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# flake8: noqa E501
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Sequence, Tuple
import torch
import torch.nn as nn
from addict import Dict
from depth_anything_3.model.dpt import _make_fusion_block, _make_scratch
from depth_anything_3.model.utils.head_utils import (
Permute,
create_uv_grid,
custom_interpolate,
position_grid_to_embed,
)
class DualDPT(nn.Module):
"""
Dual-head DPT for dense prediction with an always-on auxiliary head.
Architectural notes:
- Sky/object branches are removed.
- `intermediate_layer_idx` is fixed to (0, 1, 2, 3).
- Auxiliary head has its **own** fusion blocks (no fusion_inplace / no sharing).
- Auxiliary head is internally multi-level; **only the final level** is returned.
- Returns a **dict** with keys from `head_names`, e.g.:
{ main_name, f"{main_name}_conf", aux_name, f"{aux_name}_conf" }
- `feature_only` is fixed to False.
"""
def __init__(
self,
dim_in: int,
*,
patch_size: int = 14,
output_dim: int = 2,
activation: str = "exp",
conf_activation: str = "expp1",
features: int = 256,
out_channels: Sequence[int] = (256, 512, 1024, 1024),
pos_embed: bool = True,
down_ratio: int = 1,
aux_pyramid_levels: int = 4,
aux_out1_conv_num: int = 5,
head_names: Tuple[str, str] = ("depth", "ray"),
) -> None:
super().__init__()
# -------------------- configuration --------------------
self.patch_size = patch_size
self.activation = activation
self.conf_activation = conf_activation
self.pos_embed = pos_embed
self.down_ratio = down_ratio
self.aux_levels = aux_pyramid_levels
self.aux_out1_conv_num = aux_out1_conv_num
# names ONLY come from config (no hard-coded strings elsewhere)
self.head_main, self.head_aux = head_names
# Always expect 4 scales; enforce intermediate idx = (0, 1, 2, 3)
self.intermediate_layer_idx: Tuple[int, int, int, int] = (0, 1, 2, 3)
# -------------------- token pre-norm + per-stage projection --------------------
self.norm = nn.LayerNorm(dim_in)
self.projects = nn.ModuleList(
[nn.Conv2d(dim_in, oc, kernel_size=1, stride=1, padding=0) for oc in out_channels]
)
# -------------------- spatial re-sizers (align to common scale before fusion) --------------------
# design: stage strides (x4, x2, x1, /2) relative to patch grid to align to a common pivot scale
self.resize_layers = nn.ModuleList(
[
nn.ConvTranspose2d(
out_channels[0], out_channels[0], kernel_size=4, stride=4, padding=0
),
nn.ConvTranspose2d(
out_channels[1], out_channels[1], kernel_size=2, stride=2, padding=0
),
nn.Identity(),
nn.Conv2d(out_channels[3], out_channels[3], kernel_size=3, stride=2, padding=1),
]
)
# -------------------- scratch: stage adapters + fusion (main & aux are separate) --------------------
self.scratch = _make_scratch(list(out_channels), features, expand=False)
# Main fusion chain (independent)
self.scratch.refinenet1 = _make_fusion_block(features)
self.scratch.refinenet2 = _make_fusion_block(features)
self.scratch.refinenet3 = _make_fusion_block(features)
self.scratch.refinenet4 = _make_fusion_block(features, has_residual=False)
# Primary head neck + head (independent)
head_features_1 = features
head_features_2 = 32
self.scratch.output_conv1 = nn.Conv2d(
head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1
)
self.scratch.output_conv2 = nn.Sequential(
nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(head_features_2, output_dim, kernel_size=1, stride=1, padding=0),
)
# Auxiliary fusion chain (completely separate; no sharing, i.e., "fusion_inplace=False")
self.scratch.refinenet1_aux = _make_fusion_block(features)
self.scratch.refinenet2_aux = _make_fusion_block(features)
self.scratch.refinenet3_aux = _make_fusion_block(features)
self.scratch.refinenet4_aux = _make_fusion_block(features, has_residual=False)
# Aux pre-head per level (we will only *return final level*)
self.scratch.output_conv1_aux = nn.ModuleList(
[self._make_aux_out1_block(head_features_1) for _ in range(self.aux_levels)]
)
# Aux final projection per level
use_ln = True
ln_seq = (
[Permute((0, 2, 3, 1)), nn.LayerNorm(head_features_2), Permute((0, 3, 1, 2))]
if use_ln
else []
)
self.scratch.output_conv2_aux = nn.ModuleList(
[
nn.Sequential(
nn.Conv2d(
head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1
),
*ln_seq,
nn.ReLU(inplace=True),
nn.Conv2d(head_features_2, 7, kernel_size=1, stride=1, padding=0),
)
for _ in range(self.aux_levels)
]
)
# -------------------------------------------------------------------------
# Public forward (supports frame chunking for memory)
# -------------------------------------------------------------------------
def forward(
self,
feats: List[torch.Tensor],
H: int,
W: int,
patch_start_idx: int,
chunk_size: int = 8,
) -> Dict[str, torch.Tensor]:
"""
Args:
aggregated_tokens_list: List of 4 tensors [B, S, T, C] from transformer.
