pytorch_example_DCGAN_baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mnist'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opt = {\n",
    "    'dataset': 'mnist',\n",
    "    'dataroot': '/',\n",
    "    'workers': 2,\n",
    "    'batchSize': 64,\n",
    "    'imageSize': 28,\n",
    "    'nz': 100,\n",
    "    'ngf': 64,\n",
    "    'ndf': 64,\n",
    "    'niter': 25,\n",
    "    'lr': 0.002,\n",
    "    'beta1': 0.5,\n",
    "    'cuda': True,\n",
    "    'ngpu': 1,\n",
    "    'netG': '',\n",
    "    'netD': '',\n",
    "    'outf': ''\n",
    "#     ,\n",
    "#     'manualSeed'\n",
    "    \n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataset': 'mnist', 'dataroot': '/', 'workers': 2, 'batchSize': 64, 'imageSize': 28, 'nz': 100, 'ngf': 64, 'ndf': 64, 'niter': 25, 'lr': 0.002, 'beta1': 0.5, 'cuda': True, 'ngpu': 1, 'netG': '', 'netD': '', 'outf': ''}\n",
      "Random Seed:  6999\n",
      "<torch.utils.data.dataloader.DataLoader object at 0x7f2d75ebfc88>\n",
      "Generator(\n",
      "  (main): Sequential(\n",
      "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (2): ReLU(inplace)\n",
      "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (5): ReLU(inplace)\n",
      "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (8): ReLU(inplace)\n",
      "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (11): ReLU(inplace)\n",
      "    (12): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (13): Tanh()\n",
      "  )\n",
      ")\n",
      "Discriminator(\n",
      "  (main): Sequential(\n",
      "    (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (1): LeakyReLU(negative_slope=0.2, inplace)\n",
      "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (4): LeakyReLU(negative_slope=0.2, inplace)\n",
      "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (7): LeakyReLU(negative_slope=0.2, inplace)\n",
      "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "    (10): LeakyReLU(negative_slope=0.2, inplace)\n",
      "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
      "    (12): Sigmoid()\n",
      "  )\n",
      ")\n",
      "torch.Size([64, 1, 28, 28])\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "sizes must be non-negative",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-61-b98843c52912>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    233\u001b[0m         \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 235\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreal_cpu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m         \u001b[0merrD_real\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m         \u001b[0merrD_real\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/capsenv/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-61-b98843c52912>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparallel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_parallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngpu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/capsenv/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/capsenv/lib/python3.6/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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---> 91\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/capsenv/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    475\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[1;32m    476\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[0m\u001b[1;32m    478\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\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    479\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/capsenv/lib/python3.6/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    299\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         return F.conv2d(input, self.weight, self.bias, self.stride,\n\u001b[0;32m--> 301\u001b[0;31m                         self.padding, self.dilation, self.groups)\n\u001b[0m\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: sizes must be non-negative"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import argparse\n",
