# Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import math from functools import partial import torch import torch.nn as nn from src.masks.utils import apply_masks from src.models.utils.modules import Block from src.models.utils.pos_embs import get_2d_sincos_pos_embed, get_3d_sincos_pos_embed from src.utils.tensors import repeat_interleave_batch, trunc_normal_ class VisionTransformerPredictor(nn.Module): """Vision Transformer""" def __init__( self, img_size=(224, 224), patch_size=16, num_frames=1, tubelet_size=2, embed_dim=768, predictor_embed_dim=384, out_embed_dim=None, depth=6, num_heads=12, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=nn.LayerNorm, init_std=0.02, uniform_power=False, use_mask_tokens=False, num_mask_tokens=2, zero_init_mask_tokens=True, use_silu=False, wide_silu=True, use_activation_checkpointing=False, return_all_tokens=False, chop_last_n_tokens=0, use_rope=False, **kwargs ): super().__init__() self.return_all_tokens = return_all_tokens self.chop_last_n_tokens = chop_last_n_tokens # Map input to predictor dimension self.predictor_embed = nn.Linear(embed_dim, predictor_embed_dim, bias=True) # Mask tokens self.mask_tokens = None self.num_mask_tokens = 0 if use_mask_tokens: self.num_mask_tokens = num_mask_tokens self.mask_tokens = nn.ParameterList( [nn.Parameter(torch.zeros(1, 1, predictor_embed_dim)) for i in range(num_mask_tokens)] ) # Determine positional embedding if type(img_size) is int: img_size = (img_size, img_size) self.img_height, self.img_width = img_size self.patch_size = patch_size # -- self.num_frames = num_frames self.tubelet_size = tubelet_size self.is_video = num_frames > 1 self.grid_height = img_size[0] // self.patch_size self.grid_width = img_size[1] // self.patch_size self.grid_depth = num_frames // self.tubelet_size self.use_activation_checkpointing = use_activation_checkpointing dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule if self.is_video: self.num_patches = num_patches = ( (num_frames // tubelet_size) * (img_size[0] // patch_size) * (img_size[1] // patch_size) ) else: self.num_patches = num_patches = (img_size[0] // patch_size) * (img_size[1] // patch_size) # Position embedding self.uniform_power = uniform_power self.predictor_pos_embed = None if not use_rope: self.predictor_pos_embed = nn.Parameter( torch.zeros(1, num_patches, predictor_embed_dim), requires_grad=False ) # Attention Blocks self.use_rope = use_rope self.predictor_blocks = nn.ModuleList( [ Block( use_rope=use_rope, grid_size=self.grid_height, grid_depth=self.grid_depth, dim=predictor_embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, act_layer=nn.SiLU if use_silu else nn.GELU, wide_silu=wide_silu, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, ) for i in range(depth) ] ) if out_embed_dim is None: teacher_embed_dim = kwargs.get("teacher_embed_dim", None) if teacher_embed_dim is not None: out_embed_dim = teacher_embed_dim else: out_embed_dim = embed_dim # Normalize & project back to input dimension self.predictor_norm = norm_layer(predictor_embed_dim) self.predictor_proj = nn.Linear(predictor_embed_dim, out_embed_dim, bias=True) # ------ initialize weights if self.predictor_pos_embed is not None: self._init_pos_embed(self.predictor_pos_embed.data) # sincos pos-embed self.init_std = init_std if not zero_init_mask_tokens: for mt in self.mask_tokens: trunc_normal_(mt, std=init_std) self.apply(self._init_weights) self._rescale_blocks() def _init_pos_embed(self, pos_embed): embed_dim = pos_embed.size(-1) grid_size = self.img_height // self.patch_size # TODO: update; currently assumes square input if self.is_video: grid_depth = self.num_frames // self.tubelet_size sincos = get_3d_sincos_pos_embed( embed_dim, grid_size, grid_depth, cls_token=False, uniform_power=self.uniform_power ) else: sincos = get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False) pos_embed.copy_(torch.from_numpy(sincos).float().unsqueeze(0)) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=self.init_std) 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 _rescale_blocks(self): def rescale(param, layer_id): param.div_(math.sqrt(2.0 * layer_id)) for layer_id, layer in enumerate(self.predictor_blocks): rescale(layer.attn.proj.weight.data, layer_id + 1) rescale(layer.mlp.fc2.weight.data, layer_id + 1) def forward(self, x, masks_x, masks_y, mask_index=1, has_cls=False): """ :param x: context tokens :param masks_x: indices of context tokens in input :params masks_y: indices of target tokens in input """ assert (masks_x is not None) and (masks_y is not None), "Cannot run predictor without mask indices" if not isinstance(masks_x, list): masks_x = [masks_x] if not isinstance(masks_y, list): masks_y = [masks_y] # Batch Size B = len(x) // len(masks_x) # Map context tokens to predictor dimensions x = self.predictor_embed(x) if has_cls: x_cls = x[:, :1, :] x = x[:, 1:, :] _, N_ctxt, D = x.shape # Add positional embedding to ctxt tokens if not self.use_rope: x_pos_embed = self.predictor_pos_embed.repeat(B, 1, 1) x += apply_masks(x_pos_embed, masks_x) # Make target tokens mask_index = mask_index % self.num_mask_tokens pred_tokens = self.mask_tokens[mask_index] pred_tokens = pred_tokens.repeat(B, self.num_patches, 1) pred_tokens = apply_masks(pred_tokens, masks_y) # -- add pos embed if not self.use_rope: pos_embs = self.predictor_pos_embed.repeat(B, 1, 1) pos_embs = apply_masks(pos_embs, masks_y) pos_embs = repeat_interleave_batch(pos_embs, B, repeat=len(masks_x)) pred_tokens += pos_embs # Concatenate context & target tokens x = x.repeat(len(masks_x), 1, 1) x = torch.cat([x, pred_tokens], dim=1) # Positions of context & target tokens masks_x = torch.cat(masks_x, dim=0) masks_y = torch.cat(masks_y, dim=0) masks = torch.cat([masks_x, masks_y], dim=1) # Put tokens in sorted order argsort = torch.argsort(masks, dim=1) # [B, N] masks = torch.stack([masks[i, row] for i, row in enumerate(argsort)], dim=0) x = torch.stack([x[i, row, :] for i, row in enumerate(argsort)], dim=0) # Remove the last n tokens of sorted sequence before processing if self.chop_last_n_tokens > 0: x = x[:, : -self.chop_last_n_tokens] masks = masks[:, : -self.chop_last_n_tokens] if has_cls: x = torch.cat([x_cls, x], dim=1) # Fwd prop for i, blk in enumerate(self.predictor_blocks): if self.use_activation_checkpointing: x = torch.utils.checkpoint.checkpoint(blk, x, masks, None, use_reentrant=False) else: x = blk(x, mask=masks, attn_mask=None) x = self.predictor_norm(x) if has_cls: x = x[:, 1:, :] # Return output corresponding to target tokens if not self.return_all_tokens: reverse_argsort = torch.argsort(argsort, dim=1) # [B, N] x = torch.stack([x[i, row, :] for i, row in enumerate(reverse_argsort)], dim=0) x = x[:, N_ctxt:] x = self.predictor_proj(x) return x def vit_predictor(**kwargs): model = VisionTransformerPredictor( mlp_ratio=4, qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs ) return model