Python tensorflow.concats() Examples
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code examples of tensorflow.concats().
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Example #1
Source File: vgslspecs.py From DOTA_models with Apache License 2.0 | 5 votes |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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