images: [B, S, 3, H, W], in [0, 1].
patch_start_idx: Patch-token start in the token sequence (to drop non-patch tokens).
frames_chunk_size: Optional chunking along S for memory.
Returns:
Dict[str, Tensor] with keys based on `head_names`, e.g.:
self.head_main, f"{self.head_main}_conf",
self.head_aux, f"{self.head_aux}_conf"
Shapes:
main: [B, S, out_dim, H/down_ratio, W/down_ratio]
main_cf: [B, S, 1, H/down_ratio, W/down_ratio]
aux: [B, S, 7, H/down_ratio, W/down_ratio]
aux_cf: [B, S, 1, H/down_ratio, W/down_ratio]
"""
B, S, N, C = feats[0][0].shape
feats = [feat[0].reshape(B * S, N, C) for feat in feats]
if chunk_size is None or chunk_size >= S:
out_dict = self._forward_impl(feats, H, W, patch_start_idx)
out_dict = {k: v.reshape(B, S, *v.shape[1:]) for k, v in out_dict.items()}
return Dict(out_dict)
out_dicts = []
for s0 in range(0, S, chunk_size):
s1 = min(s0 + chunk_size, S)
out_dict = self._forward_impl(
[feat[s0:s1] for feat in feats],
H,
W,
patch_start_idx,
)
out_dicts.append(out_dict)
out_dict = {
k: torch.cat([out_dict[k] for out_dict in out_dicts], dim=0)
for k in out_dicts[0].keys()
}
out_dict = {k: v.view(B, S, *v.shape[1:]) for k, v in out_dict.items()}
return Dict(out_dict)
# -------------------------------------------------------------------------
# Internal forward (single chunk)
# -------------------------------------------------------------------------
def _forward_impl(
self,
feats: List[torch.Tensor],
H: int,
W: int,
patch_start_idx: int,
) -> Dict[str, torch.Tensor]:
B, _, C = feats[0].shape
ph, pw = H // self.patch_size, W // self.patch_size
resized_feats = []
for stage_idx, take_idx in enumerate(self.intermediate_layer_idx):
x = feats[take_idx][:, patch_start_idx:]
x = self.norm(x)
x = x.permute(0, 2, 1).reshape(B, C, ph, pw) # [B*S, C, ph, pw]
x = self.projects[stage_idx](x)
if self.pos_embed:
x = self._add_pos_embed(x, W, H)
x = self.resize_layers[stage_idx](x) # align scales
resized_feats.append(x)
# 2) Fuse pyramid (main & aux are completely independent)
fused_main, fused_aux_pyr = self._fuse(resized_feats)
# 3) Upsample to target resolution and (optional) add pos-embed again
h_out = int(ph * self.patch_size / self.down_ratio)
w_out = int(pw * self.patch_size / self.down_ratio)
fused_main = custom_interpolate(
fused_main, (h_out, w_out), mode="bilinear", align_corners=True
)
if self.pos_embed:
fused_main = self._add_pos_embed(fused_main, W, H)
# Primary head: conv1 -> conv2 -> activate
# fused_main = self.scratch.output_conv1(fused_main)
main_logits = self.scratch.output_conv2(fused_main)
fmap = main_logits.permute(0, 2, 3, 1)
main_pred = self._apply_activation_single(fmap[..., :-1], self.activation)
main_conf = self._apply_activation_single(fmap[..., -1], self.conf_activation)
# Auxiliary head (multi-level inside) -> only last level returned (after activation)
last_aux = fused_aux_pyr[-1]
if self.pos_embed:
last_aux = self._add_pos_embed(last_aux, W, H)
# neck (per-level pre-conv) then final projection (only for last level)
# last_aux = self.scratch.output_conv1_aux[-1](last_aux)
last_aux_logits = self.scratch.output_conv2_aux[-1](last_aux)
fmap_last = last_aux_logits.permute(0, 2, 3, 1)
aux_pred = self._apply_activation_single(fmap_last[..., :-1], "linear")
aux_conf = self._apply_activation_single(fmap_last[..., -1], self.conf_activation)
return {
self.head_main: main_pred.squeeze(-1),
f"{self.head_main}_conf": main_conf,
self.head_aux: aux_pred,
f"{self.head_aux}_conf": aux_conf,
}
# -------------------------------------------------------------------------
# Subroutines
# -------------------------------------------------------------------------
def _fuse(self, feats: List[torch.Tensor]) -> Tuple[torch.Tensor, List[torch.Tensor]]:
"""
Feature pyramid fusion.
