"""

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()