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android / platform / external / tensorflow / ea9d358b5aea41f40e47cf85900259689f90ae07 / . / tensorflow / tools / compatibility / tf_upgrade_v2_test.py
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# Copyright 2018 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.
# ==============================================================================
"""Tests for tf 2.0 upgrader."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import os
import tempfile
from absl.testing import parameterized
import six
import tensorflow as tf
# OSS TF V2 import placeholder.
from tensorflow.python.framework import test_util
from tensorflow.python.platform import test as test_lib
from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_export
from tensorflow.python.util import tf_inspect
from tensorflow.tools.common import public_api
from tensorflow.tools.common import traverse
from tensorflow.tools.compatibility import ast_edits
from tensorflow.tools.compatibility import tf_upgrade_v2
def get_symbol_for_name(root, name):
name_parts = name.split(".")
symbol = root
# Iterate starting with second item since 1st item is "tf.".
for part in name_parts[1:]:
symbol = getattr(symbol, part)
return symbol
def get_args(symbol):
if hasattr(inspect, "signature"):
signature = inspect.signature(symbol)
# Ignore *args and **kwargs for now.
return [param.name for param in signature.parameters.values()
if param.kind == param.POSITIONAL_OR_KEYWORD]
return tf_inspect.getargspec(symbol)[0]
def get_func_and_args_from_str(call_str):
"""Parse call string to get function and argument names.
Args:
call_str: Call string must be in the form:
`tf.foo(arg1=val1, arg2=val2, ...)`.
Returns:
(function_name, list of arg names) tuple.
"""
open_paren_index = call_str.find("(")
close_paren_index = call_str.rfind(")")
function_name = call_str[:call_str.find("(")]
args = call_str[open_paren_index+1:close_paren_index].split(",")
args = [arg.split("=")[0].strip() for arg in args]
args = [arg for arg in args if arg] # filter out empty strings
return function_name, args
class TestUpgrade(test_util.TensorFlowTestCase, parameterized.TestCase):
"""Test various APIs that have been changed in 2.0.
We also test whether a converted file is executable. test_file_v1_10.py
aims to exhaustively test that API changes are convertible and actually
work when run with current TensorFlow.
"""
@classmethod
def setUpClass(cls):
super(TestUpgrade, cls).setUpClass()
cls.v2_symbols = {}
cls.v1_symbols = {}
if hasattr(tf.compat, "v2"):
def symbol_collector(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v2 = tf_export.get_v2_names(attr)
for name in api_names_v2:
cls.v2_symbols["tf." + name] = attr
visitor = public_api.PublicAPIVisitor(symbol_collector)
visitor.private_map["tf.compat"] = ["v1"]
traverse.traverse(tf.compat.v2, visitor)
if hasattr(tf.compat, "v1"):
def symbol_collector_v1(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names_v1 = tf_export.get_v1_names(attr)
for name in api_names_v1:
cls.v1_symbols["tf." + name] = attr
visitor = public_api.PublicAPIVisitor(symbol_collector_v1)
traverse.traverse(tf.compat.v1, visitor)
def _upgrade(self, old_file_text):
in_file = six.StringIO(old_file_text)
out_file = six.StringIO()
upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())
count, report, errors = (
upgrader.process_opened_file("test.py", in_file,
"test_out.py", out_file))
return count, report, errors, out_file.getvalue()
def _upgrade_multiple(self, old_file_texts):
upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())
results = []
for old_file_text in old_file_texts:
in_file = six.StringIO(old_file_text)
out_file = six.StringIO()
count, report, errors = (
upgrader.process_opened_file("test.py", in_file,
"test_out.py", out_file))
results.append([count, report, errors, out_file.getvalue()])
return results
def testParseError(self):
_, report, unused_errors, unused_new_text = self._upgrade(
"import tensorflow as tf\na + \n")
self.assertTrue(report.find("Failed to parse") != -1)
def testReport(self):
text = "tf.angle(a)\n"
_, report, unused_errors, unused_new_text = self._upgrade(text)
# This is not a complete test, but it is a sanity test that a report
# is generating information.
self.assertTrue(report.find("Renamed function `tf.angle` to "
"`tf.math.angle`"))
def testRename(self):
text = "tf.conj(a)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.math.conj(a)\n")
text = "tf.rsqrt(tf.log_sigmoid(3.8))\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.math.rsqrt(tf.math.log_sigmoid(3.8))\n")
def testAllAPI(self):
if not hasattr(tf.compat, "v2"):
return
# Converts all symbols in the v1 namespace to the v2 namespace, raising
# an error if the target of the conversion is not in the v2 namespace.
# Please regenerate the renames file or edit any manual renames if this
# test fails.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names = tf_export.get_v1_names(attr)
for name in api_names:
_, _, _, text = self._upgrade("tf." + name)
if (text and
not text.startswith("tf.compat.v1") and
not text.startswith("tf.compat.v2") and
text not in self.v2_symbols and
# Builds currently install old version of estimator that doesn't
# have some 2.0 symbols.
not text.startswith("tf.estimator")):
self.assertFalse(
True, "Symbol %s generated from %s not in v2 API" % (
text, name))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def testAllAPIV1(self):
collect = True
v1_symbols = set([])
# Converts all symbols in the v1 namespace to the v2 namespace, raising
# an error if the target of the conversion is not in the v1 namespace.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
api_names = tf_export.get_v1_names(attr)
for name in api_names:
if collect:
v1_symbols.add("tf." + name)
else:
_, _, _, text = self._upgrade("tf." + name)
if (text and
not text.startswith("tf.compat.v1") and
not text.startswith("tf.compat.v2") and
not text.startswith("tf.estimator") and
text not in v1_symbols):
self.assertFalse(
True, "Symbol %s generated from %s not in v1 API" % (
text, name))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
collect = False
traverse.traverse(tf.compat.v1, visitor)
def testV1KeywordArgNames(self):
all_keyword_renames = (
tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)
# Visitor that verifies V1 argument names.
def arg_test_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
names_v1 = tf_export.get_v1_names(attr)
for name in names_v1:
name = "tf.%s" % name
if name not in all_keyword_renames:
continue
arg_names_v1 = tf_inspect.getargspec(attr)[0]
keyword_renames = all_keyword_renames[name]
self.assertEqual(type(keyword_renames), dict)
# Assert that v1 function has valid v1 argument names.
for from_name, _ in keyword_renames.items():
self.assertIn(
from_name, arg_names_v1,
"%s not found in %s arguments: %s" %
(from_name, name, str(arg_names_v1)))
visitor = public_api.PublicAPIVisitor(arg_test_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def testV2KeywordArgNames(self):
# This test converts a call of the form:
# tf.foo(arg1=0, arg2=1, ...)
# to 2.0. Then, checks that converted function has valid argument names.
if not hasattr(tf.compat, "v2"):
return
v2_arg_exceptions = {
"verify_shape_is_now_always_true",
# These arguments should not be used, they just specify
# that a function takes named arguments.
"keyword_required",
"_sentinel",
}
v1_name_exceptions = {
"tf.print", # requires print_function import
}
function_warnings = (
tf_upgrade_v2.TFAPIChangeSpec().function_warnings)
function_transformers = (
tf_upgrade_v2.TFAPIChangeSpec().function_transformers)
keyword_renames = (
tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)
# Visitor that converts to V2 and checks V2 argument names.
def conversion_visitor(unused_path, unused_parent, children):
for child in children:
_, attr = tf_decorator.unwrap(child[1])
if not tf_inspect.isfunction(attr):
continue
names_v1 = tf_export.get_v1_names(attr)
arg_names_v1 = get_args(attr)
for name in names_v1:
tf_name = "tf.%s" % name
if tf_name in function_warnings or tf_name in function_transformers:
continue # These require manual change
if tf_name in v1_name_exceptions:
continue
# Assert that arg names after converting to v2 are present in
# v2 function.
# 1. First, create an input of the form:
# tf.foo(arg1=val1, arg2=val2, ...)
args = ",".join(
["%s=%d" % (from_name, from_index)
for from_index, from_name in enumerate(arg_names_v1)])
text_input = "%s(%s)" % (tf_name, args)
# 2. Convert the input to V2.
_, _, _, text = self._upgrade(text_input)
new_function_name, new_args = get_func_and_args_from_str(text)
if new_function_name == "tf.compat.v1.%s" % name:
if tf_name in keyword_renames:
