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"""Contains Dense Transformer Prediction architecture.
Implements a variant of Vision Transformers for Dense Prediction, https://arxiv.org/abs/2103.13413
For licensing see accompanying LICENSE file.
Copyright (C) 2025 Apple Inc. All Rights Reserved.
"""
from __future__ import annotations
import copy
from typing import NamedTuple, Tuple
import torch
import torch.nn as nn
from sharp.models import normalizers
from sharp.models.decoders import MultiresConvDecoder, create_monodepth_decoder
from sharp.models.encoders import (
SlidingPyramidNetwork,
create_monodepth_encoder,
)
from sharp.utils import module_surgery
from .params import MonodepthAdaptorParams, MonodepthParams
DimsDecoder = Tuple[int, int, int, int, int]
class MonodepthDensePredictionTransformer(nn.Module):
"""Dense Prediction Transformer for monodepth.
Attach the disparity prediction head for monodepth prediction.
"""
def __init__(
self,
encoder: SlidingPyramidNetwork,
decoder: MultiresConvDecoder,
last_dims: tuple[int, int],
):
"""Initialize Dense Prediction Transformer.
Args:
encoder: The SlidingPyramidTransformer backbone.
decoder: The MultiresConvDecoder decoder.
last_dims: The dimension for the last convolution layers.
"""
super().__init__()
self.normalizer = normalizers.AffineRangeNormalizer(
input_range=(0, 1), output_range=(-1, 1)
)
self.encoder = encoder
self.decoder = decoder
dim_decoder = decoder.dim_out
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 convoultion layer's bias to be 0.
self.head[4].bias.data.fill_(0)
self.grad_checkpointing = False
@torch.jit.ignore
def set_grad_checkpointing(self, is_enabled=True):
"""Enable grad checkpointing."""
self.grad_checkpointing = is_enabled
self.encoder.set_grad_checkpointing(self.grad_checkpointing)
self.decoder.set_grad_checkpointing(self.grad_checkpointing)
def forward(self, image: torch.Tensor) -> torch.Tensor:
"""Decode by projection and fusion of multi-resolution encodings."""
encodings = self.encoder(self.normalizer(image))
num_encoder_features = len(self.encoder.dims_encoder)
features = self.decoder(encodings[:num_encoder_features])
disparity = self.head(features)
return disparity
def internal_resolution(self) -> int:
"""Return the internal image size of the network."""
return self.encoder.internal_resolution()
def create_monodepth_dpt(
params: MonodepthParams | None = None,
) -> MonodepthDensePredictionTransformer:
"""Creates DepthDensePredictionTransformer model.
Args:
params: Parameters of monodepth network.
Returns:
The configured monodepth DPT.
"""
if params is None:
params = MonodepthParams()
encoder: SlidingPyramidNetwork = create_monodepth_encoder(
params.patch_encoder_preset,
params.image_encoder_preset,
use_patch_overlap=params.use_patch_overlap,
last_encoder=params.dims_decoder[0],
)
decoder: MultiresConvDecoder = create_monodepth_decoder(
params.patch_encoder_preset, params.dims_decoder
)
monodepth_model = MonodepthDensePredictionTransformer(
encoder=encoder, decoder=decoder, last_dims=(32, 1)
)
# By default, we don't train the monodepth model.
# However, we allow to selectively unfreeze parts of the network.
monodepth_model.requires_grad_(False)
monodepth_model.encoder.set_requires_grad_(
patch_encoder=params.unfreeze_patch_encoder,
image_encoder=params.unfreeze_image_encoder,
)
monodepth_model.decoder.requires_grad_(params.unfreeze_decoder)
monodepth_model.head.requires_grad_(params.unfreeze_head)
if not params.unfreeze_norm_layers:
module_surgery.freeze_norm_layer(monodepth_model)
monodepth_model.set_grad_checkpointing(params.grad_checkpointing)
return monodepth_model
class MonodepthOutput(NamedTuple):
"""Output of the monodepth model."""
