import torch import torch.nn.functional as F import torch.nn as nn from scipy import interpolate import numpy as np from einops import rearrange, repeat def _init_transformer_weights(module, initializer_range=0.02): """Initialize the weights. Copied from transformers ViT/Bert model init""" if isinstance(module, (nn.Linear, nn.Conv2d)): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) def interpolate_pos_embed(pos_embed_old, pos_embed_new, num_patches_new): """ Args: pos_embed_old: (1, L_old, d), pre-trained pos_embed_new: (1, L_new, d), newly initialized, to be replaced by interpolated weights num_patches_new: """ # interpolate position embedding embedding_size = pos_embed_old.shape[-1] num_extra_tokens = pos_embed_new.shape[-2] - num_patches_new # height (== width) for the checkpoint position embedding orig_size = int((pos_embed_old.shape[-2] - num_extra_tokens) ** 0.5) # height (== width) for the new position embedding new_size = int(num_patches_new ** 0.5) if orig_size != new_size: # class_token and dist_token are kept unchanged # the extra tokens seems always at the beginning of the position embedding extra_tokens = pos_embed_old[:, :num_extra_tokens] # only the position tokens are interpolated pos_tokens = pos_embed_old[:, 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) interpolated_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) return interpolated_pos_embed else: return pos_embed_old def interpolate_pos_relative_bias_beit(state_dict_old, state_dict_new, patch_shape_new): """ Args: state_dict_old: loaded state dict state_dict_new: state dict for model with new image size patch_shape_new: new model patch_shape ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py """ all_keys = list(state_dict_old.keys()) for key in all_keys: if "relative_position_index" in key: state_dict_old.pop(key) if "relative_position_bias_table" in key: rel_pos_bias = state_dict_old[key] src_num_pos, num_attn_heads = rel_pos_bias.size() dst_num_pos, _ = state_dict_new[key].size() dst_patch_shape = patch_shape_new 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: 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) 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) state_dict_old[key] = new_rel_pos_bias return state_dict_old def interpolate_pos_relative_bias_beit_3d(state_dict_old, state_dict_new, patch_shape_new, src_t_size=1): """ Args: state_dict_old: loaded state dict state_dict_new: state dict for model with new image size patch_shape_new: new model patch_shape ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py """ all_keys = list(state_dict_old.keys()) for key in all_keys: if "relative_position_index" in key: state_dict_old.pop(key) if "relative_position_bias_table" in key: src_num_pos, num_attn_heads = state_dict_old[key].size() dst_num_pos, _ = state_dict_new[key].size() if src_num_pos == dst_num_pos: continue num_extra_tokens = dst_num_pos - np.prod([w * 2 - 1 for w in patch_shape_new]) src_s_size = int((src_num_pos - num_extra_tokens) / src_t_size) src_size = int(src_s_size ** 0.5) dst_size = patch_shape_new[-1] * 2 - 1 if src_size != dst_size: # Spatial interpolation rel_pos_bias = state_dict_old[key] 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) 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) state_dict_old[key] = new_rel_pos_bias dst_t_size = patch_shape_new[0] * 2 - 1 if src_t_size != dst_t_size: # Temporal interpolation rel_pos_bias = state_dict_old[key] extra_tokens = rel_pos_bias[-num_extra_tokens:, :] rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :] if src_t_size == 1: rel_pos_bias = repeat(rel_pos_bias, 's d -> (t s) d', t=dst_t_size) else: rel_pos_bias = rearrange(rel_pos_bias, '(t s) d -> s d t', t=src_t_size) rel_pos_bias = F.interpolate(rel_pos_bias, dst_t_size, mode='nearest') rel_pos_bias = rearrange(rel_pos_bias, 's d t -> (t s) d') new_rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0) state_dict_old[key] = new_rel_pos_bias return state_dict_old def tile(x, dim, n_tile): init_dim = x.size(dim) repeat_idx = [1] * x.dim() repeat_idx[dim] = n_tile x = x.repeat(*repeat_idx) order_index = torch.LongTensor(np.concatenate( [init_dim * np.arange(n_tile) + i for i in range(init_dim)])) return torch.index_select(x, dim, order_index.to(x.device)) def mask_logits(target, mask): return target * mask + (1 - mask) * (-1e10)