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# Copyright (C) 2024 Apple Inc. All Rights Reserved.
# Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
from __future__ import annotations
from dataclasses import dataclass
from typing import Mapping, Optional, Tuple, Union
import sys
#sys.path.append('/home/lipeng/ljh_code/Video_Depth_CVPR2025-main/ml-depth-pro')
import torch
from torch import nn
from torchvision.transforms import (
Compose,
ConvertImageDtype,
Lambda,
Normalize,
ToTensor,
)
from .network.decoder import MultiresConvDecoder
from .network.encoder import DepthProEncoder
from .network.fov import FOVNetwork
from .network.vit_factory import VIT_CONFIG_DICT, ViTPreset, create_vit
@dataclass
class DepthProConfig:
"""Configuration for DepthPro."""
patch_encoder_preset: ViTPreset
image_encoder_preset: ViTPreset
decoder_features: int
checkpoint_uri: Optional[str] = None
fov_encoder_preset: Optional[ViTPreset] = None
use_fov_head: bool = True
DEFAULT_MONODEPTH_CONFIG_DICT = DepthProConfig(
patch_encoder_preset="dinov2l16_384",
image_encoder_preset="dinov2l16_384",
checkpoint_uri="./third_party/ml-depth-pro/checkpoints/depth_pro.pt",
decoder_features=256,
use_fov_head=True,
fov_encoder_preset="dinov2l16_384",
)
def create_backbone_model(
preset: ViTPreset
) -> Tuple[nn.Module, ViTPreset]:
"""Create and load a backbone model given a config.
Args:
----
preset: A backbone preset to load pre-defind configs.
Returns:
-------
A Torch module and the associated config.
"""
if preset in VIT_CONFIG_DICT:
config = VIT_CONFIG_DICT[preset]
model = create_vit(preset=preset, use_pretrained=False)
else:
raise KeyError(f"Preset {preset} not found.")
return model, config
def create_model_and_transforms(
config: DepthProConfig = DEFAULT_MONODEPTH_CONFIG_DICT,
device: torch.device = torch.device("cpu"),
precision: torch.dtype = torch.float32,
) -> Tuple[DepthPro, Compose]:
"""Create a DepthPro model and load weights from `config.checkpoint_uri`.
Args:
----
config: The configuration for the DPT model architecture.
device: The optional Torch device to load the model onto, default runs on "cpu".
precision: The optional precision used for the model, default is FP32.
Returns:
-------
The Torch DepthPro model and associated Transform.
"""
patch_encoder, patch_encoder_config = create_backbone_model(
preset=config.patch_encoder_preset
)
image_encoder, _ = create_backbone_model(
preset=config.image_encoder_preset
)
fov_encoder = None
if config.use_fov_head and config.fov_encoder_preset is not None:
fov_encoder, _ = create_backbone_model(preset=config.fov_encoder_preset)
dims_encoder = patch_encoder_config.encoder_feature_dims
hook_block_ids = patch_encoder_config.encoder_feature_layer_ids
encoder = DepthProEncoder(
dims_encoder=dims_encoder,
patch_encoder=patch_encoder,
image_encoder=image_encoder,
hook_block_ids=hook_block_ids,
decoder_features=config.decoder_features,
)
decoder = MultiresConvDecoder(
dims_encoder=[config.decoder_features] + list(encoder.dims_encoder),
dim_decoder=config.decoder_features,
)
model = DepthPro(
encoder=encoder,
decoder=decoder,
last_dims=(32, 1),
use_fov_head=config.use_fov_head,
fov_encoder=fov_encoder,
).to(device)
if precision == torch.half:
model.half()
transform = Compose(
[
ToTensor(),
Lambda(lambda x: x.to(device)),
Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
ConvertImageDtype(precision),
]
)
if config.checkpoint_uri is not None:
state_dict = torch.load(config.checkpoint_uri, map_location="cpu")
missing_keys, unexpected_keys = model.load_state_dict(
state_dict=state_dict, strict=True
)
if len(unexpected_keys) != 0:
raise KeyError(
f"Found unexpected keys when loading monodepth: {unexpected_keys}"
)
# fc_norm is only for the classification head,
# which we would not use. We only use the encoding.
missing_keys = [key for key in missing_keys if "fc_norm" not in key]
if len(missing_keys) != 0:
raise KeyError(f"Keys are missing when loading monodepth: {missing_keys}")
return model, transform
class DepthPro(nn.Module):
"""DepthPro network."""
def __init__(
self,
encoder: DepthProEncoder,
decoder: MultiresConvDecoder,
last_dims: tuple[int, int],
use_fov_head: bool = True,
fov_encoder: Optional[nn.Module] = None,
):
"""Initialize DepthPro.
Args:
----
encoder: The DepthProEncoder backbone.
decoder: The MultiresConvDecoder decoder.
last_dims: The dimension for the last convolution layers.
use_fov_head: Whether to use the field-of-view head.
fov_encoder: A separate encoder for the field of view.
"""
super().__init__()
self.encoder = encoder
self.decoder = decoder
dim_decoder = decoder.dim_decoder
self.head = nn.Sequential(
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(True),
nn.Conv2d(last_dims[0], last_dims[1], kernel_size=1, stride=1, padding=0),
nn.ReLU(),
)
# Set the final convolution layer's bias to be 0.
self.head[4].bias.data.fill_(0)
# Set the FOV estimation head.
if use_fov_head:
self.fov = FOVNetwork(num_features=dim_decoder, fov_encoder=fov_encoder)
@property
def img_size(self) -> int:
"""Return the internal image size of the network."""
return self.encoder.img_size
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
"""Decode by projection and fusion of multi-resolution encodings.
Args:
----
x (torch.Tensor): Input image.
Returns:
-------
The canonical inverse depth map [m] and the optional estimated field of view [deg].
"""
_, _, H, W = x.shape
assert H == self.img_size and W == self.img_size
encodings = self.encoder(x)
features, features_0 = self.decoder(encodings)
canonical_inverse_depth = self.head(features)
fov_deg = None
if hasattr(self, "fov"):
fov_deg = self.fov.forward(x, features_0.detach())
return canonical_inverse_depth, fov_deg
@torch.no_grad()
def infer(
self,
x: torch.Tensor,
f_px: Optional[Union[float, torch.Tensor]] = None,
interpolation_mode="bilinear",
) -> Mapping[str, torch.Tensor]:
"""Infer depth and fov for a given image.
If the image is not at network resolution, it is resized to 1536x1536 and
the estimated depth is resized to the original image resolution.
Note: if the focal length is given, the estimated value is ignored and the provided
focal length is use to generate the metric depth values.
Args:
----
x (torch.Tensor): Input image
f_px (torch.Tensor): Optional focal length in pixels corresponding to `x`.
interpolation_mode (str): Interpolation function for downsampling/upsampling.
Returns:
-------
Tensor dictionary (torch.Tensor): depth [m], focallength [pixels].
"""
if len(x.shape) == 3:
x = x.unsqueeze(0)
_, _, H, W = x.shape
resize = H != self.img_size or W != 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, fov_deg = self.forward(x)
if f_px is None:
f_px = 0.5 * W / torch.tan(0.5 * torch.deg2rad(fov_deg.to(torch.float)))
inverse_depth = canonical_inverse_depth * (W / f_px)
f_px = f_px.squeeze()
if resize:
inverse_depth = nn.functional.interpolate(
inverse_depth, size=(H, W), mode=interpolation_mode, align_corners=False
)
depth = 1.0 / torch.clamp(inverse_depth, min=1e-4, max=1e4)
return {
"depth": depth.squeeze(),
"focallength_px": f_px,
}
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