<reponame>JessieRamaux/Food-Volume-Estimation
import tensorflow as tf
from tensorflow.python.ops import gen_logging_ops
from tensorflow.python.framework import ops as _ops
import time
import shutil
import datetime
import os
import cv2
import pickle 
import numpy as np
from tensorflow.contrib import slim
import sys
sys.path.append(os.getcwd())
from nets import model as model
from utils.tf_records import read_tfrecord_and_decode_into_image_annotation_pair_tensors
from utils.pascal_voc import pascal_segmentation_lut
from utils.augmentation import (distort_randomly_image_color,flip_randomly_left_right_image_with_annotation,
                                scale_randomly_image_with_annotation_with_fixed_size_output)


tf.app.flags.DEFINE_integer('batch_size', 3, '')
tf.app.flags.DEFINE_integer('train_size', 512, '')
tf.app.flags.DEFINE_float('learning_rate', 0.0001, '')
tf.app.flags.DEFINE_integer('max_steps', 60000, '')
tf.app.flags.DEFINE_float('moving_average_decay', 0.997, '')
tf.app.flags.DEFINE_integer('num_classes', 21, '')
tf.app.flags.DEFINE_string('gpu_list', '0,1', '')
tf.app.flags.DEFINE_string('checkpoint_path', 'checkpoints/', '')
tf.app.flags.DEFINE_string('logs_path', 'logs/', '')
tf.app.flags.DEFINE_boolean('restore', True, 'whether to resotre from checkpoint')
tf.app.flags.DEFINE_integer('save_checkpoint_steps', 2000, '')
tf.app.flags.DEFINE_integer('save_summary_steps', 10, '')
tf.app.flags.DEFINE_integer('save_image_steps', 100, '')
tf.app.flags.DEFINE_string('training_data_path', 'data/pascal_augmented_train.tfrecords', '')
tf.app.flags.DEFINE_string('pretrained_model_path', 'data/resnet_v1_101.ckpt', '')
tf.app.flags.DEFINE_integer('decay_steps',20000,'')
tf.app.flags.DEFINE_integer('decay_rate',0.1,'')
FLAGS = tf.app.flags.FLAGS


def tower_loss(images, annotation,class_labels,reuse_variables=None):
    with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):
        logits = model.model(images, is_training=True)
    pred = tf.argmax(logits, dimension=3)

    model_loss = model.loss(annotation, logits,class_labels)
    total_loss = tf.add_n([model_loss] + tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))

    # add summary
    if reuse_variables is None:
        tf.summary.scalar('model_loss', model_loss)
        tf.summary.scalar('total_loss', total_loss)
    return total_loss, model_loss,pred


def average_gradients(tower_grads):
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        grads = []
        for g, _ in grad_and_vars:
            expanded_g = tf.expand_dims(g, 0)
            grads.append(expanded_g)
        grad = tf.concat(grads, 0)
        grad = tf.reduce_mean(grad, 0)
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads

def build_image_summary():
    log_image_data = tf.placeholder(tf.uint8, [None, None, 3])
    log_image_name = tf.placeholder(tf.string)
    log_image = gen_logging_ops._image_summary(log_image_name, tf.expand_dims(log_image_data, 0), max_images=1)
    _ops.add_to_collection(_ops.GraphKeys.SUMMARIES, log_image)
    return log_image, log_image_data, log_image_name

def main(argv=None):
    gpus = range(len(FLAGS.gpu_list.split(',')))
    pascal_voc_lut = pascal_segmentation_lut()
    class_labels = pascal_voc_lut.keys()
    with open('data/color_map', 'rb') as f:
        color_map = pickle.load(f)

    os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_list
    now = datetime.datetime.now()
    StyleTime = now.strftime("%Y-%m-%d-%H-%M-%S")
    os.makedirs(FLAGS.logs_path+StyleTime)
    if not os.path.exists(FLAGS.checkpoint_path):
        os.makedirs(FLAGS.checkpoint_path)
    else:
        if not FLAGS.restore:
            if os.path.exists(FLAGS.checkpoint_path):
                shutil.rmtree(FLAGS.checkpoint_path)
                os.makedirs(FLAGS.checkpoint_path)

