fast-reid/vis_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import json\n",
    "import re\n",
    "from collections import defaultdict\n",
    "\n",
    "\n",
    "root_path = '/export/home/datasets/beijingStation/20190720/'\n",
    "label_name = 'check_20190720.txt'\n",
    "\n",
    "label_path = os.path.join(root_path, label_name)\n",
    "\n",
    "with open(label_path, 'r') as f:\n",
    "    bboxes = [i.strip() for i in f.readlines()]\n",
    "\n",
    "\n",
    "camBox = defaultdict(list)\n",
    "camPid = defaultdict(set)\n",
    "pidCam = defaultdict(set)\n",
    "pidBox = defaultdict(list)\n",
    "for i, b in enumerate(bboxes):\n",
    "    pid, cam_img, xmin, ymin, xmax, ymax = b.split(' ')\n",
    "    video_name, img_name = cam_img.split('_')\n",
    "    camBox[video_name].append(img_name)\n",
    "    camPid[video_name].add(pid)\n",
    "    pidCam[pid].add(video_name)\n",
    "    pidBox[pid].append(img_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4099"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(camPid['vedio10']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "camName = list(camBox.keys())\n",
    "imgNum = list()\n",
    "imgPid = list()\n",
    "for n in camName:\n",
    "    imgNum.append(len(camBox[n]))\n",
    "    imgPid.append(len(camPid[n]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "camPidNum = dict([(i, 0) for i in range(1, len(camName)+1)])\n",
    "for i,k in pidCam.items():\n",
    "    camPidNum[len(k)] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 9328, 2: 1241, 3: 117, 4: 0, 5: 0, 6: 0}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camPidNum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of images in different cameras')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(camName, height=imgNum, width=0.5, alpha=0.8, color='red')\n",
    "plt.title(\"Number of images in different cameras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[49455, 36068, 40793, 4363, 1602, 201]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgNum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of persons in different cameras')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(camName, height=imgPid, width=0.5, color='green')\n",
    "plt.title(\"Number of persons in different cameras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4099, 3394, 4108, 407, 137, 16]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imgPid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of persons appearance in different cameras')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(camPidNum.keys(), height=camPidNum.values(), width=0.5, color='blue')\n",
