mmc

installation

git clone https://github.com/dmarx/Multi-Modal-Comparators
cd 'Multi-Modal-Comparators'
pip install poetry
poetry build
pip install dist/mmc*.whl

# optional final step:
#poe napm_installs
python src/mmc/napm_installs/__init__.py

To see which models are immediately available, run:

python -m mmc.loaders

That optional `poe napm_installs` step

For the most convenient experience, it is recommended that you perform the final poe napm_installs step. Omitting this step will make your one-time setup faster, but will make certain use cases more complex.

If you did not perform the optional poe napm_installs step, you likely received several warnings about models whose loaders could not be registered. These are models whose codebases depend on python code which is not trivially installable. You will still have access to all of the models supported by the library as if you had run the last step, but their loaders will not be queryable from the registry (see below) and will need to be loaded via the appropriate mmc.loader directly, which may be non-trivial to identify without the ability to query it from mmc's registry.

As a concrete example, if the napm step is skipped, the model [cloob - corwsonkb - cloob_laion_400m_vit_b_16_32_epochs] will not appear in the list of registered loaders, but can still be loaded like this:

from mmc.loaders import KatCloobLoader

model = KatCloobLoader(id='cloob_laion_400m_vit_b_16_32_epochs').load()

Invoking the load() method on an unregistered loader will invoke napm to prepare any uninstallable dependencies required to load the model. Next time you run python -m mmc.loaders, the CLOOB loader will show as registered and spinning up the registry will longer emit a warning for that model.

Usage

TLDR

# spin up the registry
from mmc import loaders

from mmc.mock.openai import MockOpenaiClip
from mmc.registry import REGISTRY

cloob_query = {architecture='cloob'}
cloob_loaders = REGISTRY.find(**cloob_query)

# loader repl prints attributes for uniquely querying
print(cloob_loaders)

# loader returns a perceptor whose API is standardized across mmc
cloob_model = cloob_loaders[0].load()

# wrapper classes are provided for mocking popular implementations
# to facilitate drop-in compatibility with existing code
drop_in_replacement__cloob_model = MockOpenaiClip(cloob_model)

Querying the Model Registry

Spin up the model registry by importing the loaders module:

from mmc import loaders

To see which models are available:

from mmc.registry import REGISTRY

for loader in REGISTRY.find():
    print(loader)

You can constrain the result set by querying the registry for specific metadata attributes

# all CLIP models
clip_loaders = REGISTRY.find(architecture='clip')

# CLIP models published by openai
openai_clip_loaders = REGISTRY.find(architecture='clip', publisher='openai')

# All models published by MLFoundations (openCLIP)
mlf_loaders = REGISTRY.find(publisher='mlfoundations)'

# A specific model
rn50_loader = REGISTRY.find(architecture='clip', publisher='openai', id='RN50')
# NB: there may be multiple models matching a particular "id". the 'id' field
# only needs to be unique for a given architecture-publisher pair.

All pretrained checkpoints are uniquely identifiable by a combination of architecture, publisher, and id.

The above queries return lists of loader objects. If model artifacts (checkpoints, config) need to be downloaded, they will only be downloaded after the load() method on the loader is invoked.

loaders = REGISTRY.find(...)
loader = loaders[0] # just picking an arbitrary return value here, remember: loaders is a *list* of loaders
model = loader.load()

The load() method returns an instance of an mmc.MultiModalComparator. The MultiModalComparator class is a modality-agnostic abstraction. I'll get to the ins and outs of that another time.

API Mocking

You want something you can just drop into your code and it'll work. We got you. This library provides wrapper classes to mock the APIs of commonly used CLIP implementations. To wrap a MultiModalComparator so it can be used as a drop-in replacement with code compatible with OpenAI's CLIP:

from mmc.mock.openai import MockOpenaiClip

my_model = my_model_loader.load()
model = MockOpenaiClip(my_model)

MultiMMC: Multi-Perceptor Implementation

The MultiMMC class can be used to run inference against multiple mmc models in parallel. This form of ensemble is sometimes referred to as a "multi-perceptor".

To ensure that all models loaded into the MultiMMC are compatible, the MultiMMC instance is initialized by specifying the modalities it supports. We'll discuss modality objects in a bit.

from mmc.multimmc import MultiMMC
from mmc.modalities import TEXT, IMAGE

perceptor = MultiMMC(TEXT, IMAGE)

To load and use a model:

perceptor.load_model(
    architecture='clip', 
    publisher='openai', 
    id='RN50',
)

score = perceptor.compare(
    image=PIL.Image.open(...), 
    text=text_pos),
)

Additional models can be added to the ensemble via the load_model() method.

The MultiMMC does not support API mocking because of its reliance on the compare method.

