Python tensorflow.concats() Examples

The following are 13 code examples of tensorflow.concats(). 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: vgslspecs.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #2
Source File: vgslspecs.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #3
Source File: vgslspecs.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #4
Source File: vgslspecs.py    From ad-versarial with MIT License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index, reuse=None):
        """tf.concats outputs of layers that run on the same inputs.
    
        Args:
          prev_layer: Input tensor.
          index:      Position in model_str to start parsing
    
        Returns:
          Output tensor of the parallel,  end index in model_str.
    
        Raises:
          ValueError: If () are unbalanced or the elements don't match.
        """
        if self.model_str[index] != '(':
            return None, None
        index += 1
        layers = []
        num_dims = 0
        # Each parallel must output the same, including any reduction factor, in
        # all dimensions except depth.
        # We have to save the starting factors, so they don't get reduced by all
        # the elements of the parallel, only once.
        original_factors = self.reduction_factors
        final_factors = None
        while index < len(self.model_str) and self.model_str[index] != ')':
            self.reduction_factors = original_factors
            layer, index = self.BuildFromString(prev_layer, index)
            if num_dims == 0:
                num_dims = len(layer.get_shape())
            elif num_dims != len(layer.get_shape()):
                raise ValueError('All elements of parallel must return same num dims')
            layers.append(layer)
            if final_factors:
                if final_factors != self.reduction_factors:
                    raise ValueError('All elements of parallel must scale the same')
            else:
                final_factors = self.reduction_factors
        if index == len(self.model_str):
            raise ValueError('Missing ) at end of parallel!' + self.model_str)
        return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #5
Source File: vgslspecs.py    From Action_Recognition_Zoo with MIT License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(num_dims - 1, layers), index + 1
Example #6
Source File: vgslspecs.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(num_dims - 1, layers), index + 1
Example #7
Source File: vgslspecs.py    From hands-detection with MIT License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #8
Source File: vgslspecs.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #9
Source File: vgslspecs.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #10
Source File: vgslspecs.py    From HumanRecognition with MIT License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #11
Source File: vgslspecs.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #12
Source File: vgslspecs.py    From models with Apache License 2.0 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1
Example #13
Source File: vgslspecs.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def AddParallel(self, prev_layer, index):
    """tf.concats outputs of layers that run on the same inputs.

    Args:
      prev_layer: Input tensor.
      index:      Position in model_str to start parsing

    Returns:
      Output tensor of the parallel,  end index in model_str.

    Raises:
      ValueError: If () are unbalanced or the elements don't match.
    """
    if self.model_str[index] != '(':
      return None, None
    index += 1
    layers = []
    num_dims = 0
    # Each parallel must output the same, including any reduction factor, in
    # all dimensions except depth.
    # We have to save the starting factors, so they don't get reduced by all
    # the elements of the parallel, only once.
    original_factors = self.reduction_factors
    final_factors = None
    while index < len(self.model_str) and self.model_str[index] != ')':
      self.reduction_factors = original_factors
      layer, index = self.BuildFromString(prev_layer, index)
      if num_dims == 0:
        num_dims = len(layer.get_shape())
      elif num_dims != len(layer.get_shape()):
        raise ValueError('All elements of parallel must return same num dims')
      layers.append(layer)
      if final_factors:
        if final_factors != self.reduction_factors:
          raise ValueError('All elements of parallel must scale the same')
      else:
        final_factors = self.reduction_factors
    if index == len(self.model_str):
      raise ValueError('Missing ) at end of parallel!' + self.model_str)
    return tf.concat(axis=num_dims - 1, values=layers), index + 1