utils/visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "from skimage import transform\n",
    "# display plots in this notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "\n",
    "# set display defaults\n",
    "plt.rcParams['figure.figsize'] = (12, 9)        # small images\n",
    "plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels\n",
    "plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-image\n",
      "  Using cached scikit_image-0.18.1-cp38-cp38-manylinux1_x86_64.whl (30.2 MB)\n",
      "Collecting PyWavelets>=1.1.1\n",
      "  Using cached PyWavelets-1.1.1-cp38-cp38-manylinux1_x86_64.whl (4.4 MB)\n",
      "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,>=4.3.0 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from scikit-image) (8.1.0)\n",
      "Collecting scipy>=1.0.1\n",
      "  Downloading scipy-1.6.2-cp38-cp38-manylinux1_x86_64.whl (27.2 MB)\n",
      "\u001b[K     |████████████████████████████████| 27.2 MB 20.7 MB/s eta 0:00:01   |███████▉                        | 6.7 MB 2.7 MB/s eta 0:00:08\n",
      "\u001b[?25hCollecting imageio>=2.3.0\n",
      "  Using cached imageio-2.9.0-py3-none-any.whl (3.3 MB)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from scikit-image) (1.19.5)\n",
      "Collecting networkx>=2.0\n",
      "  Downloading networkx-2.5.1-py3-none-any.whl (1.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6 MB 2.7 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from scikit-image) (3.3.3)\n",
      "Collecting tifffile>=2019.7.26\n",
      "  Downloading tifffile-2021.4.8-py3-none-any.whl (165 kB)\n",
      "\u001b[K     |████████████████████████████████| 165 kB 26.4 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting decorator<5,>=4.3\n",
      "  Using cached decorator-4.4.2-py2.py3-none-any.whl (9.2 kB)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.4.7)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.3.1)\n",
      "Requirement already satisfied: six in /home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/lib/python3.8/site-packages (from cycler>=0.10->matplotlib!=3.0.0,>=2.0.0->scikit-image) (1.15.0)\n",
      "Installing collected packages: PyWavelets, scipy, imageio, decorator, networkx, tifffile, scikit-image\n",
      "  Attempting uninstall: decorator\n",
      "    Found existing installation: decorator 5.0.6\n",
      "    Uninstalling decorator-5.0.6:\n",
      "      Successfully uninstalled decorator-5.0.6\n",
      "Successfully installed PyWavelets-1.1.1 decorator-4.4.2 imageio-2.9.0 networkx-2.5.1 scikit-image-0.18.1 scipy-1.6.2 tifffile-2021.4.8\n",
      "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 21.0.1 is available.\n",
      "You should consider upgrading via the '/home/vincent/proj/soco/reproduce/bottom-up-attention.pytorch/env/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = '/home/vincent/proj/soco/soco/soco-image-sparta/data/flickr30k'\n",
    "\n",
    "# Load classes\n",
    "classes = ['__background__']\n",
    "with open(os.path.join(data_path, 'objects_vocab.txt')) as f:\n",
    "    for object in f.readlines():\n",
    "        classes.append(object.split(',')[0].lower().strip())\n",
    "\n",
    "# Load attributes\n",
    "attributes = ['__no_attribute__']\n",
    "with open(os.path.join(data_path, 'attributes_vocab.txt')) as f:\n",
    "    for att in f.readlines():\n",
    "        attributes.append(att.split(',')[0].lower().strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "** fvcore version of PathManager will be deprecated soon. **\n",
      "** Please migrate to the version in iopath repo. **\n",
      "https://github.com/facebookresearch/iopath \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import os\n",
    "import sys\n",
    "import torch\n",
    "import tqdm\n",
    "import cv2\n",
    "import numpy as np\n",
    "sys.path.append('../detectron2')\n",
    "sys.path.append('../')\n",
    "\n",
    "import detectron2.utils.comm as comm\n",
    "from detectron2.checkpoint import DetectionCheckpointer\n",
    "from detectron2.data import build_detection_test_loader, build_detection_train_loader\n",
    "from detectron2.config import get_cfg\n",
    "from detectron2.engine import DefaultTrainer, default_setup, launch\n",
    "from detectron2.evaluation import COCOEvaluator, verify_results\n",
    "\n",
    "from utils import mkdir, save_features\n",
    "from extract_utils import get_image_blob\n",