    "plt.title(\"Number of persons appearance in different cameras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 9328, 2: 1241, 3: 117, 4: 0, 5: 0, 6: 0}"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "camPidNum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pidName = list()\n",
    "pidNumber = list()\n",
    "for i,k in pidBox.items():\n",
    "    pidName.append(i)\n",
    "    pidNumber.append(len(k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10686 artists>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAREElEQVR4nO3db4xldX3H8fenrIj1TwEdyBZMFxJi5YlAJxZqY1rwf43wABuI0a3FbNLWRmsTu9RHJn2gTaPWtFE3ot00/oGiFoJRS1ZM06RZHQoquNAFtLoF2bGKWh9U0W8f3N/qMMwwd2buv9+d9yu5uef87rn3fH/n3P3svb97zplUFZKk/vzStAuQJG2NAS5JnTLAJalTBrgkdcoAl6RO7Zrkyp71rGfVnj17JrlKSere7bff/p2qWljdPtEA37NnD0tLS5NcpSR1L8l/rdXuEIokdcoAl6ROGeCS1CkDXJI6ZYBLUqcMcEnqlAEuSZ0ywCWpUwa4JHVqqABPcmqSG5Pck+RIkkuSnJ7k1iRH2/1p4y5WkvQLw34C/1vgs1X168DzgCPAfuBQVZ0HHGrzkqQJ2TDAkzwDeCFwHUBV/biqHgEuBw62xQ4CV4yrSEnS4w3zCfxcYBn4cJI7knwwyVOBM6vqIYB2f8ZaT06yL8lSkqXl5eWRFS5JO90wAb4LuAh4X1VdCPyITQyXVNWBqlqsqsWFhcddDVGStEXDBPgx4FhVHW7zNzII9IeT7AZo98fHU6IkaS0bBnhVfRv4VpLntKbLgK8BNwN7W9te4KaxVChJWtOwf9DhT4GPJDkZeAB4PYPwvyHJNcA3gVePp0RJ0lqGCvCquhNYXOOhy0ZbjiRpWJ6JKUmdMsAlqVMGuCR1ygCXpE4Z4JLUKQNckjplgEtSpwxwSeqUAS5JnTLAJalTBrgkdcoAl6ROGeCS1CkDXJI6ZYBLUqcMcEnqlAEuSZ0ywCWpUwa4JHXKAJekThngktQpA1ySOmWAS1KnDHBJ6pQBLkmd2jXMQkm+AfwQ+CnwaFUtJjkduB7YA3wD+P2q+t54ypQkrbaZT+C/W1UXVNVim98PHKqq84BDbV6SNCHbGUK5HDjYpg8CV2y/HEnSsIYN8AL+JcntSfa1tjOr6iGAdn/GWk9Msi/JUpKl5eXl7VcsSQKGHAMHXlBVDyY5A7g1yT3DrqCqDgAHABYXF2sLNUqS1jDUJ/CqerDdHwc+BTwfeDjJboB2f3xcRUqSHm/DAE/y1CRPPzENvAS4C7gZ2NsW2wvcNK4iJUmPN8wQypnAp5KcWP6jVfXZJF8CbkhyDfBN4NXjK1OStNqGAV5VDwDPW6P9f4DLxlGUJGljnokpSZ0ywCWpUwa4JHXKAJekThngkkZuz/5PT7uEHcEAl6ROGeCS1CkDXJI6ZYBLUqcMcEnqlAEuSZ0ywCWpUwa4JHXKAJekThngktQpA1ySOmWAS1KnDHBJ6pQBLkmdMsAlqVMGuCR1ygCXpE4Z4JLUKQNckjplgEtSp4YO8CQnJbkjyS1t/pwkh5McTXJ9kpPHV6YkabXNfAJ/E3Bkxfw7gXdX1XnA94BrRlmYJOmJDRXgSc4Gfg/4YJsPcClwY1vkIHDFOAqUJK1t2E/g7wHeCvyszT8TeKSqHm3zx4Cz1npikn1JlpIsLS8vb6tYSdIvbBjgSV4JHK+q21c2r7ForfX8qjpQVYtVtbiwsLDFMiVJq+0aYpkXAK9K8grgFOAZDD6Rn5pkV/sUfjbw4PjKlCSttuEn8Kq6tqrOrqo9wFXA56vqNcBtwJVtsb3ATWOrUpL0ONs5DvwvgLckuY/BmPh1oylJkjSMYYZQfq6qvgB8oU0/ADx/9CVJkobhmZiS1CkDXJI6ZYBLUqcMcEnq1I4P8D37Pz3tEiRpS3Z8gEtSrwxwSeqUAS5JnTLAJalTBrgkdcoAl6ROGeCaex4qqnllgEtSpwxwSeqUAS5JnTLAJalTBrgkdcoAl6ROGeCS1CkDXJI6ZYBLUqcMcEnqlAEuSZ0ywCWpUwa4JHVqwwBPckqSLyb5cpK7k7y9tZ+T5HCSo0muT3Ly+MudLK9iJ2mWDfMJ/P+AS6vqecAFwMuSXAy8E3h3VZ0HfA+4ZnxlSpJW2zDAa+B/2+yT2q2AS4EbW/tB4IqxVChJWtNQY+BJTkpyJ3AcuBW4H3ikqh5tixwDzlrnufuSLCVZWl5eHkXN0sQ4jKZZNlSAV9VPq+oC4Gzg+cBz11psneceqKrFqlpcWFjYeqWSpMfY1FEoVfUI8AXgYuDUJLvaQ2cDD462NEnSExnmKJSFJKe26acALwKOALcBV7bF9gI3javInviVe7rc/tqKXt83uzZehN3AwSQnMQj8G6rqliRfAz6e5K+AO4DrxlinJGmVDQO8