evaluate.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pdb\n",
    "\n",
    "from pathlib import Path\n",
    "from itertools import chain\n",
    "from collections import OrderedDict, defaultdict\n",
    "from io import StringIO\n",
    "from math import inf\n",
    "import json\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import torch\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "import torch.nn.functional as F\n",
    "import torchvision.transforms.functional as TF\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "from brisque import BRISQUE\n",
    "import kornia\n",
    "\n",
    "from lib.models import build_model\n",
    "from lib.attacks import BiasFieldAttack"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "ATTACK_RANDOM_SEED = 2020\n",
    "TEST_RANDOM_SEED = 1997\n",
    "\n",
    "DATA_ROOT = Path('/mnt/jiayi/cosalient/dataset')\n",
    "\n",
    "COSOD_DATASET_NAMES = ['Cosal2015', 'iCoseg', 'CoSOD3k', 'CoCA']\n",
    "\n",
    "DATASET_NAMES = COSOD_DATASET_NAMES # + SOD_DATASET_NAMES\n",
    "\n",
    "RESIZE_TARGET = \"224_mixdataset_ratio0.5_768_224\"\n",
    "\n",
    "\n",
    "RESIZED_COSOD_DATASET_NAMES = [f'{name}_{RESIZE_TARGET}' for name in COSOD_DATASET_NAMES]\n",
    "\n",
    "# NOISE_LEVELS = [4, 8, 16, 32, 64]\n",
    "\n",
    "IMAGE_EXTENSIONS = ['jpg', 'png', 'bmp']\n",
    "GROUP_SIZE = 5\n",
    "\n",
    "IMAGE_FOLDER = 'img'\n",
    "DEFENSE_PREFIX = 'defense'\n",
    "GT_FOLDER = 'gt'\n",
    "RESULT_FOLDER = 'result'\n",
    "\n",
    "BETA_SQUARE = .3\n",
    "EPSILON = 1e-5"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_CONFIGS = OrderedDict()\n",
    "\n",
    "# TEST_CONFIGS['U2Net'] = {\n",
    "#     'model': {\n",
    "#         'name': 'U2Net',\n",
    "#         'mode': 'sal',\n",
    "#         'weights_path': 'weights/U2Net.pth',\n",
    "#     },\n",
    "# }\n",
    "TEST_CONFIGS['PoolNet'] = {\n",
    "    'model': {\n",
    "        'name': 'PoolNet',\n",
    "        'backbone': 'resnet',\n",
    "        'joint': True,\n",
    "        'mode': 'sal',\n",
    "        'weights_path': 'weights/PoolNet-ResNet50-w-edge.pth',\n",
    "    },\n",
    "}\n",
    "# TEST_CONFIGS['EGNet'] = {\n",
    "#     'model': {\n",
    "#         'name': 'EGNet',\n",
    "#         'backbone': 'resnet',\n",
    "#         'mode': 'sal',\n",
    "#         'weights_path': 'weights/EGNet-ResNet50.pth',\n",
    "#     },\n",
    "# }\n",
    "# TEST_CONFIGS['EGNet_vgg'] = {\n",
    "#     'model': {\n",
    "#         'name': 'EGNet',\n",
    "#         'backbone': 'vgg',\n",
    "#         'mode': 'sal',\n",
    "#         'weights_path': 'weights/EGNet-Vgg16.pth',\n",
    "#     },\n",
    "# }\n",
    "# TEST_CONFIGS['BASNet'] = {\n",
    "#     'model': {\n",
    "#         'name': 'BASNet',\n",
    "#         'mode': 'sal',\n",
    "#         'weights_path': 'weights/BASNet.pth',\n",
    "#     },\n",
    "# }\n",
    "\n",
    "TEST_CONFIGS['GICD'] = {\n",
    "    'model': {\n",
    "        'name': 'GICD',\n",
    "        'mode': 'cosal',\n",
    "        'weights_path': 'weights/GICD-GINet.pth',\n",
    "    },\n",
    "}\n",
    "TEST_CONFIGS['GCAGC_cosal'] = {\n",
    "    'model': {\n",
    "        'name': 'GCAGC',\n",
    "        'backbone': 'hrnet',\n",
    "        'mode': 'cosal',\n",
    "        'weights_path': 'weights/GCAGC-HRNet.pth',\n",
    "    },\n",
    "}\n",
    "# TEST_CONFIGS['GCoNet'] = {\n",
    "#     'model': {\n",
    "#         'name': 'GCoNet',\n",
    "#         'mode': 'cosal',\n",
    "#         'weights_path': 'weights/GCoNet.pth',\n",
    "#     },\n",
    "# }\n",
    "\n",
    "# TEST_CONFIGS['GICD_rsattack'] = {\n",
    "#     'model': {\n",
    "#         'name': 'GICD',\n",
    "#         'mode': 'cosal',\n",
    "#         'weights_path': 'weights/GICD-GINet.pth',\n",
