Concerto-Base-Origin / concerto_model.py
Upload Concerto-Base-Origin (transformers port, original code)
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# ruff: noqa
# fmt: off
"""Concerto, vendored verbatim from Pointcept/Concerto @ 5b41117160cdd92c3538be1911277b17e7ef5871: the files below concatenated, package-internal
imports replaced by relative imports of the other vendored files (generated by scripts/model_ports/common/vendor_flat.py; do not edit by hand).
Code license: Apache-2.0."""
from .concerto_hilbert import decode as hilbert_decode_, encode as hilbert_encode_
from .concerto_z_order import key2xyz as z_order_decode_, xyz2key as z_order_encode_


# ==============================================================================================================
# concerto/utils.py
# ==============================================================================================================
"""
General utils
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""

# Copyright (c) Meta Platforms, Inc. and 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.


import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from datetime import datetime


@torch.no_grad()
def offset2bincount(offset):
    return torch.diff(
        offset, prepend=torch.tensor([0], device=offset.device, dtype=torch.long)
    )


@torch.no_grad()
def bincount2offset(bincount):
    return torch.cumsum(bincount, dim=0)


@torch.no_grad()
def offset2batch(offset):
    bincount = offset2bincount(offset)
    return torch.arange(
        len(bincount), device=offset.device, dtype=torch.long
    ).repeat_interleave(bincount)


@torch.no_grad()
def batch2offset(batch):
    return torch.cumsum(batch.bincount(), dim=0).long()


def get_random_seed():
    seed = (
        os.getpid()
        + int(datetime.now().strftime("%S%f"))
        + int.from_bytes(os.urandom(2), "big")
    )
    return seed


def set_seed(seed=None):
    if seed is None:
        seed = get_random_seed()
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    cudnn.benchmark = False
    cudnn.deterministic = True
    os.environ["PYTHONHASHSEED"] = str(seed)


# ==============================================================================================================
# concerto/serialization/default.py
# ==============================================================================================================
"""
Serialization Encoding
Pointcept detached version
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""

# Copyright (c) Meta Platforms, Inc. and 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.


import torch


@torch.inference_mode()
def encode(grid_coord, batch=None, depth=16, order="z"):
    assert order in {"z", "z-trans", "hilbert", "hilbert-trans"}
    if order == "z":
        code = z_order_encode(grid_coord, depth=depth)
    elif order == "z-trans":
        code = z_order_encode(grid_coord[:, [1, 0, 2]], depth=depth)
    elif order == "hilbert":
        code = hilbert_encode(grid_coord, depth=depth)
    elif order == "hilbert-trans":
        code = hilbert_encode(grid_coord[:, [1, 0, 2]], depth=depth)
    else:
        raise NotImplementedError
    if batch is not None:
        batch = batch.long()
        code = batch << depth * 3 | code
    return code


@torch.inference_mode()
def decode(code, depth=16, order="z"):
    assert order in {"z", "hilbert"}
    batch = code >> depth * 3
    code = code & ((1 << depth * 3) - 1)
    if order == "z":
        grid_coord = z_order_decode(code, depth=depth)
    elif order == "hilbert":
        grid_coord = hilbert_decode(code, depth=depth)
    else:
        raise NotImplementedError
    return grid_coord, batch


def z_order_encode(grid_coord: torch.Tensor, depth: int = 16):
    x, y, z = grid_coord[:, 0].long(), grid_coord[:, 1].long(), grid_coord[:, 2].long()
    # we block the support to batch, maintain batched code in Point class
    code = z_order_encode_(x, y, z, b=None, depth=depth)
    return code


def z_order_decode(code: torch.Tensor, depth):
    x, y, z = z_order_decode_(code, depth=depth)
    grid_coord = torch.stack([x, y, z], dim=-1)  # (N,  3)
    return grid_coord


def hilbert_encode(grid_coord: torch.Tensor, depth: int = 16):
    return hilbert_encode_(grid_coord, num_dims=3, num_bits=depth)


def hilbert_decode(code: torch.Tensor, depth: int = 16):
    return hilbert_decode_(code, num_dims=3, num_bits=depth)


# ==============================================================================================================
# concerto/structure.py
# ==============================================================================================================
"""
Data structure for 3D point cloud
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""