# If we rename arguments, new function must be available in 2.0.
# We should not be using compat.v1 in this case.
self.assertFalse(
"Function '%s' is not in 2.0 when converting\n%s\nto\n%s" %
(new_function_name, text_input, text))
continue
if new_function_name.startswith("tf.compat.v2"):
self.assertIn(new_function_name.replace("tf.compat.v2.", "tf."),
self.v2_symbols)
continue
# 3. Verify V2 function and arguments.
args_v2 = get_args(self.v2_symbols[new_function_name])
args_v2.extend(v2_arg_exceptions)
for new_arg in new_args:
self.assertIn(
new_arg, args_v2,
"Invalid argument '%s' in 2.0 when converting\n%s\nto\n%s.\n"
"Supported arguments: %s" % (
new_arg, text_input, text, str(args_v2)))
# 4. Verify that the argument exists in v1 as well.
if new_function_name in set(["tf.nn.ctc_loss",
"tf.saved_model.save"]):
continue
args_v1 = get_args(self.v1_symbols[new_function_name])
args_v1.extend(v2_arg_exceptions)
for new_arg in new_args:
self.assertIn(
new_arg, args_v1,
"Invalid argument '%s' in 1.0 when converting\n%s\nto\n%s.\n"
"Supported arguments: %s" % (
new_arg, text_input, text, str(args_v1)))
visitor = public_api.PublicAPIVisitor(conversion_visitor)
visitor.do_not_descend_map["tf"].append("contrib")
visitor.private_map["tf.compat"] = ["v1", "v2"]
traverse.traverse(tf.compat.v1, visitor)
def testPositionsMatchArgGiven(self):
full_dict = tf_upgrade_v2.TFAPIChangeSpec().function_arg_warnings
method_names = full_dict.keys()
for method_name in method_names:
args = full_dict[method_name].keys()
# special case for optimizer methods
if method_name.startswith("*."):
method = method_name.replace("*", "tf.train.Optimizer")
else:
method = method_name
method = get_symbol_for_name(tf, method)
arg_spec = tf_inspect.getfullargspec(method)
for (arg, pos) in args:
# to deal with the self argument on methods on objects
if method_name.startswith("*."):
pos += 1
self.assertEqual(arg_spec[0][pos], arg)
def testReorderFileNeedsUpdate(self):
reordered_function_names = (
tf_upgrade_v2.TFAPIChangeSpec().reordered_function_names)
function_reorders = (
tf_upgrade_v2.TFAPIChangeSpec().function_reorders)
manual_function_reorders = (
tf_upgrade_v2.TFAPIChangeSpec().manual_function_reorders)
added_names_message = """Some function names in
self.reordered_function_names are not in reorders_v2.py.
Please run the following commands to update reorders_v2.py:
bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
"""
removed_names_message = """%s in self.reorders_v2 does not match
any name in self.reordered_function_names.
Please run the following commands to update reorders_v2.py:
bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map
bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
"""
self.assertTrue(
reordered_function_names.issubset(function_reorders),
added_names_message)
# function_reorders should contain reordered_function_names
# and their TensorFlow V1 aliases.
for name in function_reorders:
if name in manual_function_reorders:
continue
# get other names for this function
attr = get_symbol_for_name(tf.compat.v1, name)
_, attr = tf_decorator.unwrap(attr)
v1_names = tf_export.get_v1_names(attr)
self.assertTrue(v1_names)
v1_names = ["tf.%s" % n for n in v1_names]
# check if any other name is in
self.assertTrue(
any(n in reordered_function_names for n in v1_names),
removed_names_message % name)
def testRenameConstant(self):
text = "tf.MONOLITHIC_BUILD\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "tf.sysconfig.MONOLITHIC_BUILD\n")
text = "some_call(tf.MONOLITHIC_BUILD)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, "some_call(tf.sysconfig.MONOLITHIC_BUILD)\n")
def testRenameArgs(self):
text = ("tf.nn.pool(input_a, window_shape_a, pooling_type_a, padding_a, "
"dilation_rate_a, strides_a, name_a, data_format_a)\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text,
("tf.nn.pool(input=input_a, window_shape=window_shape_a,"
" pooling_type=pooling_type_a, padding=padding_a, "
"dilations=dilation_rate_a, strides=strides_a, "
"name=name_a, data_format=data_format_a)\n"))
def testReorder(self):
text = "tf.boolean_mask(a, b, c, d)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text,
"tf.boolean_mask(tensor=a, mask=b, name=c, axis=d)\n")
def testLearningRateDecay(self):
for decay in ["tf.train.exponential_decay",
"tf.train.polynomial_decay", "tf.train.natural_exp_decay",
"tf.train.inverse_time_decay", "tf.train.cosine_decay",
"tf.train.cosine_decay_restarts",
"tf.train.linear_cosine_decay",
"tf.train.noisy_linear_cosine_decay",
"tf.train.piecewise_constant_decay",
]:
text = "%s(a, b)\n" % decay
_, report, unused_errors, _ = self._upgrade(text)
self.assertIn("switch to the schedules in "
"`tf.keras.optimizers.schedules`", report)
def verify_compat_v1_rename_correctness(self, values, ns_prefix=""):
if ns_prefix:
ns_prefix += "."
for v in values:
text = "tf." + ns_prefix + v + "(a, b)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual("tf.compat.v1." + ns_prefix + v + "(a, b)", new_text)
def testIntializers(self):
initializers = [
"zeros",
"ones",
"constant",
"random_uniform",
"random_normal",
"truncated_normal",
"variance_scaling",
"orthogonal",
"glorot_uniform",
"glorot_normal",
"identity",
"lecun_normal",
"lecun_uniform",
"he_normal",
"he_uniform",
]
self.verify_compat_v1_rename_correctness(
initializers, ns_prefix="initializers")
initializers = [
"zeros_initializer",
"ones_initializer",
"constant_initializer",
"random_uniform_initializer",
"random_normal_initializer",
"truncated_normal_initializer",
"variance_scaling_initializer",
"orthogonal_initializer",
"glorot_uniform_initializer",
"glorot_normal_initializer",
]
self.verify_compat_v1_rename_correctness(initializers)
initializers = [
"zeros",
"ones",
"Ones",
"Zeros",
"constant",
"Constant",
"VarianceScaling",
"Orthogonal",
"orthogonal",
"Identity",
"identity",
"glorot_uniform",
"glorot_normal",
"lecun_normal",
"lecun_uniform",
"he_normal",
"he_uniform",
"TruncatedNormal",
"truncated_normal",
"RandomUniform",
"uniform",
"random_uniform",
"RandomNormal",
"normal",
"random_normal",
]
self.verify_compat_v1_rename_correctness(
initializers, ns_prefix="keras.initializers")
def testContribXavierInitializer(self):
text = "tf.contrib.layers.xavier_initializer()\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, "
"mode=\"fan_avg\", "
"distribution=\"uniform\")\n",
)
text = "slim.xavier_initializer(True or False)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, "
"mode=\"fan_avg\", "
"distribution=(\"uniform\" if True or False else "
"\"truncated_normal\"))\n",
)
text = "slim.xavier_initializer(uniform=(True or False))\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, "
"mode=\"fan_avg\", "
"distribution=(\"uniform\" if True or False else "
"\"truncated_normal\"))\n",
)
text = "tf.contrib.layers.xavier_initializer_conv2d(False, 12)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, "
"mode=\"fan_avg\", "
"distribution=(\"uniform\" if False else \"truncated_normal\"), "
"seed=12)\n",
)
text = ("tf.contrib.layers.xavier_initializer_conv2d("
"False, 12, tf.float32)\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, "
"mode=\"fan_avg\", "
"distribution=(\"uniform\" if False else \"truncated_normal\"), "
"seed=12, "
"dtype=tf.float32)\n",
)
text = ("tf.contrib.layers.xavier_initializer("
"False, 12, dtypes=tf.float32)\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, "
"mode=\"fan_avg\", "
"distribution=(\"uniform\" if False else \"truncated_normal\"), "
"seed=12, "
"dtypes=tf.float32)\n",
)
def testVarianceScalingInitializer(self):
text = ("tf.contrib.layers.variance_scaling_initializer("
"mode=(\"FAN\" + \"_AVG\"))\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=2.0, "
"mode=(\"FAN\" + \"_AVG\").lower())\n",
)
text = ("slim.variance_scaling_initializer("
"uniform=(True or False), mode=(\"FAN\" + \"_AVG\"))\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=2.0, "
"distribution=(\"uniform\" if True or False else \"truncated_normal\"),"
" mode=(\"FAN\" + \"_AVG\").lower())\n",
)
text = "tf.contrib.layers.variance_scaling_initializer(factor=1.0)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0)\n",
)
text = ("tf.contrib.layers.variance_scaling_initializer("
"12.0, \"FAN_AVG\", True, dtypes=tf.float32)\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.compat.v1.keras.initializers.VarianceScaling(12.0, "
"(\"FAN_AVG\").lower(), "
"(\"uniform\" if True else \"truncated_normal\"), "
"dtypes=tf.float32)\n",
)
def testMetrics(self):
metrics = [
"accuracy",
"auc",
"average_precision_at_k",
"false_negatives",
"false_negatives_at_thresholds",
