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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 | |
| from scipy import interpolate | |
| import numpy as np | |
| 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 > 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) | |
| self.apply(self._init_weights) | |
| self.fix_init_weight() | |
| if num_classes > 0: | |
| trunc_normal_(self.head.weight, std=.02) | |
| 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) | |
| 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 | |
| #@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 | |
| def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"): | |
| missing_keys = [] | |
| unexpected_keys = [] | |
| error_msgs = [] | |
| # copy state_dict so _load_from_state_dict can modify it | |
| metadata = getattr(state_dict, '_metadata', None) | |
| state_dict = state_dict.copy() | |
| if metadata is not None: | |
| state_dict._metadata = metadata | |
| def _load(module, prefix=''): | |
| local_metadata = {} if metadata is None else metadata.get( | |
| prefix[:-1], {}) | |
| module._load_from_state_dict( | |
| state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs) | |
| for name, child in module._modules.items(): | |
| if child is not None: | |
| _load(child, prefix + name + '.') | |
| _load(model, prefix=prefix) | |
| warn_missing_keys = [] | |
| ignore_missing_keys = [] | |
| for key in missing_keys: | |
| keep_flag = True | |
| for ignore_key in ignore_missing.split('|'): | |
| if ignore_key in key: | |
| keep_flag = False | |
| break | |
| if keep_flag: | |
| warn_missing_keys.append(key) | |
| else: | |
| ignore_missing_keys.append(key) | |
| missing_keys = warn_missing_keys | |
| if len(missing_keys) > 0: | |
| print("Weights of {} not initialized from pretrained model: {}".format( | |
| model.__class__.__name__, missing_keys)) | |
| if len(unexpected_keys) > 0: | |
| print("Weights from pretrained model not used in {}: {}".format( | |
| model.__class__.__name__, unexpected_keys)) | |
| if len(ignore_missing_keys) > 0: | |
| print("Ignored weights of {} not initialized from pretrained model: {}".format( | |
| model.__class__.__name__, ignore_missing_keys)) | |
| if len(error_msgs) > 0: | |
| print('\n'.join(error_msgs)) | |
| def default_pretrained_model(args): | |
| model = beit_base_patch16_224( | |
| pretrained=False, | |
| img_size=args.image_size, | |
| num_classes=0, | |
| drop_rate=0., | |
| drop_path_rate=0.1, | |
| attn_drop_rate=0., | |
| #drop_block_rate=None, | |
| use_mean_pooling=True, | |
| init_scale=0.001, | |
| use_rel_pos_bias=True, | |
| use_abs_pos_emb=False, | |
| init_values=0.1, | |
| ) | |
| #url = 'https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k.pth' | |
| url = 'https://unilm.blob.core.windows.net/beit/beit_base_patch16_224_pt22k_ft22k.pth' | |
| checkpoint = torch.hub.load_state_dict_from_url( | |
| url, map_location='cpu', check_hash=True) | |
| print('Pretrained weights found at {}'.format(url)) | |
| # select key | |
| checkpoint_model = None | |
| for model_key in ['model', 'module']: | |
| if model_key in checkpoint: | |
| checkpoint_model = checkpoint[model_key] | |
| print("Load state_dict by model_key = %s" % model_key) | |
| break | |
| if checkpoint_model is None: | |
| checkpoint_model = checkpoint | |
| # remove head | |
| state_dict = model.state_dict() | |
| for k in ['head.weight', 'head.bias']: | |
| #if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape: | |
| if k in checkpoint_model: | |
| print(f"Removing key {k} from pretrained checkpoint") | |
| del checkpoint_model[k] | |
| # resize rel_pos_bias | |
| if model.use_rel_pos_bias and "rel_pos_bias.relative_position_bias_table" in checkpoint_model: | |
| print("Expand the shared relative position embedding to each transformer block. ") | |
