modified: .gitattributes

new file: __init__.py
new file: config.json
new file: model.onnx
new file: modeling.py

.gitattributes

@@ -32,4 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

__init__.py

@@ -0,0 +1 @@
+from .modeling import FingerNet, FingerNetConfig

config.json

@@ -0,0 +1,15 @@
+{
+  "_name_or_path": "asRobotics/fingernet",
+  "architectures": ["FingerNet"],
+  "model_type": "fingernet",
+  "x_dim": [6],
+  "y_dim": [6, 1800],
+  "h1_dim": [100, 1000],
+  "h2_dim": [100, 1000],
+  "torch_dtype": "float32",
+  "layer_norm": false,
+  "use_activation": "relu",
+  "auto_map": {
+    "AutoModel": "modeling.FingerNet"
+  }
+}

model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1138bf9e90c36e6e7e68e397dc8d109c444d96e95f3e8c45d65e5c3b756528c1
+size 15815554

modeling.py

@@ -0,0 +1,52 @@
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig
+
+class FingerNetConfig(PretrainedConfig):
+    model_type = "fingernet"
+
+    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 FingerNet(PreTrainedModel):
+    config_class = FingerNetConfig
+
+    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)