fix req

P3-SAM/demo/auto_mask.py

@@ -1253,10 +1253,11 @@ class AutoMask:
         self.model = P3SAM()
         self.model.load_state_dict(ckpt_path)
         self.model.eval()
-        # self.model_parallel = torch.nn.DataParallel(self.model)
         self.model_parallel = self.model
-        self.model.cuda()
-        self.model_parallel.cuda()
+        # self.model.cuda()
+        self.model.to('cuda')
+        self.model_parallel.to('cuda')
+        print('p3sam to cuda')
         self.point_num = point_num
         self.prompt_num = prompt_num
         self.threshold = threshold

app.py

@@ -8,39 +8,44 @@ from pathlib import Path
 import torch
 import pytorch_lightning as pl
 import spaces
-import torch.multiprocessing as mp
-mp.set_start_method('spawn')
-print('using torch spawm')
-
+# from spaces import zero
+# zero.startup()
+# import torch.multiprocessing as mp
+# mp.set_start_method('spawn')
+# print('using torch spawm')
+# print('zero gpu startup')
 
 sys.path.append('P3-SAM')
 from demo.auto_mask import AutoMask
-from demo.auto_mask_no_postprocess import AutoMask as AutoMaskNoPostProcess
-sys.path.append('XPart')
-from partgen.partformer_pipeline import PartFormerPipeline
-from partgen.utils.misc import get_config_from_file
+# from demo.auto_mask_no_postprocess import AutoMask as AutoMaskNoPostProcess
+# sys.path.append('XPart')
+# from partgen.partformer_pipeline import PartFormerPipeline
+# from partgen.utils.misc import get_config_from_file
+print('no automask no postprocess')
 
 automask = AutoMask()
-automask_no_postprocess = AutoMaskNoPostProcess(automask_instance=automask)
-
-def _load_pipeline():
-    pl.seed_everything(2026, workers=True)
-    cfg_path = str(Path(__file__).parent / "XPart/partgen/config" / "infer.yaml")
-    config = get_config_from_file(cfg_path)
-    assert hasattr(config, "ckpt") or hasattr(
-        config, "ckpt_path"
-    ), "ckpt or ckpt_path must be specified in config"
-    pipeline = PartFormerPipeline.from_pretrained(
-        config=config,
-        verbose=True,
-        ignore_keys=config.get("ignore_keys", []),
-    )
-
-    device = "cuda"
-    pipeline.to(device=device, dtype=torch.float32)
-    return pipeline
-
-_PIPELINE = _load_pipeline()
+# automask_no_postprocess = AutoMaskNoPostProcess(automask_instance=automask)
+
+# def _load_pipeline():
+#     pl.seed_everything(2026, workers=True)
+#     cfg_path = str(Path(__file__).parent / "XPart/partgen/config" / "infer.yaml")
+#     config = get_config_from_file(cfg_path)
+#     assert hasattr(config, "ckpt") or hasattr(
+#         config, "ckpt_path"
+#     ), "ckpt or ckpt_path must be specified in config"
+#     pipeline = PartFormerPipeline.from_pretrained(
+#         config=config,
+#         verbose=True,
+#         ignore_keys=config.get("ignore_keys", []),
+#     )
+
+#     device = "cuda"
+#     pipeline.to(device=device, dtype=torch.float32)
+#     return pipeline
+
+# _PIPELINE = _load_pipeline()
+
+print('no xpart pipeline')
 
 output_path = 'P3-SAM/results/gradio'
 os.makedirs(output_path, exist_ok=True)

requirements.txt

@@ -1,3 +1,5 @@
+torch==2.7.0 --index-url https://download.pytorch.org/whl/cu126
+
 # Build Tools
 ninja==1.11.1.1
 
@@ -43,5 +45,5 @@ scikit-image
 
 # sonata
 spconv-cu126
-torch-scatter -f https://data.pyg.org/whl/torch-2.8.0+cu126.html
+torch-scatter -f https://data.pyg.org/whl/torch-2.7.0+cu126.html
 git+https://github.com/Dao-AILab/flash-attention.git