"false_positives",
"false_positives_at_thresholds",
"mean",
"mean_absolute_error",
"mean_cosine_distance",
"mean_iou",
"mean_per_class_accuracy",
"mean_relative_error",
"mean_squared_error",
"mean_tensor",
"percentage_below",
"precision",
"precision_at_k",
"precision_at_thresholds",
"precision_at_top_k",
"recall",
"recall_at_k",
"recall_at_thresholds",
"recall_at_top_k",
"root_mean_squared_error",
"sensitivity_at_specificity",
"sparse_average_precision_at_k",
"sparse_precision_at_k",
"specificity_at_sensitivity",
"true_negatives",
"true_negatives_at_thresholds",
"true_positives",
"true_positives_at_thresholds",
]
for m in metrics:
text = "tf.metrics." + m + "(a, b)"
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual("tf.compat.v1.metrics." + m + "(a, b)", new_text)
self.assertIn(
"tf.metrics have been replaced with object oriented versions", report)
def testLosses(self):
losses = [
"absolute_difference",
"add_loss",
"compute_weighted_loss",
"cosine_distance",
"get_losses",
"get_regularization_loss",
"get_regularization_losses",
"get_total_loss",
"hinge_loss",
"huber_loss",
"log_loss",
"mean_pairwise_squared_error",
"mean_squared_error",
"sigmoid_cross_entropy",
"softmax_cross_entropy",
"sparse_softmax_cross_entropy",
]
for l in losses:
text = "tf.losses." + l + "(a, b)"
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual("tf.compat.v1.losses." + l + "(a, b)", new_text)
self.assertIn(
"tf.losses have been replaced with object oriented versions", report)
def testEstimatorLossReductionChange(self):
classes = [
"LinearClassifier", "LinearRegressor", "DNNLinearCombinedClassifier",
"DNNLinearCombinedRegressor", "DNNRegressor", "DNNClassifier",
"BaselineClassifier", "BaselineRegressor"
]
for c in classes:
ns = "tf.estimator." + c
text = ns + "()"
expected_text = ns + "(loss_reduction=tf.compat.v1.losses.Reduction.SUM)"
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = ns + "(loss_reduction=TEST)"
expected_text = ns + "(loss_reduction=TEST)"
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(text, new_text)
text = "tf.estimator.BaselineClassifier(m, c, w, v, o, c, lr)"
expected_text = (
"tf.compat.v1.estimator.BaselineClassifier("
"model_dir=m, n_classes=c, weight_column=w, label_vocabulary=v, "
"optimizer=o, config=c, loss_reduction=lr)")
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.estimator.BaselineClassifier(model_dir=model_dir)"
expected_text = ("tf.estimator.BaselineClassifier(" +
"model_dir=model_dir, "
"loss_reduction=tf.compat.v1.losses.Reduction.SUM)")
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testBaseEstimatorPartitioner(self):
classes = ["LinearEstimator", "DNNLinearCombinedEstimator", "DNNEstimator"]
for c in classes:
ns = "tf.estimator." + c
suffix = "(input_layer_partitioner=TEST)"
text = ns + suffix
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testCannedEstimatorPartitioner(self):
classes = [
"LinearClassifier", "LinearRegressor", "DNNLinearCombinedClassifier",
"DNNLinearCombinedRegressor", "DNNRegressor", "DNNClassifier"
]
for c in classes:
ns = "tf.estimator." + c
suffix = "(input_layer_partitioner=TEST)"
text = ns + suffix
suffix = ("(input_layer_partitioner=TEST, "
"loss_reduction=tf.compat.v1.losses.Reduction.SUM)")
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testBaseEstimatorOptimizer(self):
classes = ["BaselineEstimator", "LinearEstimator", "DNNEstimator"]
for c in classes:
ns = "tf.estimator." + c
suffix = "(optimizer=TEST)"
text = ns + suffix
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testDNNLinearCombinedEstimatorOptimizer(self):
classes = ["DNNLinearCombinedEstimator"]
for c in classes:
ns = "tf.estimator." + c
suffix = "(dnn_optimizer=TEST, linear_optimizer=Test)"
text = ns + suffix
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testCannedEstimatorOptimizer(self):
classes = [
"BaselineClassifier", "BaselineRegressor", "LinearClassifier",
"LinearRegressor", "DNNRegressor", "DNNClassifier"
]
for c in classes:
ns = "tf.estimator." + c
suffix = "(optimizer=TEST)"
text = ns + suffix
suffix = ("(optimizer=TEST, "
"loss_reduction=tf.compat.v1.losses.Reduction.SUM)")
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testDNNLinearCombinedOptimizer(self):
classes = [
"DNNLinearCombinedClassifier",
"DNNLinearCombinedRegressor",
]
for c in classes:
ns = "tf.estimator." + c
suffix = "(dnn_optimizer=TEST, linear_optimizer=Test)"
text = ns + suffix
suffix = ("(dnn_optimizer=TEST, linear_optimizer=Test, "
"loss_reduction=tf.compat.v1.losses.Reduction.SUM)")
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testBaseEstimatorPartitionerAndOptimizer(self):
classes = ["LinearEstimator", "DNNEstimator"]
for c in classes:
ns = "tf.estimator." + c
suffix = "(input_layer_partitioner=TEST, optimizer=TEST)"
text = ns + suffix
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testDNNLinearCombinedEstimatorPartitionerAndOptimizer(self):
classes = ["DNNLinearCombinedEstimator"]
for c in classes:
ns = "tf.estimator." + c
suffix = ("(input_layer_partitioner=TEST, dnn_optimizer=TEST, "
"linear_optimizer=TEST)")
text = ns + suffix
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testCannedEstimatorPartitionerAndOptimizer(self):
classes = [
"LinearClassifier", "LinearRegressor", "DNNRegressor", "DNNClassifier"
]
for c in classes:
ns = "tf.estimator." + c
suffix = "(input_layer_partitioner=TEST, optimizer=TEST)"
text = ns + suffix
suffix = ("(input_layer_partitioner=TEST, optimizer=TEST, "
"loss_reduction=tf.compat.v1.losses.Reduction.SUM)")
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testDNNLinearCombinedPartitionerAndOptimizer(self):
classes = [
"DNNLinearCombinedClassifier",
"DNNLinearCombinedRegressor",
]
for c in classes:
ns = "tf.estimator." + c
suffix = ("(input_layer_partitioner=TEST, dnn_optimizer=TEST, "
"linear_optimizer=TEST)")
text = ns + suffix
suffix = ("(input_layer_partitioner=TEST, dnn_optimizer=TEST, "
"linear_optimizer=TEST, "
"loss_reduction=tf.compat.v1.losses.Reduction.SUM)")
expected_text = "tf.compat.v1.estimator." + c + suffix
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testExtractGlimpse(self):
text = ("tf.image.extract_glimpse(x, size, off, False, "
"False, False, name=\"foo\")\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.image.extract_glimpse(x, size, off, False, "
"False, 'uniform' if (False) else 'gaussian', name=\"foo\")\n",
)
text = ("tf.image.extract_glimpse(x, size, off, centered=False, "
"normalized=False, uniform_noise=True if uniform_noise else "
"False, name=\"foo\")\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.image.extract_glimpse(x, size, off, centered=False, "
"normalized=False, noise='uniform' if (True if uniform_noise else "
"False) else 'gaussian', name=\"foo\")\n",
)
text = ("tf.image.extract_glimpse(x,\n"
" size,\n"
" off,\n"
" centered=True,\n"
" normalized=True, # Stuff before\n"
" uniform_noise=False,\n"
" name=\"foo\")# Stuff after\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text, "tf.image.extract_glimpse(x,\n"
" size,\n"
" off,\n"
" centered=True,\n"
" normalized=True, # Stuff before\n"
" noise='uniform' if (False) else 'gaussian',\n"
" name=\"foo\")# Stuff after\n")
text = "tf.image.extract_glimpse(x)\n"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, text)
self.assertEqual(errors, [])
def testDropout(self):
text = "tf.nn.dropout(x, keep_prob, name=\"foo\")\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.nn.dropout(x, 1 - (keep_prob), name=\"foo\")\n",
)
text = "tf.nn.dropout(x, keep_prob=.4, name=\"foo\")\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.nn.dropout(x, rate=1 - (.4), name=\"foo\")\n",
)
text = (
"tf.nn.dropout(x, # Stuff before\n"
" keep_prob=.4, # Stuff after\n"
" name=\"foo\")\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.nn.dropout(x, # Stuff before\n"
" rate=1 - (.4), # Stuff after\n"
" name=\"foo\")\n",
)
text = "tf.nn.dropout(x)\n"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, text)
self.assertIn("tf.nn.dropout called without arguments", errors[0])
def testDropoutExpr(self):
text = "tf.nn.dropout(x, 1 - func(3 + 4.), name=\"foo\")\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.nn.dropout(x, 1 - (1 - func(3 + 4.)), name=\"foo\")\n",
)
def testContribL1(self):
text = "tf.contrib.layers.l1_regularizer(scale)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l1(scale)\n",
)
self.assertNotIn("Dropping scope", unused_report)
text = "tf.contrib.layers.l1_regularizer(scale, scope)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l1(scale)\n",
)
