more preliminary tests with the two feature spaces

flatironinstitute · Aug 9, 2023 · 9df00ea · 9df00ea
1 parent ec4d590
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diff --git a/Lukes_folder/MMD_testing copy.ipynb b/Lukes_folder/MMD_testing copy.ipynb
diff --git a/Lukes_folder/MMD_testing.ipynb b/Lukes_folder/MMD_testing.ipynb
diff --git a/Lukes_folder/comparing_estimators copy.ipynb b/Lukes_folder/comparing_estimators copy.ipynb
diff --git a/Lukes_folder/comparing_estimators.ipynb b/Lukes_folder/comparing_estimators.ipynb
diff --git a/Lukes_folder/train_BIFE_prior.ipynb b/Lukes_folder/train_BIFE_prior.ipynb
@@ -2,18 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torch\n",
     "import matplotlib.pyplot as plt\n",
@@ -35,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -50,7 +41,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 100/100 [56:24<00:00, 33.85s/epoch, loss=-.508]\n"
+      "  1%|          | 1/100 [00:46<1:16:58, 46.65s/epoch, loss=0.407]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [5], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mempty_cache()\n\u001b[0;32m----> 2\u001b[0m npe_train_no_saving(\n\u001b[1;32m      3\u001b[0m     image_config\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mimage_params_train_cryobife.json\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      4\u001b[0m     train_config\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mresnet18_encoder.json\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      5\u001b[0m     epochs\u001b[39m=\u001b[39m\u001b[39m100\u001b[39m,\n\u001b[1;32m      6\u001b[0m     estimator_file\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcryobife_estimator_alt.estimator\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      7\u001b[0m     loss_file\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcryobife_loss\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      8\u001b[0m     device\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcuda\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m      9\u001b[0m     simulation_batch_size\u001b[39m=\u001b[39m\u001b[39m1024\u001b[39m\n\u001b[1;32m     10\u001b[0m )\n",
+      "File \u001b[0;32m/mnt/ceph/users/levans/cryo_em_SBI/src/cryo_sbi/inference/train_npe_model.py:164\u001b[0m, in \u001b[0;36mnpe_train_no_saving\u001b[0;34m(image_config, train_config, epochs, estimator_file, loss_file, train_from_checkpoint, model_state_dict, n_workers, device, saving_frequency, simulation_batch_size)\u001b[0m\n\u001b[1;32m    150\u001b[0m     images \u001b[39m=\u001b[39m cryo_em_simulator(\n\u001b[1;32m    151\u001b[0m         models,\n\u001b[1;32m    152\u001b[0m         indices\u001b[39m.\u001b[39mto(device, non_blocking\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    161\u001b[0m         pixel_size,\n\u001b[1;32m    162\u001b[0m     )\n\u001b[1;32m    163\u001b[0m     \u001b[39mfor\u001b[39;00m _indices, _images \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(indices\u001b[39m.\u001b[39msplit(train_config[\u001b[39m\"\u001b[39m\u001b[39mBATCH_SIZE\u001b[39m\u001b[39m\"\u001b[39m]), images\u001b[39m.\u001b[39msplit(train_config[\u001b[39m\"\u001b[39m\u001b[39mBATCH_SIZE\u001b[39m\u001b[39m\"\u001b[39m])):\n\u001b[0;32m--> 164\u001b[0m         losses\u001b[39m.\u001b[39mappend(step(loss(_indices\u001b[39m.\u001b[39;49mto(device, non_blocking\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m), _images\u001b[39m.\u001b[39;49mto(device, non_blocking\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m))))\n\u001b[1;32m    165\u001b[0m     \u001b[39m#torch.cuda.empty_cache()\u001b[39;00m\n\u001b[1;32m    166\u001b[0m losses \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mstack(losses)\n",
+      "File \u001b[0;32m~/.local/lib/python3.9/site-packages/lampe/utils.py:42\u001b[0m, in \u001b[0;36mGDStep.__call__\u001b[0;34m(self, loss)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, loss: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m---> 42\u001b[0m     \u001b[39mif\u001b[39;00m loss\u001b[39m.\u001b[39misfinite()\u001b[39m.\u001b[39mall():\n\u001b[1;32m     43\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m     44\u001b[0m         loss\u001b[39m.\u001b[39mbackward()\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
@@ -60,7 +64,7 @@
     "    image_config=\"image_params_train_cryobife.json\",\n",
     "    train_config=\"resnet18_encoder.json\",\n",
     "    epochs=100,\n",
-    "    estimator_file=\"cryobife_estimator.estimator\",\n",
+    "    estimator_file=\"cryobife_estimator_alt.estimator\",\n",
     "    loss_file=\"cryobife_loss\",\n",
     "    device=\"cuda\",\n",
     "    simulation_batch_size=1024\n",

diff --git a/Lukes_folder/trying_it_out.ipynb b/Lukes_folder/trying_it_out.ipynb