test

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.vscode/settings.json

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+{
+    "flake8.args": [
+        "--max-line-length=120"
+    ]
+}

LICENSE

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MODELCARD.md

@@ -0,0 +1,128 @@
+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Phenom CA-MAE-S/16
+
+Channel-agnostic image encoding model designed for microscopy image featurization. 
+The model uses a vision transformer backbone with channelwise cross-attention over patch tokens to create contextualized representations separately for each channel.
+
+
+## Model Details
+
+### Model Description
+
+This model is a [channel-agnostic masked autoencoder](https://openaccess.thecvf.com/content/CVPR2024/html/Kraus_Masked_Autoencoders_for_Microscopy_are_Scalable_Learners_of_Cellular_Biology_CVPR_2024_paper.html) trained to reconstruct microscopy images over three datasets:
+1. RxRx3
+2. JUMP-CP overexpression
+3. JUMP-CP gene-knockouts
+
+- **Developed, funded, and shared by:** Recursion
+- **Model type:** Vision transformer CA-MAE
+- **Image modality:** Optimized for microscopy images from the CellPainting assay
+- **License:** 
+
+
+### Model Sources
+
+- **Repository:** [https://github.com/recursionpharma/maes_microscopy](https://github.com/recursionpharma/maes_microscopy)
+- **Paper:** [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://openaccess.thecvf.com/content/CVPR2024/html/Kraus_Masked_Autoencoders_for_Microscopy_are_Scalable_Learners_of_Cellular_Biology_CVPR_2024_paper.html)
+
+
+## Uses
+
+NOTE: model embeddings tend to extract features only after using standard batch correction post-processing techniques. **We recommend**, at a *minimum*, after inferencing the model over your images, to do the standard `PCA-CenterScale` pattern or better yet Typical Variation Normalization:
+
+1. Fit a PCA kernel on all the *control images* (or all images if no controls) from across all experimental batches (e.g. the plates of wells from your assay),
+2. Transform all the embeddings with that PCA kernel,
+3. For each experimental batch, fit a separate StandardScaler on the transformed embeddings of the controls from step 2, then transform the rest of the embeddings from that batch with that StandardScaler.
+
+### Direct Use
+
+- Create biologically useful embeddings of microscopy images
+- Create contextualized embeddings of each channel of a microscopy image (set `return_channelwise_embeddings=True`)
+- Leverage the full MAE encoder + decoder to predict new channels / stains for images without all 6 CellPainting channels
+
+### Downstream Use
+
+- A determined ML expert could fine-tune the encoder for downstream tasks such as classification
+
+### Out-of-Scope Use
+
+- Unlikely to be especially performant on brightfield microscopy images
+- Out-of-domain medical images, such as H&E (maybe it would be a decent baseline though)
+
+## Bias, Risks, and Limitations
+
+- Primary limitation is that the embeddings tend to be more useful at scale. For example, if you only have 1 plate of microscopy images, the embeddings might underperform compared to a supervised bespoke model.
+
+## How to Get Started with the Model
+
+You should be able to successfully run the below tests, which demonstrate how to use the model at inference time.
+
+```python
+import pytest
+import torch
+
+from huggingface_mae import MAEModel
+
+huggingface_phenombeta_model_dir = "."
+# huggingface_modelpath = "recursionpharma/test-pb-model"
+
+
+@pytest.fixture
+def huggingface_model():
+    # Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/test-pb-model to this directory
+    # huggingface-cli download recursionpharma/test-pb-model --local-dir=.
+    huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
+    huggingface_model.eval()
+    return huggingface_model
+
+
+@pytest.mark.parametrize("C", [1, 4, 6, 11])
+@pytest.mark.parametrize("return_channelwise_embeddings", [True, False])
+def test_model_predict(huggingface_model, C, return_channelwise_embeddings):
+    example_input_array = torch.randint(
+        low=0,
+        high=255,
+        size=(2, C, 256, 256),
+        dtype=torch.uint8,
+        device=huggingface_model.device,
+    )
+    huggingface_model.return_channelwise_embeddings = return_channelwise_embeddings
+    embeddings = huggingface_model.predict(example_input_array)
+    expected_output_dim = 384 * C if return_channelwise_embeddings else 384
+    assert embeddings.shape == (2, expected_output_dim)
+```
+
+
+## Training, evaluation and testing details
+
+See paper linked above for details on model training and evaluation. Primary hyperparameters are included in the repo linked above.
+
+
+## Environmental Impact
+
+- **Hardware Type:** Nvidia H100 Hopper nodes
+- **Hours used:** 400
+- **Cloud Provider:** private cloud
+- **Carbon Emitted:** 138.24 kg co2 (roughly the equivalent of one car driving from Toronto to Montreal)
+
+**BibTeX:**
+
+```TeX
+@inproceedings{kraus2024masked,
+  title={Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology},
+  author={Kraus, Oren and Kenyon-Dean, Kian and Saberian, Saber and Fallah, Maryam and McLean, Peter and Leung, Jess and Sharma, Vasudev and Khan, Ayla and Balakrishnan, Jia and Celik, Safiye and others},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={11757--11768},
+  year={2024}
+}
+```
+
+## Model Card Contact
+
+- Kian Kenyon-Dean: kian.kd@recursion.com
+- Oren Kraus: oren.kraus@recursion.com
+- Or, email: info@rxrx.ai

