USAGE_v2_EN.md

Overview

nncase only provides Python APIs for compiling/inferring deep learning models on x86_64 and amd64 platforms. nncase-v2 will no longer support compilation and inference for k210 and k510, use nncase-v1 instead if needed.

nncase-v2 python APIs

Install

The nncase toolchain compiler section includes the nncase and KPU plugin wheel packages

• nncase and KPU plugin wheel packages are released at nncase github release

• nncase-v2 depends on dotnet-7.0.

• User can use pip to install nncase and KPU plugin wheel packages under linux platform directly, and apt to install dotnet under Ubuntu environment.

  pip install --upgrade pip
  pip install nncase
  pip install nncase-kpu
  
  # nncase-2.x need dotnet-7
  sudo apt-get install -y dotnet-sdk-7.0
  

• Windows platform support nncase online installation, nncase-kpu need to manually download in nncase github release and install.

Users without an Ubuntu environment can use the nncase docker (Ubuntu 20.04 + Python 3.8 + dotnet-7.0).

$ cd /path/to/nncase_sdk
$ docker pull ghcr.io/kendryte/k230_sdk
$ docker run -it --rm -v `pwd`:/mnt -w /mnt ghcr.io/kendryte/k230_sdk /bin/bash -c "/bin/bash"

Check nncase version

root@469e6a4a9e71:/mnt# python3
Python 3.8.10 (default, May 26 2023, 14:05:08)
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import _nncase
>>> print(_nncase.__version__)
2.1.0-4a87051

Examples of compilation and inference

Model compilation, inference for k230 can be found in the Jupyter script User_guide, this script contains single and multiple input examples.

If you run the Jupyter script in Docker, you can refer to the command and then open it in your browser.

docker run -it --rm --privileged=true -p 8889:8889 --name Kendryte -v `pwd`:/mnt -w /mnt ghcr.io/kendryte/k230_sdk  /bin/bash -c "/bin/bash

pip install jupyterlab

jupyter-lab --ip 0.0.0.0 --allow-root

You need to modify the following to suit your needs before executing the script:

1. Information about compile_options, ptq_options in compile_kmodel function.

   See CompileOptions for details of compile_options.

   See PTQTensorOptions for details of ptq_options.

2. In the compile kmodel single input (multiple inputs) section

   Modify model_path and dump_path to specify the model path and the file generation path during compilation.

   Modify the implementation of calib_data, see comments for data format.

3. In the run kmodel(simulate) section, modify the implementation of input_data, see comments for data format.

At the end of inference, kmodel, output result and files during compilation are generated under the dump_path path.

The processes of inferring on boards

Refer to K230_docs.

compilation APIs

CompileOptions

Parameter Description

CompileOptions is used to configure the nncase compile options

Attribute	Data Type	Required	Description
target	string	Y	Specify the compile target, such as 'cpu', 'k230'
dump_ir	bool	N	Specify whether dump IR, False by default.
dump_asm	bool	N	Specify whether dump asm file, False by default.
dump_dir	string	N	Specify dump directory
input_file	string	N	Specify .onnx_data file path when the size of onnx model lager than 2GB.
preprocess	bool	N	Specify whether to enable pre-processing, False by default. The following parameters will work when preprocess is True
input_type	string	N	Specify the input data type when turning on preprocessing, defaults to float. When preprocess is True, it must be specified as "uint8" or "float32".
input_shape	list[int]	N	Specify the shape of the input data when turning on preprocessing, [] by default. It must be specified When preprocess is True
input_range	list[float]	N	Specify the range of floating-point numbers after inverse quantization of the input data when pre-processing is turned on, [] by default.
input_layout	string	N	Specify the layout of the input data, "" by default.
swapRB	bool	N	Specify whether swap the channel of "R,B", False by default.
mean	list[float]	N	Normalize mean value for preprocess, [0, 0, 0] by default
std	list[float]	N	Normalize std value for preprocess, [1, 1, 1] by default
letterbox_value	float	N	Specify the pad value of letterbox during preprocess, 0 by default.
output_layout	string	N	Specify the layout of the output data, "" by default.

