bimm

Burn Image Models

Overview

This is a repository for burn image models; inspired by the Python timm package.

The future feature list is:

• timm => bimm

Contributing

See the CONTRIBUTING guide for build and contribution instructions.

Crates

bimm - the main crate for image models.

• bimm::cache - weight loading cache.
• bimm::layers - reusable neural network modules.
  • bimm::layers::activation - activation layers.
    • bimm::layers::activation::Activation
      • activation layer abstraction wrapper.
  • bimm::layers::blocks - miscellaneous blocks.
    • bimm::layers::blocks::conv_norm - Conv2d + BatchNorm2d block.
  • bimm::layers::drop - dropout layers.
    • bimm::layers::drop::drop_block - 2d drop block / spatial dropout.
    • bimm::layers::drop::drop_path - drop path / stochastic depth.
  • bimm::layers::patching - patching layers.
    • bimm::layers::patching::patch_embed - 2d patch embedding layer.
• bimm::models - complete model families.
  • bimm::models::resnet - ResNet
  • bimm::models::swin - The SWIN Family.
    • bimm::models::swin::v2 - The SWIN-V2 Model.

Example

Example of building a pretrained model:

use burn::backend::Wgpu;
use bimm::cache::disk::DiskCacheConfig;
use bimm::models::resnet::{PREFAB_RESNET_MAP, ResNet};

let device = Default::default();

let prefab = PREFAB_RESNET_MAP.expect_lookup_prefab("resnet18");

let weights = prefab
    .expect_lookup_pretrained_weights("tv_in1k")
    .fetch_weights(&DiskCacheConfig::default())
    .expect("Failed to fetch weights");

let model: ResNet<Wgpu> = prefab
    .to_config()
    .to_structure()
    .init(&device)
    .load_pytorch_weights(weights)
    .expect("Failed to load weights")
    // re-head the model to 10 classes:
    .with_classes(10)
    // Enable (drop_block_prob) stochastic block drops for training:
    .with_stochastic_drop_block(0.2)
    // Enable (drop_path_prob) stochastic depth for training:
    .with_stochastic_path_depth(0.1);

Example resnet_finetune - Pretrained ResNet finetuning example.

Available pretrained models:
* "resnet18"
ResNetContractConfig { layers: [2, 2, 2, 2], num_classes: 1000, stem_width: 64, output_stride: 32, bottleneck_policy: None, normalization: Batch(BatchNormConfig { num_features: 0, epsilon: 1e-5, momentum: 0.1 }), activation: Relu }
  - "resnet18.tv_in1k": TorchVision ResNet-18
  - "resnet18.a1_in1k": RSB Paper ResNet-18 a1
  - "resnet18.a2_in1k": RSB Paper ResNet-18 a2
  - "resnet18.a3_in1k": RSB Paper ResNet-18 a3
* "resnet26"
ResNetContractConfig { layers: [2, 2, 2, 2], num_classes: 1000, stem_width: 64, output_stride: 32, bottleneck_policy: Some(BottleneckPolicyConfig { pinch_factor: 4 }), normalization: Batch(BatchNormConfig { num_features: 0, epsilon: 1e-5, momentum: 0.1 }), activation: Relu }
  - "resnet26.bt_in1k": ResNet-26 pretrained on ImageNet
* "resnet34"
ResNetContractConfig { layers: [3, 4, 6, 3], num_classes: 1000, stem_width: 64, output_stride: 32, bottleneck_policy: None, normalization: Batch(BatchNormConfig { num_features: 0, epsilon: 1e-5, momentum: 0.1 }), activation: Relu }
  - "resnet34.tv_in1k": TorchVision ResNet-34
  - "resnet34.a1_in1k": RSB Paper ResNet-32 a1
  - "resnet34.a2_in1k": RSB Paper ResNet-32 a2
  - "resnet34.a3_in1k": RSB Paper ResNet-32 a3
  - "resnet34.bt_in1k": ResNet-34 pretrained on ImageNet
* "resnet50"
ResNetContractConfig { layers: [3, 4, 6, 3], num_classes: 1000, stem_width: 64, output_stride: 32, bottleneck_policy: Some(BottleneckPolicyConfig { pinch_factor: 4 }), normalization: Batch(BatchNormConfig { num_features: 0, epsilon: 1e-5, momentum: 0.1 }), activation: Relu }
  - "resnet50.tv_in1k": TorchVision ResNet-50
* "resnet101"
ResNetContractConfig { layers: [3, 4, 23, 3], num_classes: 1000, stem_width: 64, output_stride: 32, bottleneck_policy: Some(BottleneckPolicyConfig { pinch_factor: 4 }), normalization: Batch(BatchNormConfig { num_features: 0, epsilon: 1e-5, momentum: 0.1 }), activation: Relu }
  - "resnet101.tv_in1k": TorchVision ResNet-101
  - "resnet101.a1_in1k": ResNet-101 pretrained on ImageNet
* "resnet152"
ResNetContractConfig { layers: [3, 8, 36, 3], num_classes: 1000, stem_width: 64, output_stride: 32, bottleneck_policy: Some(BottleneckPolicyConfig { pinch_factor: 4 }), normalization: Batch(BatchNormConfig { num_features: 0, epsilon: 1e-5, momentum: 0.1 }), activation: Relu }
  - "resnet152.tv_in1k": TorchVision ResNet-152

bimm-contracts - a crate for static shape contracts for tensors.

This crate is now hosted in its own repository: bimm-contracts

This crate provides a stand-alone library for defining and enforcing tensor shape contracts in-line with the Burn framework modules and methods.

use bunsen::contracts::{unpack_shape_contract, shape_contract, run_periodically};

pub fn window_partition<B: Backend, K>(
    tensor: Tensor<B, 4, K>,
    window_size: usize,
) -> Tensor<B, 4, K>
where
    K: BasicOps<B>,
{
    let [b, h_wins, w_wins, c] = unpack_shape_contract!(
        [
            "batch",
            "height" = "h_wins" * "window_size",
            "width" = "w_wins" * "window_size",
            "channels"
        ],
        &tensor,
        &["batch", "h_wins", "w_wins", "channels"],
        &[("window_size", window_size)],
    );

    let tensor = tensor
        .reshape([b, h_wins, window_size, w_wins, window_size, c])
        .swap_dims(2, 3)
        .reshape([b * h_wins * w_wins, window_size, window_size, c]);

    // Run an amortized check on the output shape.
    //
    // `run_periodically!{}` runs the first 10 times,
    // then on an incrementally lengthening schedule,
    // until it reaches its default period of 1000.
    //
    // Due to amortization, in release builds, this averages ~4ns:
    assert_shape_contract_periodically!(
        [
            "batch" * "h_wins" * "w_wins",
            "window_size",
            "window_size",
            "channels"
        ],
        &tensor,
        &[
            ("batch", b),
            ("h_wins", h_wins),
            ("w_wins", w_wins),
            ("window_size", window_size),
            ("channels", c),
        ]
    );

    tensor
}

bimm-firehose - a data loading and augmentation framework.

This crate provides a SQL-inspired table + operations framework for modular data pipeline construction.

It’s still very much a work in progress, and any issues/design bugs reported are very appreciated.

This crate provides a set of image-specific operations for bimm-firehose.

Add-on crates:

• bimm-firehose-image