qr4CXLhG+wMMxsMlSVPgmZiS1CkDfAi9jo/NCrefNB4GuCR1ygCXpE7t2AD3a/3kuc035jYavXnepjs2wCWpdwa4JHXKAJekThngmlnzPHYpjYIBLkmdMsAlqVMGuKQdq/dhOgNckjplgEtSpwxwSXOp9+GRYRjgktQpA1ySOmWAS1KnDHBpCnbC+KzGzwCXpE4Z4JLUKQNckjplgEtSpwxwSeqUAS5JndowwJM8O8ltSY4kuTvJm1r76UluTXK03Z82/nIfy0OxJI1Sb5kyzCfwR4E/r6rnAhcDf5LkfGA/cKiqzgMOtXlJ0oRsGOBV9VBV/Ueb/iFwBDgLuBw42BY7CFwxriIlSY+3qTHwJHuAC4HDwJlV9RAMQh44Y53n7EuylGRpeXl5e9VK0pTN0jDL0AGe5GnAJ4A3V9UPhn1eVR2oqsWqWlxYWNhKjZKkNQwV4EmexCC8P1JVn2zNDyfZ3R7fDRwfT4mSpLUMcxRKgOuAI1X1rhUP3QzsbdN7gZtGX57WM0tf46TN8L07OruGWOYFwGuBrya5s7X9JfAO4IYk1wDfBF49nhIlSWvZMMCr6t+ArPPwZaMtR5I0LM/ElKROGeDrmMQ43Yl1rFzXWm2avnnYHz31YaNat9uXYZ7fw/YywCWpUwa4JHVqRwR4D1+FpGnp6d/Hdmoddz+nsR13RIBL0jwywCWpU3MX4D19HRyFndZfaSNrHdU1r+YuwCVppzDAJalTBrgkdcoAb4Y58+vEbTPPXe/MylGMzY3ibLWV/Rrn2OETnXU66nVu5XWGfc44x1Q3W8PK+628Fzb7Xp4V46rxic6CnoX3x1oMcEnqlAEuSZ3aMQE+LxevmbZxbcf1njPuIYthhh+GeZ1xLLvd19nshdGGGQrczOttZ329Wb19RvG+GsaOCXBJmjcGuCR1ygCXpE4Z4NswjXHbWVjfrKx/Uuud9vbtzSgPD532tp/UWPZWGeCS1CkDXJI61X2AT3sYY5xn/Wlts7j9xn124GYen/TV+Kb9b3An6z7AJWmnMsAlqVNzEeCzcIGhYV9jKxe+2ok2c/bgVr7Cj2MbT+KCR5O+qNIoLpg2TjvtiK/VNgzwJB9KcjzJXSvaTk9ya5Kj7f608ZYpSVptmE/g/wC8bFXbfuBQVZ0HHGrzkqQJ2jDAq+pfge+uar4cONimDwJXjLguSdIGtjoGfmZVPQTQ7s9Yb8Ek+5IsJVlaXl7e4uoea9bGoUZhVvu0mTHQURy+Ns4x3lndxqvNWp1eyXN2jf1HzKo6UFWLVbW4sLAw7tVJ0o6x1QB/OMlugHZ/fHQlSZKGsdUAvxnY26b3AjeNppz1DXOBnGl+jZvUGW9bvbjOuOub1TNfp3UhpUn8zdNZM66/ozqp9fVomMMIPwb8O/CcJMeSXAO8A3hxkqPAi9u8JGmCdm20QFVdvc5Dl424FknSJszFmZiStBN1FeBbGf+dpXGyWapls9Y6RHCrv0PM8kXyZ/GyDOM87X/Sf5B5mHHt3v6NT3P9XQW4JOkXDHBJ6tRcBvhmvx5O6spt0zIvF9zfztf9af3BhV7XtdX1TfvQy62sbxYPyx3WXAa4JO0EBrgkdWpHBvisfP0Zh9Vna653pMhWhiPmebttxkZ/KGIrwzeTGvIZdv3bXW7
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(np.arange(len(pidName)), height=pidNumber)\n",
    "plt.title(\"number of images for different id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ex(): return open_image('datasets/beijingStation/query/000245_c10s2_1561732033722.000000.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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
      "image/png": "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
      "text/plain": [
       "Image (3, 256, 128)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_ex().apply_tfms(crop_pad(), size=(256,128))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_dict = torch.load('logs/beijing/test/models/model_0.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from modeling import Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Baseline(2344, 1, None)\n",
    "model.load_state_dict(state_dict['model'])\n",
    "\n",
    "model.eval()\n",
    "model = model.cuda(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data.build import get_data_bunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)\n",
