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# 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
class _CDModelAdapter(object):
def __init__(self, cd_model):
super().__init__()
self.cd_model = cd_model
def __call__(self, input):
return self.cd_model(input[0], input[1])
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)
def build_model(self, spec):
model = super().build_model(spec)
return _CDModelAdapter(model)
class TestBITModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]
class TestCDNetModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]
class TestChangeStarModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]
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.custom_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)
]
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.custom_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)
]
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.custom_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)
]
class TestFCSiamConcModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]
class TestFCSiamDiffModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]
class TestSNUNetModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]
class TestSTANetModel(TestCDModel):
MODEL_CLASS = paddlers.custom_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)
]