Available Pre-trained Models

Some model comparisons here

# [<architecture> - <publisher> - <id>]
[clip - openai - RN50]
[clip - openai - RN101]
[clip - openai - RN50x4]
[clip - openai - RN50x16]
[clip - openai - RN50x64]
[clip - openai - ViT-B/32]
[clip - openai - ViT-B/16]
[clip - openai - ViT-L/14]
[clip - openai - ViT-L/[email protected]]
[clip - mlfoundations - RN50--openai]
[clip - mlfoundations - RN50--yfcc15m]
[clip - mlfoundations - RN50--cc12m]
[clip - mlfoundations - RN50-quickgelu--openai]
[clip - mlfoundations - RN50-quickgelu--yfcc15m]
[clip - mlfoundations - RN50-quickgelu--cc12m]
[clip - mlfoundations - RN101--openai]
[clip - mlfoundations - RN101--yfcc15m]
[clip - mlfoundations - RN101-quickgelu--openai]
[clip - mlfoundations - RN101-quickgelu--yfcc15m]
[clip - mlfoundations - RN50x4--openai]
[clip - mlfoundations - RN50x16--openai]
[clip - mlfoundations - ViT-B-32--openai]
[clip - mlfoundations - ViT-B-32--laion400m_e31]
[clip - mlfoundations - ViT-B-32--laion400m_e32]
[clip - mlfoundations - ViT-B-32--laion400m_avg]
[clip - mlfoundations - ViT-B-32-quickgelu--openai]
[clip - mlfoundations - ViT-B-32-quickgelu--laion400m_e31]
[clip - mlfoundations - ViT-B-32-quickgelu--laion400m_e32]
[clip - mlfoundations - ViT-B-32-quickgelu--laion400m_avg]
[clip - mlfoundations - ViT-B-16--openai]
[clip - mlfoundations - ViT-L-14--openai]
[clip - sbert - ViT-B-32-multilingual-v1]
[clip - sajjjadayobi - clipfa]

# The following models depend on napm for setup
[clip - navervision - kelip_ViT-B/32]
[cloob - crowsonkb - cloob_laion_400m_vit_b_16_16_epochs]
[cloob - crowsonkb - cloob_laion_400m_vit_b_16_32_epochs]
[clip - facebookresearch - clip_small_25ep]
[clip - facebookresearch - clip_base_25ep]
[clip - facebookresearch - clip_large_25ep]
[slip - facebookresearch - slip_small_25ep]
[slip - facebookresearch - slip_small_50ep]
[slip - facebookresearch - slip_small_100ep]
[slip - facebookresearch - slip_base_25ep]
[slip - facebookresearch - slip_base_50ep]
[slip - facebookresearch - slip_base_100ep]
[slip - facebookresearch - slip_large_25ep]
[slip - facebookresearch - slip_large_50ep]
[slip - facebookresearch - slip_large_100ep]
[simclr - facebookresearch - simclr_small_25ep]
[simclr - facebookresearch - simclr_base_25ep]
[simclr - facebookresearch - simclr_large_25ep]
[clip - facebookresearch - clip_base_cc3m_40ep]
[clip - facebookresearch - clip_base_cc12m_35ep]
[slip - facebookresearch - slip_base_cc3m_40ep]
[slip - facebookresearch - slip_base_cc12m_35ep]

VRAM Cost

The following is an estimate of the amount of space the loaded model occupies in memory:

