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{
"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(\".\")"
]
},
{
"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"
}
},
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