"""A binary to train CIFAR-10 using a single GPU.
Accuracy:
cifar10_train.py achieves ~86% accuracy after 100K steps (256 epochs of
data) as judged by cifar10_eval.py.
Speed: With batch_size 128.
System        | Step Time (sec/batch)  |     Accuracy
------------------------------------------------------------------
1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)
1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)
Usage:
Please see the tutorial and website for how to download the CIFAR-10
data set, compile the program and train the model.
http://tensorflow.org/tutorials/deep_cnn/
"""

from datetime import datetime
import os.path
import time

import numpy as np
import tensorflow as tf

from tensorflow.models.image.cifar10 import cifar10

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('train_dir', 'cifar10_train',
                           """Directory where to write event logs """
                           """and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 300000,
                            """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
                            """Whether to log device placement.""")


def put_kernels_on_grid(kernel, grid, pad=1):
    '''Visualize conv. features as an image (mostly for the 1st layer).
    Place kernel into a grid, with some paddings between adjacent filters.
    Args:
      kernel:            tensor of shape [Y, X, NumChannels, NumKernels]
      (grid_Y, grid_X):  shape of the grid. Require: NumKernels == grid_Y * grid_X
                           User is responsible of how to break into two multiples.
      pad:               number of black pixels around each filter (between them)

    Return:
      Tensor of shape [(Y+pad)*grid_Y, (X+pad)*grid_X, NumChannels, 1].
    '''
    # pad X and Y
    grid_Y,grid_X = grid
    x1 = tf.pad(kernel, tf.constant([[pad, 0], [pad, 0], [0, 0], [0, 0]]))

    # X and Y dimensions, w.r.t. padding
    Y = kernel.get_shape()[0] + pad
    X = kernel.get_shape()[1] + pad

    # put NumKernels to the 1st dimension
    x2 = tf.transpose(x1, (3, 0, 1, 2))
    # organize grid on Y axis
    x3 = tf.reshape(x2, tf.pack([grid_X, Y * grid_Y, X, 3]))

    # switch X and Y axes
    x4 = tf.transpose(x3, (0, 2, 1, 3))
    # organize grid on X axis
    x5 = tf.reshape(x4, tf.pack([1, X * grid_X, Y * grid_Y, 3]))

    # back to normal order (not combining with the next step for clarity)
    x6 = tf.transpose(x5, (2, 1, 3, 0))

    # to tf.image_summary order [batch_size, height, width, channels],
    #   where in this case batch_size == 1
    x7 = tf.transpose(x6, (3, 0, 1, 2))

    # scale to [0, 1]
    x_min = tf.reduce_min(x7)
    x_max = tf.reduce_max(x7)
    x8 = (x7 - x_min) / (x_max - x_min)

    return x8



def train():
    """Train CIFAR-10 for a number of steps."""
    with tf.Graph().as_default():
        global_step = tf.Variable(0, trainable=False)

        # Get images and labels for CIFAR-10.
        images, labels = cifar10.distorted_inputs()

        # #Adding dropout
        # keep_drop_prob = tf.placeholder(tf.float32)

        # Build a Graph that computes the logits predictions from the
        # inference model.
        logits = cifar10.inference(images)

        # ###########Changes for visualization ###############
        with tf.variable_scope('conv1') as scope_conv:
            tf.get_variable_scope().reuse_variables()
            weights = tf.get_variable('weights')
            grid_x = grid_y = 8  # to get a square grid for 64 conv1 features
            grid = put_kernels_on_grid(weights, (grid_y, grid_x))
            tf.image_summary('conv1/features', grid, max_images=1)
        # ####################################################


        # Calculate loss.
        loss = cifar10.loss(logits, labels)

        # Build a Graph that trains the model with one batch of examples and
        # updates the model parameters.
        train_op = cifar10.train(loss, global_step)

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

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.merge_all_summaries()

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

        # Start running operations on the Graph.
        sess = tf.Session(config=tf.ConfigProto(
            log_device_placement=FLAGS.log_device_placement))
        sess.run(init)

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

        summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)

        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:
                num_examples_per_step = FLAGS.batch_size
                examples_per_sec = num_examples_per_step / duration
                sec_per_batch = 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, sec_per_batch))

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

            # Save the model checkpoint periodically.
            if step % 1000 == 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)


def main(argv=None):  # pylint: disable=unused-argument
    if tf.gfile.Exists(FLAGS.train_dir):
        tf.gfile.DeleteRecursively(FLAGS.train_dir)
    tf.gfile.MakeDirs(FLAGS.train_dir)
    train()


if __name__ == '__main__':
    tf.app.run()
