ChannelPrunedLearner don't exec

[as754770178]

I compress the model by ChannelPrunedLearner, when executed to self.sess_train.run(self.train_op) in __train_pruned_model(self, finetune=False) fun, the program don't continue execution。 I don't know This is because my machine is too card or code error。

my gpu card is Tesla K80。

the program stop at :

  def __train_pruned_model(self, finetune=False):
    """Train pruned model"""
    # Initialize varialbes
    self.sess_train.run(self.train_init_op)

    if FLAGS.enbl_multi_gpu:
      self.sess_train.run(self.bcast_op)

    ## Fintuning & distilling
    self.time_prev = timer()

    nb_iters = int(FLAGS.cp_nb_iters_ft_ratio * self.nb_iters_train) \
      if finetune and not FLAGS.cp_retrain else self.nb_iters_train

    for self.idx_iter in range(nb_iters):
      # train the model
      if (self.idx_iter + 1) % FLAGS.summ_step != 0:
        self.sess_train.run(self.train_op)  # stop at here
      else:
        __, summary, log_rslt = self.sess_train.run([self.train_op, self.summary_op, self.log_op])
        self.__monitor_progress(summary, log_rslt)

      # save the model at certain steps
      if (self.idx_iter + 1) % FLAGS.save_step == 0:
        #summary, log_rslt = self.sess_train.run([self.summary_op, self.log_op])
        #self.__monitor_progress(summary, log_rslt)
        if self.__is_primary_worker():
          self.__save_model() # sess_train
          self.evaluate()

[as754770178]

This is log

INFO:tensorflow:build pruned training model
INFO:tensorflow:Restoring parameters from ./models/pruned_model.ckpt
INFO:tensorflow:Restoring parameters from ./models/pruned_model.ckpt
WARNING:tensorflow:From /home/zgz/anaconda2/envs/tf-1.8-cp3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:118: initialize_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.variables_initializer` instead.
INFO:tensorflow:training pruned model

I run pocket flow by some Learner, Can't run smoothly

[jiaxiang-wu]

You mean the program does not have any outputs? Can you take a look at the GPU utility via nvidia-smi? If you decrease the value of FLAGS.summ_step (e.g. set it to 1), will there be any outputs?

[as754770178]

nvidia-smi info:

 3  Tesla K80           On   | 00000000:86:00.0 Off |                    0 |
| N/A   74C    P0    74W / 149W |  10493MiB / 11439MiB |      0%      Default |

I change FLAGS.summ_step to 1, still don't print.

[jiaxiang-wu]

Are you using the pre-defined models in PocketFlow, or are you using a self-defined model implemented by yourself?

[as754770178]

using a self-defined model implemented by myself

[jiaxiang-wu]

Does ChannelPrunedLearner hang when using pre-defined models in PocketFlow? May it is not compatible with your self-defined model?

[as754770178]

Yes, but I want to know how to using a self-defined model implemented by myself. Can you tell me some method that positioning this problem.

[jiaxiang-wu]

Can you post the code for your self-defined model (ModelHelper and the corresponding xxx_run.py script)?

[as754770178]

I try change it, the code is too long.

[jiaxiang-wu]

Or, can you upload the file?

[as754770178]

The file is also too many, I write a demo of my net. especially the dataset, I mock it and delete the data augmentation and pre-process. net definition:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections

import tensorflow as tf
slim = tf.contrib.slim

class NoOpScope(object):
  """No-op context manager."""

  def __enter__(self):
    return None

  def __exit__(self, exc_type, exc_val, traceback):
    return False

class Block(collections.namedtuple('Block', ['scope', 'unit_fn', 'args'])):
  """A named tuple describing a ResNet block."""

def subsample(inputs, factor, scope=None):
  if factor == 1:
    return inputs
  else:
    return slim.max_pool2d(inputs, [1, 1], stride=factor, scope=scope)

