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eval.py
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eval.py
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import argparse
import os, sys
import shutil
import time
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
from torch.utils.data import DataLoader
import torch.nn.functional as F
import os.path as osp
from torch.autograd import Variable
import math
from networks.resnet import resnet50, resnet101
from dataset.dataset import VeriDataset
parser = argparse.ArgumentParser(description='PyTorch Relationship')
parser.add_argument('querypath', metavar='DIR', help='path to query set')
parser.add_argument('querylist', metavar='DIR', help='path to query list')
parser.add_argument('gallerypath', metavar='DIR', help='path to gallery set')
parser.add_argument('gallerylist', metavar='DIR', help='path to gallery list')
parser.add_argument('--dataset', default='veri', type=str,
help='dataset name (default: veri)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (defult: 4)')
parser.add_argument('--batch_size', '--batch-size', default=1, type=int, metavar='N',
help='mini-batch size (default: 1)')
parser.add_argument('-n', '--num_classes', default=576, type=int, metavar='N',
help='number of classes / categories')
parser.add_argument('--backbone', default='resnet50', type=str,
help='backbone network resnet50 or resnet101 (default: resnet50)')
parser.add_argument('--weights', default='', type=str, metavar='PATH',
help='path to weights (default: none)')
parser.add_argument('--scale-size',default=224, type=int,
help='input size')
parser.add_argument('--crop_size',default=224, type=int,
help='crop size')
parser.add_argument('--save_dir',default='./results/', type=str,
help='save_dir')
parser.add_argument('--TopK',default=100, type=int,
help='save top K indexes of results for each query (default: 100)')
def get_dataset(dataset_name, query_dir, query_list, gallery_dir, gallery_list):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
scale_size = args.scale_size
crop_size = args.crop_size
if dataset_name == 'veri':
data_transform = transforms.Compose([
transforms.Scale((scale_size,scale_size)),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop((crop_size,crop_size)),
transforms.ToTensor(),
normalize])
query_set = VeriDataset(query_dir, query_list, data_transform, is_train=False )
gallery_set = VeriDataset(gallery_dir, gallery_list, data_transform, is_train=False )
query_loader = DataLoader(dataset=query_set, num_workers=args.workers,
batch_size=args.batch_size, shuffle=False)
gallery_loader = DataLoader(dataset=gallery_set, num_workers=args.workers,
batch_size=args.batch_size, shuffle=False)
return query_loader, gallery_loader
def main():
global args
args = parser.parse_args()
print (args)
# Create dataloader
print ('====> Creating dataloader...')
query_dir = args.querypath
query_list = args.querylist
gallery_dir = args.gallerypath
gallery_list = args.gallerylist
dataset_name = args.dataset
query_loader, gallery_loader = get_dataset(dataset_name, query_dir, query_list, gallery_dir, gallery_list)
# load network
if args.backbone == 'resnet50':
model = resnet50(num_classes=args.num_classes)
elif args.backbone == 'resnet101':
model = resnet101(num_classes=args.num_classes)
print(args.weights)
if args.weights != '':
try:
model = torch.nn.DataParallel(model)
ckpt = torch.load(args.weights)
model.load_state_dict(ckpt['state_dict'])
print ('!!!load weights success !!! path is ', args.weights)
except Exception as e:
print ('!!!load weights failed !!! path is ', args.weights)
return
else:
print('!!!Load Weights PATH ERROR!!!')
return
model.cuda()
mkdir_if_missing(args.save_dir)
cudnn.benchmark = True
evaluate(query_loader, gallery_loader, model)
return
def evaluate(query_loader, gallery_loader, model):
print('Start evaluation...')
query_feats = []
query_pids = []
query_camids = []
gallery_feats = []
gallery_pids = []
gallery_camids = []
end = time.time()
# switch to eval mode
model.eval()
print('Processing query set...')