    filename_queue = tf.train.string_input_producer([FLAGS.training_data_path], num_epochs=1000)
    image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue)

    image, annotation = flip_randomly_left_right_image_with_annotation(image, annotation)
    image = distort_randomly_image_color(image)

    image_train_size=[FLAGS.train_size,FLAGS.train_size]
    resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output(image, annotation,
                                                                                                    image_train_size)
    resized_annotation = tf.squeeze(resized_annotation)

    image_batch, annotation_batch = tf.train.shuffle_batch([resized_image, resized_annotation],
                                                           batch_size=FLAGS.batch_size*len(gpus), capacity=1000, num_threads=4,
                                                           min_after_dequeue=500)

    # split
    input_images_split = tf.split(image_batch, len(gpus))
    input_segs_split = tf.split(annotation_batch, len(gpus))


    learning_rate = tf.Variable(FLAGS.learning_rate, trainable=False)
    global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
    # add summary
    tf.summary.scalar('learning_rate', learning_rate)
    opt = tf.train.AdamOptimizer(learning_rate)

    tower_grads = []
    reuse_variables = None
    for i, gpu_id in enumerate(gpus):
        with tf.device('/gpu:%d' % gpu_id):
            with tf.name_scope('model_%d' % gpu_id) as scope:
                iis = input_images_split[i]
                isms = input_segs_split[i]
                total_loss, model_loss,output_pred = tower_loss(iis, isms, class_labels, reuse_variables)
                batch_norm_updates_op = tf.group(*tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope))
                reuse_variables = True
                grads = opt.compute_gradients(total_loss)
                tower_grads.append(grads)

    grads = average_gradients(tower_grads)
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    summary_op = tf.summary.merge_all()
    log_image, log_image_data, log_image_name = build_image_summary()
    # save moving average
    variable_averages = tf.train.ExponentialMovingAverage(
        FLAGS.moving_average_decay, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())
    # batch norm updates
    with tf.control_dependencies([variables_averages_op, apply_gradient_op, batch_norm_updates_op]):
        train_op = tf.no_op(name='train_op')

    saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
    summary_writer = tf.summary.FileWriter(FLAGS.logs_path+StyleTime, tf.get_default_graph())


    if FLAGS.pretrained_model_path is not None:
        variable_restore_op = slim.assign_from_checkpoint_fn(FLAGS.pretrained_model_path,
                                                             slim.get_trainable_variables(),
                                                             ignore_missing_vars=True)

    global_vars_init_op = tf.global_variables_initializer()
    local_vars_init_op = tf.local_variables_initializer()
    init = tf.group(local_vars_init_op, global_vars_init_op)


    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
        restore_step=0
        if FLAGS.restore:
            sess.run(init)
            print('continue training from previous checkpoint')
            ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
            restore_step=int(ckpt.split('.')[0].split('_')[-1])
            saver.restore(sess, ckpt)
        else:
            sess.run(init)
            if FLAGS.pretrained_model_path is not None:
                variable_restore_op(sess)

        start = time.time()
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        try:
            while not coord.should_stop():
                for step in range(restore_step,FLAGS.max_steps):
                    if step != 0 and step % FLAGS.decay_steps == 0:
                        sess.run(tf.assign(learning_rate, learning_rate.eval() * FLAGS.decay_rate))

                    ml, tl, _ = sess.run([model_loss, total_loss, train_op])
                    if np.isnan(tl):
                        print('Loss diverged, stop training')
                        break
                    if step % 10 == 0:
                        avg_time_per_step = (time.time() - start)/10
                        start = time.time()
                        print('Step {:06d}, model loss {:.4f}, total loss {:.4f}, {:.3f} seconds/step, lr: {:.7f}').\
                            format(step, ml, tl, avg_time_per_step,learning_rate.eval())

                    if (step+1) % FLAGS.save_checkpoint_steps == 0:
                        filename = ('RefineNet'+'_step_{:d}'.format(step + 1) + '.ckpt')
                        filename = os.path.join(FLAGS.checkpoint_path,filename)
                        saver.save(sess, filename)
                        print('Write model to: {:s}'.format(filename))

                    if step % FLAGS.save_summary_steps == 0:
                        _, tl, summary_str = sess.run([train_op, total_loss, summary_op])
                        summary_writer.add_summary(summary_str, global_step=step)

                    if step % FLAGS.save_image_steps == 0:
                        log_image_name_str = ('%06d' % step)
                        img_split,seg_split,pred = sess.run([iis,isms,output_pred])

