Bugfix: correct batch dimension fixation then regenerate ONNX models

Batch dimension has been fixed properly following the guide below
https://onnxruntime.ai/docs/tutorials/mobile/helpers/make-dynamic-shape-fixed.html

convert_to_fp16_all.py

@@ -0,0 +1,35 @@
+import os
+import subprocess
+
+def convert_onnx_to_fp16_corrected(input_dir):
+    # List all files in the input directory
+    files = os.listdir(input_dir)
+    
+    # Filter out files with the .onnx extension
+    onnx_files = [file for file in files if file.endswith('.onnx')]
+    
+    # Iterate over each ONNX file to convert it to FP16
+    for onnx_file in onnx_files:
+        # Split the file name to insert '.fp16' before the last underscore
+        parts = onnx_file.rsplit('_', 1)
+        if len(parts) == 2:
+            # If there is at least one underscore, insert '.fp16' before the last part
+            output_model_name = f"{parts[0]}.fp16_{parts[1]}"
+        else:
+            # If there's no underscore, just replace '.onnx' with '.fp16.onnx'
+            output_model_name = onnx_file.replace('.onnx', '.fp16.onnx')
+        
+        # Construct the command to run the conversion script
+        command = [
+            'python3', 'convert_to_fp16.py',
+            '--input_model', os.path.join(input_dir, onnx_file),
+            '--output_model', os.path.join(input_dir, output_model_name)
+        ]
+        
+        # Execute the command
+        subprocess.run(command, check=True)
+
+if __name__ == '__main__':
+    # Assuming the current directory is the input directory
+    input_dir = os.getcwd()
+    convert_onnx_to_fp16_corrected(input_dir)

fix_batch_dimension.py

@@ -1,35 +0,0 @@
-import argparse
-import onnx
-from onnx import numpy_helper
-import numpy as np
-
-def fix_batch_dimension(input_model_path, output_model_path, batch_size=1):
-    # Load the input ONNX model
-    model = onnx.load(input_model_path)
-
-    # Iterate through the model's inputs
-    for input_tensor in model.graph.input:
-        # Get the tensor shape
-        tensor_shape = input_tensor.type.tensor_type.shape
-        
-        # Modify the batch dimension
-        if len(tensor_shape.dim) > 0:
-            tensor_shape.dim[0].dim_value = batch_size
-
-    # Save the modified model to the output path
-    onnx.save(model, output_model_path)
-    print(f"Model saved with updated batch size of {batch_size} to {output_model_path}")
-
-if __name__ == "__main__":
-    # Setup command line arguments
-    parser = argparse.ArgumentParser(description="Fix batch dimension of an ONNX model.")
-    parser.add_argument("input_model_path", type=str, help="Path to the input ONNX model.")
-    parser.add_argument("output_model_path", type=str, help="Path to save the output ONNX model with fixed batch dimension.")
-    parser.add_argument("--batch_size", type=int, default=1, help="Value of batch size to assign (default is 1).")
-
-    # Parse arguments
-    args = parser.parse_args()
-
-    # Call the function to fix the batch dimension
-    fix_batch_dimension(args.input_model_path, args.output_model_path, args.batch_size)
-

fix_batch_dimension_all.sh

@@ -5,15 +5,15 @@ for model in ./*.onnx; do
     # Exclude processing any files that already have a batch size in their name
     if [[ ! $model =~ _batch[0-9]+\.onnx$ ]]; then
         # Process for batch size 1
-        python fix_batch_dimension.py "$model" "${model%.onnx}_batch1.onnx" --batch_size 1
+        python3 -m onnxruntime.tools.make_dynamic_shape_fixed --dim_param batch --dim_value 1 "$model" "${model%.onnx}_batch1.onnx"
         echo "Generated ${model%.onnx}_batch1.onnx"
 
         # Process for batch size 2
-        python fix_batch_dimension.py "$model" "${model%.onnx}_batch2.onnx" --batch_size 2
+        python3 -m onnxruntime.tools.make_dynamic_shape_fixed --dim_param batch --dim_value 2 "$model" "${model%.onnx}_batch2.onnx"
         echo "Generated ${model%.onnx}_batch2.onnx"
 
         # Process for batch size 4
-        python fix_batch_dimension.py "$model" "${model%.onnx}_batch4.onnx" --batch_size 4
+        python3 -m onnxruntime.tools.make_dynamic_shape_fixed --dim_param batch --dim_value 4 "$model" "${model%.onnx}_batch4.onnx"
         echo "Generated ${model%.onnx}_batch4.onnx"
     fi
 done

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