    "import os\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.backends.cudnn as cudnn\n",
    "import torch.optim as optim\n",
    "# import torch.utils.data\n",
    "import torchvision.datasets as dset\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.utils as vutils\n",
    "\n",
    "\n",
    "# parser = argparse.ArgumentParser()\n",
    "# 'dataset', required=True, help='cifar10 | lsun | mnist |imagenet | folder | lfw | fake')\n",
    "# 'dataroot', required=True, help='path to dataset')\n",
    "# 'workers', type=int, help='number of data loading workers', default=2)\n",
    "# 'batchSize', type=int, default=64, help='input batch size')\n",
    "# 'imageSize', type=int, default=64, help='the height / width of the input image to network')\n",
    "# 'nz', type=int, default=100, help='size of the latent z vector')\n",
    "# 'ngf', type=int, default=64)\n",
    "# 'ndf', type=int, default=64)\n",
    "# 'niter', type=int, default=25, help='number of epochs to train for')\n",
    "# 'lr', type=float, default=0.0002, help='learning rate, default=0.0002')\n",
    "# 'beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')\n",
    "# 'cuda', action='store_true', help='enables cuda')\n",
    "# 'ngpu', type=int, default=1, help='number of GPUs to use')\n",
    "# 'netG', default='', help=\"path to netG (to continue training)\")\n",
    "# 'netD', default='', help=\"path to netD (to continue training)\")\n",
    "# 'outf', default='.', help='folder to output images and model checkpoints')\n",
    "# 'manualSeed', type=int, help='manual seed')\n",
    "\n",
    "# opt = parser.parse_args()\n",
    "\n",
    "class attr_dict(dict):\n",
    "    \"\"\"dot.notation access to dictionary attributes\"\"\"\n",
    "    __getattr__ = dict.get\n",
    "    __setattr__ = dict.__setitem__\n",
    "    __delattr__ = dict.__delitem__\n",
    "    \n",
    "opt = attr_dict({\n",
    "    'dataset': 'mnist',\n",
    "    'dataroot': '/',\n",
    "    'workers': 2,\n",
    "    'batchSize': 64,\n",
    "    'imageSize': 28,\n",
    "    'nz': 100,\n",
    "    'ngf': 64,\n",
    "    'ndf': 64,\n",
    "    'niter': 25,\n",
    "    'lr': 0.002,\n",
    "    'beta1': 0.5,\n",
    "    'cuda': True,\n",
    "    'ngpu': 1,\n",
    "    'netG': '',\n",
    "    'netD': '',\n",
    "    'outf': ''\n",
    "#     ,\n",
    "#     'manualSeed'\n",
    "    \n",
    "})\n",
    "print(opt)\n",
    "\n",
    "try:\n",
    "    os.makedirs(opt.outf)\n",
    "except OSError:\n",
    "    pass\n",
    "\n",
    "if opt.manualSeed is None:\n",
    "    opt.manualSeed = random.randint(1, 10000)\n",
    "print(\"Random Seed: \", opt.manualSeed)\n",
    "random.seed(opt.manualSeed)\n",
    "torch.manual_seed(opt.manualSeed)\n",
    "\n",
    "cudnn.benchmark = True\n",
    "\n",
    "if torch.cuda.is_available() and not opt.cuda:\n",
    "    print(\"WARNING: You have a CUDA device, so you should probably run with --cuda\")\n",
    "\n",
    "\n",
    "dataset = dset.MNIST(root='.', download=True,\n",
    "                   transform=transforms.Compose([\n",
    "                       transforms.Resize(opt.imageSize),\n",
    "                       transforms.ToTensor(),\n",
    "                       transforms.Normalize((0.5,), (0.5,)),\n",
    "                   ]))\n",