Returns:
fused_main: Tensor at finest scale (after refinenet1)
aux_pyr: List of aux tensors at each level (pre out_conv1_aux)
"""
l1, l2, l3, l4 = feats
l1_rn = self.scratch.layer1_rn(l1)
l2_rn = self.scratch.layer2_rn(l2)
l3_rn = self.scratch.layer3_rn(l3)
l4_rn = self.scratch.layer4_rn(l4)
# level 4 -> 3
out = self.scratch.refinenet4(l4_rn, size=l3_rn.shape[2:])
aux_out = self.scratch.refinenet4_aux(l4_rn, size=l3_rn.shape[2:])
aux_list: List[torch.Tensor] = []
if self.aux_levels >= 4:
aux_list.append(aux_out)
# level 3 -> 2
out = self.scratch.refinenet3(out, l3_rn, size=l2_rn.shape[2:])
aux_out = self.scratch.refinenet3_aux(aux_out, l3_rn, size=l2_rn.shape[2:])
if self.aux_levels >= 3:
aux_list.append(aux_out)
# level 2 -> 1
out = self.scratch.refinenet2(out, l2_rn, size=l1_rn.shape[2:])
aux_out = self.scratch.refinenet2_aux(aux_out, l2_rn, size=l1_rn.shape[2:])
if self.aux_levels >= 2:
aux_list.append(aux_out)
# level 1 (final)
out = self.scratch.refinenet1(out, l1_rn)
aux_out = self.scratch.refinenet1_aux(aux_out, l1_rn)
aux_list.append(aux_out)
out = self.scratch.output_conv1(out)
aux_list = [self.scratch.output_conv1_aux[i](aux) for i, aux in enumerate(aux_list)]
return out, aux_list
def _add_pos_embed(self, x: torch.Tensor, W: int, H: int, ratio: float = 0.1) -> torch.Tensor:
"""Simple UV positional embedding added to feature maps."""
pw, ph = x.shape[-1], x.shape[-2]
pe = create_uv_grid(pw, ph, aspect_ratio=W / H, dtype=x.dtype, device=x.device)
pe = position_grid_to_embed(pe, x.shape[1]) * ratio
pe = pe.permute(2, 0, 1)[None].expand(x.shape[0], -1, -1, -1)
return x + pe
def _make_aux_out1_block(self, in_ch: int) -> nn.Sequential:
"""Factory for the aux pre-head stack before the final 1x1 projection."""
if self.aux_out1_conv_num == 5:
return nn.Sequential(
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
nn.Conv2d(in_ch // 2, in_ch, 3, 1, 1),
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
nn.Conv2d(in_ch // 2, in_ch, 3, 1, 1),
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
)
if self.aux_out1_conv_num == 3:
return nn.Sequential(
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
nn.Conv2d(in_ch // 2, in_ch, 3, 1, 1),
nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1),
)
if self.aux_out1_conv_num == 1:
return nn.Sequential(nn.Conv2d(in_ch, in_ch // 2, 3, 1, 1))
raise ValueError(f"aux_out1_conv_num {self.aux_out1_conv_num} not supported")
def _apply_activation_single(
self, x: torch.Tensor, activation: str = "linear"
) -> torch.Tensor:
"""
Apply activation to single channel output, maintaining semantic consistency with value branch in multi-channel case.
