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101 lines
4.2 KiB
101 lines
4.2 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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import torch
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import torch.nn as nn
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from mmpose.core import build_optimizers
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class ExampleModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.model1 = nn.Conv2d(3, 8, kernel_size=3)
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self.model2 = nn.Conv2d(3, 4, kernel_size=3)
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def forward(self, x):
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return x
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def test_build_optimizers():
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base_lr = 0.0001
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base_wd = 0.0002
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momentum = 0.9
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# basic config with ExampleModel
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optimizer_cfg = dict(
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model1=dict(
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type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum),
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model2=dict(
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type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum))
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model = ExampleModel()
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optimizers = build_optimizers(model, optimizer_cfg)
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param_dict = dict(model.named_parameters())
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assert isinstance(optimizers, dict)
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for i in range(2):
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optimizer = optimizers[f'model{i+1}']
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param_groups = optimizer.param_groups[0]
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assert isinstance(optimizer, torch.optim.SGD)
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assert optimizer.defaults['lr'] == base_lr
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assert optimizer.defaults['momentum'] == momentum
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assert optimizer.defaults['weight_decay'] == base_wd
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assert len(param_groups['params']) == 2
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assert torch.equal(param_groups['params'][0],
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param_dict[f'model{i+1}.weight'])
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assert torch.equal(param_groups['params'][1],
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param_dict[f'model{i+1}.bias'])
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# basic config with Parallel model
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model = torch.nn.DataParallel(ExampleModel())
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optimizers = build_optimizers(model, optimizer_cfg)
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param_dict = dict(model.named_parameters())
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assert isinstance(optimizers, dict)
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for i in range(2):
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optimizer = optimizers[f'model{i+1}']
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param_groups = optimizer.param_groups[0]
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assert isinstance(optimizer, torch.optim.SGD)
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assert optimizer.defaults['lr'] == base_lr
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assert optimizer.defaults['momentum'] == momentum
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assert optimizer.defaults['weight_decay'] == base_wd
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assert len(param_groups['params']) == 2
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assert torch.equal(param_groups['params'][0],
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param_dict[f'module.model{i+1}.weight'])
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assert torch.equal(param_groups['params'][1],
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param_dict[f'module.model{i+1}.bias'])
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# basic config with ExampleModel (one optimizer)
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optimizer_cfg = dict(
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type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
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model = ExampleModel()
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optimizer = build_optimizers(model, optimizer_cfg)
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param_dict = dict(model.named_parameters())
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assert isinstance(optimizers, dict)
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param_groups = optimizer.param_groups[0]
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assert isinstance(optimizer, torch.optim.SGD)
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assert optimizer.defaults['lr'] == base_lr
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assert optimizer.defaults['momentum'] == momentum
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assert optimizer.defaults['weight_decay'] == base_wd
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assert len(param_groups['params']) == 4
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assert torch.equal(param_groups['params'][0], param_dict['model1.weight'])
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assert torch.equal(param_groups['params'][1], param_dict['model1.bias'])
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assert torch.equal(param_groups['params'][2], param_dict['model2.weight'])
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assert torch.equal(param_groups['params'][3], param_dict['model2.bias'])
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# basic config with Parallel model (one optimizer)
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model = torch.nn.DataParallel(ExampleModel())
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optimizer = build_optimizers(model, optimizer_cfg)
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param_dict = dict(model.named_parameters())
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assert isinstance(optimizers, dict)
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param_groups = optimizer.param_groups[0]
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assert isinstance(optimizer, torch.optim.SGD)
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assert optimizer.defaults['lr'] == base_lr
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assert optimizer.defaults['momentum'] == momentum
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assert optimizer.defaults['weight_decay'] == base_wd
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assert len(param_groups['params']) == 4
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assert torch.equal(param_groups['params'][0],
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param_dict['module.model1.weight'])
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assert torch.equal(param_groups['params'][1],
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param_dict['module.model1.bias'])
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assert torch.equal(param_groups['params'][2],
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param_dict['module.model2.weight'])
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assert torch.equal(param_groups['params'][3],
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param_dict['module.model2.bias'])
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