    "#     },\n",
    "#     'random_seed': ATTACK_RANDOM_SEED,\n",
    "#     'attack_keyword': '_eps16_step20'\n",
    "# }\n",
    "# TEST_CONFIGS['PoolNet_rsattack'] = {\n",
    "#     'model': {\n",
    "#         'name': 'PoolNet',\n",
    "#         'backbone': 'resnet',\n",
    "#         'joint': True,\n",
    "#         'mode': 'sal',\n",
    "#         'weights_path': 'weights/PoolNet-ResNet50-w-edge.pth',\n",
    "#     },\n",
    "#     'random_seed': ATTACK_RANDOM_SEED,\n",
    "#     'attack_keyword': 'eps16_step20'\n",
    "# }\n",
    "# TEST_CONFIGS['GCAGC_cosal_rsattack'] = {\n",
    "#     'model': {\n",
    "#         'name': 'GCAGC',\n",
    "#         'backbone': 'hrnet',\n",
    "#         'mode': 'cosal',\n",
    "#         'weights_path': 'weights/GCAGC-HRNet.pth',\n",
    "#     },\n",
    "#     'random_seed': ATTACK_RANDOM_SEED,\n",
    "#     'attack_keyword': '_eps16_step20'\n",
    "# }\n",
    "\n",
    "print(*map(str, TEST_CONFIGS.keys()), sep='\\n')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test(dataset_names, model_configs, force=False, skip_incomplete=True, attack_keyword=None):\n",
    "    trim_len = len(IMAGE_FOLDER)\n",
    "    for test_name, test_config in tqdm(model_configs.items(), desc='test'):\n",
    "        model = build_model(test_config['model']).eval().to(DEVICE)\n",
    "        cosal = test_config['model']['mode'] == 'cosal'\n",
    "        random_seed = test_config.get('random_seed', TEST_RANDOM_SEED)\n",
    "        config_attack_keyword = test_config.get('attack_keyword', None)\n",
    "\n",
    "        for dataset_name in tqdm(dataset_names, desc='dataset', leave=False):\n",
    "            dataset_root = DATA_ROOT / dataset_name\n",
    "            o_source_root = dataset_root / IMAGE_FOLDER\n",
    "            o_source_paths = chain.from_iterable(o_source_root.rglob(f'*.{e}') for e in IMAGE_EXTENSIONS)\n",
    "            o_source_paths = sorted(o_source_paths)\n",
    "            num_o_source_paths = len(o_source_paths)\n",
    "\n",
    "            source_roots = dataset_root.glob(f'{IMAGE_FOLDER}*')\n",
    "            source_roots = sorted(source_roots)\n",
    "\n",
    "            for source_root in tqdm(source_roots, desc='attack', leave=False):\n",
    "                attack_postfix = source_root.name[trim_len:]\n",
    "\n",
    "\n",
    "                # TODO: remove this later\n",
    "                if attack_postfix not in [\n",
    "                    '_jadena',\n",
    "               ]:\n",
    "                    continue\n",
    "                if not (attack_keyword is None or attack_keyword in attack_postfix or attack_postfix == ''):\n",
    "                    continue\n",
    "                if not (config_attack_keyword is None or config_attack_keyword in attack_postfix or attack_postfix == ''):\n",
    "                    continue\n",
    "                target_root = dataset_root.joinpath(f'{RESULT_FOLDER}{attack_postfix}/{test_name}')\n",
    "                source_paths = chain.from_iterable(source_root.rglob(f'*.{e}') for e in IMAGE_EXTENSIONS)\n",
    "                source_paths = sorted(source_paths)\n",
    "\n",
    "                folders = os.listdir(source_root)\n",
    "                for folder in folders:\n",
    "                    torch.manual_seed(random_seed)\n",
    "                    folder_path = os.path.join(source_root,folder)\n",
    "                    img_names = os.listdir(folder_path)\n",
    "                    if os.path.exists(os.path.join(target_root,folder)) and \\\n",
    "                        len(os.listdir(os.path.join(target_root,folder))) == len(img_names):\n",
    "                        continue\n",
    "                    source_paths = []\n",
