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| # -------------------------------------------------------- | |
| # Position embedding utils | |
| # -------------------------------------------------------- | |
| from typing import Tuple | |
| import numpy as np | |
| import torch | |
| # -------------------------------------------------------- | |
| # 2D sine-cosine position embedding | |
| # References: | |
| # Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py | |
| # MoCo v3: https://github.com/facebookresearch/moco-v3 | |
| # -------------------------------------------------------- | |
| def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): | |
| """ | |
| grid_size: int of the grid height and width | |
| return: | |
| pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) | |
| """ | |
| grid_h = np.arange(grid_size, dtype=np.float32) | |
| grid_w = np.arange(grid_size, dtype=np.float32) | |
| grid = np.meshgrid(grid_w, grid_h) # here w goes first | |
| grid = np.stack(grid, axis=0) | |
| grid = grid.reshape([2, 1, grid_size, grid_size]) | |
| pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) | |
| if cls_token: | |
| pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) | |
| return pos_embed | |
| def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): | |
| assert embed_dim % 2 == 0 | |
| # use half of dimensions to encode grid_h | |
| emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) | |
| emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) | |
| emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) | |
| return emb | |
| def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): | |
| """ | |
| embed_dim: output dimension for each position | |
| pos: a list of positions to be encoded: size (M,) | |
| out: (M, D) | |
| """ | |
| assert embed_dim % 2 == 0 | |
| omega = np.arange(embed_dim // 2, dtype=np.float) | |
| omega /= embed_dim / 2.0 | |
| omega = 1.0 / 10000 ** omega # (D/2,) | |
| pos = pos.reshape(-1) # (M,) | |
| out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product | |
| emb_sin = np.sin(out) # (M, D/2) | |
| emb_cos = np.cos(out) # (M, D/2) | |
| emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) | |
| return emb | |
| # -------------------------------------------------------- | |
| # Interpolate position embeddings for high-resolution | |
| # References: | |
| # DeiT: https://github.com/facebookresearch/deit | |
| # -------------------------------------------------------- | |
| def interpolate_pos_embed(model, checkpoint_model, pos_embed_key): | |
| if pos_embed_key in checkpoint_model: | |
| pos_embed_checkpoint = checkpoint_model[pos_embed_key] | |
| embedding_size = pos_embed_checkpoint.shape[-1] | |
| num_patches = model.num_patches | |
| if pos_embed_key.startswith("decoder"): | |
| num_extra_tokens = model.decoder_pos_embed.shape[-2] - num_patches | |
| else: | |
| 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_key] = new_pos_embed | |
| def interpolate_pos_embed_online( | |
| pos_embed, orig_size: Tuple[int], new_size: Tuple[int], num_extra_tokens: int | |
| ): | |
| extra_tokens = pos_embed[:, :num_extra_tokens] | |
| pos_tokens = pos_embed[:, num_extra_tokens:] | |
| embedding_size = pos_tokens.shape[-1] | |
| pos_tokens = pos_tokens.reshape( | |
| -1, orig_size[0], orig_size[1], embedding_size | |
| ).permute(0, 3, 1, 2) | |
| pos_tokens = torch.nn.functional.interpolate( | |
| pos_tokens, 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) | |
| return new_pos_embed | |