afe.apis.prerelease_v1

This is the pre-release API for AFE. It supports importing models, loading and storing AFE’s internal format, quantizing, executing, and simulating.

Functions

load_onnx_model(→ afe.ir.net.AwesomeNet)	Loads an ONNX model into an _AwesomeNet format.
load_pytorch_model(→ afe.ir.net.AwesomeNet)	Loads a PyTorch model into an _AwesomeNet format.
load_tensorflow_model(→ afe.ir.net.AwesomeNet)	Loads a TensorFlow model into an _AwesomeNet format
load_tensorflow2_model(→ afe.ir.net.AwesomeNet)	Loads a TensorFlow model into an _AwesomeNet format
load_tflite_model(→ afe.ir.net.AwesomeNet)	Loads a TensorFlow Lite model into an _AwesomeNet format.
load_keras_model(→ afe.ir.net.AwesomeNet)	Loads a Keras model into an _AwesomeNet format.
load_caffe_model(→ afe.ir.net.AwesomeNet)	Loads a caffe model into an _AwesomeNet format.
load_caffe2_model(→ afe.ir.net.AwesomeNet)	Loads a caffe2 model into an _AwesomeNet format.
quantize_net(net, input_generator, asymmetry, ...)	Quantizes a network.
execute_net(→ List[numpy.ndarray])	Executes a network.
compile_net(net, output_elf_path[, compress])	Compile a network using Product Compiler.
simulate_isim_module(elf_file, output_path[, ...])	Wrapper to take elf file and generate summary KPI file, and trace file.
isim_net(elf_file, output_kpi_path[, ...])	Take elf file and generate summary KPI file, and trace file.

Module Contents

afe.apis.prerelease_v1.load_onnx_model(model_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], dtype_dict: Dict[str, afe.ir.tensor_type.ScalarType], layout: str = 'NCHW', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads an ONNX model into an _AwesomeNet format.

Parameters:

• model_path – Path to a .onnx file containing the onnx model

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• dtype_dict – Dictionary of input names to input types eg. {‘input’, ‘float32’}

• layout – Input data layout. Default is ‘NCHW’

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_pytorch_model(model_path: str, input_names: List[str], input_shapes: List[Tuple[int, Ellipsis]], input_dtypes: List[afe.ir.tensor_type.ScalarType] | None = None, layout: str = 'NCHW', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a PyTorch model into an _AwesomeNet format.

Parameters:

• model_path – Path to a PyTorch file (.pt) that contains the entire model

• input_names – List of input names. eg [‘input’]

• input_shapes – List of input shapes corresponding to input names. eg. [(1, 224, 224, 3)]

• input_dtypes – List of input datatypes corresponding to input names. eg [‘float32’]

• layout – Input data layout. Default is ‘NCHW’

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_tensorflow_model(model_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], output_names: List[str], layout: str = 'NHWC', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a TensorFlow model into an _AwesomeNet format

Parameters:

• model_path – Path to a .pb TensorFlow model

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• output_names – List of output names of the network eg. [‘output1’, ‘output2’]

• layout – any variation of the characters NHWC representing Batch Size, Height, Width, and Channels

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_tensorflow2_model(model_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], output_names: List[str], layout: str = 'NHWC', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a TensorFlow model into an _AwesomeNet format

Parameters:

• model_path – Path to a SavedModel tensorflow model.

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• output_names – List of output names of the network eg. [‘output1’, ‘output2’]

• layout – any variation of the characters NHWC representing Batch Size, Height, Width, and Channels

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_tflite_model(model_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], dtype_dict: Dict[str, afe.ir.tensor_type.ScalarType], layout: str = 'NHWC', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a TensorFlow Lite model into an _AwesomeNet format.

Parameters:

• model_path – Path to a .tflite file containing the TensorFlow Lite model

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• dtype_dict – Dictionary of input names to input types eg. {‘input’, ‘float32’}

• layout – Input data layout. Default is ‘NHWC’

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_keras_model(model_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], layout: str = 'NCHW', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a Keras model into an _AwesomeNet format.

Parameters:

• model_path – Path to a .h5 file containing the keras model

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• layout – Input data layout. Default is ‘NCHW’

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_caffe_model(prototxt_file_path: str, caffemodel_file_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], dtype_dict: Dict[str, afe.ir.tensor_type.ScalarType], layout: str = 'NCHW', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a caffe model into an _AwesomeNet format.