# Copyright (c) Meta Platforms, Inc. and 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.


import torch
import spconv.pytorch as spconv
from addict import Dict



class Point(Dict):
    """
    Point Structure of Pointcept
    A Point (point cloud) in Pointcept is a dictionary that contains various properties of
    a batched point cloud. The property with the following names have a specific definition
    as follows:
    - "coord": original coordinate of point cloud;
    - "grid_coord": grid coordinate for specific grid size (related to GridSampling);
    Point also support the following optional attributes:
    - "offset": if not exist, initialized as batch size is 1;
    - "batch": if not exist, initialized as batch size is 1;
    - "feat": feature of point cloud, default input of model;
    - "grid_size": Grid size of point cloud (related to GridSampling);
    (related to Serialization)
    - "serialized_depth": depth of serialization, 2 ** depth * grid_size describe the maximum of point cloud range;
    - "serialized_code": a list of serialization codes;
    - "serialized_order": a list of serialization order determined by code;
    - "serialized_inverse": a list of inverse mapping determined by code;
    (related to Sparsify: SpConv)
    - "sparse_shape": Sparse shape for Sparse Conv Tensor;
    - "sparse_conv_feat": SparseConvTensor init with information provide by Point;
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        # If one of "offset" or "batch" do not exist, generate by the existing one
        if "batch" not in self.keys() and "offset" in self.keys():
            self["batch"] = offset2batch(self.offset)
        elif "offset" not in self.keys() and "batch" in self.keys():
            self["offset"] = batch2offset(self.batch)

    def serialization(self, order="z", depth=None, shuffle_orders=False):
        """
        Point Cloud Serialization
        relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
        """
        self["order"] = order
        assert "batch" in self.keys()
        if "grid_coord" not in self.keys():
            # if you don't want to operate GridSampling in data augmentation,
            # please add the following augmentation into your pipeline:
            # dict(type="Copy", keys_dict={"grid_size": 0.01}),
            # (adjust `grid_size` to what your want)
            assert {"grid_size", "coord"}.issubset(self.keys())

            self["grid_coord"] = torch.div(
                self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
            ).int()

        if depth is None:
            # Adaptive measure the depth of serialization cube (length = 2 ^ depth)
            depth = int(self.grid_coord.max() + 1).bit_length()
        self["serialized_depth"] = depth
        # Maximum bit length for serialization code is 63 (int64)
        assert depth * 3 + len(self.offset).bit_length() <= 63
        # Here we follow OCNN and set the depth limitation to 16 (48bit) for the point position.
        # Although depth is limited to less than 16, we can encode a 655.36^3 (2^16 * 0.01) meter^3
        # cube with a grid size of 0.01 meter. We consider it is enough for the current stage.
        # We can unlock the limitation by optimizing the z-order encoding function if necessary.
        assert depth <= 16

        # The serialization codes are arranged as following structures:
        # [Order1 ([n]),
        #  Order2 ([n]),
        #   ...
        #  OrderN ([n])] (k, n)
        code = [
            encode(self.grid_coord, self.batch, depth, order=order_) for order_ in order
        ]
        code = torch.stack(code)
        order = torch.argsort(code)
        inverse = torch.zeros_like(order).scatter_(
            dim=1,
            index=order,
            src=torch.arange(0, code.shape[1], device=order.device).repeat(
                code.shape[0], 1
            ),
        )

        if shuffle_orders:
            perm = torch.randperm(code.shape[0])
            code = code[perm]
            order = order[perm]
            inverse = inverse[perm]

        self["serialized_code"] = code
        self["serialized_order"] = order
        self["serialized_inverse"] = inverse

    def sparsify(self, pad=96):
        """
        Point Cloud Serialization
        Point cloud is sparse, here we use "sparsify" to specifically refer to
        preparing "spconv.SparseConvTensor" for SpConv.
        relay on ["grid_coord" or "coord" + "grid_size", "batch", "feat"]
        pad: padding sparse for sparse shape.
        """
        assert {"feat", "batch"}.issubset(self.keys())
        if "grid_coord" not in self.keys():
            # if you don't want to operate GridSampling in data augmentation,
            # please add the following augmentation into your pipeline:
            # dict(type="Copy", keys_dict={"grid_size": 0.01}),
            # (adjust `grid_size` to what your want)
            assert {"grid_size", "coord"}.issubset(self.keys())
            self["grid_coord"] = torch.div(
                self.coord - self.coord.min(0)[0], self.grid_size, rounding_mode="trunc"
            ).int()
        if "sparse_shape" in self.keys():
            sparse_shape = self.sparse_shape
        else:
            sparse_shape = torch.add(
                torch.max(self.grid_coord, dim=0).values, pad
            ).tolist()
        sparse_conv_feat = spconv.SparseConvTensor(
            features=self.feat,
            indices=torch.cat(
                [self.batch.unsqueeze(-1).int(), self.grid_coord.int()], dim=1
            ).contiguous(),
            spatial_shape=sparse_shape,
            batch_size=self.batch[-1].tolist() + 1,
        )
        self["sparse_shape"] = sparse_shape
        self["sparse_conv_feat"] = sparse_conv_feat


