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# --------------------------------------------------------
# Reversible Column Networks
# Copyright (c) 2022 Megvii Inc.
# Licensed under The Apache License 2.0 [see LICENSE for details]
# Written by Yuxuan Cai
# --------------------------------------------------------
import numpy as np
from torch import optim as optim
def build_optimizer(config, model):
"""
Build optimizer, set weight decay of normalization to 0 by default.
"""
skip = {}
skip_keywords = {}
if hasattr(model, 'no_weight_decay'):
skip = model.no_weight_decay()
if hasattr(model, 'no_weight_decay_keywords'):
skip_keywords = model.no_weight_decay_keywords()
elif config.MODEL.TYPE.startswith("revcol"):
parameters = param_groups_lrd(model, weight_decay=config.TRAIN.WEIGHT_DECAY, no_weight_decay_list=[], layer_decay=config.TRAIN.OPTIMIZER.LAYER_DECAY)
else:
parameters = set_weight_decay(model, skip, skip_keywords)
opt_lower = config.TRAIN.OPTIMIZER.NAME.lower()
optimizer = None
if opt_lower == 'sgd':
optimizer = optim.SGD(parameters, momentum=config.TRAIN.OPTIMIZER.MOMENTUM, nesterov=True,
lr=config.TRAIN.BASE_LR, weight_decay=config.TRAIN.WEIGHT_DECAY)
elif opt_lower == 'adamw':
optimizer = optim.AdamW(parameters, eps=config.TRAIN.OPTIMIZER.EPS, betas=config.TRAIN.OPTIMIZER.BETAS,
lr=config.TRAIN.BASE_LR)
return optimizer
def set_weight_decay(model, skip_list=(), skip_keywords=()):
has_decay = []
no_decay = []
for name, param in model.named_parameters():
if not param.requires_grad or name in ["linear_eval.weight", "linear_eval.bias"]:
continue # frozen weights
if len(param.shape) == 1 or name.endswith(".bias") or (name in skip_list) or \
check_keywords_in_name(name, skip_keywords):
no_decay.append(param)
# print(f"{name} has no weight decay")
else:
has_decay.append(param)
return [{'params': has_decay},
{'params': no_decay, 'weight_decay': 0.}]
def check_keywords_in_name(name, keywords=()):
isin = False
for keyword in keywords:
if keyword in name:
isin = True
return isin
def cal_model_depth(columns, layers):
depth = sum(layers)
dp = np.zeros((depth, columns))
dp[:,0]=np.linspace(0, depth-1, depth)
dp[0,:]=np.linspace(0, columns-1, columns)
for i in range(1, depth):
for j in range(1, columns):
dp[i][j] = min(dp[i][j-1], dp[i-1][j])+1
dp = dp.astype(int)
return dp
def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75):
"""
Parameter groups for layer-wise lr decay
Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58
"""
param_group_names = {}
param_groups = {}
dp = cal_model_depth(model.num_subnet, model.layers)+1
num_layers = dp[-1][-1] + 1
layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1))
for n, p in model.named_parameters():
if not p.requires_grad:
continue
# no decay: all 1D parameters and model specific ones
if p.ndim == 1 or n in no_weight_decay_list:# or re.match('(.*).alpha.$', n):
g_decay = "no_decay"
this_decay = 0.
else:
g_decay = "decay"
this_decay = weight_decay
layer_id = get_layer_id(n, dp, model.layers)
group_name = "layer_%d_%s" % (layer_id, g_decay)
if group_name not in param_group_names:
this_scale = layer_scales[layer_id]
param_group_names[group_name] = {
"lr_scale": this_scale,
"weight_decay": this_decay,
"params": [],
}
param_groups[group_name] = {
"lr_scale": this_scale,
"weight_decay": this_decay,
"params": [],
}
param_group_names[group_name]["params"].append(n)
param_groups[group_name]["params"].append(p)
# print("parameter groups: \n%s" % json.dumps(param_group_names, indent=2))
return list(param_groups.values())
def get_layer_id(n, dp, layers):
if n.startswith("subnet"):
name_part = n.split('.')
subnet = int(name_part[0][6:])
if name_part[1].startswith("alpha"):
id = dp[0][subnet]
else:
level = int(name_part[1][-1])
if name_part[2].startswith("blocks"):
sub = int(name_part[3])
if sub>layers[level]-1:
sub = layers[level]-1
block = sum(layers[:level])+sub
if name_part[2].startswith("fusion"):
block = sum(layers[:level])
id = dp[block][subnet]
elif n.startswith("stem"):
id = 0
else:
id = dp[-1][-1]+1
return id |