queryN = 0
for i, (image, pid, camid) in enumerate(query_loader):
# if i == 10:
# break
print('Extracting feature of image '+'%d:'%i)
query_pids.append(pid)
query_camids.append(camid)
image = torch.autograd.Variable(image).cuda()
output, feat = model(image)
query_feats.append(feat.data.cpu())
queryN = queryN+1
query_time = time.time()-end
end = time.time()
print('Processing query set... \tTime[{0:.3f}]'.format(query_time))
print('Processing gallery set...')
galleryN = 0
for i, (image, pid, camid) in enumerate(gallery_loader):
# if i == 20:
# break
print('Extracting feature of image '+'%d:'%i)
gallery_pids.append(pid)
gallery_camids.append(camid)
image = torch.autograd.Variable(image).cuda()
output, feat = model(image)
gallery_feats.append(feat.data.cpu())
galleryN = galleryN+1
gallery_time = time.time()-end
print('Processing gallery set... \tTime[{0:.3f}]'.format(gallery_time))
print('Computing CMC and mAP...')
cmc, mAP, distmat = compute(query_feats, query_pids, query_camids, gallery_feats, gallery_pids, gallery_camids)
print('Saving distmat...')
np.save(args.save_dir+'distmat.npy', np.asarray(distmat))
np.savetxt(args.save_dir+'distmat.txt', np.asarray(distmat), fmt='%.4f')
print('mAP = '+'%.4f'%mAP+'\tRank-1 = '+'%.4f'%cmc[0])
def compute(query_feats, query_pids, query_camids, gallery_feats, gallery_pids, gallery_camids):
# query
qf = torch.cat(query_feats, dim=0)
q_pids = np.asarray(query_pids)
q_camids = np.asarray(query_camids).T
# gallery
gf = torch.cat(gallery_feats, dim=0)
g_pids = np.asarray(gallery_pids)
g_camids = np.asarray(gallery_camids).T
m, n = qf.shape[0], gf.shape[0]
qf = qf.view(m, -1)
gf = gf.view(n, -1)
print('Saving feature mat...')
np.save(args.save_dir+'queryFeat.npy', qf)
np.save(args.save_dir+'galleryFeat.npy', gf)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.cpu().numpy()
q_camids = np.squeeze(q_camids)
g_camids = np.squeeze(g_camids)
cmc, mAP = eval_func(distmat, q_pids, g_pids, q_camids, g_camids)
return cmc, mAP, distmat
def eval_func(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=100):
"""Evaluation with market1501 metric
Key: for each query identity, its gallery images from the same camera view are discarded.
"""
num_q, num_g = distmat.shape
max_rank = args.TopK
if num_g < max_rank:
max_rank = num_g
print("Note: number of gallery samples is quite small, got {}".format(num_g))
indices = np.argsort(distmat, axis=1)
matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
print('Saving resulting indexes...', indices.shape)
np.save(args.save_dir+'result.npy', indices[:, :args.TopK]+1)
np.savetxt(args.save_dir+'result.txt', indices[:, :args.TopK]+1, fmt='%d')
# compute cmc curve for each query
all_cmc = []
all_AP = []
num_valid_q = 0. # number of valid query
for q_idx in range(num_q):
# get query pid and camid
q_pid = q_pids[q_idx]
q_camid = q_camids[q_idx]
# remove gallery samples that have the same pid and camid with query
order = indices[q_idx]
remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
keep = np.invert(remove)
# compute cmc curve
# binary vector, positions with value 1 are correct matches
orig_cmc = matches[q_idx][keep]
if not np.any(orig_cmc):
# this condition is true when query identity does not appear in gallery
continue
cmc = orig_cmc.cumsum()
cmc[cmc > 1] = 1
all_cmc.append(cmc[:max_rank])
num_valid_q += 1.
# compute average precision
# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
num_rel = orig_cmc.sum()
tmp_cmc = orig_cmc.cumsum()
tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
AP = tmp_cmc.sum() / num_rel
all_AP.append(AP)
assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
all_cmc = np.asarray(all_cmc).astype(np.float32)
all_cmc = all_cmc.sum(0) / num_valid_q
mAP = np.mean(all_AP)
return all_cmc, mAP
def mkdir_if_missing(dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
if __name__=='__main__':
main()