test.py

from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import random
import numpy as np
import sys

import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader

from caj import datasets
from caj import models
from caj.models.dsbn import convert_dsbn, convert_bn
from caj.evaluators import Evaluator
from caj.utils.data import transforms as T
from caj.utils.data.preprocessor import Preprocessor
from caj.utils.logging import Logger
from caj.utils.serialization import load_checkpoint, copy_state_dict


def get_data(name, data_dir, height, width, batch_size, workers):
    root = osp.join(data_dir, name)

    dataset = datasets.create(name, root)

    normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])

    test_transformer = T.Compose([
             T.Resize((height, width), interpolation=3),
             T.ToTensor(),
             normalizer
         ])

    test_loader = DataLoader(
        Preprocessor(list(set(dataset.query) | set(dataset.gallery)),
                     root=dataset.images_dir, transform=test_transformer),
        batch_size=batch_size, num_workers=workers,
        shuffle=False, pin_memory=True)
    return dataset, test_loader


def main():
    args = parser.parse_args()

    if args.seed is not None:
        random.seed(args.seed)
        np.random.seed(args.seed)
        torch.manual_seed(args.seed)
        cudnn.deterministic = True

    main_worker(args)


def main_worker(args):
    cudnn.benchmark = True

    log_dir = osp.dirname(args.resume)
    sys.stdout = Logger(osp.join(log_dir, 'log_test.txt'))
    print("==========\nArgs:{}\n==========".format(args))

    # Create data loaders
    dataset, test_loader = get_data(args.dataset, args.data_dir, args.height,
                                    args.width, args.batch_size, args.workers)

    # Create model
    model = models.create(args.arch, pretrained=False, num_features=args.features, dropout=args.dropout,
                          num_classes=0, pooling_type=args.pooling_type)
    if args.dsbn:
        print("==> Load the model with domain-specific BNs")
        convert_dsbn(model)

    # Load from checkpoint
    checkpoint = load_checkpoint(args.resume)
    copy_state_dict(checkpoint['state_dict'], model, strip='module.')

    if args.dsbn:
        print("==> Test with {}-domain BNs".format("source" if args.test_source else "target"))
        convert_bn(model, use_target=(not args.test_source))

    model.cuda()
    model = nn.DataParallel(model)

    # Evaluator
    model.eval()
    evaluator = Evaluator(model)
    evaluator.evaluate(test_loader, dataset.query, dataset.gallery, args, cmc_flag=True, rerank=args.rerank)
    return


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Testing the model")
    # data
    parser.add_argument('-d', '--dataset', type=str, default='market1501')
    parser.add_argument('-b', '--batch-size', type=int, default=256)
    parser.add_argument('-j', '--workers', type=int, default=4)
    parser.add_argument('--height', type=int, default=256, help="input height")
    parser.add_argument('--width', type=int, default=128, help="input width")
    # model
    parser.add_argument('-a', '--arch', type=str, default='resnet50',
                        choices=models.names())
    parser.add_argument('--features', type=int, default=0)
    parser.add_argument('--dropout', type=float, default=0)

    parser.add_argument('--resume', type=str,
                        default="pretrained_models/",
                        metavar='PATH')
    # testing configs
    parser.add_argument('--dsbn', action='store_true',
                        help="test on the model with domain-specific BN")
    parser.add_argument('--test-source', action='store_true',
                        help="test on the source domain")
    parser.add_argument('--seed', type=int, default=1)
    # path
    working_dir = osp.dirname(osp.abspath(__file__))
    parser.add_argument('--data-dir', type=str, metavar='PATH',
                        default='data')
    parser.add_argument('--pooling-type', type=str, default='avg')
    parser.add_argument('--embedding_features_path', type=str,
                        default='')

    # rerank setting
    parser.add_argument('--rerank', action='store_true')
    # Jaccard
    parser.add_argument('--k1', type=int, default=30,
                        help="hyperparameter for jaccard distance")
    parser.add_argument('--k2', type=int, default=6,
                        help="hyperparameter for jaccard distance")

    # CKRNNs
    parser.add_argument('--ckrnns', action='store_true')
    parser.add_argument('--k1-intra', type=int, default=5)
    parser.add_argument('--k1-inter', type=int, default=20)

    # CLQE
    parser.add_argument('--clqe', action='store_true')
    parser.add_argument('--k2-intra', type=int, default=3)
    parser.add_argument('--k2-inter', type=int, default=3)
    main()