#!/usr/bin/env python
# coding: utf-8
# wut-train-cluster-fn.py --- What U Think? SatNOGS Observation AI, training application cluster edition.
#
# https://spacecruft.org/spacecruft/satnogs-wut
#
# Based on data/train and data/val directories builds a wut.tf file.
# GPLv3+
# Built using Jupyter, Tensorflow, Keras
from __future__ import absolute_import, division, print_function, unicode_literals
from __future__ import print_function
import os
import numpy as np
import simplejson as json
import datetime
import tensorflow as tf
import tensorflow.python.keras
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.python.keras import optimizers
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
from tensorflow.python.keras.layers import Input, concatenate
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.preprocessing import image
from tensorflow.python.keras.preprocessing.image import img_to_array
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.preprocessing.image import load_img

# Environmetal variables are set by shell script that launches this python script.
#os.environ["TF_CONFIG"] = json.dumps({
#    "cluster": {
#        "worker": [ "10.100.100.130:2222", "ml1:2222", "ml2:2222", "ml3:2222", "ml4:2222", "ml5:2222" ]
#    },
#   "task": {"type": "worker", "index": 0 },
#   "num_workers": 5
#})
IMG_HEIGHT = 416
IMG_WIDTH= 804
batch_size = 32
epochs = 4

BUFFER_SIZE = 10000
NUM_WORKERS = 6
GLOBAL_BATCH_SIZE = 64 * NUM_WORKERS

# XXX
#tf.keras.backend.clear_session()

options = tf.data.Options()

strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
    tf.distribute.experimental.CollectiveCommunication.RING)

train_dir = os.path.join('data/', 'train')
val_dir = os.path.join('data/', 'val')
train_good_dir = os.path.join(train_dir, 'good')
train_bad_dir = os.path.join(train_dir, 'bad')
val_good_dir = os.path.join(val_dir, 'good')
val_bad_dir = os.path.join(val_dir, 'bad')
num_train_good = len(os.listdir(train_good_dir))
num_train_bad = len(os.listdir(train_bad_dir))
num_val_good = len(os.listdir(val_good_dir))
num_val_bad = len(os.listdir(val_bad_dir))
total_train = num_train_good + num_train_bad
total_val = num_val_good + num_val_bad

print('total training good images:', num_train_good)
print('total training bad images:', num_train_bad)
print("--")
print("Total training images:", total_train)
print('total validation good images:', num_val_good)
print('total validation bad images:', num_val_bad)
print("--")
print("Total validation images:", total_val)
print("--")
print("Reduce training and validation set when testing")
#total_train = 16
#total_val = 16
print("Reduced training images:", total_train)
print("Reduced validation images:", total_val)

train_image_generator = ImageDataGenerator(
    rescale=1./255
)
val_image_generator = ImageDataGenerator(
    rescale=1./255
)
train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='binary')
val_data_gen = val_image_generator.flow_from_directory(batch_size=batch_size,
                                                       directory=val_dir,
                                                       target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                       class_mode='binary')

def get_uncompiled_model():
  model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
    MaxPooling2D(),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(1, activation='sigmoid')
  ])
  return model

def get_compiled_model():
  model = get_uncompiled_model()
  model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])
  return model


with strategy.scope():
    model = get_compiled_model()
    model.fit(
        train_data_gen,
        steps_per_epoch=total_train // batch_size,
        epochs=epochs,
        validation_data=val_data_gen,
        validation_steps=total_val // batch_size,
        verbose=2
        )

print("TRAINING info")
print(train_dir)
print(train_good_dir)
print(train_bad_dir)
print(train_image_generator)
print(train_data_gen)

# The End