Merge pull request #219 from jonnor/installable-package

Create installable Python package

docs/api_nnom.md

@@ -2,7 +2,7 @@
 # NNoM Utils - Structured API
 
 What makes NNoM easy to use is the models can be deployed to MCU automatically or manually with the help of NNoM utils. 
-These functions are located in `scripts/nnom.py`
+These functions are located in the `nnom` Python package.
 
 This tools, will generate NNoM model using **Structured API** instead of 'layer API' in the previous script.
 
@@ -11,7 +11,6 @@ Usage in short, in your python file, add the scripts the directory to your envir
 ~~~
 import sys
 import os
-sys.path.append(os.path.abspath("../../scripts"))
 from nnom import *
 ~~~
 

examples/auto_test/main.py

@@ -10,8 +10,6 @@
 
 import sys
 import os
-sys.path.append(os.path.abspath("../../scripts"))
-print(sys.path)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/keyword_spotting/kws-rnn.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/keyword_spotting/kws.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/mnist-densenet/mnist_densenet.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/mnist-simple/mnist_simple.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/octave-conv/mnist_octaveconv.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/rnn-denoise/main.py

@@ -10,10 +10,6 @@
 import tensorflow as tf
 import numpy as np
 
-import sys
-sys.path.append(os.path.abspath("../../scripts"))
-print(sys.path)
-
 from gen_dataset import *
 from nnom import *
 

examples/uci-har-rnn/uci_rnn.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

examples/uci-inception/uci_test.py

@@ -11,8 +11,6 @@
 
 import matplotlib.pyplot as plt
 import os
-nnscript = os.path.abspath('../../scripts')
-os.sys.path.append(nnscript)
 
 from tensorflow.keras import *
 from tensorflow.keras.datasets import mnist

pyproject.toml

@@ -0,0 +1,35 @@
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["scripts", "src", "inc", "port"]
+
+[tool.hatch.build.targets.wheel.sources]
+"scripts" = "nnom"
+"src" = "nnom_core/src"
+"inc" = "nnom_core/inc"
+"port" = "nnom_core/port"
+
+[project]
+name = "nnom"
+readme = "README.md"
+requires-python = ">= 3.8"
+keywords = ["machine learning", "deep learning", "microcontroller", "embedded"]
+version = "0.5.0a1"
+
+dependencies = [
+  "keras<3.0.0",
+]
+
+classifiers = [
+  "License :: OSI Approved :: MIT License",
+]
+
+
+[project.urls]
+Homepage = "https://github.com/majianjia/nnom"
+Documentation = "https://majianjia.github.io/nnom/"
+Repository = "https://github.com/majianjia/nnom"
+

scripts/__init__.py

@@ -1 +1,5 @@
+
 # package
+
+from .nnom import *
+

scripts/nnom.py

@@ -16,8 +16,8 @@
 import tensorflow.keras.backend as K
 from tensorflow.keras import *
 from tensorflow.keras.layers import *
-from fully_connected_opt_weight_generation import *
-from gen_config import *
+from .fully_connected_opt_weight_generation import *
+from .gen_config import *
 import scipy.stats
 import time
 import warnings