lobe-python 0.3 refactor (#15)

* refactor! more flexible and backwards-compatible with lobe signature files. tf_backend uses the tf 1.15 session and saved model loader rather than the contrib inference library.

* update to tensorflow 2.4

* add onnx backend!

* add note about libjpeg62-turbo for raspbian

* onnxruntime not on arm

* remove tflite install resource (taken care of in setup.py), and add link to trash classifier example

Author: mbeissinger

README.md

@@ -1,8 +1,9 @@
-# lobe-python
-Code to run exported Lobe models in Python.
+# Lobe Python API
+Code to run exported Lobe models in Python using the TensorFlow, TensorFlow Lite, or ONNX options.
 
 ## Install
-```
+### Linux
+```shell script
 # Install Python3
 sudo apt update
 sudo apt install -y python3-dev python3-pip
@@ -13,15 +14,36 @@ sudo apt install -y \
     libatlas-base-dev \
     libopenjp2-7 \
     libtiff5 \
-    libjpeg62-turbo
+    libjpeg62-dev
 
 # Install lobe-python
 pip3 install setuptools git
 pip3 install git+https://github.com/lobe/lobe-python
 ```
 
-## Usage
+_Note for Raspbian OS (Raspberry Pi)_: Please install `libjpeg62-turbo` instead of `libjpeg62-dev`
+
+### Mac/Windows
+Use a virtual environment with Python 3.7
+```shell script
+python3 -m venv .venv
+
+# Mac:
+source .venv/bin/activate
+
+# Windows:
+.venv\Scripts\activate
+```
+Install the library
+```shell script
+# make sure pip is up to date
+python -m pip install --upgrade pip
+# install
+pip install git+https://github.com/lobe/lobe-python
 ```
+
+## Usage
+```python
 from lobe import ImageModel
 
 model = ImageModel.load('path/to/exported/model')
@@ -30,7 +52,7 @@ model = ImageModel.load('path/to/exported/model')
 result = model.predict_from_file('path/to/file.jpg')
 
 # OPTION 2: Predict from an image url
-result = model.predict_from_url('http://path/to/file.jpg')
+result = model.predict_from_url('http://url/to/file.jpg')
 
 # OPTION 3: Predict from Pillow image
 from PIL import Image
@@ -41,21 +63,12 @@ result = model.predict(img)
 print(result.prediction)
 
 # Print all classes
-for label, prop in result.labels:
-    print(f"{label}: {prop*100}%")
+for label, confidence in result.labels:
+    print(f"{label}: {confidence*100}%")
 
 ```
+Note: model predict functions should be thread-safe. If you find bugs please file an issue.
 
 ## Resources
 
-If you're running this on a Pi and having issues, and seeing this error:
-
-```bash
-Could not install packages due to an EnvironmentError: 404 Client Error: Not Found for url:  https://pypi.org/simple/tflite-runtime/ 
-```
-
-running this may help:
-
-```bash
-pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl
-```
+See the [Raspberry Pi Trash Classifier](https://github.com/microsoft/TrashClassifier) example, and its [Adafruit Tutorial](https://learn.adafruit.com/lobe-trash-classifier-machine-learning).

RELEASE.md

@@ -0,0 +1,12 @@
+# Release 0.3.0
+___
+## Breaking Changes
+* Previous use of Signature should be ImageClassificationSignature. `from lobe.signature import Signature` -> 
+  `from lobe.signature import ImageClassificationSignature`
+
+## Bug Fixes and Other Improvements
+* Update to TensorFlow 2.4 from 1.15.4
+* Add ONNX runtime backend
+* Use requests instead of urllib
+* Make backends thread-safe
+* Added constants file for signature keys to enable backwards-compatibility

examples/basic_usage.py

@@ -7,7 +7,7 @@
 result = model.predict_from_file('path/to/file.jpg')
 
 # Predict from an image url
-result = model.predict_from_url('http://path/to/file.jpg')
+result = model.predict_from_url('http://url/to/file.jpg')
 
 # Predict from Pillow image
 from PIL import Image
@@ -18,5 +18,5 @@
 print("Top prediction:", result.prediction)
 
 # Print all classes
-for label, prop in result.labels:
-    print(f"{label}: {prop*100:.6f}%")
+for label, confidence in result.labels:
+    print(f"{label}: {confidence*100:.6f}%")

setup.py

@@ -1,18 +1,55 @@
 from setuptools import setup, find_packages
+import sys
+import platform
+
+
+python_version = platform.python_version().rsplit('.', maxsplit=1)[0]
+
+requirements = [
+    "pillow",
+    "requests",
+    "numpy==1.19.3",
+    "tensorflow==2.4;platform_machine!='armv7l'",
+    "onnxruntime==1.6.0;platform_machine!='armv7l'"
+]
+
+# get the right TF Lite runtime packages based on OS and python version: https://www.tensorflow.org/lite/guide/python#install_just_the_tensorflow_lite_interpreter
+tflite_python = None
+tflite_machine = None
+
+# get the right python string for the version
+if python_version == '3.5':
+    tflite_python = 'cp35-cp35m'
+elif python_version == '3.6':
+    tflite_python = 'cp36-cp36m'
+elif python_version == '3.7':
+    tflite_python = 'cp37-cp37m'
+elif python_version == '3.8':
+    tflite_python = 'cp38-cp38'
+
+# get the right machine string
+if sys.platform == 'win32':
+    tflite_machine = 'win_amd64'
+elif sys.platform == 'darwin':
+    tflite_machine = 'macosx_10_15_x86_64'
+elif sys.platform == 'linux':
+    if platform.machine() == 'x86_64':
+        tflite_machine = 'linux_x86_64'
+    elif platform.machine() == 'armv7l':
+        tflite_machine = 'linux_armv7l'
+
+# add it to the requirements, or print the location to find the version to install
+if tflite_python and tflite_machine:
+    requirements.append(f"tflite_runtime @ https://github.com/google-coral/pycoral/releases/download/release-frogfish/tflite_runtime-2.5.0-{tflite_python}-{tflite_machine}.whl")
+else:
+    print(
+        f"Couldn't find tflite_runtime for your platform {sys.platform}, machine {platform.machine()}, and python version {python_version}, please see the install guide for the right version: https://www.tensorflow.org/lite/guide/python#install_just_the_tensorflow_lite_interpreter"
+    )
+
 setup(
     name="lobe",
-    version="0.2.1",
+    version="0.3.0",
     packages=find_packages("src"),
     package_dir={"": "src"},
-    install_requires=[
-        "numpy",
-        "pillow",
-        "requests",
-        "tensorflow>=1.15.2,<2;platform_machine!='armv7l'",
-        "tflite_runtime ; platform_machine=='armv7l'"
-    ],
-    dependency_links=[
-        "https://www.piwheels.org/simple/tensorflow",
-        "https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl"
-    ]
+    install_requires=requirements,
 )

src/lobe/ImageModel.py

@@ -0,0 +1,2 @@
+from .signature import Signature
+from .model.image_model import ImageModel