embodied_gen/utils/monkey_patch/sam3d.py

# Project EmbodiedGen
#
# Copyright (c) 2025 Horizon Robotics. All Rights Reserved.
#
# 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 sys
from copy import deepcopy
from typing import Optional, Union

import numpy as np
import torch
from PIL import Image


def monkey_patch_sam3d():
    """Monkey patches SAM3D inference pipelines with custom initialization and execution logic."""
    from embodied_gen.data.utils import model_device_ctx
    from embodied_gen.utils.log import logger

    os.environ["LIDRA_SKIP_INIT"] = "true"
    os.environ['ATTN_BACKEND'] = "xformers"

    current_file_path = os.path.abspath(__file__)
    current_dir = os.path.dirname(current_file_path)
    sam3d_root = os.path.abspath(
        os.path.join(current_dir, "../../../thirdparty/sam3d")
    )
    if sam3d_root not in sys.path:
        sys.path.insert(0, sam3d_root)

    def patch_pointmap_infer_pipeline():
        """Patches InferencePipelinePointMap.run to handle pointmap generation and 3D structure sampling."""
        try:
            from sam3d_objects.pipeline.inference_pipeline_pointmap import (
                InferencePipelinePointMap,
            )
        except ImportError:
            logger.error(
                "[MonkeyPatch]: Could not import sam3d_objects directly. Check paths."
            )
            return

        def patch_run(
            self,
            image: Union[None, Image.Image, np.ndarray],
            mask: Union[None, Image.Image, np.ndarray] = None,
            seed: Optional[int] = None,
            stage1_only=False,
            with_mesh_postprocess=True,
            with_texture_baking=True,
            with_layout_postprocess=True,
            use_vertex_color=False,
            stage1_inference_steps=None,
            stage2_inference_steps=None,
            use_stage1_distillation=False,
            use_stage2_distillation=False,
            pointmap=None,
            decode_formats=None,
            estimate_plane=False,
        ) -> dict:
            """Execute the inference pipeline: process image/mask, generate layouts (SS), and decode 3D shapes (SLAT)."""
            image = self.merge_image_and_mask(image, mask)
            with self.device:
                pointmap_dict = self.compute_pointmap(image, pointmap)
                pointmap = pointmap_dict["pointmap"]
                pts = type(self)._down_sample_img(pointmap)
                pts_colors = type(self)._down_sample_img(
                    pointmap_dict["pts_color"]
                )

                if estimate_plane:
                    return self.estimate_plane(pointmap_dict, image)

                ss_input_dict = self.preprocess_image(
                    image, self.ss_preprocessor, pointmap=pointmap
                )

                slat_input_dict = self.preprocess_image(
                    image, self.slat_preprocessor
                )
                if seed is not None:
                    torch.manual_seed(seed)

                with model_device_ctx(
                    self.models["ss_generator"],
                    self.models["ss_decoder"],
                    self.condition_embedders["ss_condition_embedder"],
                ):
                    ss_return_dict = self.sample_sparse_structure(
                        ss_input_dict,
                        inference_steps=stage1_inference_steps,
                        use_distillation=use_stage1_distillation,
                    )

                # We could probably use the decoder from the models themselves
                pointmap_scale = ss_input_dict.get("pointmap_scale", None)
                pointmap_shift = ss_input_dict.get("pointmap_shift", None)
                ss_return_dict.update(
                    self.pose_decoder(
                        ss_return_dict,
                        scene_scale=pointmap_scale,
                        scene_shift=pointmap_shift,
                    )
                )

                ss_return_dict["scale"] = (
                    ss_return_dict["scale"]
                    * ss_return_dict["downsample_factor"]
                )

                if stage1_only:
                    logger.info("Finished!")
                    ss_return_dict["voxel"] = (
                        ss_return_dict["coords"][:, 1:] / 64 - 0.5
                    )
                    return {
                        **ss_return_dict,
                        "pointmap": pts.cpu().permute((1, 2, 0)),  # HxWx3
                        "pointmap_colors": pts_colors.cpu().permute(
                            (1, 2, 0)
                        ),  # HxWx3
                    }
                    # return ss_return_dict