The pipeline of preprocessing

At present, there is no support for custom pre-processing order. You can choose the required pre-processing parameters to configure according to the following flow diagram.

graph TD;

    NewInput("NewInput\n(shape = input_shape\ndtype = input_type)") -->a(input_layout != ' ')-.Y.->Transpose1["transpose"] -.->b("SwapRB == True")-.Y.->SwapRB["SwapRB"]-.->c("input_type != float32")-.Y.->Dequantize["Dequantize"]-.->d("input_HW != model_HW")-.Y.->LetterBox["LetterBox"] -.->e("std not empty\nmean not empty")-.Y.->Normalization["Normalization"]-.->OldInput-->Model_body-->OldOutput-->f("output_layout != ' '")-.Y.->Transpose2["Transpose"]-.-> NewOutput;
	a--N-->b--N-->c--N-->d--N-->e--N-->OldInput; f--N-->NewOutput;

	subgraph origin_model
		OldInput; Model_body ; OldOutput;
	end

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Parameter explanations:

1. input_range is the range of input data after be dequantized to "float32" when input_type is "uint8".

   a. When input type is "uint8",range is "[0,255]",input_range is "[0,255]", the Dequantize_op only convert the type of input data to "float32". meanand std are still specificed according to data with range "[0,255]".

   b. When input type is "uint8",range is "[0,255]",input_range is [0,1],the input data will be dequantized to "float32" with range "[0,1]",meanand std need to specify according to data with range "[0,1]".

   graph TD;
   	NewInput_uint8("NewInput_uint8 \n[input_type:uint8]") --input_range:0,255 -->dequantize_0["Dequantize"]--float range:0,255--> OldInput_float32
   	NewInput_uint81("NewInput_uint8 \n[input_type:uint8]") --input_range:0,1 -->dequantize_1["Dequantize"]--float range:0,1--> OldInput_float32
   

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2. input_shape is the shape of input data,input_layout is the layout of input data,Both strings ("NHWC", "NCHW") and indexes are now supported as input_layout, and non-4D data handling is supported.

   When input_layout is configured in the form of a string, it indicates the layout of the input data; when input_layout is configured in the form of an index, it indicates that the input data will be transposed in accordance with the currently configured input_layout. input_layout is the perm parameter of Transpose.

graph TD;
	subgraph B
    	NewInput1("NewInput: 1,4,10") --"input_layout:"0,2,1""-->Transpose2("Transpose perm: 0,2,1") --> OldInput2("OldInput: 1,10,4");
    end
	subgraph A
    	NewInput --"input_layout:"NHWC""--> Transpose0("Transpose: NHWC2NCHW") --> OldInput;
    	NewInput("NewInput: 1,224,224,3 (NHWC)") --"input_layout:"0,3,1,2""--> Transpose1("Transpose perm: 0,3,1,2") --> OldInput("OldInput: 1,3,224,224 (NCHW)");
	end

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`output_layout` is similar to `input_layout`

graph TD;
subgraph B
    OldOutput1("OldOutput: 1,10,4,5,2") --"output_layout: "0,2,3,1,4""--> Transpose5("Transpose perm: 0,2,3,1,4") --> NewOutput1("NewOutput: 1,4,5,10,2");
    end
subgraph A
    OldOutput --"output_layout: "NHWC""--> Transpose3("Transpose: NCHW2NHWC") --> NewOutput("NewOutput\nNHWC");
    OldOutput("OldOutput: (NCHW)") --"output_layout: "0,2,3,1""--> Transpose4("Transpose perm: 0,2,3,1") --> NewOutput("NewOutput\nNHWC");
    end

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If you have utilized pre-processing configurations when compiling the kmodel, when you need to verify the results using the ONNX or TFLite framework, you must add the corresponding pre-processing operations to your ONNX or TFLite pipeline to ensure equivalence between the kmodel pipeline.

Dynamice shape args

Refer to Dynamic shape args description

Example

compile_options = nncase.CompileOptions()

compile_options.target = "cpu" #"k230"
compile_options.dump_ir = True  # if False, will not dump the compile-time result.
compile_options.dump_asm = True
compile_options.dump_dir = "dump_path"
compile_options.input_file = ""

# preprocess args
compile_options.preprocess = False
if compile_options.preprocess:
    compile_options.input_type = "uint8" # "uint8" "float32"
    compile_options.input_shape = [1,224,320,3]
    compile_options.input_range = [0,1]
    compile_options.input_layout = "NHWC" # "NHWC"
    compile_options.swapRB = False
    compile_options.mean = [0,0,0]
    compile_options.std = [1,1,1]
    compile_options.letterbox_value = 0
    compile_options.output_layout = "NHWC" # "NHWC"

ImportOptions

Definition

The details of all attributes are following.