    "                    for img_name in img_names:\n",
    "                        source_paths.append(os.path.join(folder_path,img_name))\n",
    "                    ims = map(lambda p: Image.open(p).convert('RGB'), source_paths)\n",
    "                    ims = map(TF.to_tensor, ims)\n",
    "                    ims = torch.stack(list(ims), dim=0).to(DEVICE)\n",
    "                    res = model(ims) \n",
    "                    for res_index in range(len(img_names)):\n",
    "                        img_name = img_names[res_index]\n",
    "                        res_part = res[res_index]\n",
    "                        res_part = TF.to_pil_image(res_part.cpu())\n",
    "                        target_path = os.path.join(target_root,folder,img_name)[:-3] + \"png\"\n",
    "\n",
    "                        if not os.path.exists(os.path.join(target_root,folder)):\n",
    "                            os.makedirs(os.path.join(target_root,folder))\n",
    "                        res_part.save(target_path)\n",
    "\n",
    "\n",
    "test(\n",
    "    dataset_names=RESIZED_COSOD_DATASET_NAMES,\n",
    "    model_configs=TEST_CONFIGS,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chosen_ratio = 0.5\n",
    "degradation_type = \"jadena\"\n",
    "metrics_path = Path('metrics_eval/{}_{}_mix{}.json'.format(COSOD_DATASET_NAMES[0],degradation_type,chosen_ratio))\n",
    "print(metrics_path)\n",
    "\n",
    "\n",
    "def ap(result, gt):\n",
    "    result = result.flatten()\n",
    "    gt = gt.flatten()\n",
    "    \n",
    "    desc_score_indices = torch.argsort(result, descending=True)\n",
    "\n",
    "    result = result[desc_score_indices]\n",
    "    gt = gt[desc_score_indices]\n",
    "\n",
    "    distinct_indices = torch.where(result[1:] - result[:-1])[0]\n",
    "    threshold_indices = F.pad(distinct_indices, (0, 1), value=gt.numel() - 1)\n",
    "\n",
    "    tps = gt.cumsum(dim=0)[threshold_indices]\n",
    "    fps = 1 + threshold_indices - tps\n",
    "    \n",
    "    precision = tps / (tps + fps)\n",
    "    recall = tps / tps[-1]\n",
    "    \n",
    "    precision = F.pad(precision, (1, 0), value=1)\n",
    "    recall = F.pad(recall, (1, 0), value=0)\n",
    "\n",
    "    return ((recall[1:] - recall[:-1]) * (precision[1:] + precision[:-1]) / 2).sum().item()\n",
    "\n",
    "    \n",
    "def fmeasure_etc(result, gt):\n",
    "    result = result >= .5\n",
    "    gt = gt >= .5\n",
    "\n",
    "    tp = (gt & result).sum().item()\n",
    "    fp = (~gt & result).sum().item()\n",
    "    fn = (gt & ~result).sum().item()\n",
    "    u = (gt | result).sum().item()\n",
    "\n",
    "    p = (tp + EPSILON) / (tp + fp + EPSILON)\n",
    "    r = (tp + EPSILON) / (tp + fn + EPSILON)\n",
    "    fs = ((BETA_SQUARE + 1) * p * r + EPSILON) / (BETA_SQUARE * p + r + EPSILON)\n",
    "    iou = (tp + EPSILON) / (u + EPSILON)\n",
    "    \n",
    "    return {'fmeasure': fs, 'recall': r, 'iou': iou, 'success': 1 if iou > .5 else 0}\n",
    "\n",
    "\n",
    "def mae(result, gt):\n",
    "    return F.l1_loss(result, gt).item()\n",
    "\n",
    "\n",
    "def evaluate_images(image_root, o_image_root):\n",
    "    image_paths = chain.from_iterable(image_root.rglob(f'*.{e}') for e in IMAGE_EXTENSIONS)\n",
    "    image_paths = sorted(image_paths)\n",
    "\n",
    "    brisq = BRISQUE()\n",
    "    \n",
    "    metrics = defaultdict(list)\n",
    "    for image_path in image_paths:\n",
    "        relative_path = image_path.relative_to(image_root)\n",
    "        o_image_path = o_image_root.joinpath(relative_path).with_suffix('.jpg')\n",
    "\n",
    "        im = Image.open(image_path).convert('RGB')\n",
    "        o_im = Image.open(o_image_path).convert('RGB')\n",
    "        \n",
    "        im = TF.to_tensor(im)\n",
