prototype.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37664bitpytorchenvcondac65bc6c7ab474553ae2b82822152b36f",
   "display_name": "Python 3.7.6 64-bit ('pytorch_env': conda)",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[2 8 7 1 3 4 9 0 6 5]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.random.permutation(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.metrics import structural_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = 40000\n",
    "dev = 1000\n",
    "test = 1000\n",
    "\n",
    "img_list = np.random.permutation(train+dev+test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "i = (1, 3, 5)\n",
    "i[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import torchvision.models as tmodels\n",
    "from functools import partial\n",
    "import collections\n",
    "\n",
    "# dummy data: 10 batches of images with batch size 16\n",
    "dataset = [torch.rand(16,3,224,224).cuda() for _ in range(10)]\n",
    "\n",
    "# network: a resnet50\n",
    "net = tmodels.resnet50(pretrained=True).cuda()\n",
    "\n",
    "# a dictionary that keeps saving the activations as they come\n",
    "activations = collections.defaultdict(list)\n",
    "def save_activation(name, mod, inp, out):\n",
    "\tactivations[name].append(out.cpu())\n",
    "\n",
    "# Registering hooks for all the Conv2d layers\n",
    "# Note: Hooks are called EVERY TIME the module performs a forward pass. For modules that are\n",
    "# called repeatedly at different stages of the forward pass (like RELUs), this will save different\n",
    "# activations. Editing the forward pass code to save activations is the way to go for these cases.\n",
    "for name, m in net.named_modules():\n",
    "\tif type(m)==nn.Conv2d:\n",
    "\t\t# partial to assign the layer name to each hook\n",
    "\t\tm.register_forward_hook(partial(save_activation, name))\n",
    "\n",
    "# forward pass through the full dataset\n",
    "for batch in dataset:\n",
    "\tout = net(batch)\n",
    "\n",
    "# concatenate all the outputs we saved to get the the activations for each layer for the whole dataset\n",
    "activations = {name: torch.cat(outputs, 0) for name, outputs in activations.items()}\n",
    "\n",
    "# just print out the sizes of the saved activations as a sanity check\n",
    "for k,v in activations.items():\n",
    "\tprint (k, v.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mse_froim_psnr(psnr):\n",
    "    mse = 10**-(psnr/20)\n",
    "    return mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.05604024173641845\n"
     ]
    }
   ],
   "source": [
    "print(mse_froim_psnr(25.030))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percent_improv(a, b):\n",
    "    return 100*((a-b)/b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "51.78571428571429\n"
     ]
    }
   ],
   "source": [
    "print(percent_improv(0.085, 0.056))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_dataset_vs_time():\n",
    "\n",
    "    # Create some mock data\n",
    "    dset= ['1k', '10k', '20k', '40k', '50k']\n",
    "    data1 = [25.15, 26.83, 26.98, 27.28, 27.31]\n",
    "    data2 = [2.55, 21.10, 41.90, 83.78, 104.68]\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "\n",
    "    color = 'purple'\n",
    "    ax1.set_xlabel('Dataset Size')\n",
    "    ax1.set_ylabel('PSNR (dB)', color=color)\n",
    "    ax1.plot(dset, data1, color=color, marker='o', ls='--')\n",
    "    ax1.plot('40k', 27.28, 'ko')\n",
    "    ax1.tick_params(axis='y', labelcolor=color)\n",
    "    ax1.yaxis.label.set_size(16)\n",
    "\n",
    "    ax1.xaxis.label.set_size(16)\n",
    "\n",
    "    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "    color = 'green'\n",
    "    ax2.set_ylabel('Training Time (s)', color=color)  # we already handled the x-label with ax1\n",
    "    ax2.plot(dset, data2, color=color, marker='o', ls='--')\n",
    "    ax2.plot('40k', 83.27, 'ko')\n",
    "    ax2.tick_params(axis='y', labelcolor=color)\n",
    "    ax2.yaxis.label.set_size(16)\n",
    "\n",
    "    fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "    # plt.show()\n",
    "    plt.savefig('datasetVtime.eps', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Unrecognized character 0 in format string",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-a9f96989632b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_dataset_vs_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-36-96bbe650757c>\u001b[0m in \u001b[0;36mplot_dataset_vs_time\u001b[0;34m()\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'PSNR (dB)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'--'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'40k'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m27.28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'k0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtick_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabelcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_env/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m         \"\"\"\n\u001b[1;32m   1664\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1665\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1666\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    223\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    364\u001b[0m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 366\u001b[0;31m             \u001b[0mlinestyle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_plot_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    367\u001b[0m             \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch_env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_process_plot_format\u001b[0;34m(fmt)\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             raise ValueError(\n\u001b[0;32m--> 106\u001b[0;31m                 'Unrecognized character %c in format string' % c)\n\u001b[0m\u001b[1;32m    107\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlinestyle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmarker\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unrecognized character 0 in format string"
     ]
    }
   ],
   "source": [
    "plot_dataset_vs_time()"
   ]
  }
 ]
}