train_sup.py

# Lint as: python2, python3
# Copyright 2020 Google LLC

# 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

#     https://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.
# ==============================================================================
"""Training script for supervised baseline.

Always train DeepLab decoder.
Optionally, train a classifier with image-level labeled data.
"""

from absl import flags
import six
#import tensorflow.compat.v1 as tf
import tensorflow as tf
from tensorflow.contrib import slim as contrib_slim
from tensorflow.contrib import tfprof as contrib_tfprof
from third_party.deeplab import common
from third_party.deeplab.core import feature_extractor
from third_party.deeplab.utils import train_utils
from third_party.slim.deployment import model_deploy
# Custom import
from core import data_generator
from core import model
from core import preprocess_utils

slim = contrib_slim
FLAGS = flags.FLAGS

## DeepLab options
# Settings for multi-GPUs/multi-replicas training.

flags.DEFINE_integer('num_clones', 16, 'Number of clones to deploy.')

flags.DEFINE_boolean('clone_on_cpu', False, 'Use CPUs to deploy clones.')

flags.DEFINE_integer('num_replicas', 1, 'Number of worker replicas.')

flags.DEFINE_integer('startup_delay_steps', 15,
                     'Number of training steps between replicas startup.')

flags.DEFINE_integer(
    'num_ps_tasks', 0,
    'The number of parameter servers. If the value is 0, then '
    'the parameters are handled locally by the worker.')

flags.DEFINE_string('master', '', 'BNS name of the tensorflow server')

flags.DEFINE_integer('task', 0, 'The task ID.')

# Settings for logging.

flags.DEFINE_string('train_logdir', None,
                    'Where the checkpoint and logs are stored.')

flags.DEFINE_integer('log_steps', 10,
                     'Display logging information at every log_steps.')

flags.DEFINE_integer('save_interval_secs', 1200,
                     'How often, in seconds, we save the model to disk.')

flags.DEFINE_integer('save_summaries_secs', 600,
                     'How often, in seconds, we compute the summaries.')

flags.DEFINE_boolean(
    'save_summaries_images', False,
    'Save sample inputs, labels, and semantic predictions as '
    'images to summary.')

# Settings for profiling.

flags.DEFINE_string('profile_logdir', None,
                    'Where the profile files are stored.')

# Settings for training strategy.

flags.DEFINE_enum('optimizer', 'momentum', ['momentum', 'adam'],
                  'Which optimizer to use.')


# Momentum optimizer flags

flags.DEFINE_enum('learning_policy', 'poly', ['poly', 'step'],
                  'Learning rate policy for training.')

# Use 0.007 when training on PASCAL augmented training set, train_aug. When
# fine-tuning on PASCAL trainval set, use learning rate=0.0001.
flags.DEFINE_float('base_learning_rate', .0001,
                   'The base learning rate for model training.')

flags.DEFINE_float('decay_steps', 0.0,
                   'Decay steps for polynomial learning rate schedule.')

flags.DEFINE_float('end_learning_rate', 0.0,
                   'End learning rate for polynomial learning rate schedule.')

flags.DEFINE_float('learning_rate_decay_factor', 0.1,
                   'The rate to decay the base learning rate.')

flags.DEFINE_integer('learning_rate_decay_step', 2000,
                     'Decay the base learning rate at a fixed step.')

flags.DEFINE_float('learning_power', 0.9,
                   'The power value used in the poly learning policy.')

flags.DEFINE_integer('training_number_of_steps', 30000,
                     'The number of steps used for training')

flags.DEFINE_float('momentum', 0.9, 'The momentum value to use')

# Adam optimizer flags
flags.DEFINE_float('adam_learning_rate', 0.001,
                   'Learning rate for the adam optimizer.')
flags.DEFINE_float('adam_epsilon', 1e-08, 'Adam optimizer epsilon.')

# When fine_tune_batch_norm=True, use at least batch size larger than 12
# (batch size more than 16 is better). Otherwise, one could use smaller batch
# size and set fine_tune_batch_norm=False.
flags.DEFINE_integer('train_batch_size', 64,
                     'The number of images in each batch during training.')