	publisher	architecture	model_name	vram_mb
0	openai	clip	RN50	358
1	openai	clip	RN101	294
2	openai	clip	RN50x4	424
3	openai	clip	RN50x16	660
4	openai	clip	RN50x64	1350
5	openai	clip	ViT-B/32	368
6	openai	clip	ViT-B/16	348
7	openai	clip	ViT-L/14	908
8	openai	clip	ViT-L/[email protected]	908
9	mlfoundations	clip	RN50--openai	402
10	mlfoundations	clip	RN50--yfcc15m	402
11	mlfoundations	clip	RN50--cc12m	402
12	mlfoundations	clip	RN50-quickgelu--openai	402
13	mlfoundations	clip	RN50-quickgelu--yfcc15m	402
14	mlfoundations	clip	RN50-quickgelu--cc12m	402
15	mlfoundations	clip	RN101--openai	476
16	mlfoundations	clip	RN101--yfcc15m	476
17	mlfoundations	clip	RN101-quickgelu--openai	476
18	mlfoundations	clip	RN101-quickgelu--yfcc15m	476
19	mlfoundations	clip	RN50x4--openai	732
20	mlfoundations	clip	RN50x16--openai	1200
21	mlfoundations	clip	ViT-B-32--openai	634
22	mlfoundations	clip	ViT-B-32--laion400m_e31	634
23	mlfoundations	clip	ViT-B-32--laion400m_e32	634
24	mlfoundations	clip	ViT-B-32--laion400m_avg	634
25	mlfoundations	clip	ViT-B-32-quickgelu--openai	634
26	mlfoundations	clip	ViT-B-32-quickgelu--laion400m_e31	634
27	mlfoundations	clip	ViT-B-32-quickgelu--laion400m_e32	634
28	mlfoundations	clip	ViT-B-32-quickgelu--laion400m_avg	634
29	mlfoundations	clip	ViT-B-16--openai	634
30	mlfoundations	clip	ViT-L-14--openai	1688
32	sajjjadayobi	clip	clipfa	866
33	crowsonkb	cloob	cloob_laion_400m_vit_b_16_16_epochs	610
34	crowsonkb	cloob	cloob_laion_400m_vit_b_16_32_epochs	610
36	facebookresearch	slip	slip_small_25ep	728
37	facebookresearch	slip	slip_small_50ep	650
38	facebookresearch	slip	slip_small_100ep	650
39	facebookresearch	slip	slip_base_25ep	714
40	facebookresearch	slip	slip_base_50ep	714
41	facebookresearch	slip	slip_base_100ep	714
42	facebookresearch	slip	slip_large_25ep	1534
43	facebookresearch	slip	slip_large_50ep	1522
44	facebookresearch	slip	slip_large_100ep	1522
45	facebookresearch	slip	slip_base_cc3m_40ep	714
46	facebookresearch	slip	slip_base_cc12m_35ep	714

Contributing

Suggest a pre-trained model

If you would like to suggest a pre-trained model for future addition, you can add a comment to this issue

Add a pre-trained model

Create a loader class that encapsulates the logic for importing the model, loading weights, preprocessing inputs, and performing projections.
At the bottom of the file defining the loader class should be a code snippet that adds each respective checkpoint's loader to the registry.
Add an import for the new file to mmc/loaders/__init__.py. The imports in this file are the reason import mmc.loaders "spins up" the registry.
If the codebase on which the model depends can be installed, update pytproject.toml to install it.
Otherwise, add napm preparation at the top of the loaders load method (see cloob or kelip for examples), and also add napm setup to mmc/napm_installs/__init__.py
Add a test case to tests/test_mmc_loaders.py
Add a test script for the loader (see test_mmc_katcloob as an example)

Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

Related tags

Overview

mmc

installation

That optional `poe napm_installs` step

Usage

Querying the Model Registry

API Mocking

MultiMMC: Multi-Perceptor Implementation

Available Pre-trained Models

VRAM Cost

Contributing

Suggest a pre-trained model

Add a pre-trained model

Owner

David Marx

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

Starter Code for VALUE benchmark

Outlier Exposure with Confidence Control for Out-of-Distribution Detection

Companion repository to the paper accepted at the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

Official pytorch code for "APP: Anytime Progressive Pruning"

A two-stage U-Net for high-fidelity denoising of historical recordings

😮The official implementation of "CoNeRF: Controllable Neural Radiance Fields" 😮

A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

Official implementation of MSR-GCN (ICCV 2021 paper)

LIVECell - A large-scale dataset for label-free live cell segmentation

This project aims at building a real-time wide band channel sounder using USRPs

code for Grapadora research paper experimentation

Unified API to facilitate usage of pre-trained "perceptor" models, a la CLIP

Related tags

Overview

mmc

installation

That optional poe napm_installs step

Usage

Querying the Model Registry

API Mocking

MultiMMC: Multi-Perceptor Implementation

Available Pre-trained Models

VRAM Cost

Contributing

Suggest a pre-trained model

Add a pre-trained model

Owner

David Marx

Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators.

ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.

RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation

U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"

Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

code for "AttentiveNAS Improving Neural Architecture Search via Attentive Sampling"

Starter Code for VALUE benchmark

Outlier Exposure with Confidence Control for Out-of-Distribution Detection

Companion repository to the paper accepted at the 4th ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

Official pytorch code for "APP: Anytime Progressive Pruning"

A two-stage U-Net for high-fidelity denoising of historical recordings

😮The official implementation of "CoNeRF: Controllable Neural Radiance Fields" 😮

A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

Code for the ECCV2020 paper "A Differentiable Recurrent Surface for Asynchronous Event-Based Data"

Official implementation of MSR-GCN (ICCV 2021 paper)

LIVECell - A large-scale dataset for label-free live cell segmentation

This project aims at building a real-time wide band channel sounder using USRPs

code for Grapadora research paper experimentation

That optional `poe napm_installs` step