Attribute	Data Type	Required	Description
output_arrays	string	N	output array name

Example

# import_options
import_options = nncase.ImportOptions()
import_options.output_arrays = 'output' # Your output node name

PTQTensorOptions

Definition

PTQTensorOptions is used to configure PTQ options. The details of all attributes are following.

Attribute	Data Type	Required	Description
calibrate_method	string	N	Specify calibrate method, 'NoClip' by default. 'Kld' is optional. Must be configured when use quantification.
samples_count	int	N	The number of calibration data sets. Must be configured when use quantification.
finetune_weights_method	string	N	Finetune weights method,'NoFineTuneWeights' by default. 'UseSquant' is optional.
quant_type	string	N	Type of data quantification,'uint8' by default. 'int8','int16' are optional.
w_quant_type	string	N	Type of weights quantification,'uint8' by default. 'int8','int16' are optional.
dump_quant_error	bool	N	Specify whether dump quantification error, False by default. The parameters following worked when dump_ir=True.
dump_quant_error_symmetric_for_signed	bool	N	Specify whether dump quantification error by symmetric for signed number,True by default.
quant_scheme	string	N	specify the path of quantification scheme file,"" by default.
quant_scheme_strict_mode	bool	N	Specify whether strictly follow quant_scheme for quantification, False by default.
export_quant_scheme	bool	N	Specify whether export quantification scheme, False by default.
export_weight_range_by_channel	bool	N	Specify whether export weights range by channel, False by default.

Detailed information about quantitative profiles can be found at Mix Quant

set_tensor_data()

Definition

set_tensor_data(calib_data)

Parameters

Attribute	Data Type	Required	Description
calib_data	byte[]	Y	The data for calibrating.

Returns

N/A

Example

# If model has multiple inputs, calib_data format is "[[x1, x2,...], [y1, y2,...], ...]"
# e.g. Model has three inputs (x, y, z), the calib_data is '[[x1, x2, x3],[y1, y2, y3],[z1, z2, z3]]'

calib_data = [[np.random.rand(1, 3, 224, 224).astype(np.float32), np.random.rand(1, 3, 224, 224).astype(np.float32)]]

# ptq_options
ptq_options = nncase.PTQTensorOptions()
ptq_options.samples_count = len(calib_data[0])
ptq_options.set_tensor_data(calib_data)

Compiler

Description

Compiler is used to compile models.

Example

compiler = nncase.Compiler(compile_options)

import_tflite()

Description

Import tflite model.

Definition

import_tflite(model_content, import_options)

Parameters

Attribute	Data Type	Required	Description
model_content	byte[]	Y	The content of model.
import_options	ImportOptions	Y	Import options

Returns

N/A

Example

model_content = read_model_file(model)
compiler.import_tflite(model_content, import_options)

import_onnx()

Description

Import onnx model.

Definition

import_onnx(model_content, import_options)

Parameters

Attribute	Data Type	Required	Description
model_content	byte[]	Y	The content of model.
import_options	ImportOptions	Y	Import options

Returns

N/A

Example

model_content = read_model_file(model)
compiler.import_onnx(model_content, import_options)

use_ptq()

Description

Enable PTQ.

Definition

use_ptq(ptq_options)

Parameters

Attribute	Data Type	Required	Description
ptq_options	PTQTensorOptions	Y	PTQ options.

Returns

N/A

Example

compiler.use_ptq(ptq_options)

compile()

Description

Compile model.

Definition

compile()

Parameters

N/A

Returns

N/A

Example

compiler.compile()

gencode_tobytes()

Description

Generate byte code for model.