@slim.add_arg_scope
def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None, data_format='NHWC'):
  if stride == 1:
    return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate,
                       padding='SAME', scope=scope)
  else:
    kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
    pad_total = kernel_size_effective - 1
    pad_beg = pad_total // 2
    pad_end = pad_total - pad_beg
    if data_format == 'NHWC':
        inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
    else:
        inputs = tf.pad(inputs, [[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
    return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride,
                       rate=rate, padding='VALID', scope=scope)

@slim.add_arg_scope
def bottleneck(inputs, depth, depth_bottleneck, stride, rate=1,
               outputs_collections=None, scope=None, use_bounded_activations=False,
               data_format='NHWC'):
  """Bottleneck residual unit variant with BN after convolutions.
  """
  with tf.variable_scope(scope, 'bottleneck_v1', [inputs]) as sc:
    if data_format == 'NHWC':
      depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    else:
      depth_in = inputs.get_shape().as_list()[1]
    if depth == depth_in:
      shortcut = subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride,
                             activation_fn=tf.nn.relu6 if use_bounded_activations else None,
                             scope='shortcut')

    residual = slim.conv2d(inputs, depth_bottleneck, [1, 1], stride=1,
                           scope='conv1')
    residual = conv2d_same(residual, depth_bottleneck, 3, stride,
                                        rate=rate, scope='conv2')
    residual = slim.conv2d(residual, depth, [1, 1], stride=1,
                           activation_fn=None, scope='conv3')

    if use_bounded_activations:
      # Use clip by value to simulate bandpass activatin
      residual = tf.clip_by_value(residual, -6.0, 6.0)
      output = tf.nn.relu6(shortcut + residual)
    else:
      output = tf.nn.relu(shortcut + residual)

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.original_name_scope,
                                            output)

@slim.add_arg_scope
def residual_unit(inputs, depth, stride, rate=1,
                  outputs_collections=None, scope=None, data_format='NHWC'):
  with tf.variable_scope(scope, 'residual_unit_v1', [inputs]) as sc:
    if data_format == 'NHWC':
      depth_in = slim.utils.last_dimension(inputs.get_shape(), min_rank=4)
    else:
      depth_in = inputs.get_shape().as_list()[1]
    if depth == depth_in:
      shortcut = subsample(inputs, stride, 'shortcut')
    else:
      shortcut = slim.conv2d(inputs, depth, [1, 1], stride=stride,
                             activation_fn=None, scope='shortcut')

    residual = slim.conv2d(inputs, depth, [3, 3], stride=1,
                           scope='conv1')

    residual = slim.conv2d(residual, depth, [3, 3], stride=stride,
                           activation_fn=None, scope='conv2')

    output = tf.nn.relu(shortcut + residual)

    return slim.utils.collect_named_outputs(outputs_collections,
                                            sc.original_name_scope,
                                            output)

def resnet_v1_block(scope, base_depth, num_units, stride, use_bottleneck=True):

  if use_bottleneck is True:
      return Block(scope, bottleneck, [{
          'depth': base_depth * 4,
          'depth_bottleneck': base_depth,
          'stride': 1
      }] * (num_units - 1) + [{
          'depth': base_depth * 4,
          'depth_bottleneck': base_depth,
          'stride': stride
      }])
  else:
      return Block(scope, residual_unit, [{
          'depth': base_depth,
          'stride': 1,
      }] + (num_units - 1) * [{
          'depth': base_depth,
          'stride': stride,
      }])

@slim.add_arg_scope
def stack_blocks_dense(net, blocks, output_stride=None,
                       store_non_strided_activations=False,
                       outputs_collections=None):
  current_stride = 1

  # The atrous convolution rate parameter.
  rate = 1

  for block in blocks:
    with tf.variable_scope(block.scope, 'block', [net]) as sc:
      block_stride = 1
      for i, unit in enumerate(block.args):
        if store_non_strided_activations and i == len(block.args) - 1:
          # Move stride from the block's last unit to the end of the block
          block_stride = unit.get('stride', 1)
          unit = dict(unit, stride=1)
        if output_stride is not None and current_stride > output_stride:
          raise ValueError('The target output_stride cannot be reached.')

