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import cv2import torchimport fractionsimport numpy as npfrom PIL import Imageimport torch.nn.functional as Ffrom torchvision import transformsfrom models.models import create_modelfrom options.test_options import TestOptionsdef lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0transformer = transforms.Compose([        transforms.ToTensor(),        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])    ])transformer_Arcface = transforms.Compose([        transforms.ToTensor(),        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])    ])detransformer = transforms.Compose([        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])    ])if __name__ == '__main__':    opt = TestOptions().parse()    start_epoch, epoch_iter = 1, 0    torch.nn.Module.dump_patches = True    model = create_model(opt)    model.eval()    with torch.no_grad():                pic_a = opt.pic_a_path        img_a = Image.open(pic_a).convert('RGB')        img_a = transformer_Arcface(img_a)        img_id = img_a.view(-1, img_a.shape[0], img_a.shape[1], img_a.shape[2])        pic_b = opt.pic_b_path        img_b = Image.open(pic_b).convert('RGB')        img_b = transformer(img_b)        img_att = img_b.view(-1, img_b.shape[0], img_b.shape[1], img_b.shape[2])        # convert numpy to tensor        img_id = img_id.cuda()        img_att = img_att.cuda()        #create latent id        img_id_downsample = F.interpolate(img_id, scale_factor=0.5)        latend_id = model.netArc(img_id_downsample)        latend_id = latend_id.detach().to('cpu')        latend_id = latend_id/np.linalg.norm(latend_id,axis=1,keepdims=True)        latend_id = latend_id.to('cuda')        ############## Forward Pass ######################        img_fake = model(img_id, img_att, latend_id, latend_id, True)        for i in range(img_id.shape[0]):            if i == 0:                row1 = img_id[i]                row2 = img_att[i]                row3 = img_fake[i]            else:                row1 = torch.cat([row1, img_id[i]], dim=2)                row2 = torch.cat([row2, img_att[i]], dim=2)                row3 = torch.cat([row3, img_fake[i]], dim=2)        #full = torch.cat([row1, row2, row3], dim=1).detach()        full = row3.detach()        full = full.permute(1, 2, 0)        output = full.to('cpu')        output = np.array(output)        output = output[..., ::-1]        output = output*255        cv2.imwrite(opt.output_path + 'result.jpg',output)
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