# For weight_decay, use 0.00004 for MobileNet-V2 or Xcpetion model variants.
# Use 0.0001 for ResNet model variants.
flags.DEFINE_float('weight_decay', 0.00004,
                   'The value of the weight decay for training.')

flags.DEFINE_list('train_crop_size', '513,513',
                  'Image crop size [height, width] during training.')

flags.DEFINE_float(
    'last_layer_gradient_multiplier', 1.0,
    'The gradient multiplier for last layers, which is used to '
    'boost the gradient of last layers if the value > 1.')

flags.DEFINE_boolean('upsample_logits', True,
                     'Upsample logits during training.')

# Hyper-parameters for NAS training strategy.

flags.DEFINE_float(
    'drop_path_keep_prob', 1.0,
    'Probability to keep each path in the NAS cell when training.')

# Settings for fine-tuning the network.

flags.DEFINE_string('tf_initial_checkpoint', None,
                    'The initial checkpoint in tensorflow format.')

# Set to False if one does not want to re-use the trained classifier weights.
flags.DEFINE_boolean('initialize_last_layer', True,
                     'Initialize the last layer.')

flags.DEFINE_boolean('last_layers_contain_logits_only', False,
                     'Only consider logits as last layers or not.')

flags.DEFINE_integer('slow_start_step', 0,
                     'Training model with small learning rate for few steps.')

flags.DEFINE_float('slow_start_learning_rate', 1e-4,
                   'Learning rate employed during slow start.')

# Set to True if one wants to fine-tune the batch norm parameters in DeepLabv3.
# Set to False and use small batch size to save GPU memory.
flags.DEFINE_boolean('fine_tune_batch_norm', True,
                     'Fine tune the batch norm parameters or not.')

flags.DEFINE_float('min_scale_factor', 0.5,
                   'Mininum scale factor for data augmentation.')

flags.DEFINE_float('max_scale_factor', 2.,
                   'Maximum scale factor for data augmentation.')

flags.DEFINE_float('scale_factor_step_size', 0.25,
                   'Scale factor step size for data augmentation.')

# For `xception_65`, use atrous_rates = [12, 24, 36] if output_stride = 8, or
# rates = [6, 12, 18] if output_stride = 16. For `mobilenet_v2`, use None. Note
# one could use different atrous_rates/output_stride during training/evaluation.
flags.DEFINE_multi_integer('atrous_rates', None,
                           'Atrous rates for atrous spatial pyramid pooling.')

flags.DEFINE_integer('output_stride', 16,
                     'The ratio of input to output spatial resolution.')

# Hard example mining related flags.
flags.DEFINE_integer(
    'hard_example_mining_step', 0,
    'The training step in which exact hard example mining kicks off. Note we '
    'gradually reduce the mining percent to the specified '
    'top_k_percent_pixels. For example, if hard_example_mining_step=100K and '
    'top_k_percent_pixels=0.25, then mining percent will gradually reduce from '
    '100% to 25% until 100K steps after which we only mine top 25% pixels.')

flags.DEFINE_float(
    'top_k_percent_pixels', 1.0,
    'The top k percent pixels (in terms of the loss values) used to compute '
    'loss during training. This is useful for hard pixel mining.')

# Quantization setting.
flags.DEFINE_integer(
    'quantize_delay_step', -1,
    'Steps to start quantized training. If < 0, will not quantize model.')

# Dataset settings.
flags.DEFINE_string('dataset', 'pascal_voc_seg',
                    'Name of the segmentation dataset.')

flags.DEFINE_string('train_split', 'train',
                    'Which split of the dataset to be used for training (seg)')

flags.DEFINE_string('dataset_dir', None, 'Where the dataset reside.')

