From 93c55972d7a1beae02b4203baf71d6a5764341b1 Mon Sep 17 00:00:00 2001 From: Noah Kasmanoff Date: Mon, 8 Jul 2024 13:00:37 +0000 Subject: [PATCH] embedding visual: mean vs class pooling --- notebooks/embedding_visualizer.ipynb | 10529 +++++++++++++++++++++++++ requirements-dev.txt | 1 + 2 files changed, 10530 insertions(+) create mode 100644 notebooks/embedding_visualizer.ipynb diff --git a/notebooks/embedding_visualizer.ipynb b/notebooks/embedding_visualizer.ipynb new file mode 100644 index 0000000..73ecffa --- /dev/null +++ b/notebooks/embedding_visualizer.ipynb @@ -0,0 +1,10529 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "from tqdm import tqdm\n", + "\n", + "import pytorch_lightning as pl\n", + "import torch\n", + "import wandb\n", + "from sdofm import utils\n", + "from sdofm.datasets import SDOMLDataModule\n", + "from sdofm.pretraining import MAE\n", + "from scripts.pretrain import Pretrainer\n", + "from lightning.pytorch.loggers.wandb import WandbLogger" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import omegaconf\n", + "\n", + "cfg = omegaconf.OmegaConf.load(\"../experiments/finetune_32.2M_mae_virtualeve.yaml\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[* CACHE SYSTEM *] Found cached index data in /mnt/sdoml/cache/aligndata_AIA_FULL_12min.csv.\n", + "[* CACHE SYSTEM *] Found cached normalization data in /mnt/sdoml/cache/normalizations_AIA_FULL_12min.json.\n", + "[* CACHE SYSTEM *] Found cached HMI mask data in /mnt/sdoml/cache/hmi_mask_512x512.npy.\n" + ] + } + ], + "source": [ + "data_module = SDOMLDataModule(\n", + " hmi_path=None,\n", + " aia_path=os.path.join(\n", + " cfg.data.sdoml.base_directory, cfg.data.sdoml.sub_directory.aia\n", + " ),\n", + " eve_path=None,\n", + " components=cfg.data.sdoml.components,\n", + " wavelengths=cfg.data.sdoml.wavelengths,\n", + " ions=cfg.data.sdoml.ions,\n", + " frequency=cfg.data.sdoml.frequency,\n", + " batch_size=cfg.model.opt.batch_size,\n", + " num_workers=cfg.data.num_workers,\n", + " val_months=cfg.data.month_splits.val,\n", + " test_months=cfg.data.month_splits.test,\n", + " holdout_months=cfg.data.month_splits.holdout,\n", + " cache_dir=os.path.join(\n", + " cfg.data.sdoml.base_directory, cfg.data.sdoml.sub_directory.cache\n", + " ),\n", + ")\n", + "data_module.setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[* CACHE SYSTEM *] Found cached index data in /mnt/sdoml/cache/aligndata_AIA_FULL_12min.csv.\n", + "[* CACHE SYSTEM *] Found cached normalization data in /mnt/sdoml/cache/normalizations_AIA_FULL_12min.json.\n", + "[* CACHE SYSTEM *] Found cached HMI mask data in /mnt/sdoml/cache/hmi_mask_512x512.npy.\n", + "Loading checkpoint...\n", + "Found pre-downloaded checkpoint at artifacts/model-tk45el88:v12/model.ckpt\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/envs/sdofm/lib/python3.10/site-packages/lightning/pytorch/utilities/migration/utils.py:56: The loaded checkpoint was produced with Lightning v2.2.5, which is newer than your current Lightning version: v2.2.1\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checkpoint loaded from artifacts/model-tk45el88:v12/model.ckpt\n" + ] + } + ], + "source": [ + "#model = MAE(\n", + "# **cfg.model.mae,\n", + " # **cfg.model.samae,\n", + " # hmi_mask=data_module.hmi_mask,\n", + "# optimiser=cfg.model.opt.optimiser,\n", + "# lr=cfg.model.opt.learning_rate,\n", + "# weight_decay=cfg.model.opt.weight_decay,\n", + " \n", + "#)\n", + "\n", + "logger = WandbLogger(\n", + " # WandbLogger params\n", + " name=cfg.experiment.name,\n", + " project=cfg.experiment.project,\n", + " dir=cfg.experiment.wandb.output_directory,\n", + " log_model=cfg.experiment.wandb.log_model,\n", + " # kwargs