| num_layers = model.get_num_layers() | |
| rel_pos_bias = checkpoint_model["rel_pos_bias.relative_position_bias_table"] | |
| for i in range(num_layers): | |
| checkpoint_model["blocks.%d.attn.relative_position_bias_table" % i] = rel_pos_bias.clone() | |
| checkpoint_model.pop("rel_pos_bias.relative_position_bias_table") | |
| all_keys = list(checkpoint_model.keys()) | |
| for key in all_keys: | |
| if "relative_position_index" in key: | |
| checkpoint_model.pop(key) | |
| if "relative_position_bias_table" in key: | |
| rel_pos_bias = checkpoint_model[key] | |
| src_num_pos, num_attn_heads = rel_pos_bias.size() | |
| dst_num_pos, _ = model.state_dict()[key].size() | |
| dst_patch_shape = model.patch_embed.patch_shape | |
| if dst_patch_shape[0] != dst_patch_shape[1]: | |
| raise NotImplementedError() | |
| num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1) | |
| src_size = int((src_num_pos - num_extra_tokens) ** 0.5) | |
| dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5) | |
| if src_size != dst_size: | |
| print("Position interpolate for %s from %dx%d to %dx%d" % ( | |
| key, src_size, src_size, dst_size, dst_size)) | |
| extra_tokens = rel_pos_bias[-num_extra_tokens:, :] | |
| rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] | |
| def geometric_progression(a, r, n): | |
| return a * (1.0 - r ** n) / (1.0 - r) | |
| left, right = 1.01, 1.5 | |
| while right - left > 1e-6: | |
| q = (left + right) / 2.0 | |
| gp = geometric_progression(1, q, src_size // 2) | |
| if gp > dst_size // 2: | |
| right = q | |
| else: | |
| left = q | |
| # if q > 1.090307: | |
| # q = 1.090307 | |
| dis = [] | |
| cur = 1 | |
| for i in range(src_size // 2): | |
| dis.append(cur) | |
| cur += q ** (i + 1) | |
| r_ids = [-_ for _ in reversed(dis)] | |
| x = r_ids + [0] + dis | |
| y = r_ids + [0] + dis | |
| t = dst_size // 2.0 | |
| dx = np.arange(-t, t + 0.1, 1.0) | |
| dy = np.arange(-t, t + 0.1, 1.0) | |
| print("Original positions = %s" % str(x)) | |
| print("Target positions = %s" % str(dx)) | |
| all_rel_pos_bias = [] | |
| for i in range(num_attn_heads): | |
| z = rel_pos_bias[:, i].view(src_size, src_size).float().numpy() | |
| f = interpolate.interp2d(x, y, z, kind='cubic') | |
| all_rel_pos_bias.append( | |
| torch.Tensor(f(dx, dy)).contiguous().view(-1, 1).to(rel_pos_bias.device)) | |
| rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1) | |
| new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) | |
| checkpoint_model[key] = new_rel_pos_bias | |
| # interpolate position embedding | |
| if 'pos_embed' in checkpoint_model: | |
| pos_embed_checkpoint = checkpoint_model['pos_embed'] | |
| embedding_size = pos_embed_checkpoint.shape[-1] | |
| num_patches = model.patch_embed.num_patches | |
| num_extra_tokens = model.pos_embed.shape[-2] - num_patches | |
| # height (== width) for the checkpoint position embedding | |
| orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) | |
| # height (== width) for the new position embedding | |
| new_size = int(num_patches ** 0.5) | |
| # class_token and dist_token are kept unchanged | |
| if orig_size != new_size: | |
| print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size)) | |
| extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] | |
| # only the position tokens are interpolated | |
| pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] | |
| pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) | |
| pos_tokens = torch.nn.functional.interpolate( | |
| pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) | |
| pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) | |
| new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) | |
| checkpoint_model['pos_embed'] = new_pos_embed | |
| load_state_dict(model, checkpoint_model) | |
| return model | |