    "nc=1\n",
    "\n",
    "# train_loader = torch.utils.data.DataLoader(\n",
    "#         datasets.MNIST('../data', train=True, download=True,\n",
    "#                        transform=transforms.Compose([\n",
    "#                            transforms.ToTensor(),\n",
    "#                            transforms.Normalize((0.1307,), (0.3081,))\n",
    "#                        ])),\n",
    "#         batch_size=64, shuffle=True, **kwargs)\n",
    "\n",
    "# train_loader = torch.utils.data.DataLoader(\n",
    "#         datasets.MNIST('../data', train=True, download=True,\n",
    "#                        transform=transforms.Compose([\n",
    "#                            transforms.ToTensor(),\n",
    "#                            transforms.Normalize((0.1307,), (0.3081,))\n",
    "#                        ])),\n",
    "#         batch_size=64, shuffle=True, **kwargs)\n",
    "\n",
    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,\n",
    "                                         shuffle=True, num_workers=int(opt.workers))\n",
    "\n",
    "\n",
    "print(dataloader)\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda:0\" if opt.cuda else \"cpu\")\n",
    "ngpu = int(opt.ngpu)\n",
    "nz = int(opt.nz)\n",
    "ngf = int(opt.ngf)\n",
    "ndf = int(opt.ndf)\n",
    "\n",
    "\n",
    "# custom weights initialization called on netG and netD\n",
    "def weights_init(m):\n",
    "    classname = m.__class__.__name__\n",
    "    if classname.find('Conv') != -1:\n",
    "        m.weight.data.normal_(0.0, 0.02)\n",
    "    elif classname.find('BatchNorm') != -1:\n",
    "        m.weight.data.normal_(1.0, 0.02)\n",
    "        m.bias.data.fill_(0)\n",
    "\n",
    "\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self, ngpu):\n",
    "        super(Generator, self).__init__()\n",
    "        self.ngpu = ngpu\n",
    "        self.main = nn.Sequential(\n",
    "            # input is Z, going into a convolution\n",
    "            nn.ConvTranspose2d(     nz, ngf * 8, 4, 1, 0, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 8),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*8) x 4 x 4\n",
    "            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 4),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*4) x 8 x 8\n",
    "            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf * 2),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf*2) x 16 x 16\n",
    "            nn.ConvTranspose2d(ngf * 2,     ngf, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ngf),\n",
    "            nn.ReLU(True),\n",
    "            # state size. (ngf) x 32 x 32\n",
    "            nn.ConvTranspose2d(    ngf,      nc, 4, 2, 1, bias=False),\n",
    "            nn.Tanh()\n",
    "            # state size. (nc) x 64 x 64\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        if input.is_cuda and self.ngpu > 1:\n",
    "            output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))\n",
    "        else:\n",
    "            output = self.main(input)\n",
    "        return output\n",
    "\n",
    "\n",
    "netG = Generator(ngpu).to(device)\n",
    "netG.apply(weights_init)\n",
    "if opt.netG != '':\n",
    "    netG.load_state_dict(torch.load(opt.netG))\n",
    "print(netG)\n",
    "\n",
    "\n",
    "class Discriminator(nn.Module):\n",
    "    def __init__(self, ngpu):\n",
    "        super(Discriminator, self).__init__()\n",
    "        self.ngpu = ngpu\n",
    "        self.main = nn.Sequential(\n",
    "            # input is (nc) x 64 x 64\n",
    "            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf) x 32 x 32\n",
