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test.py
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test.py
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# -*- coding: utf-8 -*-
from __future__ import print_function, division
import argparse
import os
import scipy.io
import yaml
import torch
from torchvision import datasets, transforms
from model import ft_net_dense, ft_net, DisentangleNet
from model import load_network, load_whole_network
######################################################################
# Options
# --------
parser = argparse.ArgumentParser(description='Testing')
parser.add_argument('--which_epoch', default='last', type=str, help='0,1,2,3...or last')
parser.add_argument('--test_dir', default='duke', type=str, help='./test_data')
parser.add_argument('--name', default='', type=str, help='save model path')
parser.add_argument('--batchsize', default=64, type=int, help='batchsize')
parser.add_argument('--net_loss_model', default=1, type=int, help='net_loss_model')
parser.add_argument('--domain_num', default=5, type=int, help='domain_num')
parser.add_argument('--gpu', type=str, default='0', help='GPU id to use.')
opt = parser.parse_args()
print('opt = %s' % opt)
def test_function(test_dir=None, net_loss_model=None, domain_num=None, which_epoch=None):
if test_dir != None:
opt.test_dir = test_dir
if net_loss_model != None:
opt.net_loss_model = net_loss_model
if domain_num != None:
opt.domain_num = domain_num
if which_epoch != None:
opt.which_epoch = which_epoch
print('opt.which_epoch = %s' % opt.which_epoch)
print('opt.test_dir = %s' % opt.test_dir)
print('opt.name = %s' % opt.name)
print('opt.batchsize = %s' % opt.batchsize)
name = opt.name
data_dir = os.path.join('data', opt.test_dir, 'pytorch')
print('data_dir = %s' % data_dir)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
######################################################################
# Load Data
# ---------
data_transforms = transforms.Compose([
transforms.Resize((256, 128), interpolation=3),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dataset_list = ['gallery', 'query']
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms) for x in dataset_list}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=opt.batchsize,
shuffle=False, num_workers=0) for x in dataset_list}
class_names = image_datasets[dataset_list[1]].classes
use_gpu = torch.cuda.is_available()
######################################################################
# Extract feature
# ----------------------
#
# Extract feature from a trained model.
#
def fliplr(img):
'''flip horizontal'''
inv_idx = torch.arange(img.size(3) - 1, -1, -1).long() # N x C x H x W
img_flip = img.index_select(3, inv_idx)
return img_flip
def extract_feature_original(model, dataloaders):
features = torch.FloatTensor()
for data in dataloaders:
img, label = data
n, c, h, w = img.size()
ff_d = torch.FloatTensor(n, 512).zero_().cuda()
for i in range(2):
if (i == 1):
img = fliplr(img)
input_img = img.cuda()
ret = model(input_img)
did_outputs = ret[1]
# did_outputs = ret[5]
ff_d = ff_d + did_outputs
# norm feature
fnorm = torch.norm(ff_d, p=2, dim=1, keepdim=True)
ff_d = ff_d.div(fnorm.expand_as(ff_d))
ff_d = ff_d.detach().cpu().float()
features = torch.cat((features, ff_d), 0)
return features
def extract_feature(model, dataloaders):
features = torch.FloatTensor()
for data in dataloaders:
img, label = data
n, c, h, w = img.size()
ff_d = torch.FloatTensor(n, 512).zero_().cuda()
ff_s = torch.FloatTensor(n, 512).zero_().cuda()
for i in range(2):
if (i == 1):
img = fliplr(img)
input_img = img.cuda()
did_outputs = model(input_img)[1]
sid_outputs = model(input_img)[3]
ff_d = ff_d + did_outputs
ff_s = ff_s + sid_outputs
# norm feature
fnorm = torch.norm(ff_d, p=2, dim=1, keepdim=True)
ff_d = ff_d.div(fnorm.expand_as(ff_d))
ff_d = ff_d.detach().cpu().float()
fnorm = torch.norm(ff_s, p=2, dim=1, keepdim=True)
ff_s = ff_s.div(fnorm.expand_as(ff_s))
ff_s = ff_s.detach().cpu().float()
features = torch.cat((features, torch.cat((ff_d, ff_s), 1)), 0)
return features
def get_id(img_path):
camera_id = []
labels = []
for path, v in img_path:
# filename = path.split('/')[-1]
filename = os.path.basename(path)
label = filename[0:4]
if 'msmt' in opt.test_dir:
camera = filename[9:11]
else:
camera = filename.split('c')[1][0]
if label[0:2] == '-1':
labels.append(-1)
else:
labels.append(int(label))
camera_id.append(int(camera))
return camera_id, labels
dataset_path = []
for i in range(len(dataset_list)):
dataset_path.append(image_datasets[dataset_list[i]].imgs)
dataset_cam = []
dataset_label = []
for i in range(len(dataset_list)):
cam, label = get_id(dataset_path[i])
dataset_cam.append(cam)
dataset_label.append(label)
######################################################################
# Load Collected data Trained model
print('---------test-----------')
class_num = len(os.listdir(os.path.join(data_dir, 'train_all_new')))
sid_num = class_num
did_num = class_num * opt.domain_num
did_embedding_net = ft_net(id_num=did_num)
sid_embedding_net = ft_net(id_num=sid_num)
model = DisentangleNet(did_embedding_net, sid_embedding_net)
if use_gpu:
model.cuda()
if 'best' in opt.which_epoch or 'last' in opt.which_epoch:
model = load_whole_network(model, name, opt.which_epoch + '_' + str(opt.net_loss_model))
else:
model = load_whole_network(model, name, opt.which_epoch)
model = model.eval()
if use_gpu:
model = model.cuda()
# Extract feature
dataset_feature = []
with torch.no_grad():
for i in range(len(dataset_list)):
dataset_feature.append(extract_feature(model, dataloaders[dataset_list[i]]))
result = {'gallery_f': dataset_feature[0].numpy(), 'gallery_label': dataset_label[0], 'gallery_cam': dataset_cam[0],
'query_f': dataset_feature[1].numpy(), 'query_label': dataset_label[1], 'query_cam': dataset_cam[1]}
scipy.io.savemat('pytorch_result.mat', result)
if __name__ == '__main__':
test_function(test_dir=opt.test_dir, net_loss_model=opt.net_loss_model, domain_num=opt.domain_num,
which_epoch=opt.which_epoch)