        with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
          # If we have reached the target output_stride, then we need to employ
          # atrous convolution with stride=1 and multiply the atrous rate by the
          # current unit's stride for use in subsequent layers.
          if output_stride is not None and current_stride == output_stride:
            net = block.unit_fn(net, rate=rate, **dict(unit, stride=1))
            rate *= unit.get('stride', 1)

          else:
            net = block.unit_fn(net, rate=1, **unit)
            current_stride *= unit.get('stride', 1)
            if output_stride is not None and current_stride > output_stride:
              raise ValueError('The target output_stride cannot be recalled')

      net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net)

      # Subsampling of the block's output activations.
      if output_stride is not None and current_stride == output_stride:
        rate *= block_stride
      else:
        net = subsample(net, block_stride)
        current_stride *= block_stride
        if output_stride is not None and current_stride > output_stride:
          raise ValueError('The target output_stride cannot be reached.')

  if output_stride is not None and current_stride != output_stride:
    raise ValueError('The target output_stride cannot be reached.')

  return net

@slim.add_arg_scope
def resnet_v1(inputs,
              blocks,
              num_classes=None,
              is_training=True,
              global_pool=True,
              output_stride=None,
              include_root_block=True,
              spatial_squeeze=False,
              store_non_strided_activations=False,
              reuse=None,
              scope=None,
              data_format='NHWC'):
  with tf.variable_scope(scope, 'resnet_v1', [inputs], reuse=reuse) as sc:
    end_points_collection = sc.name + '_end_points'
    with slim.arg_scope([slim.conv2d, bottleneck,
                         stack_blocks_dense],
                        outputs_collections=end_points_collection):
      with (slim.arg_scope([slim.batch_norm], is_training=is_training)
            if is_training is not None else NoOpScope()):
        net = inputs
        if include_root_block:
          if output_stride is not None:
            if output_stride % 4 != 0:
              raise ValueError('The output_stride needs to be a multiple of 4.')
            output_stride /= 4
          net = conv2d_same(net, 64, 7, stride=2, scope='conv1')
          net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1')
        net = stack_blocks_dense(net, blocks, output_stride,
                                              store_non_strided_activations)

        # Convert end_points_collection into a dictionary of end_points.
        end_points = slim.utils.convert_collection_to_dict(
          end_points_collection)

        if global_pool:
          # Global average pooling.
          if data_format == 'NHWC':
            net = tf.reduce_mean(net, [1, 2], name='pool5', keepdims=True)
            end_points['global_pool'] = net
          else:
            net = tf.reduce_mean(net, [2, 3], name='pool5', keepdims=True)
            end_points['global_pool'] = net
        if num_classes is not None:
          net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                            normalizer_fn=None, scope='logits')
          end_points[sc.name + '/logits'] = net
          if spatial_squeeze:
            if data_format == 'NHWC':
              net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
              end_points[sc.name + '/spatial_squeeze'] = net
            else:
              net = tf.squeeze(net, [2, 3], name='SpatialSqueeze')
              end_points[sc.name + '/spatial_squeeze'] = net
          end_points['predictions'] = slim.softmax(net, scope='predictions')
        return net, end_points
resnet_v1.default_image_size = 224
resnet_v1.default_labels_offset = 1
resnet_v1.default_logits_pattern = 'logits'
run
def resnet_v1_18(inputs,
                 num_classes=None,
                 is_training=True,
                 global_pool=True,
                 output_stride=None,
                 spatial_squeeze=True,
                 store_non_strided_activations=False,
                 reuse=None,
                 scope='resnet_v1_18'):
  """ResNet-18 model of [1]. See resnet_v1() for arg and return description."""
  blocks = [
      resnet_v1_block('block1', base_depth=64, num_units=2, stride=2, use_bottleneck=False),
      resnet_v1_block('block2', base_depth=128, num_units=2, stride=2, use_bottleneck=False),
      resnet_v1_block('block3', base_depth=256, num_units=2, stride=2, use_bottleneck=False),
      resnet_v1_block('block4', base_depth=512, num_units=2, stride=1, use_bottleneck=False),
  ]
  return resnet_v1(inputs, blocks, num_classes, is_training,
                   global_pool=global_pool, output_stride=output_stride,
                   include_root_block=True, spatial_squeeze=spatial_squeeze,
                   store_non_strided_activations=store_non_strided_activations,
                   reuse=reuse, scope=scope)
resnet_v1_18.default_image_size = resnet_v1.default_image_size
resnet_v1_18.default_labels_offset = resnet_v1.default_labels_offset
resnet_v1_18.default_logits_pattern = resnet_v1.default_logits_pattern

