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#
# 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"
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_infer_init()
return
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