Python resnet_model.HParams() Examples

The following are 13 code examples of resnet_model.HParams(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module resnet_model , or try the search function .
Example #1
Source File: resnet_main.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #2
Source File: resnet_main.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #3
Source File: resnet_main.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #4
Source File: resnet_main.py    From deeplearning-benchmark with Apache License 2.0 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

#   with tf.device(dev):
  if FLAGS.mode == 'train':
    train(hps)
  elif FLAGS.mode == 'eval':
    evaluate(hps)
Example #5
Source File: resnet_main.py    From Action_Recognition_Zoo with MIT License 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #6
Source File: resnet_main.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #7
Source File: resnet_main.py    From hands-detection with MIT License 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #8
Source File: resnet_main.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #9
Source File: resnet_main.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #10
Source File: resnet_main.py    From HumanRecognition with MIT License 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #11
Source File: resnet_main.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #12
Source File: resnet_main.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def main(_):
  if FLAGS.num_gpus == 0:
    dev = '/cpu:0'
  elif FLAGS.num_gpus == 1:
    dev = '/gpu:0'
  else:
    raise ValueError('Only support 0 or 1 gpu.')

  if FLAGS.mode == 'train':
    batch_size = 128
  elif FLAGS.mode == 'eval':
    batch_size = 100

  if FLAGS.dataset == 'cifar10':
    num_classes = 10
  elif FLAGS.dataset == 'cifar100':
    num_classes = 100

  hps = resnet_model.HParams(batch_size=batch_size,
                             num_classes=num_classes,
                             min_lrn_rate=0.0001,
                             lrn_rate=0.1,
                             num_residual_units=5,
                             use_bottleneck=False,
                             weight_decay_rate=0.0002,
                             relu_leakiness=0.1,
                             optimizer='mom')

  with tf.device(dev):
    if FLAGS.mode == 'train':
      train(hps)
    elif FLAGS.mode == 'eval':
      evaluate(hps)
Example #13
Source File: cifar_eval.py    From mentornet with Apache License 2.0 4 votes vote down vote up 
def eval_resnet():
  """Evaluates the resnet model."""
  if not os.path.exists(FLAGS.eval_dir):
    os.makedirs(FLAGS.eval_dir)
  g = tf.Graph()
  with g.as_default():
    # pylint: disable=line-too-long
    images, one_hot_labels, num_samples, num_of_classes = cifar_data_provider.provide_resnet_data(
        FLAGS.dataset_name,
        FLAGS.split_name,
        FLAGS.batch_size,
        dataset_dir=FLAGS.data_dir,
        num_epochs=None)

    hps = resnet_model.HParams(
        batch_size=FLAGS.batch_size,
        num_classes=num_of_classes,
        min_lrn_rate=0.0001,
        lrn_rate=0,
        num_residual_units=9,
        use_bottleneck=False,
        weight_decay_rate=0.0002,
        relu_leakiness=0.1,
        optimizer='mom')

    # Define the model:
    images.set_shape([FLAGS.batch_size, 32, 32, 3])
    resnet = resnet_model.ResNet(hps, images, one_hot_labels, mode='test')

    logits = resnet.build_model()

    total_loss = tf.nn.softmax_cross_entropy_with_logits(
        labels=one_hot_labels, logits=logits)
    total_loss = tf.reduce_mean(total_loss, name='xent')

    slim.summaries.add_scalar_summary(
        total_loss, 'total_loss', print_summary=True)

    # Define the metrics:
    predictions = tf.argmax(logits, 1)
    labels = tf.argmax(one_hot_labels, 1)

    names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
        'accuracy': tf.metrics.accuracy(predictions, labels),
    })

    for name, value in names_to_values.iteritems():
      slim.summaries.add_scalar_summary(
          value, name, prefix='eval', print_summary=True)

    # This ensures that we make a single pass over all of the data.
    num_batches = math.ceil(num_samples / float(FLAGS.batch_size))

    slim.evaluation.evaluation_loop(
        master=FLAGS.master,
        checkpoint_dir=FLAGS.checkpoint_dir,
        logdir=FLAGS.eval_dir,
        num_evals=num_batches,
        eval_op=names_to_updates.values(),
        eval_interval_secs=FLAGS.eval_interval_secs)