mirror of
https://github.com/open-mmlab/mmselfsup.git
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20488d01b4
* [Refactor]: Refactor data flow * [Fix]: Change data sample to data samples * [Fix]: Change batch_inputs to inputs * [Fix]: Fix lint and UT * [Fix]: Fix UT * [Fix]: Fix lint * [Fix]: Fix docstring * [Fix]: Fix UT * [Refactor]: Add assert in data preprocessor * [Fix]: Fix lint
108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import tempfile
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from unittest import TestCase
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import torch
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import torch.nn as nn
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from mmengine.model import BaseModule
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from mmengine.runner import Runner
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from mmengine.structures import LabelData
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from torch.utils.data import Dataset
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from mmselfsup.engine import SimSiamHook
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from mmselfsup.models.algorithms import BaseModel
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from mmselfsup.registry import MODELS
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from mmselfsup.structures import SelfSupDataSample
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class DummyDataset(Dataset):
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METAINFO = dict() # type: ignore
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data = torch.randn(12, 2)
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label = torch.ones(12)
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@property
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def metainfo(self):
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return self.METAINFO
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def __len__(self):
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return self.data.size(0)
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def __getitem__(self, index):
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data_sample = SelfSupDataSample()
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gt_label = LabelData(value=self.label[index])
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setattr(data_sample, 'gt_label', gt_label)
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return dict(inputs=[self.data[index]], data_samples=data_sample)
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@MODELS.register_module()
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class SimSiamDummyLayer(BaseModule):
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def __init__(self, init_cfg=None):
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super().__init__(init_cfg)
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self.predictor = nn.Linear(2, 1)
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def forward(self, x):
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return self.predictor(x)
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class ToyModel(BaseModel):
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def __init__(self):
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super().__init__(backbone=dict(type='SimSiamDummyLayer'))
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def loss(self, batch_inputs, data_samples):
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labels = []
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for x in data_samples:
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labels.append(x.gt_label.value)
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labels = torch.stack(labels)
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outputs = self.backbone(batch_inputs[0])
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loss = (labels - outputs).sum()
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outputs = dict(loss=loss)
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return outputs
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class TestSimSiamHook(TestCase):
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def setUp(self):
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_simsiam_hook(self):
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device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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dummy_dataset = DummyDataset()
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toy_model = ToyModel().to(device)
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simsiam_hook = SimSiamHook(
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fix_pred_lr=True, lr=0.05, adjust_by_epoch=False)
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# test SimSiamHook
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runner = Runner(
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model=toy_model,
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work_dir=self.temp_dir.name,
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train_dataloader=dict(
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dataset=dummy_dataset,
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sampler=dict(type='DefaultSampler', shuffle=True),
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collate_fn=dict(type='default_collate'),
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batch_size=1,
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num_workers=0),
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optim_wrapper=dict(
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optimizer=dict(type='SGD', lr=0.05),
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paramwise_cfg=dict(
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custom_keys={'predictor': dict(fix_lr=True)})),
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param_scheduler=dict(type='MultiStepLR', milestones=[1]),
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train_cfg=dict(by_epoch=True, max_epochs=2),
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custom_hooks=[simsiam_hook],
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default_hooks=dict(logger=None),
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log_processor=dict(window_size=1),
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experiment_name='test_simsiam_hook',
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default_scope='mmselfsup')
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runner.train()
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for param_group in runner.optim_wrapper.optimizer.param_groups:
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if 'fix_lr' in param_group and param_group['fix_lr']:
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assert param_group['lr'] == 0.05
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else:
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assert param_group['lr'] != 0.05
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