for wandb.init\n", + " tags=cfg.experiment.wandb.tags,\n", + " notes=cfg.experiment.wandb.notes,\n", + " group=cfg.experiment.wandb.group,\n", + " save_code=True,\n", + " job_type=cfg.experiment.wandb.job_type,\n", + "\n", + ")\n", + "model = Pretrainer(cfg, logger=logger, is_backbone=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "val_dataset = data_module.valid_ds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "model.model.to(\"cuda\");" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1024/1024 [02:36<00:00, 6.54it/s]\n" + ] + } + ], + "source": [ + "num_samples = 1024\n", + "cls_embeddings = []\n", + "mean_embeddings = []\n", + "names = []\n", + "for i in tqdm(range(num_samples)):\n", + " batch = val_dataset[i]\n", + " name = val_dataset.aligndata.iloc[i].name\n", + " batch = torch.tensor(batch).unsqueeze(0) \n", + " batch = batch.to(\"cuda\") \n", + " x, mask, ids_restore = model.model.forward_encoder(batch, mask_ratio = 0)\n", + " # cls_token \n", + " cls_embedding = x[:,0,:].detach().cpu()\n", + " mean_embedding = x[:,1:,:].mean(dim=1).detach().cpu()\n", + " cls_embeddings.append(cls_embedding)\n", + " mean_embeddings.append(mean_embedding)\n", + " names.append(name)\n", + "cls_embeddings = torch.cat(cls_embeddings, dim=0)\n", + "mean_embeddings = torch.cat(mean_embeddings, dim=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.manifold import TSNE\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1024, 2)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "tsne = TSNE(n_components=2, random_state=0)\n", + "\n", + "cls_embeddings_np = cls_embeddings.numpy()\n", + "cls_embeddings_tsne = tsne.fit_transform(cls_embeddings_np)\n", + "\n", + "cls_embeddings_tsne.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a color scale for the dates we see. Darker = earlier\n", + "import matplotlib.colors as mcolors\n", + "import matplotlib.cm as cm\n", + "\n", + "norm = mcolors.Normalize(vmin=0, vmax=num_samples-1)\n", + "cmap = cm.ScalarMappable(norm=norm, cmap=cm.viridis)\n", + "cmap.set_array([])\n", + "colors = cmap.to_rgba(np.arange(num_samples ))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "%{hovertext}

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x=%{x}
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Mean pooling\n", + "fig = px.scatter(x=mean_embeddings_tsne[:,0], y=mean_embeddings_tsne[:,1], color=range(num_samples), color_discrete_map={i: f\"rgb({int(colors[i][0]*255)},{int(colors[i][1]*255)},{int(colors[i][2]*255)})\" for i in range(num_samples)}\n", + " \n", + " # only show text on hover\n", + " , hover_name=names\n", + "\n", + " )\n", + "fig.update_traces(marker=dict(size=12, line=dict(width=2, color='DarkSlateGrey')), selector=dict(mode='markers'))\n", + "# hide the colorbar\n", + "fig.update_layout(coloraxis_showscale=False)\n", + "\n", + "\n", + "fig.update_traces(textposition='top center', textfont_size=8)\n", + "fig.update_layout(showlegend=False, autosize=False, width=800, height=800, margin=dict(l=0, r=0, b=0, t=0))\n", + "\n", + "\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install plotly" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "sdofm", + "language": "python", + "name": "python3" + }, + "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": 2 +} diff --git a/requirements-dev.txt b/requirements-dev.txt index 13ed8e1..c250684 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -3,6 +3,7 @@ ## helpful functions for notebooks scikit-image zarr[jupyter] +plotly ## for profiling tensorflow-cpu