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"""MetricAnything DepthMap model."""
from __future__ import annotations
from dataclasses import dataclass
from pathlib import Path
from typing import IO, Any, Dict, Mapping, Optional, Union
import torch
from torch import nn
from network.decoder import MultiresConvDecoder
from network.encoder import MetricAnythingEncoder
from network.vit_factory import VIT_CONFIG_DICT, ViTConfig, ViTPreset, create_vit
@dataclass(frozen=True)
class MetricAnythingConfig:
"""Configuration for MetricAnything DepthMap."""
patch_encoder_preset: ViTPreset
decoder_features: int
DEFAULT_CONFIG = MetricAnythingConfig(
patch_encoder_preset="dinov3_vith16plus_224",
decoder_features=256,
)
def _create_backbone(preset: ViTPreset) -> tuple[nn.Module, ViTConfig]:
"""Load a ViT backbone and its preset config."""
if preset not in VIT_CONFIG_DICT:
raise KeyError(f"Unknown ViT preset: {preset}")
return create_vit(preset), VIT_CONFIG_DICT[preset]
def create_model(
config: MetricAnythingConfig = DEFAULT_CONFIG,
device: torch.device | str | int = "cpu",
) -> "MetricAnythingDepthMap":
"""Build the MetricAnything DepthMap model."""
patch_encoder, patch_cfg = _create_backbone(config.patch_encoder_preset)
encoder = MetricAnythingEncoder(
dims_encoder=patch_cfg.encoder_feature_dims,
patch_encoder=patch_encoder,
hook_block_ids=patch_cfg.encoder_feature_layer_ids,
)
decoder_dims = (
[config.decoder_features]
+ [encoder.dims_encoder[0]] * 2
+ list(encoder.dims_encoder)
)
decoder = MultiresConvDecoder(dims_encoder=decoder_dims, dim_decoder=config.decoder_features)
return MetricAnythingDepthMap(
encoder=encoder,
decoder=decoder,
last_dims=(32, 1),
).to(device)
class MetricAnythingDepthMap(nn.Module):
"""MetricAnything DepthMap network."""
def __init__(
self,
encoder: MetricAnythingEncoder,
decoder: MultiresConvDecoder,
last_dims: tuple[int, int],
) -> None:
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.head = self._build_head(decoder.dim_decoder, last_dims)
# Initialize the final conv bias for stable depth scaling.
self.head[-2].bias.data.fill_(0)
@staticmethod
def _build_head(dim_decoder: int, last_dims: tuple[int, int]) -> nn.Sequential:
layers: list[nn.Module] = [
nn.Conv2d(dim_decoder, dim_decoder // 2, kernel_size=3, stride=1, padding=1),
nn.ConvTranspose2d(
in_channels=dim_decoder // 2,
out_channels=dim_decoder // 2,
kernel_size=2,
stride=2,
padding=0,
bias=True,
),
nn.Conv2d(dim_decoder // 2, last_dims[0], kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
]
# Extra refinement layers at the final resolution.
for _ in range(4):
layers += [
nn.Conv2d(last_dims[0], last_dims[0], kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
]
layers += [
nn.Conv2d(last_dims[0], last_dims[1], kernel_size=1, stride=1, padding=0),
nn.ReLU(inplace=True),
]
return nn.Sequential(*layers)
@property
def img_size(self) -> int:
"""Network input resolution."""
return self.encoder.img_size
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Predict canonical inverse depth at the network resolution."""
_, _, height, width = x.shape
assert height == self.img_size and width == self.img_size
encodings = self.encoder(x)
features, _ = self.decoder(encodings)
return self.head(features)
@torch.no_grad()
def infer(
self,
x: torch.Tensor,
f_px: float | torch.Tensor | None = None,
interpolation_mode: str = "bilinear",
) -> Mapping[str, torch.Tensor]:
"""Infer metric depth for an input image tensor."""
if x.ndim == 3:
x = x.unsqueeze(0)
_, _, height, width = x.shape
resize = height != self.img_size or width != self.img_size
if resize:
x = nn.functional.interpolate(
x,
size=(self.img_size, self.img_size),
mode=interpolation_mode,
align_corners=False,
)
canonical_inverse_depth = self.forward(x)
if f_px is None:
f_px = 1000
inverse_depth = canonical_inverse_depth * (width / f_px)
if resize:
inverse_depth = nn.functional.interpolate(
inverse_depth,
size=(height, width),
mode=interpolation_mode,
align_corners=False,
)
depth = 1.0 / torch.clamp(inverse_depth, min=1e-3, max=1e3)
return {"depth": depth.squeeze()}
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, Path, IO[bytes]],
model_kwargs: Optional[Dict[str, Any]] = None,
**hf_kwargs: Any,
) -> "MetricAnythingDepthMap":
"""Load weights from a local path or a Hugging Face Hub repo."""
model_kwargs = dict(model_kwargs or {})
config = model_kwargs.pop("config", DEFAULT_CONFIG)
device = model_kwargs.pop("device", "cpu")
strict = model_kwargs.pop("strict", True)
weights_only = model_kwargs.pop("weights_only", True)
def _resolve_map_location(value: Any) -> torch.device | str:
if isinstance(value, int):
return torch.device(f"cuda:{value}") if torch.cuda.is_available() else torch.device("cpu")
return value
map_location = _resolve_map_location(model_kwargs.pop("map_location", device))
if isinstance(pretrained_model_name_or_path, (str, Path)):
path = Path(pretrained_model_name_or_path)
if path.exists():
checkpoint_path = path
else:
try:
from huggingface_hub import hf_hub_download
except ImportError as exc:
raise ImportError(
"huggingface_hub is required for loading from the Hub. "
"Install it with `pip install huggingface_hub`."
) from exc
filename = hf_kwargs.pop("filename", "model.pt")
checkpoint_path = hf_hub_download(
repo_id=str(pretrained_model_name_or_path),
repo_type="model",
filename=filename,
**hf_kwargs,
)
checkpoint = torch.load(checkpoint_path, map_location=map_location, weights_only=weights_only)
else:
checkpoint = torch.load(pretrained_model_name_or_path, map_location=map_location, weights_only=weights_only)
if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
model = create_model(config=config, device=device)
model.load_state_dict(checkpoint, strict=strict)
return model
__all__ = [
"MetricAnythingConfig",
"DEFAULT_CONFIG",
"MetricAnythingDepthMap",
"create_model",
]
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