# ==============================================================================================================
# concerto/module.py
# ==============================================================================================================
"""
Point Modules
Pointcept detached version
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""

# Copyright (c) Meta Platforms, Inc. and 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.


import sys
import torch.nn as nn
import spconv.pytorch as spconv
from collections import OrderedDict



class PointModule(nn.Module):
    r"""PointModule
    placeholder, all module subclass from this will take Point in PointSequential.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)


class PointSequential(PointModule):
    r"""A sequential container.
    Modules will be added to it in the order they are passed in the constructor.
    Alternatively, an ordered dict of modules can also be passed in.
    """

    def __init__(self, *args, **kwargs):
        super().__init__()
        if len(args) == 1 and isinstance(args[0], OrderedDict):
            for key, module in args[0].items():
                self.add_module(key, module)
        else:
            for idx, module in enumerate(args):
                self.add_module(str(idx), module)
        for name, module in kwargs.items():
            if sys.version_info < (3, 6):
                raise ValueError("kwargs only supported in py36+")
            if name in self._modules:
                raise ValueError("name exists.")
            self.add_module(name, module)

    def __getitem__(self, idx):
        if not (-len(self) <= idx < len(self)):
            raise IndexError("index {} is out of range".format(idx))
        if idx < 0:
            idx += len(self)
        it = iter(self._modules.values())
        for i in range(idx):
            next(it)
        return next(it)

    def __len__(self):
        return len(self._modules)

    def add(self, module, name=None):
        if name is None:
            name = str(len(self._modules))
            if name in self._modules:
                raise KeyError("name exists")
        self.add_module(name, module)

    def forward(self, input):
        for k, module in self._modules.items():
            # Point module
            if isinstance(module, PointModule):
                input = module(input)
            # Spconv module
            elif spconv.modules.is_spconv_module(module):
                if isinstance(input, Point):
                    input.sparse_conv_feat = module(input.sparse_conv_feat)
                    input.feat = input.sparse_conv_feat.features
                else:
                    input = module(input)
            # PyTorch module
            else:
                if isinstance(input, Point):
                    input.feat = module(input.feat)
                    if "sparse_conv_feat" in input.keys():
                        input.sparse_conv_feat = input.sparse_conv_feat.replace_feature(
                            input.feat
                        )
                elif isinstance(input, spconv.SparseConvTensor):
                    if input.indices.shape[0] != 0:
                        input = input.replace_feature(module(input.features))
                else:
                    input = module(input)
        return input


# ==============================================================================================================
# concerto/model.py
# ==============================================================================================================
"""
Point Transformer - V3 Mode2 - Sonata & Concerto
Pointcept detached version
Author: Xiaoyang Wu (xiaoyang.wu.cs@gmail.com)
Please cite our work if the code is helpful to you.
"""

# Copyright (c) Meta Platforms, Inc. and 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.


import os
from packaging import version
from huggingface_hub import hf_hub_download, PyTorchModelHubMixin
from addict import Dict
import torch
import torch.nn as nn
from torch.nn.init import trunc_normal_
import spconv.pytorch as spconv
import torch_scatter
from timm.layers import DropPath


try:
    import flash_attn
except ImportError:
    flash_attn = None


MODELS = [
    "sonata",
    "concerto_large_outdoor",
    "concerto_large",
    "concerto_base",
    "concerto_small",
    "concerto_large_linear_prob_head_sc",
]


class LayerScale(nn.Module):
    def __init__(
        self,
        dim: int,
        init_values: float = 1e-5,
        inplace: bool = False,
    ) -> None:
        super().__init__()
        self.inplace = inplace
        self.gamma = nn.Parameter(init_values * torch.ones(dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x.mul_(self.gamma) if self.inplace else x * self.gamma


class RPE(torch.nn.Module):
    def __init__(self, patch_size, num_heads):
        super().__init__()
        self.patch_size = patch_size
        self.num_heads = num_heads
        self.pos_bnd = int((4 * patch_size) ** (1 / 3) * 2)
        self.rpe_num = 2 * self.pos_bnd + 1
        self.rpe_table = torch.nn.Parameter(torch.zeros(3 * self.rpe_num, num_heads))
        torch.nn.init.trunc_normal_(self.rpe_table, std=0.02)