    "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 2),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*2) x 16 x 16\n",
    "            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 4),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*4) x 8 x 8\n",
    "            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n",
    "            nn.BatchNorm2d(ndf * 8),\n",
    "            nn.LeakyReLU(0.2, inplace=True),\n",
    "            # state size. (ndf*8) x 4 x 4\n",
    "            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, input):\n",
    "        print(input.shape)\n",
    "        if input.is_cuda and self.ngpu > 1:\n",
    "            output = nn.parallel.data_parallel(self.main, input, range(self.ngpu))\n",
    "        else:\n",
    "            output = self.main(input)\n",
    "\n",
    "        return output.view(-1, 1).squeeze(1)\n",
    "\n",
    "\n",
    "netD = Discriminator(ngpu).to(device)\n",
    "netD.apply(weights_init)\n",
    "if opt.netD != '':\n",
    "    netD.load_state_dict(torch.load(opt.netD))\n",
    "print(netD)\n",
    "\n",
    "criterion = nn.BCELoss()\n",
    "\n",
    "fixed_noise = torch.randn(opt.batchSize, nz, 1, 1, device=device)\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "# setup optimizer\n",
    "optimizerD = optim.Adam(netD.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n",
    "optimizerG = optim.Adam(netG.parameters(), lr=opt.lr, betas=(opt.beta1, 0.999))\n",
    "\n",
    "for epoch in range(opt.niter):\n",
    "    for i, (data, _) in enumerate(dataloader, 0):\n",
    "        ############################\n",
    "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
    "        ###########################\n",
    "        # train with real\n",
    "        netD.zero_grad()\n",
    "        real_cpu = data.to(device)\n",
    "        batch_size = real_cpu.size(0)\n",
    "        label = torch.full((batch_size,), real_label, device=device)\n",
    "\n",
    "        output = netD(real_cpu)\n",
    "        errD_real = criterion(output, label)\n",
    "        errD_real.backward()\n",
    "        D_x = output.mean().item()\n",
    "\n",
    "        # train with fake\n",
    "        noise = torch.randn(batch_size, nz, 1, 1, device=device)\n",
    "        fake = netG(noise)\n",
    "        label.fill_(fake_label)\n",
    "        output = netD(fake.detach())\n",
    "        errD_fake = criterion(output, label)\n",
    "        errD_fake.backward()\n",
    "        D_G_z1 = output.mean().item()\n",
    "        errD = errD_real + errD_fake\n",
    "        optimizerD.step()\n",
    "\n",
    "        ############################\n",
    "        # (2) Update G network: maximize log(D(G(z)))\n",
    "        ###########################\n",
    "        netG.zero_grad()\n",
    "        label.fill_(real_label)  # fake labels are real for generator cost\n",
    "        output = netD(fake)\n",
    "        errG = criterion(output, label)\n",
    "        errG.backward()\n",
    "        D_G_z2 = output.mean().item()\n",
    "        optimizerG.step()\n",
    "\n",
    "        print('[%d/%d][%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f / %.4f'\n",
    "              % (epoch, opt.niter, i, len(dataloader),\n",
    "                 errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n",
    "        if i % 100 == 0:\n",
    "            vutils.save_image(real_cpu,\n",
    "                    '%s/real_samples.png' % opt.outf,\n",
    "                    normalize=True)\n",
    "            fake = netG(fixed_noise)\n",
    "            vutils.save_image(fake.detach(),\n",
    "                    '%s/fake_samples_epoch_%03d.png' % (opt.outf, epoch),\n",
    "                    normalize=True)\n",
