape = sample['mask'].shape
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self.mask_values = set(sample['mask'].ravel())
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if 'aux_masks' in sample:
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self.aux_mask_shapes = [
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aux_mask.shape for aux_mask in sample['aux_masks']
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]
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self.aux_mask_values = [
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set(aux_mask.ravel()) for aux_mask in sample['aux_masks']
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]
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return sample
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def _out_hook_not_keep_ratio(sample):
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self.check_output_equal(sample['image'].shape[:2], TARGET_SIZE)
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if 'image2' in sample:
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self.check_output_equal(sample['image2'].shape[:2], TARGET_SIZE)
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if 'mask' in sample:
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self.check_output_equal(sample['mask'].shape[:2], TARGET_SIZE)
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self.assertLessEqual(
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set(sample['mask'].ravel()), self.mask_values)
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if 'aux_masks' in sample:
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for aux_mask in sample['aux_masks']:
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self.check_output_equal(aux_mask.shape[:2], TARGET_SIZE)
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for aux_mask, amv in zip(sample['aux_masks'],
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self.aux_mask_values):
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self.assertLessEqual(set(aux_mask.ravel()), amv)
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# TODO: Test gt_bbox and gt_poly
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return sample
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def _out_hook_keep_ratio(sample):
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def __check_ratio(shape1, shape2):
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self.check_output_equal(shape1[0] / shape1[1],
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shape2[0] / shape2[1])
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__check_ratio(sample['image'].shape, self.image_shape)
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if 'image2' in sample:
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__check_ratio(sample['image2'].shape, self.image_shape)
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if 'mask' in sample:
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__check_ratio(sample['mask'].shape, self.mask_shape)
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if 'aux_masks' in sample:
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for aux_mask, ori_aux_mask_shape in zip(sample['aux_masks'],
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self.aux_mask_shapes):
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__check_ratio(aux_mask.shape, ori_aux_mask_shape)
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# TODO: Test gt_bbox and gt_poly
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return sample
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test_func_not_keep_ratio = make_test_func(
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T.Resize,
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in_hook=_in_hook,
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out_hook=_out_hook_not_keep_ratio,
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target_size=TARGET_SIZE,
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keep_ratio=False)
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test_func_not_keep_ratio(self)
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test_func_keep_ratio = make_test_func(
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T.Resize,
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in_hook=_in_hook,
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out_hook=_out_hook_keep_ratio,
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target_size=TARGET_SIZE,
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keep_ratio=True)
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test_func_keep_ratio(self)
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class TestCompose(CpuCommonTest):
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pass
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class TestArrange(CpuCommonTest):
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pass
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