391 lines
16 KiB
Python
391 lines
16 KiB
Python
from __future__ import print_function
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from __future__ import division
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import os
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import sys
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import time
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import datetime
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import argparse
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import os.path as osp
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.backends.cudnn as cudnn
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from torch.utils.data import DataLoader
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from torch.optim import lr_scheduler
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from torchreid import data_manager
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from torchreid.dataset_loader import ImageDataset, VideoDataset
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from torchreid import transforms as T
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from torchreid import models
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from torchreid.losses import CrossEntropyLabelSmooth, TripletLoss, DeepSupervision
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from torchreid.utils.iotools import save_checkpoint, check_isfile
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from torchreid.utils.avgmeter import AverageMeter
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from torchreid.utils.logger import Logger
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from torchreid.utils.torchtools import count_num_param
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from torchreid.utils.reidtools import visualize_ranked_results
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from torchreid.eval_metrics import evaluate
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from torchreid.samplers import RandomIdentitySampler
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from torchreid.optimizers import init_optim
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parser = argparse.ArgumentParser(description='Train video model with cross entropy loss')
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# Datasets
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parser.add_argument('--root', type=str, default='data',
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help="root path to data directory")
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parser.add_argument('-d', '--dataset', type=str, default='mars',
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choices=data_manager.get_names())
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parser.add_argument('-j', '--workers', default=4, type=int,
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help="number of data loading workers (default: 4)")
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parser.add_argument('--height', type=int, default=256,
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help="height of an image (default: 256)")
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parser.add_argument('--width', type=int, default=128,
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help="width of an image (default: 128)")
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parser.add_argument('--seq-len', type=int, default=15,
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help="number of images to sample in a tracklet")
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# Optimization options
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parser.add_argument('--optim', type=str, default='adam',
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help="optimization algorithm (see optimizers.py)")
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parser.add_argument('--max-epoch', default=500, type=int,
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help="maximum epochs to run")
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parser.add_argument('--start-epoch', default=0, type=int,
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help="manual epoch number (useful on restarts)")
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parser.add_argument('--train-batch', default=32, type=int,
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help="train batch size")
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parser.add_argument('--test-batch', default=5, type=int,
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help="test batch size (number of tracklets)")
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parser.add_argument('--lr', '--learning-rate', default=0.0003, type=float,
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help="initial learning rate")
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parser.add_argument('--stepsize', default=[300, 400], nargs='+', type=int,
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help="stepsize to decay learning rate")
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parser.add_argument('--gamma', default=0.1, type=float,
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help="learning rate decay")
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parser.add_argument('--weight-decay', default=5e-04, type=float,
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help="weight decay (default: 5e-04)")
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parser.add_argument('--margin', type=float, default=0.3,
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help="margin for triplet loss")
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parser.add_argument('--num-instances', type=int, default=4,
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help="number of instances per identity")
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parser.add_argument('--htri-only', action='store_true',
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help="only use hard triplet loss (default: Fasle)")
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parser.add_argument('--lambda-xent', type=float, default=1,
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help="weight to balance cross entropy loss")
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parser.add_argument('--lambda-htri', type=float, default=1,
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help="weight to balance hard triplet loss")
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parser.add_argument('--label-smooth', action='store_true',
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help="use label smoothing regularizer in cross entropy loss")
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# Architecture
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parser.add_argument('-a', '--arch', type=str, default='resnet50', choices=models.get_names())
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parser.add_argument('--pool', type=str, default='avg', choices=['avg', 'max'])
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# Miscs
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parser.add_argument('--print-freq', type=int, default=10,
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help="print frequency")
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parser.add_argument('--seed', type=int, default=1,
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help="manual seed")
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parser.add_argument('--resume', type=str, default='', metavar='PATH')
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parser.add_argument('--load-weights', type=str, default='',
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help="load pretrained weights but ignores layers that don't match in size")
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parser.add_argument('--evaluate', action='store_true',
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help="evaluation only")
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parser.add_argument('--eval-step', type=int, default=-1,
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help="run evaluation for every N epochs (set to -1 to test after training)")
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parser.add_argument('--start-eval', type=int, default=0,
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help="start to evaluate after specific epoch")
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parser.add_argument('--save-dir', type=str, default='log')
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parser.add_argument('--use-cpu', action='store_true',
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help="use cpu")
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parser.add_argument('--gpu-devices', default='0', type=str,
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help='gpu device ids for CUDA_VISIBLE_DEVICES')
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parser.add_argument('--use-avai-gpus', action='store_true',
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help="use available gpus instead of specified devices (this is useful when using managed clusters)")
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parser.add_argument('--visualize-ranks', action='store_true',
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help="visualize ranked results, only available in evaluation mode (default: False)")
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# global variables
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args = parser.parse_args()
