Python tensorflow.greater_equal方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def central_crop(image, crop_size):
  """Returns a central crop for the specified size of an image.

  Args:
    image: A tensor with shape [height, width, channels]
    crop_size: A tuple (crop_width, crop_height)

  Returns:
    A tensor of shape [crop_height, crop_width, channels].
  """
  with tf.variable_scope('CentralCrop'):
    target_width, target_height = crop_size
    image_height, image_width = tf.shape(image)[0], tf.shape(image)[1]
    assert_op1 = tf.Assert(
        tf.greater_equal(image_height, target_height),
        ['image_height < target_height', image_height, target_height])
    assert_op2 = tf.Assert(
        tf.greater_equal(image_width, target_width),
        ['image_width < target_width', image_width, target_width])
    with tf.control_dependencies([assert_op1, assert_op2]):
      offset_width = (image_width - target_width) / 2
      offset_height = (image_height - target_height) / 2
      return tf.image.crop_to_bounding_box(image, offset_height, offset_width,
                                           target_height, target_width) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def prune_small_boxes(boxlist, min_side, scope=None):
  """Prunes small boxes in the boxlist which have a side smaller than min_side.

  Args:
    boxlist: BoxList holding N boxes.
    min_side: Minimum width AND height of box to survive pruning.
    scope: name scope.

  Returns:
    A pruned boxlist.
  """
  with tf.name_scope(scope, 'PruneSmallBoxes'):
    height, width = height_width(boxlist)
    is_valid = tf.logical_and(tf.greater_equal(width, min_side),
                              tf.greater_equal(height, min_side))
    return gather(boxlist, tf.reshape(tf.where(is_valid), [-1])) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def testRandomPixelValueScale(self):
    preprocessing_options = []
    preprocessing_options.append((preprocessor.normalize_image, {
        'original_minval': 0,
        'original_maxval': 255,
        'target_minval': 0,
        'target_maxval': 1
    }))
    preprocessing_options.append((preprocessor.random_pixel_value_scale, {}))
    images = self.createTestImages()
    tensor_dict = {fields.InputDataFields.image: images}
    tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options)
    images_min = tf.to_float(images) * 0.9 / 255.0
    images_max = tf.to_float(images) * 1.1 / 255.0
    images = tensor_dict[fields.InputDataFields.image]
    values_greater = tf.greater_equal(images, images_min)
    values_less = tf.less_equal(images, images_max)
    values_true = tf.fill([1, 4, 4, 3], True)
    with self.test_session() as sess:
      (values_greater_, values_less_, values_true_) = sess.run(
          [values_greater, values_less, values_true])
      self.assertAllClose(values_greater_, values_true_)
      self.assertAllClose(values_less_, values_true_) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def assert_box_normalized(boxes, maximum_normalized_coordinate=1.1):
  """Asserts the input box tensor is normalized.

  Args:
    boxes: a tensor of shape [N, 4] where N is the number of boxes.
    maximum_normalized_coordinate: Maximum coordinate value to be considered
      as normalized, default to 1.1.

  Returns:
    a tf.Assert op which fails when the input box tensor is not normalized.

  Raises:
    ValueError: When the input box tensor is not normalized.
  """
  box_minimum = tf.reduce_min(boxes)
  box_maximum = tf.reduce_max(boxes)
  return tf.Assert(
      tf.logical_and(
          tf.less_equal(box_maximum, maximum_normalized_coordinate),
          tf.greater_equal(box_minimum, 0)),
      [boxes]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def mode(cls, parameters: Dict[str, Tensor]) -> Tensor:
        mu = parameters["mu"]
        tau = parameters["tau"]
        nu = parameters["nu"]
        beta = parameters["beta"]

