def test(model_path, data_path, target_tag, pair_num, save_result=False):
model = SiameseNetwork()
model.load_state_dict(
torch.load(model_path, map_location='cpu')['model_state_dict'])
result = []
for i in range(0, pair_num):
im1, im2, is_same, first_image, second_image = random_data_generator(
data_path, target_tag)
predict = model(im1.unsqueeze(0), im2.unsqueeze(0)).item()
result.append((first_image, second_image, predict, is_same))
if save_result:
plt.figure(figsize=(4, 12), dpi=300)
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=1.5)
for i, item in enumerate(result):
first_image, second_image, predict, is_same = item[0], item[
1], item[2], item[3]
img1 = Image.open(first_image)
img2 = Image.open(second_image)
img1 = img1.convert('RGB')
img2 = img2.convert('RGB')
plt.subplot(pair_num, 2, 2 * i + 1)
plt.axis('off')
plt.title('Label=%s, Prediction=%s' % (str(is_same), str(predict)),
fontsize=4)
plt.imshow(img1)
plt.subplot(pair_num, 2, 2 * i + 2)
plt.axis('off')
plt.title('Real Label=%s' % second_image.split('/')[-2],
fontsize=4)
plt.imshow(img2)
plt.savefig('./result.png', dpi=300)
return result
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-img', type=str, default='test.jpg')
# parser.add_argument('-ques', type=str, default='What vechile is in the picture?')
args = parser.parse_args()
img = args.img
folder_dataset_test = dset.ImageFolder(root=Config.testing_dir)
siamese_dataset = SiameseNetworkDataset(
imageFolderDataset=folder_dataset_test,
transform=transforms.Compose(
[transforms.Resize((100, 100)),
transforms.ToTensor()]),
should_invert=False)
img = Image.open(img)
img = img.convert("L")
transform = transforms.Compose(
[transforms.Resize((100, 100)),
transforms.ToTensor()])
img = transform(img)
# Add a dimension to image to make padding possible.
img = img[None, :, :, :]
test_dataloader = DataLoader(siamese_dataset, num_workers=3, batch_size=1)
dataiter = iter(test_dataloader)
net = SiameseNetwork()
net.load_state_dict(torch.load("trained_weights.pt"))
for i in range(4):
_, x1, label2 = next(dataiter)
concatenated = torch.cat((img, x1), 0)
output1, output2 = net(Variable(img), Variable(x1))
euclidean_distance = F.pairwise_distance(output1, output2)
imshow(
torchvision.utils.make_grid(concatenated), 'Dissimilarity: \
{:.2f}'.format(euclidean_distance.data.numpy()[0][0]))
from siamese_dataset import SiameseNetworkDataset
from siamese_network import SiameseNetwork
from helpers import imshow
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
class Config():
testing_dir = "/home/wingman2/datasets/personas/test/"
# testing_dir = "/home/wingman2/code/Facial-Similarity-with-Siamese-Networks-in-Pytorch/data/faces/testing/"
model = SiameseNetwork().cuda()
model.load_state_dict(torch.load('/home/wingman2/models/siamese-faces-160.pt'))
model.eval()
data_transforms_test = transforms.Compose(
[transforms.Resize((100, 100)),
transforms.ToTensor()])
folder_dataset_test = ImageFolder(root=Config.testing_dir)
siamese_dataset = SiameseNetworkDataset(imageFolderDataset=folder_dataset_test,
transform=data_transforms_test,
should_invert=False)
test_dataloader = DataLoader(siamese_dataset,
num_workers=8,
batch_size=1,
shuffle=True)
dataiter = iter(test_dataloader)