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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tools for analyzing the operations and variables in a TensorFlow graph.
To analyze the operations in a graph:
images, labels = LoadData(...)
predictions = MyModel(images)
slim.model_analyzer.analyze_ops(tf.get_default_graph(), print_info=True)
To analyze the model variables in a graph:
variables = tf.model_variables()
slim.model_analyzer.analyze_vars(variables, print_info=False)
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
def tensor_description(var):
"""Returns a compact and informative string about a tensor.
Args:
var: A tensor variable.
Returns:
a string with type and size, e.g.: (float32 1x8x8x1024).
"""
description = '(' + str(var.dtype.name) + ' '
sizes = var.get_shape()
for i, size in enumerate(sizes):
description += str(size)
if i < len(sizes) - 1:
description += 'x'
description += ')'
return description
def analyze_ops(graph, print_info=False):
"""Compute the estimated size of the ops.outputs in the graph.
Args:
graph: the graph containing the operations.
print_info: Optional, if true print ops and their outputs.
Returns:
total size of the ops.outputs
"""
if print_info:
print('---------')
print('Operations: name -> (type shapes) [size]')
print('---------')
total_size = 0
for op in graph.get_operations():
op_size = 0
shapes = []
for output in op.outputs:
# if output.num_elements() is None or [] assume size 0.
output_size = output.get_shape().num_elements() or 0
if output.get_shape():
shapes.append(tensor_description(output))
op_size += output_size
if print_info:
print(op.name, '\t->', ', '.join(shapes), '[' + str(op_size) + ']')
total_size += op_size
return total_size
def analyze_vars(variables, print_info=False):
"""Prints the names and shapes of the variables.
Args:
variables: list of variables, for example tf.all_variables().
print_info: Optional, if true print variables and their shape.
Returns:
(total size of the variables, total bytes of the variables)
"""
if print_info:
print('---------')
print('Variables: name (type shape) [size]')
print('---------')
total_size = 0
total_bytes = 0
for var in variables:
# if var.num_elements() is None or [] assume size 0.
var_size = var.get_shape().num_elements() or 0
var_bytes = var_size * var.dtype.size
total_size += var_size
total_bytes += var_bytes
if print_info:
print(var.name, tensor_description(var), '[%d, bytes: %d]' %
(var_size, var_bytes))
if print_info:
print('Total size of variables: %d' % total_size)
print('Total bytes of variables: %d' % total_bytes)
return total_size, total_bytes
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