def resnet_arg_scope(weight_decay=0.0001,
                     batch_norm_decay=0.997,
                     batch_norm_epsilon=1e-5,
                     batch_norm_scale=True,
                     data_format='NHWC',
                     use_batch_norm=True,
                     batch_norm_updates_colections=tf.GraphKeys.UPDATE_OPS,
                     **kwargs):
  batch_norm_params = {
      'decay': batch_norm_decay,
      'epsilon': batch_norm_epsilon,
      'scale': batch_norm_scale,
      'updates_collections': batch_norm_updates_colections,
  }

  if 'weights_initializer_params' in kwargs:
    weights_initializer_params = kwargs['weights_initializer_params']
  else:
    weights_initializer_params = {}

  with slim.arg_scope(
      [slim.conv2d],
      weights_regularizer=slim.l2_regularizer(weight_decay),
      weights_initializer=slim.variance_scaling_initializer(**weights_initializer_params),
      activation_fn=tf.nn.relu,
      normalizer_fn=slim.batch_norm if use_batch_norm else None,
      normalizer_params=batch_norm_params):
    with slim.arg_scope([conv2d_same], data_format=data_format):
      with slim.arg_scope([slim.batch_norm], **batch_norm_params):
        with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc:
          return arg_sc

my model helper:

import tensorflow as tf
import numpy as np

from pocket_flow.nets.abstract_model_helper import AbstractModelHelper
from pocket_flow.utils.lrn_rate_utils import setup_lrn_rate_piecewise_constant
from pocket_flow.utils.lrn_rate_utils import setup_lrn_rate_exponential_decay
from .nets.resnet_v1_18 import resnet_v1_18
from .nets.resnet_v1_18 import resnet_arg_scope
slim = tf.contrib.slim

FLAGS = tf.flags.FLAGS

tf.flags.DEFINE_float('lrn_rate_init', 1e-2, 'initial learning rate')
tf.flags.DEFINE_float('batch_size_norm', 128, 'normalization factor of batch size')
tf.flags.DEFINE_float('loss_w_dcy', 2e-4, 'weight decaying loss\'s coefficient')
tf.flags.DEFINE_float('nb_epochs_rat', 1.0, '# of training epochs\'s ratio')
tf.flags.DEFINE_integer('learning_rate_version', 1, 'Learning rate\'s version '
                                                        '(1: Setup the learning rate with piecewise constant strategy.'
                                                        ' or 2: Setup the learning rate with exponential decaying strategy.)')
tf.flags.DEFINE_integer('compress_nb_epochs', 100,
                       'Max number of epochs to compress once.')
tf.flags.DEFINE_string('compress_idxs_epoch', '30,50,100',
                       'Use with compress_decay_rates in Learning rate version 1.')
tf.flags.DEFINE_string('compress_decay_rates', '0.1,0.01,0.001,0.0001',
                       'Use with compress_idxs_epoch in Learning rate version 1.')
tf.flags.DEFINE_float('compress_epoch_step', 2.5,
                       'Use with compress_decay_rate in Learning rate version 2.')
tf.flags.DEFINE_float('compress_decay_rate', 0.98,
                       'Use with compress_epoch_step in Learning rate version 2.')

class ModelHelper(AbstractModelHelper):
  """Model helper for creating a ResNet model for the CIFAR-10 dataset."""

  def __init__(self, image_size=224, data_fromat='NHWC', num_classes=5, batch_size=64):
    """Constructor function."""

