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import torch
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import torch.nn as nn
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from mmaction.engine.optimizers import SwinOptimWrapperConstructor
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class SubModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(2, 2, kernel_size=1, groups=2)
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self.gn = nn.GroupNorm(2, 2)
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self.fc = nn.Linear(2, 2)
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self.param1 = nn.Parameter(torch.ones(1))
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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.param1 = nn.Parameter(torch.ones(1))
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self.conv1 = nn.Conv2d(3, 4, kernel_size=1, bias=False)
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self.conv2 = nn.Conv2d(4, 2, kernel_size=1)
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self.bn = nn.BatchNorm2d(2)
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self.sub = SubModel()
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self.fc = nn.Linear(2, 1)
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base_lr = 0.01
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base_wd = 0.0001
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betas = (0.9, 0.999)
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def test_swin_optim_wrapper_constructor():
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model = ExampleModel()
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optim_wrapper_cfg = dict(
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optimizer=dict(
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type='AdamW', lr=base_lr, weight_decay=base_wd, betas=betas))
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paramwise_cfg = {
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'base.param1': dict(lr_mult=2.),
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'base.conv1.weight': dict(lr_mult=3.),
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'bn': dict(decay_mult=0.),
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'sub': dict(lr_mult=0.1),
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'sub.conv1.bias': dict(decay_mult=0.1),
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'gn': dict(decay_mult=0.),
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}
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constructor = SwinOptimWrapperConstructor(optim_wrapper_cfg, paramwise_cfg)
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optim_wrapper = constructor(model)
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optimizer = optim_wrapper.optimizer
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param_groups = optimizer.param_groups
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assert isinstance(optimizer, torch.optim.AdamW)
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assert optimizer.defaults['lr'] == base_lr
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assert optimizer.defaults['weight_decay'] == base_wd
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model_parameters = list(model.parameters())
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assert len(param_groups) == len(model_parameters)
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for i, param in enumerate(model_parameters):
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param_group = param_groups[i]
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assert torch.equal(param_group['params'][0], param)
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assert param_group['betas'] == betas
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param1 = param_groups[0]
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assert param1['lr'] == base_lr * paramwise_cfg['base.param1']['lr_mult']
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assert param1['weight_decay'] == base_wd
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conv1_weight = param_groups[1]
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assert conv1_weight['lr'] == \
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base_lr * paramwise_cfg['base.conv1.weight']['lr_mult']
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assert conv1_weight['weight_decay'] == base_wd
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conv2_weight = param_groups[2]
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assert conv2_weight['lr'] == base_lr
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assert conv2_weight['weight_decay'] == base_wd
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conv2_bias = param_groups[3]
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assert conv2_bias['lr'] == base_lr
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assert conv2_bias['weight_decay'] == base_wd
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bn_weight = param_groups[4]
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assert bn_weight['lr'] == base_lr
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assert bn_weight['weight_decay'] == \
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base_wd * paramwise_cfg['bn']['decay_mult']
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bn_bias = param_groups[5]
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assert bn_bias['lr'] == base_lr
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assert bn_bias['weight_decay'] == \
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base_wd * paramwise_cfg['bn']['decay_mult']
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sub_param1 = param_groups[6]
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assert sub_param1['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_param1['weight_decay'] == base_wd
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sub_conv1_weight = param_groups[7]
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assert sub_conv1_weight['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_conv1_weight['weight_decay'] == base_wd
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sub_conv1_bias = param_groups[8]
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assert sub_conv1_bias['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_conv1_bias['weight_decay'] == \
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base_wd * paramwise_cfg['sub.conv1.bias']['decay_mult']
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sub_gn_weight = param_groups[9]
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assert sub_gn_weight['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_gn_weight['weight_decay'] == \
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base_wd * paramwise_cfg['gn']['decay_mult']
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sub_gn_bias = param_groups[10]
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assert sub_gn_bias['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_gn_bias['weight_decay'] == \
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base_wd * paramwise_cfg['gn']['decay_mult']
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sub_fc_weight = param_groups[11]
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assert sub_fc_weight['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_fc_weight['weight_decay'] == base_wd
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sub_fc_bias = param_groups[12]
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assert sub_fc_bias['lr'] == base_lr * paramwise_cfg['sub']['lr_mult']
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assert sub_fc_bias['weight_decay'] == base_wd
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fc_weight = param_groups[13]
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assert fc_weight['lr'] == base_lr
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assert fc_weight['weight_decay'] == base_wd
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fc_bias = param_groups[14]
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assert fc_bias['lr'] == base_lr
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assert fc_bias['weight_decay'] == base_wd
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