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Rishit-dagli

Rishit-dagli

private val objectDetector = ObjectDetection.getClient(customObjectDetectorOptions) objectDetector.process(image) 
    .addOnFailureListener(Log.d(...))

    .addOnSuccessListener{ 
        graphicsOverlay.clear() 
        for (detectedObject in it){ 
            graphicsOverlay.add(ObjectGraphic(graphicsOverlay, detectedObject))
        } 
        graphicsOverlay.postInvalidate()} 

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Rishit-dagli

converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8

tflite_model_quant = converter.convert()
tflite_model_quant_file = "model.tflite"
tflite_model_quant_file.write_bytes(tflite_model_quant)

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converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.experimental_new_converter = True

# Post training quantization
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_quant_model = converter.convert()
tflite_model_quant_file = "model.tflite"
tflite_model_quant_file.write_bytes(tflite_quant_model)

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# Use torch.onnx.export function

model_pytorch = SimpleModel(input_size=input_size, hidden_sizes=hidden_sizes, output_size=output_size)
model_pytorch.load_state_dict(torch.load('./models/model_simple.pt'))

sample_input = torch.from_numpy(X_test[0].reshape(1, -1)).float().to(device)
sample_output = model_pytorch(dummy_input)

# Export to ONNX format
torch.onnx.export(model_pytorch, sample_input, './models/model_simple.onnx', input_names=['test_input'], output_names=['test_output'])

Rishit-dagli

# Use torch.onnx.export function

model_pytorch = SimpleModel(input_size=input_size, hidden_sizes=hidden_sizes, output_size=output_size)
model_pytorch.load_state_dict(torch.load('./models/model_simple.pt'))

sample_input = torch.from_numpy(X_test[0].reshape(1, -1)).float().to(device)
sample_output = model_pytorch(dummy_input)

# Export to ONNX format
torch.onnx.export(model_pytorch, sample_input, './models/model_simple.onnx', input_names=['test_input'], output_names=['test_output'])

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Contribution License Agreement

This Contribution License Agreement (“Agreement”) is agreed to by the party signing below (“You”), and conveys certain license rights to Rishit Dagli for Your contributions to Rishit Dagli's open source projects. This Agreement is effective upon Your acknowledgment via the CLA Assistant tool. All prospective contributors to Rishit Dagli's open source projects must sign this Agreement (digitally or otherwise) before any changes will be merged.

1. Definitions.

“Code” means the computer software code, whether in human-readable or machine-executable form, that is delivered by You to Rishit Dagli under this Agreement.

“Project” means any of the projects owned or managed by Rishit Dagli in which software is offered under a license approved by the Open Source Initiative (OSI) (www.opensource.org) and/or documentation offered under an OSI or a Creative Commons license (https://creativecommons.org/licenses).

“Submit” is the act of uploading, submitting, transmitting, or distribu

Rishit-dagli

Contribution License Agreement

This Contribution License Agreement (“Agreement”) is agreed to by the party signing below (“You”), and conveys certain license rights to Rishit Dagli for Your contributions to Rishit Dagli's open source projects. This Agreement is effective upon Your acknowledgment via the CLA Assistant tool. All prospective contributors to Rishit Dagli's open source projects must sign this Agreement (digitally or otherwise) before any changes will be merged.

1. Definitions.

“Code” means the computer software code, whether in human-readable or machine-executable form, that is delivered by You to Rishit Dagli under this Agreement.

“Project” means any of the projects owned or managed by Rishit Dagli in which software is offered under a license approved by the Open Source Initiative (OSI) (www.opensource.org) and/or documentation offered under an OSI or a Creative Commons license (https://creativecommons.org/licenses).

“Submit” is the act of uploading, submitting, transmitting, or distribu