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| # -------------------------------------------------------- | |
| # BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254) | |
| # Github source: https://github.com/microsoft/unilm/tree/master/beit | |
| # Copyright (c) 2021 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # By Hangbo Bao | |
| # Based on timm and DeiT code bases | |
| # https://github.com/rwightman/pytorch-image-models/tree/master/timm | |
| # https://github.com/facebookresearch/deit/ | |
| # --------------------------------------------------------' | |
| import math | |
| import torch | |
| import torch.nn as nn | |
| from functools import partial | |
| from modeling_finetune import Block, _cfg, PatchEmbed, RelativePositionBias | |
| from timm.models.registry import register_model | |
| from timm.models.layers import trunc_normal_ as __call_trunc_normal_ | |
| def trunc_normal_(tensor, mean=0., std=1.): | |
| __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) | |
| __all__ = [ | |
| 'beit_base_patch16_224_8k_vocab', | |
| 'beit_large_patch16_224_8k_vocab', | |
| ] | |
| class VisionTransformerForMaskedImageModeling(nn.Module): | |
| def __init__(self, img_size=224, patch_size=16, in_chans=3, vocab_size=8192, embed_dim=768, depth=12, | |
| num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., | |
| drop_path_rate=0., norm_layer=None, init_values=None, attn_head_dim=None, | |
| use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False, init_std=0.02, **kwargs): | |
| super().__init__() | |
| self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models | |
| self.patch_embed = PatchEmbed( | |
| img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) | |
| num_patches = self.patch_embed.num_patches | |
| self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) | |
| self.mask_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) | |
| if use_abs_pos_emb: | |
| self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) | |
| else: | |
| self.pos_embed = None | |
| self.pos_drop = nn.Dropout(p=drop_rate) | |
| if use_shared_rel_pos_bias: | |
| self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads) | |
| else: | |
| self.rel_pos_bias = None | |
| dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule | |
| self.blocks = nn.ModuleList([ | |
| Block( | |
| dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, | |
| drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, | |
| init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None, | |
| attn_head_dim=attn_head_dim, | |
| ) | |
| for i in range(depth)]) | |
| self.norm = norm_layer(embed_dim) | |
| self.init_std = init_std | |
| self.lm_head = nn.Linear(embed_dim, vocab_size) | |
| if self.pos_embed is not None: | |
| trunc_normal_(self.pos_embed, std=self.init_std) | |
| trunc_normal_(self.cls_token, std=self.init_std) | |
| trunc_normal_(self.mask_token, std=self.init_std) | |
| trunc_normal_(self.lm_head.weight, std=self.init_std) | |
| self.apply(self._init_weights) | |
| self.fix_init_weight() | |
| def fix_init_weight(self): | |
| def rescale(param, layer_id): | |
| param.div_(math.sqrt(2.0 * layer_id)) | |
| for layer_id, layer in enumerate(self.blocks): | |
| rescale(layer.attn.proj.weight.data, layer_id + 1) | |
| rescale(layer.mlp.fc2.weight.data, layer_id + 1) | |
| def _init_weights(self, m): | |
| if isinstance(m, nn.Linear): | |
| trunc_normal_(m.weight, std=self.init_std) | |
| if isinstance(m, nn.Linear) and m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.LayerNorm): | |
| nn.init.constant_(m.bias, 0) | |
| nn.init.constant_(m.weight, 1.0) | |
| elif isinstance(m, nn.Conv2d): | |
| trunc_normal_(m.weight, std=self.init_std) | |
| if m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| def no_weight_decay(self): | |
| return {'pos_embed', 'cls_token'} | |
| def get_num_layers(self): | |
| return len(self.blocks) | |
| def forward_features(self, x, bool_masked_pos): | |
| x = self.patch_embed(x, bool_masked_pos=bool_masked_pos) | |
| batch_size, seq_len, _ = x.size() | |
| cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks | |
| mask_token = self.mask_token.expand(batch_size, seq_len, -1) | |
| # replace the masked visual tokens by mask_token | |
| w = bool_masked_pos.unsqueeze(-1).type_as(mask_token) | |
| x = x * (1 - w) + mask_token * w | |
| x = torch.cat((cls_tokens, x), dim=1) | |
| if self.pos_embed is not None: | |
| x = x + self.pos_embed | |
| x = self.pos_drop(x) | |
| rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None | |
| for blk in self.blocks: | |
| x = blk(x, rel_pos_bias=rel_pos_bias) | |
| return self.norm(x) | |
| def forward(self, x, bool_masked_pos, return_all_tokens=False): | |
| x = self.forward_features(x, bool_masked_pos=bool_masked_pos) | |
| x = x[:, 1:] | |
| if return_all_tokens: | |
| return self.lm_head(x) | |
| else: | |
| # return the masked tokens | |
| return self.lm_head(x[bool_masked_pos]) | |
| def beit_base_patch16_224_8k_vocab(pretrained=False, **kwargs): | |
| model = VisionTransformerForMaskedImageModeling( | |
| patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6), vocab_size=8192, **kwargs) | |
| model.default_cfg = _cfg() | |
| if pretrained: | |
| checkpoint = torch.load( | |
| kwargs["init_ckpt"], map_location="cpu" | |
| ) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |
| def beit_large_patch16_224_8k_vocab(pretrained=False, **kwargs): | |
| model = VisionTransformerForMaskedImageModeling( | |
| patch_size=16, embed_dim=1024, depth=24, num_heads=16, mlp_ratio=4, qkv_bias=True, | |
| norm_layer=partial(nn.LayerNorm, eps=1e-6), vocab_size=8192, **kwargs) | |
| model.default_cfg = _cfg() | |
| if pretrained: | |
| checkpoint = torch.load( | |
| kwargs["init_ckpt"], map_location="cpu" | |
| ) | |
| model.load_state_dict(checkpoint["model"]) | |
| return model | |