## Pseudo_seg options
flags.DEFINE_boolean('weakly', False, 'Using image-level labeled data or not')

flags.DEFINE_string('train_split_cls', 'train_aug',
                    'Which split of the dataset to be used for training (cls)')

# Others
flags.DEFINE_integer('seed', 0, 'Random seed')


def _build_deeplab(iterator_seg, iterator, outputs_to_num_classes,
                   ignore_label):
  """Builds a clone of Supervised DeepLab.

  Args:
    iterator_seg: An iterator of type tf.data.Iterator for images and labels.
    (seg)
    iterator: An iterator of type tf.data. Iterator for images and labels.
    outputs_to_num_classes: A map from output type to the number of classes. For
      example, for the task of semantic segmentation with 21 semantic classes,
      we would have outputs_to_num_classes['semantic'] = 21.
    ignore_label: Ignore label.
  """
  if FLAGS.weakly:
    samples = iterator.get_next()
    samples[common.IMAGE] = tf.identity(
        samples[common.IMAGE], name=common.IMAGE)
    samples[common.LABEL] = tf.identity(
        samples[common.LABEL], name=common.LABEL)

  samples_seg = iterator_seg.get_next()
  samples_seg[common.IMAGE] = tf.identity(
      samples_seg[common.IMAGE], name=common.IMAGE + '_seg')
  samples_seg[common.LABEL] = tf.identity(
      samples_seg[common.LABEL], name=common.LABEL + '_seg')

  model_options = common.ModelOptions(
      outputs_to_num_classes=outputs_to_num_classes,
      crop_size=[int(sz) for sz in FLAGS.train_crop_size],
      atrous_rates=FLAGS.atrous_rates,
      output_stride=FLAGS.output_stride)

  ### Cls data
  if FLAGS.weakly:
    _, end_points_cls = feature_extractor.extract_features(
        samples[common.IMAGE],
        output_stride=model_options.output_stride,
        multi_grid=model_options.multi_grid,
        model_variant=model_options.model_variant,
        depth_multiplier=model_options.depth_multiplier,
        divisible_by=model_options.divisible_by,
        weight_decay=FLAGS.weight_decay,
        reuse=tf.AUTO_REUSE,
        is_training=True,
        preprocessed_images_dtype=model_options.preprocessed_images_dtype,
        fine_tune_batch_norm=FLAGS.fine_tune_batch_norm,
        global_pool=True,
        num_classes=outputs_to_num_classes[common.OUTPUT_TYPE] - 1)

    # ResNet beta version has an additional suffix in FLAGS.model_variant, but
    # it shares the same variable names with original version. Add a special
    # handling here for beta version ResNet.
    logits_cls = end_points_cls['{}/logits'.format(FLAGS.model_variant).replace(
        '_beta', '')]
    logits_cls = tf.reshape(
        logits_cls, [-1, outputs_to_num_classes[common.OUTPUT_TYPE] - 1])
    # Seems that people usually use multi-label soft margin loss
    loss_cls = tf.nn.sigmoid_cross_entropy_with_logits(
        labels=samples['cls_label'],
        logits=logits_cls)
    loss_cls = tf.reduce_mean(loss_cls)
    loss_cls = tf.identity(loss_cls, name='loss_cls')
    tf.compat.v1.losses.add_loss(loss_cls)

  ### Seg data
  outputs_to_scales_to_logits = model.multi_scale_logits(
      samples_seg[common.IMAGE],
      model_options=model_options,
      image_pyramid=FLAGS.image_pyramid,
      weight_decay=FLAGS.weight_decay,
      is_training=True,
      fine_tune_batch_norm=FLAGS.fine_tune_batch_norm,
      nas_training_hyper_parameters={
          'drop_path_keep_prob': FLAGS.drop_path_keep_prob,
          'total_training_steps': FLAGS.training_number_of_steps,
      })