README.md

@@ -1,127 +1,42 @@
----
-library_name: transformers
-tags: []
----
+# Masked Autoencoders are Scalable Learners of Cellular Morphology
+Official repo for Recursion's two recently accepted papers:
+- Spotlight full-length paper at [CVPR 2024](https://cvpr.thecvf.com/Conferences/2024/AcceptedPapers) -- Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
+  - Paper: https://arxiv.org/abs/2404.10242
+  - CVPR poster page with video: https://cvpr.thecvf.com/virtual/2024/poster/31565
+- Spotlight workshop paper at [NeurIPS 2023 Generative AI & Biology workshop](https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/GenBio)
+  - Paper: https://arxiv.org/abs/2309.16064
 
-# Model Card for OpenPhenom-S/16
+![vit_diff_mask_ratios](https://github.com/recursionpharma/maes_microscopy/assets/109550980/c15f46b1-cdb9-41a7-a4af-bdc9684a971d)
 
-Channel-agnostic image encoding model CA-MAE with a ViT-S/16 encoder backbone designed for microscopy image featurization. 
-The model uses a vision transformer backbone with channelwise cross-attention over patch tokens to create contextualized representations separately for each channel.
 
+## Provided code
+See the repo for ingredients required for defining our MAEs. Users seeking to re-implement training will need to stitch together the Encoder and Decoder modules according to their usecase.
 
