def optimize(args): """ Gatys et al. CVPR 2017 ref: Image Style Transfer Using Convolutional Neural Networks """ # load the content and style target content_image = utils.tensor_load_rgbimage(args.content_image, size=args.content_size, keep_asp=True) content_image = content_image.unsqueeze(0) content_image = Variable(utils.preprocess_batch(content_image), requires_grad=False) utils.subtract_imagenet_mean_batch(content_image) style_image = utils.tensor_load_rgbimage(args.style_image, size=args.style_size) style_image = style_image.unsqueeze(0) style_image = Variable(utils.preprocess_batch(style_image), requires_grad=False) utils.subtract_imagenet_mean_batch(style_image) # load the pre-trained vgg-16 and extract features vgg = Vgg16() utils.init_vgg16(args.vgg_model_dir) vgg.load_state_dict(torch.load(os.path.join(args.vgg_model_dir, "vgg16.weight"))) if args.cuda: content_image = content_image.cuda() style_image = style_image.cuda() vgg.cuda() features_content = vgg(content_image) f_xc_c = Variable(features_content[1].data, requires_grad=False) features_style = vgg(style_image) gram_style = [utils.gram_matrix(y) for y in features_style] # init optimizer output = Variable(content_image.data, requires_grad=True) optimizer = Adam([output], lr=args.lr) mse_loss = torch.nn.MSELoss() # optimizing the images for e in range(args.iters): utils.add_imagenet_mean_batch(output) output.data.clamp_(0, 255) utils.subtract_imagenet_mean_batch(output) optimizer.zero_grad() features_y = vgg(output) content_loss = args.content_weight * mse_loss(features_y[1], f_xc_c) style_loss = 0. for m in range(len(features_y)): gram_y = utils.gram_matrix(features_y[m]) gram_s = Variable(gram_style[m].data, requires_grad=False) style_loss += args.style_weight * mse_loss(gram_y, gram_s) total_loss = content_loss + style_loss if (e + 1) % args.log_interval == 0: print(total_loss.data.cpu().numpy()[0]) total_loss.backward() optimizer.step() # save the image utils.tensor_save_bgrimage(output.data[0], args.output_image, args.cuda)
def optimize(args): """ Gatys et al. CVPR 2017 ref: Image Style Transfer Using Convolutional Neural Networks """ if args.cuda: ctx = mx.gpu(0) else: ctx = mx.cpu(0) # load the content and style target content_image = utils.tensor_load_rgbimage(args.content_image, ctx, size=args.content_size, keep_asp=True) content_image = utils.subtract_imagenet_mean_preprocess_batch( content_image) style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size) style_image = utils.subtract_imagenet_mean_preprocess_batch(style_image) # load the pre-trained vgg-16 and extract features vgg = net.Vgg16() utils.init_vgg_params(vgg, 'models', ctx=ctx) # content feature f_xc_c = vgg(content_image)[1] # style feature features_style = vgg(style_image) gram_style = [net.gram_matrix(y) for y in features_style] # output output = Parameter('output', shape=content_image.shape) output.initialize(ctx=ctx) output.set_data(content_image) # optimizer trainer = gluon.Trainer([output], 'adam', {'learning_rate': args.lr}) mse_loss = gluon.loss.L2Loss() # optimizing the images for e in range(args.iters): utils.imagenet_clamp_batch(output.data(), 0, 255) # fix BN for pre-trained vgg with autograd.record(): features_y = vgg(output.data()) content_loss = 2 * args.content_weight * mse_loss( features_y[1], f_xc_c) style_loss = 0. for m in range(len(features_y)): gram_y = net.gram_matrix(features_y[m]) gram_s = gram_style[m] style_loss = style_loss + 2 * args.style_weight * mse_loss( gram_y, gram_s) total_loss = content_loss + style_loss total_loss.backward() trainer.step(1) if (e + 1) % args.log_interval == 0: print('loss:{:.2f}'.format(total_loss.asnumpy()[0])) # save the image output = utils.add_imagenet_mean_batch(output.data()) utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)
def optimize(args): """ Gatys et al. CVPR 2017 ref: Image Style Transfer Using Convolutional Neural Networks """ if args.cuda: ctx = mx.gpu(0) else: ctx = mx.cpu(0) # load the content and style target content_image = utils.tensor_load_rgbimage(args.content_image,ctx, size=args.content_size, keep_asp=True) content_image = utils.subtract_imagenet_mean_preprocess_batch(content_image) style_image = utils.tensor_load_rgbimage(args.style_image, ctx, size=args.style_size) style_image = utils.subtract_imagenet_mean_preprocess_batch(style_image) # load the pre-trained vgg-16 and extract features vgg = net.Vgg16() utils.init_vgg_params(vgg, 'models', ctx=ctx) # content feature f_xc_c = vgg(content_image)[1] # style feature features_style = vgg(style_image) gram_style = [net.gram_matrix(y) for y in features_style] # output output = Parameter('output', shape=content_image.shape) output.initialize(ctx=ctx) output.set_data(content_image) # optimizer trainer = gluon.Trainer([output], 'adam', {'learning_rate': args.lr}) mse_loss = gluon.loss.L2Loss() # optimizing the images for e in range(args.iters): utils.imagenet_clamp_batch(output.data(), 0, 255) # fix BN for pre-trained vgg with autograd.record(): features_y = vgg(output.data()) content_loss = 2 * args.content_weight * mse_loss(features_y[1], f_xc_c) style_loss = 0. for m in range(len(features_y)): gram_y = net.gram_matrix(features_y[m]) gram_s = gram_style[m] style_loss = style_loss + 2 * args.style_weight * mse_loss(gram_y, gram_s) total_loss = content_loss + style_loss total_loss.backward() trainer.step(1) if (e + 1) % args.log_interval == 0: print('loss:{:.2f}'.format(total_loss.asnumpy()[0])) # save the image output = utils.add_imagenet_mean_batch(output.data()) utils.tensor_save_bgrimage(output[0], args.output_image, args.cuda)