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best_rank1 = -np.inf
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def main():
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global args, best_rank1
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torch.manual_seed(args.seed)
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if not args.use_avai_gpus: os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
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use_gpu = torch.cuda.is_available()
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if args.use_cpu: use_gpu = False
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if not args.evaluate:
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sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
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else:
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sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
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print("==========\nArgs:{}\n==========".format(args))
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if use_gpu:
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print("Currently using GPU {}".format(args.gpu_devices))
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cudnn.benchmark = True
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torch.cuda.manual_seed_all(args.seed)
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else:
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print("Currently using CPU (GPU is highly recommended)")
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print("Initializing dataset {}".format(args.dataset))
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dataset = data_manager.init_vidreid_dataset(root=args.root, name=args.dataset)
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transform_train = T.Compose([
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T.Random2DTranslation(args.height, args.width),
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T.RandomHorizontalFlip(),
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T.ToTensor(),
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T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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transform_test = T.Compose([
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T.Resize((args.height, args.width)),
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T.ToTensor(),
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T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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pin_memory = True if use_gpu else False
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# decompose tracklets into images for image-based training
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new_train = []
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for img_paths, pid, camid in dataset.train:
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for img_path in img_paths:
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new_train.append((img_path, pid, camid))
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trainloader = DataLoader(
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ImageDataset(new_train, transform=transform_train),
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sampler=RandomIdentitySampler(new_train, args.train_batch, args.num_instances),
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batch_size=args.train_batch, num_workers=args.workers,
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pin_memory=pin_memory, drop_last=True,
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)
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queryloader = DataLoader(
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VideoDataset(dataset.query, seq_len=args.seq_len, sample='evenly', transform=transform_test),
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batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
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pin_memory=pin_memory, drop_last=False,
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)
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galleryloader = DataLoader(
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VideoDataset(dataset.gallery, seq_len=args.seq_len, sample='evenly', transform=transform_test),
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batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
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pin_memory=pin_memory, drop_last=False,
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)
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print("Initializing model: {}".format(args.arch))
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model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={'xent', 'htri'})
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print("Model size: {:.3f} M".format(count_num_param(model)))
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if args.label_smooth:
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criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
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else:
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criterion_xent = nn.CrossEntropyLoss()
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criterion_htri = TripletLoss(margin=args.margin)
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optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
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scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)
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if args.load_weights and check_isfile(args.load_weights):
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# load pretrained weights but ignore layers that don't match in size
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checkpoint = torch.load(args.load_weights)
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pretrain_dict = checkpoint['state_dict']
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model_dict = model.state_dict()
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pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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print("Loaded pretrained weights from '{}'".format(args.load_weights))
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if args.resume and check_isfile(args.resume):
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checkpoint = torch.load(args.resume)
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model.load_state_dict(checkpoint['state_dict'])
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args.start_epoch = checkpoint['epoch'] + 1
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best_rank1 = checkpoint['rank1']
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print("Loaded checkpoint from '{}'".format(args.resume))
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print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch, best_rank1))
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if use_gpu:
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model = nn.DataParallel(model).cuda()
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if args.evaluate:
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print("Evaluate only")
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distmat = test(model, queryloader, galleryloader, args.pool, use_gpu, return_distmat=True)
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if args.visualize_ranks:
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visualize_ranked_results(
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distmat, dataset,
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save_dir=osp.join(args.save_dir, 'ranked_results'),
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topk=20,
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)
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return
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start_time = time.time()
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train_time = 0
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best_epoch = args.start_epoch
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print("==> Start training")
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for epoch in range(args.start_epoch, args.max_epoch):
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start_train_time = time.time()
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train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu)
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train_time += round(time.time() - start_train_time)
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scheduler.step()
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if (epoch + 1) > args.start_eval and args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (epoch + 1) == args.max_epoch:
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print("==> Test")
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rank1 = test(model, queryloader, galleryloader, args.pool, use_gpu)
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is_best = rank1 > best_rank1
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if is_best:
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best_rank1 = rank1
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best_epoch = epoch + 1
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if use_gpu:
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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save_checkpoint({