# Disparity output from the monodepth model.
disparity: torch.Tensor
# Multi-level features from monodepth encoder.
encoder_features: list[torch.Tensor]
# Single-level feature from monodepth decoder.
decoder_features: torch.Tensor
# List of monodepth features to be used in gaussian predictor.
output_features: list[torch.Tensor]
# List of intermediate encoder features to be used in distillation.
intermediate_features: list[torch.Tensor] = []
class MonodepthWithEncodingAdaptor(nn.Module):
"""Monodepth model with feature maps."""
def __init__(
self,
monodepth_predictor: MonodepthDensePredictionTransformer,
return_encoder_features: bool,
return_decoder_features: bool,
num_monodepth_layers: int,
sorting_monodepth: bool,
):
"""Initialize MonodepthWithEncodingAdaptor.
Args:
monodepth_predictor: The monodepth model.
return_encoder_features: Whether to return encoder features from monodepth model.
return_decoder_features: Whether to return decoder features from monodepth model.
num_monodepth_layers: How many layers the monodepth model predicts.
sorting_monodepth: Whether to sort the monodepth output (for two layer monodepth).
"""
super().__init__()
self.monodepth_predictor = monodepth_predictor
self.return_encoder_features = return_encoder_features
self.return_decoder_features = return_decoder_features
self.num_monodepth_layers = num_monodepth_layers
self.sorting_monodepth = sorting_monodepth
def forward(self, image: torch.Tensor) -> MonodepthOutput:
"""Process image and return disparity and feature maps."""
inputs = self.monodepth_predictor.normalizer(image)
encoder_output = self.monodepth_predictor.encoder(inputs)
num_encoder_features = len(self.monodepth_predictor.encoder.dims_encoder)
# NOTE: whether intermediate features are empty have already been decided
# in monodepth_predictor during create_monodepth_dpt.
encoder_features = encoder_output[:num_encoder_features]
intermediate_features = encoder_output[num_encoder_features:]
decoder_features = self.monodepth_predictor.decoder(encoder_features)
disparity = self.monodepth_predictor.head(decoder_features)
# We cannot use disparity.shape[1], otherwise the tracer will fail.
if self.num_monodepth_layers == 2 and self.sorting_monodepth:
first_layer_disparity = disparity.max(dim=1, keepdims=True).values
second_layer_disparity = disparity.min(dim=1, keepdims=True).values
disparity = torch.cat([first_layer_disparity, second_layer_disparity], dim=1)
output_features = []
if self.return_encoder_features:
output_features.extend(encoder_features)
if self.return_decoder_features:
output_features.append(decoder_features)
return MonodepthOutput(
disparity=disparity,
encoder_features=encoder_features,
decoder_features=decoder_features,
output_features=output_features,
intermediate_features=intermediate_features,
)
def get_feature_dims(self) -> list[int]:
"""Return dimensions of output feature maps."""
dims = []
if self.return_encoder_features:
dims.extend(self.monodepth_predictor.encoder.dims_encoder)
if self.return_decoder_features:
dims.append(self.monodepth_predictor.decoder.dim_out)
return dims
def internal_resolution(self) -> int:
"""Return the internal image size of the network."""
return self.monodepth_predictor.internal_resolution()
def replicate_head(self, num_repeat: int):
"""Replicate the last convolution layer (head[4] in DPT) for multi layer depth."""
conv_last = copy.deepcopy(self.monodepth_predictor.head[4])
self.monodepth_predictor.head[4].out_channels = num_repeat
self.monodepth_predictor.head[4].weight = nn.Parameter(
conv_last.weight.repeat(num_repeat, 1, 1, 1)
)
self.monodepth_predictor.head[4].bias = nn.Parameter(conv_last.bias.repeat(num_repeat))
def create_monodepth_adaptor(
monodepth_predictor: MonodepthDensePredictionTransformer,
params: MonodepthAdaptorParams,
num_monodepth_layers: int,
sorting_monodepth: bool,
) -> MonodepthWithEncodingAdaptor:
"""Create an adaptor that returns both disparity and features."""
adaptor = MonodepthWithEncodingAdaptor(
monodepth_predictor=monodepth_predictor,
return_encoder_features=params.encoder_features,
return_decoder_features=params.decoder_features,
num_monodepth_layers=num_monodepth_layers,
sorting_monodepth=sorting_monodepth,
)
return adaptor
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