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| from tags import tags | |
| class argHandler(dict): | |
| __getattr__ = dict.get | |
| __setattr__ = dict.__setitem__ | |
| __delattr__ = dict.__delitem__ | |
| _descriptions = {'help, --h, -h': 'show this super helpful message and exit'} | |
| def setDefaults(self): | |
| self.define('train_csv', './IU-XRay/training_set.csv', | |
| 'path to training csv containing the images names and the labels') | |
| self.define('test_csv', './IU-XRay/testing_set.csv', | |
| 'path to testing csv containing the images names and the labels') | |
| self.define('all_data_csv', './IU-XRay/all_data.csv', | |
| 'path to all data csv containing the images names and the labels') | |
| self.define('image_directory', './IU-XRay/images', | |
| 'path to folder containing the patient folders which containing the images') | |
| self.define('output_images_folder', './outputs/CDGPT2', | |
| 'path to folder containing output images') | |
| self.define('data_dir', './IU-XRay', | |
| 'path to folder containing the patient folders which containing the images') | |
| # self.define('visual_model_name', 'fine_tuned_chexnet', | |
| self.define('visual_model_name', 'fine_tuned_chexnet', | |
| 'path to folder containing the patient folders which containing the images') | |
| self.define('finetune_visual_model', False, | |
| 'option if you want to finetune the visual model') | |
| self.define('visual_model_pop_layers', 2, | |
| 'number of conv layers to pop to get visual features') | |
| self.define('csv_label_columns', ['Caption'], 'the name of the label columns in the csv') | |
| self.define('image_target_size', (224, 224, 3), 'the target size to resize the image') | |
| self.define('max_sequence_length', 200, | |
| 'Maximum number of words in a sentence') | |
| self.define('num_epochs', 100, 'maximum number of epochs') | |
| self.define('encoder_layers', [0.4], 'a list describing the hidden layers of the encoder. Example [10,0.4,5] will create a hidden layer with size 10 then dropout wth drop prob 0.4, then hidden layer with size 5. If empty it will connect to output nodes directly.') | |
| self.define('classifier_layers', [0.4], 'a list describing the hidden layers of the encoder. Example [10,0.4,5] will create a hidden layer with size 10 then dropout wth drop prob 0.4, then hidden layer with size 5. If empty it will connect to output nodes directly.') | |
| self.define('tags_threshold', -1, | |
| 'The threshold from which to detect a tag. -1 will multiply the tags embeddings according to prediction') | |
| self.define('tokenizer_vocab_size', 1001, | |
| 'The number of words to tokinze, the rest will be set as <unk>') | |
| self.define('batch_size', 16, 'batch size for training and testing') | |
| self.define('generator_workers', 8, 'The number of cpu workers generating batches.') | |
| self.define('beam_width', 7, 'The beam search width during evaluation') | |
| self.define('epochs_to_evaluate', 3, 'The number of epochs to train before evaluating on the test set.') | |
| self.define('generator_queue_length', 24, 'The maximum number of batches in the queue to be trained on.') | |
| self.define('ckpt_path', './checkpoints/CDGPT2/', | |
| 'where to save the checkpoints. The path will be created if it does not exist. The system saves every epoch by default') | |
| self.define('continue_from_last_ckpt', True, | |
| 'continue training from last ckpt or not') | |
| self.define('calculate_loss_after_epoch', False, | |
| 'if True it will calculate the train and test loss by passing over the data again and append it to losses_csv') | |
| self.define('learning_rate', 1e-3, 'The optimizer learning rate') | |
| self.define('optimizer_type', 'Adam', 'Choose from (Adam, SGD, RMSprop, Adagrad, Adadelta, Adamax, Nadam)') | |
| self.define('tags', tags, | |
| 'the names of the tags') | |
| def define(self, argName, default, description): | |
| self[argName] = default | |
| self._descriptions[argName] = description | |
| def help(self): | |
| print('Arguments:') | |
| spacing = max([len(i) for i in self._descriptions.keys()]) + 2 | |
| for item in self._descriptions: | |
| currentSpacing = spacing - len(item) | |
| print(' --' + item + (' ' * currentSpacing) + self._descriptions[item]) | |
| exit() | |