Unofficial implementation of Dynamic Convolutions. Approach from paper Dynamic Convolution: Attention over Convolution Kernels.

Source code of the Project for the Machine Learning course at Skoltech 2021.

• Timotei Ardelean
• Andreea Dogaru
• Alexey Larionov
• Oleg Maslov
• Saian Protasov

• configs/ - configuration files to be used with train.py to train different combinations of models / datasets / optimizers / schedulers and their parameters
• data/ - datasets downloading and setting up:
  • imagenette_dataset.py - Imagenette dataset (download), which is a small ImageNet subset of 10 classes.
  • tinyimagenet_dataset.py - Tiny ImageNet dataset (download), which is a small ImageNet subset of 200 classes with 600 images per class.
  • pascalvoc2012_dataset.py - Pascal VOC 2012 segmentation dataset (official page), used to train and evaluate DeepLabV3+
  • sb_dataset.py - Semantic Boundaries segmentation dataset (official page), used to train DeepLabV3+
  • mnist_dataset.py - MNIST dataset, provided to test correctness of installation with a trivial classification CNN
• datasets/ -default storage of downloaded and extracted datasets (can be configured to be stored somewhere else)
• experiments/ - default storage of checkpoints and tensorboard logs of experiments (can be configured to be stored somewhere else)
• models/ - different models' source code on which we apply Dynamic Convolutions
  • common.py - base classes that allow integration of Dynamic Convolutions into existing models. Models derive from BaseModel class which allows to construct a model with a custom convolutional layer class (either nn.Conv2d or our DynamicConvolution), submodules inside models derive from TempModule class, which allows pass a temperature argument to forward() method
  • deeplab.py and deeplab_details/ folder - DeepLabV3+
  • mobilenetv3.py - MobileNetV3
  • mobilenetv2.py - MobileNetV2
  • resnet.py - general ResNet, ResNet10 and ResNet18
  • simple_conv.py - a trivial classification CNN provided to test correctness of installation
• tensorboard_events/ - a collection of training logs: loss and scores progression over epochs, made to be parsed/visualized in TensorBoard library
• notebooks/ - check out section below
• utils/ - framework auxiliary code for parsing options from configuration files and command line, loading and storing of checkpoints, custom metrics, losses
• 👉dynamic_convolutions.py - implementation of DynamicConvolution class, i.e. a drop-in replacement for nn.Conv2d with learnable per-sample attention
• inspect_attention.py - methods to extract and analyze Dynamic Convolution state in trained models
• 👉 train.py - entry point for training of models, using a configuration file (or other options, see Reproduce training section)

A GPU is recommended to perform the experiments. The code is set up to be effortlessly run using Google Colab. Main prerequisites are:

• pytorch, torchvision
• numpy, tensorboard, pillow, tqdm

For convenience, a ready to use conda environment is provided. To create a new python environment with all the required packages, you can run:

conda env create -f environment.yml
conda activate dyconv

The training process can be started with the provided script:

python train.py --config_path "configs/<name>.yaml"

where <name>.yaml refers to a configuration file (configs/) facilitating experiment reproducibility. Among the available training setups there are:

• MobileNetV2_[type]_[width-multiplier]_[temperature] - various setups of MobileNetV2 architecture
• MobileNetV3_[type]_[width-multiplier]_[dataset] - various setups of MobileNetV3 architecture
• deeplabv3plus.yaml, dy_deeplabv3plus.yaml - DeepLabV3+ with MobileNetV2 backbone trained on Pascal VOC 2012 dataset (and Dynamic Convolution variant)
• deeplabv3plus_0.5.yaml, dy_deeplabv3plus_0.5.yaml - baseline of DeepLabV3+ with MobileNetV2 backbone and x0.5 width convolutions, trained on a combination of Pascal VOC 2012 and Semantic Boundaries datasets (and Dynamic Convolution variant)
• Resnet_[type]_[width-multiplier] - various setups of ResNet architecture
• config.yaml - toy example of classifying MNIST digits with a trivial CNN, provided to test correctness of installation

