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from copy import deepcopy
from dataclasses import dataclass
from typing import Literal
import torch
from torch import nn
from .croco.blocks import DecoderBlock
from .croco.croco import CroCoNet
from .croco.misc import fill_default_args, freeze_all_params, transpose_to_landscape, is_symmetrized, interleave, \
make_batch_symmetric
from .croco.patch_embed import get_patch_embed
from .backbone import Backbone
from src.geometry.camera_emb import get_intrinsic_embedding
inf = float('inf')
croco_params = {
'ViTLarge_BaseDecoder': {
'enc_depth': 24,
'dec_depth': 12,
'enc_embed_dim': 1024,
'dec_embed_dim': 768,
'enc_num_heads': 16,
'dec_num_heads': 12,
'pos_embed': 'RoPE100',
'img_size': (512, 512),
},
}
default_dust3r_params = {
'enc_depth': 24,
'dec_depth': 12,
'enc_embed_dim': 1024,
'dec_embed_dim': 768,
'enc_num_heads': 16,
'dec_num_heads': 12,
'pos_embed': 'RoPE100',
'patch_embed_cls': 'PatchEmbedDust3R',
'img_size': (512, 512),
'head_type': 'dpt',
'output_mode': 'pts3d',
'depth_mode': ('exp', -inf, inf),
'conf_mode': ('exp', 1, inf)
}
@dataclass
class BackboneCrocoCfg:
name: Literal["croco", "croco_multi"]
model: Literal["ViTLarge_BaseDecoder", "ViTBase_SmallDecoder", "ViTBase_BaseDecoder"] # keep interface for the last two models, but they are not supported
patch_embed_cls: str = 'PatchEmbedDust3R' # PatchEmbedDust3R or ManyAR_PatchEmbed
asymmetry_decoder: bool = True
intrinsics_embed_loc: Literal["encoder", "decoder", "none"] = 'none'
intrinsics_embed_degree: int = 0
intrinsics_embed_type: Literal["pixelwise", "linear", "token"] = 'token' # linear or dpt
class AsymmetricCroCo(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, cfg: BackboneCrocoCfg, d_in: int) -> None:
self.intrinsics_embed_loc = cfg.intrinsics_embed_loc
self.intrinsics_embed_degree = cfg.intrinsics_embed_degree
self.intrinsics_embed_type = cfg.intrinsics_embed_type
self.intrinsics_embed_encoder_dim = 0
self.intrinsics_embed_decoder_dim = 0
if self.intrinsics_embed_loc == 'encoder' and self.intrinsics_embed_type == 'pixelwise':
self.intrinsics_embed_encoder_dim = (self.intrinsics_embed_degree + 1) ** 2 if self.intrinsics_embed_degree > 0 else 3
elif self.intrinsics_embed_loc == 'decoder' and self.intrinsics_embed_type == 'pixelwise':
self.intrinsics_embed_decoder_dim = (self.intrinsics_embed_degree + 1) ** 2 if self.intrinsics_embed_degree > 0 else 3
self.patch_embed_cls = cfg.patch_embed_cls
self.croco_args = fill_default_args(croco_params[cfg.model], CroCoNet.__init__)
super().__init__(**croco_params[cfg.model])
if cfg.asymmetry_decoder:
self.dec_blocks2 = deepcopy(self.dec_blocks) # This is used in DUSt3R and MASt3R
if self.intrinsics_embed_type == 'linear' or self.intrinsics_embed_type == 'token':
self.intrinsic_encoder = nn.Linear(9, 1024)
# self.set_freeze(freeze)
def _set_patch_embed(self, img_size=224, patch_size=16, enc_embed_dim=768, in_chans=3):
in_chans = in_chans + self.intrinsics_embed_encoder_dim
self.patch_embed = get_patch_embed(self.patch_embed_cls, img_size, patch_size, enc_embed_dim, in_chans)
def _set_decoder(self, enc_embed_dim, dec_embed_dim, dec_num_heads, dec_depth, mlp_ratio, norm_layer, norm_im2_in_dec):
self.dec_depth = dec_depth
self.dec_embed_dim = dec_embed_dim
# transfer from encoder to decoder
enc_embed_dim = enc_embed_dim + self.intrinsics_embed_decoder_dim
self.decoder_embed = nn.Linear(enc_embed_dim, dec_embed_dim, bias=True)
# transformer for the decoder
self.dec_blocks = nn.ModuleList([
DecoderBlock(dec_embed_dim, dec_num_heads, mlp_ratio=mlp_ratio, qkv_bias=True, norm_layer=norm_layer, norm_mem=norm_im2_in_dec, rope=self.rope)
for i in range(dec_depth)])
# final norm layer
self.dec_norm = norm_layer(dec_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
assert freeze in ['none', 'mask', 'encoder'], f"unexpected freeze={freeze}"
to_be_frozen = {
'none': [],
'mask': [self.mask_token],
'encoder': [self.mask_token, self.patch_embed, self.enc_blocks],
'encoder_decoder': [self.mask_token, self.patch_embed, self.enc_blocks, self.enc_norm, self.decoder_embed, self.dec_blocks, self.dec_blocks2, self.dec_norm],
}
freeze_all_params(to_be_frozen[freeze])
def _set_prediction_head(self, *args, **kwargs):
""" No prediction head """
return
def _encode_image(self, image, true_shape, intrinsics_embed=None):
# embed the image into patches (x has size B x Npatches x C)
x, pos = self.patch_embed(image, true_shape=true_shape)
