# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A library to train Inception using multiple GPU's with synchronous updates.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys

sys.path.insert(0, '/home/arjun/MS/Thesis/CAMELYON-16/source')
import copy
import os.path
import re
import time
from datetime import datetime

import numpy as np
import tensorflow as tf

from camelyon16.inception import image_processing
from camelyon16.inception import inception_model as inception
from camelyon16.inception.dataset import Dataset
from camelyon16.inception.slim import slim
import camelyon16.utils as utils

DATA_SET_NAME = 'Camelyon'

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('train_dir', utils.TRAIN_DIR,
                           """Directory where to write event logs """
                           """and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000,  # 1M
                            """Number of batches to run.""")
tf.app.flags.DEFINE_string('subset', 'train',
                           """Either 'train' or 'validation'.""")

# Flags governing the hardware employed for running tf.
tf.app.flags.DEFINE_integer('num_gpus', 2,
                            """How many GPUs to use.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
                            """Whether to log device placement.""")

# Flags governing the type of training.
tf.app.flags.DEFINE_boolean('fine_tune', False,
                            """If set, randomly initialize the final layer """
                            """of weights in order to train the network on a """
                            """new task.""")
tf.app.flags.DEFINE_string('pretrained_model_checkpoint_path', utils.FINE_TUNE_MODEL_CKPT_PATH,
                           """If specified, restore this pretrained model """
                           """before beginning any training.""")

# **IMPORTANT**
# Please note that this learning rate schedule is heavily dependent on the
# hardware architecture, batch size and any changes to the model architecture
# specification. Selecting a finely tuned learning rate schedule is an
# empirical process that requires some experimentation. Please see README.md
# more guidance and discussion.
#
# With 8 Tesla K40's and a batch size = 256, the following setup achieves
# precision@1 = 73.5% after 100 hours and 100K steps (20 epochs).
# Learning rate decay factor selected from http://arxiv.org/abs/1404.5997.
tf.app.flags.DEFINE_float('initial_learning_rate', 0.01,  # 1*e-2
                          """Initial learning rate.""")
tf.app.flags.DEFINE_float('num_epochs_per_decay', 30.0,
                          """Epochs after which learning rate decays.""")
tf.app.flags.DEFINE_integer('num_steps_per_decay', 60000,
                            """Steps after which learning rate decays.""")

tf.app.flags.DEFINE_float('learning_rate_decay_factor', 0.1,
                          """Learning rate decay factor.""")

# Constants dictating the learning rate schedule.
RMSPROP_DECAY = 0.9  # Decay term for RMSProp.
RMSPROP_MOMENTUM = 0.9  # Momentum in RMSProp.
RMSPROP_EPSILON = 1.0  # Epsilon term for RMSProp.


def _tower_loss(images, labels, num_classes, scope, reuse_variables=None):
    """Calculate the total loss on a single tower running the ImageNet model.

    We perform 'batch splitting'. This means that we cut up a batch across
    multiple GPU's. For instance, if the batch size = 32 and num_gpus = 2,
    then each tower will operate on an batch of 16 images.

    Args:
      images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
                                         FLAGS.image_size, 3].
      labels: 1-D integer Tensor of [batch_size].
      num_classes: number of classes
      scope: unique prefix string identifying the ImageNet tower, e.g.
        'tower_0'.

    Returns:
       Tensor of shape [] containing the total loss for a batch of data
    """
    # When fine-tuning a model, we do not restore the logits but instead we
    # randomly initialize the logits. The number of classes in the output of the
    # logit is the number of classes in specified Dataset.
    restore_logits = not FLAGS.fine_tune

    # Build inference Graph.
    with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):
        logits = inception.inference(images, num_classes, for_training=True,
                                     restore_logits=restore_logits,
                                     scope=scope)

    # Build the portion of the Graph calculating the losses. Note that we will
    # assemble the total_loss using a custom function below.
    split_batch_size = images.get_shape().as_list()[0]
    inception.loss(logits, labels, batch_size=split_batch_size)

    # Assemble all of the losses for the current tower only.
    losses = tf.get_collection(slim.losses.LOSSES_COLLECTION, scope)

    # Calculate the total loss for the current tower.
    regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
    total_loss = tf.add_n(losses + regularization_losses, name='total_loss')

    # Compute the moving average of all individual losses and the total loss.
    loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
    loss_averages_op = loss_averages.apply(losses + [total_loss])