    def forward(self, coord):
        idx = (
            coord.clamp(-self.pos_bnd, self.pos_bnd)  # clamp into bnd
            + self.pos_bnd  # relative position to positive index
            + torch.arange(3, device=coord.device) * self.rpe_num  # x, y, z stride
        )
        out = self.rpe_table.index_select(0, idx.reshape(-1))
        out = out.view(idx.shape + (-1,)).sum(3)
        out = out.permute(0, 3, 1, 2)  # (N, K, K, H) -> (N, H, K, K)
        return out


class SerializedAttention(PointModule):
    def __init__(
        self,
        channels,
        num_heads,
        patch_size,
        qkv_bias=True,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        order_index=0,
        enable_rpe=False,
        enable_flash=True,
        upcast_attention=True,
        upcast_softmax=True,
    ):
        super().__init__()
        assert channels % num_heads == 0
        self.channels = channels
        self.num_heads = num_heads
        self.scale = qk_scale or (channels // num_heads) ** -0.5
        self.order_index = order_index
        self.upcast_attention = upcast_attention
        self.upcast_softmax = upcast_softmax
        self.enable_rpe = enable_rpe
        self.enable_flash = enable_flash
        if enable_flash:
            assert (
                enable_rpe is False
            ), "Set enable_rpe to False when enable Flash Attention"
            assert (
                upcast_attention is False
            ), "Set upcast_attention to False when enable Flash Attention"
            assert (
                upcast_softmax is False
            ), "Set upcast_softmax to False when enable Flash Attention"
            assert flash_attn is not None, "Make sure flash_attn is installed."
            self.patch_size = patch_size
            self.attn_drop = attn_drop
        else:
            # when disable flash attention, we still don't want to use mask
            # consequently, patch size will auto set to the
            # min number of patch_size_max and number of points
            self.patch_size_max = patch_size
            self.patch_size = 0
            self.attn_drop = torch.nn.Dropout(attn_drop)

        self.qkv = torch.nn.Linear(channels, channels * 3, bias=qkv_bias)
        self.proj = torch.nn.Linear(channels, channels)
        self.proj_drop = torch.nn.Dropout(proj_drop)
        self.softmax = torch.nn.Softmax(dim=-1)
        self.rpe = RPE(patch_size, num_heads) if self.enable_rpe else None

    @torch.no_grad()
    def get_rel_pos(self, point, order):
        K = self.patch_size
        rel_pos_key = f"rel_pos_{self.order_index}"
        if rel_pos_key not in point.keys():
            grid_coord = point.grid_coord[order]
            grid_coord = grid_coord.reshape(-1, K, 3)
            point[rel_pos_key] = grid_coord.unsqueeze(2) - grid_coord.unsqueeze(1)
        return point[rel_pos_key]

    @torch.no_grad()
    def get_padding_and_inverse(self, point):
        pad_key = "pad"
        unpad_key = "unpad"
        cu_seqlens_key = "cu_seqlens_key"
        if (
            pad_key not in point.keys()
            or unpad_key not in point.keys()
            or cu_seqlens_key not in point.keys()
        ):
            offset = point.offset
            bincount = offset2bincount(offset)
            bincount_pad = (
                torch.div(
                    bincount + self.patch_size - 1,
                    self.patch_size,
                    rounding_mode="trunc",
                )
                * self.patch_size
            )
            # only pad point when num of points larger than patch_size
            mask_pad = bincount > self.patch_size
            bincount_pad = ~mask_pad * bincount + mask_pad * bincount_pad
            _offset = nn.functional.pad(offset, (1, 0))
            _offset_pad = nn.functional.pad(torch.cumsum(bincount_pad, dim=0), (1, 0))
            pad = torch.arange(_offset_pad[-1], device=offset.device)
            unpad = torch.arange(_offset[-1], device=offset.device)
            cu_seqlens = []
            for i in range(len(offset)):
                unpad[_offset[i] : _offset[i + 1]] += _offset_pad[i] - _offset[i]
                if bincount[i] != bincount_pad[i]:
                    pad[
                        _offset_pad[i + 1]
                        - self.patch_size
                        + (bincount[i] % self.patch_size) : _offset_pad[i + 1]
                    ] = pad[
                        _offset_pad[i + 1]
                        - 2 * self.patch_size
                        + (bincount[i] % self.patch_size) : _offset_pad[i + 1]
                        - self.patch_size
                    ]
                pad[_offset_pad[i] : _offset_pad[i + 1]] -= _offset_pad[i] - _offset[i]
                cu_seqlens.append(
                    torch.arange(
                        _offset_pad[i],
                        _offset_pad[i + 1],
                        step=self.patch_size,
                        dtype=torch.int32,
                        device=offset.device,
                    )
                )
            point[pad_key] = pad
            point[unpad_key] = unpad
            point[cu_seqlens_key] = nn.functional.pad(
                torch.concat(cu_seqlens), (0, 1), value=_offset_pad[-1]
            )
        return point[pad_key], point[unpad_key], point[cu_seqlens_key]

    def forward(self, point):
        if not self.enable_flash:
            self.patch_size = min(
                offset2bincount(point.offset).min().tolist(), self.patch_size_max
            )