    "from frcnn_ext_models.bua import add_bottom_up_attention_config\n",
    "from frcnn_ext_models.bua.layers.nms import nms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Config '../configs/bua-caffe/extract-bua-caffe-r101.yaml' has no VERSION. Assuming it to be compatible with latest v2.\n"
     ]
    }
   ],
   "source": [
    "config_file = '../configs/bua-caffe/extract-bua-caffe-r101.yaml'\n",
    "\n",
    "cfg = get_cfg()\n",
    "add_bottom_up_attention_config(cfg, True)\n",
    "cfg.merge_from_file(config_file)\n",
    "cfg.freeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ResNet.make_stage(first_stride=) is deprecated!  Use 'stride_per_block' or 'stride' instead.\n",
      "ResNet.make_stage(first_stride=) is deprecated!  Use 'stride_per_block' or 'stride' instead.\n",
      "ResNet.make_stage(first_stride=) is deprecated!  Use 'stride_per_block' or 'stride' instead.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GeneralizedBUARCNN(\n",
       "  (backbone): ResNet(\n",
       "    (stem): BUABasicStem(\n",
       "      (conv1): Conv2d(\n",
       "        3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
       "        (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "      )\n",
       "    )\n",
       "    (res2): Sequential(\n",
       "      (0): BottleneckBlock(\n",
       "        (shortcut): Conv2d(\n",
       "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
       "        )\n",
       "        (conv1): Conv2d(\n",
       "          64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "      (1): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "      (2): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (res3): Sequential(\n",
       "      (0): BottleneckBlock(\n",
       "        (shortcut): Conv2d(\n",
       "          256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
       "        )\n",
       "        (conv1): Conv2d(\n",
       "          256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "      (1): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "      (2): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "      (3): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (res4): Sequential(\n",
       "      (0): BottleneckBlock(\n",
       "        (shortcut): Conv2d(\n",
       "          512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv1): Conv2d(\n",
       "          512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (2): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (3): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (4): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (5): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (6): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (7): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (8): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (9): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (10): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (11): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (12): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (13): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (14): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (15): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (16): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (17): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (18): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (19): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (20): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (21): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (22): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (proposal_generator): BUARPN(\n",
       "    (anchor_generator): DefaultAnchorGenerator(\n",
       "      (cell_anchors): BufferList()\n",
       "    )\n",
       "    (rpn_head): StandardBUARPNHead(\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (objectness_logits): Conv2d(512, 24, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (anchor_deltas): Conv2d(512, 48, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (roi_heads): BUACaffeRes5ROIHeads(\n",
       "    (pooler): ROIPooler(\n",
       "      (level_poolers): ModuleList(\n",
       "        (0): RoIPool(output_size=(14, 14), spatial_scale=0.0625)\n",
       "      )\n",
       "    )\n",
       "    (res5): Sequential(\n",
       "      (0): BottleneckBlock(\n",
