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#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.
import torch
from dinov3.eval.dense.depth.utils import cast_to
from .dpt_head import DPTHead
from .encoder import BackboneLayersSet, DinoVisionTransformerWrapper, PatchSizeAdaptationStrategy
class FeaturesToDepth(torch.nn.Module):
def __init__(
self,
min_depth=0.001,
max_depth=80,
bins_strategy="linear",
norm_strategy="linear",
):
"""
Module which converts a feature maps into a depth map
Args:
min_depth (float): minimum depth, used to calibrate the depth range
max_depth (float): maximum depth, used to calibrate the depth range
bins_strategy (str): Choices are 'linear' or 'log', for Uniform or Scale Invariant distributions for depth bins.
See AdaBins [1] for more details.
norm_strategy (str): Choices are 'linear', 'softmax' or 'sigmoid', for the conversion of features to depth logits
scale_up (bool): If true, and only if regression by classification is not used, the result is multiplied by max_depth
Example:
x = depth_model(input_image) # N C H W
- If pure regression (C == 1), depth is obtained by scaling and/or shifting x
- If C > 1, bins are used:
Depth is obtained as a weighted sum of depth bins, where weights are predicted logits. (see AdaBins [1] for more details)
[1] AdaBins: https://github.com/shariqfarooq123/AdaBins
"""
super().__init__()
self.min_depth = min_depth
self.max_depth = max_depth
assert bins_strategy in ["linear", "log"], "Support bins_strategy: linear, log"
assert norm_strategy in ["linear", "softmax", "sigmoid"], "Support norm_strategy: linear, softmax, sigmoid"
self.bins_strategy = bins_strategy
self.norm_strategy = norm_strategy
def forward(self, x):
n_bins = x.shape[1] # N n_bins H W
if n_bins > 1:
if self.bins_strategy == "linear":
bins = torch.linspace(self.min_depth, self.max_depth, n_bins, device=x.device)
elif self.bins_strategy == "log":
bins = torch.linspace(
torch.log(torch.tensor(self.min_depth)),
torch.log(torch.tensor(self.max_depth)),
n_bins,
device=x.device,
)
bins = torch.exp(bins)
# following Adabins, default linear
if self.norm_strategy == "linear":
logit = torch.relu(x)
eps = 0.1
logit = logit + eps
logit = logit / logit.sum(dim=1, keepdim=True)
elif self.norm_strategy == "softmax":
logit = torch.softmax(x, dim=1)
elif self.norm_strategy == "sigmoid":
logit = torch.sigmoid(x)
logit = logit / logit.sum(dim=1, keepdim=True)
output = torch.einsum("ikmn,k->imn", [logit, bins]).unsqueeze(dim=1)
else:
# standard regression
output = torch.relu(x) + self.min_depth
return output
def make_head(
embed_dims: int | list[int],
n_output_channels: int,
use_batchnorm: bool = False,
use_cls_token: bool = False,
# upsample: int = 4,
head_type: str = "linear",
**kwargs,
) -> torch.nn.Module:
if isinstance(embed_dims, int):
embed_dims = [embed_dims]
if head_type == "linear":
raise NotImplementedError
elif head_type == "dpt":
decoder = DPTHead(
in_channels=embed_dims,
n_output_channels=n_output_channels,
readout_type="project" if use_cls_token else "ignore",
use_batchnorm=use_batchnorm,
**kwargs, # TODO add here post_process_channels, n_hidden_channels
)
else:
raise NotImplementedError("only linear and DPT head supported")
return decoder
class EncoderDecoder(torch.nn.Module):
def __init__(
self,
encoder: torch.nn.Module,
decoder: torch.nn.Module,
encoder_dtype=torch.float,
decoder_dtype=torch.float,
):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.encoder_dtype = encoder_dtype
self.decoder_dtype = decoder_dtype
self.is_cuda = torch.cuda.is_available()
def forward(self, x):
with torch.autocast("cuda" if torch.cuda.is_available() else "cpu", enabled=False):
x = x.to(self.encoder_dtype)
x = self.encoder(x)
x = cast_to(x, self.decoder_dtype)
return self.decoder(x)
def build_depther(
backbone: torch.nn.Module,
backbone_out_layers: list[int] | BackboneLayersSet,
n_output_channels: int,
use_backbone_norm: bool = False,
use_batchnorm: bool = False,
use_cls_token: bool = False,
adapt_to_patch_size: PatchSizeAdaptationStrategy = PatchSizeAdaptationStrategy.CENTER_PADDING,
head_type: str = "dpt",
encoder_dtype: torch.dtype = torch.float,
decoder_dtype: torch.dtype = torch.float,
# depth args
min_depth: float = 0.001,
max_depth: float = 10.0,
bins_strategy: str = "linear",
norm_strategy: str = "linear",
**kwargs,
):
encoder = DinoVisionTransformerWrapper(
backbone_model=backbone,
backbone_out_layers=backbone_out_layers,
use_backbone_norm=use_backbone_norm,
adapt_to_patch_size=adapt_to_patch_size,
)
encoder = encoder.to(encoder_dtype)
encoder.eval()
decoder = make_head(
encoder.embed_dims,
n_output_channels=n_output_channels,
use_batchnorm=use_batchnorm,
use_cls_token=use_cls_token,
head_type=head_type,
**kwargs,
)
decoder.eval()
features_to_depth = FeaturesToDepth(
min_depth=min_depth,
max_depth=max_depth,
bins_strategy=bins_strategy,
norm_strategy=norm_strategy,
)
return torch.nn.Sequential(
EncoderDecoder(
encoder,
decoder,
encoder_dtype=encoder_dtype,
decoder_dtype=decoder_dtype,
),
features_to_depth,
)
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