        H = self.num_heads
        K = self.patch_size
        C = self.channels

        pad, unpad, cu_seqlens = self.get_padding_and_inverse(point)

        order = point.serialized_order[self.order_index][pad]
        inverse = unpad[point.serialized_inverse[self.order_index]]

        # padding and reshape feat and batch for serialized point patch
        qkv = self.qkv(point.feat)[order]

        if not self.enable_flash:
            # encode and reshape qkv: (N', K, 3, H, C') => (3, N', H, K, C')
            q, k, v = (
                qkv.reshape(-1, K, 3, H, C // H).permute(2, 0, 3, 1, 4).unbind(dim=0)
            )
            # attn
            if self.upcast_attention:
                q = q.float()
                k = k.float()
            attn = (q * self.scale) @ k.transpose(-2, -1)  # (N', H, K, K)
            if self.enable_rpe:
                attn = attn + self.rpe(self.get_rel_pos(point, order))
            if self.upcast_softmax:
                attn = attn.float()
            attn = self.softmax(attn)
            attn = self.attn_drop(attn).to(qkv.dtype)
            feat = (attn @ v).transpose(1, 2).reshape(-1, C)
        else:
            feat = flash_attn.flash_attn_varlen_qkvpacked_func(
                qkv.half().reshape(-1, 3, H, C // H),
                cu_seqlens,
                max_seqlen=self.patch_size,
                dropout_p=self.attn_drop if self.training else 0,
                softmax_scale=self.scale,
            ).reshape(-1, C)
            feat = feat.to(qkv.dtype)
        feat = feat[inverse]

        # ffn
        feat = self.proj(feat)
        feat = self.proj_drop(feat)
        point.feat = feat
        return point


class MLP(nn.Module):
    def __init__(
        self,
        in_channels,
        hidden_channels=None,
        out_channels=None,
        act_layer=nn.GELU,
        drop=0.0,
    ):
        super().__init__()
        out_channels = out_channels or in_channels
        hidden_channels = hidden_channels or in_channels
        self.fc1 = nn.Linear(in_channels, hidden_channels)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_channels, out_channels)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block(PointModule):
    def __init__(
        self,
        channels,
        num_heads,
        patch_size=48,
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        drop_path=0.0,
        layer_scale=None,
        norm_layer=nn.LayerNorm,
        act_layer=nn.GELU,
        pre_norm=True,
        order_index=0,
        cpe_indice_key=None,
        enable_rpe=False,
        enable_flash=True,
        upcast_attention=True,
        upcast_softmax=True,
    ):
        super().__init__()
        self.channels = channels
        self.pre_norm = pre_norm

        self.cpe = PointSequential(
            spconv.SubMConv3d(
                channels,
                channels,
                kernel_size=3,
                bias=True,
                indice_key=cpe_indice_key,
            ),
            nn.Linear(channels, channels),
            norm_layer(channels),
        )

        self.norm1 = PointSequential(norm_layer(channels))
        self.ls1 = PointSequential(
            LayerScale(channels, init_values=layer_scale)
            if layer_scale is not None
            else nn.Identity()
        )
        self.attn = SerializedAttention(
            channels=channels,
            patch_size=patch_size,
            num_heads=num_heads,
            qkv_bias=qkv_bias,
            qk_scale=qk_scale,
            attn_drop=attn_drop,
            proj_drop=proj_drop,
            order_index=order_index,
            enable_rpe=enable_rpe,
            enable_flash=enable_flash,
            upcast_attention=upcast_attention,
            upcast_softmax=upcast_softmax,
        )
        self.norm2 = PointSequential(norm_layer(channels))
        self.ls2 = PointSequential(
            LayerScale(channels, init_values=layer_scale)
            if layer_scale is not None
            else nn.Identity()
        )
        self.mlp = PointSequential(
            MLP(
                in_channels=channels,
                hidden_channels=int(channels * mlp_ratio),
                out_channels=channels,
                act_layer=act_layer,
                drop=proj_drop,
            )
        )
        self.drop_path = PointSequential(
            DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
        )