                        img_split=np.squeeze(img_split)[0]
                        seg_split=np.squeeze(seg_split)[0]
                        pred=np.squeeze(pred)[0]

                        #img_split=cv2.resize(img_split,(128,128))

                        color_seg = np.zeros((seg_split.shape[0], seg_split.shape[1], 3))
                        for i in range(seg_split.shape[0]):
                            for j in range(seg_split.shape[1]):
                                color_seg[i, j, :] = color_map[str(seg_split[i][j])]

                        color_pred = np.zeros((pred.shape[0], pred.shape[1], 3))
                        for i in range(pred.shape[0]):
                            for j in range(pred.shape[1]):
                                color_pred[i, j, :] = color_map[str(pred[i][j])]

                        write_img=np.hstack((color_seg,color_pred))
                        log_image_summary_op = sess.run(log_image,feed_dict={log_image_name: log_image_name_str, \
                                                                   log_image_data: write_img})
                        summary_writer.add_summary(log_image_summary_op, global_step=step)
        except tf.errors.OutOfRangeError:
            print('finish')
        finally:
            coord.request_stop()
        coord.join(threads)


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


import tensorflow as tf
from tensorflow.python.ops import gen_logging_ops
from tensorflow.python.framework import ops as _ops
import time
import shutil
import datetime
import os
import cv2
import pickle 
import numpy as np
from tensorflow.contrib import slim
import sys
sys.path.append(os.getcwd())
from nets import model as model
from utils.tf_records import read_tfrecord_and_decode_into_image_annotation_pair_tensors
from utils.pascal_voc import pascal_segmentation_lut
from utils.augmentation import (distort_randomly_image_color,flip_randomly_left_right_image_with_annotation,
                                scale_randomly_image_with_annotation_with_fixed_size_output)


tf.app.flags.DEFINE_integer('batch_size', 3, '')
tf.app.flags.DEFINE_integer('train_size', 512, '')
tf.app.flags.DEFINE_float('learning_rate', 0.0001, '')
tf.app.flags.DEFINE_integer('max_steps', 60000, '')
tf.app.flags.DEFINE_float('moving_average_decay', 0.997, '')
tf.app.flags.DEFINE_integer('num_classes', 21, '')
tf.app.flags.DEFINE_string('gpu_list', '0,1', '')
tf.app.flags.DEFINE_string('checkpoint_path', 'checkpoints/', '')
tf.app.flags.DEFINE_string('logs_path', 'logs/', '')
tf.app.flags.DEFINE_boolean('restore', True, 'whether to resotre from checkpoint')
tf.app.flags.DEFINE_integer('save_checkpoint_steps', 2000, '')
tf.app.flags.DEFINE_integer('save_summary_steps', 10, '')
tf.app.flags.DEFINE_integer('save_image_steps', 100, '')
tf.app.flags.DEFINE_string('training_data_path', 'data/pascal_augmented_train.tfrecords', '')
tf.app.flags.DEFINE_string('pretrained_model_path', 'data/resnet_v1_101.ckpt', '')
tf.app.flags.DEFINE_integer('decay_steps',20000,'')
tf.app.flags.DEFINE_integer('decay_rate',0.1,'')
FLAGS = tf.app.flags.FLAGS


def tower_loss(images, annotation,class_labels,reuse_variables=None):
    with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables):
        logits = model.model(images, is_training=True)
    pred = tf.argmax(logits, dimension=3)

    model_loss = model.loss(annotation, logits,class_labels)
    total_loss = tf.add_n([model_loss] + tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))