-## Model Details
-
-### Model Description
-
-This model is a [channel-agnostic masked autoencoder](https://openaccess.thecvf.com/content/CVPR2024/html/Kraus_Masked_Autoencoders_for_Microscopy_are_Scalable_Learners_of_Cellular_Biology_CVPR_2024_paper.html) trained to reconstruct microscopy images over three datasets:
-1. RxRx3
-2. JUMP-CP overexpression
-3. JUMP-CP gene-knockouts
-
-- **Developed, funded, and shared by:** Recursion
-- **Model type:** Vision transformer CA-MAE
-- **Image modality:** Optimized for microscopy images from the CellPainting assay
-- **License:** [Non-Commercial End User License Agreement](https://huggingface.co/recursionpharma/OpenPhenom/blob/main/LICENSE)
-
-
-### Model Sources
-
-- **Repository:** [https://github.com/recursionpharma/maes_microscopy](https://github.com/recursionpharma/maes_microscopy)
-- **Paper:** [Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology](https://openaccess.thecvf.com/content/CVPR2024/html/Kraus_Masked_Autoencoders_for_Microscopy_are_Scalable_Learners_of_Cellular_Biology_CVPR_2024_paper.html)
-
-
-## Uses
-
-NOTE: model embeddings tend to extract features only after using standard batch correction post-processing techniques. **We recommend**, at a *minimum*, after inferencing the model over your images, to do the standard `PCA-CenterScale` pattern or better yet Typical Variation Normalization:
-
-1. Fit a PCA kernel on all the *control images* (or all images if no controls) from across all experimental batches (e.g. the plates of wells from your assay),
-2. Transform all the embeddings with that PCA kernel,
-3. For each experimental batch, fit a separate StandardScaler on the transformed embeddings of the controls from step 2, then transform the rest of the embeddings from that batch with that StandardScaler.
-
-### Direct Use
-
-- Create biologically useful embeddings of microscopy images
-- Create contextualized embeddings of each channel of a microscopy image (set `return_channelwise_embeddings=True`)
-- Leverage the full MAE encoder + decoder to predict new channels / stains for images without all 6 CellPainting channels
-
-### Downstream Use
-
-- A determined ML expert could fine-tune the encoder for downstream tasks such as classification
-
-### Out-of-Scope Use
-
-- Unlikely to be especially performant on brightfield microscopy images
-- Out-of-domain medical images, such as H&E (maybe it would be a decent baseline though)
-
-## Bias, Risks, and Limitations
-
-- Primary limitation is that the embeddings tend to be more useful at scale. For example, if you only have 1 plate of microscopy images, the embeddings might underperform compared to a supervised bespoke model.
-
-## How to Get Started with the Model
-
-You should be able to successfully run the below tests, which demonstrate how to use the model at inference time.
-
-```python
-import pytest
-import torch
-
-from huggingface_mae import MAEModel
-
-# huggingface_openphenom_model_dir = "."
-huggingface_modelpath = "recursionpharma/OpenPhenom"
-
-
-@pytest.fixture
-def huggingface_model():
-    # This step downloads the model to a local cache, takes a bit to run
-    huggingface_model = MAEModel.from_pretrained(huggingface_modelpath)
-    huggingface_model.eval()
-    return huggingface_model
-
-
-@pytest.mark.parametrize("C", [1, 4, 6, 11])
-@pytest.mark.parametrize("return_channelwise_embeddings", [True, False])
-def test_model_predict(huggingface_model, C, return_channelwise_embeddings):
-    example_input_array = torch.randint(
-        low=0,
-        high=255,
-        size=(2, C, 256, 256),
-        dtype=torch.uint8,
-        device=huggingface_model.device,
-    )
-    huggingface_model.return_channelwise_embeddings = return_channelwise_embeddings
-    embeddings = huggingface_model.predict(example_input_array)
-    expected_output_dim = 384 * C if return_channelwise_embeddings else 384
-    assert embeddings.shape == (2, expected_output_dim)
+Furthermore the baseline Vision Transformer architecture backbone used in this work can be built with the following code snippet from Timm:
 ```
-We also provide a [notebook](https://huggingface.co/recursionpharma/OpenPhenom/blob/main/RxRx3-core_inference.ipynb) for running inference on [RxRx3-core](https://huggingface.co/datasets/recursionpharma/rxrx3-core).
-
-## Training, evaluation and testing details
-
-See paper linked above for details on model training and evaluation. Primary hyperparameters are included in the repo linked above.
-
-
-## Environmental Impact
-
-- **Hardware Type:** Nvidia H100 Hopper nodes
-- **Hours used:** 400
-- **Cloud Provider:** private cloud
-- **Carbon Emitted:** 138.24 kg co2 (roughly the equivalent of one car driving from Toronto to Montreal)
-
-**BibTeX:**
-
-```TeX
-@inproceedings{kraus2024masked,
-  title={Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology},
-  author={Kraus, Oren and Kenyon-Dean, Kian and Saberian, Saber and Fallah, Maryam and McLean, Peter and Leung, Jess and Sharma, Vasudev and Khan, Ayla and Balakrishnan, Jia and Celik, Safiye and others},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={11757--11768},
-  year={2024}
-}
+import timm.models.vision_transformer as vit
+
+def vit_base_patch16_256(**kwargs):
+    default_kwargs = dict(
+        img_size=256,
+        in_chans=6,
+        num_classes=0,
+        fc_norm=None,
+        class_token=True,
+        drop_path_rate=0.1,
+        init_values=0.0001,
+        block_fn=vit.ParallelScalingBlock,
+        qkv_bias=False,
+        qk_norm=True,
+    )
+    for k, v in kwargs.items():
+        default_kwargs[k] = v
+    return vit.vit_base_patch16_224(**default_kwargs)
 ```
 
-## Model Card Contact
+## Provided models
+A publicly available model for research can be found via Nvidia's BioNemo platform, which handles inference and auto-scaling: https://www.rxrx.ai/phenom
 
-- Kian Kenyon-Dean: kian.kd@recursion.com
-- Oren Kraus: oren.kraus@recursion.com
-- Or, email: info@rxrx.ai
+We have partnered with Nvidia to host a publicly-available smaller and more flexible version of the MAE phenomics foundation model, called Phenom-Beta. Interested parties can access it directly through the Nvidia BioNemo API:
+- https://blogs.nvidia.com/blog/drug-discovery-bionemo-generative-ai/
+- https://www.youtube.com/watch?v=Gch6bX1toB0