Parameters:

• prototxt_file_path – filepath to the caffe .prototxt file

• caffemodel_file_path – filepath to the caffe .caffemodel file

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• dtype_dict – Dictionary of input names to input types eg. {‘input’, ‘float32’}

• layout – Input data layout. Default is ‘NCHW’

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.load_caffe2_model(init_net_file_path: str, predict_net_file_path: str, shape_dict: Dict[str, Tuple[int, Ellipsis]], dtype_dict: Dict[str, afe.ir.tensor_type.ScalarType], layout: str = 'NCHW', is_quantized: bool = False) → afe.ir.net.AwesomeNet

Loads a caffe2 model into an _AwesomeNet format.

Parameters:

• init_net_file_path – filepath to the caffe2 .pb init_net file

• predict_net_file_path – filepath to the caffe2 .pb predict_net file

• shape_dict – Dictionary of input names to input shapes eg. {‘input’, (1,224,224,3)}

• dtype_dict – Dictionary of input names to input types eg. {‘input’, ‘float32’}

• layout – Input data layout. Default is ‘NCHW’

• is_quantized – Whether the model is pre-quantized. Default is False

Returns:

An _AwesomeNet

afe.apis.prerelease_v1.quantize_net(net: afe.ir.net.AwesomeNet, input_generator: sima_utils.data.data_generator.DataGenerator, asymmetry: bool, per_channel: bool, max_calibration_samples: int)

Quantizes a network.

Parameters:

• net – an _AwesomeNet

• input_generator – a DataGenerator we use to feed inputs into the _AwesomeNet during calibration

• asymmetry – If True this function performs asymmetric quantization. Otherwise, it performs symmetric quantization

• per_channel – If True this function performs per_channel quantization.

• max_calibration_samples – Maximum number of samples we use for calibration

afe.apis.prerelease_v1.execute_net(net: afe.ir.net.AwesomeNet, inputs: Dict[str, numpy.ndarray], dequantize_outputs: bool = True) → List[numpy.ndarray]

Executes a network.

Parameters:

• net – an _AwesomeNet

• inputs – Dictionary of placeholder node names (str) to the input data

• dequantize_outputs – Whether to dequantize the output data to floating point

:return The result of executing the output node

afe.apis.prerelease_v1.compile_net(net: afe.ir.net.AwesomeNet, output_elf_path: str, compress: bool = True)

Compile a network using Product Compiler.

Parameters:

• net – an _AwesomeNet.

• output_elf_path – Path in which elf files should be created.

• compress – If True mlc file is compressed before generating .elf file.

afe.apis.prerelease_v1.simulate_isim_module(elf_file: str, output_path: str, output_kpi_file_name: str | None = None, output_trace_file_name: str | None = None, generate_csv_kpi: bool | None = True, override_existing: bool | None = False, target: sima_utils.common.Platform | None = None)

Wrapper to take elf file and generate summary KPI file, and trace file.

Parameters:

• elf_file – Path to the location of the elf file to simulate in ISIM.

• output_path – Location of where to store all output files. If location does not exist, it will be created.

• output_kpi_file_name – Name for output kpi file. Default: elf_file name without ‘.elf’ extension and finishing with ‘_kpis.txt’ postfix.

• output_trace_file_name – Name for output trace file. Default: elf_file name without ‘.elf’ extension and finishing with ‘_trace.mpack’ postfix.

• generate_csv_kpi – Whether to generate a CSV file with the KPIs in addition to the txt.

• override_existing – Whether to override existing kpis and trace files from a previous run.

• target – [Optional] platform target.

afe.apis.prerelease_v1.isim_net(elf_file: str, output_kpi_path: str, output_trace_path: str | None = None, output_kpi_csv_path: str | None = None, csv_format: bool | None = False, target: sima_utils.common.Platform | None = None)

Take elf file and generate summary KPI file, and trace file.

Parameters:

• elf_file – a elf_file

• output_kpi_path – Path in which kpi files should be created

• output_trace_path – Optional Path in which trace files should be created. Only mla-isim supports –mpack.

• output_kpi_csv_path – Path in which kpi files in csv format should be created

• csv_format – Generate the Summary KPI in CVS format

• target – [Optional] platform target.