    # Attach a scalar summary to all individual losses and the total loss; do the
    # same for the averaged version of the losses.
    for l in losses + [total_loss]:
        # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
        # session. This helps the clarity of presentation on TensorBoard.
        loss_name = re.sub('%s_[0-9]*/' % inception.TOWER_NAME, '', l.op.name)
        # Name each loss as '(raw)' and name the moving average version of the loss
        # as the original loss name.
        tf.summary.scalar(loss_name + ' (raw)', l)
        tf.summary.scalar(loss_name, loss_averages.average(l))

    with tf.control_dependencies([loss_averages_op]):
        total_loss = tf.identity(total_loss)
    return total_loss


def _average_gradients(tower_grads):
    """Calculate the average gradient for each shared variable across all towers.

    Note that this function provides a synchronization point across all towers.

    Args:
      tower_grads: List of lists of (gradient, variable) tuples. The outer list
        is over individual gradients. The inner list is over the gradient
        calculation for each tower.
    Returns:
       List of pairs of (gradient, variable) where the gradient has been averaged
       across all towers.
    """
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        # Note that each grad_and_vars looks like the following:
        #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))
        grads = []
        for g, _ in grad_and_vars:
            # Add 0 dimension to the gradients to represent the tower.
            expanded_g = tf.expand_dims(g, 0)

            # Append on a 'tower' dimension which we will average over below.
            grads.append(expanded_g)

        # Average over the 'tower' dimension.
        grad = tf.concat_v2(grads, 0)
        grad = tf.reduce_mean(grad, 0)

        # Keep in mind that the Variables are redundant because they are shared
        # across towers. So .. we will just return the first tower's pointer to
        # the Variable.
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads


def train(dataset):
    """Train on dataset for a number of steps."""
    with tf.Graph().as_default(), tf.device('/cpu:0'):
        # Create a variable to count the number of train() calls. This equals the
        # number of batches processed * FLAGS.num_gpus.
        global_step = tf.get_variable(
            'global_step', [],
            initializer=tf.constant_initializer(0), trainable=False)

        # Calculate the learning rate schedule.
        num_batches_per_epoch = (dataset.num_examples_per_epoch() /
                                 FLAGS.batch_size)
        decay_steps = int(num_batches_per_epoch * FLAGS.num_epochs_per_decay)

        # Decay the learning rate exponentially based on the number of steps.
        lr = tf.train.exponential_decay(FLAGS.initial_learning_rate,
                                        global_step,
                                        60000,
                                        FLAGS.learning_rate_decay_factor,
                                        staircase=True)

        # Create an optimizer that performs gradient descent.
        opt = tf.train.GradientDescentOptimizer(lr)

        # opt = tf.train.RMSPropOptimizer(lr, RMSPROP_DECAY,
        #                                 momentum=RMSPROP_MOMENTUM,
        #                                 epsilon=RMSPROP_EPSILON)

        # Get images and labels for ImageNet and split the batch across GPUs.
        assert FLAGS.batch_size % FLAGS.num_gpus == 0, (
            'Batch size must be divisible by number of GPUs')
        split_batch_size = int(FLAGS.batch_size / FLAGS.num_gpus)

        # Override the number of preprocessing threads to account for the increased
        # number of GPU towers.
        num_preprocess_threads = FLAGS.num_preprocess_threads * FLAGS.num_gpus
        images, labels = image_processing.distorted_inputs(
            dataset,
            num_preprocess_threads=num_preprocess_threads)

        input_summaries = copy.copy(tf.get_collection(tf.GraphKeys.SUMMARIES))

        # updated - Arjun
        num_classes = dataset.num_classes()

        # Split the batch of images and labels for towers.
        images_splits = tf.split(images, FLAGS.num_gpus, axis=0)
        labels_splits = tf.split(labels, FLAGS.num_gpus, axis=0)

        # Calculate the gradients for each model tower.
        tower_grads = []
        reuse_variables = None
        for i in range(FLAGS.num_gpus):
            with tf.device('/gpu:%d' % i):
                with tf.name_scope('%s_%d' % (inception.TOWER_NAME, i)) as scope:
                    # Force all Variables to reside on the CPU.
                    with slim.arg_scope([slim.variables.variable], device='/cpu:0'):
                        # Calculate the loss for one tower of the ImageNet model. This
                        # function constructs the entire ImageNet model but shares the
                        # variables across all towers.
                        loss = _tower_loss(images_splits[i], labels_splits[i], num_classes,
                                           scope, reuse_variables)

