/FLIM

Feature Learning from Image Markers

Primary LanguageJupyter NotebookMIT LicenseMIT

FLIM - Feature Learning from Image Markers

Feature Learning from Image Markers is a technique to learn the filter of convolutional neural networks' feature extractors from user-drawn image markers.

This package provides an implementation of this technique powered by Pytorch, Scikit-learn, and Scikit-image.

To install it, go to the folder where the package it and run the command

pip install .

To install dependencies run the command

pip install -r requirements.txt

⚠️ It is recommended to create a virtual enviroment before instaling this package.

To use NVIDIA GPUs, install the PyTorch version for your specific CUDA version. Instructions can be found here.

To build the package API reference documentation, go to the folder docs and run the command

make html

You can run a simple HTTP server to serve the documetation page.

python -m http.server

Go to localhost:8000 and you navigate through the documentation.

How to define the model architecture with JSON

It is possible to define the model architecture using a JSON that must respect the following structure:

{
    "module_name1": ...
    "module_name2": ...
    ...
    "module_nameN": ...
}

An example of a module:

"features": {
        "type": "sequential",
        "layers": {
            "conv1": {
                "operation": "conv2d",
                "params": {
                    "kernel_size": 5,
                    "stride": 1,
                    "padding": 2,
                    "dilation": 1,
                    "number_of_kernels_per_marker": 8,
                    "out_channels": 32
                }
            },
            "activation": {
                "operation": "relu",
                "params": {
                    "inplace": true
                }
            },

            "pool": {
                "operation": "max_pool2d",
                "params": {
                    "kernel_size": 3,
                    "stride": 4,
                    "padding": 0
                }
            }
        }
    }

In this example, "features" is a module of type sequential - that is, each sub-module is applied in the order that is specified. This "features" module has a field called "layers" that determines its layers. Layers can be modules.

Each layer has a name, which is the key, and it is necessary to inform the type of the layer through the "operation" field. A list of currently supported operations:

  • "max_pool2d"
  • "max_pool3d"
  • "conv2d"
  • "conv3d"
  • "relu"
  • "linear"
  • "batch_norm2d"
  • "batch_norm3d"
  • "dropout"
  • "adap_avg_pool2d"
  • "adap_max_pool2d"
  • "unfold"
  • "fold"
  • "m_norm2d"
  • "m_norm3d"

Every layer must be specified as follows:

"layer_name": {
    "operation": "operation_name",
    "params: {
        any necessary parameter to create this layer
    }
}

To know which parameters for each type of operation, just look at the PyTorch documentation. All parameters specified there are supported with the same name.

Convolutional and pooling layers have a parameter called "train_dilation" that must be set to "true" if the layer must used dilated kernels during training due to previous pooling layers. A convolutional layer also has a "number_of_kernels_per_marker" parameter that determines the number of kernels that must be created from the markers.

An example of a classifier follows:

"classifier": {
    "layers": {
        "linear1": {
            "operation": "linear",
            "params": {
                "in_features": -1,
                "out_features": 512
            }
        },
       
        "relu3": {
            "operation": "relu",
            "params": {
                "inplace": true
            }
        },

        "linear2": {
            "operation": "linear",
            "params": {
                "in_features": 512,
                "out_features": 512
            }
        }
    }
}

Layers with "linear" operation (fully connected) with the parameter "in_feature" as -1 have this information determined automatically. The number of features may depend on the feature extractor (number of kernels, padding, stride, etc.).

CLI Tools

When the package is installed with pip, there are two command line tools that are available for use in any directory.

The available tools are train and validate. For a description of the parameters, run train -h or validate -h on the console.

Following, there are usage examples for each tool:

train train -d lids-dataset -ts train_split.txt -ad arch.json -md markers-dir -od outputs-dir -mn trained-model.pt -g 0 -e 120 -bs 64 -lr 0.001 -wd 0.001

validate -d lids-dataset -vs val_split.txt -ad arch.json -mp outputs-dir/trained-model.pt -g 0

To save the output of intermediate layers, run:

train train -md markers/ -d lids-dataset/ -ts train.txt -ad arch.json -g 0 -od outputs -mn model.pt -i -f zip -l layer_name1 layer_name2

The possible formats are OPFDataset (zip), MImage (mimg), and NumPy array (npy).

The dataset must be in LIDS format. It is also possible to train an SVM model by running train passing the -s argument.

Please, feel free to give feedback and to contribute. If you have any question, you can open an issue requesting help or you can contact me.