  # Add name to graph node so we can add to summary.
  output_type_dict = outputs_to_scales_to_logits[common.OUTPUT_TYPE]
  output_type_dict[model.MERGED_LOGITS_SCOPE] = tf.identity(
      output_type_dict[model.MERGED_LOGITS_SCOPE],
      name=common.OUTPUT_TYPE + '_seg')

  for output, num_classes in six.iteritems(outputs_to_num_classes):
    train_utils.add_softmax_cross_entropy_loss_for_each_scale(
        outputs_to_scales_to_logits[output],
        samples_seg[common.LABEL],
        num_classes,
        ignore_label,
        loss_weight=model_options.label_weights,
        upsample_logits=FLAGS.upsample_logits,
        hard_example_mining_step=FLAGS.hard_example_mining_step,
        top_k_percent_pixels=FLAGS.top_k_percent_pixels,
        scope=output)

  ## Sanity check. Monitor pixel accuracy
  logits_seg = outputs_to_scales_to_logits[common.OUTPUT_TYPE][
      model.MERGED_LOGITS_SCOPE]
  temp_label = tf.compat.v1.image.resize_nearest_neighbor(
      samples_seg[common.LABEL],
      preprocess_utils.resolve_shape(logits_seg, 4)[1:3])
  temp_label = tf.reshape(temp_label, [-1])

  dump = tf.concat(
      [tf.range(outputs_to_num_classes[common.OUTPUT_TYPE]), temp_label],
      axis=-1)
  _, _, count = tf.unique_with_counts(dump)
  num_pixel_list = count - 1
  # Exclude the ignore region
  num_pixel_list = num_pixel_list[:outputs_to_num_classes[common.OUTPUT_TYPE]]
  num_pixel_list = tf.cast(num_pixel_list, tf.float32)
  inverse_ratio = train_utils._div_maybe_zero(1, num_pixel_list)
  inverse_ratio = inverse_ratio / tf.reduce_sum(inverse_ratio)

  # Create weight mask to balance each class
  weight_mask = tf.einsum(
      '...y,y->...',
      tf.one_hot(
          temp_label,
          outputs_to_num_classes[common.OUTPUT_TYPE],
          dtype=tf.float32), inverse_ratio)
  temp_valid = tf.not_equal(temp_label, ignore_label)
  temp_label_valid = tf.boolean_mask(temp_label, temp_valid)
  weight_mask_valid = tf.boolean_mask(weight_mask, temp_valid)

  pred_seg = tf.argmax(logits_seg, axis=-1)
  pred_seg = tf.reshape(pred_seg, [-1])
  acc_seg, acc_seg_op = tf.metrics.mean_per_class_accuracy(
      temp_label_valid,
      tf.boolean_mask(pred_seg, temp_valid),
      outputs_to_num_classes[common.OUTPUT_TYPE],
      weights=weight_mask_valid)
  with tf.control_dependencies([acc_seg_op]):
    acc_seg = tf.identity(acc_seg, name='acc_seg')


def main(unused_argv):
  tf.logging.set_verbosity(tf.logging.INFO)
  tf.set_random_seed(FLAGS.seed)
  # Set up deployment (i.e., multi-GPUs and/or multi-replicas).
  config = model_deploy.DeploymentConfig(
      num_clones=FLAGS.num_clones,
      clone_on_cpu=FLAGS.clone_on_cpu,
      replica_id=FLAGS.task,
      num_replicas=FLAGS.num_replicas,
      num_ps_tasks=FLAGS.num_ps_tasks)

  # Split the batch across GPUs.
  assert FLAGS.train_batch_size % config.num_clones == 0, (
      'Training batch size not divisble by number of clones (GPUs).')
  clone_batch_size = FLAGS.train_batch_size // config.num_clones

  tf.gfile.MakeDirs(FLAGS.train_logdir)
  tf.logging.info('Training segmentation on %s set', FLAGS.train_split)
  if FLAGS.weakly:
    tf.logging.info('Training classification on %s set', FLAGS.train_split_cls)