self.assertIn("Dropping scope", unused_report)
text = (
"slim.l1_regularizer( # Stuff before\n"
" scale=.4,"
" scope=\"foo\")\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l1( # Stuff before\n"
" l=.4)\n",
)
self.assertIn("Dropping scope", unused_report)
def testContribL2(self):
text = "tf.contrib.layers.l2_regularizer(scale)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l2(0.5 * (scale))\n",
)
self.assertNotIn("Dropping scope", unused_report)
text = "tf.contrib.layers.l2_regularizer(scale, scope)\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l2(0.5 * (scale))\n",
)
self.assertIn("Dropping scope", unused_report)
text = (
"slim.l2_regularizer( # Stuff before\n"
" scale=.4,"
" scope=\"foo\")\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l2( # Stuff before\n"
" l=0.5 * (.4))\n",
)
self.assertIn("Dropping scope", unused_report)
def testContribL2Expr(self):
text = "tf.contrib.layers.l2_regularizer(1 - func(3 + 4.), scope=\"foo\")\n"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(
new_text,
"tf.keras.regularizers.l2(0.5 * (1 - func(3 + 4.)))\n",
)
def testMathCountNonZeroChanges(self):
text = (
"tf.math.count_nonzero(input_tensor=input, dtype=dtype, name=name, "
"reduction_indices=axis, keep_dims=keepdims)\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
expected_text = (
"tf.math.count_nonzero(input=input, dtype=dtype, name=name, "
"axis=axis, keepdims=keepdims)\n"
)
self.assertEqual(new_text, expected_text)
def testCountNonZeroChanges(self):
text = (
"tf.count_nonzero(input_tensor=input, dtype=dtype, name=name, "
"reduction_indices=axis, keep_dims=keepdims)\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
expected_text = (
"tf.math.count_nonzero(input=input, dtype=dtype, name=name, "
"axis=axis, keepdims=keepdims)\n"
)
self.assertEqual(new_text, expected_text)
def testRandomMultinomialToRandomCategorical(self):
text = (
"tf.random.multinomial(logits, samples, seed, name, output_dtype)\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
expected_text = (
"tf.random.categorical(logits=logits, num_samples=samples, seed=seed, "
"name=name, dtype=output_dtype)\n"
)
self.assertEqual(new_text, expected_text)
text = (
"tf.multinomial(logits, samples, seed, name, output_dtype)\n"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
expected_text = (
"tf.random.categorical(logits=logits, num_samples=samples, seed=seed, "
"name=name, dtype=output_dtype)\n"
)
self.assertEqual(new_text, expected_text)
def testRandomPoissonConversion(self):
text1 = "tf.random_poisson(lam, shape, dtype)"
text2 = "tf.random.poisson(lam, shape, dtype)"
expected_text = "tf.random.poisson(lam=lam, shape=shape, dtype=dtype)"
_, unused_report, unused_errors, new_text1 = self._upgrade(text1)
self.assertEqual(new_text1, expected_text)
_, unused_report, unused_errors, new_text2 = self._upgrade(text2)
self.assertEqual(new_text2, expected_text)
def testConvolutionOpUpdate(self):
text = (
"tf.nn.convolution(input, filter, padding, strides, dilation_rate, "
"name, data_format)"
)
_, unused_report, unused_errors, new_text = self._upgrade(text)
expected_text = (
"tf.nn.convolution(input=input, filters=filter, padding=padding, "
"strides=strides, dilations=dilation_rate, name=name, "
"data_format=data_format)"
)
self.assertEqual(new_text, expected_text)
def test_substr(self):
text = "tf.substr(input, pos, len, name, unit)\n"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual("tf.strings.substr(input=input, pos=pos, len=len, "
"name=name, unit=unit)\n", new_text)
self.assertEqual(errors, [])
def testColocateGradientsWithOps(self):
text = "tf.gradients(yx=a, foo=False)\n"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(text, new_text)
self.assertEqual(errors, [])
text = "tf.gradients(yx=a, colocate_gradients_with_ops=False)\n"
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual("tf.gradients(yx=a)\n", new_text)
self.assertIn("tf.gradients no longer takes", report)
text = "tf.gradients(y, x, grad_ys, name, colocate, gate)\n"
expected = ("tf.gradients(ys=y, xs=x, grad_ys=grad_ys, name=name, "
"gate_gradients=gate)\n")
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def testColocateGradientsWithOpsMinimize(self):
text = "optimizer.minimize(a, foo=False)\n"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(text, new_text)
self.assertEqual(errors, [])
text = "optimizer.minimize(a, colocate_gradients_with_ops=False)\n"
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual("optimizer.minimize(a)\n", new_text)
self.assertIn("Optimizer.minimize no longer takes", report)
def testColocateGradientsWithOpsComputeGradients(self):
text = "optimizer.compute_gradients(a, foo=False)\n"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(text, new_text)
self.assertEqual(errors, [])
text = "optimizer.compute_gradients(a, colocate_gradients_with_ops=False)\n"
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual("optimizer.compute_gradients(a)\n", new_text)
self.assertIn("Optimizer.compute_gradients no longer takes", report)
def testExportSavedModelRename(self):
text = "self.est.export_savedmodel(path)"
_, report, unused_errors, unused_new_text = self._upgrade(text)
self.assertIn(
"rename the method export_savedmodel() to export_saved_model()",
report)
def testArgmin(self):
text = "tf.argmin(input, name=n, dimension=1, output_type=type)"
expected_text = "tf.argmin(input=input, name=n, axis=1, output_type=type)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.argmin(input, 0)"
expected_text = "tf.argmin(input=input, axis=0)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.arg_min(input, 0)"
expected_text = "tf.argmin(input, 0)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testArgmax(self):
text = "tf.argmax(input, name=n, dimension=1, output_type=type)"
expected_text = "tf.argmax(input=input, name=n, axis=1, output_type=type)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.argmax(input, 0)"
expected_text = "tf.argmax(input=input, axis=0)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.arg_max(input, 0)"
expected_text = "tf.argmax(input, 0)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testAutograph(self):
text = "tf.autograph.to_graph(f, True, arg_values=None, arg_types=None)"
expected_text = "tf.autograph.to_graph(f, True)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = ("tf.autograph.to_code"
"(f, False, arg_values=None, arg_types=None, indentation=' ')")
expected_text = "tf.autograph.to_code(f, False)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testEstimatorInputs(self):
text = "tf.estimator.inputs.numpy_input_fn(0)"
expected_text = "tf.compat.v1.estimator.inputs.numpy_input_fn(0)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.estimator.inputs.pandas_input_fn(0)"
expected_text = "tf.compat.v1.estimator.inputs.pandas_input_fn(0)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testBatchToSpace(self):
text = "tf.batch_to_space_nd(input, block_shape, crops, name)"
expected_text = "tf.batch_to_space(input, block_shape, crops, name)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.batch_to_space(input, crops, block_size, name)"
expected_text = (
"tf.batch_to_space(input=input, crops=crops, block_shape=block_size, "
"name=name)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.manip.batch_to_space_nd(input, block_shape, crops, name)"
expected_text = "tf.batch_to_space(input, block_shape, crops, name)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testExtractImagePatches(self):
text = (
"tf.extract_image_patches(images, ksizes=ksizes, strides=strides,"
"rates=rates, padding=padding, name=name)")
expected_text = (
"tf.image.extract_patches(images, sizes=ksizes, strides=strides,"
"rates=rates, padding=padding, name=name)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testKerasSavedModel(self):
text = (
"tf.contrib.saved_model.save_keras_model(model, './saved_models')\n"
"tf.contrib.saved_model.load_keras_model(saved_model_path)\n")
expected_text = (
"tf.keras.experimental.export_saved_model(model, './saved_models')\n"
"tf.keras.experimental.load_from_saved_model(saved_model_path)\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testStatelessMultinomial(self):
text = (
"tf.random.stateless_multinomial(logits, num_samples, seed, "
"output_dtype=dtype, name=name)")
expected_text = (
"tf.random.stateless_categorical(logits, num_samples, seed, "
"dtype=dtype, name=name)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testSoftMaxCrossEntropyWithLogitsV2(self):
text = (
"tf.nn.softmax_cross_entropy_with_logits_v2("
"labels=labels, logits=logits, dim=2)")