Definition

gencode_tobytes()

Parameters

N/A

Returns

bytes[]

Example

kmodel = compiler.gencode_tobytes()
with open(os.path.join(infer_dir, 'test.kmodel'), 'wb') as f:
    f.write(kmodel)

nncase inference APIs

Nncase provides inference APIs to inference kmodel. You can make use of it to check the result with runtime for deep learning frameworks.

MemoryRange

Description

MemoryRange is used to describe the range to memory.

Attribute	Data Type	Required	Description
location	int	N	Specify the location of memory. 0 means input, 1 means output, 2 means rdata, 3 means data, 4 means shared_data.
dtype	python data type	N	data type
start	int	N	The start of memory
size	int	N	The size of memory

Example

mr = nncase.MemoryRange()

RuntimeTensor

Description

RuntimeTensor is used to describe the runtime tensor. The details of all attributes are following.

Attribute	Data Type	Required	Description
dtype	int	N	The data type of tensor
shape	list	N	The shape of tensor

from_numpy()

Description

Construct RuntimeTensor from numpy.ndarray

Definition

from_numpy(py::array arr)

Parameters

Attribute	Data Type	Required	Description
arr	numpy.ndarray	Y	numpy.ndarray

Returns

RuntimeTensor

Example

tensor = nncase.RuntimeTensor.from_numpy(self.inputs[i]['data'])

copy_to()

Description

Copy RuntimeTensor

Definition

copy_to(RuntimeTensor to)

Parameters

Attribute	Data Type	Required	Description
to	RuntimeTensor	Y	RuntimeTensor

Returns

N/A

Example

sim.get_output_tensor(i).copy_to(to)

to_numpy()

Description

Convert RuntimeTensor to numpy.ndarray.

Definition

to_numpy()

Parameters

N/A

Returns

numpy.ndarray

Example

arr = sim.get_output_tensor(i).to_numpy()

Simulator

Description

Simulator is used to inference kmodel on PC. The details of all attributes are following.

Attribute	Data Type	Required	Description
inputs_size	int	N	The number of inputs.
outputs_size	int	N	The number of outputs.

Example

sim = nncase.Simulator()

load_model()

Description

Load kmodel.

Definition

load_model(model_content)

Parameters

Attribute	Data Type	Required	Description
model_content	byte[]	Y	kmodel byte stream

Returns

N/A

Example

sim.load_model(kmodel)

get_input_desc()

Description

Get description for input.

Definition

get_input_desc(index)

Parameters

Attribute	Data Type	Required	Description
index	int	Y	The index for input.

Returns

MemoryRange

Example

input_desc_0 = sim.get_input_desc(0)

get_output_desc()

Description

Get description for output.

Definition

get_output_desc(index)

Parameters

Attribute	Data Type	Required	Description
index	int	Y	The index for output.

Returns

MemoryRange

Example

output_desc_0 = sim.get_output_desc(0)

get_input_tensor()

Description

Get the input runtime tensor with specified index.

Definition

get_input_tensor(index)

Parameters

Attribute	Data Type	Required	Description
index	int	Y	The index for input tensor.

Returns

RuntimeTensor

Example

input_tensor_0 = sim.get_input_tensor(0)

set_input_tensor()

Description

Set the input runtime tensor with specified index.

Definition

set_input_tensor(index, tensor)

Parameters

Attribute	Data Type	Required	Description
index	int	Y	The index for input tensor.
tensor	RuntimeTensor	Y	RuntimeTensor

Returns

N/A

Example

sim.set_input_tensor(0, nncase.RuntimeTensor.from_numpy(self.inputs[0]['data']))

get_output_tensor()

Description

Get the output runtime tensor with specified index.

Definition

get_output_tensor(index)

Parameters

Attribute	Data Type	Required	Description
index	int	Y	The index for output tensor.

Returns

RuntimeTensor

Example

output_arr_0 = sim.get_output_tensor(0).to_numpy()

set_output_tensor()

Description

Set the RuntimeTensor with specified index.

Definition

set_output_tensor(index, tensor)

Parameters

Attribute	Data Type	Required	Description
index	int	Y	The index for output tensor.
tensor	RuntimeTensor	Y	RuntimeTensor

Returns

N/A

Example

sim.set_output_tensor(0, tensor)

run()

Description

Run kmodel for inferencing.

Definition

run()

Parameters

N/A

Returns

N/A

Example

sim.run()