        lam = 1./beta
        mode = tf.zeros_like(mu) * tf.zeros_like(mu)
        mode = tf.where(tf.logical_and(tf.greater(nu, mu),
                                       tf.less(mu+lam/tau, nu)),
                        mu+lam/tau,
                        mode)
        mode = tf.where(tf.logical_and(tf.greater(nu, mu),
                                       tf.greater_equal(mu+lam/tau, nu)),
                        nu,
                        mode)
        mode = tf.where(tf.logical_and(tf.less_equal(nu, mu),
                                       tf.greater(mu-lam/tau, nu)),
                        mu-lam/tau,
                        mode)
        mode = tf.where(tf.logical_and(tf.less_equal(nu, mu),
                                       tf.less_equal(mu-lam/tau, nu)),
                        nu,
                        mode)
        return(mode) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def show_range_image(self, range_image, layout_index_start=1):
        """Shows range image.
        Args:
          range_image: the range image data from a given lidar of type MatrixFloat.
          layout_index_start: layout offset
        """
        range_image_tensor = tf.convert_to_tensor(range_image.data)
        range_image_tensor = tf.reshape(range_image_tensor, range_image.shape.dims)
        lidar_image_mask = tf.greater_equal(range_image_tensor, 0)
        range_image_tensor = tf.where(lidar_image_mask, range_image_tensor,
                                      tf.ones_like(range_image_tensor) * 1e10)
        range_image_range = range_image_tensor[..., 0]
        range_image_intensity = range_image_tensor[..., 1]
        range_image_elongation = range_image_tensor[..., 2]
        self.plot_range_image_helper(range_image_range.numpy(), 'range',
                                [8, 1, layout_index_start], vmax=75, cmap='gray')
        self.plot_range_image_helper(range_image_intensity.numpy(), 'intensity',
                                [8, 1, layout_index_start + 1], vmax=1.5, cmap='gray')
        self.plot_range_image_helper(range_image_elongation.numpy(), 'elongation',
                                [8, 1, layout_index_start + 2], vmax=1.5, cmap='gray') 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def filter_outside_boxes(boxes, img_h, img_w):
    '''
    :param anchors:boxes with format [xmin, ymin, xmax, ymax]
    :param img_h: height of image
    :param img_w: width of image
    :return: indices of anchors that inside the image boundary
    '''

    with tf.name_scope('filter_outside_boxes'):
        xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1)

        xmin_index = tf.greater_equal(xmin, 0)
        ymin_index = tf.greater_equal(ymin, 0)
        xmax_index = tf.less_equal(xmax, tf.cast(img_w, tf.float32))
        ymax_index = tf.less_equal(ymax, tf.cast(img_h, tf.float32))

        indices = tf.transpose(tf.stack([xmin_index, ymin_index, xmax_index, ymax_index]))
        indices = tf.cast(indices, dtype=tf.int32)
        indices = tf.reduce_sum(indices, axis=1)
        indices = tf.where(tf.equal(indices, 4))
        # indices = tf.equal(indices, 4)
        return tf.reshape(indices, [-1]) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def smooth_l1_loss_rpn(bbox_pred, bbox_targets, label, sigma=1.0):
    '''

    :param bbox_pred: [-1, 4]
    :param bbox_targets: [-1, 4]
    :param label: [-1]
    :param sigma:
    :return:
    '''
    value = _smooth_l1_loss_base(bbox_pred, bbox_targets, sigma=sigma)

    value = tf.reduce_sum(value, axis=1)  # to sum in axis 1

    # rpn_select = tf.reshape(tf.where(tf.greater_equal(label, 0)), [-1])
    rpn_select = tf.where(tf.greater(label, 0))

    # rpn_select = tf.stop_gradient(rpn_select) # to avoid
    selected_value = tf.gather(value, rpn_select)

    non_ignored_mask = tf.stop_gradient(
        1.0 - tf.to_float(tf.equal(label, -1)))  # positve is 1.0 others is 0.0

    bbox_loss = tf.reduce_sum(selected_value) / tf.maximum(1.0, tf.reduce_sum(non_ignored_mask))

    return bbox_loss 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def _filter_negative_samples(labels, tensors):
    """keeps only samples with none-negative labels 
    Params:
    -----
    labels: of shape (N,)
    tensors: a list of tensors, each of shape (N, .., ..) the first axis is sample number

