core/encoders/dinov2/hub/depthers.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.

from enum import Enum
from functools import partial
from typing import Optional, Tuple, Union

import torch

from .backbones import _make_dinov2_model
from .depth import BNHead, DepthEncoderDecoder, DPTHead
from .utils import _DINOV2_BASE_URL, _make_dinov2_model_name, CenterPadding


class Weights(Enum):
    NYU = "NYU"
    KITTI = "KITTI"


def _get_depth_range(pretrained: bool, weights: Weights = Weights.NYU) -> Tuple[float, float]:
    if not pretrained:  # Default
        return (0.001, 10.0)

    # Pretrained, set according to the training dataset for the provided weights
    if weights == Weights.KITTI:
        return (0.001, 80.0)

    if weights == Weights.NYU:
        return (0.001, 10.0)

    return (0.001, 10.0)


def _make_dinov2_linear_depth_head(
    *,
    embed_dim: int,
    layers: int,
    min_depth: float,
    max_depth: float,
    **kwargs,
):
    if layers not in (1, 4):
        raise AssertionError(f"Unsupported number of layers: {layers}")

    if layers == 1:
        in_index = [0]
    else:
        assert layers == 4
        in_index = [0, 1, 2, 3]

    return BNHead(
        classify=True,
        n_bins=256,
        bins_strategy="UD",
        norm_strategy="linear",
        upsample=4,
        in_channels=[embed_dim] * len(in_index),
        in_index=in_index,
        input_transform="resize_concat",
        channels=embed_dim * len(in_index) * 2,
        align_corners=False,
        min_depth=0.001,
        max_depth=80,
        loss_decode=(),
    )


def _make_dinov2_linear_depther(
    *,
    arch_name: str = "vit_large",
    layers: int = 4,
    pretrained: bool = True,
    weights: Union[Weights, str] = Weights.NYU,
    depth_range: Optional[Tuple[float, float]] = None,
    **kwargs,
):
    if layers not in (1, 4):
        raise AssertionError(f"Unsupported number of layers: {layers}")
    if isinstance(weights, str):
        try:
            weights = Weights[weights]
        except KeyError:
            raise AssertionError(f"Unsupported weights: {weights}")

    if depth_range is None:
        depth_range = _get_depth_range(pretrained, weights)
    min_depth, max_depth = depth_range

    backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)

    embed_dim = backbone.embed_dim
    patch_size = backbone.patch_size
    model_name = _make_dinov2_model_name(arch_name, patch_size)
    linear_depth_head = _make_dinov2_linear_depth_head(
        embed_dim=embed_dim,
        layers=layers,
        min_depth=min_depth,
        max_depth=max_depth,
    )

    layer_count = {
        "vit_small": 12,
        "vit_base": 12,
        "vit_large": 24,
        "vit_giant2": 40,
    }[arch_name]

    if layers == 4:
        out_index = {
            "vit_small": [2, 5, 8, 11],
            "vit_base": [2, 5, 8, 11],
            "vit_large": [4, 11, 17, 23],
            "vit_giant2": [9, 19, 29, 39],
        }[arch_name]
    else:
        assert layers == 1
        out_index = [layer_count - 1]

    model = DepthEncoderDecoder(backbone=backbone, decode_head=linear_depth_head)
    model.backbone.forward = partial(
        backbone.get_intermediate_layers,
        n=out_index,
        reshape=True,
        return_class_token=True,
        norm=False,
    )
    model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(patch_size)(x[0]))

    if pretrained:
        layers_str = str(layers) if layers == 4 else ""
        weights_str = weights.value.lower()
        url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_linear{layers_str}_head.pth"
        checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
        if "state_dict" in checkpoint:
            state_dict = checkpoint["state_dict"]
        model.load_state_dict(state_dict, strict=False)

    return model


def dinov2_vits14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_linear_depther(
        arch_name="vit_small", layers=layers, pretrained=pretrained, weights=weights, **kwargs
    )


def dinov2_vitb14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_linear_depther(
        arch_name="vit_base", layers=layers, pretrained=pretrained, weights=weights, **kwargs
    )


def dinov2_vitl14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_linear_depther(
        arch_name="vit_large", layers=layers, pretrained=pretrained, weights=weights, **kwargs
    )


def dinov2_vitg14_ld(*, layers: int = 4, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_linear_depther(
        arch_name="vit_giant2", layers=layers, ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
    )


def _make_dinov2_dpt_depth_head(*, embed_dim: int, min_depth: float, max_depth: float):
    return DPTHead(
        in_channels=[embed_dim] * 4,
        channels=256,
        embed_dims=embed_dim,
        post_process_channels=[embed_dim // 2 ** (3 - i) for i in range(4)],
        readout_type="project",
        min_depth=min_depth,
        max_depth=max_depth,
        loss_decode=(),
    )


def _make_dinov2_dpt_depther(
    *,
    arch_name: str = "vit_large",
    pretrained: bool = True,
    weights: Union[Weights, str] = Weights.NYU,
    depth_range: Optional[Tuple[float, float]] = None,
    **kwargs,
):
    if isinstance(weights, str):
        try:
            weights = Weights[weights]
        except KeyError:
            raise AssertionError(f"Unsupported weights: {weights}")

    if depth_range is None:
        depth_range = _get_depth_range(pretrained, weights)
    min_depth, max_depth = depth_range

    backbone = _make_dinov2_model(arch_name=arch_name, pretrained=pretrained, **kwargs)

    model_name = _make_dinov2_model_name(arch_name, backbone.patch_size)
    dpt_depth_head = _make_dinov2_dpt_depth_head(embed_dim=backbone.embed_dim, min_depth=min_depth, max_depth=max_depth)

    out_index = {
        "vit_small": [2, 5, 8, 11],
        "vit_base": [2, 5, 8, 11],
        "vit_large": [4, 11, 17, 23],
        "vit_giant2": [9, 19, 29, 39],
    }[arch_name]

    model = DepthEncoderDecoder(backbone=backbone, decode_head=dpt_depth_head)
    model.backbone.forward = partial(
        backbone.get_intermediate_layers,
        n=out_index,
        reshape=True,
        return_class_token=True,
        norm=False,
    )
    model.backbone.register_forward_pre_hook(lambda _, x: CenterPadding(backbone.patch_size)(x[0]))

    if pretrained:
        weights_str = weights.value.lower()
        url = _DINOV2_BASE_URL + f"/{model_name}/{model_name}_{weights_str}_dpt_head.pth"
        checkpoint = torch.hub.load_state_dict_from_url(url, map_location="cpu")
        if "state_dict" in checkpoint:
            state_dict = checkpoint["state_dict"]
        model.load_state_dict(state_dict, strict=False)

    return model


def dinov2_vits14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_dpt_depther(arch_name="vit_small", pretrained=pretrained, weights=weights, **kwargs)


def dinov2_vitb14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_dpt_depther(arch_name="vit_base", pretrained=pretrained, weights=weights, **kwargs)


def dinov2_vitl14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_dpt_depther(arch_name="vit_large", pretrained=pretrained, weights=weights, **kwargs)


def dinov2_vitg14_dd(*, pretrained: bool = True, weights: Union[Weights, str] = Weights.NYU, **kwargs):
    return _make_dinov2_dpt_depther(
        arch_name="vit_giant2", ffn_layer="swiglufused", pretrained=pretrained, weights=weights, **kwargs
    )