break if opt.data_type == 16: data['label'] = data['label'].half() data['inst'] = data['inst'].half() elif opt.data_type == 8: data['label'] = data['label'].uint8() data['inst'] = data['inst'].uint8() if opt.export_onnx: print ("Exporting to ONNX: ", opt.export_onnx) assert opt.export_onnx.endswith("onnx"), "Export model file should end with .onnx" torch.onnx.export(model, [data['label'], data['inst']], opt.export_onnx, verbose=True) exit(0) minibatch = 1 if opt.engine: generated = run_trt_engine(opt.engine, minibatch, [data['label'], data['inst']]) elif opt.onnx: generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['label'], data['inst']]) else: generated = model.inference(data['label'], data['inst'], data['image']) visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)), ('synthesized_image', util.tensor2im(generated.data[0]))]) img_path = data['path'] print('process image... %s' % img_path) visualizer.save_images(webpage, visuals, img_path) print("----{}s seconds----".format(time.time() - start_time)) webpage.save()
for i, data in enumerate(dataset): if i >= opt.how_many: break if opt.data_type == 16: data['label'] = data['label'].half() data['inst'] = data['inst'].half() elif opt.data_type == 8: data['label'] = data['label'].uint8() data['inst'] = data['inst'].uint8() if opt.export_onnx: print ("Exporting to ONNX: ", opt.export_onnx) assert opt.export_onnx.endswith("onnx"), "Export model file should end with .onnx" torch.onnx.export(model, [data['label'], data['inst']], opt.export_onnx, verbose=True) exit(0) minibatch = 1 if opt.engine: generated = run_trt_engine(opt.engine, minibatch, [data['label'], data['inst']]) elif opt.onnx: generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['label'], data['inst']]) else: generated = model.inference(data['label'], data['inst']) visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)), ('synthesized_image', util.tensor2im(generated.data[0]))]) img_path = data['path'] print('process image... %s' % img_path) visualizer.save_images(webpage, visuals, img_path) webpage.save()
def infer(n, image_label_path, image_inst_path): opt = TestOptions().parse(save=False) opt.nThreads = 1 # test code only supports nThreads = 1 opt.batchSize = 1 # test code only supports batchSize = 1 opt.serial_batches = True # no shuffle opt.no_flip = True # no flip opt.name = "label2city_1024p" opt.netG = "local" opt.ngf = 32 opt.resize_or_crop = "none" data_loader = CreateOneDataLoader(opt) dataset = data_loader.load_data(image_label_path, image_inst_path) visualizer = Visualizer(opt) # create website #web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch)) #webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch)) # test if not opt.engine and not opt.onnx: model = create_model(opt) if opt.data_type == 16: model.half() elif opt.data_type == 8: model.type(torch.uint8) if opt.verbose: print(model) else: from run_engine import run_trt_engine, run_onnx for i, data in enumerate(dataset): if i >= opt.how_many: break if opt.data_type == 16: data['label'] = data['label'].half() data['inst'] = data['inst'].half() elif opt.data_type == 8: data['label'] = data['label'].uint8() data['inst'] = data['inst'].uint8() if opt.export_onnx: print("Exporting to ONNX: ", opt.export_onnx) assert opt.export_onnx.endswith( "onnx"), "Export model file should end with .onnx" torch.onnx.export(model, [data['label'], data['inst']], opt.export_onnx, verbose=True) exit(0) minibatch = 1 if opt.engine: generated = run_trt_engine(opt.engine, minibatch, [data['label'], data['inst']]) elif opt.onnx: generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['label'], data['inst']]) else: generated = model.inference(data['label'], data['inst']) visuals = OrderedDict([ ('input_label', util.tensor2label(data['label'][0], opt.label_nc)), ('synthesized_image', util.tensor2im(generated.data[0])) ]) img_path = data['path'] print('process image... %s' % img_path) visualizer.save_image(visuals, n)
data['sketch_deform'] = data['sketch_deform'].uint8() data['photo'] = data['photo'].uint8() if opt.export_onnx: print("Exporting to ONNX: ", opt.export_onnx) assert opt.export_onnx.endswith( "onnx"), "Export model file should end with .onnx" torch.onnx.export( model, [data['sketch'], data['sketch_deform'], data['photo']], opt.export_onnx, verbose=True) exit(0) minibatch = 1 if opt.engine: generated = run_trt_engine(opt.engine, minibatch, [data['sketch'], data['photo']]) elif opt.onnx: generated = run_onnx(opt.onnx, opt.data_type, minibatch, [data['sketch'], data['photo']]) else: model_output = model.inference(data['sketch'], data['photo']) visuals = OrderedDict([ ('sketch', util.tensor2label(data['sketch'][0], opt.label_nc)), ('synthesized', util.tensor2im(model_output['fake_image'].data[0])), ]) img_path = data['path'] print('process image... %s' % img_path) race_str = data_loader.dataset.getLabelEncoder().inverse_transform(