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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.
# ==============================================================================
"""A simple script for inspect checkpoint files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import re
import sys
import numpy as np
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.platform import app
from tensorflow.python.platform import flags
FLAGS = None
def _count_total_params(reader, count_exclude_pattern=""):
"""Count total number of variables."""
var_to_shape_map = reader.get_variable_to_shape_map()
# Filter out tensors that we don't want to count
if count_exclude_pattern:
regex_pattern = re.compile(count_exclude_pattern)
new_var_to_shape_map = {}
exclude_num_tensors = 0
exclude_num_params = 0
for v in var_to_shape_map:
if regex_pattern.search(v):
exclude_num_tensors += 1
exclude_num_params += np.prod(var_to_shape_map[v])
else:
new_var_to_shape_map[v] = var_to_shape_map[v]
var_to_shape_map = new_var_to_shape_map
print("# Excluding %d tensors (%d params) that match %s when counting." % (
exclude_num_tensors, exclude_num_params, count_exclude_pattern))
var_sizes = [np.prod(var_to_shape_map[v]) for v in var_to_shape_map]
return np.sum(var_sizes, dtype=int)
def print_tensors_in_checkpoint_file(file_name, tensor_name, all_tensors,
all_tensor_names=False,
count_exclude_pattern=""):
"""Prints tensors in a checkpoint file.
If no `tensor_name` is provided, prints the tensor names and shapes
in the checkpoint file.
If `tensor_name` is provided, prints the content of the tensor.
Args:
file_name: Name of the checkpoint file.
tensor_name: Name of the tensor in the checkpoint file to print.
all_tensors: Boolean indicating whether to print all tensors.
all_tensor_names: Boolean indicating whether to print all tensor names.
count_exclude_pattern: Regex string, pattern to exclude tensors when count.
"""
try:
reader = pywrap_tensorflow.NewCheckpointReader(file_name)
if all_tensors or all_tensor_names:
var_to_shape_map = reader.get_variable_to_shape_map()
var_to_dtype_map = reader.get_variable_to_dtype_map()
for key, value in sorted(var_to_shape_map.items()):
print("tensor: %s (%s) %s" % (key, var_to_dtype_map[key].name, value))
if all_tensors:
print(reader.get_tensor(key))
elif not tensor_name:
print(reader.debug_string().decode("utf-8", errors="ignore"))
else:
if not reader.has_tensor(tensor_name):
print("Tensor %s not found in checkpoint" % tensor_name)
return
var_to_shape_map = reader.get_variable_to_shape_map()
var_to_dtype_map = reader.get_variable_to_dtype_map()
print("tensor: %s (%s) %s" %
(tensor_name, var_to_dtype_map[tensor_name].name,
var_to_shape_map[tensor_name]))
print(reader.get_tensor(tensor_name))
# Count total number of parameters
print("# Total number of params: %d" % _count_total_params(
reader, count_exclude_pattern=count_exclude_pattern))
except Exception as e: # pylint: disable=broad-except
print(str(e))
if "corrupted compressed block contents" in str(e):
print("It's likely that your checkpoint file has been compressed "
"with SNAPPY.")
if ("Data loss" in str(e) and
any(e in file_name for e in [".index", ".meta", ".data"])):
proposed_file = ".".join(file_name.split(".")[0:-1])
v2_file_error_template = """
It's likely that this is a V2 checkpoint and you need to provide the filename
*prefix*. Try removing the '.' and extension. Try:
inspect checkpoint --file_name = {}"""
print(v2_file_error_template.format(proposed_file))
def parse_numpy_printoption(kv_str):
"""Sets a single numpy printoption from a string of the form 'x=y'.
See documentation on numpy.set_printoptions() for details about what values
x and y can take. x can be any option listed there other than 'formatter'.
Args:
kv_str: A string of the form 'x=y', such as 'threshold=100000'
Raises:
argparse.ArgumentTypeError: If the string couldn't be used to set any
nump printoption.
"""
k_v_str = kv_str.split("=", 1)
if len(k_v_str) != 2 or not k_v_str[0]:
raise argparse.ArgumentTypeError("'%s' is not in the form k=v." % kv_str)
k, v_str = k_v_str
printoptions = np.get_printoptions()
if k not in printoptions:
raise argparse.ArgumentTypeError("'%s' is not a valid printoption." % k)
v_type = type(printoptions[k])
if v_type is type(None):
raise argparse.ArgumentTypeError(
"Setting '%s' from the command line is not supported." % k)
try:
v = (
v_type(v_str)
if v_type is not bool else flags.BooleanParser().parse(v_str))
except ValueError as e:
raise argparse.ArgumentTypeError(e.message)
np.set_printoptions(**{k: v})
def main(unused_argv):
if not FLAGS.file_name:
print("Usage: inspect_checkpoint --file_name=checkpoint_file_name "
"[--tensor_name=tensor_to_print] "
"[--all_tensors] "
"[--all_tensor_names] "
"[--printoptions]")
sys.exit(1)
else:
print_tensors_in_checkpoint_file(
FLAGS.file_name, FLAGS.tensor_name,
FLAGS.all_tensors, FLAGS.all_tensor_names,
count_exclude_pattern=FLAGS.count_exclude_pattern)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.register("type", "bool", lambda v: v.lower() == "true")
parser.add_argument(
"--file_name",
type=str,
default="",
help="Checkpoint filename. "
"Note, if using Checkpoint V2 format, file_name is the "
"shared prefix between all files in the checkpoint.")
parser.add_argument(
"--tensor_name",
type=str,
default="",
help="Name of the tensor to inspect")
parser.add_argument(
"--count_exclude_pattern",
type=str,
default="",
help="Pattern to exclude tensors, e.g., from optimizers, when counting.")
parser.add_argument(
"--all_tensors",
nargs="?",
const=True,
type="bool",
default=False,
help="If True, print the names and values of all the tensors.")
parser.add_argument(
"--all_tensor_names",
nargs="?",
const=True,
type="bool",
default=False,
help="If True, print the names of all the tensors.")
parser.add_argument(
"--printoptions",
nargs="*",
type=parse_numpy_printoption,
help="Argument for numpy.set_printoptions(), in the form 'k=v'.")
FLAGS, unparsed = parser.parse_known_args()
app.run(main=main, argv=[sys.argv[0]] + unparsed)
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