import cv2 import torch import fractions import numpy as np from PIL import Image import torch.nn.functional as F from torchvision import transforms from models.models import create_model from options.test_options import TestOptions def lcm(a, b): return abs(a * b) / fractions.gcd(a, b) if a and b else 0 transformer = 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)