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226 lines
7.3 KiB
226 lines
7.3 KiB
# 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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from itertools import cycle |
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import paddlers |
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from rs_models.test_model import TestModel |
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class _CDModelAdapter(object): |
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def __init__(self, cd_model): |
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super().__init__() |
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self.cd_model = cd_model |
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def __call__(self, input): |
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return self.cd_model(input[0], input[1]) |
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class TestCDModel(TestModel): |
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EF_MODE = 'None' # Early-fusion strategy |
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def check_output(self, output, target): |
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self.assertIsInstance(output, list) |
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self.check_output_equal(len(output), len(target)) |
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for o, t in zip(output, target): |
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o = o.numpy() |
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self.check_output_equal(o.shape[0], t.shape[0]) |
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self.check_output_equal(len(o.shape), 4) |
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self.check_output_equal(o.shape[2:], t.shape[2:]) |
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def set_inputs(self): |
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if self.EF_MODE == 'Concat': |
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# Early-fusion |
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def _gen_data(specs): |
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for spec in specs: |
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c = spec['in_channels'] // 2 |
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assert c * 2 == spec['in_channels'] |
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yield [self.get_randn_tensor(c), self.get_randn_tensor(c)] |
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elif self.EF_MODE == 'None': |
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# Late-fusion |
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def _gen_data(specs): |
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for spec in specs: |
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c = spec.get('in_channels', 3) |
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yield [self.get_randn_tensor(c), self.get_randn_tensor(c)] |
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else: |
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raise ValueError |
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self.inputs = _gen_data(self.specs) |
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def set_targets(self): |
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def _gen_data(specs): |
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for spec in specs: |
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c = spec.get('num_classes', 2) |
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yield [self.get_zeros_array(c)] |
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self.targets = _gen_data(self.specs) |
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def build_model(self, spec): |
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model = super().build_model(spec) |
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return _CDModelAdapter(model) |
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class TestBITModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.BIT |
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def set_specs(self): |
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base_spec = dict(in_channels=3, num_classes=2) |
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self.specs = [ |
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base_spec, dict( |
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**base_spec, backbone='resnet34'), dict( |
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**base_spec, n_stages=3), dict( |
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**base_spec, enc_depth=4, dec_head_dim=16), dict( |
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in_channels=4, num_classes=2), dict( |
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in_channels=3, num_classes=8) |
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] |
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class TestCDNetModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.CDNet |
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EF_MODE = 'Concat' |
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def set_specs(self): |
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self.specs = [ |
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dict( |
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in_channels=6, num_classes=2), dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=6, num_classes=8) |
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] |
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class TestChangeStarModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.ChangeStar |
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def set_specs(self): |
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self.specs = [ |
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dict(num_classes=2), dict(num_classes=10), dict( |
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num_classes=2, mid_channels=128, num_convs=2), dict( |
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num_classes=2, _phase='eval', _stop_grad=True) |
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] |
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def set_targets(self): |
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# Avoid allocation of large memories |
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tar_c2 = [self.get_zeros_array(2)] * 4 |
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self.targets = [ |
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tar_c2, |
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[self.get_zeros_array(10)] * 2 + [self.get_zeros_array(2)] * 2, |
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tar_c2, [self.get_zeros_array(2)] |
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] |
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class TestDSAMNetModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.DSAMNet |
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def set_specs(self): |
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base_spec = dict(in_channels=3, num_classes=2) |
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self.specs = [ |
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base_spec, dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=3, num_classes=8), dict( |
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**base_spec, ca_ratio=4, sa_kernel=5), dict( |
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**base_spec, _phase='eval', _stop_grad=True) |
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] |
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def set_targets(self): |
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# Avoid allocation of large memories |
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tar_c2 = [self.get_zeros_array(2)] * 3 |
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self.targets = [ |
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tar_c2, tar_c2, [self.get_zeros_array(8)] * 3, tar_c2, |
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[self.get_zeros_array(2)] |
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] |
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class TestDSIFNModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.DSIFN |
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def set_specs(self): |
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self.specs = [ |
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dict(num_classes=2), dict(num_classes=10), dict( |
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num_classes=2, use_dropout=True), dict( |
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num_classes=2, _phase='eval', _stop_grad=True) |
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] |
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def set_targets(self): |
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# Avoid allocation of large memories |
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tar_c2 = [self.get_zeros_array(2)] * 5 |
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self.targets = [ |
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tar_c2, [self.get_zeros_array(10)] * 5, tar_c2, |
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[self.get_zeros_array(2)] |
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] |
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class TestFCEarlyFusionModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.FCEarlyFusion |
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EF_MODE = 'Concat' |
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def set_specs(self): |
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self.specs = [ |
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dict( |
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in_channels=6, num_classes=2), dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=6, num_classes=8), dict( |
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in_channels=6, num_classes=2, use_dropout=True) |
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] |
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class TestFCSiamConcModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.FCSiamConc |
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def set_specs(self): |
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self.specs = [ |
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dict( |
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in_channels=3, num_classes=2), dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=3, num_classes=8), dict( |
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in_channels=3, num_classes=2, use_dropout=True) |
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] |
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class TestFCSiamDiffModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.FCSiamDiff |
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def set_specs(self): |
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self.specs = [ |
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dict( |
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in_channels=3, num_classes=2), dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=3, num_classes=8), dict( |
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in_channels=3, num_classes=2, use_dropout=True) |
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] |
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class TestSNUNetModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.SNUNet |
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def set_specs(self): |
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self.specs = [ |
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dict( |
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in_channels=3, num_classes=2), dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=3, num_classes=8), dict( |
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in_channels=3, num_classes=2, width=64) |
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] |
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class TestSTANetModel(TestCDModel): |
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MODEL_CLASS = paddlers.custom_models.cd.STANet |
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def set_specs(self): |
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base_spec = dict(in_channels=3, num_classes=2) |
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self.specs = [ |
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base_spec, dict( |
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in_channels=8, num_classes=2), dict( |
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in_channels=3, num_classes=8), dict( |
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**base_spec, att_type='PAM'), dict( |
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**base_spec, ds_factor=4) |
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]
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