2. Error accumulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstruction errors\n",
    "\n",
    "This notebook shows how numerical errors propagate through the invertible architectures \n",
    "by showing the evolution of the SNR through the model layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tristan/anaconda3/envs/torch4/lib/python3.7/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n",
      "  warnings.warn(warning.format(ret))\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "from torch import nn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from helpers import *\n",
    "from rec_error_helpers import log_rec, summarize_errors, init_data\n",
    "from models import IFlatXD, IResXD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "device = 1\n",
    "batch_size = 256\n",
    "\n",
    "dataset = cifar10(root='./data/cifar10')\n",
    "train_set = list(zip(transpose(normalise(pad(dataset['train']['data'], 4))), dataset['train']['labels']))\n",
    "transforms = [Crop(32, 32), FlipLR(), Cutout(8, 8)]\n",
    "train_batches = Batches(Transform(train_set, transforms), batch_size, shuffle=True, \n",
    "                        set_random_choices=True, drop_last=True, device=device)\n",
    "batch  = next(iter(train_batches))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "invert = True\n",
    "skip_invert = True\n",
    "invert = True\n",
    "negative_slope = 0.2\n",
    "ieps = 0.0001\n",
    "nlayers=[2, 3, 3]\n",
    "infeatures = 32\n",
    "pools = [\"channel\", \"channel\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SNR through layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data \n",
    "inp    = batch[\"input\"].clone()\n",
    "target = batch[\"target\"].clone()\n",
    "\n",
    "# Model\n",
    "model = IFlatXD(infeatures=infeatures, invert=invert, layers=nlayers, pools=pools,\n",
    "                negative_slope=negative_slope, bn_ieps=ieps, inchannels=3).cuda(device)\n",
    "handles, data=log_rec(model)\n",
    "\n",
    "# Run fwd + bwd\n",
    "output = model(inp)\n",
    "output.sum().backward()\n",
    "\n",
    "# Process and plot\n",
    "df = summarize_errors(data)\n",
    "init_data(data)\n",
    "\n",
    "plt.style.use(\"default\")\n",
    "plt.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False) # labels along the bottom edge are off\n",
    "\n",
    "df1 = df[df.layer.isin([\"IConv2dXD\", \"IBatchNorm3d\", \"ILeakyReLU\"])]\n",
    "ax=df1[\"snr\"].plot(logy=True)\n",
    "ax.figure.savefig(\"figures/Error propagation flat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2, figsize=(10,5), sharey=True)\n",
    "plt.style.use(\"default\")\n",
    "\n",
    "ax[0].tick_params(\n",
    "    axis='x',        \n",
    "    which='both',    \n",
    "    bottom=False,     \n",
    "    top=False,         \n",
    "    labelbottom=False) \n",
    "\n",
    "ax[1].tick_params(\n",
    "    axis='x',        \n",
    "    which='both',    \n",
    "    bottom=False,     \n",
    "    top=False,         \n",
    "    labelbottom=False) \n",
    "\n",
    "df1[\"snr\"].plot(logy=True, ax=ax[0])\n",
    "df2[\"snr\"].iloc[9:].plot(logy=True, ax=ax[1])\n",
    "\n",
    "fig.savefig(\"figures/Error propagation residual\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>layer</th>\n",
       "      <th>bwd_cnt</th>\n",
       "      <th>snr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>IConv2dXD</td>\n",
       "      <td>1</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>IBatchNorm3d</td>\n",
       "      <td>1</td>\n",
       "      <td>10705435648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ILeakyReLU</td>\n",
       "      <td>1</td>\n",
       "      <td>10679172096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>IConv2dXD</td>\n",
       "      <td>1</td>\n",
       "      <td>114619621376.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>IBatchNorm3d</td>\n",
       "      <td>1</td>\n",
       "      <td>131419054080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>IBatchNorm3d</td>\n",
       "      <td>1</td>\n",
       "      <td>1092363136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>150</td>\n",
       "      <td>ILeakyReLU</td>\n",
       "      <td>1</td>\n",
       "      <td>346250528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>IConv2dXD</td>\n",
       "      <td>1</td>\n",
       "      <td>1184569344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>IBatchNorm3d</td>\n",
       "      <td>1</td>\n",
       "      <td>1179111552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>ILeakyReLU</td>\n",
       "      <td>1</td>\n",
       "      <td>3366377918300160.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id         layer bwd_cnt                 snr\n",
       "1      1     IConv2dXD       1                 inf\n",
       "3      3  IBatchNorm3d       1       10705435648.0\n",
       "4      4    ILeakyReLU       1       10679172096.0\n",
       "8      8     IConv2dXD       1      114619621376.0\n",
       "12    12  IBatchNorm3d       1      131419054080.0\n",
       "..   ...           ...     ...                 ...\n",
       "149  149  IBatchNorm3d       1        1092363136.0\n",
       "150  150    ILeakyReLU       1         346250528.0\n",
       "151  151     IConv2dXD       1        1184569344.0\n",
       "155  155  IBatchNorm3d       1        1179111552.0\n",
       "156  156    ILeakyReLU       1  3366377918300160.0\n",
       "\n",
       "[69 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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