expected_text = (
"tf.nn.softmax_cross_entropy_with_logits("
"labels=labels, logits=logits, axis=2)")
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertFalse(errors)
def testSoftMaxCrossEntropyWithLogits(self):
text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=labels, logits=logits, dim=2)")
expected_text = (
"tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(labels), logits=logits, axis=2)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=foo(bar))")
expected_text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(foo(bar)))")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testSoftMaxCrossEntropyWithLogitsDoesntNest(self):
text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(labels), logits=logits, dim=2)")
expected_text = (
"tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(labels), logits=logits, axis=2)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(foo(bar)))")
expected_text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(foo(bar)))")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=foo())")
expected_text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(foo()))")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=foo().zz())")
expected_text = ("tf.nn.softmax_cross_entropy_with_logits("
"labels=tf.stop_gradient(foo().zz()))")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testSparseMatmul(self):
text = ("tf.sparse_matmul(a, b, c, d, e, f, g)\n")
expected_text = ("tf.linalg.matmul(a=a, b=b, transpose_a=c, transpose_b=d, "
"a_is_sparse=e, b_is_sparse=f, name=g)\n")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testWeightedMoments(self):
text = "tf.nn.weighted_moments(x, axes, freq, name, kd)"
expected_text = (
"tf.nn.weighted_moments(x=x, axes=axes, frequency_weights=freq, "
"name=name, keepdims=kd)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testSparseAdd(self):
text = "tf.sparse.add(a, b, t)"
expected_text = "tf.sparse.add(a=a, b=b, threshold=t)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testSparseConcat(self):
text = "tf.sparse.concat(ax, inp, name, exp, concat)"
expected_text = (
"tf.sparse.concat(axis=ax, sp_inputs=inp, name=name, "
"expand_nonconcat_dims=exp, axis=concat)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testSeparableConv2D(self):
text = "tf.nn.separable_conv2d(inp, d, pt, strides, pad, rate, name, fmt)"
expected_text = (
"tf.nn.separable_conv2d(input=inp, depthwise_filter=d, "
"pointwise_filter=pt, strides=strides, padding=pad, "
"dilations=rate, name=name, data_format=fmt)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testConv2D(self):
text = (
"tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu, "
"data_format)")
expected_text = (
"tf.nn.conv2d(input=input, filters=filter, strides=strides, "
"padding=padding, data_format=data_format)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = (
"tf.nn.conv2d(input, filter=filter, strides=strides, padding=padding, "
"use_cudnn_on_gpu=use_cudnn_on_gpu)")
expected_text = ("tf.nn.conv2d(input=input, filters=filter, "
"strides=strides, padding=padding)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testConv2DBackpropFilter(self):
text = (
"tf.nn.conv2d_backprop_filter(input, filter_sizes, out_backprop, "
"strides, padding, use_cudnn_on_gpu, data_format)")
expected_text = (
"tf.compat.v1.nn.conv2d_backprop_filter(input, filter_sizes, "
"out_backprop, strides, padding, use_cudnn_on_gpu, data_format)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testConv2DBackpropInput(self):
text = (
"tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, "
"strides, padding, use_cudnn_on_gpu, data_format)")
expected_text = (
"tf.nn.conv2d_transpose(output_shape=input_sizes, filters=filter, "
"input=out_backprop, strides=strides, padding=padding, "
"data_format=data_format)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testSpacetoBatch(self):
text = "tf.space_to_batch_nd(input, shape, paddings, name)"
expected_text = "tf.space_to_batch(input, shape, paddings, name)"
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.nn.space_to_batch(input, paddings, block_size, name)"
expected_text = (
"tf.space_to_batch(input=input, paddings=paddings, "
"block_shape=block_size, name=name)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testInTopK(self):
text = "tf.math.in_top_k(a, b, c, n)"
expected_text = (
"tf.math.in_top_k(predictions=a, targets=b, k=c, name=n)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testDepthToSpace(self):
text = "tf.nn.depth_to_space(input, block_size, name, data_format)"
expected_text = (
"tf.nn.depth_to_space(input=input, block_size=block_size, "
"name=name, data_format=data_format)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testEmbeddingLookup(self):
text = ("tf.nn.embedding_lookup(params, ids, partition_strategy, name, "
"validate_indices, max_norm)")
expected_text = ("tf.nn.embedding_lookup(params=params, ids=ids, "
"partition_strategy=partition_strategy, name=name, "
"max_norm=max_norm)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testEmbeddingLookupSparse(self):
text = ("tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, "
"partition_strategy, name, combiner, max_norm)")
expected_text = ("tf.nn.embedding_lookup_sparse(params=params, "
"sp_ids=sp_ids, sp_weights=sp_weights, "
"partition_strategy=partition_strategy, name=name, "
"combiner=combiner, max_norm=max_norm)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testNnInTopK(self):
text = "tf.nn.in_top_k(predictions, targets, k, name)"
expected_text = ("tf.nn.in_top_k(predictions=predictions, "
"targets=targets, k=k, name=name)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testSpaceToDepth(self):
text = "tf.nn.space_to_depth(input, block_size, name, data_format)"
expected_text = ("tf.nn.space_to_depth(input=input, block_size=block_size, "
"name=name, data_format=data_format)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testPrint(self):
# tf.print() cannot be parsed unless we import print_function
text = """from __future__ import print_function
tf.print()
tf.print('abc')
"""
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, text) # Text should stay the same
def testSparseSplit(self):
text = (
"tf.sparse_split(sp_input=sp_input, num_split=num_split, axis=axis, "
"name=name)")
expected_text = (
"tf.sparse.split(sp_input=sp_input, num_split=num_split, axis=axis, "
"name=name)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = (
"tf.sparse_split(sp_input=sp_input, num_split=num_split, "
"name=name, split_dim=axis)")
expected_text = (
"tf.sparse.split(sp_input=sp_input, num_split=num_split, "
"name=name, axis=axis)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = (
"tf.sparse.split(sp_input=sp_input, num_split=num_split, "
"name=name, split_dim=axis)")
expected_text = (
"tf.sparse.split(sp_input=sp_input, num_split=num_split, "
"name=name, axis=axis)")
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testIterators(self):
for (text, expected) in [
("(expr + yielding(data)).make_one_shot_iterator()",
"tf.compat.v1.data.make_one_shot_iterator((expr + yielding(data)))"),
("dataset.make_one_shot_iterator()",
"tf.compat.v1.data.make_one_shot_iterator(dataset)"),
("dataset.make_one_shot_iterator(shared_name=foo)",
"tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)"),
("dataset.make_one_shot_iterator(x, y, z)",
"tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)"),
("dataset.make_initializable_iterator()",
"tf.compat.v1.data.make_initializable_iterator(dataset)"),
("ds.make_initializable_iterator(shared_name=foo)",
"tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)"),
("dataset.make_initializable_iterator(x, y, z)",
"tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)"),
("tf.data.make_one_shot_iterator(dataset)",
"tf.compat.v1.data.make_one_shot_iterator(dataset)"),
("tf.data.make_one_shot_iterator(dataset, shared_name=foo)",
"tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)"),
("tf.data.make_one_shot_iterator(dataset, x, y, z)",
"tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)"),
("tf.data.make_initializable_iterator(dataset)",
"tf.compat.v1.data.make_initializable_iterator(dataset)"),
("tf.data.make_initializable_iterator(ds, shared_name=foo)",
"tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)"),
("tf.data.make_initializable_iterator(dataset, x, y, z)",
"tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)"),
("tf.compat.v1.data.make_one_shot_iterator(dataset)",
"tf.compat.v1.data.make_one_shot_iterator(dataset)"),
("tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)",
"tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)"),
("tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)",
"tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)"),
("tf.compat.v1.data.make_initializable_iterator(dataset)",
"tf.compat.v1.data.make_initializable_iterator(dataset)"),
("tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)",
"tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)"),
("tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)",
"tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)")]:
_, unused_report, unused_errors, actual = self._upgrade(text)
self.assertEqual(actual, expected)
def testStructure(self):
for (text, expected) in [
("tf.data.experimental.DatasetStructure", "tf.data.DatasetSpec"),
("tf.data.experimental.OptionalStructure", "tf.OptionalSpec"),
("tf.data.experimental.RaggedTensorStructure", "tf.RaggedTensorSpec"),
("tf.data.experimental.SparseTensorStructure", "tf.SparseTensorSpec"),
("tf.data.experimental.Structure", "tf.TypeSpec"),
("tf.data.experimental.TensorArrayStructure", "tf.TensorArraySpec"),
("tf.data.experimental.TensorStructure", "tf.TensorSpec"),
]:
_, unused_report, unused_errors, actual = self._upgrade(text)
self.assertEqual(actual, expected)
def testMapAndBatch(self):
suffix = ".data.experimental.map_and_batch_with_legacy_function(args)"
text = "tf" + suffix
expected = "tf.compat.v1" + suffix
_, unused_report, unused_errors, actual = self._upgrade(text)
self.assertEqual(actual, expected)
def testCast(self):
for (name, dtype) in [("int32", "int32"),
("int64", "int64"),
("float", "float32"),
("double", "float64"),
("complex64", "complex64"),
("complex128", "complex128"),
("bfloat16", "bfloat16")]:
text = "tf.to_%s(x, name='test')" % name
expected_text = "tf.cast(x, name='test', dtype=tf.%s)" % dtype
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testCastPositionalSecondArgument(self):
for (name, dtype) in [("int32", "int32"),
("int64", "int64"),
("float", "float32"),
("double", "float64"),
("complex64", "complex64"),
("complex128", "complex128"),
("bfloat16", "bfloat16")]:
text = "tf.to_%s(x, 'test')" % name
expected_text = "tf.cast(x, name='test', dtype=tf.%s)" % dtype
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testImageResize(self):
for method in ["bilinear", "area", "bicubic", "nearest_neighbor"]:
text = "tf.image.resize_%s(i, s)" % method
expected_text = ("tf.image.resize(i, s, "
"method=tf.image.ResizeMethod.%s)" % method.upper())
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testImageResizeExtraPositionalArgs(self):
for method in ["bilinear", "area", "bicubic", "nearest_neighbor"]:
text = "tf.image.resize_%s(i, s, a, p)" % method
expected_text = [
"tf.image.resize(i, s, ", "preserve_aspect_ratio=p, ",
"method=tf.image.ResizeMethod.%s)" % method.upper()
]
_, unused_report, unused_errors, new_text = self._upgrade(text)
for s in expected_text:
self.assertIn(s, new_text)
def testCond(self):
text = "tf.cond(a, b, c, True)"
expected_text = "tf.cond(pred=a, true_fn=b, false_fn=c)"
_, unused_report, errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
self.assertIn("tf.cond", errors[0])
self.assertIn("requires manual check", errors[0])
def testParens(self):
text = """
def _log_prob(self, x):
return tf.reduce_logsumexp(
(self.mixture_distribution.logits + self.distribution.log_prob(
x[..., tf.newaxis])),
axis=-1)"""
expected_text = """
def _log_prob(self, x):
return tf.reduce_logsumexp(
input_tensor=(self.mixture_distribution.logits + self.distribution.log_prob(
x[..., tf.newaxis])),
axis=-1)"""
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testAssertStatements(self):
for name in ["assert_greater", "assert_equal", "assert_none_equal",
"assert_less", "assert_negative", "assert_positive",
"assert_non_negative", "assert_non_positive", "assert_near",
"assert_less", "assert_less_equal", "assert_greater",
"assert_greater_equal", "assert_integer", "assert_type",
"assert_scalar"]:
text = "tf.%s(a)" % name
expected_text = "tf.compat.v1.%s(a)" % name
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
self.assertIn("%s has been" % name, report)
text = "tf.debugging.%s(a)" % name
expected_text = "tf.compat.v1.debugging.%s(a)" % name
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
self.assertIn("%s has been" % name, report)
def testAssertRankStatements(self):
for name in ["assert_rank", "assert_rank_at_least", "assert_rank_in"]:
text = "tf.%s(a)" % name
expected_text = "tf.compat.v1.%s(a)" % name
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
self.assertIn("%s has been" % name, report)
text = "tf.debugging.%s(a)" % name
expected_text = "tf.compat.v1.debugging.%s(a)" % name
_, report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
self.assertIn("%s has been" % name, report)
def test_assert_equal_graph_def(self):
text = ("tf.test.assert_equal_graph_def(a, b, checkpoint_v2=x, "
"hash_table_shared_name=y)")
expected = "tf.test.assert_equal_graph_def(actual=a, expected=b)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_is_tensor_upgrade(self):
text = "tf.contrib.framework.is_tensor(x)"
expected = "tf.is_tensor(x)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_CriticalSection_upgrade(self):
text = "tf.contrib.framework.CriticalSection(shared_name='blah')"
expected = "tf.CriticalSection(shared_name='blah')"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_sample_distorted_bounding_box(self):
# pylint: disable=line-too-long
text = "tf.image.sample_distorted_bounding_box(a, b, c, d, e, f, g, h, i, j)"
expected = "tf.image.sample_distorted_bounding_box(image_size=a, bounding_boxes=b, seed=c, min_object_covered=e, aspect_ratio_range=f, area_range=g, max_attempts=h, use_image_if_no_bounding_boxes=i, name=j)"
# pylint: enable=line-too-long
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_contrib_initialize(self):
text = "tf.contrib.summary.initialize"
expected = "tf.compat.v1.summary.initialize"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_contrib_framework_argsort(self):
text = "tf.contrib.framework.argsort"
expected = "tf.argsort"
# pylint: enable=line-too-long
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_flags_bare(self):
_, _, errors, _ = self._upgrade("tf.flags")
self.assertIn("tf.flags has been removed", errors[0])
def test_flags_flags(self):
_, _, errors, _ = self._upgrade("tf.flags.FLAGS")
self.assertIn("tf.flags has been removed", errors[0])
def test_contrib_estimator_head_deprecation(self):
api_symbols = ["binary_classification_head", "logistic_regression_head",
"multi_class_head", "multi_head", "multi_label_head",
"poisson_regression_head", "regression_head"]
for symbol in api_symbols:
text = "tf.contrib.estimator." + symbol
_, report, _, _ = self._upgrade(text)
self.assertIn("`tf.contrib.estimator.*_head` has been deprecated", report)
def test_contrib_layers_layer_norm_deprecation(self):
_, report, _, _ = self._upgrade("tf.contrib.layers.layer_norm")
self.assertIn("`tf.contrib.layers.layer_norm` has been deprecated", report)
def test_contrib_rnn_deprecation(self):
_, report, _, _ = self._upgrade("tf.contrib.rnn")
self.assertIn("tf.contrib.rnn.* has been deprecated", report)
def test_contrib_cudnn_rnn_deprecation(self):
_, report, _, _ = self._upgrade("tf.contrib.cudnn_rnn")
self.assertIn("tf.contrib.cudnn_rnn.* has been deprecated", report)
def test_max_pool_2d(self):
text = "tf.nn.max_pool(value=4)"
expected_text = "tf.nn.max_pool2d(input=4)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_contrib_estimator_early_stopping(self):
api_symbols = [
"make_early_stopping_hook", "stop_if_higher_hook", "stop_if_lower_hook",
"stop_if_no_decrease_hook", "stop_if_no_increase_hook"
]
for symbol in api_symbols:
text = "tf.contrib.estimator." + symbol
expected_text = "tf.estimator.experimental." + symbol
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_contrib_rnn_cell(self):
api_symbols = ["RNNCell", "BasicLSTMCell", "BasicRNNCell", "GRUCell",
"LSTMCell", "MultiRNNCell"]
for symbol in api_symbols:
text = "tf.contrib.rnn." + symbol
expected_text = "tf.compat.v1.nn.rnn_cell." + symbol
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_contrib_rnn_function(self):
api_symbols = ["static_rnn", "static_state_saving_rnn",
"static_bidirectional_rnn"]
for symbol in api_symbols:
text = "tf.contrib.rnn." + symbol
expected_text = "tf.compat.v1.nn." + symbol
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_contrib_summary_generic(self):
text = "tf.contrib.summary.generic('foo', myval, meta, 'fam', 42)"
expected = ("tf.compat.v2.summary.write(tag='foo', data=myval, "
