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# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# modified from DUSt3R
import torch
import dust3r.utils.path_to_croco # noqa: F401
from models.blocks import PatchEmbed # noqa
def get_patch_embed(patch_embed_cls, img_size, patch_size, enc_embed_dim, in_chans=3):
assert patch_embed_cls in ["PatchEmbedDust3R", "ManyAR_PatchEmbed"]
patch_embed = eval(patch_embed_cls)(img_size, patch_size, in_chans, enc_embed_dim)
return patch_embed
class PatchEmbedDust3R(PatchEmbed):
def forward(self, x, **kw):
B, C, H, W = x.shape
assert (
H % self.patch_size[0] == 0
), f"Input image height ({H}) is not a multiple of patch size ({self.patch_size[0]})."
assert (
W % self.patch_size[1] == 0
), f"Input image width ({W}) is not a multiple of patch size ({self.patch_size[1]})."
x = self.proj(x)
pos = self.position_getter(B, x.size(2), x.size(3), x.device)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x, pos
class ManyAR_PatchEmbed(PatchEmbed):
"""Handle images with non-square aspect ratio.
All images in the same batch have the same aspect ratio.
true_shape = [(height, width) ...] indicates the actual shape of each image.
"""
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
embed_dim=768,
norm_layer=None,
flatten=True,
):
self.embed_dim = embed_dim
super().__init__(img_size, patch_size, in_chans, embed_dim, norm_layer, flatten)
def forward(self, img, true_shape):
B, C, H, W = img.shape
assert (
H % self.patch_size[0] == 0
), f"Input image height ({H}) is not a multiple of patch size ({self.patch_size[0]})."
assert (
W % self.patch_size[1] == 0
), f"Input image width ({W}) is not a multiple of patch size ({self.patch_size[1]})."
assert true_shape.shape == (
B,
2,
), f"true_shape has the wrong shape={true_shape.shape}"
W //= self.patch_size[0]
H //= self.patch_size[1]
n_tokens = H * W
height, width = true_shape.T
is_landscape = torch.ones_like(width, dtype=torch.bool)
is_portrait = ~is_landscape
x = img.new_zeros((B, n_tokens, self.embed_dim))
pos = img.new_zeros((B, n_tokens, 2), dtype=torch.int64)
x[is_landscape] = (
self.proj(img[is_landscape]).permute(0, 2, 3, 1).flatten(1, 2).float()
)
x[is_portrait] = (
self.proj(img[is_portrait].swapaxes(-1, -2))
.permute(0, 2, 3, 1)
.flatten(1, 2)
.float()
)
pos[is_landscape] = self.position_getter(1, H, W, pos.device)
pos[is_portrait] = self.position_getter(1, W, H, pos.device)
x = self.norm(x)
return x, pos
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