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• Linux|Windows operating system
• 30 GB+ of storage, 4 GB+ RAM
• Python 3.8+, <3.10
• Poetry 1.1+

Let's take a look at the following .yaml file

task:
  - image-segmentation

implementations:
  torch: # framework name
    JaccardLoss: # name of the loss function(can any name)
      weight: 0.3  # weight of the loss function(can be any float)
      object: # can be `function`
        # path to the class/function(can be a local path or a installed library code)
        _target_: pytorch_toolbelt.losses.JaccardLoss
        mode: binary  # additional argument for the class
    BinaryFocalLoss:  # another loss, structure is similar to above described loss 
      weight: 0.7
      object:
        _target_: pytorch_toolbelt.losses.BinaryFocalLoss

task denotes the type of the problem these loss functions were designed to be used.

implementations contains information on how to instantiate the loss functions for different frameworks.

Inner level is for framework names. Here we can use torch, sklearn, xgboost etc. Inside of the framework level we have the names of the objects. Names are later used during logging. You are free to select any name.

Latter if we go inside the "name's level" we will have two fields: weight, object/function. Weight is used to specify the weight of the loss function.

TL;DR

• if code to be instantiated is a function then name this field function
• if code to be instantiated is an object then name this field object

Here we are choosing the type of the code we want to instantiate.

It can be an object of a class or a function. As functions cannot be instantiated right away without arguments. We need to instantiate function later in the code when we receive arguments.

Under the hood:

object - gets instantiated
function - gets wrapped into a lambda function

this allows us to have the same interface for both objects and functions later on.

Example:

In the following snippet we initialize the loss object BinaryFocalLoss

from pytorch_toolbelt.losses import BinaryFocalLoss
import torch

criterion = BinaryFocalLoss()

pred = torch.tensor([0.0, 0.0, 0.0])
target = torch.tensor([1, 1, 1])

pred.unsqueeze_(0)
target.unsqueeze_(0)

loss1 = criterion(pred, target)

In the following snippet we initialize the function binary_cross_entropy and pass arguments right away.

import torch
import torch.nn.functional as F

pred = torch.tensor([0.0, 0.0, 0.0])
target = torch.tensor([1, 1, 1])

pred.unsqueeze_(0)
target.unsqueeze_(0)

loss1 = F.binary_cross_entropy(pred, target)

Now we will consider adding your custom dataset into the framework.

1. Split your data into two folders: train and test.
2. Make sure that you have the corresponding datamodule to process your data. All the available datamodules stored in innofw/core/datamodules/. Each datamodule has a task and framework attributes*. Pair of task and framework can be duplicated, in this case difference is in the data retrieval logic, select one that is more suitable for your problem.
   1. In case you have not found suitable datamodule then write your own. Refer to section 2.2.
3. Create a configuration file in config/datasets/[dataset_name].yaml
   Dataset config file should be structured as follows:

      task:
        - [dedicated task]
   
      name: [name of the dataset]
      description: [specify dataset description]
   
      markup_info: [specify markup information]
      date_time: [specify date]
   
      _target_: innofw.core.datamodules.[submodule].[submodule].[class_name]
   
    # =============== Data Paths ================= #
    # use one of the following:
   
      # ====== 1. local data ====== #
      train:
        source: /path/to/file/or/folder
      test:
        source: /path/to/file/or/folder   
      # ====== 2. remote data ====== #
      train:
        source: https://api.blackhole.ai.innopolis.university/public-datasets/folder/train.zip
        target: folder/to/extract/train/
      test:
        source: https://api.blackhole.ai.innopolis.university/public-datasets/folder/test.zip
        target: folder/to/extract/test/
    # ================================== #
           
      # some datamodules require additional arguments
      # look for them in the documentation of each datamodule
      # arguments passed in the following way:
      arg1: value1  # here arg1 - name of the argument, value1 - value for the arg1
      arg2: value2
      # ... same for other datamodule arguments
   

4. To run prediction on new data you should create an inference datamodule configuration file. Configuration file is alike to file created in 3.

      task:
        - [dedicated task]
   
      name: [name of the dataset]
      description: [specify dataset description]
   
      markup_info: [specify markup information]
      date_time: [specify date]
   
      _target_: innofw.core.datamodules.[submodule].[submodule].[class_name]
   
