Python tensorflow.conv3d_transpose() Examples

The following are 8 code examples of tensorflow.conv3d_transpose(). 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 tensorflow , or try the search function .
Example #1
Source File: ptn_vox_decoder.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #2
Source File: ptn_vox_decoder.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #3
Source File: ptn_vox_decoder.py    From hands-detection with MIT License 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #4
Source File: ptn_vox_decoder.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #5
Source File: ptn_vox_decoder.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #6
Source File: ptn_vox_decoder.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #7
Source File: ptn_vox_decoder.py    From models with Apache License 2.0 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3
Example #8
Source File: ptn_vox_decoder.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def model(identities, params, is_training):
  """Model transforming embedding to voxels."""
  del is_training  # Unused
  f_dim = params.f_dim

  # Please refer to the original implementation: github.com/xcyan/nips16_PTN
  # In TF replication, we use a slightly different architecture.
  with slim.arg_scope(
      [slim.fully_connected, conv3d_transpose],
      weights_initializer=tf.truncated_normal_initializer(stddev=0.02, seed=1)):
    h0 = slim.fully_connected(
        identities, 4 * 4 * 4 * f_dim * 8, activation_fn=tf.nn.relu)
    h1 = tf.reshape(h0, [-1, 4, 4, 4, f_dim * 8])
    h1 = conv3d_transpose(
        h1, f_dim * 4, [4, 4, 4], stride=2, activation_fn=tf.nn.relu)
    h2 = conv3d_transpose(
        h1, int(f_dim * 3 / 2), [5, 5, 5], stride=2, activation_fn=tf.nn.relu)
    h3 = conv3d_transpose(
        h2, 1, [6, 6, 6], stride=2, activation_fn=tf.nn.sigmoid)
  return h3