    "\n",
    "    # do checkpointing\n",
    "    torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opt.outf, epoch))\n",
    "    torch.save(netD.state_dict(), '%s/netD_epoch_%d.pth' % (opt.outf, epoch))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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cw8ymZB/EyMymSLpczTf78CZJq7L7qyQ9XWAvH9EsMzeXmllaBT93zTbjdSEX+WRDGX8r\nqVXSenf/q4Y3MQ4zO0ejZ3tpdBLTh4vszcw2SrpUo9/6GpR0m6SnJD0m6ZOS9kj6srs3/IO3Er1d\nqtGXrr+cufnIe+wG9/abkn4iaaekkWzxGo2+vy7suUv0tVIFPG9c4QcExRV+QFCEHwiK8ANBEX4g\nKMIPBEX4gaAIPxAU4QeC+j+/aRI99fx/jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d7651bd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ulX5Wlm4LRVe8LlsV4d8uaXKf+5Mk7aigj365+47s5y5JD6j9Vh/eeXSR1Oznror7+al2\nWrm5v5Wl1QbPXTuteF1F+J+SNM3MTjezYZLmS1pXQR/vYGajsj/EyMxGSfqw2m/14XWSFma3F0pa\nW2EvP6ddVm6utbK0Kn7u2m3F60o+5JNNZXxO0lBJK9z9sy1voh9m9h71nu2l3kVMv15lb2a2RtKF\n6v3W105JN0n6B0n3SZoiaZukj7l7y//wVqO3C9X70vWnKzcffY/d4t4+KOlRSS9I6sk2L1Xv++vK\nnrtEXwtUwfPGJ/yAoPiEHxAU4QeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoP4f3FciqdI9KgcAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d763264a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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tTkmPl9jLWzTLys15K0ur5Neu2Va8LuUkn2wq428ktUjqcfe/bHgTYzCzd2vkaC+NLGL6\ncJm9mdkaSZdo5KqvfZK+IulfJH1L0lmSfibpGndv+C/ecnq7RCNvXX+1cvOxz9gN7u13Jf1A0nOS\nhrPNKzXy+bq01y7R1wqV8Lpxhh8QFGf4AUERfiAowg8ERfiBoAg/EBThB4Ii/EBQhB8I6v8BCRL3\nTPkoMtUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d7614e3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZpNfKnrk5m1BmfO+ZpSVdKOlSlfjaJfq6WCW8bmXs+adL2u7uL7j7m5LuljS3hD6anrs/\nJGn/MYvnSlqV3V6lnn88DZfTW1Nw993u/kR2+1VJR2eWLvW1S/RVijLCf5qknb3ud6q5pvx2ST8z\ns8fNbGHZzfRhXDZt+tHp08eW3M+xKs7c3EjHzCzdNK9dNTNeF62M8Pc1+08zDTnMdPcPSPqkpMXZ\nx1v0T79mbm6UPmaWbgrVznhdtDLC3ylpYq/7EyTtKqGPPrn7ruzvXklr1HyzD+85Oklq9ndvyf38\nVjPN3NzXzNJqgteumWa8LiP8j0lqM7MzzOwkSV+QtLaEPt7GzIZnP8TIzIZL+oSab/bhtZLmZ7fn\nS7qvxF7eollmbs6bWVolv3bNNuN1KQf5ZEMZ35PUImmlu3+j4U30wczOVM/eXuqZxPRHZfZmZndJ\nmqWes772SLpW0k8l3SvpdyW9KOnz7t7wH95yepulno+uv525+eh37Ab3dr6k9ZK2SOrOFi9Vz/fr\n0l67RF/zVMLrxhF+QFAc4QcERfiBoAg/EBThB4Ii/EBQhB8IivADQRF+IKj/A6ChYgF6G1UWAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d76125b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADPlJREFUeJzt3W2MXOV5xvHr8sYvjYHWFNtYxi2O\n40IQSR26MqROWiJE6kSRDB+gWApyJNqNIlCLmlRB/gKKWolGISlSKyqnWBg1kJAQij+4rYmVlqAS\nykKcmNRpcWEDjrdeiJNimsbY3rsf9jjamJ1nxzNn5ox9/3+SNTPnPi+3Rr72zMwzcx5HhADkM6fp\nBgA0g/ADSRF+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0jqLf082DzPjwVa2M9DAqn8TP+rN+KI21m3\nq/DbXi/pbklDkv42Iu4srb9AC3W5r+rmkAAKnopdba/b8ct+20OS/lrSByVdImmj7Us63R+A/urm\nPf9aSfsi4oWIeEPSlyRtqKctAL3WTfiXS3p52uP91bJfYHvE9qjt0aM60sXhANSpm/DP9KHCm34f\nHBFbImI4Iobnan4XhwNQp27Cv1/SimmPL5B0oLt2APRLN+F/WtJq2yttz5N0g6Tt9bQFoNc6HuqL\niGO2b5H0T5oa6tsaEd+rrTMAPdXVOH9E7JC0o6ZeAPQRX+8FkiL8QFKEH0iK8ANJEX4gKcIPJEX4\ngaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/kBThB5Ii/EBShB9IivADSRF+IKm+TtEN\nTPeDh95ZrO9Zd19X+7/4Kze3rL391m91te8zAWd+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iqq3F+\n22OSDks6LulYRAzX0RRyuOuyrxTrk5os1v/hp4uK9SUXvXLKPWVSx5d83h8Rr9awHwB9xMt+IKlu\nwx+Sdtp+xvZIHQ0B6I9uX/avi4gDtpdIesz29yPi8ekrVH8URiRpgd7a5eEA1KWrM39EHKhuJyQ9\nImntDOtsiYjhiBieq/ndHA5AjToOv+2Fts8+cV/SByQ9V1djAHqrm5f9SyU9YvvEfh6IiH+spSsA\nPddx+CPiBUm/WWMvwCm5f/w9xfqiG3/Ssna87mZOQwz1AUkRfiApwg8kRfiBpAg/kBThB5Li0t3o\nqTnvurhl7fyhf5tlaxer43+zqlg/51Uuz13CmR9IivADSRF+ICnCDyRF+IGkCD+QFOEHkmKcHz31\n/T86q2Xt0nnlcfxNY79XrC/asbdY52e7ZZz5gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApxvnRlYmb\nf7tY373+rkK1/N/v5cO/Uqyf9ZMXinWUceYHkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaRmHee3vVXS\nhyVNRMSl