      "Split index = 0\n"
     ]
    }
   ],
   "source": [
    "data_bunch, test_labels, num_query = get_data_bunch(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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
      "image/png": "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
      "text/plain": [
       "Image (3, 256, 128)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_bunch.train_ds[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/export/home/lxy/reid_baseline/data/datasets/eval_reid.py:18: UserWarning: Cython evaluation is UNAVAILABLE, which is highly recommended\n",
      "  warnings.warn(\"Cython evaluation is UNAVAILABLE, which is highly recommended\")\n"
     ]
    }
   ],
   "source": [
    "from data.datasets.eval_reid import evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "pids = []\n",
    "camids = []\n",
    "for i in test_labels:\n",
    "    pids.append(i[0])\n",
    "    camids.append(i[1])\n",
    "q_pids = np.asarray(pids[:num_query])\n",
    "q_camids = np.asarray(camids[:num_query])\n",
    "g_pids = np.asarray(pids[num_query:])\n",
    "g_camids = np.asarray(camids[num_query:])\n",
    "\n",
    "feats, pids, camids = [], [], []\n",
    "for imgs, _ in data_bunch.test_dl:\n",
    "    with torch.no_grad():\n",
    "        feat = model(imgs)\n",
    "        feats.append(feat)\n",
    "feats = torch.cat(feats, dim=0)\n",
    "feats = F.normalize(feats, p=2, dim=1)\n",
    "\n",
    "qf = feats[:num_query]\n",
    "gf = feats[num_query:]\n",
    "\n",
    "m, n = qf.shape[0], gf.shape[0]\n",
    "distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()\n",
    "distmat.addmm_(1, -2, qf, gf.t())\n",
    "distmat = to_np(distmat)\n",
    "cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import get_data_bunch\n",
    "from modeling import build_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not load imagenet pretrained model!\n"
     ]
    }
   ],
   "source": [
    "model = build_model(cfg, 751)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg.DATASETS.NAMES = ('market1501',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, _, _ = get_data_bunch(cfg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from layers import make_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = Learner(data=data, model=model, loss_func=make_loss(cfg), path='logs', )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)\n",
      "Split index = 0\n",
      "not load imagenet pretrained model!\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from fastai.basic_train import Learner\n",
    "\n",
    "from engine.interpreter import ReidInterpretation\n",
