# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import platform from itertools import cycle import paddlers from rs_models.test_model import TestModel __all__ = [ 'TestBITModel', 'TestCDNetModel', 'TestChangeStarModel', 'TestDSAMNetModel', 'TestDSIFNModel', 'TestFCEarlyFusionModel', 'TestFCSiamConcModel', 'TestFCSiamDiffModel', 'TestSNUNetModel', 'TestSTANetModel' ] class TestCDModel(TestModel): EF_MODE = 'None' # Early-fusion strategy def check_output(self, output, target): self.assertIsInstance(output, list) self.check_output_equal(len(output), len(target)) for o, t in zip(output, target): o = o.numpy() self.check_output_equal(o.shape[0], t.shape[0]) self.check_output_equal(len(o.shape), 4) self.check_output_equal(o.shape[2:], t.shape[2:]) def set_inputs(self): if self.EF_MODE == 'Concat': # Early-fusion def _gen_data(specs): for spec in specs: c = spec['in_channels'] // 2 assert c * 2 == spec['in_channels'] yield [self.get_randn_tensor(c), self.get_randn_tensor(c)] elif self.EF_MODE == 'None': # Late-fusion def _gen_data(specs): for spec in specs: c = spec.get('in_channels', 3) yield [self.get_randn_tensor(c), self.get_randn_tensor(c)] else: raise ValueError self.inputs = _gen_data(self.specs) def set_targets(self): def _gen_data(specs): for spec in specs: c = spec.get('num_classes', 2) yield [self.get_zeros_array(c)] self.targets = _gen_data(self.specs) class TestBITModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.BIT def set_specs(self): base_spec = dict(in_channels=3, num_classes=2) self.specs = [ base_spec, dict(**base_spec, backbone='resnet34'), dict(**base_spec, n_stages=3), dict(**base_spec, enc_depth=4, dec_head_dim=16), dict(in_channels=4, num_classes=2), dict(in_channels=3, num_classes=8) ] # yapf: disable class TestCDNetModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.CDNet EF_MODE = 'Concat' def set_specs(self): self.specs = [ dict(in_channels=6, num_classes=2), dict(in_channels=8, num_classes=2), dict(in_channels=6, num_classes=8) ] # yapf: disable class TestChangeStarModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.ChangeStar def set_specs(self): self.specs = [ dict(num_classes=2), dict(num_classes=10), dict(num_classes=2, mid_channels=128, num_convs=2), dict(num_classes=2, _phase='eval', _stop_grad=True) ] # yapf: disable def set_targets(self): # Avoid allocation of large memories tar_c2 = [self.get_zeros_array(2)] * 4 self.targets = [ tar_c2, [self.get_zeros_array(10)] * 2 + [self.get_zeros_array(2)] * 2, tar_c2, [self.get_zeros_array(2)] ] class TestDSAMNetModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.DSAMNet def set_specs(self): base_spec = dict(in_channels=3, num_classes=2) self.specs = [ base_spec, dict(in_channels=8, num_classes=2), dict(in_channels=3, num_classes=8), dict(**base_spec, ca_ratio=4, sa_kernel=5), dict(**base_spec, _phase='eval', _stop_grad=True) ] # yapf: disable def set_targets(self): # Avoid allocation of large memories tar_c2 = [self.get_zeros_array(2)] * 3 self.targets = [ tar_c2, tar_c2, [self.get_zeros_array(8)] * 3, tar_c2, [self.get_zeros_array(2)] ] class TestDSIFNModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.DSIFN def set_specs(self): self.specs = [ dict(num_classes=2), dict(num_classes=10), dict(num_classes=2, use_dropout=True), dict(num_classes=2, _phase='eval', _stop_grad=True) ] # yapf: disable def set_targets(self): # Avoid allocation of large memories tar_c2 = [self.get_zeros_array(2)] * 5 self.targets = [ tar_c2, [self.get_zeros_array(10)] * 5, tar_c2, [self.get_zeros_array(2)] ] class TestFCEarlyFusionModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.FCEarlyFusion EF_MODE = 'Concat' def set_specs(self): self.specs = [ dict(in_channels=6, num_classes=2), dict(in_channels=8, num_classes=2), dict(in_channels=6, num_classes=8), dict(in_channels=6, num_classes=2, use_dropout=True) ] # yapf: disable class TestFCSiamConcModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.FCSiamConc def set_specs(self): self.specs = [ dict(in_channels=3, num_classes=2), dict(in_channels=8, num_classes=2), dict(in_channels=3, num_classes=8), dict(in_channels=3, num_classes=2, use_dropout=True) ] # yapf: disable class TestFCSiamDiffModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.FCSiamDiff def set_specs(self): self.specs = [ dict(in_channels=3, num_classes=2), dict(in_channels=8, num_classes=2), dict(in_channels=3, num_classes=8), dict(in_channels=3, num_classes=2, use_dropout=True) ] # yapf: disable class TestSNUNetModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.SNUNet def set_specs(self): self.specs = [ dict(in_channels=3, num_classes=2), dict(in_channels=8, num_classes=2), dict(in_channels=3, num_classes=8), dict(in_channels=3, num_classes=2, width=64) ] # yapf: disable class TestSTANetModel(TestCDModel): MODEL_CLASS = paddlers.rs_models.cd.STANet def set_specs(self): base_spec = dict(in_channels=3, num_classes=2) self.specs = [ base_spec, dict(in_channels=8, num_classes=2), dict(in_channels=3, num_classes=8), dict(**base_spec, att_type='PAM'), dict(**base_spec, ds_factor=4) ] # yapf: disable # HACK:FIXME: We observe an OOM error when running TestSTANetModel.test_forward() on a Windows machine. # Currently, we do not perform this test. if platform.system() == 'Windows': TestSTANetModel.test_forward = lambda self: None