Version 0.1.0 (#41)

* Extended unit tests to classifier and fixed pooling (#17) * Extended unit tests to classifier and fixed pooling * Changed trigger of doctest workflow * Fixing issue #18 * fixed linters * Add pre-commit hooks * Doctest only on PRs * Fixed network conversion from GPU Also tested on Windows machine. * Create python_versions.yml * Update and rename python_versions.yml to tests.yml * Update export.yaml * CI fix (#21) * Create pre-commit.yaml * remove code.yaml * fixing pre-commit * Doctest with pytest * change trigger * change trigger * Delete LICENSE * checkpoint from filesystem (#20) * checkpoint from filesystem * fixed deps * Update README.md * Update LICENSE * Updating LICENSE --------- Co-authored-by: fpaissan <[email protected]> Co-authored-by: Francesco Paissan <[email protected]> * Create LICENSE (#22) * Update README.md (#23) * new min python version to 3.8 * 🐛 extra_requirements now have a version - fixed CI (#24) * 🐛 extra_requirements now have a version * fixed linter errors * testing actions * fixed linter * removing tf_probability * fixed tf prob version --------- Co-authored-by: fpaissan <[email protected]> * Documentation upgrade - guide for contribution (#25) * add contribution guide to docs * documentation with contribution guide * cosmetic * bump version 0.0.4 -> 0.0.5 * Bump requests from 2.28.2 to 2.31.0 (#27) Bumps [requests](https://github.com/psf/requests) from 2.28.2 to 2.31.0. - [Release notes](https://github.com/psf/requests/releases) - [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md) - [Commits](psf/requests@v2.28.2...v2.31.0) --- updated-dependencies: - dependency-name: requests dependency-type: direct:production ... Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * fix pypi release * Update README.md (#29) * Patch for faster GPU inference (#35) * Patch for faster GPU inference * remove unused zeropad def --------- Co-authored-by: fpaissan <[email protected]> * initial commit * add eval loop * add acceleration * modules as dict * add checkpointer * minor * load best checkpoint * restore epoch, optimizer, lr sched * fix logging on multi-gpu * minor fixes * working on single gpu * fix checkpointer + multi-gpu * fp16 might not be ok yet * load_modules and unwrap_model * fixed convert and export * cosmetic on export * add argparse * add metrics -- check something is off with acc * its print strange * fixed checkpointer viz * fix checkpointers and metrics * cosmetic * linters * add credits * fix requirements * fix unittest * remove recipes * remove unused files * remove unused fuctions from networks * fix tests * hot fix * onnx conversion without convert * fix requirements * add default class config and temp folder for debug mode * add doc for class Metric * finish doc MicroMind * update docs * linters fix * new initial page * bump version 0.0.5 -> 0.1.0 * final touches and bumpver --------- Signed-off-by: dependabot[bot] <[email protected]> Co-authored-by: Matteo Beltrami <[email protected]> Co-authored-by: SebastianCavada <[email protected]> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Matteo Tremonti <[email protected]> Co-authored-by: Matteo Beltrami <[email protected]>
micromind-toolkit · Oct 12, 2023 · d318e0b · d318e0b
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diff --git a/README.md b/README.md
@@ -45,6 +45,14 @@ for the basic install. To install `micromind` with the full exportability featur
 pip install -e .[conversion]
 ```
 
+### Training networks with recipes
+
+After the installation, get started looking at the examples and the docs!
+
+### Export your model and run it on your MCU
+Check out [this](https://docs.google.com/document/d/1zt5urvNtI9VSJcoJdIeo10YrdH-tZNcS4JHbT1z5udI/edit?usp=sharing)
+tutorial and have fun deploying your network on MCU!
+
 ---------------------------------------------------------------------------------------------------------
 
 ## 📧 Contact

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -6,6 +6,74 @@
 Welcome to micromind's documentation!
 =====================================
 
+.. image:: https://img.shields.io/badge/python-3.9%20|%203.10-blue
+    :target: https://www.python.org/downloads/
+
+.. image:: https://img.shields.io/badge/License-Apache_2.0-blue.svg
+    :target: https://github.com/fpaissan/micromind/blob/main/LICENSE
+
+.. image:: https://img.shields.io/pypi/v/micromind
+
+This is the official repository of `micromind`, a toolkit that aims to bridge two communities: artificial intelligence and embedded systems. `micromind` is based on `PyTorch <https://pytorch.org>`_ and provides exportability for the supported models in ONNX, Intel OpenVINO, and TFLite.
+
+Key Features
+------------
+
+- Smooth flow from research to deployment;
+- Support for multimedia analytics recipes (image classification, sound event detection, etc);
+- Detailed API documentation;
+- Tutorials for embedded deployment.
+
+Installation
+------------
+
+Using Pip
+~~~~~~~~~
+
+First of all, install `Python 3.8 or later <https://www.python.org>`_. Open a terminal and run:
+
+.. code:: shell
+
+    pip install micromind
+
+for the basic install. To install `micromind` with the full exportability features, run
+
+.. code:: shell
+
+    pip install micromind[conversion]
+
+
+Basic how-to
+------------
+
+If you want to launch a simple training on an image classification model, you just need to define a class that extends `MicroMind <https://micromind.readthedocs.org/en/latest/micromind.html#micromind.core.MicroMind>`_, defining the modules you want to use, such as a `PhiNet`, the forward method of the model and the way in which to calculate your loss function. micromind takes care of the rest for you.
