| import torch
|
| from torch import optim as optim
|
|
|
| from timm.optim.adafactor import Adafactor
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| from timm.optim.adahessian import Adahessian
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| from timm.optim.adamp import AdamP
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| from timm.optim.lookahead import Lookahead
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| from timm.optim.nvnovograd import NvNovoGrad
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| from timm.optim.rmsprop_tf import RMSpropTF
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| from timm.optim.sgdp import SGDP
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|
|
| import json
|
|
|
| try:
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| from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD
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| has_apex = True
|
| except ImportError:
|
| has_apex = False
|
|
|
|
|
| def get_num_layer_for_convnext(var_name):
|
| """
|
| Divide [3, 3, 27, 3] layers into 12 groups; each group is three
|
| consecutive blocks, including possible neighboring downsample layers;
|
| adapted from https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py
|
| """
|
| num_max_layer = 12
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| if var_name.startswith("downsample_layers"):
|
| stage_id = int(var_name.split('.')[1])
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| if stage_id == 0:
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| layer_id = 0
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| elif stage_id == 1 or stage_id == 2:
|
| layer_id = stage_id + 1
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| elif stage_id == 3:
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| layer_id = 12
|
| return layer_id
|
|
|
| elif var_name.startswith("stages"):
|
| stage_id = int(var_name.split('.')[1])
|
| block_id = int(var_name.split('.')[2])
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| if stage_id == 0 or stage_id == 1:
|
| layer_id = stage_id + 1
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| elif stage_id == 2:
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| layer_id = 3 + block_id // 3
|
| elif stage_id == 3:
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| layer_id = 12
|
| return layer_id
|
| else:
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| return num_max_layer + 1
|
|
|
| class LayerDecayValueAssigner(object):
|
| def __init__(self, values):
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| self.values = values
|
|
|
| def get_scale(self, layer_id):
|
| return self.values[layer_id]
|
|
|
| def get_layer_id(self, var_name):
|
| return get_num_layer_for_convnext(var_name)
|
|
|
|
|
| def get_parameter_groups(model, weight_decay=1e-5, skip_list=(), get_num_layer=None, get_layer_scale=None):
|
| parameter_group_names = {}
|
| parameter_group_vars = {}
|
|
|
| for name, param in model.named_parameters():
|
| if not param.requires_grad:
|
| continue
|
| if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list:
|
| group_name = "no_decay"
|
| this_weight_decay = 0.
|
| else:
|
| group_name = "decay"
|
| this_weight_decay = weight_decay
|
| if get_num_layer is not None:
|
| layer_id = get_num_layer(name)
|
| group_name = "layer_%d_%s" % (layer_id, group_name)
|
| else:
|
| layer_id = None
|
|
|
| if group_name not in parameter_group_names:
|
| if get_layer_scale is not None:
|
| scale = get_layer_scale(layer_id)
|
| else:
|
| scale = 1.
|
|
|
| parameter_group_names[group_name] = {
|
| "weight_decay": this_weight_decay,
|
| "params": [],
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| "lr_scale": scale
|
| }
|
| parameter_group_vars[group_name] = {
|
| "weight_decay": this_weight_decay,
|
| "params": [],
|
| "lr_scale": scale
|
| }
|
|
|
| parameter_group_vars[group_name]["params"].append(param)
|
| parameter_group_names[group_name]["params"].append(name)
|
| print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
|
| return list(parameter_group_vars.values())
|
|
|
|
|
| def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None):
|
| opt_lower = args.opt.lower()
|
| weight_decay = args.weight_decay
|
|
|
| if filter_bias_and_bn:
|
| skip = {}
|
| if skip_list is not None:
|
| skip = skip_list
|
| elif hasattr(model, 'no_weight_decay'):
|
| skip = model.no_weight_decay()
|
| parameters = get_parameter_groups(model, weight_decay, skip, get_num_layer, get_layer_scale)
|
| weight_decay = 0.
|
| else:
|
| parameters = model.parameters()
|
|
|
| if 'fused' in opt_lower:
|
| assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers'
|
|
|
| opt_args = dict(lr=args.lr, weight_decay=weight_decay)
|
| if hasattr(args, 'opt_eps') and args.opt_eps is not None:
|
| opt_args['eps'] = args.opt_eps
|
| if hasattr(args, 'opt_betas') and args.opt_betas is not None:
|
| opt_args['betas'] = args.opt_betas
|
|
|
| opt_split = opt_lower.split('_')
|
| opt_lower = opt_split[-1]
|
| if opt_lower == 'sgd' or opt_lower == 'nesterov':
|
| opt_args.pop('eps', None)
|
| optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
|
| elif opt_lower == 'momentum':
|
| opt_args.pop('eps', None)
|
| optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
|
| elif opt_lower == 'adam':
|
| optimizer = optim.Adam(parameters, **opt_args)
|
| elif opt_lower == 'adamw':
|
| optimizer = optim.AdamW(parameters, **opt_args)
|
| elif opt_lower == 'adamp':
|
| optimizer = AdamP(parameters, wd_ratio=0.01, nesterov=True, **opt_args)
|
| elif opt_lower == 'sgdp':
|
| optimizer = SGDP(parameters, momentum=args.momentum, nesterov=True, **opt_args)
|
| elif opt_lower == 'adadelta':
|
| optimizer = optim.Adadelta(parameters, **opt_args)
|
| elif opt_lower == 'adafactor':
|
| if not args.lr:
|
| opt_args['lr'] = None
|
| optimizer = Adafactor(parameters, **opt_args)
|
| elif opt_lower == 'adahessian':
|
| optimizer = Adahessian(parameters, **opt_args)
|
| elif opt_lower == 'rmsprop':
|
| optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
|
| elif opt_lower == 'rmsproptf':
|
| optimizer = RMSpropTF(parameters, alpha=0.9, momentum=args.momentum, **opt_args)
|
| elif opt_lower == 'nvnovograd':
|
| optimizer = NvNovoGrad(parameters, **opt_args)
|
| elif opt_lower == 'fusedsgd':
|
| opt_args.pop('eps', None)
|
| optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=True, **opt_args)
|
| elif opt_lower == 'fusedmomentum':
|
| opt_args.pop('eps', None)
|
| optimizer = FusedSGD(parameters, momentum=args.momentum, nesterov=False, **opt_args)
|
| elif opt_lower == 'fusedadam':
|
| optimizer = FusedAdam(parameters, adam_w_mode=False, **opt_args)
|
| elif opt_lower == 'fusedadamw':
|
| optimizer = FusedAdam(parameters, adam_w_mode=True, **opt_args)
|
| elif opt_lower == 'fusedlamb':
|
| optimizer = FusedLAMB(parameters, **opt_args)
|
| elif opt_lower == 'fusednovograd':
|
| opt_args.setdefault('betas', (0.95, 0.98))
|
| optimizer = FusedNovoGrad(parameters, **opt_args)
|
| else:
|
| assert False and "Invalid optimizer"
|
|
|
| if len(opt_split) > 1:
|
| if opt_split[0] == 'lookahead':
|
| optimizer = Lookahead(optimizer)
|
|
|
| return optimizer
|
|
|
