paddlers/tests/rs_models/test_cd_models.py

# 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.

from itertools import cycle

import paddlers
from rs_models.test_model import TestModel, allow_oom

__all__ = [
    'TestBITModel', 'TestCDNetModel', 'TestChangeStarModel', 'TestDSAMNetModel',
    'TestDSIFNModel', 'TestFCEarlyFusionModel', 'TestFCSiamConcModel',
    'TestFCSiamDiffModel', 'TestSNUNetModel', 'TestSTANetModel',
    'TestChangeFormerModel', 'TestFCCDNModel'
]


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):
            if isinstance(o, list):
                self.check_output(o, t)
            else:
                o = o.numpy()
                self.check_output_equal(o.shape, t.shape)

    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


@allow_oom
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


@allow_oom
class TestChangeFormerModel(TestCDModel):
    MODEL_CLASS = paddlers.rs_models.cd.ChangeFormer

    def set_specs(self):
        base_spec = dict(in_channels=3, num_classes=2)
        self.specs = [
            base_spec,
            dict(**base_spec, decoder_softmax=True),
            dict(**base_spec, embed_dim=56)
        ]   # yapf: disable


class TestFCCDNModel(TestCDModel):
    MODEL_CLASS = paddlers.rs_models.cd.FCCDN

    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, _phase='eval', _stop_grad=True)
        ]   # yapf: disable

    def set_targets(self):
        b = self.DEFAULT_BATCH_SIZE
        h = self.DEFAULT_HW[0] // 2
        w = self.DEFAULT_HW[1] // 2
        tar_c2 = [
            self.get_zeros_array(2), [self.get_zeros_array(1, b, h, w)] * 2
        ]
        self.targets = [
            tar_c2, tar_c2, [self.get_zeros_array(8), tar_c2[1]],
            [self.get_zeros_array(2)]
        ]