RxRx3-core_inference.ipynb

@@ -1,195 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5edcb7d2-53dc-4170-9f2f-619c0da0ae4c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import numpy as np\n",
-    "from torch.utils.data import DataLoader\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f839c8fb-b018-4ab6-86a9-7d5bf7883b45",
-   "metadata": {},
-   "source": [
-    "# Load OpenPhenom"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "84b9324d-fde9-4c43-bc5a-eb66cdb4f891",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load model directly\n",
-    "from huggingface_mae import MAEModel\n",
-    "open_phenom = MAEModel.from_pretrained(\"recursionpharma/OpenPhenom\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "57d918c5-78de-4b36-9f46-4652c5da93f2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "open_phenom.eval()\n",
-    "cuda_available = torch.cuda.is_available()\n",
-    "if cuda_available:\n",
-    "    open_phenom.cuda()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c89d82d-5365-4492-b496-adb3bbd71b32",
-   "metadata": {},
-   "source": [
-    "# Load Rxrx3-core"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "deeff3a8-db67-4905-a7e9-c43aad614a84",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datasets import load_dataset\n",
-    "rxrx3_core = load_dataset(\"recursionpharma/rxrx3-core\")['train']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8f2226ce-9415-4dd8-932e-54e4e1bd8c1a",
-   "metadata": {},
-   "source": [
-    "# Infernce loop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa1218ab-f9cd-413b-9228-c1146df978be",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def convert_path_to_well_id(path_str):\n",
-    "    \n",
-    "    return path_str.split('_')[0].replace('/','_').replace('Plate','')\n",
-    "    \n",
-    "def collate_rxrx3_core(batch):\n",
-    "    \n",
-    "    images = np.stack([np.array(i['jp2']) for i in batch]).reshape(-1,6,512,512)\n",
-    "    images = np.vstack([patch_image(i) for i in images]) # convert to 4 256x256 patches\n",
-    "    images = torch.from_numpy(images)\n",
-    "    well_ids = [convert_path_to_well_id(i['__key__']) for i in batch[::6]]\n",
-    "    return images, well_ids\n",
-    "\n",
-    "def iter_border_patches(width, height, patch_size):\n",
-    "    \n",
-    "    x_start, x_end, y_start, y_end = (0, width, 0, height)\n",
-    "\n",
-    "    for x in range(x_start, x_end - patch_size + 1, patch_size):\n",
-    "        for y in range(y_start, y_end - patch_size + 1, patch_size):\n",
-    "            yield x, y\n",
-    "\n",
-    "def patch_image(image_array, patch_size=256):\n",
-    "    \n",
-    "    _, width, height = image_array.shape\n",
-    "    output_patches = []\n",
-    "    patch_count = 0\n",
-    "    for x, y in iter_border_patches(width, height, patch_size):\n",
-    "        patch = image_array[:, y : y + patch_size, x : x + patch_size].copy()\n",
-    "        output_patches.append(patch)\n",
-    "    \n",
-    "    output_patches = np.stack(output_patches)\n",
-    "    \n",
-    "    return output_patches"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "de308003-bcfc-4b59-9715-dd884b9b2536",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Convert to PyTorch DataLoader\n",
-    "batch_size = 128\n",
-    "num_workers = 4\n",
-    "rxrx3_core_dataloader = DataLoader(rxrx3_core, batch_size=batch_size*6, shuffle=False, \n",
-    "                                   collate_fn=collate_rxrx3_core, num_workers=num_workers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e3ea6c2-d1aa-4e20-a175-d72ea636153e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Inference loop\n",
-    "num_features = 384\n",
-    "n_crops = 4\n",
-    "well_ids = []\n",
-    "emb_ind = 0\n",
-    "embeddings = np.zeros(\n",
-    "    ((len(rxrx3_core_dataloader.dataset)//6), num_features), dtype=np.float32\n",
-    ")\n",
-    "forward_pass_counter = 0\n",
-    "\n",
-    "for imgs, batch_well_ids in rxrx3_core_dataloader:\n",
-    "\n",
-    "    if cuda_available:\n",
-    "        with torch.amp.autocast(\"cuda\"), torch.no_grad():\n",
-    "            latent = open_phenom.predict(imgs.cuda())\n",
-    "    else:\n",
-    "        latent = open_phenom.predict(imgs)\n",
-    "    \n",
-    "    latent = latent.view(-1, n_crops, num_features).mean(dim=1)  # average over 4 256x256 crops per image\n",
-    "    embeddings[emb_ind : (emb_ind + len(latent))] = latent.detach().cpu().numpy()\n",
-    "    well_ids.extend(batch_well_ids)\n",
-    "\n",
-    "    emb_ind += len(latent)\n",
-    "    forward_pass_counter += 1\n",
-    "    if forward_pass_counter % 5 == 0:\n",
-    "        print(f\"forward pass {forward_pass_counter} of {len(rxrx3_core_dataloader)} done, wells inferenced {emb_ind}\")\n",
-    "\n",
-    "embedding_df = embeddings[:emb_ind]\n",
-    "embedding_df = pd.DataFrame(embedding_df)\n",
-    "embedding_df.columns = [f\"feature_{i}\" for i in range(num_features)]\n",
-    "embedding_df['well_id'] = well_ids\n",
-    "embedding_df = embedding_df[['well_id']+[f\"feature_{i}\" for i in range(num_features)]]\n",
-    "embedding_df.to_parquet('OpenPhenom_rxrx3-core_embeddings.parquet')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "photo2",
-   "language": "python",
-   "name": "photo2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