    # add summary
    if reuse_variables is None:
        tf.summary.scalar('model_loss', model_loss)
        tf.summary.scalar('total_loss', total_loss)
    return total_loss, model_loss,pred


def average_gradients(tower_grads):
    average_grads = []
    for grad_and_vars in zip(*tower_grads):
        grads = []
        for g, _ in grad_and_vars:
            expanded_g = tf.expand_dims(g, 0)
            grads.append(expanded_g)
        grad = tf.concat(grads, 0)
        grad = tf.reduce_mean(grad, 0)
        v = grad_and_vars[0][1]
        grad_and_var = (grad, v)
        average_grads.append(grad_and_var)
    return average_grads

def build_image_summary():
    log_image_data = tf.placeholder(tf.uint8, [None, None, 3])
    log_image_name = tf.placeholder(tf.string)
    log_image = gen_logging_ops._image_summary(log_image_name, tf.expand_dims(log_image_data, 0), max_images=1)
    _ops.add_to_collection(_ops.GraphKeys.SUMMARIES, log_image)
    return log_image, log_image_data, log_image_name

def main(argv=None):
    gpus = range(len(FLAGS.gpu_list.split(',')))
    pascal_voc_lut = pascal_segmentation_lut()
    class_labels = pascal_voc_lut.keys()
    with open('data/color_map', 'rb') as f:
        color_map = pickle.load(f)

    os.environ['CUDA_VISIBLE_DEVICES'] = FLAGS.gpu_list
    now = datetime.datetime.now()
    StyleTime = now.strftime("%Y-%m-%d-%H-%M-%S")
    os.makedirs(FLAGS.logs_path+StyleTime)
    if not os.path.exists(FLAGS.checkpoint_path):
        os.makedirs(FLAGS.checkpoint_path)
    else:
        if not FLAGS.restore:
            if os.path.exists(FLAGS.checkpoint_path):
                shutil.rmtree(FLAGS.checkpoint_path)
                os.makedirs(FLAGS.checkpoint_path)

    filename_queue = tf.train.string_input_producer([FLAGS.training_data_path], num_epochs=1000)
    image, annotation = read_tfrecord_and_decode_into_image_annotation_pair_tensors(filename_queue)

    image, annotation = flip_randomly_left_right_image_with_annotation(image, annotation)
    image = distort_randomly_image_color(image)

    image_train_size=[FLAGS.train_size,FLAGS.train_size]
    resized_image, resized_annotation = scale_randomly_image_with_annotation_with_fixed_size_output(image, annotation,
                                                                                                    image_train_size)
    resized_annotation = tf.squeeze(resized_annotation)

    image_batch, annotation_batch = tf.train.shuffle_batch([resized_image, resized_annotation],
                                                           batch_size=FLAGS.batch_size*len(gpus), capacity=1000, num_threads=4,
                                                           min_after_dequeue=500)

    # split
    input_images_split = tf.split(image_batch, len(gpus))
    input_segs_split = tf.split(annotation_batch, len(gpus))


    learning_rate = tf.Variable(FLAGS.learning_rate, trainable=False)
    global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
    # add summary
    tf.summary.scalar('learning_rate', learning_rate)
    opt = tf.train.AdamOptimizer(learning_rate)

    tower_grads = []
    reuse_variables = None
    for i, gpu_id in enumerate(gpus):
        with tf.device('/gpu:%d' % gpu_id):
            with tf.name_scope('model_%d' % gpu_id) as scope:
                iis = input_images_split[i]
                isms = input_segs_split[i]
                total_loss, model_loss,output_pred = tower_loss(iis, isms, class_labels, reuse_variables)
                batch_norm_updates_op = tf.group(*tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope))
                reuse_variables = True
                grads = opt.compute_gradients(total_loss)
                tower_grads.append(grads)

    grads = average_gradients(tower_grads)
    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

