# Copyright (C) 2024-present Naver Corporation. All rights reserved. # Licensed under CC BY-NC-SA 4.0 (non-commercial use only). # # -------------------------------------------------------- # DUSt3R model class # -------------------------------------------------------- from copy import deepcopy import torch from .utils.misc import fill_default_args, freeze_all_params, is_symmetrized, interleave, transpose_to_landscape from .heads import head_factory from dust3r.patch_embed import get_patch_embed import dust3r.utils.path_to_croco # noqa: F401 from croco.models.croco import CroCoNet # noqa inf = float('inf') class AsymmetricCroCo3DStereo (CroCoNet): """ Two siamese encoders, followed by two decoders. The goal is to output 3d points directly, both images in view1's frame (hence the asymmetry). """ def __init__(self, output_mode='pts3d', head_type='linear', depth_mode=('exp', -inf, inf), conf_mode=('exp', 1, inf), freeze='none', landscape_only=True, patch_embed_cls='PatchEmbedDust3R', # PatchEmbedDust3R or ManyAR_PatchEmbed **croco_kwargs): self.patch_embed_cls = patch_embed_cls self.croco_args = fill_default_args(croco_kwargs, super().__init__) super().__init__(**croco_kwargs) # dust3rWithSam2 specific initialization self.dec_blocks2 = deepcopy(self.dec_blocks) self.set_downstream_head(output_mode, head_type, landscape_only, depth_mode, conf_mode, **croco_kwargs) self.set_freeze(freeze) def _set_patch_embed(self, img_size=224, patch_size=16, enc_embed_dim=768): self.patch_embed = get_patch_embed(self.patch_embed_cls, img_size, patch_size, enc_embed_dim) def load_state_dict(self, ckpt, **kw): # duplicate all weights for the second decoder if not present new_ckpt = dict(ckpt) if not any(k.startswith('dec_blocks2') for k in ckpt): for key, value in ckpt.items(): if key.startswith('dec_blocks'): new_ckpt[key.replace('dec_blocks', 'dec_blocks2')] = value return super().load_state_dict(new_ckpt, **kw) def set_freeze(self, freeze): # this is for use by downstream models self.freeze = freeze to_be_frozen = { 'none': [], 'mask': [self.mask_token], 'encoder': [self.mask_token, self.patch_embed, self.enc_blocks], } freeze_all_params(to_be_frozen[freeze]) def _set_prediction_head(self, *args, **kwargs): """ No prediction head """ return def set_downstream_head(self, output_mode, head_type, landscape_only, depth_mode, conf_mode, patch_size, img_size, **kw): assert img_size[0] % patch_size == 0 and img_size[1] % patch_size == 0, \ f'{img_size=} must be multiple of {patch_size=}' self.output_mode = output_mode self.head_type = head_type self.depth_mode = depth_mode self.conf_mode = conf_mode # allocate heads self.downstream_head1 = head_factory(head_type, output_mode, self, has_conf=bool(conf_mode)) self.downstream_head2 = head_factory(head_type, output_mode, self, has_conf=bool(conf_mode)) # magic wrapper self.head1 = transpose_to_landscape(self.downstream_head1, activate=landscape_only) self.head2 = transpose_to_landscape(self.downstream_head2, activate=landscape_only) def _encode_image(self, image, true_shape): # image：输入的两张图片在batch维度上连接后的结果 # embed the image into patches (x has size B x Npatches x C) x, pos = self.patch_embed(image, true_shape=true_shape) # 调用PatchEmbedDust3R，进行patch_embedding和位置编码 # add positional embedding without cls token assert self.enc_pos_embed is None # now apply the transformer encoder and normalization for blk in self.enc_blocks: # 一共有24层block的encoder x = blk(x, pos) x = self.enc_norm(x) # LayerNorm return x, pos, None def _encode_image_pairs(self, img1, img2, true_shape1, true_shape2): if img1.shape[-2:] == img2.shape[-2:]: out, pos, _ = self._encode_image(torch.cat((img1, img2), dim=0), # 将两张图片在batch维度上连接 torch.cat((true_shape1, true_shape2), dim=0)) out, out2 = out.chunk(2, dim=0) pos, pos2 = pos.chunk(2, dim=0) else: #******************************* 输入ViT encoder ************************ out, pos, _ = self._encode_image(img1, true_shape1) out2, pos2, _ = self._encode_image(img2, true_shape2) return out, out2, pos, pos2 def _encode_symmetrized(self, view1, view2): img1 = view1['img'] img2 = view2['img'] B = img1.shape[0] # Recover true_shape when available, otherwise assume that the img shape is the true one shape1 = view1.get('true_shape', torch.tensor(img1.shape[-2:])[None].repeat(B, 1)) shape2 = view2.get('true_shape', torch.tensor(img2.shape[-2:])[None].repeat(B, 1)) # warning! maybe the images have different portrait/landscape orientations if is_symmetrized(view1, view2): # computing half of forward pass!' feat1, feat2, pos1, pos2 = self._encode_image_pairs(img1[::2], img2[::2], shape1[::2], shape2[::2]) feat1, feat2 = interleave(feat1, feat2) pos1, pos2 = interleave(pos1, pos2) else: #******************************* 输入ViT encoder ************************ feat1, feat2, pos1, pos2 = self._encode_image_pairs(img1, img2, shape1, shape2) return (shape1, shape2), (feat1, feat2), (pos1, pos2) def _decoder(self, f1, pos1, f2, pos2): final_output = [(f1, f2)] # 来自encoder的两个编码 # 映射前的两个编码 # project to decoder dim # 一个Linear映射层 f1 = self.decoder_embed(f1) # Linear层，channel：1024->768 f2 = self.decoder_embed(f2) final_output.append((f1, f2)) # 映射后的两个编码 for blk1, blk2 in zip(self.dec_blocks, self.dec_blocks2): #dec_blocks2是由dec_blocks deepcopy过来的，所以是一样的 # img1 side，*final_output[-1][::+1]表示输入f1，f2 f1, _ = blk1(*final_output[-1][::+1], pos1, pos2) # img2 side *final_output[-1][::-1]表示输入f2，f1 f2, _ = blk2(*final_output[-1][::-1], pos2, pos1) # store the result final_output.append((f1, f2)) # normalize last output del final_output[1] # duplicate with final_output[0]，即删除 映射后的两个编码 final_output[-1] = tuple(map(self.dec_norm, final_output[-1])) return zip(*final_output) def _downstream_head(self, head_num, decout, img_shape): B, S, D = decout[-1].shape # img_shape = tuple(map(int, img_shape)) head = getattr(self, f'head{head_num}') return head(decout, img_shape) def forward(self, view1, view2): # *****encode the two images --> B,S,D ** 输入ViT encoder ************************ (shape1, shape2), (feat1, feat2), (pos1, pos2) = self._encode_symmetrized(view1, view2) # combine all ref images into object-centric representation **输入decoder************* dec1, dec2 = self._decoder(feat1, pos1, feat2, pos2) with torch.cuda.amp.autocast(enabled=False): #Decoder的结果分别输入 Head1 和 Head2 res1 = self._downstream_head(1, [tok.float() for tok in dec1], shape1) # PixelwiseTaskWithDPT res2 = self._downstream_head(2, [tok.float() for tok in dec2], shape2) res2['pts3d_in_other_view'] = res2.pop('pts3d') # predict view2's pts3d in view1's frame，即res2中的三维点云坐标是在view1的相机坐标系下的 return res1, res2