       "        (shortcut): Conv2d(\n",
       "          1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv1): Conv2d(\n",
       "          1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False\n",
       "          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (1): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False\n",
       "          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (2): BottleneckBlock(\n",
       "        (conv1): Conv2d(\n",
       "          2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv2): Conv2d(\n",
       "          512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(2, 2), dilation=(2, 2), bias=False\n",
       "          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (conv3): Conv2d(\n",
       "          512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
       "          (norm): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (box_predictor): BUACaffeFastRCNNOutputLayers(\n",
       "      (cls_score): Linear(in_features=2048, out_features=1601, bias=True)\n",
       "      (bbox_pred): Linear(in_features=2048, out_features=6404, bias=True)\n",
       "      (cls_embed): Embedding(1601, 256)\n",
       "      (attr_linear1): Linear(in_features=2304, out_features=512, bias=True)\n",
       "      (attr_linear2): Linear(in_features=512, out_features=401, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MIN_BOXES = 10\n",
    "MAX_BOXES = 20\n",
    "CONF_THRESH = 0.4\n",
    "\n",
    "model = DefaultTrainer.build_model(cfg)\n",
    "DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(\n",
    "    '../'+cfg.MODEL.WEIGHTS, resume=True\n",
    ")\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "boxes=11\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im_file = '/home/vincent/proj/vln/cs-eqa/data/debug/unnamed.jpg'\n",
    "\n",
    "im = cv2.imread(im_file)\n",
    "dataset_dict = get_image_blob(im, cfg.MODEL.PIXEL_MEAN)\n",
    "\n",
    "with torch.set_grad_enabled(False):\n",
    "    boxes, scores, features_pooled, attr_scores = model([dataset_dict])\n",
    "\n",
    "dets = boxes[0].tensor.cpu() / dataset_dict['im_scale']\n",
    "scores = scores[0].cpu()\n",
    "feats = features_pooled[0].cpu()\n",
    "attr_scores = attr_scores[0].cpu()\n",
    "\n",
    "max_conf = torch.zeros((scores.shape[0])).to(scores.device)\n",
    "for cls_ind in range(1, scores.shape[1]):\n",
    "        cls_scores = scores[:, cls_ind]\n",
    "        keep = nms(dets, cls_scores, 0.3)\n",
    "        max_conf[keep] = torch.where(cls_scores[keep] > max_conf[keep],\n",
    "                                    cls_scores[keep],\n",
    "                                    max_conf[keep])\n",
    "            \n",
    "keep_boxes = torch.nonzero(max_conf >= CONF_THRESH).flatten()\n",
    "if len(keep_boxes) < MIN_BOXES:\n",
    "    keep_boxes = torch.argsort(max_conf, descending=True)[:MIN_BOXES]\n",
    "elif len(keep_boxes) > MAX_BOXES:\n",
    "    keep_boxes = torch.argsort(max_conf, descending=True)[:MAX_BOXES]\n",
    "\n",
    "im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
    "plt.axis('off')\n",
    "plt.imshow(im)\n",
    "\n",
    "boxes = dets[keep_boxes].numpy()\n",
    "objects = np.argmax(scores[keep_boxes].numpy()[:,1:], axis=1)\n",
    "attr_thresh = 0.1\n",
    "attr = np.argmax(attr_scores[keep_boxes].numpy()[:,1:], axis=1)\n",
    "attr_conf = np.max(attr_scores[keep_boxes].numpy()[:,1:], axis=1)\n",
    "\n",
    "for i in range(len(keep_boxes)):\n",
    "    bbox = boxes[i]\n",
    "    if bbox[0] == 0:\n",
    "        bbox[0] = 1\n",
    "    if bbox[1] == 0:\n",
    "        bbox[1] = 1\n",
    "    cls = classes[objects[i]+1]\n",
    "    if attr_conf[i] > attr_thresh:\n",
    "        cls = attributes[attr[i]+1] + \" \" + cls\n",
    "    plt.gca().add_patch(\n",
    "        plt.Rectangle((bbox[0], bbox[1]),\n",
    "                      bbox[2] - bbox[0],\n",
    "                      bbox[3] - bbox[1], fill=False,\n",
    "                      edgecolor='red', linewidth=2, alpha=0.5)\n",
    "            )\n",
    "    plt.gca().text(bbox[0], bbox[1] - 2,\n",
    "                '%s' % (cls),\n",
    "                bbox=dict(facecolor='blue', alpha=0.5),\n",
    "                fontsize=10, color='white')\n",
    "print('boxes={}'.format(len(keep_boxes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}