Image reconstruct

.gitignore

@@ -0,0 +1,4 @@
+work_dir
+cifar-10-batches-py
+code/nestml-target
+code/__pycache__

code/contrast_calculator.ipynb

@@ -0,0 +1,109 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pickle\n",
+    "\n",
+    "cifar_dir = \"/storage/ice1/7/8/jyou79/cifar-10-batches-py\"\n",
+    "cifar_65_dir = \"/storage/ice1/7/8/jyou79/cifar-10-batches-py-65\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rgb_to_grayscale(row):\n",
+    "        R = row[:1024].reshape(32, 32)\n",
+    "        G = row[1024:2048].reshape(32, 32)\n",
+    "        B = row[2048:].reshape(32, 32)\n",
+    "        grayscale = 0.299 * R + 0.587 * G + 0.114 * B\n",
+    "        return grayscale\n",
+    "   \n",
+    "with open(f'%s/data_batch_1'%(cifar_dir), 'rb') as f:\n",
+    "        cifar = pickle.load(f, encoding='bytes') \n",
+    "cifar = np.array([rgb_to_grayscale(row) for row in cifar[b'data']])\n",
+    "cifar = cifar.astype(np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50.21379848461852\n",
+      "15.546079322771272\n"
+     ]
+    }
+   ],
+   "source": [
+    "stds = np.std(cifar, axis=(1, 2), keepdims=True)\n",
+    "avg_std = np.mean(stds)\n",
+    "std_std = np.std(stds)\n",
+    "means = np.mean(cifar, axis=(1, 2), keepdims=True)\n",
+    "print(avg_std)\n",
+    "print(std_std)\n",
+    "avg_std = 65"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "adjusted_cifar = (cifar - means) / stds * avg_std + means\n",
+    "print(np.std(adjusted_cifar[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(f'%s/data_batch_1'%(cifar_65_dir), 'wb') as f:\n",
+    "    cifar = pickle.dump(adjusted_cifar, f) "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nest",
+   "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

code/custom_model/install_dendritic.py

@@ -7,10 +7,10 @@
 print(NEST_SIMULATOR_INSTALL_LOCATION)
 
 generate_nest_target(input_path="iaf_cond_exp_dend.nestml",
-                     target_path="/tmp/nestml-target",
+                     target_path="tmp/nestml-target",
                      module_name="nestml_iaf_module",
                      suffix="_nestml",
-                     logging_level="ERROR",  # try "INFO" for more debug information
+                     logging_level="INFO",  # try "INFO" for more debug information
                      codegen_opts={"nest_path": NEST_SIMULATOR_INSTALL_LOCATION})
 
 nest.Install("nestml_iaf_module")