                coords = ss_return_dict["coords"]
                with model_device_ctx(
                    self.models["slat_generator"],
                    self.condition_embedders["slat_condition_embedder"],
                ):
                    slat = self.sample_slat(
                        slat_input_dict,
                        coords,
                        inference_steps=stage2_inference_steps,
                        use_distillation=use_stage2_distillation,
                    )

                with model_device_ctx(
                    self.models["slat_decoder_mesh"],
                    self.models["slat_decoder_gs"],
                    self.models["slat_decoder_gs_4"],
                ):
                    outputs = self.decode_slat(
                        slat,
                        (
                            self.decode_formats
                            if decode_formats is None
                            else decode_formats
                        ),
                    )

                outputs = self.postprocess_slat_output(
                    outputs,
                    with_mesh_postprocess,
                    with_texture_baking,
                    use_vertex_color,
                )
                glb = outputs.get("glb", None)

                try:
                    if (
                        with_layout_postprocess
                        and self.layout_post_optimization_method is not None
                    ):
                        assert (
                            glb is not None
                        ), "require mesh to run postprocessing"
                        logger.info(
                            "Running layout post optimization method..."
                        )
                        postprocessed_pose = self.run_post_optimization(
                            deepcopy(glb),
                            pointmap_dict["intrinsics"],
                            ss_return_dict,
                            ss_input_dict,
                        )
                        ss_return_dict.update(postprocessed_pose)
                except Exception as e:
                    logger.error(
                        f"Error during layout post optimization: {e}",
                        exc_info=True,
                    )

                result = {
                    **ss_return_dict,
                    **outputs,
                    "pointmap": pts.cpu().permute((1, 2, 0)),
                    "pointmap_colors": pts_colors.cpu().permute((1, 2, 0)),
                }
                return result

        InferencePipelinePointMap.run = patch_run

    def patch_infer_init():
        """Patches InferencePipeline.__init__ to allow CPU offloading during model initialization."""
        import torch

        try:
            from sam3d_objects.pipeline import preprocess_utils
            from sam3d_objects.pipeline.inference_pipeline_pointmap import (
                InferencePipeline,
            )
            from sam3d_objects.pipeline.inference_utils import (
                SLAT_MEAN,
                SLAT_STD,
            )
        except ImportError:
            print(
                "[MonkeyPatch] Error: Could not import sam3d_objects directly for infer pipeline."
            )
            return

        def patch_init(
            self,
            ss_generator_config_path,
            ss_generator_ckpt_path,
            slat_generator_config_path,
            slat_generator_ckpt_path,
            ss_decoder_config_path,
            ss_decoder_ckpt_path,
            slat_decoder_gs_config_path,
            slat_decoder_gs_ckpt_path,
            slat_decoder_mesh_config_path,
            slat_decoder_mesh_ckpt_path,
            slat_decoder_gs_4_config_path=None,
            slat_decoder_gs_4_ckpt_path=None,
            ss_encoder_config_path=None,
            ss_encoder_ckpt_path=None,
            decode_formats=["gaussian", "mesh"],
            dtype="bfloat16",
            pad_size=1.0,
            version="v0",
            device="cuda",
            ss_preprocessor=preprocess_utils.get_default_preprocessor(),
            slat_preprocessor=preprocess_utils.get_default_preprocessor(),
            ss_condition_input_mapping=["image"],
            slat_condition_input_mapping=["image"],
            pose_decoder_name="default",
            workspace_dir="",
            downsample_ss_dist=0,  # the distance we use to downsample
            ss_inference_steps=25,
            ss_rescale_t=3,
            ss_cfg_strength=7,
            ss_cfg_interval=[0, 500],
            ss_cfg_strength_pm=0.0,
            slat_inference_steps=25,
            slat_rescale_t=3,
            slat_cfg_strength=5,
            slat_cfg_interval=[0, 500],
            rendering_engine: str = "nvdiffrast",  # nvdiffrast OR pytorch3d,
            shape_model_dtype=None,
            compile_model=False,
            slat_mean=SLAT_MEAN,
            slat_std=SLAT_STD,
        ):
            """Initialize pipeline components on CPU first to save GPU memory, then move necessary parts later."""
            self.rendering_engine = rendering_engine
            self.device = torch.device(device)
            self.compile_model = compile_model
            with self.device:
                self.decode_formats = decode_formats
                self.pad_size = pad_size
                self.version = version
                self.ss_condition_input_mapping = ss_condition_input_mapping
                self.slat_condition_input_mapping = (
                    slat_condition_input_mapping
                )
                self.workspace_dir = workspace_dir
                self.downsample_ss_dist = downsample_ss_dist
                self.ss_inference_steps = ss_inference_steps
                self.ss_rescale_t = ss_rescale_t
                self.ss_cfg_strength = ss_cfg_strength
                self.ss_cfg_interval = ss_cfg_interval
                self.ss_cfg_strength_pm = ss_cfg_strength_pm
                self.slat_inference_steps = slat_inference_steps
                self.slat_rescale_t = slat_rescale_t
                self.slat_cfg_strength = slat_cfg_strength
                self.slat_cfg_interval = slat_cfg_interval

