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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/
- # https://github.com/facebookresearch/dino
- # --------------------------------------------------------'
- import math
- from functools import partial
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- from timm.models.layers import drop_path, to_2tuple, trunc_normal_
- from timm.models.registry import register_model
-
-
- def _cfg(url='', **kwargs):
- return {
- 'url': url,
- 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
- 'crop_pct': .9, 'interpolation': 'bicubic',
- 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
- **kwargs
- }
-
-
- class DropPath(nn.Module):
- """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
- """
- def __init__(self, drop_prob=None):
- super(DropPath, self).__init__()
- self.drop_prob = drop_prob
-
- def forward(self, x):
- return drop_path(x, self.drop_prob, self.training)
-
- def extra_repr(self) -> str:
- return 'p={}'.format(self.drop_prob)
-
-
- class Mlp(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- # x = self.drop(x)
- # commit this for the orignal BERT implement
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
- class Attention(nn.Module):
- def __init__(
- self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
- proj_drop=0., window_size=None, attn_head_dim=None):
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- if attn_head_dim is not None:
- head_dim = attn_head_dim
- all_head_dim = head_dim * self.num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
- if qkv_bias:
- self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
- self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
- else:
- self.q_bias = None
- self.v_bias = None
-
- if window_size:
- self.window_size = window_size
- self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
- # cls to token & token 2 cls & cls to cls
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(window_size[0])
- coords_w = torch.arange(window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * window_size[1] - 1
- relative_position_index = \
- torch.zeros(size=(window_size[0] * window_size[1] + 1, ) * 2, dtype=relative_coords.dtype)
- relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- relative_position_index[0, 0:] = self.num_relative_distance - 3
- relative_position_index[0:, 0] = self.num_relative_distance - 2
- relative_position_index[0, 0] = self.num_relative_distance - 1
-
- self.register_buffer("relative_position_index", relative_position_index)
- else:
- self.window_size = None
- self.relative_position_bias_table = None
- self.relative_position_index = None
-
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(all_head_dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- def forward(self, x, rel_pos_bias=None):
- B, N, C = x.shape
- qkv_bias = None
- if self.q_bias is not None:
- qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
- # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
- qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- if self.relative_position_bias_table is not None:
- relative_position_bias = \
- self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1] + 1,
- self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if rel_pos_bias is not None:
- attn = attn + rel_pos_bias
-
- attn = attn.softmax(dim=-1)
- attn = self.attn_drop(attn)
-
- x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-
- class Block(nn.Module):
-
- def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
- drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
- window_size=None, attn_head_dim=None):
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
- attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim)
- # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- if init_values is not None and init_values > 0:
- self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
- self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)),requires_grad=True)
- else:
- self.gamma_1, self.gamma_2 = None, None
-
- def forward(self, x, rel_pos_bias=None):
- if self.gamma_1 is None:
- x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
- x = x + self.drop_path(self.mlp(self.norm2(x)))
- else:
- x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
- x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
- return x
-
-
- class PatchEmbed(nn.Module):
- """ Image to Patch Embedding
- """
- def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
- super().__init__()
- img_size = to_2tuple(img_size)
- patch_size = to_2tuple(patch_size)
- num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
- self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
- self.img_size = img_size
- self.patch_size = patch_size
- self.num_patches = num_patches
-
- self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-
- def forward(self, x, **kwargs):
- B, C, H, W = x.shape
- # FIXME look at relaxing size constraints
- assert H == self.img_size[0] and W == self.img_size[1], \
- f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
- x = self.proj(x).flatten(2).transpose(1, 2)
- return x
-
-
- class RelativePositionBias(nn.Module):
-
- def __init__(self, window_size, num_heads):
- super().__init__()
- self.window_size = window_size
- self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros(self.num_relative_distance, num_heads)) # 2*Wh-1 * 2*Ww-1, nH
- # cls to token & token 2 cls & cls to cls
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(window_size[0])
- coords_w = torch.arange(window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * window_size[1] - 1
- relative_position_index = \
- torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
- relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- relative_position_index[0, 0:] = self.num_relative_distance - 3
- relative_position_index[0:, 0] = self.num_relative_distance - 2
- relative_position_index[0, 0] = self.num_relative_distance - 1
-
- self.register_buffer("relative_position_index", relative_position_index)
-
- # trunc_normal_(self.relative_position_bias_table, std=.02)
-
- def forward(self):
- relative_position_bias = \
- self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1] + 1,
- self.window_size[0] * self.window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
- return relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
-
-
- class VisionTransformer(nn.Module):
- """ Vision Transformer with support for patch or hybrid CNN input stage
- """
- def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
- num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
- drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None,
- use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
- use_mean_pooling=True, init_scale=0.001):
- super().__init__()
- self.num_classes = num_classes
- 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.use_rel_pos_bias = use_rel_pos_bias
- 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)
- for i in range(depth)])
- self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
- self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
- self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
- if self.pos_embed is not None:
- trunc_normal_(self.pos_embed, std=.02)
- trunc_normal_(self.cls_token, std=.02)
- # trunc_normal_(self.mask_token, std=.02)
- if isinstance(self.head, nn.Linear):
- trunc_normal_(self.head.weight, std=.02)
- self.apply(self._init_weights)
- self.fix_init_weight()
-
- if isinstance(self.head, nn.Linear):
- self.head.weight.data.mul_(init_scale)
- self.head.bias.data.mul_(init_scale)
-
- 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=.02)
- 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)
-
- def get_num_layers(self):
- return len(self.blocks)
-
- @torch.jit.ignore
- def no_weight_decay(self):
- return {'pos_embed', 'cls_token'}
-
- def get_classifier(self):
- return self.head
-
- def reset_classifier(self, num_classes, global_pool=''):
- self.num_classes = num_classes
- self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
-
- def forward_features(self, x):
- x = self.patch_embed(x)
- batch_size, seq_len, _ = x.size()
-
- cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- 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)
-
- x = self.norm(x)
- if self.fc_norm is not None:
- t = x[:, 1:, :]
- return self.fc_norm(t.mean(1))
- else:
- return x[:, 0]
-
- def forward(self, x):
- x = self.forward_features(x)
- x = self.head(x)
- return x
-
- def get_intermediate_layers(self, x):
- x = self.patch_embed(x)
- batch_size, seq_len, _ = x.size()
-
- cls_tokens = self.cls_token.expand(batch_size, -1, -1) # stole cls_tokens impl from Phil Wang, thanks
- 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)
-
- features = []
- 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)
- features.append(x)
-
- return features
-
-
- @register_model
- def beit_base_patch16_224(pretrained=False, **kwargs):
- model = VisionTransformer(
- 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), **kwargs)
- model.default_cfg = _cfg()
- return model
-
-
- @register_model
- def beit_base_patch16_384(pretrained=False, **kwargs):
- model = VisionTransformer(
- img_size=384, 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), **kwargs)
- model.default_cfg = _cfg()
- return model
-
-
- @register_model
- def beit_large_patch16_224(pretrained=False, **kwargs):
- model = VisionTransformer(
- 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), **kwargs)
- model.default_cfg = _cfg()
- return model
-
-
- @register_model
- def beit_large_patch16_384(pretrained=False, **kwargs):
- model = VisionTransformer(
- img_size=384, 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), **kwargs)
- model.default_cfg = _cfg()
- return model
-
-
- @register_model
- def beit_large_patch16_512(pretrained=False, **kwargs):
- model = VisionTransformer(
- img_size=512, 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), **kwargs)
- model.default_cfg = _cfg()
- return model
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