    Returns:
    -----
    tensors: filtered tensors
    """
    # return tensors
    keeps = tf.where(tf.greater_equal(labels, 0))
    keeps = tf.reshape(keeps, [-1])

    filtered = []
    for t in tensors:
        tf.assert_equal(tf.shape(t)[0], tf.shape(labels)[0])
        f = tf.gather(t, keeps)
        filtered.append(f)

    return filtered 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def _crop(image, offset_height, offset_width, crop_height, crop_width):
  original_shape = tf.shape(image)

  rank_assertion = tf.Assert(
      tf.equal(tf.rank(image), 3),
      ['Rank of image must be equal to 3.'])
  cropped_shape = control_flow_ops.with_dependencies(
      [rank_assertion],
      tf.stack([crop_height, crop_width, original_shape[2]]))

  size_assertion = tf.Assert(
      tf.logical_and(
          tf.greater_equal(original_shape[0], crop_height),
          tf.greater_equal(original_shape[1], crop_width)),
      ['Crop size greater than the image size.'])

  offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))

  # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
  # define the crop size.
  image = control_flow_ops.with_dependencies(
      [size_assertion],
      tf.slice(image, offsets, cropped_shape))
  return tf.reshape(image, cropped_shape) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def _crop(image, offset_height, offset_width, crop_height, crop_width):
  """Crops the given image using the provided offsets and sizes.

  Note that the method doesn't assume we know the input image size but it does
  assume we know the input image rank.

  Args:
    image: an image of shape [height, width, channels].
    offset_height: a scalar tensor indicating the height offset.
    offset_width: a scalar tensor indicating the width offset.
    crop_height: the height of the cropped image.
    crop_width: the width of the cropped image.

  Returns:
    the cropped (and resized) image.

  Raises:
    InvalidArgumentError: if the rank is not 3 or if the image dimensions are
      less than the crop size.
  """
  original_shape = tf.shape(image)

  rank_assertion = tf.Assert(
      tf.equal(tf.rank(image), 3),
      ['Rank of image must be equal to 3.'])
  with tf.control_dependencies([rank_assertion]):
    cropped_shape = tf.stack([crop_height, crop_width, original_shape[2]])

  size_assertion = tf.Assert(
      tf.logical_and(
          tf.greater_equal(original_shape[0], crop_height),
          tf.greater_equal(original_shape[1], crop_width)),
      ['Crop size greater than the image size.'])

  offsets = tf.to_int32(tf.stack([offset_height, offset_width, 0]))

  # Use tf.slice instead of crop_to_bounding box as it accepts tensors to
  # define the crop size.
  with tf.control_dependencies([size_assertion]):
    image = tf.slice(image, offsets, cropped_shape)
  return tf.reshape(image, cropped_shape) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def setup_training(loss_op, initial_learning_rate, steps_per_decay,
                   learning_rate_decay, momentum, max_steps,
                   sync=False, adjust_lr_sync=True,
                   num_workers=1, replica_id=0, vars_to_optimize=None, 
                   clip_gradient_norm=0, typ=None, momentum2=0.999,
                   adam_eps=1e-8):
  if sync and adjust_lr_sync:
    initial_learning_rate = initial_learning_rate * num_workers
    max_steps = np.int(max_steps / num_workers)
    steps_per_decay = np.int(steps_per_decay / num_workers)

