Export a MNIST model to a CPP standalone project#

[1]:

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[notice] A new release of pip is available: 24.1 -> 24.2
[notice] To update, run: pip install --upgrade pip
Note: you may need to restart the kernel to use updated packages.

Download the model#

[2]:

import os
import requests

[3]:

# Download onnx file if it has not been done before
if not os.path.isfile("./lenet_mnist.onnx"):
    response = requests.get("https://huggingface.co/vtemplier/LeNet_MNIST/resolve/main/lenet_mnist.onnx?download=true")
    if response.status_code == 200:
        with open("lenet_mnist.onnx", 'wb') as f:
            f.write(response.content)
        print("ONNX model downloaded successfully.")
    else:
        print("Failed to download ONNX model. Status code:", response.status_code)

ONNX model downloaded successfully.

Load the model in Aidge and manipulate it#

[4]:

import aidge_core
import aidge_backend_cpu
import aidge_onnx
import aidge_export_cpp

[5]:

model = aidge_onnx.load_onnx("lenet_mnist.onnx")

- feature_feature_0_Conv_output_0 (Conv)
- feature_feature_1_Relu (Relu)
- feature_feature_2_MaxPool_output_0 (MaxPool)
- feature_feature_3_Conv_output_0 (Conv)
- feature_feature_4_Relu (Relu)
- feature_feature_5_MaxPool_output_0 (MaxPool)
- classifier_classifier_0_Flatten (Flatten | GenericOperator)
        - axis : 1
- classifier_classifier_1_Gemm_output_0 (Gemm)
- classifier_classifier_2_Relu (Relu)
- classifier_classifier_3_Gemm_output_0 (Gemm)
- classifier_classifier_4_Relu (Relu)
- output (Gemm)

[6]:

# Remove Flatten node, useless in the CPP export
aidge_core.remove_flatten(model)

# Freeze the model by setting constant to parameters producers
for node in model.get_nodes():
    if node.type() == "Producer":
        node.get_operator().attr.set_attr("constant", True)

# Create Producer Node for the Graph
input_node = aidge_core.Producer([1, 1, 28, 28], "input")
input_node.add_child(model)
model.add(input_node)

# Configuration for the model + forward dimensions
model.compile("cpu", aidge_core.dtype.float32)

[7]:

# Generate scheduling of the model
scheduler = aidge_core.SequentialScheduler(model)
scheduler.generate_scheduling()

[8]:

model.save("test")

Export the model#

[9]:

aidge_export_cpp.export("lenet_export_fp32", model, scheduler)

Draw your own number#

[10]:

from ipywidgets import HBox, VBox, Button, Layout
from ipycanvas import RoughCanvas, hold_canvas

img_name = "my_number.png"

canvas = RoughCanvas(width=28, height=28, sync_image_data=True)

button_gen = Button(description="Generate PNG")
button_clear = Button(description="Clear")

drawing = False
position = None
shape = []

def on_erase_button_clicked(b):
    canvas.clear()

def on_generate_button_clicked(b):
    try:
        canvas.to_file(img_name)
        print(f"Image generated to {img_name} !")
    except:
        print("Draw a number before generating the image.")

button_clear.on_click(on_erase_button_clicked)
button_gen.on_click(on_generate_button_clicked)

def on_mouse_down(x, y):
    global drawing
    global position
    global shape

    drawing = True
    position = (x, y)
    shape = [position]

def on_mouse_move(x, y):
    global drawing
    global position
    global shape

    if not drawing:
        return

    with hold_canvas():
        canvas.stroke_line(position[0], position[1], x, y)
        position = (x, y)

    shape.append(position)

def on_mouse_up(x, y):
    global drawing
    global position
    global shape

    drawing = False

    with hold_canvas():
        canvas.stroke_line(position[0], position[1], x, y)

    shape = []

canvas.on_mouse_down(on_mouse_down)
canvas.on_mouse_move(on_mouse_move)
canvas.on_mouse_up(on_mouse_up)

canvas.stroke_style = "#000000"

VBox((canvas, HBox((button_gen, button_clear))),
     layout=Layout(height='auto', width="300px"))

[10]:

Generate inputs for testing the model from your drawing#

[11]:

try:
    number_np = canvas.get_image_data()
    # We got a numpy array with the shape of (28,28,4)
    # Transform it to (28,28)
    x = number_np[:, :, 3].astype("float32")
    # Convert from [0, 255] to [0, 1] and export it
    aidge_export_cpp.generate_input_file(export_folder="lenet_export_fp32",
                                         array_name="inputs",
                                         array=x / 255)
except:
    print("Please draw a number in the previous cell before running this one.")

Please draw a number in the previous cell before running this one.

Compile the export and test it#

[12]:

!cd lenet_export_fp32 && make

make[1]: Entering directory '/builds/eclipse/aidge/aidge/docs/source/Tutorial/lenet_export_fp32'
g++  -O2 -Wall -Wextra -MMD -fopenmp -I. -I./dnn -I./dnn/include -I./dnn/layers -I./dnn/parameters -c dnn/src/forward.cpp -o build/./dnn/src/forward.o
g++  -O2 -Wall -Wextra -MMD -fopenmp -I. -I./dnn -I./dnn/include -I./dnn/layers -I./dnn/parameters -c main.cpp -o build/./main.o
main.cpp:4:10: fatal error: inputs.h: No such file or directory
    4 | #include "inputs.h"
      |          ^~~~~~~~~~
compilation terminated.
make[1]: *** [Makefile:23: build/./main.o] Error 1
make[1]: Leaving directory '/builds/eclipse/aidge/aidge/docs/source/Tutorial/lenet_export_fp32'

[13]:

!./lenet_export_fp32/bin/run_export

/usr/bin/sh: 1: ./lenet_export_fp32/bin/run_export: not found