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test_by_patches.py
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test_by_patches.py
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"""
Copyright (c) 2018, National Institute of Informatics
All rights reserved.
Author: Huy H. Nguyen
-----------------------------------------------------
Script for testing Capsule-Forensics on large images using patch aggregation strategy
"""
import sys
sys.setrecursionlimit(15000)
import os
import torch
import numpy as np
from torch.autograd import Variable
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
from tqdm import tqdm
import argparse
from sklearn import metrics
import math
import model
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', default ='datasets/cgvsphotos', help='path to dataset')
parser.add_argument('--test_set', default ='test', help='path to test dataset')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=0)
parser.add_argument('--batchSize', type=int, default=10, help='batch size')
parser.add_argument('--imageSize', type=int, default=100, help='the height / width of the input image to network')
parser.add_argument('--gpu_id', type=int, default=0, help='GPU ID')
parser.add_argument('--outf', default='checkpoints/cgvsphotos', help='folder to output images and model checkpoints')
parser.add_argument('--random_sample', type=int, default=0, help='number of random sample to test')
parser.add_argument('--random', action='store_true', default=False, help='enable randomness for routing matrix')
parser.add_argument('--id', type=int)
opt = parser.parse_args()
print(opt)
if __name__ == '__main__':
text_writer = open(os.path.join(opt.outf, 'test_by_patches.txt'), 'w')
transform_fwd = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
# folder dataset
dataset_test = dset.ImageFolder(root=os.path.join(opt.dataset, opt.test_set), transform=transform_fwd)
assert dataset_test
dataloader_test = torch.utils.data.DataLoader(dataset_test, batch_size=1, shuffle=False, num_workers=int(opt.workers))
def extract_subimages(image, subimage_size=100):
subimages = []
width = image.shape[3]
height = image.shape[2]
current_height = 0
while current_height + subimage_size <= height:
current_width = 0
while current_width + subimage_size <= width:
sub = image[:,:,current_height:current_height+subimage_size, current_width:current_width+subimage_size]
subimages.append(sub)
current_width += subimage_size
current_height += subimage_size
return subimages
vgg_ext = model.VggExtractor()
model = model.CapsuleNet(opt.gpu_id)
model.load_state_dict(torch.load(os.path.join(opt.outf,'capsule_' + str(opt.id) + '.pt')))
model.eval()
if opt.gpu_id >= 0:
vgg_ext.cuda(opt.gpu_id)
model.cuda(opt.gpu_id)
##################################################################################
predict_lst = np.array([], dtype=np.float)
labels_lst = np.array([], dtype=np.float)
for img_data, labels_data in dataloader_test:
img_label = labels_data.numpy().astype(np.float)
subimages = extract_subimages(img_data, opt.imageSize)
prob = np.array([[0.0, 0.0]])
n_sub_imgs = len(subimages)
if (opt.random_sample > 0):
if n_sub_imgs > opt.random_sample:
np.random.shuffle(subimages)
n_sub_imgs = opt.random_sample
img_tmp = torch.FloatTensor([]).view(0, 3, opt.imageSize, opt.imageSize)
for i in range(n_sub_imgs):
img_tmp = torch.cat((img_tmp, subimages[i]), dim=0)
if opt.gpu_id >= 0:
img_tmp = img_tmp.cuda(opt.gpu_id)
input_v = Variable(img_tmp, requires_grad = False)
x = vgg_ext(input_v)
classes, class_ = model(x, random=opt.random)
output_pred = class_.data.cpu().numpy()
else:
batchSize = opt.batchSize
steps = int(math.ceil(n_sub_imgs*1.0/batchSize))
output_pred = np.array([], dtype=np.float).reshape(0,2)
for i in range(steps):
img_tmp = torch.FloatTensor([]).view(0, 3, opt.imageSize, opt.imageSize)
end = (i + 1)*batchSize
if end > n_sub_imgs:
end = n_sub_imgs - i*batchSize
else:
end = batchSize
for j in range(end):
img_tmp = torch.cat((img_tmp, subimages[i*batchSize + j]), dim=0)
if opt.gpu_id >= 0:
img_tmp = img_tmp.cuda(opt.gpu_id)
input_v = Variable(img_tmp, requires_grad = False)
x = vgg_ext(input_v)
classes, class_ = model(x, random=opt.random)
output_p = class_.data.cpu().numpy()
output_pred = np.concatenate((output_pred, output_p), axis=0)
output_pred = output_pred.mean(0)
if output_pred[1] >= output_pred[0]:
pred = 1.0
else:
pred = 0.0
text_writer.write('%d,%.2f\n' % (img_label, output_pred[1]))
predict_lst = np.concatenate((predict_lst, np.array([pred])), axis=0)
labels_lst = np.concatenate((labels_lst, img_label), axis=0)
acc = metrics.accuracy_score(labels_lst, predict_lst)
print(len(predict_lst))
print('Test accuracy: %.4f' % (acc))
text_writer.flush()
text_writer.close()