  with tf.Graph().as_default() as graph:
    with tf.device(config.inputs_device()):
      dataset = data_generator.Dataset(
          dataset_name=FLAGS.dataset,
          split_name=FLAGS.train_split,
          dataset_dir=FLAGS.dataset_dir,
          batch_size=clone_batch_size,
          crop_size=[int(sz) for sz in FLAGS.train_crop_size],
          min_resize_value=FLAGS.min_resize_value,
          max_resize_value=FLAGS.max_resize_value,
          resize_factor=FLAGS.resize_factor,
          min_scale_factor=FLAGS.min_scale_factor,
          max_scale_factor=FLAGS.max_scale_factor,
          scale_factor_step_size=FLAGS.scale_factor_step_size,
          model_variant=FLAGS.model_variant,
          num_readers=4,
          is_training=True,
          should_shuffle=True,
          should_repeat=True,
          with_cls=True,
          cls_only=False)

      if FLAGS.weakly:
        dataset_cls = data_generator.Dataset(
            dataset_name=FLAGS.dataset,
            split_name=FLAGS.train_split_cls,
            dataset_dir=FLAGS.dataset_dir,
            batch_size=clone_batch_size,
            crop_size=[int(sz) for sz in FLAGS.train_crop_size],
            min_resize_value=FLAGS.min_resize_value,
            max_resize_value=FLAGS.max_resize_value,
            resize_factor=FLAGS.resize_factor,
            min_scale_factor=FLAGS.min_scale_factor,
            max_scale_factor=FLAGS.max_scale_factor,
            scale_factor_step_size=FLAGS.scale_factor_step_size,
            model_variant=FLAGS.model_variant,
            num_readers=4,
            is_training=True,
            should_shuffle=True,
            should_repeat=True,
            with_cls=True,
            cls_only=False)
      else:
        dataset_cls = dataset

    # Create the global step on the device storing the variables.
    with tf.device(config.variables_device()):
      global_step = tf.train.get_or_create_global_step()

      # Define the model and create clones.
      model_fn = _build_deeplab
      model_args = (dataset.get_one_shot_iterator(),
                    dataset_cls.get_one_shot_iterator(), {
                        common.OUTPUT_TYPE: dataset.num_of_classes
                    }, dataset.ignore_label)
      clones = model_deploy.create_clones(config, model_fn, args=model_args)

      # Gather update_ops from the first clone. These contain, for example,
      # the updates for the batch_norm variables created by model_fn.
      first_clone_scope = config.clone_scope(0)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)

    # Gather initial summaries.
    summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))

    # Add summaries for model variables.
    for model_var in tf.model_variables():
      summaries.add(tf.summary.histogram(model_var.op.name, model_var))

    # Add summaries for images, labels, semantic predictions
    # Visualize seg image and predictions
    if FLAGS.save_summaries_images:
      summary_image = graph.get_tensor_by_name(
          ('%s/%s:0' % (first_clone_scope, common.IMAGE+'_seg')).strip('/'))
      summaries.add(
          tf.summary.image('samples/%s' % common.IMAGE+'_seg', summary_image))

      first_clone_label = graph.get_tensor_by_name(
          ('%s/%s:0' % (first_clone_scope, common.LABEL+'_seg')).strip('/'))
      # Scale up summary image pixel values for better visualization.
      pixel_scaling = max(1, 255 // dataset.num_of_classes)
      summary_label = tf.cast(first_clone_label * pixel_scaling, tf.uint8)
      summaries.add(
          tf.summary.image('samples/%s' % common.LABEL+'_seg', summary_label))

      first_clone_output = graph.get_tensor_by_name(
          ('%s/%s:0' %
           (first_clone_scope, common.OUTPUT_TYPE + '_seg')).strip('/'))
      predictions = tf.expand_dims(tf.argmax(first_clone_output, 3), -1)
      summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8)
      summaries.add(
          tf.summary.image(
              'samples/%s' % common.OUTPUT_TYPE+'_seg', summary_predictions))

    # Add summaries for losses.
    for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
      summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))

    # Monitor pseudo label quality
    summary = graph.get_tensor_by_name(
        ('%s/%s:0' % (first_clone_scope, 'acc_seg')).strip('/'))
    summaries.add(tf.summary.scalar('sanity_check/acc_seg', summary))