    summary_op = tf.summary.merge_all()
    log_image, log_image_data, log_image_name = build_image_summary()
    # save moving average
    variable_averages = tf.train.ExponentialMovingAverage(
        FLAGS.moving_average_decay, global_step)
    variables_averages_op = variable_averages.apply(tf.trainable_variables())
    # batch norm updates
    with tf.control_dependencies([variables_averages_op, apply_gradient_op, batch_norm_updates_op]):
        train_op = tf.no_op(name='train_op')

    saver = tf.train.Saver(tf.global_variables(), max_to_keep=100)
    summary_writer = tf.summary.FileWriter(FLAGS.logs_path+StyleTime, tf.get_default_graph())


    if FLAGS.pretrained_model_path is not None:
        variable_restore_op = slim.assign_from_checkpoint_fn(FLAGS.pretrained_model_path,
                                                             slim.get_trainable_variables(),
                                                             ignore_missing_vars=True)

    global_vars_init_op = tf.global_variables_initializer()
    local_vars_init_op = tf.local_variables_initializer()
    init = tf.group(local_vars_init_op, global_vars_init_op)


    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
        restore_step=0
        if FLAGS.restore:
            sess.run(init)
            print('continue training from previous checkpoint')
            ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
            restore_step=int(ckpt.split('.')[0].split('_')[-1])
            saver.restore(sess, ckpt)
        else:
            sess.run(init)
            if FLAGS.pretrained_model_path is not None:
                variable_restore_op(sess)

        start = time.time()
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        try:
            while not coord.should_stop():
                for step in range(restore_step,FLAGS.max_steps):
                    if step != 0 and step % FLAGS.decay_steps == 0:
                        sess.run(tf.assign(learning_rate, learning_rate.eval() * FLAGS.decay_rate))

                    ml, tl, _ = sess.run([model_loss, total_loss, train_op])
                    if np.isnan(tl):
                        print('Loss diverged, stop training')
                        break
                    if step % 10 == 0:
                        avg_time_per_step = (time.time() - start)/10
                        start = time.time()
                        print('Step {:06d}, model loss {:.4f}, total loss {:.4f}, {:.3f} seconds/step, lr: {:.7f}').\
                            format(step, ml, tl, avg_time_per_step,learning_rate.eval())

                    if (step+1) % FLAGS.save_checkpoint_steps == 0:
                        filename = ('RefineNet'+'_step_{:d}'.format(step + 1) + '.ckpt')
                        filename = os.path.join(FLAGS.checkpoint_path,filename)
                        saver.save(sess, filename)
                        print('Write model to: {:s}'.format(filename))

                    if step % FLAGS.save_summary_steps == 0:
                        _, tl, summary_str = sess.run([train_op, total_loss, summary_op])
                        summary_writer.add_summary(summary_str, global_step=step)

                    if step % FLAGS.save_image_steps == 0:
                        log_image_name_str = ('%06d' % step)
                        img_split,seg_split,pred = sess.run([iis,isms,output_pred])

                        img_split=np.squeeze(img_split)[0]
                        seg_split=np.squeeze(seg_split)[0]
                        pred=np.squeeze(pred)[0]

                        #img_split=cv2.resize(img_split,(128,128))

                        color_seg = np.zeros((seg_split.shape[0], seg_split.shape[1], 3))
                        for i in range(seg_split.shape[0]):
                            for j in range(seg_split.shape[1]):
                                color_seg[i, j, :] = color_map[str(seg_split[i][j])]

                        color_pred = np.zeros((pred.shape[0], pred.shape[1], 3))
                        for i in range(pred.shape[0]):
                            for j in range(pred.shape[1]):
                                color_pred[i, j, :] = color_map[str(pred[i][j])]

                        write_img=np.hstack((color_seg,color_pred))
                        log_image_summary_op = sess.run(log_image,feed_dict={log_image_name: log_image_name_str, \
                                                                   log_image_data: write_img})
                        summary_writer.add_summary(log_image_summary_op, global_step=step)
        except tf.errors.OutOfRangeError:
            print('finish')
        finally:
            coord.request_stop()
        coord.join(threads)


if __name__ == '__main__':
    tf.app.run()
# add summary # split # add summary # save moving average # batch norm updates #img_split=cv2.resize(img_split,(128,128))