"metadata=meta, step=42)")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
# Arg errors come in alphabetical order of arguments, not appearance order.
self.assertIn("'family' argument", errors[0])
self.assertIn("'name' argument", errors[1])
self.assertIn("tf.compat.v2.summary.*", errors[2])
def test_contrib_summary_audio(self):
text = "tf.contrib.summary.audio('foo', myval, 44100, 3, 'fam', 42)"
expected = ("tf.compat.v2.summary.audio(name='foo', data=myval, "
"sample_rate=44100, max_outputs=3, step=42)")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'family' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_histogram(self):
text = "tf.contrib.summary.histogram('foo', myval, 'fam', 42)"
expected = ("tf.compat.v2.summary.histogram(name='foo', data=myval, "
"step=42)")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'family' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_image(self):
text = "tf.contrib.summary.image('foo', myval, red, 3, 'fam', 42)"
expected = ("tf.compat.v2.summary.image(name='foo', data=myval, "
"max_outputs=3, step=42)")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'bad_color' argument", errors[0])
self.assertIn("'family' argument", errors[1])
self.assertIn("tf.compat.v2.summary.*", errors[2])
def test_contrib_summary_scalar(self):
text = "tf.contrib.summary.scalar('foo', myval, 'fam', 42)"
expected = ("tf.compat.v2.summary.scalar(name='foo', data=myval, "
"step=42)")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'family' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_generic_nostep(self):
text = "tf.contrib.summary.generic('foo', myval)"
expected = ("tf.compat.v2.summary.write(tag='foo', data=myval, "
"step=tf.compat.v1.train.get_or_create_global_step())")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'name' argument", errors[0])
self.assertIn("'step' argument", errors[1])
self.assertIn("tf.compat.v2.summary.*", errors[2])
def test_contrib_summary_audio_nostep(self):
text = "tf.contrib.summary.audio('foo', myval, 44100)"
expected = ("tf.compat.v2.summary.audio(name='foo', data=myval, "
"sample_rate=44100, "
"step=tf.compat.v1.train.get_or_create_global_step())")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'step' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_histogram_nostep(self):
text = "tf.contrib.summary.histogram('foo', myval)"
expected = ("tf.compat.v2.summary.histogram(name='foo', data=myval, "
"step=tf.compat.v1.train.get_or_create_global_step())")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'step' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_image_nostep(self):
text = "tf.contrib.summary.image('foo', myval)"
expected = ("tf.compat.v2.summary.image(name='foo', data=myval, "
"step=tf.compat.v1.train.get_or_create_global_step())")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'step' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_scalar_nostep(self):
text = "tf.contrib.summary.scalar('foo', myval)"
expected = ("tf.compat.v2.summary.scalar(name='foo', data=myval, "
"step=tf.compat.v1.train.get_or_create_global_step())")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'step' argument", errors[0])
self.assertIn("tf.compat.v2.summary.*", errors[1])
def test_contrib_summary_graph(self):
text = "tf.contrib.summary.graph(my_graph)"
_, _, errors, _ = self._upgrade(text)
expected_error = "tf.compat.v2.summary.trace"
self.assertIn(expected_error, errors[0])
def test_contrib_summary_import_event(self):
text = "tf.contrib.summary.import_event(my_event)"
_, _, errors, _ = self._upgrade(text)
expected_error = "tf.compat.v2.summary.experimental.write_raw_pb"
self.assertIn(expected_error, errors[0])
def test_contrib_summary_flush(self):
text = "tf.contrib.summary.flush(writer=foo)"
expected = "tf.compat.v2.summary.flush(writer=foo)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_contrib_summary_create_file_writer(self):
text = ("tf.contrib.summary.create_file_writer('my_logdir', 0, 1000, "
"'.foo', 'shared-name')")
expected = ("tf.compat.v2.summary.create_file_writer(logdir='my_logdir', "
"max_queue=0, flush_millis=1000, filename_suffix='.foo')")
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("'name' argument", errors[0])
self.assertIn("no longer re-uses existing event files", errors[1])
def test_contrib_summary_always_record_summaries(self):
text = "tf.contrib.summary.always_record_summaries()"
expected = "tf.compat.v2.summary.record_if(True)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_contrib_summary_never_record_summaries(self):
text = "tf.contrib.summary.never_record_summaries()"
expected = "tf.compat.v2.summary.record_if(False)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_contrib_summary_record_summaries_every_n_global_steps(self):
text = "tf.contrib.summary.record_summaries_every_n_global_steps(10)"
_, _, errors, _ = self._upgrade(text)
expected_error = "replaced by a call to tf.compat.v2.summary.record_if()"
self.assertIn(expected_error, errors[0])
def test_contrib_summary_all_summary_ops(self):
text = "tf.contrib.summary.all_summary_ops()"
expected = "tf.compat.v1.summary.all_v2_summary_ops()"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_contrib_summary_full_example(self):
deindent = lambda n, s: "\n".join(line[n:] for line in s.split("\n"))
text = deindent(4, """
import tensorflow as tf
tf.enable_eager_execution()
writer = tf.contrib.summary.create_file_writer(
"/tmp/migration_test", flush_millis=1000)
with writer.as_default(), tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("loss", 0.42)
tf.contrib.summary.histogram("weights", [1.0, 2.0], step=7)
tf.contrib.summary.flush()
""")
expected = deindent(4, """
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
writer = tf.compat.v2.summary.create_file_writer(
logdir="/tmp/migration_test", flush_millis=1000)
with writer.as_default(), tf.compat.v2.summary.record_if(True):
tf.compat.v2.summary.scalar(name="loss", data=0.42, step=tf.compat.v1.train.get_or_create_global_step())
tf.compat.v2.summary.histogram(name="weights", data=[1.0, 2.0], step=7)
tf.compat.v2.summary.flush()
""")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_summary_api_warning(self):
text = "tf.summary.scalar('foo', 42)"
_, report, _, _ = self._upgrade(text)
expected_info = "TF 1.x summary API cannot be automatically migrated"
self.assertIn(expected_info, report)
def test_avg_pool_2d(self):
text = "tf.nn.avg_pool(value=4)"
expected_text = "tf.nn.avg_pool2d(input=4)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_saved_model_load(self):
text = "tf.saved_model.load(sess, ['foo_graph'])"
expected = "tf.compat.v1.saved_model.load(sess, ['foo_graph'])"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_saved_model_load_v2(self):
text = "tf.saved_model.load_v2('/tmp/blah')"
expected = "tf.compat.v2.saved_model.load('/tmp/blah')"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_uniform_unit_scaling_initializer(self):
text = "tf.uniform_unit_scaling_initializer(0.5)"
expected_text = ("tf.compat.v1.keras.initializers.VarianceScaling("
"scale=0.5, distribution=\"uniform\")")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.initializers.uniform_unit_scaling(0.5)"
expected_text = ("tf.compat.v1.keras.initializers.VarianceScaling("
"scale=0.5, distribution=\"uniform\")")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_name_scope(self):
text = "tf.name_scope(None, default_name, [some, values])"
expected_text = "tf.name_scope(name=default_name)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.name_scope(default_name=default_name, values=stuff)"
expected_text = "tf.name_scope(name=default_name)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.name_scope(name=n, default_name=d, values=s)"
expected_text = "tf.compat.v1.name_scope(name=n, default_name=d, values=s)"
_, report, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
self.assertIn("`name` passed to `name_scope`", report)
text = "tf.name_scope(name=None, values=stuff)"
_, _, errors, _ = self._upgrade(text)
self.assertIn("name_scope call with neither name nor default_name",
errors[0])