    # =============== Data Paths ================= #
    # use one of the following:
   
      # ====== 1. local data ====== #
      infer:
        source: /path/to/file/or/folder   
      # ====== 2. remote data ====== #
      infer:
        source: https://api.blackhole.ai.innopolis.university/public-datasets/folder/infer.zip
        target: folder/to/extract/infer/
    # ================================== #
           
      # some datamodules require additional arguments
      # look for them in the documentation of each datamodule
      # arguments passed in the following way:
      arg1: value1  # here arg1 - name of the argument, value1 - value for the arg1
      arg2: value2
      # ... same for other datamodule arguments
   
   

• * task refers to the problem type where this datamodule is used. framework refers to the framework type where this datamodule is used

Datamodule is a class which has the following responsibilities:

1. creation of data loaders for each dataset type: train, test, val and infer.
2. dataset setting up(e.g. downloading, preprocessing, creating additional files etc.)
3. model predictions saving - formatting the predictions provided by a model

For now all of our data modules inherit from following two classes: PandasDataModule, BaseLightningDataModule

PandasDataModule is suitable for tasks with input provided as table. The class provides the data by first uploading it into RAM. BaseLightningDataModule is suitable for tasks where notion of 'batches' is reasonable for the data and the model.

from innofw.core.datamodules.lightning_datamodules.base import (
    BaseLightningDataModule,
)


class DataModule(BaseLightningDataModule):
   def setup(self, *args, **kwargs):
      pass
      
   def train_dataloader(self):
      pass

   def val_dataloader(self):
      pass

   def test_dataloader(self):
      pass

Where each dataloader utilizes the dataset(similar term as torch's Dataset)

If you have written your own model, for instance this dummy model:

import torch.nn as nn


class MNISTClassifier(nn.Module):
    def __init__(self, hidden_dim: int = 100):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(28 * 28, hidden_dim),
            nn.Linear(hidden_dim, 10)
        )

    def forward(self, x):
        return self.layers(x)

And you would like to add train it. Then you should do the following:

1. add task and framework parameters

   import torch.nn as nn
   
   
   class MNISTClassifier(nn.Module):
       task = ['image-classification']
       framework = ['torch']
       # rest of the code is the same

   standard list of tasks:

   • image-classification
   • image-segmentation
   • image-detection
   • table-regression
   • table-classification
   • table-clustering ...

   standard list of frameworks:

   • torch
   • sklearn
   • xgboost

2. add the file with model to innofw/core/models/torch/architectures/[task]/file_with_nn_module.py

3. make sure dictionary in get_default in innofw/utils/defaults.py contains a mapping between your task and a lightning module

   if task has no corresponding pytorch_lightning.LightningModule add new implementation in this folder innofw/core/models/torch/lightning_modules/[task].py.

   for more information on lightning modules visit official documentation

4. make sure you have suitable dataset class for your model. Refer to chapter 2

5. add configuration file to your model.

   in config/models/[model_name].yaml define a _target_ field and arguments for your model.

   For example:

   _target_: innofw.core.models.torch.architectures.classification.MNISTClassifier
   hidden_dim: 256

Now you are able to train and test your model! 😊

1. Make sure you have needed working dataset configuration. See Section 2.

2. Make sure you have needed working model configuration file. See Section 3.

3. Write an experiment file

   For instance file in folder config/experiments named KA_130722_yolov5.yaml with contents:

   # @package _global_
   defaults:
     - override /models: [model_config_name]
     - override /datasets: [dataset_config_name]
   
   project: [project_name]
   task: [task_name]
   seed: 42
   epochs: 300
   batch_size: 4
   weights_path: /path/to/store/weights
   weights_freq: 1  # weights saving frequency
   
   ckpt_path: /path/to/saved/model/weights.pt

4. Launch training

python train.py experiments=KA_130722_yolov5.yaml

5. Launch testing

python test.py experiments=KA_130722_yolov5.yaml

6. Launch inference

python infer.py experiments=KA_130722_yolov5.yaml

References:

1. https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html

1. Framework versions must be specified in X.Y.Z format, where: ‒ X – older version (updates in case of big changes); ‒ Y – younger version (updates in case of small changes); ‒ Z - tiny changes (updates in case of tiny changes).
2. When one of the numbers is increased, all numbers after it must be set to zero.
3. Backward compatibility in software must be maintained in all versions with the same older version.