Supports: exp / relu / sigmoid / softplus / tanh / linear / expp1
"""
act = activation.lower() if isinstance(activation, str) else activation
if act == "exp":
return torch.exp(x)
if act == "expm1":
return torch.expm1(x)
if act == "expp1":
return torch.exp(x) + 1
if act == "relu":
return torch.relu(x)
if act == "sigmoid":
return torch.sigmoid(x)
if act == "softplus":
return torch.nn.functional.softplus(x)
if act == "tanh":
return torch.tanh(x)
# Default linear
return x
# # -----------------------------------------------------------------------------
# # Building blocks (tidy)
# # -----------------------------------------------------------------------------
# def _make_fusion_block(
# features: int,
# size: Tuple[int, int] = None,
# has_residual: bool = True,
# groups: int = 1,
# inplace: bool = False, # <- activation uses inplace=True by default; not related to "fusion_inplace"
# ) -> nn.Module:
# return FeatureFusionBlock(
# features=features,
# activation=nn.ReLU(inplace=inplace),
# deconv=False,
# bn=False,
# expand=False,
# align_corners=True,
# size=size,
# has_residual=has_residual,
# groups=groups,
# )
# def _make_scratch(
# in_shape: List[int], out_shape: int, groups: int = 1, expand: bool = False
# ) -> nn.Module:
# scratch = nn.Module()
# # optionally expand widths by stage
# c1 = out_shape
# c2 = out_shape * (2 if expand else 1)
# c3 = out_shape * (4 if expand else 1)
# c4 = out_shape * (8 if expand else 1)
# scratch.layer1_rn = nn.Conv2d(in_shape[0], c1, 3, 1, 1, bias=False, groups=groups)
# scratch.layer2_rn = nn.Conv2d(in_shape[1], c2, 3, 1, 1, bias=False, groups=groups)
# scratch.layer3_rn = nn.Conv2d(in_shape[2], c3, 3, 1, 1, bias=False, groups=groups)
# scratch.layer4_rn = nn.Conv2d(in_shape[3], c4, 3, 1, 1, bias=False, groups=groups)
# return scratch
# class ResidualConvUnit(nn.Module):
# """Lightweight residual conv block used within fusion."""
# def __init__(self, features: int, activation: nn.Module, bn: bool, groups: int = 1) -> None:
# super().__init__()
# self.bn = bn
# self.groups = groups
# self.conv1 = nn.Conv2d(features, features, 3, 1, 1, bias=True, groups=groups)
# self.conv2 = nn.Conv2d(features, features, 3, 1, 1, bias=True, groups=groups)
# self.norm1 = None
# self.norm2 = None
# self.activation = activation
# self.skip_add = nn.quantized.FloatFunctional()
# def forward(self, x: torch.Tensor) -> torch.Tensor: # type: ignore[override]
# out = self.activation(x)
# out = self.conv1(out)
# if self.norm1 is not None:
# out = self.norm1(out)
# out = self.activation(out)
# out = self.conv2(out)
# if self.norm2 is not None:
# out = self.norm2(out)
# return self.skip_add.add(out, x)
# class FeatureFusionBlock(nn.Module):
# """Top-down fusion block: (optional) residual merge + upsample + 1x1 shrink."""
# def __init__(
# self,
# features: int,
# activation: nn.Module,
# deconv: bool = False,
# bn: bool = False,
# expand: bool = False,
# align_corners: bool = True,
# size: Tuple[int, int] = None,
# has_residual: bool = True,
# groups: int = 1,
# ) -> None:
# super().__init__()
# self.align_corners = align_corners
# self.size = size
# self.has_residual = has_residual
# self.resConfUnit1 = (
# ResidualConvUnit(features, activation, bn, groups=groups) if has_residual else None
# )
# self.resConfUnit2 = ResidualConvUnit(features, activation, bn, groups=groups)
# out_features = (features // 2) if expand else features
# self.out_conv = nn.Conv2d(features, out_features, 1, 1, 0, bias=True, groups=groups)
# self.skip_add = nn.quantized.FloatFunctional()
# def forward(self, *xs: torch.Tensor, size: Tuple[int, int] = None) -> torch.Tensor: # type: ignore[override]
# """
# xs:
# - xs[0]: top input
# - xs[1]: (optional) lateral (to be added with residual)
# """
# y = xs[0]
# if self.has_residual and len(xs) > 1 and self.resConfUnit1 is not None:
# y = self.skip_add.add(y, self.resConfUnit1(xs[1]))
# y = self.resConfUnit2(y)
# # upsample
# if (size is None) and (self.size is None):
# up_kwargs = {"scale_factor": 2}
# elif size is None:
# up_kwargs = {"size": self.size}
# else:
# up_kwargs = {"size": size}
# y = custom_interpolate(y, **up_kwargs, mode="bilinear", align_corners=self.align_corners)
# y = self.out_conv(y)
# return y
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