completed converting the pytorch model -> ONNX

checkpoint_files/__init__.py

@@ -0,0 +1,6 @@
+import os
+
+CHECKPOINTS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+if __name__ == "__main__":
+    print(CHECKPOINTS_DIR)

convert_to_onnx.py

@@ -0,0 +1,84 @@
+import os
+
+import numpy as np
+import torch
+
+from data.training.mobilenetv3 import mobilenetv3, RFClassifier
+from onnx_files import ONNX_DIR
+from helpers.app_settings import get_app_settings
+
+
+
+def convert_to_onnx(ckpt_path, fp16=False):
+    """
+    Convert a PyTorch model to ONNX format.
+
+    Parameters:
+        model (torch.nn.Module): The PyTorch model to convert.
+        input_shape (tuple): The shape of the input tensor.
+        output_path (str): The path to save the converted ONNX model.
+    """
+    settings = get_app_settings()
+    
+    inference_cfg = settings.inference
+    dataset_cfg = settings.dataset
+    
+    
+    in_channels = 2
+    batch_size = 1
+    slice_length = int(1024/dataset_cfg.num_slices)
+    num_classes = inference_cfg.num_classes
+    
+    model = RFClassifier(
+        model=mobilenetv3(
+            model_size="mobilenetv3_small_050",
+            num_classes=num_classes,
+            in_chans=in_channels,
+        )
+    )
+    
+    checkpoint = torch.load(
+        ckpt_path, weights_only = True, map_location=torch.device("cpu")
+    )
+    model.load_state_dict(checkpoint["state_dict"])
+    
+    if fp16:
+        model.half()
+    
+    model.eval()
+    
+    
+    # Generate random sample data
+    base, ext = os.path.splitext(os.path.basename(ckpt_path))
+    if fp16:
+        output_path = os.path.join(ONNX_DIR, f"{base}_fp16.onnx")
+        sample_input = torch.from_numpy(
+            np.random.rand(batch_size, in_channels, slice_length).astype(np.float16)
+        )
+    else:
+        output_path = os.path.join(ONNX_DIR, f"{base}_fp32.onnx")
+        sample_input = torch.rand(batch_size, in_channels, slice_length)
+        
+    
+    torch.onnx.export(
+        model=model,
+        args=sample_input,
+        f=output_path,
+        export_params=True,
+        opset_version=20,  # Last compatible with ORT v1.15.1 is 18, 21 not supported by torch export
+        do_constant_folding=True,
+        input_names=["input"],
+        output_names=["output"],
+        dynamo=False,  # Requires onnxscript
+    )
+    
+if __name__ == "__main__":
+    from checkpoint_files import CHECKPOINTS_DIR
+
+    model_checkpoint = "interference_recognition_model.ckpt"
+    
+    print("Converting to ONNX...")
+
+    convert_to_onnx(ckpt_path=os.path.join(CHECKPOINTS_DIR, model_checkpoint), fp16=False)
+    
+    print("Conversion complete stored at: ", os.path.join(ONNX_DIR, model_checkpoint))

onnx_files/__init__.py

@@ -0,0 +1,6 @@
+import os
+
+ONNX_DIR = os.path.dirname(os.path.abspath(__file__))
+
+if __name__ == "__main__":
+    print(ONNX_DIR)