    # class-independent initialization
    super(ModelHelper, self).__init__()
    self.image_size = image_size
    self.data_fromat = data_fromat
    self.num_classes = num_classes
    self.batch_size = batch_size

  def build_dataset_train(self, enbl_trn_val_split=False):
    """Build the data subset for training, usually with data augmentation."""

    dataset = tf.data.Dataset.from_tensor_slices({
      "image":np.random.uniform(size=(640, self.image_size, self.image_size, 3)),
      "label":np.random.uniform(size=(self.batch_size, self.num_classes))
    })

    dataset = dataset.shuffle(1000).batch(self.batch_size)
    iterator = dataset.make_initializable_iterator()

    return iterator

  def build_dataset_eval(self):
    """Build the data subset for evaluation, usually without data augmentation."""

    dataset = tf.data.Dataset.from_tensor_slices({
      "image": np.random.uniform(
        size=(640, self.image_size, self.image_size, 3)),
      "label": np.random.uniform(size=(self.batch_size, self.num_classes))
    })

    dataset = dataset.shuffle(1000).batch(self.batch_size)
    iterator = dataset.make_initializable_iterator()

    return iterator

  def forward_train(self, inputs):
    arg_scope = resnet_arg_scope(weight_decay=FLAGS.weight_decay,
                                 data_format=self.data_fromat)
    with slim.arg_scope(arg_scope):
      with slim.arg_scope([slim.bias_add, slim.batch_norm, slim.conv2d,
                           slim.conv2d_in_plane, slim.conv2d_transpose,
                           slim.avg_pool2d, slim.max_pool2d,
                           slim.unit_norm],
                          data_format=self.data_fromat):
        with slim.arg_scope([slim.batch_norm], fused=True,
                            renorm=False):
          logits, end_points = resnet_v1_18(inputs,
                                            num_classes=5,
                                            is_training=True)
    return logits

  def forward_eval(self, inputs):
    arg_scope = resnet_arg_scope(weight_decay=FLAGS.weight_decay,
                                     data_format=self.data_fromat)
    with slim.arg_scope(arg_scope):
      with slim.arg_scope([slim.bias_add, slim.batch_norm, slim.conv2d,
                           slim.conv2d_in_plane, slim.conv2d_transpose,
                           slim.avg_pool2d, slim.max_pool2d,
                           slim.unit_norm],
                          data_format=self.data_fromat):
        with slim.arg_scope([slim.batch_norm], fused=True,
                            renorm=False):
          logits, end_points = resnet_v1_18(inputs,
                                            num_classes=5,
                                            is_training=False)
    return logits

  def calc_loss(self, labels, outputs, trainable_vars):
    """Calculate loss (and some extra evaluation metrics)."""

    loss = tf.losses.softmax_cross_entropy(labels, outputs)
    loss_filter = lambda var: 'batch_normalization' not in var.name
    reg_vars = [tf.nn.l2_loss(var) for var in trainable_vars if loss_filter(var)]
    loss += FLAGS.loss_w_dcy \
      * tf.add_n(reg_vars)
    accuracy = tf.reduce_mean(
      tf.cast(tf.equal(tf.argmax(labels, axis=1), tf.argmax(outputs, axis=1)), tf.float32))
    metrics = {'accuracy': accuracy}

    return loss, metrics

  def setup_lrn_rate(self, global_step):
    """Setup the learning rate (and number of training iterations)."""

