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Test.py
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# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import time
import os
import scipy.io
import yaml
from Debug import Test_Model, Train_Model
######################################################################
# Load Data
# ---------
#
# We will use torchvision and torch.utils.data packages for loading the
# data.
#
data_transforms = transforms.Compose([
transforms.Resize((256, 128)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
data_dir = './pytorch'
image_datasets = {x: datasets.ImageFolder(os.path.join(
data_dir, x), data_transforms) for x in ['gallery', 'query']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=64,
shuffle=False, num_workers=16) for x in ['gallery', 'query']}
class_names = image_datasets['query'].classes
use_gpu = torch.cuda.is_available()
######################################################################
# Load model
# ---------------------------
def load_network(network):
# TODO
save_path = './Model0_119.pth'
network.load_state_dict(torch.load(save_path))
return network
######################################################################
# 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(model, dataloaders, device):
features = torch.FloatTensor()
count = 0
for data in dataloaders:
img, label = data
n, c, h, w = img.size()
count += n
print(count)
ff = torch.FloatTensor(n, 2048).zero_()
for i in range(2):
if(i == 1):
img = fliplr(img)
input_img = img.to(device)
# if opt.fp16:
# input_img = input_img.half()
outputs = model(input_img)
f = outputs.data.cpu().float()
ff = ff+f
fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)
ff = ff.div(fnorm.expand_as(ff))
features = torch.cat((features, ff), 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]
camera = filename.split('c')[1]
if label[0:2] == '-1':
labels.append(-1)
else:
labels.append(int(label))
camera_id.append(int(camera[0]))
return camera_id, labels
gallery_path = image_datasets['gallery'].imgs
query_path = image_datasets['query'].imgs
gallery_cam, gallery_label = get_id(gallery_path)
query_cam, query_label = get_id(query_path)
######################################################################
# Load Collected data Trained model
print('-------test-----------')
model = Train_Model()
model = load_network(model)
model = Test_Model(model)
# Change to test mode
model = model.eval()
device = torch.device('cuda:1')
if use_gpu:
model = model.to(device)
# Extract feature
with torch.no_grad():
gallery_feature = extract_feature(model, dataloaders['gallery'], device)
query_feature = extract_feature(model, dataloaders['query'], device)
# Save to Matlab for check
result = {'gallery_f': gallery_feature.numpy(), 'gallery_label': gallery_label, 'gallery_cam': gallery_cam,
'query_f': query_feature.numpy(), 'query_label': query_label, 'query_cam': query_cam}
scipy.io.savemat('pytorch_result.mat', result)