    "        o_im = TF.to_tensor(o_im)\n",
    "\n",
    "        mae = F.l1_loss(im, o_im).item()\n",
    "        mse = F.mse_loss(im, o_im).item()\n",
    "        psnr = (10. * torch.log10(1. / F.mse_loss(im, o_im))).item()\n",
    "        # ssim = kornia.ssim(im[None], o_im[None], 11)[0].mean().item()\n",
    "        ssim = kornia.losses.ssim(im[None], o_im[None], 11)[0].mean().item()\n",
    "        brisque = brisq.get_score(str(image_path))\n",
    "\n",
    "        metrics['mae'].append(mae)\n",
    "        metrics['mse'].append(mse)\n",
    "        metrics['psnr'].append(psnr)\n",
    "        metrics['ssim'].append(ssim)\n",
    "        metrics['brisque'].append(brisque)\n",
    "\n",
    "    avg_metrics = {n: sum(m) / len(m) for n, m in metrics.items()}\n",
    "\n",
    "    return avg_metrics, metrics, list(map(str, image_paths))\n",
    "\n",
    "\n",
    "def evaluate_results(result_root, gt_root):\n",
    "    result_paths = chain.from_iterable(result_root.rglob(f'*.{e}') for e in IMAGE_EXTENSIONS)\n",
    "    result_paths = sorted(result_paths)\n",
    "\n",
    "\n",
    "    metrics = defaultdict(list)\n",
    "\n",
    "    basename = os.path.basename(result_root)\n",
    "    not_processed_root = os.path.join(os.path.dirname(os.path.dirname(result_root)),\"result{}\".format(degradation_type),basename)\n",
    "\n",
    "    relative_paths_select = []\n",
    "    relative_paths_notselect = []\n",
    "\n",
    "    folders = os.listdir(result_root)\n",
    "    for folder in folders:\n",
    "        folder_path = os.path.join(result_root,folder)\n",
    "        img_names = os.listdir(folder_path)\n",
    "        len_imgs = len(img_names)\n",
    "        chosen_corrupt_num = int(len_imgs * chosen_ratio)\n",
    "\n",
    "        for i in range(len_imgs):\n",
    "        # for i in range(chosen_corrupt_num):\n",
    "        # for i in range(chosen_corrupt_num,len_imgs):\n",
    "            img_name = img_names[i]\n",
    "            relative_paths_select.append(os.path.join(folder,img_name))\n",
    "\n",
    "        # for i in range(chosen_corrupt_num, len_imgs):\n",
    "        #     img_name = img_names[i]\n",
    "        #     relative_paths_notselect.append(os.path.join(folder,img_name))\n",
    "\n",
    "    for relative_path in relative_paths_select:\n",
    "        result_path = os.path.join(result_root, relative_path)\n",
    "        gt_path = os.path.join(gt_root, relative_path)\n",
    "        \n",
    "        result = Image.open(result_path).convert('L')\n",
    "        gt = Image.open(gt_path).convert('L')\n",
    "\n",
    "        result = TF.to_tensor(result)\n",
    "        gt = TF.to_tensor(gt)\n",
    "\n",
    "        metrics['ap'].append(ap(result, gt))\n",
    "        metrics['mae'].append(mae(result, gt))\n",
    "        for k, v in fmeasure_etc(result, gt).items():\n",
    "            metrics[k].append(v)\n",
    "\n",
    "    for relative_path in relative_paths_notselect:\n",
    "        result_path = os.path.join(not_processed_root, relative_path)\n",
    "        gt_path = os.path.join(gt_root, relative_path)\n",
    "        \n",
    "        result = Image.open(result_path).convert('L')\n",
    "        gt = Image.open(gt_path).convert('L')\n",
    "\n",
    "        result = TF.to_tensor(result)\n",
    "        gt = TF.to_tensor(gt)\n",
    "\n",
    "        metrics['ap'].append(ap(result, gt))\n",
    "        metrics['mae'].append(mae(result, gt))\n",
    "        for k, v in fmeasure_etc(result, gt).items():\n",
    "            metrics[k].append(v)\n",
    "\n",
    "    avg_metrics = {n: sum(m) / len(m) for n, m in metrics.items()}\n",
    "\n",
    "    return avg_metrics, metrics, list(map(str, result_paths))\n",
    "\n",
    "\n",
    "def find(metrics, dataset_name, attack_postfix, method):\n",
