notebooks/Caffe2_Inference.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "# Download ResNet50 pre-trained\n",
    "#wget https://github.com/leonardvandriel/caffe2_models/raw/master/model/resnet50_init_net.pb\n",
    "#wget https://github.com/leonardvandriel/caffe2_models/raw/master/model/resnet50_predict_net.pb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_net_loc = 'resnet50_init_net.pb'\n",
    "predict_net_loc = 'resnet50_predict_net.pb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import caffe2\n",
    "from caffe2.proto import caffe2_pb2\n",
    "from caffe2.python import model_helper, core, workspace, models\n",
    "from common.params_inf import *\n",
    "from common.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Force one-gpu\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_net(INIT_NET, PREDICT_NET, device_opts):\n",
    "    init_def = caffe2_pb2.NetDef()\n",
    "    with open(INIT_NET, 'rb') as f:\n",
    "        init_def.ParseFromString(f.read())\n",
    "        init_def.device_option.CopyFrom(device_opts)\n",
    "        workspace.RunNetOnce(init_def.SerializeToString())\n",
    "    net_def = caffe2_pb2.NetDef()\n",
    "    with open(PREDICT_NET, 'rb') as f:\n",
    "        net_def.ParseFromString(f.read())\n",
    "        net_def.device_option.CopyFrom(device_opts)\n",
    "        workspace.CreateNet(net_def.SerializeToString(), overwrite=True)\n",
    "    return net_def.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_fn(classifier, data, batchsize, device_opts):\n",
    "    \"\"\" Return features from classifier \"\"\"\n",
    "    out = np.zeros((len(data), RESNET_FEATURES), np.float32)\n",
    "    for idx, dta in yield_mb_X(data, batchsize):\n",
    "        workspace.FeedBlob(\"data\", dta, device_option=device_opts)\n",
    "        workspace.RunNet(classifier, 1)\n",
    "        out[idx*batchsize:(idx+1)*batchsize] = workspace.FetchBlob('pool5').squeeze()\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0) \n",
    "test_net = load_net(init_net_loc, \n",
    "                    predict_net_loc,\n",
    "                    device_opts=device_opts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cold_start = predict_fn(test_net, fake_input_data_cf, BATCH_SIZE, device_opts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.28 s, sys: 1.84 s, total: 10.1 s\n",
      "Wall time: 10.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "features = predict_fn(test_net, fake_input_data_cf, BATCH_SIZE, device_opts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images per second 126.73267326732673\n"
     ]
    }
   ],
   "source": [
    "print(\"Images per second {}\".format((BATCH_SIZE*BATCHES_GPU)/10.1))"
   ]
  }
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