narchi 1.6.0

Main features

• Network architectures are written in jsonnet format, which provides useful features like input parameters and functions to define repeated blocks.

• The shapes of the tensors internal to the networks are automatically deduced by propagating the shapes of the inputs, thus requiring less effort and being less error prone.

• Propagation of shapes is done using symbolic arithmetic which makes it simple to understand relationships between inputs and the derived shapes.

• Architecture files can reference other architecture files, thus making this approach modular.

• A command line tool is included to validate jsonnet architecture files and to create detailed diagrams of the respective network architectures.

• Several examples intended to illustrate different features supported.

• Includes basic implementations that allows to instantiate pytorch modules:

  • Instantiation only requires a jsonnet architecture file.

  • No need to write module classes or forward function for each new architecture.

  • One basic implementation that supports instatiating several of the examples.

  • A second example that supports packed 1d and 2d sequences which illustrates the implementation independent nature of the architecture files.

Teaser example

Here you can see an example that illustrates what narchi provides. The example is for resnet18 as implemented in torchvision, though bare in mind that the potential of narchi is the ease of configurability of network architectures, not the reimplementation of existing architectures.

Instantiating a pytorch module from the architecture file can be easily done as follows.

from narchi.instantiators.pytorch import StandardModule
module = StandardModule('resnet.jsonnet',
                        state_dict='resnet18-5c106cde.pth',
                        cfg={'ext_vars': {"num_blocks": [2, 2, 2, 2]}})

Creating a diagram of the architecture requires a single command like the following.

narchi_cli.py render \
  --ext_vars '{"num_blocks": [2, 2, 2, 2]}' \
  --nested_depth 4 \
  resnet.jsonnet \
  resnet18.pdf

Below you can see a small part of the rendered diagram of the start of the first downsample layer of resnet18.

The part of the json that generated the previous crop of the architecture diagram can be seen below. Note that information of the shapes is not included, since these are derived automatically.

{
  "_class": "Sequential",
  "_id": "layer2",
  "blocks": [
    {
      "_class": "Group",
      "_name": "ResBlock",
      "blocks": [
        {
          "_class": "Identity",
          "_id": "ident"
        },
        {
          "_class": "Conv2d",
          "_id": "conv1",
          "bias": false,
          "dilation": 1,
          "kernel_size": 3,
          "output_size": 128,
          "padding": 1,
          "stride": 2
        },
        {
          "_class": "BatchNorm2d",
          "_id": "bn1"
        },
        {
          "_class": "Sequential",
          "_id": "downsample",
          "blocks": [
            {
              "_class": "Conv2d",
              "bias": false,
              "kernel_size": 1,
              "output_size": 128,
              "padding": 0,
              "stride": 2
            },
            {
              "_class": "BatchNorm2d"
            }
          ]
        },
        {"...": "..."}
      ],
      "graph": [
        "ident -> conv1 -> bn1 -> relu1 -> conv2 -> bn2 -> add -> relu2",
        "ident -> downsample -> add"
      ],
      "input": "ident",
      "output": "relu2"
    }
  ]
}