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Commit a03373da authored by Cyril Moineau's avatar Cyril Moineau
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Remove unused templates.

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aidge_export_arm_cortexm/templates/memory/mem_info.jinja deleted 100644 → 0
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#ifndef MEM_INFO_H
#define MEM_INFO_H
#define MEMORY_SIZE {{ mem_size }}
#define MEMORY_ALIGNMENT {{ mem_alignment }}
{% for i in range(mem_info|length) -%}
{%- set layer_name = mem_info[i][0] %}
/* {{layer_name}} memory */
{% for j in range(1, mem_info[i]|length) %}
#define {{ layer_name|upper }}_MEM_{{ mem_info_legends[j]|upper }} {{ mem_info[i][j] }}
{%- endfor %}
{% endfor %}
#endif /* MEM_INFO_H */
aidge_export_arm_cortexm/templates/network/dnn_header.jinja deleted 100644 → 0
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{#- For name header -#}
#ifndef DNN_H
#define DNN_H
#ifdef __cplusplus
extern "C" {
#endif
{#- For libraries #}
{% for lib in libraries %}
#include <{{ lib }}>
{%- endfor %}
{% for func in functions %}
{{ func }}
{% endfor %}
#ifdef __cplusplus
}
#endif
#endif /* DNN_H */
\ No newline at end of file
aidge_export_arm_cortexm/templates/network/network_forward.jinja deleted 100644 → 0
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{#- For libraries -#}
#include <stdint.h>
#include "dnn.h"
#include "network_functions.h"
// Layer & memory configurations
{%- for header in headers %}
#include "{{ header }}"
{%- endfor %}
{# mem has the datatype of the firt input #}
{#- Change here to improve it -#}
{% if inputs[0][0] %}
static {{inputs[0][0]}} mem[MEMORY_SIZE];
{% else %}
static float mem[MEMORY_SIZE];
{% endif %}
{# Forward function #}
{#- Support multiple inputs with different datatypes and multiple outputs with different datatypes -#}
void model_forward({% for inp in inputs %}const {{inp[0]}}* {{inp[1]}}, {% endfor %}{% for out in outputs %}{{out[0]}}* {{out[1]}}{{ ", " if not loop.last else "" }}{% endfor %})
{
{%- for action in actions %}
{{ action }}
{%- endfor %}
}
aidge_export_arm_cortexm/templates/network/network_prototypes.jinja deleted 100644 → 0
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{#- For name header -#}
#ifndef NETWORK_FUNCTIONS_HPP
#define NETWORK_FUNCTIONS_HPP
{#- For libraries #}
{% for lib in libraries %}
#include <{{ lib }}>
{%- endfor %}
{% for file in files %}
#include "{{ file }}"
{%- endfor %}
{% for func in functions %}
{{ func }}
{% endfor %}
#endif /* NETWORK_FUNCTIONS_HPP */
\ No newline at end of file
aidge_export_arm_cortexm/utils/__init__.py deleted 100644 → 0
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from pathlib import Path
# Constants
FILE = Path(__file__).resolve()
ROOT = FILE.parents[1]
def get_all_available_boards():
boards = {}
directory_path = Path(str(ROOT / "boards"))
for subfolder in directory_path.rglob('*'):
if subfolder.is_dir() and \
subfolder.name != "__pycache__" and \
(subfolder.parent / '__init__.py').exists() and \
not (subfolder / '__init__.py').exists():
# Get relative path to boards directory
relpath = str(subfolder.relative_to(directory_path))
# Get board name
board_name = relpath.replace('/', '').replace('\\', '')
boards[board_name.lower()] = str(subfolder)
return boards
AVAILABLE_BOARDS = get_all_available_boards()
def has_board(board_name: str) -> bool:
return board_name.lower() in AVAILABLE_BOARDS.keys()
aidge_export_arm_cortexm/utils/converter.py deleted 100644 → 0
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import numpy as np
import aidge_core
def numpy_dtype2ctype(dtype):
if dtype == np.int8:
return "int8_t"
elif dtype == np.uint8:
return "uint8_t"
elif dtype == np.int16:
return "int16_t"
elif dtype == np.int32:
return "int32_t"
elif dtype == np.int64:
return "int64_t"
elif dtype == np.float32:
return "float"
elif dtype == np.float64:
return "double"
# Add more dtype mappings as needed
else:
raise ValueError(f"Unsupported {dtype} dtype")
def aidge_datatype2ctype(datatype):
if datatype == aidge_core.dtype.int8:
return "int8_t"
elif datatype == aidge_core.dtype.uint8:
return "uint8_t"
elif datatype == aidge_core.dtype.int32:
return "int32_t"
elif datatype == aidge_core.dtype.int64:
return "int64_t"
elif datatype == aidge_core.dtype.float32:
return "float"
elif datatype == aidge_core.dtype.float64:
return "double"
# Add more dtype mappings as needed
else:
raise ValueError(f"Unsupported {datatype} aidge dtype")
def aidge_datatype2dataformat(datatype):
if datatype == aidge_core.dtype.int8:
return "int8"
elif datatype == aidge_core.dtype.int32:
return "int32"
elif datatype == aidge_core.dtype.int64:
return "int64"
elif datatype == aidge_core.dtype.float32:
return "float32"
elif datatype == aidge_core.dtype.float64:
return "float64"
# Add more dtype mappings as needed
else:
raise ValueError(f"Unsupported {datatype} aidge dtype")
examples/README.md deleted 100644 → 0
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− 6
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# Examples on how to use this module Aidge ARM CortexM Export
This folder contains some examples on how to use the `Aidge ARM CortexM Export` module in your projects.
- [LeNet export for MNIST dataset](./export_LeNet/)
Feel free to propose your own contributions with this module !
\ No newline at end of file
examples/export_LeNet/export_lenet_fp32.ipynb deleted 100644 → 0
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%% Cell type:markdown id: tags:
# Export a MNIST model to a CPP standalone project
%% Cell type:code id: tags:
``` python
%pip install requests numpy ipywidgets ipycanvas
```
%% Cell type:markdown id: tags:
## Download the model
%% Cell type:code id: tags:
``` python
import os
import requests
```
%% Cell type:code id: tags:
``` python
# 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)
```
%% Cell type:markdown id: tags:
## Load the model in Aidge and manipulate it
%% Cell type:code id: tags:
``` python
import aidge_core
import aidge_backend_cpu
import aidge_onnx
import aidge_export_cpp
import aidge_export_arm_cortexm
```
%% Cell type:code id: tags:
``` python
model = aidge_onnx.load_onnx("lenet_mnist.onnx")
```
%% Cell type:code id: tags:
``` python
# 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().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.DataType.Float32)
```
%% Cell type:code id: tags:
``` python
# Generate scheduling of the model
scheduler = aidge_core.SequentialScheduler(model)
scheduler.generate_scheduling()
```
%% Cell type:markdown id: tags:
## Export the model
%% Cell type:code id: tags:
``` python
aidge_export_arm_cortexm.export("lenet_export_fp32", model, scheduler, board="stm32h7")
```
%% Cell type:markdown id: tags:
### Draw your own number
%% Cell type:code id: tags:
``` python
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"))
```
%% Cell type:markdown id: tags:
### Generate inputs for testing the model from your drawing
%% Cell type:code id: tags:
``` python
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.")
```
%% Cell type:markdown id: tags:
### Compile the export and test it
%% Cell type:code id: tags:
``` python
!cd lenet_export_fp32 && make build_image_docker && make build_docker
```
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