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1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#     http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ==============================================================================
15"""A simple script for inspect checkpoint files."""
16from __future__ import absolute_import
17from __future__ import division
18from __future__ import print_function
19
20import argparse
21import re
22import sys
23
24import numpy as np
25
26from tensorflow.python.platform import app
27from tensorflow.python.platform import flags
28from tensorflow.python.training import py_checkpoint_reader
29
30FLAGS = None
31
32
33def _count_total_params(reader, count_exclude_pattern=""):
34  """Count total number of variables."""
35  var_to_shape_map = reader.get_variable_to_shape_map()
36
37  # Filter out tensors that we don't want to count
38  if count_exclude_pattern:
39    regex_pattern = re.compile(count_exclude_pattern)
40    new_var_to_shape_map = {}
41    exclude_num_tensors = 0
42    exclude_num_params = 0
43    for v in var_to_shape_map:
44      if regex_pattern.search(v):
45        exclude_num_tensors += 1
46        exclude_num_params += np.prod(var_to_shape_map[v])
47      else:
48        new_var_to_shape_map[v] = var_to_shape_map[v]
49    var_to_shape_map = new_var_to_shape_map
50    print("# Excluding %d tensors (%d params) that match %s when counting." % (
51        exclude_num_tensors, exclude_num_params, count_exclude_pattern))
52
53  var_sizes = [np.prod(var_to_shape_map[v]) for v in var_to_shape_map]
54  return np.sum(var_sizes, dtype=int)
55
56
57def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors,
58                                     all_tensor_names=False,
59                                     count_exclude_pattern=""):
60  """Prints tensors in a checkpoint file.
61
62  If no `tensor_name` is provided, prints the tensor names and shapes
63  in the checkpoint file.
64
65  If `tensor_name` is provided, prints the content of the tensor.
66
67  Args:
68    file_name: Name of the checkpoint file.
69    tensor_name: Name of the tensor in the checkpoint file to print.
70    all_tensors: Boolean indicating whether to print all tensors.
71    all_tensor_names: Boolean indicating whether to print all tensor names.
72    count_exclude_pattern: Regex string, pattern to exclude tensors when count.
73  """
74  try:
75    reader = py_checkpoint_reader.NewCheckpointReader(file_name)
76    if all_tensors or all_tensor_names:
77      var_to_shape_map = reader.get_variable_to_shape_map()
78      var_to_dtype_map = reader.get_variable_to_dtype_map()
79      for key, value in sorted(var_to_shape_map.items()):
80        print("tensor: %s (%s) %s" % (key, var_to_dtype_map[key].name, value))
81        if all_tensors:
82          print(reader.get_tensor(key))
83    elif not tensor_name:
84      print(reader.debug_string().decode("utf-8", errors="ignore"))
85    else:
86      if not reader.has_tensor(tensor_name):
87        print("Tensor %s not found in checkpoint" % tensor_name)
88        return
89
90      var_to_shape_map = reader.get_variable_to_shape_map()
91      var_to_dtype_map = reader.get_variable_to_dtype_map()
92      print("tensor: %s (%s) %s" %
93            (tensor_name, var_to_dtype_map[tensor_name].name,
94             var_to_shape_map[tensor_name]))
95      print(reader.get_tensor(tensor_name))
96
97    # Count total number of parameters
98    print("# Total number of params: %d" % _count_total_params(
99        reader, count_exclude_pattern=count_exclude_pattern))
100  except Exception as e:  # pylint: disable=broad-except
101    print(str(e))
102    if "corrupted compressed block contents" in str(e):
103      print("It's likely that your checkpoint file has been compressed "
104            "with SNAPPY.")
105    if ("Data loss" in str(e) and
106        any(e in file_name for e in [".index", ".meta", ".data"])):
107      proposed_file = ".".join(file_name.split(".")[0:-1])
108      v2_file_error_template = """
109It's likely that this is a V2 checkpoint and you need to provide the filename
110*prefix*.  Try removing the '.' and extension.  Try:
111inspect checkpoint --file_name = {}"""
112      print(v2_file_error_template.format(proposed_file))
113
114
115def parse_numpy_printoption(kv_str):
116  """Sets a single numpy printoption from a string of the form 'x=y'.
117
118  See documentation on numpy.set_printoptions() for details about what values
119  x and y can take. x can be any option listed there other than 'formatter'.
120
121  Args:
122    kv_str: A string of the form 'x=y', such as 'threshold=100000'
123
124  Raises:
125    argparse.ArgumentTypeError: If the string couldn't be used to set any
126        nump printoption.
127  """
128  k_v_str = kv_str.split("=", 1)
129  if len(k_v_str) != 2 or not k_v_str[0]:
130    raise argparse.ArgumentTypeError("'%s' is not in the form k=v." % kv_str)
131  k, v_str = k_v_str
132  printoptions = np.get_printoptions()
133  if k not in printoptions:
134    raise argparse.ArgumentTypeError("'%s' is not a valid printoption." % k)
135  v_type = type(printoptions[k])
136  if v_type is type(None):
137    raise argparse.ArgumentTypeError(
138        "Setting '%s' from the command line is not supported." % k)
139  try:
140    v = (
141        v_type(v_str)
142        if v_type is not bool else flags.BooleanParser().parse(v_str))
143  except ValueError as e:
144    raise argparse.ArgumentTypeError(e.message)
145  np.set_printoptions(**{k: v})
146
147
148def main(unused_argv):
149  if not FLAGS.file_name:
150    print("Usage: inspect_checkpoint --file_name=checkpoint_file_name "
151          "[--tensor_name=tensor_to_print] "
152          "[--all_tensors] "
153          "[--all_tensor_names] "
154          "[--printoptions]")
155    sys.exit(1)
156  else:
157    print_tensors_in_checkpoint_file(
158        FLAGS.file_name, FLAGS.tensor_name,
159        FLAGS.all_tensors, FLAGS.all_tensor_names,
160        count_exclude_pattern=FLAGS.count_exclude_pattern)
161
162
163if __name__ == "__main__":
164  parser = argparse.ArgumentParser()
165  parser.register("type", "bool", lambda v: v.lower() == "true")
166  parser.add_argument(
167      "--file_name",
168      type=str,
169      default="",
170      help="Checkpoint filename. "
171      "Note, if using Checkpoint V2 format, file_name is the "
172      "shared prefix between all files in the checkpoint.")
173  parser.add_argument(
174      "--tensor_name",
175      type=str,
176      default="",
177      help="Name of the tensor to inspect")
178  parser.add_argument(
179      "--count_exclude_pattern",
180      type=str,
181      default="",
182      help="Pattern to exclude tensors, e.g., from optimizers, when counting.")
183  parser.add_argument(
184      "--all_tensors",
185      nargs="?",
186      const=True,
187      type="bool",
188      default=False,
189      help="If True, print the names and values of all the tensors.")
190  parser.add_argument(
191      "--all_tensor_names",
192      nargs="?",
193      const=True,
194      type="bool",
195      default=False,
196      help="If True, print the names of all the tensors.")
197  parser.add_argument(
198      "--printoptions",
199      nargs="*",
200      type=parse_numpy_printoption,
201      help="Argument for numpy.set_printoptions(), in the form 'k=v'.")
202  FLAGS, unparsed = parser.parse_known_args()
203  app.run(main=main, argv=[sys.argv[0]] + unparsed)
204