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# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.core.optimizers import LearningRateDecayOptimizerConstructor
base_lr = 1
decay_rate = 2
base_wd = 0.05
weight_decay = 0.05
expected_stage_wise_lr_wd_convnext = [{
'weight_decay': 0.0,
'lr_scale': 128
}, {
'weight_decay': 0.0,
'lr_scale': 1
}, {
'weight_decay': 0.05,
'lr_scale': 64
}, {
'weight_decay': 0.0,
'lr_scale': 64
}, {
'weight_decay': 0.05,
'lr_scale': 32
}, {
'weight_decay': 0.0,
'lr_scale': 32
}, {
'weight_decay': 0.05,
'lr_scale': 16
}, {
'weight_decay': 0.0,
'lr_scale': 16
}, {
'weight_decay': 0.05,
'lr_scale': 8
}, {
'weight_decay': 0.0,
'lr_scale': 8
}, {
'weight_decay': 0.05,
'lr_scale': 128
}, {
'weight_decay': 0.05,
'lr_scale': 1
}]
expected_layer_wise_lr_wd_convnext = [{
'weight_decay': 0.0,
'lr_scale': 128
}, {
'weight_decay': 0.0,
'lr_scale': 1
}, {
'weight_decay': 0.05,
'lr_scale': 64
}, {
'weight_decay': 0.0,
'lr_scale': 64
}, {
'weight_decay': 0.05,
'lr_scale': 32
}, {
'weight_decay': 0.0,
'lr_scale': 32
}, {
'weight_decay': 0.05,
'lr_scale': 16
}, {
'weight_decay': 0.0,
'lr_scale': 16
}, {
'weight_decay': 0.05,
'lr_scale': 2
}, {
'weight_decay': 0.0,
'lr_scale': 2
}, {
'weight_decay': 0.05,
'lr_scale': 128
}, {
'weight_decay': 0.05,
'lr_scale': 1
}]
class ToyConvNeXt(nn.Module):
def __init__(self):
super().__init__()
self.stages = nn.ModuleList()
for i in range(4):
stage = nn.Sequential(ConvModule(3, 4, kernel_size=1, bias=True))
self.stages.append(stage)
self.norm0 = nn.BatchNorm2d(2)
# add some variables to meet unit test coverate rate
self.cls_token = nn.Parameter(torch.ones(1))
self.mask_token = nn.Parameter(torch.ones(1))
self.pos_embed = nn.Parameter(torch.ones(1))
self.stem_norm = nn.Parameter(torch.ones(1))
self.downsample_norm0 = nn.BatchNorm2d(2)
self.downsample_norm1 = nn.BatchNorm2d(2)
self.downsample_norm2 = nn.BatchNorm2d(2)
self.lin = nn.Parameter(torch.ones(1))
self.lin.requires_grad = False
self.downsample_layers = nn.ModuleList()
for _ in range(4):
stage = nn.Sequential(nn.Conv2d(3, 4, kernel_size=1, bias=True))
self.downsample_layers.append(stage)
class ToyDetector(nn.Module):
def __init__(self, backbone):
super().__init__()
self.backbone = backbone
self.head = nn.Conv2d(2, 2, kernel_size=1, groups=2)
class PseudoDataParallel(nn.Module):
def __init__(self, model):
super().__init__()
self.module = model
def check_optimizer_lr_wd(optimizer, gt_lr_wd):
assert isinstance(optimizer, torch.optim.AdamW)
assert optimizer.defaults['lr'] == base_lr
assert optimizer.defaults['weight_decay'] == base_wd
param_groups = optimizer.param_groups
print(param_groups)
assert len(param_groups) == len(gt_lr_wd)
for i, param_dict in enumerate(param_groups):
assert param_dict['weight_decay'] == gt_lr_wd[i]['weight_decay']
assert param_dict['lr_scale'] == gt_lr_wd[i]['lr_scale']
assert param_dict['lr_scale'] == param_dict['lr']
def test_learning_rate_decay_optimizer_constructor():
# Test lr wd for ConvNeXT
backbone = ToyConvNeXt()
model = PseudoDataParallel(ToyDetector(backbone))
optimizer_cfg = dict(
type='AdamW', lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05)
# stagewise decay
stagewise_paramwise_cfg = dict(
decay_rate=decay_rate, decay_type='stage_wise', num_layers=6)
optim_constructor = LearningRateDecayOptimizerConstructor(
optimizer_cfg, stagewise_paramwise_cfg)
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_stage_wise_lr_wd_convnext)
# layerwise decay
layerwise_paramwise_cfg = dict(
decay_rate=decay_rate, decay_type='layer_wise', num_layers=6)
optim_constructor = LearningRateDecayOptimizerConstructor(
optimizer_cfg, layerwise_paramwise_cfg)
optimizer = optim_constructor(model)
check_optimizer_lr_wd(optimizer, expected_layer_wise_lr_wd_convnext)
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