from absl import app, flags, logging
from absl.flags import FLAGS

import tensorflow as tf
import numpy as np
import cv2
from tensorflow.keras.callbacks import (
    ReduceLROnPlateau,
    EarlyStopping,
    ModelCheckpoint,
    TensorBoard
)
from yolov3_tf2.models import (
    YoloV3, YoloV3Tiny, YoloLoss,
    yolo_anchors, yolo_anchor_masks,
    yolo_tiny_anchors, yolo_tiny_anchor_masks
)
from yolov3_tf2.utils import freeze_all
import yolov3_tf2.dataset as dataset

class FLAGS:
    dataset = './data/voc2012_train.tfrecord'
    val_dataset= None
    tiny = False
    weights = None
    classes = './data/voc2012.names'
    mode = 'eager_tf'
    transfer = 'none'
    size  = 416
    epochs =  2
    batch_size =  8
    learning_rate= 1e-3
    num_classes= 20
    weights_num_classes=None



def main(_argv):
    physical_devices = tf.config.experimental.list_physical_devices('GPU')
    for physical_device in physical_devices:
        tf.config.experimental.set_memory_growth(physical_device, True)

    if FLAGS.tiny:
        model = YoloV3Tiny(FLAGS.size, training=True,
                           classes=FLAGS.num_classes)
        anchors = yolo_tiny_anchors
        anchor_masks = yolo_tiny_anchor_masks
    else:
        model = YoloV3(FLAGS.size, training=True, classes=FLAGS.num_classes)
        anchors = yolo_anchors
        anchor_masks = yolo_anchor_masks

    if FLAGS.dataset:
        train_dataset = dataset.load_tfrecord_dataset(FLAGS.dataset, FLAGS.classes, FLAGS.size)
    else:
        train_dataset = dataset.load_fake_dataset()
    train_dataset = train_dataset.shuffle(buffer_size=512)
    train_dataset = train_dataset.batch(FLAGS.batch_size)
    train_dataset = train_dataset.map(lambda x, y: (
        dataset.transform_images(x, FLAGS.size),
        dataset.transform_targets(y, anchors, anchor_masks, FLAGS.size)))
    train_dataset = train_dataset.prefetch(
        buffer_size=tf.data.experimental.AUTOTUNE)

    if FLAGS.val_dataset:
        val_dataset = dataset.load_tfrecord_dataset(
            FLAGS.val_dataset, FLAGS.classes, FLAGS.size)
    else:
        val_dataset = dataset.load_fake_dataset()
    val_dataset = val_dataset.batch(FLAGS.batch_size)
    val_dataset = val_dataset.map(lambda x, y: (
        dataset.transform_images(x, FLAGS.size),
        dataset.transform_targets(y, anchors, anchor_masks, FLAGS.size)))

    # Configure the model for transfer learning
    if FLAGS.transfer == 'none':
        pass  # Nothing to do
    elif FLAGS.transfer in ['darknet', 'no_output']:
        # Darknet transfer is a special case that works
        # with incompatible number of classes

        # reset top layers
        if FLAGS.tiny:
            model_pretrained = YoloV3Tiny(
                FLAGS.size, training=True, classes=FLAGS.weights_num_classes or FLAGS.num_classes)
        else:
            model_pretrained = YoloV3(
                FLAGS.size, training=True, classes=FLAGS.weights_num_classes or FLAGS.num_classes)
        #model_pretrained.load_weights(FLAGS.weights)

        if FLAGS.transfer == 'darknet':
            model.get_layer('yolo_darknet').set_weights(
                model_pretrained.get_layer('yolo_darknet').get_weights())
            freeze_all(model.get_layer('yolo_darknet'))

        elif FLAGS.transfer == 'no_output':
            for l in model.layers:
                if not l.name.startswith('yolo_output'):
                    l.set_weights(model_pretrained.get_layer(
                        l.name).get_weights())
                    freeze_all(l)

    else:
        # All other transfer require matching classes
        model.load_weights(FLAGS.weights)
        if FLAGS.transfer == 'fine_tune':
            # freeze darknet and fine tune other layers
            darknet = model.get_layer('yolo_darknet')
            freeze_all(darknet)
        elif FLAGS.transfer == 'frozen':
            # freeze everything
            freeze_all(model)

    optimizer = tf.keras.optimizers.Adam(lr=FLAGS.learning_rate)
    loss = [YoloLoss(anchors[mask], classes=FLAGS.num_classes)
            for mask in anchor_masks]
    print(model)
    if FLAGS.mode == 'eager_tf':
        # Eager mode is great for debugging
        # Non eager graph mode is recommended for real training
        avg_loss = tf.keras.metrics.Mean('loss', dtype=tf.float32)
        avg_val_loss = tf.keras.metrics.Mean('val_loss', dtype=tf.float32)

        for epoch in range(1, FLAGS.epochs + 1):
            for batch, (images, labels) in enumerate(train_dataset):
                with tf.GradientTape() as tape:
                    outputs = model(images, training=True)
                    regularization_loss = tf.reduce_sum(model.losses)
                    pred_loss = []
                    wh_batch_losses = []
                    for output, label, loss_fn in zip(outputs, labels, loss):
                        batch_xy_loss, batch_wh_loss, batch_obj_loss, batch_class_loss = loss_fn(label, output)
                        pred_total_loss = batch_xy_loss + batch_wh_loss + batch_obj_loss + batch_class_loss
                        pred_loss.append(pred_total_loss)
                        wh_batch_losses.append(batch_wh_loss)
                    total_loss = tf.reduce_sum(pred_loss) + regularization_loss
                    total_wh_batch_loss = tf.reduce_sum(wh_batch_losses)
                grads = tape.gradient(total_loss, model.trainable_variables)
                optimizer.apply_gradients(
                    zip(grads, model.trainable_variables))

                logging.info("{}_train_{}, {}, {} , {} ".format(
                    epoch, batch, total_loss.numpy(), total_wh_batch_loss.numpy(),
                    list(map(lambda x: np.sum(x.numpy()), pred_loss))))
                avg_loss.update_state(total_loss)

            for batch, (images, labels) in enumerate(val_dataset):
                outputs = model(images)
                regularization_loss = tf.reduce_sum(model.losses)
                pred_loss = []
                for output, label, loss_fn in zip(outputs, labels, loss):
                    pred_loss.append(loss_fn(label, output))
                total_loss = tf.reduce_sum(pred_loss) + regularization_loss

                logging.info("{}_val_{}, {}, {}".format(
                    epoch, batch, total_loss.numpy(),
                    list(map(lambda x: np.sum(x.numpy()), pred_loss))))
                avg_val_loss.update_state(total_loss)

            logging.info("{}, train: {}, val: {}".format(
                epoch,
                avg_loss.result().numpy(),
                avg_val_loss.result().numpy()))

            avg_loss.reset_states()
            avg_val_loss.reset_states()
            model.save_weights(
                'checkpoints/yolov3_train_{}.tf'.format(epoch))
    else:
        model.compile(optimizer=optimizer, loss=loss,
                      run_eagerly=(FLAGS.mode == 'eager_fit'))

        callbacks = [
            ReduceLROnPlateau(verbose=1),
            EarlyStopping(patience=3, verbose=1),
            ModelCheckpoint('checkpoints/yolov3_train_{epoch}.tf',
                            verbose=1, save_weights_only=True),
            TensorBoard(log_dir='logs')
        ]

        history = model.fit(train_dataset,
                            epochs=FLAGS.epochs,
                            callbacks=callbacks,
                            validation_data=val_dataset)


try:
    app.run(main)
except SystemExit:
    pass
