Fix FP16 models having poor accuracy

Keep only dynamic-batch ONNX models, because
it's compatible with TRT EP as well as we've just verified.

convert_to_fp16.py

@@ -17,7 +17,7 @@ def main():
 
     # Convert model from Float32 to Float16
     print("Converting model...")
-    new_onnx_model = convert_float_to_float16(onnx_model, keep_io_types=True)
+    new_onnx_model = convert_float_to_float16(onnx_model, min_positive_val=1e-7, max_finite_val=1e4, keep_io_types=True)
 
     # Save the converted model
     print(f"Saving converted model to {args.output_model}")

demo.sh

@@ -1,2 +1,2 @@
 #!/bin/sh
-python3 rtmo_demo.py ./video rtmo-s.fp16_batch1.onnx
+python3 rtmo_demo.py ./video rtmo-s.fp16.onnx

demo_batch.sh

@@ -1,2 +1,2 @@
 #!/bin/sh
-python3 rtmo_demo_batch.py ./video rtmo-s.fp16_batch4.onnx 4
+python3 rtmo_demo_batch.py ./video rtmo-t.fp16.onnx 4

fix_input_batch_size.py

@@ -1,31 +0,0 @@
-import onnx
-import argparse
-
-def modify_batch_size(input_model_path, output_model_path, batch_size):
-    # Load the ONNX model
-    model = onnx.load(input_model_path)
-
-    # Modify the batch size of the first input tensor
-    input_tensor = model.graph.input[0]
-    input_tensor.type.tensor_type.shape.dim[0].dim_value = batch_size
-
-    # Save the modified model
-    onnx.save(model, output_model_path)
-    print(f"Modified model saved to {output_model_path}")
-
-if __name__ == "__main__":
-    # Parse command line arguments
-    parser = argparse.ArgumentParser(description="Modify the batch size of the first input tensor of an ONNX model.")
-    parser.add_argument("input_model_path", type=str, help="Path to the input ONNX model file.")
-    parser.add_argument("output_model_path", type=str, help="Path to save the modified ONNX model file.")
-    parser.add_argument("--batch_size", type=int, help="Desired batch size for the first input tensor.")
-
-    args = parser.parse_args()
-
-    # Ensure that the batch_size argument has been provided
-    if args.batch_size is None:
-        raise ValueError("Please specify the new batch size for the input tensor using --batch_size.")
-
-    # Call the function to modify the model's batch size
-    modify_batch_size(args.input_model_path, args.output_model_path, args.batch_size)
-

fix_input_batch_size_all.sh

@@ -1,38 +0,0 @@
-#!/bin/bash
-
-# Directory containing the original ONNX models
-MODEL_DIR="."
-
-# Directory to save the modified ONNX models
-OUTPUT_DIR="."
-
-# Ensure output directory exists
-mkdir -p "$OUTPUT_DIR"
-
-# Path to the Python script
-PYTHON_SCRIPT="fix_input_batch_size.py"
-
-# Loop through each ONNX model in the directory
-for model in "$MODEL_DIR"/*.onnx; do
-    echo "Processing $model..."
-    filename=$(basename -- "$model")
-    base="${filename%.*}"
-    
-    # Generate models with batch size 1
-    output_model_1="$OUTPUT_DIR/${base}_batch1.onnx"
-    python "$PYTHON_SCRIPT" "$model" "$output_model_1" --batch_size 1
-    echo "Generated model with batch size 1: $output_model_1"
-
-    # Generate models with batch size 2
-    output_model_2="$OUTPUT_DIR/${base}_batch2.onnx"
-    python "$PYTHON_SCRIPT" "$model" "$output_model_2" --batch_size 1
-    echo "Generated model with batch size 2: $output_model_2"
-    
-    # Generate models with batch size 4
-    output_model_4="$OUTPUT_DIR/${base}_batch4.onnx"
-    python "$PYTHON_SCRIPT" "$model" "$output_model_4" --batch_size 4
-    echo "Generated model with batch size 4: $output_model_4"
-done
-
-echo "Batch size modification complete."
-

rtmo-l.fp16.onnx

@@ -1,3 +1,3 @@
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+size 88036653

rtmo-l.fp16_batch1.onnx

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rtmo-l.fp16_batch2.onnx

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rtmo-l.fp16_batch4.onnx

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rtmo-l_batch1.onnx

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rtmo-l_batch2.onnx

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rtmo-l_batch4.onnx

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rtmo-m.fp16.onnx

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rtmo-m.fp16_batch1.onnx

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rtmo-m.fp16_batch2.onnx

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rtmo-m.fp16_batch4.onnx

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rtmo-m_batch1.onnx

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rtmo-m_batch2.onnx

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rtmo-m_batch4.onnx

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rtmo-s.fp16.onnx

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rtmo-s.fp16_batch1.onnx

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rtmo-s.fp16_batch2.onnx

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rtmo-s_batch1.onnx

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rtmo-s_batch2.onnx

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rtmo-t.fp16.onnx

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