    def forward(self, point: Point):
        shortcut = point.feat
        point = self.cpe(point)
        point.feat = shortcut + point.feat
        shortcut = point.feat
        if self.pre_norm:
            point = self.norm1(point)
        point = self.drop_path(self.ls1(self.attn(point)))
        point.feat = shortcut + point.feat
        if not self.pre_norm:
            point = self.norm1(point)

        shortcut = point.feat
        if self.pre_norm:
            point = self.norm2(point)
        point = self.drop_path(self.ls2(self.mlp(point)))
        point.feat = shortcut + point.feat
        if not self.pre_norm:
            point = self.norm2(point)
        point.sparse_conv_feat = point.sparse_conv_feat.replace_feature(point.feat)
        return point


class GridPooling(PointModule):
    def __init__(
        self,
        in_channels,
        out_channels,
        stride=2,
        norm_layer=None,
        act_layer=None,
        reduce="max",
        shuffle_orders=True,
        traceable=True,  # record parent and cluster
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels

        self.stride = stride
        assert reduce in ["sum", "mean", "min", "max"]
        self.reduce = reduce
        self.shuffle_orders = shuffle_orders
        self.traceable = traceable

        self.proj = nn.Linear(in_channels, out_channels)
        if norm_layer is not None:
            self.norm = PointSequential(norm_layer(out_channels))
        if act_layer is not None:
            self.act = PointSequential(act_layer())

    def forward(self, point: Point):
        if "grid_coord" in point.keys():
            grid_coord = point.grid_coord
        elif {"coord", "grid_size"}.issubset(point.keys()):
            grid_coord = torch.div(
                point.coord - point.coord.min(0)[0],
                point.grid_size,
                rounding_mode="trunc",
            ).int()
        else:
            raise AssertionError(
                "[gird_coord] or [coord, grid_size] should be include in the Point"
            )
        grid_coord = torch.div(grid_coord, self.stride, rounding_mode="trunc")
        grid_coord = grid_coord | point.batch.view(-1, 1) << 48
        grid_coord, cluster, counts = torch.unique(
            grid_coord,
            sorted=True,
            return_inverse=True,
            return_counts=True,
            dim=0,
        )
        grid_coord = grid_coord & ((1 << 48) - 1)
        # indices of point sorted by cluster, for torch_scatter.segment_csr
        _, indices = torch.sort(cluster)
        # index pointer for sorted point, for torch_scatter.segment_csr
        idx_ptr = torch.cat([counts.new_zeros(1), torch.cumsum(counts, dim=0)])
        # head_indices of each cluster, for reduce attr e.g. code, batch
        head_indices = indices[idx_ptr[:-1]]
        point_dict = Dict(
            feat=torch_scatter.segment_csr(
                self.proj(point.feat)[indices], idx_ptr, reduce=self.reduce
            ),
            coord=torch_scatter.segment_csr(
                point.coord[indices], idx_ptr, reduce="mean"
            ),
            grid_coord=grid_coord,
            batch=point.batch[head_indices],
        )
        if "origin_coord" in point.keys():
            point_dict["origin_coord"] = torch_scatter.segment_csr(
                point.origin_coord[indices], idx_ptr, reduce="mean"
            )
        if "condition" in point.keys():
            point_dict["condition"] = point.condition
        if "context" in point.keys():
            point_dict["context"] = point.context
        if "name" in point.keys():
            point_dict["name"] = point.name
        if "split" in point.keys():
            point_dict["split"] = point.split
        if "color" in point.keys():
            point_dict["color"] = torch_scatter.segment_csr(
                point.color[indices], idx_ptr, reduce="mean"
            )
        if "grid_size" in point.keys():
            point_dict["grid_size"] = point.grid_size * self.stride

        if self.traceable:
            point_dict["pooling_inverse"] = cluster
            point_dict["pooling_parent"] = point
            point_dict["idx_ptr"] = idx_ptr
        order = point.order
        point = Point(point_dict)
        if self.norm is not None:
            point = self.norm(point)
        if self.act is not None:
            point = self.act(point)
        point.serialization(order=order, shuffle_orders=self.shuffle_orders)
        point.sparsify()
        return point


class GridUnpooling(PointModule):
    def __init__(
        self,
        in_channels,
        skip_channels,
        out_channels,
        norm_layer=None,
        act_layer=None,
        traceable=False,  # record parent and cluster
    ):
        super().__init__()
        self.proj = PointSequential(nn.Linear(in_channels, out_channels))
        self.proj_skip = PointSequential(nn.Linear(skip_channels, out_channels))

        if norm_layer is not None:
            self.proj.add(norm_layer(out_channels))
            self.proj_skip.add(norm_layer(out_channels))

        if act_layer is not None:
            self.proj.add(act_layer())
            self.proj_skip.add(act_layer())

