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.gitattributes

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README.md

@@ -9,7 +9,7 @@ tags:
   - pytorch
 library_name: transformers
 datasets:
-  - asRobotics/ballnet-100k
+  - han-xudong/ballnet-100k
 ---
 
 # Model Card for BallNet
@@ -27,7 +27,7 @@ datasets:
 
 BallNet is an MLP model designed for the Metaball. It can predict both 6D force and 3D shape (mesh nodes) from the 6D motion of the ball.
 
-Try it out on the [Spaces demo](https://huggingface.co/spaces/asRobotics/ballnet-demo).
+Try it out on the [Spaces demo](https://huggingface.co/spaces/han-xudong/ballnet-demo).
 
 - Developer: Xudong Han, Tianyu Wu, Fang Wan, and Chaoyang Song.
 - Model type: MLP
@@ -42,7 +42,7 @@ To load the model:
 ```python
 from transformers import AutoModel
 
-model = AutoModel.from_pretrained("asRobotics/ballnet", trust_remote_code=True)
+model = AutoModel.from_pretrained("han-xudong/ballnet", trust_remote_code=True)
 x = torch.zeros((1, 6))  # Example input: batch size of 1, 6D motion
 output = model(x)
 ```
@@ -55,7 +55,7 @@ import onnxruntime as ort
 import numpy as np
 from huggingface_hub import hf_hub_download
 
-onnx_model_path = hf_hub_download("asRobotics/ballnet", filename="model.onnx")
+onnx_model_path = hf_hub_download("han-xudong/ballnet", filename="model.onnx")
 ort_session = ort.InferenceSession(onnx_model_path)
 
 # Example input
@@ -65,7 +65,7 @@ output = ort_session.run(None, {"motion": x})
 
 ## Training Data
 
-The model was trained on the [BallNet-100K](https://huggingface.co/datasets/asRobotics/ballnet-100k) dataset, which includes a variety of motion, force, and shape data collected by finite element simulations.
+The model was trained on the [BallNet-100K](https://huggingface.co/datasets/han-xudong/ballnet-100k) dataset, which includes a variety of motion, force, and shape data collected by finite element simulations.
 
 ## Citation
 

config.json

@@ -1,15 +1,16 @@
-{
-  "_name_or_path": "asRobotics/ballnet",
-  "architectures": ["BallNet"],
-  "model_type": "ballnet",
-  "x_dim": [6],
-  "y_dim": [6, 2931],
-  "h1_dim": [100, 1000],
-  "h2_dim": [100, 1000],
-  "torch_dtype": "float32",
-  "layer_norm": false,
-  "use_activation": "relu",
-  "auto_map": {
-    "AutoModel": "modeling.FingerNet"
-  }
-}
+{
+  "_name_or_path": "asRobotics/ballnet",
+  "architectures": ["BallNet"],
+  "model_type": "ballnet",
+  "x_dim": [6],
+  "y_dim": [6, 2931],
+  "h1_dim": [100, 1000],
+  "h2_dim": [100, 1000],
+  "torch_dtype": "float32",
+  "layer_norm": false,
+  "use_activation": "relu",
+  "auto_map": {
+    "AutoConfig": "modeling.BallNetConfig",
+    "AutoModel": "modeling.BallNet"
+  }
+}

modeling.py

@@ -1,52 +1,54 @@
-import torch
-import torch.nn as nn
-from transformers import PreTrainedModel, PretrainedConfig
-
-class BallNetConfig(PretrainedConfig):
-    model_type = "ballnet"
-
-    def __init__(
-        self,
-        x_dim=[6],
-        y_dim=[6, 1800],
-        h1_dim=[100, 1000],
-        h2_dim=[100, 1000],
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.x_dim = x_dim
-        self.y_dim = y_dim
-        self.h1_dim = h1_dim
-        self.h2_dim = h2_dim
-
-
-class BallNet(PreTrainedModel):
-    config_class = BallNetConfig
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.x_dim = config.x_dim
-        self.y_dim = config.y_dim
-        self.h1_dim = config.h1_dim
-        self.h2_dim = config.h2_dim
-
-        # build sub-networks
-        self.branches = nn.ModuleList()
-        for i in range(len(self.y_dim)):
-            net = nn.Sequential(
-                nn.Linear(self.x_dim[0], self.h1_dim[i]),
-                nn.ReLU(),
-                nn.Linear(self.h1_dim[i], self.h2_dim[i]),
-                nn.ReLU(),
-                nn.Linear(self.h2_dim[i], self.y_dim[i]),
-            )
-            self.branches.append(net)
-
-        # initialize weights
-        self.post_init()
-
-    def forward(self, x):
-        if isinstance(x, (list, tuple)):
-            x = torch.tensor(x, dtype=torch.float32)
-        outputs = [branch(x) for branch in self.branches]
-        return tuple(outputs)
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig
+
+class BallNetConfig(PretrainedConfig):
+    model_type = "ballnet"
+
+    def __init__(
+        self,
+        x_dim=[6],
+        y_dim=[6, 1800],
+        h1_dim=[100, 1000],
+        h2_dim=[100, 1000],
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.x_dim = x_dim
+        self.y_dim = y_dim
+        self.h1_dim = h1_dim
+        self.h2_dim = h2_dim
+
+
+class BallNet(PreTrainedModel):
+    config_class = BallNetConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.x_dim = config.x_dim
+        self.y_dim = config.y_dim
+        self.h1_dim = config.h1_dim
+        self.h2_dim = config.h2_dim
+
+        self.model = nn.ModuleDict()
+        # Define the model architecture
+        for i in range(len(self.y_dim)):
+            self.model[f"estimator_{i}"] = nn.Sequential(
+                nn.Linear(self.x_dim[0], self.h1_dim[i]),
+                nn.ReLU(),
+                nn.Linear(self.h1_dim[i], self.h2_dim[i]),
+                nn.ReLU(),
+                nn.Linear(self.h2_dim[i], self.y_dim[i]),
+            )
+
+        # initialize weights
+        self.post_init()
+
+    def forward(self, x):
+        outputs = []
+        for i in range(len(self.y_dim)):
+            # Get the estimator for the i-th output
+            estimator = self.model[f"estimator_{i}"]
+            y = estimator(x)
+            outputs.append(y)
+        return outputs

modify_safetensors.py

@@ -0,0 +1,32 @@
+from safetensors import safe_open
+from safetensors.torch import save_file
+import torch
+
+# ==========================================================
+# 1. Load original safetensors
+# ==========================================================
+input_path = "model.safetensors"
+output_path = "model_fixed.safetensors"
+
+new_state_dict = {}
+
+with safe_open(input_path, framework="pt", device="cpu") as f:
+    for key in f.keys():
+        tensor = f.get_tensor(key)
+        # Add prefix "model." if not already
+        new_key = key if key.startswith("model.") else f"model.{key}"
+        new_state_dict[new_key] = tensor
+
+metadata = {
+    "format": "pt",
+}
+# ==========================================================
+# 2. Save to new safetensors file
+# ==========================================================
+save_file(new_state_dict, output_path, metadata=metadata)
+print(f"✅ Saved updated safetensors to {output_path}")
+print(f"✅ Total tensors: {len(new_state_dict)}")
+
+# Optional: verify one sample
+print("Example tensor key:", list(new_state_dict.keys())[0])
+print("Shape:", new_state_dict[list(new_state_dict.keys())[0]].shape)