52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import pytest
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import torch
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import torch.nn as nn
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from mmselfsup.core import LARS, build_optimizer
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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.predictor = nn.Linear(2, 1)
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def forward(self, img, img_metas, test_mode=False, **kwargs):
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res = self.predictor(img)
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return res
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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.sum())
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def test_lars():
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optimizer = dict(
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type='LARS',
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lr=0.3,
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momentum=0.9,
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weight_decay=1e-6,
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paramwise_options={'bias': dict(weight_decay=0., lars_exclude=True)})
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model = ExampleModel()
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optimizer = build_optimizer(model, optimizer)
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for i in range(2):
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loss = model.train_step(
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dict(img=torch.ones(2, 2), img_metas=None), optimizer)
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optimizer.zero_grad()
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loss['loss'].backward()
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optimizer.step()
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with pytest.raises(ValueError):
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optimizer = LARS(model.parameters(), lr=-1)
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with pytest.raises(ValueError):
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optimizer = LARS(model.parameters(), lr=0.1, momentum=-1)
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with pytest.raises(ValueError):
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optimizer = LARS(model.parameters(), lr=0.1, weight_decay=-1)
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with pytest.raises(ValueError):
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optimizer = LARS(model.parameters(), lr=0.1, eta=-1)
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