def main(): net = Net().to(setting.device) net.eval() net.load_state_dict(torch.load('model.pt')) print('model has been loaded.') correct = 0 valid_dataloader = data_preprocess.get_valid_dataloader() total = len(os.listdir(setting.valid_folder_path)) with torch.no_grad(): miss_character = {} for (imgs, labels) in tqdm((valid_dataloader)): imgs, labels = imgs.to(setting.device), labels.to(setting.device) labels_ohe_predict = net(imgs) # for each img in one batch for single in range(labels_ohe_predict.shape[0]): single_labels_ohe_predict = labels_ohe_predict[single, :] predict_label = '' # get predict_label for slice in range(setting.char_num): char = ohe.num2char[np.argmax( single_labels_ohe_predict[slice*setting.pool_length:(slice+1)*setting.pool_length].cpu().data.numpy())] predict_label += char # get true label true_label = ohe.decode(labels[single, :].cpu().numpy()) # print('true label:', true_label, ' predict label:', predict_label) if predict_label == true_label: correct += 1 else: for i in range(setting.char_num): if predict_label[i] != true_label[i]: error_info = '{} -> {}'.format(true_label[i], predict_label[i]) if error_info in miss_character: miss_character[error_info] +=1 else: miss_character[error_info] =1 sorted_miss = sorted(miss_character.items(), key=lambda kv:kv[1], reverse=True) sorted_miss=collections.OrderedDict(sorted_miss) with open('miss_character.txt','w') as f: for i in sorted_miss: f.write('{} : {}\n'.format(i, sorted_miss[i])) print('accuracy: {}/{} -- {:.4f}'.format(correct, total, correct/total))
animals_dataset = AnimalsDataset(filename='meta-data_new/meta-data/train.csv', root_dir='./train_new/train/', train=True, transform=data_transform1) test_dataset = AnimalsDataset(filename='meta-data_new/meta-data/test.csv', root_dir='./test_new/test', train=False, transform=data_transform1) test_loader = DataLoader(test_dataset, batch_size=60) use_cuda = torch.cuda.is_available() device = torch.device("cuda" if use_cuda else "cpu") print(device) net = Net().to(device) print(net) optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) net.load_state_dict(torch.load("./models/model_ep50.net")) net.eval() ####################### # Testing an image data_iter = iter(test_dataset) sample = next(data_iter) data, labels = sample['image'], sample['labels'] img = data img = torch.unsqueeze(img, 0) img = img.float().to(device) out = net(img) prediction = torch.nn.functional.softmax(out)
def process_img(original_image): processed_img = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY) #processed_img = cv2.Canny(processed_img, threshold1 = 100, threshold2 = 200) return processed_img def image_to_tensor(img): img = cv2.resize(img, dsize=(48, 48), interpolation=cv2.INTER_CUBIC) return torch.Tensor(img.reshape(1, 1, 48, 48)) model = Net() dir_path = os.path.dirname(os.path.realpath(__file__)) model.load_state_dict(torch.load(dir_path + '/models/current_model.pth')) curr_emotion = "Neutral" emotion_ave = [] last_time = time.time() while (True): #Grabs image from screen screen = np.array(ImageGrab.grab(bbox=(300, 300, 500, 500))) processed_img = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB) new_screen = process_img(screen) # Convert iamge to tensor and predict the emotion x = new_screen x = image_to_tensor(x) output = model(x)