1bJzJX1Z0oWSxiRdHxE/7l2bGFSvr4hifYE7/yrJwR/9crHe+koBaEc7Z/77JK0/adlt\nknZFxGpJu6rHAE4js4Y/Ih6XdOikxRskbavub5N0Tc19AeixTt/zL42IcUmqbpfU1xKAfuj5d/tt\nj0gakaQFemuvDwegTZ2e+Q/aXiZJ1e1EqxUjYktEDEfE8FzN7/BwAOrWafi3S9pU3d8k6dF62gHQ\nL7OG3/aDkp6UdJHt/bZvknSnpKttPy/p6uoxgNPIrO/5I2Jji9JVNfeCATS0eHGx/vvrn+h43weO\nHSnWV3/mZ8X6ZMdHhsQ3/IC0CD+QFOEHkiL8QFKEH0iK8ANJcenu5OYsXFisv3PnK8X67Yt3F+ul\n4bjrPv2nxW1/9TtPFuvoDmd+ICnCDyRF+IGkCD+QFOEHkiL8QFKEH0iKcf7k/EsLivVPL/nnWfbQ\n+fljyb/8d7F+vOM9ox2c+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gKcb5k/vRh36jWJ+jncX6XA8V\n62/bOdKytnrfM8Vt0Vuc+YGkCD+QFOEHkiL8QFKEH0iK8ANJEX4gqVnH+W1vlfRhSRMRcWm17A5J\nfyjpxEXdN0fEjl41ic791wNrivVvve+zxfqk5hXrnzr4W8X6xZ8ca1nj9/rNaufMf5+k9TMs/3xE\nrKn+EXzgNDNr+CPicUmH+tALgD7q5j3/Lba/a3ur7UW1dQSgLzoN/z2SVklaI2lc0l2tVrQ9YnvU\n9uhRHenwcADq1lH4I+JgRByPiElJX5C0trDulogYjojhuZrfaZ8AatZR+G0vm/bwWknP1dMOgH5p\nZ6jvQUlXSjrP9n5Jt0u60vYaSSFpTNLHetgjgB6YNfwRsXGGxff2oBd0aGjx4pa19696vrjt2XPK\n4/iz+fo97ynWz3v1ya72j97hG35AUoQfSIrwA0kRfiApwg8kRfiBpLh092ngLecvLdZf/INVLWuP\nXHB3cdufTh4t1t/3V58s1pdv+ddiHYOLMz+QFOEHkiL8QFKEH0iK8ANJEX4gKcIPJMU4/2ng/961\nolj/9sfLY/klB467WF/+F4zjn6k48wNJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUozznwZe+uixjrf9\nn8k3ivVrHyj/Xn+luPT2mYozP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kNes4v+0Vku6XdL6kSUlb\nIuJu2+dK+rKkCyWNSbo+In7cu1bPXJPvXVOsb73ivo73fcWjf1Ksr97MOH5W7Zz5j0n6RES8Q9IV\nkm62fYmk2yTtiojVknZVjwGcJmYNf0SMR8Sz1f3DkvZKWi5pg6Rt1WrbJF3TqyYB1O+U3vPbvlDS\nuyU9JWlpRIxLU38gJC2puzkAvdN2+G2fJelhSbdGxGunsN2I7VHbo0d1pJMeAfRAW+G3PVdTwf9i\nRHytWnzQ9rKqvkzSxEzbRsSWiBiOiOG5ml9HzwBqMGv4bVvSvZL2RsTnppW2S9pU3d8k6dH62wPQ\nK+38pHedpBsl7bG9u1q2WdKdkh6yfZOklyRd15sWz3wvfrx8+ezL55en0S5Z/Y4fFutDb19ZrB/f\n92LHx8ZgmzX8EfGEpFb/O6+qtx0A/cI3/ICkCD+QFOEHkiL8QFKEH0iK8ANJcenuAfC7q57v2b4f\nvuirxfplfzZSrK+8oc5uMEg48wNJEX4gKcIPJEX4gaQIP5AU4QeSIvxAUozzD4Dxm5YX63/31RXF\n+kfOebll7bJvfqy47cqN3ynWcebizA8kRfiBpAg/kBThB5Ii/EBShB9IivADSTki+nawc3xuXG6u\n9g30ylOxS6/FofJEEBXO/EBShB9IivADSRF+ICnCDyRF+IGkCD+Q1Kzht73C9jds77X9Pdt/XC2/\nw/YPbe+u/n2o9+0CqEs7F/M4JukTEfGs7bMlPWP7sar2+Yj4bO/aA9Ars4Y/IsYljVf3D9veK6l8\n6RkAA++U3vPbvlDSuyU9VS26xfZ3bW+1vajFNiO2R22PHtWRrpoFUJ+2w2/7LEkPS7o1Il6TdI+k\nVZLWaOqVwV0zbRcRWyJiOCKG52p+DS0DqENb4bc9V1PB/2JEfE2SIuJgRByPiElJX5C0tndtAqhb\nO5/2W9K9kvZGxOemLV82bbVrJT1Xf3sAeqWdT/vXSbpR0h7bu6tlmyVttL1GUkgak1S+RjSAgdLO\np/1PSJrp98E76m8HQL/wDT8gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0kRfiApwg8kRfiBpAg/\nkBThB5Ii/EBSfZ2i2/Yrkn4wbdF5kl7tWwOnZlB7G9S+JHrrVJ29/XpELG5nxb6G/00Ht0cjYrix\nBgoGtbdB7Uuit0411Rsv+4GkCD+QVNPh39Lw8UsGtbdB7Uuit0410luj7/kBNKfpMz+AhjQSftvr\nbf+H7X22b2uih1Zsj9neU808PNpwL1ttT9h+btqyc20/Zvv56nbGadIa6m0gZm4uzCzd6HM3aDNe\n9/1lv+0hSf8p6WpJ+yU9LWljRPx7XxtpwfaYpOGIaHxM2PbvSHpd0v0RcWm17DOSDkXEndUfzkUR\n8akB6e0OSa83PXNzNaHMsukzS0u6RtJH1eBzV+jrejXwvDVx5l8raV9EvBARb0j6kqQNDfQx8CLi\ncUmHTlq8QdK26v42Tf3n6bsWvQ2EiBiPiGer+4clnZhZutHnrtBXI5oI/3JJL097vF+DNeV3SNpp\n+xnbI003M4Ol1bTpJ6ZPX9JwPyebdebmfjppZumBee46mfG6bk2Ef6bZfwZpyGFdRFwm6YOSbq5e\n3qI9bc3c3C8zzCw9EDqd8bpuTYR/v6QV0x5fIOlAA33MKCIOVLcTkh7R4M0+fPDEJKnV7UTD/fzc\nIM3cPNPM0hqA526QZrxuIvxPS1pte6XteZJukLS9gT7exPbC6oMY2V4o6QMavNmHt0vaVN3fJOnR\nBnv5BYMyc3OrmaXV8HM3aDNeN/Iln2oo4y8lDUnaGhF/3vcmZmD7bZo620tTk5g+0GRvth+UdKWm\nfvV1UNLtkv5e0kOSfk3SS5Kui4i+f/DWorcrNfXS9eczN594j93n3t4r6ZuS9kiarBZv1tT768ae\nu0JfG9XA88Y3/ICk+IYfkBThB5Ii/EBShB9IivADSRF+ICnCDyRF+IGk/h+CgoxFDvUfnwAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d7605a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d7601b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAADHxJREFUeJzt3X+oZPV5x/H34/a64sY0K0bdrlqt\n1RJrm016sWkNrUEMJrRdhShZaNiEpBtohASkVBZKpNAipfkhTRvY1CUrJJqExGhBWkXEH7Qxrnar\nprbG6lbX3e4qWmIC6v54+sc9m97onTN3Z87MmfV5v2CZmfM9M+fDYT/3zMyZmW9kJpLqOabvAJL6\nYfmloiy/VJTll4qy/FJRll8qyvJLRVl+qSjLLxX1c9Pc2LGxMo9j1TQ3KZXyCj/htXw1lrPuWOWP\niEuB64EVwN9n5nVt6x/HKn4zLh5nk5JaPJB3LXvdkZ/2R8QK4G+BDwDnARsi4rxRH0/SdI3zmv8C\n4MnMfCozXwNuBtZ3E0vSpI1T/rXAs4tu72qW/YyI2BQR2yNi+35eHWNzkro0TvmXelPhDd8Pzswt\nmTmfmfNzrBxjc5K6NE75dwGnL7p9GrB7vDiSpmWc8j8InBMRZ0XEscCHgdu6iSVp0kY+1ZeZByLi\nKuCfWDjVtzUzf9BZMkkTNdZ5/sy8Hbi9oyySpsiP90pFWX6pKMsvFWX5paIsv1SU5ZeKsvxSUZZf\nKsryS0VZfqkoyy8VZfmloiy/VJTll4qy/FJRll8qyvJLRVl+qSjLLxVl+aWiLL9UlOWXirL8UlGW\nXyrK8ktFWX6pKMsvFWX5paIsv1TUWLP0RsRO4GXgIHAgM+e7CCVp8sYqf+N9mflCB48jaYp82i8V\nNW75E7gjIh6KiE1dBJI0HeM+7b8wM3dHxMnAnRHxH5l57+IVmj8KmwCO4/gxNyepK2Md+TNzd3O5\nD7gFuGCJdbZk5nxmzs+xcpzNSerQyOWPiFURccLh68D7gce6CiZpssZ52n8KcEtEHH6cr2fmP3aS\nStLEjVz+zHwKeGeHWSRNkaf6pKIsv1SU5ZeKsvxSUZZfKsryS0VZfqkoyy8VZfmloiy/VJTll4qy\n/FJRll8qyvJLRXXx673lHbPuvNbx3e9725SSHF3+7U/+rnX8e68cbB3/s499onX8mHv+9YgzVeKR\nXyrK8ktFWX6pKMsvFWX5paIsv1SU5ZeKOqrO869YvXrg2BNfOrP1vu856+mO0/y/33rbfa3jH3vr\nf01s28PMxYrW8f3Zfi59kvZne7ZfP7Y929N/0D4D1Nn3HHGkUjzyS0VZfqkoyy8VZfmloiy/VJTl\nl4qy/FJRQ8/zR8RW4PeAfZl5frPsROAbwJnATuDKzHxpcjEXPPfRdwwce+R3vzjpzQ80/Fz6lIJI\nR2A5R/6vApe+btk1wF2ZeQ5wV3Nb0lFkaPkz817gxdctXg9sa65vAy7rOJekCRv1Nf8pmbkHoLk8\nubtIkqZh4p/tj4hNwCaA4zh+0puTtEyjHvn3RsQagOZy36AVM3NLZs5n5vwc7V/EkDQ9o5b/NmBj\nc30jcGs3cSRNy9DyR8RNwL8AvxIRuyLi48B1wCUR8UPgkua2pKPI0Nf8mblhwNDFHWcZ6vj/OTRw\n7IkhJ9PPnYuu4yzbX77wG63jT/7k7RPb9jG075dDjLdffnnV863jm096aKzH1+T4CT+pKMsvFWX5\npaIsv1SU5ZeKsvxSUUfVT3e/9abvDRz74/x0631/csoE/84NOVt22nd3tY4f2PlMh2Gm657Lf7t1\nfPPfeKpvVnnkl4qy/FJRll8qyvJLRVl+qSjLLxVl+aWijqrz/G1OuHnwZwAATphSjqUc6HHbfWv7\nWfNhP3muyfLILxVl+aWiLL9UlOWXirL8UlGWXyrK8ktFvWnO82s27c+DvdxXw3nkl4qy/FJRll8q\nyvJLRVl+qSjLLxVl+aWihpY/IrZGxL6IeGzRsmsj4rmI2NH8++BkY0rq2nKO/F8FLl1i+Rcyc13z\n7/ZuY0matKHlz8x7gRenkEXSFI3zmv+qiHikeVmwurNEkqZi1PJ/GTgbWAfsAT43aMWI2BQR2yNi\n+35eHXFzkro2Uvkzc29mHszMQ8BXgAta1t2SmfOZOT/HylFzSurYSOWPiDWLbl4OPDZoXUmzaehX\neiPiJuAi4KSI2AV8FrgoItYBCewEPjnBjJImYGj5M3PDEotvmEAWSVPkJ/ykoiy/VJTll4qy/FJR\nll8qyvJLRVl+qSjLLxVl+aWiLL9UlOWXirL8UlGWXyrK8ktFOUW3JmouVow0psnzyC8VZfmloiy/\nVJTll4qy/FJRll8qyvJLRXmeX2M54Z+fbh1f/8TvDxy79dx/aL3v/jw4UiYtj0d+qSjLLxVl+aWi\nLL9UlOWXirL8UlGWXypq6Hn+iDgduBE4FTgEbMnM6yPiROAbwJnATuDKzHxpclE1iw7u3dc6/swL\nvzZ48NyOw+iILOfIfwC4OjPfAbwH+FREnAdcA9yVmecAdzW3JR0lhpY/M/dk5sPN9ZeBx4G1wHpg\nW7PaNuCySYWU1L0jes0fEWcC7wIeAE7JzD2w8AcCOLnrcJImZ9nlj4i3AN8GPpOZPzqC+22KiO0R\nsX0/r46SUdIELKv8ETHHQvG/lpnfaRbvjYg1zfgaYMl3fjJzS2bOZ+b8HCu7yCypA0PLHxEB3AA8\nnpmfXzR0G7Cxub4RuLX7eJImZTlf6b0Q+AjwaETsaJZtBq4DvhkRHweeAa6YTERJkzC0/Jl5PxAD\nhi/uNo6kafETflJRll8qyvJLRVl+qSjLLxVl+aWiLL9UlOWXirL8UlGWXyrK8ktFWX6pKMsvFWX5\npaKcolsTdcYVjw4cu+r7722975fW3t86fuqvtv9s+IrVqweOHXzJX5n3yC8VZfmloiy/VJTll4qy\n/FJRll8qyvJLRXmeX705lO3Hnv15sHX8jvNvbh3/0Jo/HDzoeX6P/FJVll8qyvJLRVl+qSjLLxVl\n+aWiLL9U1NDz/BFxOnAjcCpwCNiSmddHxLXAHwHPN6tuzszbJxVUbz7fv/md7StcffdYj//0h04a\nOHbGnz8x1mO/GSznQz4HgKsz8+GIOAF4KCLubMa+kJl/Pbl4kiZlaPkzcw+wp7n+ckQ8DqyddDBJ\nk3VEr/kj4kzgXcADzaKrIuKRiNgaEUv+ZlJEbIqI7RGxfT+vjhVWUneWXf6IeAvwbeAzmfkj4MvA\n2cA6Fp4ZfG6p+2Xmlsycz8z5OVZ2EFlSF5ZV/oiYY6H4X8vM7wBk5t7MPJiZh4CvABdMLqakrg0t\nf0QEcAPweGZ+ftHyNYtWuxx4rPt4kiZlOe/2Xwh8BHg0InY0yzYDGyJiHZDATuCTE0kojWjtfa/0\nHWGmLefd/vuBWGLIc/rSUcxP+ElFWX6pKMsvFWX5paIsv1SU5ZeK8qe71ZtfuPt/W8e3fOLc1vFv\nPfvu1vGf3/H0wLH2HwWvwSO/VJTll4qy/FJRll8qyvJLRVl+qSjLLxUVmTm9jUU8D/z3okUnAS9M\nLcCRmdVss5oLzDaqLrP9Yma+fTkrTrX8b9h4xPbMnO8tQItZzTarucBso+orm0/7paIsv1RU3+Xf\n0vP228xqtlnNBWYbVS/Zen3NL6k/fR/5JfWkl/JHxKUR8Z8R8WREXNNHhkEiYmdEPBoROyJie89Z\ntkbEvoh4bNGyEyPizoj4YXO55DRpPWW7NiKea/bdjoj4YE/ZTo+IuyPi8Yj4QUR8ulne675rydXL\nfpv60/6IWAE8AVwC7AIeBDZk5r9PNcgAEbETmM/M3s8JR8TvAD8GbszM85tlfwW8mJnXNX84V2fm\nn85ItmuBH/c9c3MzocyaxTNLA5cBH6XHfdeS60p62G99HPkvAJ7MzKcy8zXgZmB9DzlmXmbeC7z4\nusXrgW3N9W0s/OeZugHZZkJm7snMh5vrLwOHZ5budd+15OpFH+VfCzy76PYuZmvK7wTuiIiHImJT\