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