        self.traceable = traceable

    def forward(self, point):
        assert "pooling_parent" in point.keys()
        assert "pooling_inverse" in point.keys()
        parent = point.pop("pooling_parent")
        inverse = point.pooling_inverse
        feat = point.feat

        parent = self.proj_skip(parent)
        parent.feat = parent.feat + self.proj(point).feat[inverse]
        parent.sparse_conv_feat = parent.sparse_conv_feat.replace_feature(parent.feat)

        if self.traceable:
            point.feat = feat
            parent["unpooling_parent"] = point
        return parent


class Embedding(PointModule):
    def __init__(
        self,
        in_channels,
        embed_channels,
        norm_layer=None,
        act_layer=None,
        mask_token=False,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.embed_channels = embed_channels

        self.stem = PointSequential(linear=nn.Linear(in_channels, embed_channels))
        if norm_layer is not None:
            self.stem.add(norm_layer(embed_channels), name="norm")
        if act_layer is not None:
            self.stem.add(act_layer(), name="act")

        if mask_token:
            self.mask_token = nn.Parameter(torch.zeros(1, embed_channels))
        else:
            self.mask_token = None

    def forward(self, point: Point):
        point = self.stem(point)
        if "mask" in point.keys():
            point.feat = torch.where(
                point.mask.unsqueeze(-1),
                self.mask_token.to(point.feat.dtype),
                point.feat,
            )
        return point


class PointTransformerV3(PointModule, PyTorchModelHubMixin):
    def __init__(
        self,
        in_channels=6,
        order=("z", "z-trans"),
        stride=(2, 2, 2, 2),
        enc_depths=(3, 3, 3, 12, 3),
        enc_channels=(48, 96, 192, 384, 512),
        enc_num_head=(3, 6, 12, 24, 32),
        enc_patch_size=(1024, 1024, 1024, 1024, 1024),
        dec_depths=(3, 3, 3, 3),
        dec_channels=(96, 96, 192, 384),
        dec_num_head=(6, 6, 12, 32),
        dec_patch_size=(1024, 1024, 1024, 1024),
        mlp_ratio=4,
        qkv_bias=True,
        qk_scale=None,
        attn_drop=0.0,
        proj_drop=0.0,
        drop_path=0.3,
        layer_scale=None,
        pre_norm=True,
        shuffle_orders=True,
        enable_rpe=False,
        enable_flash=True,
        upcast_attention=False,
        upcast_softmax=False,
        traceable=False,
        mask_token=False,
        enc_mode=False,
        freeze_encoder=False,
    ):
        super().__init__()
        self.num_stages = len(enc_depths)
        self.order = [order] if isinstance(order, str) else order
        self.enc_mode = enc_mode
        self.shuffle_orders = shuffle_orders
        self.freeze_encoder = freeze_encoder

        assert self.num_stages == len(stride) + 1
        assert self.num_stages == len(enc_depths)
        assert self.num_stages == len(enc_channels)
        assert self.num_stages == len(enc_num_head)
        assert self.num_stages == len(enc_patch_size)
        assert self.enc_mode or self.num_stages == len(dec_depths) + 1
        assert self.enc_mode or self.num_stages == len(dec_channels) + 1
        assert self.enc_mode or self.num_stages == len(dec_num_head) + 1
        assert self.enc_mode or self.num_stages == len(dec_patch_size) + 1

        # normalization layer
        ln_layer = nn.LayerNorm
        # activation layers
        act_layer = nn.GELU

        self.embedding = Embedding(
            in_channels=in_channels,
            embed_channels=enc_channels[0],
            norm_layer=ln_layer,
            act_layer=act_layer,
            mask_token=mask_token,
        )

        # encoder
        enc_drop_path = [
            x.item() for x in torch.linspace(0, drop_path, sum(enc_depths))
        ]
        self.enc = PointSequential()
        for s in range(self.num_stages):
            enc_drop_path_ = enc_drop_path[
                sum(enc_depths[:s]) : sum(enc_depths[: s + 1])
            ]
            enc = PointSequential()
            if s > 0:
                enc.add(
                    GridPooling(
                        in_channels=enc_channels[s - 1],
                        out_channels=enc_channels[s],
                        stride=stride[s - 1],
                        norm_layer=ln_layer,
                        act_layer=act_layer,
                    ),
                    name="down",
                )
            for i in range(enc_depths[s]):
                enc.add(
                    Block(
                        channels=enc_channels[s],
                        num_heads=enc_num_head[s],
                        patch_size=enc_patch_size[s],
                        mlp_ratio=mlp_ratio,
                        qkv_bias=qkv_bias,
                        qk_scale=qk_scale,
                        attn_drop=attn_drop,
                        proj_drop=proj_drop,
                        drop_path=enc_drop_path_[i],
                        layer_scale=layer_scale,
                        norm_layer=ln_layer,
                        act_layer=act_layer,
                        pre_norm=pre_norm,
                        order_index=i % len(self.order),
                        cpe_indice_key=f"stage{s}",
                        enable_rpe=enable_rpe,
                        enable_flash=enable_flash,
                        upcast_attention=upcast_attention,
                        upcast_softmax=upcast_softmax,
                    ),
                    name=f"block{i}",
                )
            if len(enc) != 0:
                self.enc.add(module=enc, name=f"enc{s}")

