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| 1 | +# Copyright (c) OpenMMLab. All rights reserved. |
| 2 | +import pytest |
| 3 | +import torch |
| 4 | +import torch.nn as nn |
| 5 | +from mmcv.runner import DefaultOptimizerConstructor |
| 6 | + |
| 7 | +from mmseg.core.builder import (OPTIMIZER_BUILDERS, build_optimizer, |
| 8 | + build_optimizer_constructor) |
| 9 | + |
| 10 | + |
| 11 | +class ExampleModel(nn.Module): |
| 12 | + |
| 13 | + def __init__(self): |
| 14 | + super().__init__() |
| 15 | + self.param1 = nn.Parameter(torch.ones(1)) |
| 16 | + self.conv1 = nn.Conv2d(3, 4, kernel_size=1, bias=False) |
| 17 | + self.conv2 = nn.Conv2d(4, 2, kernel_size=1) |
| 18 | + self.bn = nn.BatchNorm2d(2) |
| 19 | + |
| 20 | + def forward(self, x): |
| 21 | + return x |
| 22 | + |
| 23 | + |
| 24 | +base_lr = 0.01 |
| 25 | +base_wd = 0.0001 |
| 26 | +momentum = 0.9 |
| 27 | + |
| 28 | + |
| 29 | +def test_build_optimizer_constructor(): |
| 30 | + optimizer_cfg = dict( |
| 31 | + type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum) |
| 32 | + optim_constructor_cfg = dict( |
| 33 | + type='DefaultOptimizerConstructor', optimizer_cfg=optimizer_cfg) |
| 34 | + optim_constructor = build_optimizer_constructor(optim_constructor_cfg) |
| 35 | + # Test whether optimizer constructor can be built from parent. |
| 36 | + assert type(optim_constructor) is DefaultOptimizerConstructor |
| 37 | + |
| 38 | + @OPTIMIZER_BUILDERS.register_module() |
| 39 | + class MyOptimizerConstructor(DefaultOptimizerConstructor): |
| 40 | + pass |
| 41 | + |
| 42 | + optim_constructor_cfg = dict( |
| 43 | + type='MyOptimizerConstructor', optimizer_cfg=optimizer_cfg) |
| 44 | + optim_constructor = build_optimizer_constructor(optim_constructor_cfg) |
| 45 | + # Test optimizer constructor can be built from child registry. |
| 46 | + assert type(optim_constructor) is MyOptimizerConstructor |
| 47 | + |
| 48 | + # Test unregistered constructor cannot be built |
| 49 | + with pytest.raises(KeyError): |
| 50 | + build_optimizer_constructor(dict(type='A')) |
| 51 | + |
| 52 | + |
| 53 | +def test_build_optimizer(): |
| 54 | + model = ExampleModel() |
| 55 | + optimizer_cfg = dict( |
| 56 | + type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum) |
| 57 | + optimizer = build_optimizer(model, optimizer_cfg) |
| 58 | + # test whether optimizer is successfully built from parent. |
| 59 | + assert isinstance(optimizer, torch.optim.SGD) |
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