    # Build the optimizer based on the device specification.
    with tf.device(config.optimizer_device()):
      learning_rate = train_utils.get_model_learning_rate(
          FLAGS.learning_policy,
          FLAGS.base_learning_rate,
          FLAGS.learning_rate_decay_step,
          FLAGS.learning_rate_decay_factor,
          FLAGS.training_number_of_steps,
          FLAGS.learning_power,
          FLAGS.slow_start_step,
          FLAGS.slow_start_learning_rate,
          decay_steps=FLAGS.decay_steps,
          end_learning_rate=FLAGS.end_learning_rate)

      summaries.add(tf.summary.scalar('learning_rate', learning_rate))

      if FLAGS.optimizer == 'momentum':
        optimizer = tf.train.MomentumOptimizer(learning_rate, FLAGS.momentum)
      elif FLAGS.optimizer == 'adam':
        optimizer = tf.train.AdamOptimizer(
            learning_rate=FLAGS.adam_learning_rate, epsilon=FLAGS.adam_epsilon)
      else:
        raise ValueError('Unknown optimizer')

    startup_delay_steps = FLAGS.task * FLAGS.startup_delay_steps
    with tf.device(config.variables_device()):
      total_loss, grads_and_vars = model_deploy.optimize_clones(
          clones, optimizer)
      total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
      summaries.add(tf.summary.scalar('total_loss', total_loss))

      # Modify the gradients for biases and last layer variables.
      last_layers = model.get_extra_layer_scopes(
          FLAGS.last_layers_contain_logits_only)
      grad_mult = train_utils.get_model_gradient_multipliers(
          last_layers, FLAGS.last_layer_gradient_multiplier)
      if grad_mult:
        grads_and_vars = slim.learning.multiply_gradients(
            grads_and_vars, grad_mult)
      # NOTE: Neither last cls nor last seg layer loads pre-trained weights
      last_layers += [
          '{}/logits'.format(FLAGS.model_variant).replace('_beta', '')
      ]

      # Create gradient update op.
      grad_updates = optimizer.apply_gradients(
          grads_and_vars, global_step=global_step)
      update_ops.append(grad_updates)
      update_op = tf.group(*update_ops)
      with tf.control_dependencies([update_op]):
        train_tensor = tf.identity(total_loss, name='train_op')

    # Add the summaries from the first clone. These contain the summaries
    # created by model_fn and either optimize_clones() or _gather_clone_loss().
    summaries |= set(
        tf.get_collection(tf.GraphKeys.SUMMARIES, first_clone_scope))

    # Merge all summaries together.
    summary_op = tf.summary.merge(list(summaries))

    # Soft placement allows placing on CPU ops without GPU implementation.
    session_config = tf.ConfigProto(
        allow_soft_placement=True, log_device_placement=False)
    session_config.gpu_options.allow_growth = True

    # Start the training.
    profile_dir = FLAGS.profile_logdir
    if profile_dir is not None:
      tf.gfile.MakeDirs(profile_dir)

    with contrib_tfprof.ProfileContext(
        enabled=profile_dir is not None, profile_dir=profile_dir):
      init_fn = None
      if FLAGS.tf_initial_checkpoint:
        init_fn = train_utils.get_model_init_fn(
            FLAGS.train_logdir,
            FLAGS.tf_initial_checkpoint,
            FLAGS.initialize_last_layer,
            last_layers,
            ignore_missing_vars=True)

      slim.learning.train(
          train_tensor,
          logdir=FLAGS.train_logdir,
          log_every_n_steps=FLAGS.log_steps,
          master=FLAGS.master,
          number_of_steps=FLAGS.training_number_of_steps,
          is_chief=(FLAGS.task == 0),
          session_config=session_config,
          startup_delay_steps=startup_delay_steps,
          init_fn=init_fn,
          summary_op=summary_op,
          save_summaries_secs=FLAGS.save_summaries_secs,
          save_interval_secs=FLAGS.save_interval_secs)


if __name__ == '__main__':
  flags.mark_flag_as_required('train_logdir')
  flags.mark_flag_as_required('dataset_dir')
  tf.app.run()