config.json

@@ -1,9 +1,5 @@
 {
   "_attn_implementation_autoset": true,
-  "auto_map": {
-    "AutoModel": "huggingface_mae.MAEModel",
-    "AutoConfig": "huggingface_mae.MAEConfig"
-  },
   "apply_loss_unmasked": false,
   "architectures": [
     "MAEModel"

huggingface_mae.py

@@ -4,13 +4,12 @@ import torch
 import torch.nn as nn
 
 from transformers import PretrainedConfig, PreTrainedModel
-from transformers.utils import cached_file
 
-from .loss import FourierLoss
-from .normalizer import Normalizer
-from .mae_modules import CAMAEDecoder, MAEDecoder, MAEEncoder
-from .mae_utils import flatten_images
-from .vit import (
+from loss import FourierLoss
+from normalizer import Normalizer
+from mae_modules import CAMAEDecoder, MAEDecoder, MAEEncoder
+from mae_utils import flatten_images
+from vit import (
     generate_2d_sincos_pos_embeddings,
     sincos_positional_encoding_vit,
     vit_small_patch16_256,
@@ -286,9 +285,9 @@ class MAEModel(PreTrainedModel):
     def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         filename = kwargs.pop("filename", "model.safetensors")
 
+        modelpath = f"{pretrained_model_name_or_path}/{filename}"
         config = MAEConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        modelpath = cached_file(pretrained_model_name_or_path, filename=filename)
         state_dict = torch.load(modelpath, map_location="cpu")
-        model = cls(config)
+        model = cls(config, *model_args, **kwargs)
         model.load_state_dict(state_dict["state_dict"])
         return model

mae_modules.py

@@ -7,8 +7,8 @@ import torch.nn as nn
 from timm.models.helpers import checkpoint_seq
 from timm.models.vision_transformer import Block, Mlp, VisionTransformer
 
-from .masking import transformer_random_masking
-from .vit import channel_agnostic_vit
+from masking import transformer_random_masking
+from vit import channel_agnostic_vit
 
 # If interested in training new MAEs, combine an encoder and decoder into a new module, and you should
 # leverage the flattening and unflattening utilities as needed from mae_utils.py.

model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f6e5f1c97101331b1574c9e4b99623260191c55eea5d98e40460849c0e4c4d47
-size 712434294

test_huggingface_mae.py

@@ -1,16 +1,17 @@
 import pytest
 import torch
 
-# huggingface_openphenom_model_dir = "."
-huggingface_modelpath = "recursionpharma/OpenPhenom"
+from huggingface_mae import MAEModel
 
-from .huggingface_mae import MAEModel
+huggingface_phenombeta_model_dir = "."
+# huggingface_modelpath = "recursionpharma/test-pb-model"
 
 
 @pytest.fixture
 def huggingface_model():
-    # This step downloads the model to a local cache, takes a bit to run
-    huggingface_model = MAEModel.from_pretrained(huggingface_modelpath)
+    # Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/test-pb-model to this directory
+    # huggingface-cli download recursionpharma/test-pb-model --local-dir=.
+    huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
     huggingface_model.eval()
     return huggingface_model