cfm-task-models 0.0.15

CFM - model zoo

CFM-Task-Models

Popular CV models modified for various approaches of Compression for Machines (aka Coding for Machines)

Installation

The easiest way to install CFM-Task-Models is through PyPI, to do this simply use pip install cfm-task-models However, CFM-Task-Models has requirements that currently cannot be handled by pip, thus before using CFM-Task-Models for the first time please run miminstaller.py in your virutal environment. If using poetry consider: poetry run python -m cfm_task_models.split_utils.miminstaller

Requirements

CFM-Task-Models currently relies on several tools from OpenMMLab, which require custom installation using the openmim installer. Openmim is a tool provided by OpenMMLab which installs their libraries based on the user's pytorch and cuda versions.

See pyproject.toml for standard requirements and miminstaller.py for OpenMMLAB requirements

Usage

To test Swin_Transformer, run the following command from the root directory:

python models/Swin-Transformer/models/swin_transformer_v2.py

Semantic Segmentation

Dataset

To downald the ADE20K dataset, run the following command from the root directory:

python cfm_task_models/dataset_download.py --dataset-name ade20k_2016 --save-dir data/ade --unzip --delete

Model Zoo

The model configs can be found in configs/segmentation.

ADE20K dataset

Model	Backbone	config	checkpoint	mIoU
UperNet	Swin-T	UPerNet	download model	44.41
Mask2Former	Swin-T	Mask2Former	download model	48.66
KNet + UperNet	Swin-T	KNet	download model	45.84

Download from mim:

mim download mmsegmentation --config swin-tiny-patch4-window7-in1k-pre_upernet_8xb2-160k_ade20k-512x512 --dest ./configs/segmentation

mim download mmsegmentation --config mask2former_swin-t_8xb2-160k_ade20k-512x512 --dest ./configs/segmentation

mim download mmsegmentation --config knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512 --dest ./configs/segmentation

Evaluation

python