    "\n",
    "from data import get_data_bunch\n",
    "\n",
    "from config import cfg\n",
    "cfg.DATASETS.TEST_NAMES = 'bj'\n",
    "cfg.MODEL.BACKBONE = 'resnet50_ibn'\n",
    "\n",
    "data_bunch, test_labels, num_query = get_data_bunch(cfg)\n",
    "\n",
    "from modeling import Baseline\n",
    "\n",
    "model = Baseline(cfg.MODEL.BACKBONE, 10, 1)\n",
    "model.load_params_wo_fc(torch.load('logs/2019.8.12/bj/ibn_lighting/models/model_119.pth')['model'])\n",
    "learn = Learner(data_bunch, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "reidInterpreter = ReidInterpretation(learn, test_labels, num_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAExCAYAAACZG/jkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOy9ebhlR1no/Xur1lp7n7FPD+kxJEBmMhMDckUBmSSKIkFUFAQEB/QqPN/9Pu5VQUTkUb+LwOeE1wkhYhgEFAFFZMZEEExISEKmTtKd9Nxn2mcPa62q9/ujau+z++ScPt1N53RI6pdnp8/ea6jaa73rrXeq2qKqJBKJRCKRSCQSiUTi+DCnugOJRCKRSCQSiUQi8Z1IcqYSiUQikUgkEolE4gRIzlQikUgkEolEIpFInADJmUokEolEIpFIJBKJEyA5U4lEIpFIJBKJRCJxAiRnKpFIJBKJRCKRSCROgORMJRKJxKMEEWmJyONP8NhvisjT499vEpFrvo1+vEtE3nCix58KROQ5IvLRofffIyJ3xGv6glWOfbqI7P42298iIreKSOPbOU8ikUgkTi7Zqe5AIpFIJNYGVR3/No698CT24xf6f0cH7RpVPf14ziEi/wf4T6AEXqWqTz1Z/VuBtwK/PPT+zcAfqeo7H+J2AVDVfSLyWeDngD9cizYTiUQisTopM5VIJBIPI0TkER3kEhF7kk71A8An1qJNEbkSWKeq1w99fCbwzW/nvCfA3wI/v8ZtJhKJROIoJGcqkUgkjgERuVxEvi4i8yLyfhG5VkTeEre9XES+tGR/FZGz498NEfnfInKfiOyLZW4jcdvTRWS3iLxeRPYCfy0iN4vI84fOlYvIQRG57Bj6ebaIfF5EZuMx71+hT+8WkT8RkU/GUrUvi8hWEXmHiEyLyG0icvnQsfeIyLNWaPODIrI3tvkFEblwaNu7ReRPReQTIrIAPCN+9hYRGQM+CWyPfWiJyHYRaYvIxqFzXCEiB0Qkj+8vAWaACeBdwFPisTNHafNzIvKqoXMecc9E5HwR+VcROSwi3xKRFw99xecBnx/a9y7g8cDHYrsNEXlFLMObF5G7RWRFpyfe6/vjvt8SkWfGz42I/E8RuUtEDonIB0Rkw9Ch/wE8XkTOXOnciUQikVhbkjOVSCQSqyAiBfBR4L3ABuCDwNXHcYrfA84FLgPOBnYAbxzavjWe90xCGdd7gJ8e2n4VsEdVbziGtn4b+BSwHjido5eEvRj4DWAT0AOuA74e338I+INjaA+CQ3QOsDke/7dLtr8E+B2C8zNwYFR1geCoPKCq4/H1APC52Lc+Pw1cq6pVfH8V8HFVvRX4BeC6eOzUam0uR3Tq/hV4X/wOPwn8yZBTeDHwraF+nwXcBzw/ttsD9gM/BEwCrwDeLiJPXKat8wjlgleq6gTwXOCeuPlXgBcATwO2A9PAHw+1WwN3Apce7fskEolEYu1IzlQikUiszncDOfAOVa1U9UPAV4/lQBER4NXA61T1sKrOE+bf/MTQbh74TVXtqWoHuAa4SkQm4/aXEhy5Y6EiOGXbVbWrqkdzJD6iql9T1S7wEaCrqu9RVQe8H7j8KMcOUNW/UtX56FS8CbhURNYN7fIPqvplVfWxrdX4G6IzGUv0fpIjv/8PsnqJ3/G0+UPAPar616paq+rXgb8HXhS3TwHzRzuBqn5cVe/SwOcJDu33LrOrAxrAE0QkV9V7VPWuuO3ngV9X1d1D1/JFS0o/52N/EolEIvEwIDlTiUQisTrbgftVVYc+u/cYjz0NGAW+JiIzsRTtn+PnfQ4MG/wxO/Nl4GoRmSJkb5Zme1bi/wEE+IqEFfheeZR99w393Vnm/aoLVoiIFZHfjaVpcyxmWTYN7bbrmHq+yD8QnI3HA88GZlX1K7G9KeB84N9XOcfxtHkm8OT+/Yn36KcIGUMIGaKJo51ARJ4nItfHMsEZQvZs09L9VPVO4LUER2m/hHLR7UP9+MhQH24lOF9bhk4xQShxTCQSicTDgEf0ROdEIpE4SewBdoiIDDlUZwD9jMICwWECQES2Dh17kOCYXKiq969wfl3ms78BXkXQ09cd5dgjT6S6l5AJQ0SeCnxaRL4QjfiHgpcAPwI8i+BIrSM4HzLcraMc/6BtqtoVkQ8QHJrzOTIr9