    "    candidates = [\n",
    "        m for m in metrics\n",
    "        if m['dataset_name'] == dataset_name and\n",
    "        m['attack_postfix'] == attack_postfix and\n",
    "        ((m['type'] == 'image' and method == '') or\n",
    "         (m['type'] == 'result' and m['model'] == method))\n",
    "    ]\n",
    "    assert len(candidates) <= 1\n",
    "    return candidates[0] if len(candidates) == 1 else None\n",
    "\n",
    "\n",
    "def evaluate(dataset_names, prev_metrics=None, attack_keyword=None, dataset_keyword=None):\n",
    "    trim_len = len(IMAGE_FOLDER)\n",
    "    all_metrics = [] if prev_metrics is None else list(prev_metrics)\n",
    "\n",
    "    for dataset_name in tqdm(dataset_names, desc='dataset'):\n",
    "        if not (dataset_keyword is None or dataset_keyword in dataset_name):\n",
    "            continue\n",
    "        dataset_root = DATA_ROOT / dataset_name\n",
    "        gt_root = dataset_root.joinpath(GT_FOLDER)\n",
    "        o_image_root = dataset_root.joinpath(IMAGE_FOLDER)\n",
    "\n",
    "        image_roots = dataset_root.glob(f'{IMAGE_FOLDER}*')\n",
    "        image_roots = sorted(image_roots)\n",
    "\n",
    "        for image_root in tqdm(image_roots, desc='attack', leave=False):\n",
    "            attack_postfix = image_root.name[trim_len:]\n",
    "            if degradation_type not in attack_postfix:\n",
    "                continue\n",
    "            if not (attack_keyword is None or attack_keyword in attack_postfix or attack_postfix == ''):\n",
    "                continue\n",
    "            attack_result_root = dataset_root.joinpath(RESULT_FOLDER + attack_postfix)\n",
    "            result_roots = attack_result_root.iterdir() if attack_result_root.exists() else []\n",
    "            result_roots = sorted(result_roots)\n",
    "\n",
    "            # if find(all_metrics, dataset_name, attack_postfix, '') is None:\n",
    "            #     avg_metrics, metrics, paths = evaluate_images(image_root, o_image_root)\n",
    "            #     all_metrics.append({\n",
    "            #         'dataset_name': dataset_name,\n",
    "            #         'attack_postfix': attack_postfix,\n",
    "            #         'type': 'image',\n",
    "            #         'avg_metrics': avg_metrics,\n",
    "            #         'metrics': metrics,\n",
    "            #         'paths': paths,\n",
    "            #     })\n",
    "\n",
    "            for result_root in tqdm(result_roots, desc='method', leave=False):\n",
    "                method = result_root.name\n",
    "                if method.endswith('_intragroup'):\n",
    "                    continue\n",
    "                if find(all_metrics, dataset_name, attack_postfix, method) is None:\n",
    "                    avg_metrics, metrics, paths = evaluate_results(result_root, gt_root)\n",
    "                    all_metrics.append({\n",
    "                        'dataset_name': dataset_name,\n",
    "                        'attack_postfix': attack_postfix,\n",
    "                        'type': 'result',\n",
    "                        'model': method,\n",
    "                        'avg_metrics': avg_metrics,\n",
    "                        # 'metrics': metrics,\n",
    "                        # 'paths': paths,\n",
    "                    })\n",
    "\n",
    "    return all_metrics\n",
    "\n",
    "\n",
    "prev_metrics = json.loads(metrics_path.read_text()) if metrics_path.exists() else None\n",
    "\n",
    "metrics = evaluate(\n",
    "    dataset_names=RESIZED_COSOD_DATASET_NAMES,\n",
    "    prev_metrics=prev_metrics,\n",
    ")\n",
    "metrics_path.write_text(json.dumps(metrics, indent=4, sort_keys=True))"
   ]
  }
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