if intrinsics_embed is not None:
if self.intrinsics_embed_type == 'linear':
x = x + intrinsics_embed
elif self.intrinsics_embed_type == 'token':
x = torch.cat((x, intrinsics_embed), dim=1)
add_pose = pos[:, 0:1, :].clone()
add_pose[:, :, 0] += (pos[:, -1, 0].unsqueeze(-1) + 1)
pos = torch.cat((pos, add_pose), dim=1)
# 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:
x = blk(x, pos)
x = self.enc_norm(x)
return x, pos, None
def _encode_image_pairs(self, img1, img2, true_shape1, true_shape2, intrinsics_embed1=None, intrinsics_embed2=None):
if img1.shape[-2:] == img2.shape[-2:]:
out, pos, _ = self._encode_image(torch.cat((img1, img2), dim=0),
torch.cat((true_shape1, true_shape2), dim=0),
torch.cat((intrinsics_embed1, intrinsics_embed2), dim=0) if intrinsics_embed1 is not None else None)
out, out2 = out.chunk(2, dim=0)
pos, pos2 = pos.chunk(2, dim=0)
else:
out, pos, _ = self._encode_image(img1, true_shape1, intrinsics_embed1)
out2, pos2, _ = self._encode_image(img2, true_shape2, intrinsics_embed2)
return out, out2, pos, pos2
def _encode_symmetrized(self, view1, view2, force_asym=False):
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
intrinsics_embed1 = view1.get('intrinsics_embed', None)
intrinsics_embed2 = view2.get('intrinsics_embed', None)
if force_asym or not is_symmetrized(view1, view2):
feat1, feat2, pos1, pos2 = self._encode_image_pairs(img1, img2, shape1, shape2, intrinsics_embed1, intrinsics_embed2)
else:
# 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)
return (shape1, shape2), (feat1, feat2), (pos1, pos2)
def _decoder(self, f1, pos1, f2, pos2, extra_embed1=None, extra_embed2=None):
final_output = [(f1, f2)] # before projection
if extra_embed1 is not None:
f1 = torch.cat((f1, extra_embed1), dim=-1)
if extra_embed2 is not None:
f2 = torch.cat((f2, extra_embed2), dim=-1)
# project to decoder dim
f1 = self.decoder_embed(f1)
f2 = self.decoder_embed(f2)
final_output.append((f1, f2))
for blk1, blk2 in zip(self.dec_blocks, self.dec_blocks2):
# img1 side
f1, _ = blk1(*final_output[-1][::+1], pos1, pos2)
# img2 side
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]
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,
context: dict,
symmetrize_batch=False,
return_views=False,
):
b, v, _, h, w = context["image"].shape
device = context["image"].device
view1, view2 = ({'img': context["image"][:, 0]},
{'img': context["image"][:, 1]})
# camera embedding in the encoder
if self.intrinsics_embed_loc == 'encoder' and self.intrinsics_embed_type == 'pixelwise':
intrinsic_emb = get_intrinsic_embedding(context, degree=self.intrinsics_embed_degree)
view1['img'] = torch.cat((view1['img'], intrinsic_emb[:, 0]), dim=1)
view2['img'] = torch.cat((view2['img'], intrinsic_emb[:, 1]), dim=1)
if self.intrinsics_embed_loc == 'encoder' and (self.intrinsics_embed_type == 'token' or self.intrinsics_embed_type == 'linear'):
intrinsic_embedding = self.intrinsic_encoder(context["intrinsics"].flatten(2))
view1['intrinsics_embed'] = intrinsic_embedding[:, 0].unsqueeze(1)
view2['intrinsics_embed'] = intrinsic_embedding[:, 1].unsqueeze(1)
if symmetrize_batch:
instance_list_view1, instance_list_view2 = [0 for _ in range(b)], [1 for _ in range(b)]
view1['instance'] = instance_list_view1
view2['instance'] = instance_list_view2
view1['idx'] = instance_list_view1
view2['idx'] = instance_list_view2
view1, view2 = make_batch_symmetric(view1, view2)
# encode the two images --> B,S,D
(shape1, shape2), (feat1, feat2), (pos1, pos2) = self._encode_symmetrized(view1, view2, force_asym=False)
else:
# encode the two images --> B,S,D
(shape1, shape2), (feat1, feat2), (pos1, pos2) = self._encode_symmetrized(view1, view2, force_asym=True)
if self.intrinsics_embed_loc == 'decoder':
# FIXME: downsample is hardcoded to 16
intrinsic_emb = get_intrinsic_embedding(context, degree=self.intrinsics_embed_degree, downsample=16, merge_hw=True)
dec1, dec2 = self._decoder(feat1, pos1, feat2, pos2, intrinsic_emb[:, 0], intrinsic_emb[:, 1])
else:
dec1, dec2 = self._decoder(feat1, pos1, feat2, pos2)
if self.intrinsics_embed_loc == 'encoder' and self.intrinsics_embed_type == 'token':
dec1, dec2 = list(dec1), list(dec2)
for i in range(len(dec1)):
dec1[i] = dec1[i][:, :-1]
dec2[i] = dec2[i][:, :-1]
if return_views:
return dec1, dec2, shape1, shape2, view1, view2
return dec1, dec2, shape1, shape2
@property
def patch_size(self) -> int:
return 16
@property
def d_out(self) -> int:
return 1024
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