+
+.. code-block:: python
+
+   class ImageClassification(MicroMind):
+      def __init__(self, *args, **kwargs):
+         super().__init__(*args, **kwargs)
+
+         self.modules["classifier"] = PhiNet(
+            (3, 32, 32), include_top=True, num_classes=10
+         )
+
+      def forward(self, batch):
+         return self.modules["classifier"](batch[0])
+
+      def compute_loss(self, pred, batch):
+         return nn.CrossEntropyLoss()(pred, batch[1])
+
+Afterwards, you can export the model in the format you like best between **ONNX**, **TFLite** and **OpenVINO**, just run this simple code:
+
+.. code-block:: python
+
+   m = ImageClassification()
+   m.export("output_onnx", "onnx", (3, 32, 32))
+
+
+Here is the link to the Python `file <https://github.com/micromind-toolkit/micromind/blob/mm_refactor/examples/mind.py>`_ inside our repository that illustrates how to use the MicroMind class.
+
 .. toctree::
    :maxdepth: 2
    :caption: Contents:

diff --git a/docs/source/micromind.conversion.rst b/docs/source/micromind.conversion.rst
diff --git a/docs/source/micromind.networks.rst b/docs/source/micromind.networks.rst
@@ -11,11 +11,3 @@ micromind.networks.phinet module
    :members:
    :undoc-members:
    :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: micromind.networks
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/micromind.rst b/docs/source/micromind.rst
@@ -1,20 +1,27 @@
 micromind package
 =================
 
+micromind.core module
+---------------------
+
+.. automodule:: micromind.core
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+micromind.convert module
+------------------------
+
+.. automodule:: micromind.convert
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 Subpackages
 -----------
 
 .. toctree::
-   :maxdepth: 4
+   :maxdepth: 2
 
-   micromind.conversion
    micromind.networks
    micromind.utils
-
-Module contents
----------------
-
-.. automodule:: micromind
-   :members:
-   :undoc-members:
-   :show-inheritance:
diff --git a/docs/source/micromind.utils.rst b/docs/source/micromind.utils.rst
@@ -4,18 +4,26 @@ micromind.utils package
 Submodules
 ----------
 
-micromind.utils.configlib module
---------------------------------
+micromind.utils.checkpointer module
+-----------------------------------
 
-.. automodule:: micromind.utils.configlib
+.. automodule:: micromind.utils.checkpointer
    :members:
    :undoc-members:
    :show-inheritance:
 
-Module contents
----------------
+micromind.utils.helpers module
+------------------------------
 
-.. automodule:: micromind.utils
+.. automodule:: micromind.utils.helpers
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+micromind.utils.parse module
+----------------------------
+
+.. automodule:: micromind.utils.parse
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/examples/mind.py b/examples/mind.py
@@ -0,0 +1,67 @@
+from micromind import MicroMind, Metric
+from micromind.networks import PhiNet
+from micromind.utils.parse import parse_arguments
+
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+batch_size = 128
+
+
+class ImageClassification(MicroMind):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.modules["classifier"] = PhiNet(
+            (3, 32, 32), include_top=True, num_classes=10
+        )
+
+    def forward(self, batch):
+        return self.modules["classifier"](batch[0])
+
+    def compute_loss(self, pred, batch):
+        return nn.CrossEntropyLoss()(pred, batch[1])
+
+
+if __name__ == "__main__":
+    hparams = parse_arguments()
+    m = ImageClassification(hparams)
+
+    def compute_accuracy(pred, batch):
+        tmp = (pred.argmax(1) == batch[1]).float()
+        return tmp
+
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+
+    trainset = torchvision.datasets.CIFAR10(
+        root="data/cifar-10", train=True, download=True, transform=transform
+    )
+    trainloader = torch.utils.data.DataLoader(
+        trainset, batch_size=batch_size, shuffle=True, num_workers=1
+    )
+
+    testset = torchvision.datasets.CIFAR10(
+        root="data/cifar-10", train=False, download=True, transform=transform
+    )
+    testloader = torch.utils.data.DataLoader(
+        testset, batch_size=batch_size, shuffle=False, num_workers=1
+    )
+
+    acc = Metric(name="accuracy", fn=compute_accuracy)
+
+    m.train(
+        epochs=10,
+        datasets={"train": trainloader, "val": testloader, "test": testloader},
+        metrics=[acc],
+        debug=hparams.debug,
+    )
+
+    m.test(
+        datasets={"test": testloader},
+    )
+
+    m.export("output_onnx", "onnx", (3, 32, 32))
diff --git a/micromind/__init__.py b/micromind/__init__.py
@@ -1,9 +1,7 @@
-from .networks.phinet import PhiNet
-from .utils import configlib
-
+from .core import MicroMind, Metric, Stage
 
 # Package version
-__version__ = "0.0.5"
+__version__ = "0.1.0"
 
 """datasets_info is a dictionary that contains information about the attributes
 of the datasets.

diff --git a/micromind/conversion/__init__.py b/micromind/conversion/__init__.py