Vzg32L27GjnXvr5EfeIRUcJguP1eVV99grn+gahTHNZJCxX/vfAywgZsUrCMuqy3P6q+j7gfTHz+GeEMtCXxn68UlW/vEI7GaFM9MaV+pJIJBKJtSVlphKJRGJ1rgNq4FdEJBORFwJPGtp+I3ChiFwmIk1C1gEAVfXAnxPm0GwGEJEdIvLcVdr8KPBE4FcJc6iOCRH5MRHpLzM+TXAq3FEO+XaZIMy3OkRwVt56nMfvAzYuKQuE8J1fDvwwoeyxz9ISv33A6XFe29G4AXihiIzGRTh+dmjbPwHnishLJSz2kYvIlSJyQdz+CcI8ppUoCKV7B4BaRJ4HPGe5HUXkPBH5/uiAdQmOdv/+vAv4nf4CEyJymoj8yNDhTyKUIx5rVjSRSCQSDzHJmUokEolVUNUSeCHBuJ8Gfhz48ND22wm/O/Rp4A4evODB6wkLB1wfS+E+DZy3SpsdQrbjccNtHQNXAv8hIi3gH4FfVdWdx3H88fIeQsnj/cAtwPVH3/1IVPU24O+Au2N52/b4+ZcJc8m+rqr3wGD+2bMJZZJ9PkNYonyviBw8SlNvJ/wm1T5C1m9QNhnnsT2HMI/tAWAvIVvUiNu/DsyKyJNX+A7zhMUjPkCQj5cQrv1yNIDfJWQs9xIWvPi1uO2d8bhPicg84VoOt/lTBIcrkUgkEg8T5MgpAIlEIpE4FkTk3cBuVf2Nh7CNNwLnqupPr7rzIxAR+QzwPlX9i/j+SYQfyn3S0Y98SPryHOA1qvqCtW47tr+ZsDz75ce4iEcikUgk1oA0ZyqRSCQehsTfF/pZwlyaRx0Sfij3iYT5WMP85inoDqr6KcIKfacEVd0PXLDqjolEIpFYU1KZXyKRSDzMEJFXExYj+KSqfuFU92etEZG/IZRCvjaW0AGgql9R1U+eup4lEolEInEkqcwvkUgkEolEIpFIJE6AlJlKJBKJRCKRSCQSiRMgOVOJRCKRSCQSiUQicQIkZyqRSCQSiUQikUgkToDkTCUSiUQikUgkEonECZCcqUQikUgkEolEIpE4AZIzlUgkEolEIpFIJBInQHKmEolEIpFIJBKJROIESM5UIpFIJBKJRCKRSJwAyZlKJBKJRCKRSCQSiRMgOVOJRCKRSCQSiUQicQIkZyqRSCQSiUQikUgkToDkTCUSiUQikUgkEonECZCcqUQikUgkEolEIpE4AZIzlUgkEolEIpFIJBInQHKmEolEIpFIJBKJROIESM5UIpFIJBKJRCKRSJwAyZlKJBKJRCKRSCQSiRMgOVOJRCKRSCQSiUQicQIkZyqRSCQSiUQikUgkToDkTCUSiUQikUgkEonECZCcqUQikUgkEolEIpE4AZIzlUgkEolEIpFIJBInQHKmEolEIpFIJBKJROIESM5UIpFIfIcgIt8jIneISEtEXrDKvk8Xkd1r1bfEwxsR+UUR2RdlZ+Mq+75JRK5Zq74lHv6IyI+KyK4oP5evsu/LReRLa9W3xMObR4PuSc5U4jsaEdkgIh8RkQURuVdEXnIMxxQicttSQ1NEni8iN8cH/t9F5AlLtr9ORPaKyKyI/JWINFZp5yUi8r5lPk9G7inmJMuNFZG3iMgDIjIvIv8lIlNxm8Rt90e5+ZyIXLhKO9uPIh9vBv5IVcdV9aPH+n0TJ481lJ2Xi4iL+qj/evoq7aykc3LgD4DnRNk5dFxfOnHSOMnyo/E8ffn4i6FtDRF5VzRiD4vIx0Rkxyrt/DcR+fcVNv9v4Jej/PzXsXzXxMnlZMrO0PafiXL0qqHPPrlE75QictMq7TyqdU9ypo4TEclOdR8SR/DHQAlsAX4K+NPVjFXg/wb2D38gIucAfwv8AjAFfAz4x/79FpHnAv8TeCbwWODxwG+t0s5VwCeO47sM9yfJ2UP
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = reidInterpreter.plot_rank_result(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = reidInterpreter.plot_rank_result(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1080x2880 with 60 Axes>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1080x2880 with 60 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "reidInterpreter.plot_top_error(10, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_sim,neg_sim=reidInterpreter.plot_positve_negative_dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Figure size 720x360 with 1 Axes>\n"
     ]
    }
   ],
   "source": [
    "reidInterpreter.plot_same_cam_diff_cam_dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "language": "python",
   "name": "torch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}