                self.dtype = self._get_dtype(dtype)
                if shape_model_dtype is None:
                    self.shape_model_dtype = self.dtype
                else:
                    self.shape_model_dtype = self._get_dtype(shape_model_dtype)

                # Setup preprocessors
                self.pose_decoder = self.init_pose_decoder(
                    ss_generator_config_path, pose_decoder_name
                )
                self.ss_preprocessor = self.init_ss_preprocessor(
                    ss_preprocessor, ss_generator_config_path
                )
                self.slat_preprocessor = slat_preprocessor

                raw_device = self.device
                self.device = torch.device("cpu")
                ss_generator = self.init_ss_generator(
                    ss_generator_config_path, ss_generator_ckpt_path
                )
                slat_generator = self.init_slat_generator(
                    slat_generator_config_path, slat_generator_ckpt_path
                )
                ss_decoder = self.init_ss_decoder(
                    ss_decoder_config_path, ss_decoder_ckpt_path
                )
                ss_encoder = self.init_ss_encoder(
                    ss_encoder_config_path, ss_encoder_ckpt_path
                )
                slat_decoder_gs = self.init_slat_decoder_gs(
                    slat_decoder_gs_config_path, slat_decoder_gs_ckpt_path
                )
                slat_decoder_gs_4 = self.init_slat_decoder_gs(
                    slat_decoder_gs_4_config_path, slat_decoder_gs_4_ckpt_path
                )
                slat_decoder_mesh = self.init_slat_decoder_mesh(
                    slat_decoder_mesh_config_path, slat_decoder_mesh_ckpt_path
                )

                # Load conditioner embedder so that we only load it once
                ss_condition_embedder = self.init_ss_condition_embedder(
                    ss_generator_config_path, ss_generator_ckpt_path
                )
                slat_condition_embedder = self.init_slat_condition_embedder(
                    slat_generator_config_path, slat_generator_ckpt_path
                )
                self.device = raw_device

                self.condition_embedders = {
                    "ss_condition_embedder": ss_condition_embedder,
                    "slat_condition_embedder": slat_condition_embedder,
                }

                # override generator and condition embedder setting
                self.override_ss_generator_cfg_config(
                    ss_generator,
                    cfg_strength=ss_cfg_strength,
                    inference_steps=ss_inference_steps,
                    rescale_t=ss_rescale_t,
                    cfg_interval=ss_cfg_interval,
                    cfg_strength_pm=ss_cfg_strength_pm,
                )
                self.override_slat_generator_cfg_config(
                    slat_generator,
                    cfg_strength=slat_cfg_strength,
                    inference_steps=slat_inference_steps,
                    rescale_t=slat_rescale_t,
                    cfg_interval=slat_cfg_interval,
                )

                self.models = torch.nn.ModuleDict(
                    {
                        "ss_generator": ss_generator,
                        "slat_generator": slat_generator,
                        "ss_encoder": ss_encoder,
                        "ss_decoder": ss_decoder,
                        "slat_decoder_gs": slat_decoder_gs,
                        "slat_decoder_gs_4": slat_decoder_gs_4,
                        "slat_decoder_mesh": slat_decoder_mesh,
                    }
                )
                logger.info("Loading SAM3D model weights completed.")

                if self.compile_model:
                    logger.info("Compiling model...")
                    self._compile()
                    logger.info("Model compilation completed!")
                self.slat_mean = torch.tensor(slat_mean)
                self.slat_std = torch.tensor(slat_std)

        InferencePipeline.__init__ = patch_init

    # patch_pointmap_infer_pipeline() # patch
    # patch_infer_init() # patch

    return