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| # -------------------------------------------------------- | |
| # Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks (https://arxiv.org/abs/2208.10442) | |
| # Github source: https://github.com/microsoft/unilm/tree/master/beit3 | |
| # Copyright (c) 2023 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # --------------------------------------------------------' | |
| from torch import optim as optim | |
| from timm.optim.lookahead import Lookahead | |
| import json | |
| def get_num_layer_for_vit(var_name, num_max_layer): | |
| if "embed" in var_name: | |
| return 0 | |
| elif var_name in ( | |
| "cls_token", "mask_token", "pos_embed", "language_pos_embed", | |
| "word_embeddings.weight", "vision_cls_token", "vision_pos_embed" | |
| ): | |
| return 0 | |
| elif var_name.startswith("patch_embed"): | |
| return 0 | |
| elif var_name.startswith("rel_pos_bias"): | |
| return num_max_layer - 1 | |
| elif "layers." in var_name: | |
| layer_id = int(var_name.split('layers.')[1].split('.')[0]) | |
| return layer_id + 1 | |
| else: | |
| return num_max_layer - 1 | |
| def get_is_head_flag_for_vit(var_name, num_max_layer): | |
| if var_name.startswith("head"): | |
| return 1 | |
| # elif var_name.startswith("pooler"): | |
| # return 1 | |
| else: | |
| return 0 | |
| class LayerDecayValueAssigner(object): | |
| def __init__(self, values, scale_handler=None): | |
| self.scale_handler = scale_handler or get_num_layer_for_vit | |
| self.values = values | |
| def get_scale(self, layer_id): | |
| return self.values[layer_id] | |
| def get_layer_id(self, var_name): | |
| return self.scale_handler(var_name, len(self.values)) | |
| # The implementation code is modified from Timm (https://github.com/huggingface/pytorch-image-models/tree/main/timm | |
| 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 # frozen weights | |
| 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": [], | |
| "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 weight_decay and 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() | |
| 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 == 'adamw': | |
| optimizer = optim.AdamW(parameters, **opt_args) | |
| else: | |
| raise ValueError("Invalid optimizer") | |
| if len(opt_split) > 1: | |
| if opt_split[0] == 'lookahead': | |
| optimizer = Lookahead(optimizer) | |
| return optimizer | |