    #batch_size = FLAGS.batch_size * (1 if not FLAGS.enbl_multi_gpu else mgw.size())
    batch_size = FLAGS.batch_size
    if FLAGS.learning_rate_version == 1:
      nb_epochs = FLAGS.compress_nb_epochs or 100
      idxs_epoch = [int(int_item) for int_item in FLAGS.compress_idxs_epoch.split(',')] or [30, 60, 80, 90]
      decay_rates = [float(float_item) for float_item in FLAGS.compress_decay_rates.split(',')] or [1.0, 0.1, 0.01, 0.001, 0.0001]
      lrn_rate = setup_lrn_rate_piecewise_constant(global_step, batch_size, idxs_epoch, decay_rates, self.train_dataset_meta)
      nb_iters = int(self.train_dataset_meta.total_num_samples * nb_epochs * FLAGS.nb_epochs_rat / batch_size)
    elif FLAGS.learning_rate_version == 2:
      nb_epochs = FLAGS.compress_nb_epochs or 412
      epoch_step = FLAGS.compress_epoch_step or 2.5
      decay_rate = FLAGS.compress_decay_rate or 0.98
      decay_rate = decay_rate ** epoch_step  # which is better, 0.98 OR (0.98 ** epoch_step)?
      lrn_rate = setup_lrn_rate_exponential_decay(global_step, batch_size, epoch_step, decay_rate, self.train_dataset_meta)
      nb_iters = int(self.train_dataset_meta.total_num_samples * nb_epochs * FLAGS.nb_epochs_rat / batch_size)
    else:
      raise ValueError('invalid MobileNet version: {}'.format(FLAGS.learning_rate_version))

    return lrn_rate, nb_iters

  @property
  def model_name(self):
    """Model's name."""

    return FLAGS.model_name

  @property
  def dataset_name(self):
    """Dataset's name."""

    return FLAGS.dataset_name

my call func:

model_helper = ModelHelper()
learner = create_learner(sm_writer, model_helper)
learner.train()

[as754770178]

@jiaxiang-wu The input of pretrained model , I define in next code.

  def input_fn(run_mode, **kwargs):
    if use_eval_data_in_training:
      dataset = eval_dataset
    else:
      dataset = train_dataset

    with tf.variable_scope("data"):
      image, label = dataset.get(['image', 'label'])
      mem_images = tf.placeholder(dtype=image.dtype,
                                  shape=image.shape)
      mem_labels = tf.placeholder(dtype=label.dtype,
                                  shape=label.shape)

    tf.add_to_collection('mem_images', mem_images)
    tf.add_to_collection('mem_labels', mem_labels)
    if run_mode == 'train':
      tf.add_to_collection('train_images', image)
      tf.add_to_collection('train_labels', label)
    else:
      tf.add_to_collection('eval_images', image)
      tf.add_to_collection('eval_labels', label)

    image, label = dataset.get(['image', 'label'])
    return image, label

I only define placeholder as mem_images or mem_labels and add to collection. Do you do like this?

[as754770178]

I read the code in learner.py.

  def __finetune_pruned_model(self, path=None, finetune=False):
    if path is None:
      path = FLAGS.cp_channel_pruned_path
    start = timer()
    tf.logging.info('build pruned evaluating model')
    self.__build_pruned_evaluate_model(path)
    tf.logging.info('build pruned training model')
    self.__build_pruned_train_model(path, finetune=finetune)
    tf.logging.info('training pruned model')
    self.__train_pruned_model(finetune=finetune)
    tf.logging.info('fintuning time cost: {}s'.format(timer() - start))

Why edit the graph in __build_pruned_train_model ?

train_images = tf.get_collection('train_images')[0]
      train_labels = tf.get_collection('train_labels')[0]
      mem_images = tf.get_collection('mem_images')[0]
      mem_labels = tf.get_collection('mem_labels')[0]

      self.sess_train.close()

      graph_editor.reroute_ts(train_images, mem_images)
      graph_editor.reroute_ts(train_labels, mem_labels)

      self.sess_train = tf.Session(config=config)
      self.saver_train.restore(self.sess_train, path)

When train the pruned model in __train_pruned_model, the code don't use mem_images. and my code stop at self.sess_train.run(self.train_op).

    for self.idx_iter in range(nb_iters):
      # train the model
      if (self.idx_iter + 1) % FLAGS.summ_step != 0:
        self.sess_train.run(self.train_op)
      else:
        __, summary, log_rslt = self.sess_train.run([self.train_op, self.summary_op, self.log_op])
        self.__monitor_progress(summary, log_rslt)