n32GWcEozbfrh6dNP7jnP6w2duXmaXjez9Mzsu1FmvO5aH+VfavafWTrlcGFmvhv4APCp5umtlmdZ\nMzdPyxIzS8+EUWe87lof5d8FnL7o9mnA7h5yLCkzdzeX+4BbmL3Zh/ceniS1udzXc56fmqWZm5ea\nWZoZ2HezNON1H+V/EDgnIs6KiGOBDwO39ZDjDSJiVfNGDBGxCng/szf78G3Axub6RuDWHrP8jFmZ\nuXnQzNL0vO9mbcbrXj7k05zK+CKwAtiamX8x9RBLiIhfYuFoDwu/bPz1PrNFxE3ARSx862sv8Fng\nu8A3gTOAZ4ArMnPqb7wNyHYRC09dfzpz8+HX2FPO9l7gPuBR4FCzeDMLr69723ctuTbQw37zE35S\nUX7CTyrK8ktFWX6pKMsvFWX5paIsv1SU5ZeKsvxSUf8Hetl0PSE/3yoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d76339c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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vtldKmq/hT31tk3SNpPsk3SPpeEm/kPSpiOj5H94a9DZfwy9d31y5+cB77B73drakH0h6\nStJQtXmJht9f1/bcFfpapBqeN67wA5LiCj8gKcIPJEX4gaQIP5AU4QeSIvxAUoQfSIrwA0n9P+oa\n5oPSSKvOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d7604c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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MPyApzvADkiL8QFKEH0iK8ANJEX4gKcIPJEX4gaQIP5DU/wPE+tuvv82tWQAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d75fb8048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d75f6a4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d75ee5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2d75ed4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <bound method _DataLoaderIter.__del__ of <torch.utils.data.dataloader._DataLoaderIter object at 0x7f2d76313dd8>>\n",
      "Traceback (most recent call last):\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 399, in __del__\n",
      "    self._shutdown_workers()\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/site-packages/torch/utils/data/dataloader.py\", line 378, in _shutdown_workers\n",
      "    self.worker_result_queue.get()\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/queues.py\", line 337, in get\n",
      "    return _ForkingPickler.loads(res)\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/site-packages/torch/multiprocessing/reductions.py\", line 151, in rebuild_storage_fd\n",
      "    fd = df.detach()\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/resource_sharer.py\", line 57, in detach\n",
      "    with _resource_sharer.get_connection(self._id) as conn:\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/resource_sharer.py\", line 87, in get_connection\n",
      "    c = Client(address, authkey=process.current_process().authkey)\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/connection.py\", line 494, in Client\n",
      "    deliver_challenge(c, authkey)\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/connection.py\", line 722, in deliver_challenge\n",
      "    response = connection.recv_bytes(256)        # reject large message\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/connection.py\", line 216, in recv_bytes\n",
      "    buf = self._recv_bytes(maxlength)\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/connection.py\", line 407, in _recv_bytes\n",
      "    buf = self._recv(4)\n",
      "  File \"/u/53/aftenim1/unix/.conda/envs/capsenv/lib/python3.6/multiprocessing/connection.py\", line 379, in _recv\n",
      "    chunk = read(handle, remaining)\n",
      "ConnectionResetError: [Errno 104] Connection reset by peer\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "for i, (data, _) in enumerate(dataloader):\n",
    "    \n",
    "    print(data[0][0].shape)\n",
    "    plt.imshow(data[0][0])\n",
    "    plt.show()\n",
    "    if i  > 10: break\n",
    "\n",
    "# plt.imshow(np.arange(28*28).reshape((28, 28)))\n",
    "# plt.show()\n",
    "        "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "capsenv",
   "language": "python",
   "name": "capsenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}