Besides providing the config file, you can also pass options as command line arguments, which will override those used in the config. For the full list of available (and default) options refer to utils/options.py file.

python train.py --config_path "configs/<name>.yaml" --device "cpu" --batch_size 100

TensorBoard facilitates tracking the training process and comparing experiments:

tensorboard --logdir experiments

The following Google Colab compatible Jupyter notebooks are available:

• notebooks/DyConv_training.ipynb - Self-explanatory training procedure
• notebooks/DyConv_profiling.ipynb - Profile number of parameters, FLOPS and inference time
• notebooks/DyConv_inspect.ipynb - Inspect the distribution of attentions computed by Dynamic Convolutions
• notebooks/DyConv_inspect_segmentation.ipynb - Inspect learning progress and statistics of segmentation models training (DeepLabV3+)

• Dataset: Tiny ImageNet

  Network	Number of parameters	Accuracy	Config file
  ResNet-10	5.00M	56.86	Resnet_Vanilla_1.yaml
  DY-ResNet-10	19.80M	58.17	Resnet_DY_1.yaml
  ResNet-10 x 0.5	1.27M	52.78	Resnet_Vanilla_05.yaml
  DY-ResNet-10 x 0.5	4.97M	53.92	Resnet_DY_05.yaml
  DY-ResNet-10 x 0.5 (**)	4.97M	55.38	Resnet_DY_Leaky_05_smooth.yaml
  ResNet-10 x 0.25	0.33M	46.17	Resnet_Vanilla_025.yaml
  DY-ResNet-10 x 0.25	1.25M	48.05	Resnet_DY_025.yaml
  MobileNetV2 x 0.35	0.65M	34.05	MobileNetV2_Vanilla_035.yaml
  DY-MobileNetV2 x 0.35	2.09M	31.91	MobileNetV2_DY_035.yaml
  MobileNetV3 x 0.5	0.87M	53.36	MobileNetV3_Vanilla_05_Tiny.yaml
  DY-MobileNetV3 x 0.5	2.52M	50.95	MobileNetV3_DY_05_Tiny.yaml
  MobileNetV3	1.91M	55.53	MobileNetV3_Vanilla_1_Tiny.yaml
  DY-MobileNetV3	6.04M	53.59	MobileNetV3_DY_1_Tiny.yaml

• Dataset: Imagenette

  Network	Number of parameters	Accuracy	Config file
  MobileNetV3 x 0.25	0.27M	85.07	MobileNetV3_Vanilla_025_Imagenette.yaml
  DY-MobileNetV3 x 0.25	0.99M	85.24	MobileNetV3_DY_025_Imagenette.yaml
  MobileNetV3 x 0.5	0.63M	87.31	MobileNetV3_Vanilla_05_Imagenette.yaml
  DY-MobileNetV3 x 0.5	2.28M	87.89	MobileNetV3_DY_05_Imagenette.yaml
  MobileNetV3	1.67M	88.40	MobileNetV3_Vanilla_1_Imagenette.yaml
  DY-MobileNetV3	5.80M	89.32	MobileNetV3_DY_1_Imagenette.yaml

• Dataset: Pascal VOC 2012

  Network	Number of parameters	mIoU	Config file
  DeepLabV3+	5.81M	65.09	deeplabv3plus.yaml
  DY-DeepLabV3+	18.4M	65.45	dy_deeplabv3plus.yaml

• Dataset: Pascal VOC 2012 + SBD (only used for training)

  Network	Number of parameters	mIoU	Config file
  DeepLabV3+ x 0.5	3.40M	70.23	deeplabv3plus_0.5.yaml
  DY-DeepLabV3+ x 0.5	8.44M	71.09	dy_deeplabv3plus_0.5.yaml