                    # Reuse variables for the next tower.
                    reuse_variables = True

                    # Retain the summaries from the final tower.
                    summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)

                    # Retain the Batch Normalization updates operations only from the
                    # final tower. Ideally, we should grab the updates from all towers
                    # but these stats accumulate extremely fast so we can ignore the
                    # other stats from the other towers without significant detriment.
                    batch_norm_updates = tf.get_collection(slim.ops.UPDATE_OPS_COLLECTION,
                                                           scope)

                    # Calculate the gradients for the batch of data on this ImageNet
                    # tower.
                    grads = opt.compute_gradients(loss)

                    # Keep track of the gradients across all towers.
                    tower_grads.append(grads)

        # We must calculate the mean of each gradient. Note that this is the
        # synchronization point across all towers.
        grads = _average_gradients(tower_grads)

        # Add a summaries for the input processing and global_step.
        summaries.extend(input_summaries)

        # Add a summary to track the learning rate.
        summaries.append(tf.summary.scalar('learning_rate', lr))

        # Add histograms for gradients.
        for grad, var in grads:
            if grad is not None:
                summaries.append(
                    tf.summary.histogram(var.op.name + '/gradients', grad))

        # Apply the gradients to adjust the shared variables.
        apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

        # Add histograms for trainable variables.
        for var in tf.trainable_variables():
            summaries.append(tf.summary.histogram(var.op.name, var))

        # Track the moving averages of all trainable variables.
        # Note that we maintain a "double-average" of the BatchNormalization
        # global statistics. This is more complicated then need be but we employ
        # this for backward-compatibility with our previous models.
        variable_averages = tf.train.ExponentialMovingAverage(
            inception.MOVING_AVERAGE_DECAY, global_step)

        # Another possiblility is to use tf.slim.get_variables().
        variables_to_average = (tf.trainable_variables() +
                                tf.moving_average_variables())
        variables_averages_op = variable_averages.apply(variables_to_average)

        # Group all updates to into a single train op.
        batch_norm_updates_op = tf.group(*batch_norm_updates)
        train_op = tf.group(apply_gradient_op, variables_averages_op,
                            batch_norm_updates_op)

        # Create a saver.
        saver = tf.train.Saver(tf.all_variables(), max_to_keep=1000)

        # Build the summary operation from the last tower summaries.
        summary_op = tf.summary.merge(summaries)

        # Build an initialization operation to run below.
        init = tf.initialize_all_variables()

        # Start running operations on the Graph. allow_soft_placement must be set to
        # True to build towers on GPU, as some of the ops do not have GPU
        # implementations.
        config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=FLAGS.log_device_placement)
        config.gpu_options.per_process_gpu_memory_fraction = 0.5
        sess = tf.Session(config=config)
        sess.run(init)

        if FLAGS.pretrained_model_checkpoint_path:
            # assert tf.gfile.Exists(FLAGS.pretrained_model_checkpoint_path)
            print('model path: %s' % FLAGS.pretrained_model_checkpoint_path)
            variables_to_restore = tf.get_collection(
                slim.variables.VARIABLES_TO_RESTORE)
            restorer = tf.train.Saver(variables_to_restore)
            restorer.restore(sess, FLAGS.pretrained_model_checkpoint_path)
            print('%s: Pre-trained model restored from %s' %
                  (datetime.now(), FLAGS.pretrained_model_checkpoint_path))

        # Start the queue runners.
        tf.train.start_queue_runners(sess=sess)

        summary_writer = tf.summary.FileWriter(
            FLAGS.train_dir,
            graph_def=sess.graph.as_graph_def(add_shapes=True))

        for step in range(FLAGS.max_steps):
            start_time = time.time()
            _, loss_value = sess.run([train_op, loss])
            duration = time.time() - start_time

            assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

            if step % 10 == 0:
                examples_per_sec = FLAGS.batch_size / float(duration)
                format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                              'sec/batch)')
                print(format_str % (datetime.now(), step, loss_value,
                                    examples_per_sec, duration))

            if step % 100 == 0:
                summary_str = sess.run(summary_op)
                summary_writer.add_summary(summary_str, step)

            # Save the model checkpoint periodically.
            if step % 5000 == 0 or (step + 1) == FLAGS.max_steps:
                checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
                saver.save(sess, checkpoint_path, global_step=step)


dataset = Dataset(DATA_SET_NAME, utils.data_subset[0])
train(dataset)
