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fc69e38009
* [Feature] Add MoCo v3 (#194) * [Feature] add position embedding function * [Fature] modify nonlinear neck for vit backbone * [Feature] add mocov3 head * [Feature] modify cls_head for vit backbone * [Feature] add ViT backbone * [Feature] add mocov3 algorithm * [Docs] revise BYOL hook docstring * [Feature] add mocov3 vit small config files * [Feature] add mocov3 vit small linear eval config files * [Fix] solve conflict * [Fix] add mmcls * [Fix] fix docstring format * [Fix] fix isort * [Fix] add mmcls to runtime requirements * [Feature] remove duplicated codes * [Feature] add mocov3 related unit test * [Feature] revise position embedding function * [Feature] add UT codes * [Docs] add README.md * [Docs] add model links and results to model zoo * [Docs] fix model links * [Docs] add metafile * [Docs] modify install.md and add mmcls requirements * [Docs] modify description * [Fix] using specific arch name `mocov3-small` rather than general arch name `small` * [Fix] add mmcls * [Fix] fix arch name * [Feature] change name to `MoCoV3` * [Fix] fix unit test bug * [Feature] change `BYOLHook` name to `MomentumUpdateHook` * [Feature] change name to MoCoV3 * [Docs] modify description Co-authored-by: fangyixiao18 <fangyx18@hotmail.com> Co-authored-by: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com> * [Docs] update model zoo results (#195) * Bump version to v0.6.0 (#198) * [Docs] update model zoo results * Bump version to v0.6.0 Co-authored-by: fangyixiao18 <fangyx18@hotmail.com> Co-authored-by: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com>
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import logging
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import tempfile
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from unittest.mock import MagicMock
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import torch
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import torch.nn as nn
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from mmcv.parallel import MMDataParallel
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from mmcv.runner import build_runner, obj_from_dict
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from torch.utils.data import DataLoader, Dataset
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from mmselfsup.core.hooks import MomentumUpdateHook
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class ExampleDataset(Dataset):
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def __getitem__(self, idx):
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results = dict(img=torch.tensor([1]), img_metas=dict())
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return results
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def __len__(self):
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return 1
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class ExampleModel(nn.Module):
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def __init__(self):
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super(ExampleModel, self).__init__()
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self.test_cfg = None
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self.online_net = nn.Conv2d(3, 3, 3)
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self.target_net = nn.Conv2d(3, 3, 3)
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self.base_momentum = 0.96
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self.momentum = self.base_momentum
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def forward(self, img, img_metas, test_mode=False, **kwargs):
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return img
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def train_step(self, data_batch, optimizer):
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loss = self.forward(**data_batch)
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return dict(loss=loss)
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@torch.no_grad()
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def _momentum_update(self):
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"""Momentum update of the target network."""
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for param_ol, param_tgt in zip(self.online_net.parameters(),
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self.target_net.parameters()):
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param_tgt.data = param_tgt.data * self.momentum + \
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param_ol.data * (1. - self.momentum)
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@torch.no_grad()
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def momentum_update(self):
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self._momentum_update()
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def test_byol_hook():
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test_dataset = ExampleDataset()
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test_dataset.evaluate = MagicMock(return_value=dict(test='success'))
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data_loader = DataLoader(
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test_dataset, batch_size=1, sampler=None, num_workers=0, shuffle=False)
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runner_cfg = dict(type='EpochBasedRunner', max_epochs=2)
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optim_cfg = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005)
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# test MomentumUpdateHook
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with tempfile.TemporaryDirectory() as tmpdir:
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model = MMDataParallel(ExampleModel())
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optimizer = obj_from_dict(optim_cfg, torch.optim,
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dict(params=model.parameters()))
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momentum_hook = MomentumUpdateHook()
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runner = build_runner(
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runner_cfg,
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default_args=dict(
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model=model,
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optimizer=optimizer,
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work_dir=tmpdir,
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logger=logging.getLogger()))
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runner.register_hook(momentum_hook)
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runner.run([data_loader], [('train', 1)])
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assert runner.model.module.momentum == 0.98
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