notebooks/PyTorch_Inference.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import torch\n",
    "import torchvision.models as models\n",
    "from torch.autograd import Variable\n",
    "from common.params_inf import *\n",
    "from common.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Force one-gpu\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OS:  linux\n",
      "Python:  3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "Numpy:  1.14.1\n",
      "GPU:  ['Tesla P100-PCIE-16GB', 'Tesla P100-PCIE-16GB']\n",
      "CUDA Version 8.0.61\n",
      "CuDNN Version  6.0.21\n"
     ]
    }
   ],
   "source": [
    "print(\"OS: \", sys.platform)\n",
    "print(\"Python: \", sys.version)\n",
    "print(\"Numpy: \", np.__version__)\n",
    "print(\"GPU: \", get_gpu_name())\n",
    "print(get_cuda_version())\n",
    "print(\"CuDNN Version \", get_cudnn_version())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1280, 224, 224, 3) (1280, 3, 224, 224)\n"
     ]
    }
   ],
   "source": [
    "# Create batches of fake data\n",
    "fake_input_data_cl, fake_input_data_cf = give_fake_data(BATCH_SIZE*BATCHES_GPU)\n",
    "print(fake_input_data_cl.shape, fake_input_data_cf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear(in_features=2048, out_features=1000, bias=True)\n",
      "AvgPool2d(kernel_size=7, stride=7, padding=0, ceil_mode=False, count_include_pad=True)\n"
     ]
    }
   ],
   "source": [
    "# Download ResNet\n",
    "resnet50 = models.resnet50(pretrained=True)\n",
    "# Chop-off last FC layer\n",
    "print(list(resnet50.children())[-1])\n",
    "chopped_resnet50 = torch.nn.Sequential(*list(resnet50.children())[:-1])\n",
    "# CUDA\n",
    "chopped_resnet50.cuda()\n",
    "# Last layer is now avgpool2d\n",
    "print(list(chopped_resnet50.children())[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_fn(classifier, data, batchsize):\n",
    "    \"\"\" Return features from classifier \"\"\"\n",
    "    classifier.eval()\n",
    "    out = np.zeros((len(data), RESNET_FEATURES), np.float32)\n",
    "    for idx, dta in yield_mb_X(data, batchsize):\n",
    "        pred = classifier(Variable(torch.FloatTensor(dta).cuda()))\n",
    "        out[idx*batchsize:(idx+1)*batchsize] = pred.data.cpu().numpy().squeeze()\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cold_start = predict_fn(chopped_resnet50, fake_input_data_cf, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.05 s, sys: 397 ms, total: 2.44 s\n",
      "Wall time: 2.44 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "features = predict_fn(chopped_resnet50, fake_input_data_cf, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images per second 524.5901639344262\n"
     ]
    }
   ],
   "source": [
    "print(\"Images per second {}\".format((BATCH_SIZE*BATCHES_GPU)/2.44))"
   ]
  }
 ],
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