  global_step_op = slim.get_or_create_global_step()
  lr_op          = tf.train.exponential_decay(initial_learning_rate,
    global_step_op, steps_per_decay, learning_rate_decay, staircase=True)
  if typ == 'sgd':
    optimizer      = tf.train.MomentumOptimizer(lr_op, momentum)
  elif typ == 'adam':
    optimizer      = tf.train.AdamOptimizer(learning_rate=lr_op, beta1=momentum,
                                            beta2=momentum2, epsilon=adam_eps)
  
  if sync:
    
    sync_optimizer = tf.train.SyncReplicasOptimizer(optimizer, 
                                               replicas_to_aggregate=num_workers, 
                                               replica_id=replica_id, 
                                               total_num_replicas=num_workers)
    train_op       = slim.learning.create_train_op(loss_op, sync_optimizer,
                                                   variables_to_train=vars_to_optimize,
                                                   clip_gradient_norm=clip_gradient_norm)
  else:
    sync_optimizer = None
    train_op       = slim.learning.create_train_op(loss_op, optimizer,
                                                   variables_to_train=vars_to_optimize,
                                                   clip_gradient_norm=clip_gradient_norm)
    should_stop_op = tf.greater_equal(global_step_op, max_steps)
  return lr_op, global_step_op, train_op, should_stop_op, optimizer, sync_optimizer 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def extract_features(self, preprocessed_inputs):
    """Extract features from preprocessed inputs.

    Args:
      preprocessed_inputs: a [batch, height, width, channels] float tensor
        representing a batch of images.

    Returns:
      feature_maps: a list of tensors where the ith tensor has shape
        [batch, height_i, width_i, depth_i]
    """
    preprocessed_inputs.get_shape().assert_has_rank(4)
    shape_assert = tf.Assert(
        tf.logical_and(tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
                       tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
        ['image size must at least be 33 in both height and width.'])

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
    }

    with tf.control_dependencies([shape_assert]):
      with slim.arg_scope(self._conv_hyperparams):
        with tf.variable_scope('MobilenetV1',
                               reuse=self._reuse_weights) as scope:
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              preprocessed_inputs,
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              scope=scope)
          feature_maps = feature_map_generators.multi_resolution_feature_maps(
              feature_map_layout=feature_map_layout,
              depth_multiplier=self._depth_multiplier,
              min_depth=self._min_depth,
              insert_1x1_conv=True,
              image_features=image_features)

    return feature_maps.values() 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def matched_column_indicator(self):
    """Returns column indices that are matched.

    Returns:
      column_indices: int32 tensor of shape [K] with column indices.
    """
    return tf.greater_equal(self._match_results, 0) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import greater_equal [as 别名]
def prune_completely_outside_window(boxlist, window, scope=None):
  """Prunes bounding boxes that fall completely outside of the given window.

  The function clip_to_window prunes bounding boxes that fall
  completely outside the window, but also clips any bounding boxes that
  partially overflow. This function does not clip partially overflowing boxes.

  Args:
    boxlist: a BoxList holding M_in boxes.
    window: a float tensor of shape [4] representing [ymin, xmin, ymax, xmax]
      of the window
    scope: name scope.

  Returns:
    pruned_corners: a tensor with shape [M_out, 4] where M_out <= M_in
    valid_indices: a tensor with shape [M_out] indexing the valid bounding boxes
     in the input tensor.
  """
  with tf.name_scope(scope, 'PruneCompleteleyOutsideWindow'):
    y_min, x_min, y_max, x_max = tf.split(
        value=boxlist.get(), num_or_size_splits=4, axis=1)
    win_y_min, win_x_min, win_y_max, win_x_max = tf.unstack(window)
    coordinate_violations = tf.concat([
        tf.greater_equal(y_min, win_y_max), tf.greater_equal(x_min, win_x_max),
        tf.less_equal(y_max, win_y_min), tf.less_equal(x_max, win_x_min)
    ], 1)
    valid_indices = tf.reshape(
        tf.where(tf.logical_not(tf.reduce_any(coordinate_violations, 1))), [-1])
    return gather(boxlist, valid_indices), valid_indices