        # decoder
        if not self.enc_mode:
            dec_drop_path = [
                x.item() for x in torch.linspace(0, drop_path, sum(dec_depths))
            ]
            self.dec = PointSequential()
            dec_channels = list(dec_channels) + [enc_channels[-1]]
            for s in reversed(range(self.num_stages - 1)):
                dec_drop_path_ = dec_drop_path[
                    sum(dec_depths[:s]) : sum(dec_depths[: s + 1])
                ]
                dec_drop_path_.reverse()
                dec = PointSequential()
                dec.add(
                    GridUnpooling(
                        in_channels=dec_channels[s + 1],
                        skip_channels=enc_channels[s],
                        out_channels=dec_channels[s],
                        norm_layer=ln_layer,
                        act_layer=act_layer,
                        traceable=traceable,
                    ),
                    name="up",
                )
                for i in range(dec_depths[s]):
                    dec.add(
                        Block(
                            channels=dec_channels[s],
                            num_heads=dec_num_head[s],
                            patch_size=dec_patch_size[s],
                            mlp_ratio=mlp_ratio,
                            qkv_bias=qkv_bias,
                            qk_scale=qk_scale,
                            attn_drop=attn_drop,
                            proj_drop=proj_drop,
                            drop_path=dec_drop_path_[i],
                            layer_scale=layer_scale,
                            norm_layer=ln_layer,
                            act_layer=act_layer,
                            pre_norm=pre_norm,
                            order_index=i % len(self.order),
                            cpe_indice_key=f"stage{s}",
                            enable_rpe=enable_rpe,
                            enable_flash=enable_flash,
                            upcast_attention=upcast_attention,
                            upcast_softmax=upcast_softmax,
                        ),
                        name=f"block{i}",
                    )
                self.dec.add(module=dec, name=f"dec{s}")
        if self.freeze_encoder:
            for p in self.embedding.parameters():
                p.requires_grad = False
            for p in self.enc.parameters():
                p.requires_grad = False
        self.apply(self._init_weights)

    @staticmethod
    def _init_weights(module):
        if isinstance(module, nn.Linear):
            trunc_normal_(module.weight, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)
        elif isinstance(module, spconv.SubMConv3d):
            trunc_normal_(module.weight, std=0.02)
            if module.bias is not None:
                nn.init.zeros_(module.bias)

    def forward(self, data_dict):
        point = Point(data_dict)
        point = self.embedding(point)

        point.serialization(order=self.order, shuffle_orders=self.shuffle_orders)
        point.sparsify()

        point = self.enc(point)
        if not self.enc_mode:
            point = self.dec(point)
        return point


def load(
    name: str = "concerto_large",
    repo_id="Pointcept/Concerto",
    download_root: str = None,
    custom_config: dict = None,
    ckpt_only: bool = False,
):
    if name in MODELS:
        print(f"Loading checkpoint from HuggingFace: {name} ...")
        ckpt_path = hf_hub_download(
            repo_id=repo_id,
            filename=f"{name}.pth",
            repo_type="model",
            revision="main",
            local_dir=download_root or os.path.expanduser("~/.cache/concerto/ckpt"),
        )
    elif os.path.isfile(name):
        print(f"Loading checkpoint in local path: {name} ...")
        ckpt_path = name
    else:
        raise RuntimeError(f"Model {name} not found; available models = {MODELS}")

    if version.parse(torch.__version__) >= version.parse("2.4"):
        ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=True)
    else:
        ckpt = torch.load(ckpt_path, map_location="cpu")
    if custom_config is not None:
        for key, value in custom_config.items():
            ckpt["config"][key] = value

    if ckpt_only:
        return ckpt

    model = PointTransformerV3(**ckpt["config"])
    model.load_state_dict(ckpt["state_dict"])
    n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
    print(f"Model params: {n_parameters / 1e6:.2f}M")
    return model