adm18/IMPAX/nni/nb1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import argparse\n",
    "import logging\n",
    "import nni\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataset import *\n",
    "from models import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_STEP = 5\n",
    "\n",
    "testset  = IMPAXDataset('/shares/Public/IMPAX/test')\n",
    "\n",
    "testloader = torch.utils.data.DataLoader(\n",
    "    testset, \n",
    "    batch_size=TEST_STEP,\n",
    "    shuffle=True, \n",
    "    num_workers=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "\n",
      "GeForce RTX 2080 Ti\n",
      "Memory Usage:\n",
      "Allocated: 0.0 GB\n",
      "Cached:    0.0 GB\n"
     ]
    }
   ],
   "source": [
    "# setting device on GPU if available, else CPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('Using device:', device)\n",
    "print()\n",
    "\n",
    "#Additional Info when using cuda\n",
    "if device.type == 'cuda':\n",
    "    print(torch.cuda.get_device_name(0))\n",
    "    print('Memory Usage:')\n",
    "    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')\n",
    "    print('Cached:   ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')\n",
    "\n",
    "# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 256, 256]             640\n",
      "            Conv2d-2         [-1, 64, 256, 256]          36,928\n",
      "            Conv2d-3         [-1, 64, 256, 256]          36,928\n",
      "            Conv2d-4         [-1, 64, 256, 256]          36,928\n",
      "            Conv2d-5         [-1, 64, 256, 256]          36,928\n",
      "            Conv2d-6         [-1, 64, 256, 256]          36,928\n",
      "            Conv2d-7          [-1, 2, 256, 256]           1,154\n",
      "================================================================\n",
      "Total params: 186,434\n",
      "Trainable params: 186,434\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.25\n",
      "Forward/backward pass size (MB): 193.00\n",
      "Params size (MB): 0.71\n",
      "Estimated Total Size (MB): 193.96\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "PATH = '/home/xfr/nni/model-5-64/TwNuKtj7/best_zdoyO.pth'\n",
    "\n",
    "m = re.search('model-(\\d*)-(\\d*)', PATH)\n",
    "\n",
    "hidden_layer = int(m[1])\n",
    "hidden_size  = int(m[2])\n",
    "\n",
    "model = Net(hidden_layer, hidden_size)\n",
    "model.load_state_dict(torch.load(PATH))\n",
    "model.to(device)\n",
    "model.eval()\n",
    "\n",
    "summary(model, (1,256,256))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x2160 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = [20, 30]\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "# dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "with torch.no_grad():\n",
    "    output = model(images.to(device))\n",
    "\n",
    "# torch.set_printoptions(profile=\"full\")\n",
    "# print(labels[0])\n",
    "# torch.set_printoptions(profile=\"default\")\n",
    "\n",
    "for j in range(TEST_STEP):\n",
    "    out = output[j]\n",
    "    plt.subplot(TEST_STEP,5,j*5+1)\n",
    "    plt.imshow(images[j][0,:,:], cmap='gray')\n",
    "    \n",
    "    plt.subplot(TEST_STEP,5,j*5+2)\n",
    "    plt.imshow(labels[j][0,:,:], cmap='gray')\n",
    "    plt.subplot(TEST_STEP,5,j*5+3)\n",
    "    plt.imshow(out[0,:,:].cpu().detach().numpy(), cmap='gray')\n",
    "    \n",
    "    plt.subplot(TEST_STEP,5,j*5+4)\n",
    "    plt.imshow(labels[j][1,:,:], cmap='gray')\n",
    "    plt.subplot(TEST_STEP,5,j*5+5)\n",
    "    plt.imshow(out[1,:,:].cpu().detach().numpy(), cmap='gray')\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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