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509565d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | # 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 importlib
import os
import sys
import torch
import torch.nn as nn
def monkey_patch_p3sam():
"""Patch P3-SAM model construction to use the shared Hugging Face cache."""
current_file_path = os.path.abspath(__file__)
current_dir = os.path.dirname(current_file_path)
hunyuan_part_root = os.path.abspath(
os.path.join(current_dir, "../../../thirdparty/Hunyuan3D-Part")
)
p3sam_root = os.path.join(hunyuan_part_root, "P3-SAM")
partgen_root = os.path.join(hunyuan_part_root, "XPart/partgen")
for path in [p3sam_root, partgen_root]:
if path not in sys.path:
sys.path.insert(0, path)
from models import sonata
p3sam_model = importlib.import_module("model")
def build_P3SAM(self):
self.sonata = sonata.load(
"sonata",
repo_id="facebook/sonata",
download_root=os.path.expanduser(
"~/.cache/huggingface/hub/sonata"
),
)
self.mlp = nn.Sequential(
nn.Linear(1232, 512),
nn.GELU(),
nn.Linear(512, 512),
nn.GELU(),
nn.Linear(512, 512),
)
self.transform = sonata.transform.default()
self.seg_mlp_1 = nn.Sequential(
nn.Linear(512 + 3 + 3, 512),
nn.GELU(),
nn.Linear(512, 512),
nn.GELU(),
nn.Linear(512, 1),
)
self.seg_mlp_2 = nn.Sequential(
nn.Linear(512 + 3 + 3, 512),
nn.GELU(),
nn.Linear(512, 512),
nn.GELU(),
nn.Linear(512, 1),
)
self.seg_mlp_3 = nn.Sequential(
nn.Linear(512 + 3 + 3, 512),
nn.GELU(),
nn.Linear(512, 512),
nn.GELU(),
nn.Linear(512, 1),
)
self.seg_s2_mlp_g = nn.Sequential(
nn.Linear(512 + 3 + 3 + 3, 256),
nn.GELU(),
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 256),
)
self.seg_s2_mlp_1 = nn.Sequential(
nn.Linear(512 + 3 + 3 + 3 + 256, 256),
nn.GELU(),
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 1),
)
self.seg_s2_mlp_2 = nn.Sequential(
nn.Linear(512 + 3 + 3 + 3 + 256, 256),
nn.GELU(),
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 1),
)
self.seg_s2_mlp_3 = nn.Sequential(
nn.Linear(512 + 3 + 3 + 3 + 256, 256),
nn.GELU(),
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 1),
)
self.iou_mlp = nn.Sequential(
nn.Linear(512 + 3 + 3 + 3 + 256, 256),
nn.GELU(),
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 256),
)
self.iou_mlp_out = nn.Sequential(
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 256),
nn.GELU(),
nn.Linear(256, 3),
)
self.iou_criterion = torch.nn.MSELoss()
p3sam_model.build_P3SAM = build_P3SAM
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