bugfix & remove redundent uni3d

app.py

@@ -6,8 +6,8 @@ import functools
 from datasets import load_dataset
 from feature_extractors.uni3d_embedding_encoder import Uni3dEmbeddingEncoder
 
-# os.environ['HTTP_PROXY'] = 'http://192.168.48.17:18000'
-# os.environ['HTTPS_PROXY'] = 'http://192.168.48.17:18000'
+os.environ['HTTP_PROXY'] = 'http://192.168.48.17:18000'
+os.environ['HTTPS_PROXY'] = 'http://192.168.48.17:18000'
 
 MAX_BATCH_SIZE = 16
 MAX_QUEUE_SIZE = 10
@@ -119,8 +119,8 @@ def launch():
         
     demo.queue(max_size=10)
 
-    # os.environ.pop('HTTP_PROXY')
-    # os.environ.pop('HTTPS_PROXY')
+    os.environ.pop('HTTP_PROXY')
+    os.environ.pop('HTTPS_PROXY')
 
     demo.launch(server_name='0.0.0.0')
 

dockerfile

@@ -7,13 +7,13 @@ LABEL email="yuanze1024@gmail.com"
 # Install webp support
 RUN apt update && apt install libwebp-dev -y
 
-RUN pip install -r requirements.txt
+RUN pip install -r requirements.txt 
 
 # note that you may need to modify the TORCH_CUDA_ARCH_LIST in the setup.py file
 ENV TORCH_CUDA_ARCH_LIST="8.6"
 
 # Install Pointnet2_PyTorch
 RUN git clone https://github.com/erikwijmans/Pointnet2_PyTorch.git \
-    && mv -f backup_install.txt Pointnet2_PyTorch/pointnet2_ops_lib/setup.py \
+    && cp -f change_setup.txt Pointnet2_PyTorch/pointnet2_ops_lib/setup.py \
     && cd Pointnet2_PyTorch/pointnet2_ops_lib \
-    && python install .
+    && pip install .

feature_extractors/uni3d_embedding_encoder.py

@@ -281,21 +281,21 @@ def create_uni3d(uni3d_path):
 class Uni3dEmbeddingEncoder(FeatureExtractor):
     def __init__(self, cache_dir, **kwargs) -> None:
         bpe_path = "utils/bpe_simple_vocab_16e6.txt.gz"
-        uni3d_path = os.path.join(cache_dir, "Uni3D", "modelzoo", "uni3d-g", "model.pt") # concat the subfolder as hf_hub_download will put it here
+        # uni3d_path = os.path.join(cache_dir, "Uni3D", "modelzoo", "uni3d-g", "model.pt") # concat the subfolder as hf_hub_download will put it here
         clip_path = os.path.join(cache_dir, "Uni3D", "open_clip_pytorch_model.bin")
 
-        if not os.path.exists(uni3d_path):
-            hf_hub_download("BAAI/Uni3D", "model.pt", subfolder="modelzoo/uni3d-g", cache_dir=cache_dir, 
-                            local_dir=cache_dir + os.sep + "Uni3D")
+        # if not os.path.exists(uni3d_path):
+        #     hf_hub_download("BAAI/Uni3D", "model.pt", subfolder="modelzoo/uni3d-g", cache_dir=cache_dir, 
+        #                     local_dir=cache_dir + os.sep + "Uni3D")
         if not os.path.exists(clip_path):
             hf_hub_download("timm/eva02_enormous_patch14_plus_clip_224.laion2b_s9b_b144k", "open_clip_pytorch_model.bin", 
                             cache_dir=cache_dir, local_dir=cache_dir + os.sep + "Uni3D")
 
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.tokenizer = SimpleTokenizer(bpe_path)
-        self.model = create_uni3d(uni3d_path)
-        self.model.eval()
-        self.model.to(self.device)
+        # self.model = create_uni3d(uni3d_path)
+        # self.model.eval()
+        # self.model.to(self.device)
         self.clip_model, _, self.preprocess = open_clip.create_model_and_transforms(model_name="EVA02-E-14-plus", pretrained=clip_path)
         self.clip_model.to(self.device)
         
@@ -309,10 +309,11 @@ class Uni3dEmbeddingEncoder(FeatureExtractor):
 
     @torch.no_grad()
     def encode_3D(self, data):
-        pc = data.to(device=self.device, non_blocking=True)
-        pc_features = self.model.encode_pc(pc)
-        pc_features = pc_features / pc_features.norm(dim=-1, keepdim=True)
-        return pc_features.float()
+        pass
+    #     pc = data.to(device=self.device, non_blocking=True)
+    #     pc_features = self.model.encode_pc(pc)
+    #     pc_features = pc_features / pc_features.norm(dim=-1, keepdim=True)
+    #     return pc_features.float()
 
     @torch.no_grad()
     def encode_text(self, input_text):