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'state_dict': state_dict,
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'rank1': rank1,
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'epoch': epoch,
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}, is_best, osp.join(args.save_dir, 'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
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print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(best_rank1, best_epoch))
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elapsed = round(time.time() - start_time)
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elapsed = str(datetime.timedelta(seconds=elapsed))
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train_time = str(datetime.timedelta(seconds=train_time))
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print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
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def train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu):
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losses = AverageMeter()
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batch_time = AverageMeter()
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data_time = AverageMeter()
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model.train()
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end = time.time()
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for batch_idx, (imgs, pids, _) in enumerate(trainloader):
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data_time.update(time.time() - end)
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if use_gpu:
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imgs, pids = imgs.cuda(), pids.cuda()
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outputs, features = model(imgs)
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if args.htri_only:
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if isinstance(features, (tuple, list)):
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loss = DeepSupervision(criterion_htri, features, pids)
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else:
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loss = criterion_htri(features, pids)
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else:
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if isinstance(outputs, (tuple, list)):
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xent_loss = DeepSupervision(criterion_xent, outputs, pids)
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else:
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xent_loss = criterion_xent(outputs, pids)
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if isinstance(features, (tuple, list)):
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htri_loss = DeepSupervision(criterion_htri, features, pids)
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else:
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htri_loss = criterion_htri(features, pids)
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loss = args.lambda_xent * xent_loss + args.lambda_htri * htri_loss
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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batch_time.update(time.time() - end)
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losses.update(loss.item(), pids.size(0))
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if (batch_idx + 1) % args.print_freq == 0:
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print('Epoch: [{0}][{1}/{2}]\t'
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'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
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'Data {data_time.val:.4f} ({data_time.avg:.4f})\t'
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'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(
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epoch + 1, batch_idx + 1, len(trainloader), batch_time=batch_time,
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data_time=data_time, loss=losses))
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end = time.time()
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def test(model, queryloader, galleryloader, pool, use_gpu, ranks=[1, 5, 10, 20], return_distmat=False):
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batch_time = AverageMeter()
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model.eval()
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with torch.no_grad():
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qf, q_pids, q_camids = [], [], []
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for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
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if use_gpu: imgs = imgs.cuda()
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b, s, c, h, w = imgs.size()
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imgs = imgs.view(b*s, c, h, w)
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end = time.time()
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features = model(imgs)
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batch_time.update(time.time() - end)
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features = features.view(b, s, -1)
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if pool == 'avg':
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features = torch.mean(features, 1)
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else:
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features, _ = torch.max(features, 1)
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features = features.data.cpu()
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qf.append(features)
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q_pids.extend(pids)
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q_camids.extend(camids)
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qf = torch.cat(qf, 0)
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q_pids = np.asarray(q_pids)
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q_camids = np.asarray(q_camids)
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print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
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gf, g_pids, g_camids = [], [], []
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for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
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if use_gpu: imgs = imgs.cuda()
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b, s, c, h, w = imgs.size()
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imgs = imgs.view(b*s, c, h, w)
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end = time.time()
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features = model(imgs)
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batch_time.update(time.time() - end)
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features = features.view(b, s, -1)
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if pool == 'avg':
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features = torch.mean(features, 1)
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else:
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features, _ = torch.max(features, 1)
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features = features.data.cpu()
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gf.append(features)
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g_pids.extend(pids)
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g_camids.extend(camids)
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gf = torch.cat(gf, 0)
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g_pids = np.asarray(g_pids)
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g_camids = np.asarray(g_camids)
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print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
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print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch*args.seq_len))
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m, n = qf.size(0), gf.size(0)
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distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
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torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
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distmat.addmm_(1, -2, qf, gf.t())
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distmat = distmat.numpy()
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print("Computing CMC and mAP")
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cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
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print("Results ----------")
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print("mAP: {:.1%}".format(mAP))
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print("CMC curve")
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for r in ranks:
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print("Rank-{:<3}: {:.1%}".format(r, cmc[r-1]))
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print("------------------")
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if return_distmat:
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return distmat
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return cmc[0]
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if __name__ == '__main__':
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main()
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