""" Retrain the YOLO model for your own dataset. """ import numpy as np import keras.backend as K from keras.layers import Input, Lambda from keras.models import Model from keras.optimizers import Adam from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss from yolo3.utils import get_random_data from keras.utils import plot_model # plot model import argparse def _main(annotation_path, classes_path, output_model_path): # return annotation_path = annotation_path log_dir = 'logs/000/' classes_path = classes_path anchors_path = 'model_data/yolo_anchors.txt' class_names = get_classes(classes_path) num_classes = len(class_names) anchors = get_anchors(anchors_path) input_shape = (416,416) # multiple of 32, hw is_tiny_version = len(anchors)==6 # default setting if is_tiny_version: model = create_tiny_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5') else: model = create_model(input_shape, anchors, num_classes, freeze_body=2, weights_path='model_data/yolo_weights.h5') # make sure you know what you freeze # model.save('yolo_model_retrain.h5') # creates a HDF5 file 'my_model.h5' print(model.input) print(model.output) plot_model(model, to_file='model_data/retrained_model.png', show_shapes = True) logging = TensorBoard(log_dir=log_dir) checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5', monitor='val_loss', save_weights_only=True, save_best_only=True, period=3) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1) early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1) val_split = 0.1 with open(annotation_path) as f: lines = f.readlines() np.random.seed(10101) np.random.shuffle(lines) np.random.seed(None) num_val = int(len(lines)*val_split) num_train = len(lines) - num_val # Train with frozen layers first, to get a stable loss. # Adjust num epochs to your dataset. This step is enough to obtain a not bad model. if True: # model.compile(optimizer=Adam(lr=1e-3), loss={ # # use custom yolo_loss Lambda layer. # 'yolo_loss': lambda y_true, y_pred: y_pred}) model.compile(optimizer=Adam(lr=1e-3), loss='mean_squared_error') batch_size = 32 print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=50, initial_epoch=0, callbacks=[logging, checkpoint]) # model.save_weights(log_dir + 'trained_weights_stage_1.h5') # model.save(log_dir + 'trained_model_stage_1.h5') # Unfreeze and continue training, to fine-tune. # Train longer if the result is not good. if True: for i in range(len(model.layers)): model.layers[i].trainable = True model.compile(optimizer=Adam(lr=1e-4), loss='mean_squared_error') # recompile to apply the change print('Unfreeze all of the layers.') batch_size = 1 # note that more GPU memory is required after unfreezing the body print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size)) model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes), steps_per_epoch=max(1, num_train//batch_size), validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes), validation_steps=max(1, num_val//batch_size), epochs=100, initial_epoch=50, callbacks=[logging, checkpoint, reduce_lr, early_stopping]) # model.save_weights(log_dir + 'trained_weights_final.h5') # model.save(log_dir + 'trained_model_final.h5') # Further training if needed. # print('model.input = ',model.input) # print('len(model.layers) = ',len(model.layers)) # print('model.layers[-1]: ',model.layers[-1].output) # print('model.layers[-2]: ',model.layers[-2].output) # print('model.layers[-3]: ',model.layers[-3].output) # print('model.layers[-4]: ',model.layers[-4].output,'\n') # # original yolo model outputs: # print('model.layers[-5]: ',model.layers[-5].output) # print('model.layers[-6]: ',model.layers[-6].output) # print('model.layers[-7]: ',model.layers[-7].output) # save the derived model for detection(using yolo_video.py) derived_model = Model(model.input[0], [model.layers[249].output, model.layers[250].output, model.layers[251].output]) plot_model(derived_model, to_file=output_model_path[:-3]+'.png', show_shapes = True) derived_model.save(output_model_path) def get_classes(classes_path): '''loads the classes''' with open(classes_path) as f: class_names = f.readlines() class_names = [c.strip() for c in class_names] return class_names def get_anchors(anchors_path): '''loads the anchors from a file''' with open(anchors_path) as f: anchors = f.readline() anchors = [float(x) for x in anchors.split(',')] return np.array(anchors).reshape(-1, 2) def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/yolo_weights.h5'): '''create the training model''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) # y_true = [Input(shape=(416//{0:32, 1:16, 2:8}[l], 416//{0:32, 1:16, 2:8}[l], 9//3, 80+5)) for l in range(3)] y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], num_anchors//3, num_classes+5)) for l in range(3)] model_body = yolo_body(image_input, num_anchors//3, num_classes) print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze darknet53 body or freeze all but 3 output layers. num = (185, len(model_body.layers)-3)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) print('model_body.input: ', model_body.input) print('model.input: ', model.input) return model def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2, weights_path='model_data/tiny_yolo_weights.h5'): '''create the training model, for Tiny YOLOv3''' K.clear_session() # get a new session image_input = Input(shape=(None, None, 3)) h, w = input_shape num_anchors = len(anchors) y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \ num_anchors//2, num_classes+5)) for l in range(2)] model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes) print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes)) if load_pretrained: model_body.load_weights(weights_path, by_name=True, skip_mismatch=True) print('Load weights {}.'.format(weights_path)) if freeze_body in [1, 2]: # Freeze the darknet body or freeze all but 2 output layers. num = (20, len(model_body.layers)-2)[freeze_body-1] for i in range(num): model_body.layers[i].trainable = False print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers))) model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})( [*model_body.output, *y_true]) model = Model([model_body.input, *y_true], model_loss) return model def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes): '''data generator for fit_generator''' n = len(annotation_lines) i = 0 while True: image_data = [] box_data = [] for b in range(batch_size): if i==0: np.random.shuffle(annotation_lines) image, box = get_random_data(annotation_lines[i], input_shape, random=True) image_data.append(image) box_data.append(box) i = (i+1) % n image_data = np.array(image_data) # input of original yolo: image box_data = np.array(box_data) # output of original yolo: boxes y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) # some kind of output description?! yield [image_data, *y_true], np.zeros(batch_size) def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes): n = len(annotation_lines) if n==0 or batch_size<=0: return None return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("-a", "--annotation_path", type=str, default='test_data/training_data/annotation.txt', help="input annotation_path") parser.add_argument("-c", "--classes_path", type=str, default='test_data/training_data/pedestrian_classes.txt', help="input classes_path") parser.add_argument("-o", "--output_model_path", type=str, default='model_data/pedestrian_model.h5', help="input output_model_path") args = parser.parse_args() print('annotation_path = ', args.annotation_path) print('classes_path = ', args.classes_path) print('output_model_path = ', args.output_model_path) _main(args.annotation_path, args.classes_path, args.output_model_path)