def main(): with K.get_session(): """ Used for testing' """ train_name = 'with_bridge' use_bridge = True dataset = ClopemaLoader() m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(), dataset.n_landmark_cats(), use_bridge) display = Display(dataset, m.bb_size) data = dataset.fetch_data('val') data_x, data_y = data predictions = m.predict_combined(data_x, train_name) pred = m.loader.as_output(tuple(predictions)) gt_loss = m.loader.as_input(tuple(data)) display.show_landmark_stats(dataset.landmark_names, predictions, gt_loss) display.show_garment_stats(pred, data_y) display.history_charts(load_losses(train_name)) display.show_multiple_results( data_x, pred, annotations=data_y, rpn_ground_truths=m.get_rpn_ground_truths(data))
def main(): with K.get_session(): """ Used for testing' """ dataset = ClopemaLoader() plot = Display(dataset) m = TreeNet((224, 224, 3), dataset.n_garment_cats(), dataset.n_landmark_cats()) train_data, val_data = dataset.fetch_multiple_data(['train', 'val']) val_data = (filter_by_n_landmarks(filter_class(val_data, 0))) # plot.show_results(val_data[0], predictions=m.load_losses(val_data), annotations=val_data[1]) train_data = augment(filter_by_n_landmarks(filter_class(train_data, 0))) results = m.train_roi_pants('pants_overfit_again_night_1', train_data, val_data, n_epochs=10) plot.history_charts(results[0]) plot.show_results(val_data[0], predictions=results[1], annotations=val_data[1])
def main(): with K.get_session(): """ Used for testing' """ dataset = ClopemaLoader() m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(), dataset.n_landmark_cats(), False) display = Display(dataset, m.bb_size) train_data, val_data = dataset.fetch_multiple_data(['train', 'val']) # train_data = augment(train_data) results = m.train_global('global', train_data, val_data, n_epochs=40) display.history_charts(results[0])
def main(): with K.get_session(): """ Used for testing' """ dataset = ClopemaLoader() m = TridNet((224, 224, 3), dataset.n_garment_cats(), dataset.n_landmark_cats()) display = Display(dataset) data = dataset.fetch_data('train') pred = m.predict_rpns(data[0], 'train_rpn') display.history_charts(load_losses('train_rpn')) display.show_multiple_results( data[0], pred, annotations=data[1], rpn_ground_truths=m.get_rpn_ground_truths(data))
def main(): with K.get_session(): """ Used for testing' """ dataset = ClopemaLoader() plot = Display(dataset) m = TridNet((224, 224, 3), dataset.n_garment_cats(), dataset.n_landmark_cats()) train_data, val_data = dataset.fetch_multiple_data(['train', 'val']) results = m.train_roi('train_roi', train_data, val_data, n_epochs=10) plot.history_charts(results[0]) plot.show_results(val_data[0], results[1], val_data[1], ground_truths=results[2])
def main(): with K.get_session(): """ Used for testing' """ dataset = ClopemaLoader() m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(), dataset.n_landmark_cats()) display = Display(dataset, m.bb_size) data = dataset.fetch_data('val') predictions = m.predict_landmarks(data[0], 'landmarks_with_spacial_constraint', ground_truths=data) pred = m.loader.as_output(tuple(predictions) + (None, None)) gt_loss = m.loader.as_input(tuple(data)) display.show_landmark_stats(dataset.landmark_names, predictions, gt_loss) display.history_charts(load_losses('landmarks_with_spacial_constraint')) display.show_multiple_results(data[0], pred, annotations=data[1], rpn_ground_truths=m.get_rpn_ground_truths(data))
def main(): with K.get_session(): """ Used for testing' """ dataset = ClopemaLoader() m = SimpleNet(dataset.image_shape(), dataset.n_garment_cats(), dataset.n_landmark_cats(), False) display = Display(dataset, m.bb_size) data = dataset.fetch_data('val') data_x, data_y = data predictions = m.predict_global(data_x, 'global') pred = m.loader.as_output((None, None) + tuple(predictions)) display.show_garment_stats(pred, data_y) display.history_charts(load_losses('global')) display.show_multiple_results(data_x, pred, data_y)