""" Retrain the YOLO model for your own dataset. """ import os 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 def _main(): annotation_path = 'train.txt' log_dir = 'logs/000/' classes_path = 'model_data/coco_classes.txt' 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 model, bottleneck_model, last_layer_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 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: # perform bottleneck training if not os.path.isfile("bottlenecks.npz"): print("calculating bottlenecks") batch_size=8 bottlenecks=bottleneck_model.predict_generator(data_generator_wrapper(lines, batch_size, input_shape, anchors, num_classes, random=False, verbose=True), steps=(len(lines)//batch_size)+1, max_queue_size=1) np.savez("bottlenecks.npz", bot0=bottlenecks[0], bot1=bottlenecks[1], bot2=bottlenecks[2]) # load bottleneck features from file dict_bot=np.load("bottlenecks.npz") bottlenecks_train=[dict_bot["bot0"][:num_train], dict_bot["bot1"][:num_train], dict_bot["bot2"][:num_train]] bottlenecks_val=[dict_bot["bot0"][num_train:], dict_bot["bot1"][num_train:], dict_bot["bot2"][num_train:]] # train last layers with fixed bottleneck features batch_size=8 print("Training last layers with bottleneck features") print('with {} samples, val on {} samples and batch size {}.'.format(num_train, num_val, batch_size)) last_layer_model.compile(optimizer='adam', loss={'yolo_loss': lambda y_true, y_pred: y_pred}) last_layer_model.fit_generator(bottleneck_generator(lines[:num_train], batch_size, input_shape, anchors, num_classes, bottlenecks_train), steps_per_epoch=max(1, num_train//batch_size), validation_data=bottleneck_generator(lines[num_train:], batch_size, input_shape, anchors, num_classes, bottlenecks_val), validation_steps=max(1, num_val//batch_size), epochs=30, initial_epoch=0, max_queue_size=1) model.save_weights(log_dir + 'trained_weights_stage_0.h5') # train last layers with random augmented data model.compile(optimizer=Adam(lr=1e-3), loss={ # use custom yolo_loss Lambda layer. 'yolo_loss': lambda y_true, y_pred: y_pred}) batch_size = 16 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') # 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={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change print('Unfreeze all of the layers.') batch_size = 4 # 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') # Further training if needed. 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=(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))) # get output of second last layers and create bottleneck model of it out1=model_body.layers[246].output out2=model_body.layers[247].output out3=model_body.layers[248].output bottleneck_model = Model([model_body.input, *y_true], [out1, out2, out3]) # create last layer model of last layers from yolo model in0 = Input(shape=bottleneck_model.output[0].shape[1:].as_list()) in1 = Input(shape=bottleneck_model.output[1].shape[1:].as_list()) in2 = Input(shape=bottleneck_model.output[2].shape[1:].as_list()) last_out0=model_body.layers[249](in0) last_out1=model_body.layers[250](in1) last_out2=model_body.layers[251](in2) model_last=Model(inputs=[in0, in1, in2], outputs=[last_out0, last_out1, last_out2]) model_loss_last =Lambda(yolo_loss, output_shape=(1,), name='yolo_loss', arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})( [*model_last.output, *y_true]) last_layer_model = Model([in0,in1,in2, *y_true], model_loss_last) 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) return model, bottleneck_model, last_layer_model def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False): '''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 and random: np.random.shuffle(annotation_lines) image, box = get_random_data(annotation_lines[i], input_shape, random=random) image_data.append(image) box_data.append(box) i = (i+1) % n image_data = np.array(image_data) if verbose: print("Progress: ",i,"/",n) box_data = np.array(box_data) y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) yield [image_data, *y_true], np.zeros(batch_size) def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes, random=True, verbose=False): 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, random, verbose) def bottleneck_generator(annotation_lines, batch_size, input_shape, anchors, num_classes, bottlenecks): n = len(annotation_lines) i = 0 while True: box_data = [] b0=np.zeros((batch_size,bottlenecks[0].shape[1],bottlenecks[0].shape[2],bottlenecks[0].shape[3])) b1=np.zeros((batch_size,bottlenecks[1].shape[1],bottlenecks[1].shape[2],bottlenecks[1].shape[3])) b2=np.zeros((batch_size,bottlenecks[2].shape[1],bottlenecks[2].shape[2],bottlenecks[2].shape[3])) for b in range(batch_size): _, box = get_random_data(annotation_lines[i], input_shape, random=False, proc_img=False) box_data.append(box) b0[b]=bottlenecks[0][i] b1[b]=bottlenecks[1][i] b2[b]=bottlenecks[2][i] i = (i+1) % n box_data = np.array(box_data) y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes) yield [b0, b1, b2, *y_true], np.zeros(batch_size) if __name__ == '__main__': _main()