@parameterized.parameters(
# Rename parameter: delimiter -> sep and add .to_sparse()
["tf.string_split('test', delimiter=' ')",
"tf.strings.split(input='test', sep=' ').to_sparse()"],
# Rename parameter: source -> input
["tf.strings.split(source='test1')",
"tf.strings.split(input='test1').to_sparse()"],
# Use compat.v1 for skip_empty parameter.
["tf.string_split('test', ' ', True)",
"tf.compat.v1.string_split(source='test', sep=' ', skip_empty=True)"],
["tf.string_split('test', ' ', skip_empty=False)",
"tf.strings.split(input='test', sep=' ').to_sparse()"],
# Split behavior for sep=None changed. (In particular, it now splits on
# all whitespace, not just the space character)
["tf.string_split(x)",
"tf.compat.v1.string_split(source=x)"],
# Split behavior for sep='' changed:
["tf.string_split(x, '')",
"tf.strings.bytes_split(input=x).to_sparse()"],
["tf.string_split(x, sep='')",
"tf.strings.bytes_split(input=x).to_sparse()"],
["tf.string_split(x, delimiter='')",
"tf.strings.bytes_split(input=x).to_sparse()"],
["tf.string_split(x, '', result_type='RaggedTensor')",
"tf.strings.bytes_split(input=x)"],
# If sep is a variable, we can't tell if it's empty:
["tf.string_split(x, sep)",
"tf.compat.v1.string_split(source=x, sep=sep)"],
# If sep is a non-empty string literal, then we don't need compat.v1.
["tf.string_split(x, 'non-empty-sep')",
"tf.strings.split(input=x, sep='non-empty-sep').to_sparse()"],
# Add to_sparse unless result_type is RaggedTensor:
["tf.string_split(x, ' ')",
"tf.strings.split(input=x, sep=' ').to_sparse()"],
["tf.string_split(x, ' ', result_type='SparseTensor')",
"tf.strings.split(input=x, sep=' ').to_sparse()"],
["tf.string_split(x, ' ', result_type='RaggedTensor')",
"tf.strings.split(input=x, sep=' ')"],
["tf.string_split(x, ' ', result_type=x)",
"tf.compat.v1.string_split(source=x, sep=' ', result_type=x)"],
) # pyformat: disable
# TODO(b/129398290)
def DISABLED_test_string_split(self, text, expected_text):
"""Tests for transforming from tf.string_split."""
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
@parameterized.parameters(
# Add to_sparse unless result_type is RaggedTensor:
["tf.strings.split(x, sep)",
"tf.strings.split(x, sep).to_sparse()"],
["tf.strings.split(x, sep, result_type='SparseTensor')",
"tf.strings.split(x, sep).to_sparse()"],
["tf.strings.split(x, sep, result_type='RaggedTensor')",
"tf.strings.split(x, sep)"],
["tf.strings.split(x, sep, result_type=x)",
"tf.compat.v1.strings.split(x, sep, result_type=x)"],
) # pyformat: disable
def test_strings_split(self, text, expected_text):
"""Tests for transforming from tf.strings.split."""
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_sdca_to_raw_ops(self):
text = "tf.train.sdca_fprint(input_tensor)"
expected_text = "tf.raw_ops.SdcaFprint(input=input_tensor)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.train.sdca_fprint(input, name=n)"
expected_text = "tf.raw_ops.SdcaFprint(input=input, name=n)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.train.sdca_shrink_l1(w, l, ll)"
expected_text = "tf.raw_ops.SdcaShrinkL1(weights=w, l1=l, l2=ll)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = (
"tf.train.sdca_optimizer(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o)")
expected_text = (
"tf.raw_ops.SdcaOptimizer(sparse_example_indices=a, "
"sparse_feature_indices=b, sparse_feature_values=c, dense_features=d, "
"example_weights=e, example_labels=f, sparse_indices=g, "
"sparse_weights=h, dense_weights=i, example_state_data=j, loss_type=k, "
"l1=l, l2=m, num_loss_partitions=n, num_inner_iterations=o)")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_contrib_to_addons_move(self):
small_mapping = {
"tf.contrib.layers.poincare_normalize":
"tfa.layers.PoincareNormalize",
"tf.contrib.layers.maxout":
"tfa.layers.Maxout",
"tf.contrib.layers.group_norm":
"tfa.layers.GroupNormalization",
"tf.contrib.layers.instance_norm":
"tfa.layers.InstanceNormalization",
}
for symbol, replacement in small_mapping.items():
text = "{}('stuff', *args, **kwargs)".format(symbol)
_, report, _, _ = self._upgrade(text)
self.assertIn(replacement, report)
def testXlaExperimental(self):
text = "tf.xla.experimental.jit_scope(0)"
expected_text = "tf.xla.experimental.jit_scope(0)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
text = "tf.xla.experimental.compile(0)"
expected_text = "tf.xla.experimental.compile(0)"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testNnErosion2d(self):
text = "tf.nn.erosion2d(v, k, s, r, p)"
expected_text = "tf.nn.erosion2d(v, k, s, r, p, data_format='NHWC')"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
def testPywrapTensorflowWarning(self):
text = "tf.pywrap_tensorflow.foo()"
expected = "tf.pywrap_tensorflow.foo()"
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("`tf.pywrap_tensorflow` will not be distributed", errors[0])
def testKerasSaveModelFormat(self):
text = "tf.keras.models.save_model(model, path)"
expected_text = "tf.keras.models.save_model(model, path, save_format='h5')"
_, report, _, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertNotIn(
"saves to the Tensorflow SavedModel format by default", report)
_, report, _, _ = self._upgrade("model.save(path)")
self.assertIn(
"saves to the Tensorflow SavedModel format by default", report)
def test_distribute_strategy(self):
text = "tf.contrib.distribute.CrossDeviceOps()"
expected = "tf.distribute.CrossDeviceOps()"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
text = "tf.contrib.distribute.MirroredStrategy"
expected = "tf.contrib.distribute.MirroredStrategy"
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("migrated to tf.distribute.MirroredStrategy", errors[0])
text = "tf.distribute.MirroredStrategy"
expected = "tf.distribute.MirroredStrategy"
_, report, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("tf.distribute.MirroredStrategy API has changed", report)
self.assertIn("make_dataset_iterator->experimental_distribute_dataset",
report)
text = "tf.contrib.distribute.TPUStrategy"
expected = "tf.contrib.distribute.TPUStrategy"
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("migrated to tf.distribute.experimental.TPUStrategy",
errors[0])
text = "tf.contrib.distribute.foo"
expected = "tf.contrib.distribute.foo"
_, report, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
self.assertIn("tf.contrib.distribute.* have been migrated", report)
def test_decode_raw(self):
text = "tf.io.decode_raw(bytes=[1,2,3], output_dtype=tf.int32)"
expected_text = (
"tf.io.decode_raw(input_bytes=[1,2,3], output_dtype=tf.int32)")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def testRecomputeGrad(self):
text = "tf.contrib.layers.recompute_grad()"
expected = "tf.recompute_grad()"
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected, new_text)
def test_load_variable(self):
text = "tf.contrib.framework.load_variable('a')"
expected_text = (
"tf.train.load_variable('a')")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
text = "tf.contrib.framework.load_variable(checkpoint_dir='a')"
expected_text = (
"tf.train.load_variable(ckpt_dir_or_file='a')")
_, _, _, new_text = self._upgrade(text)
self.assertEqual(expected_text, new_text)
def test_import_analysis(self):
old_symbol = "tf.conj(a)"
new_symbol = "tf.math.conj(a)"
# We upgrade the base un-versioned tensorflow aliased as tf
import_header = "import tensorflow as tf\n"
text = import_header + old_symbol
expected_text = import_header + new_symbol
_, unused_report, unused_errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
import_header = ("import tensorflow as tf\n"
"import tensorflow.compat.v1 as tf_v1\n"
"import tensorflow.compat.v2 as tf_v2\n")
text = import_header + old_symbol
expected_text = import_header + new_symbol
_, _, _, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
# We don't handle unaliased tensorflow imports currently,
# So the upgrade script show log errors
import_header = "import tensorflow\n"
text = import_header + old_symbol
expected_text = import_header + old_symbol
_, _, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertIn("unaliased `import tensorflow`", "\n".join(errors))
# Upgrading explicitly-versioned tf code is unsafe, but we don't
# need to throw errors when we detect explicitly-versioned tf.
import_header = "import tensorflow.compat.v1 as tf\n"
text = import_header + old_symbol
expected_text = import_header + old_symbol
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertIn("`tensorflow.compat.v1` was directly imported as `tf`",
report)
self.assertEmpty(errors)
import_header = "from tensorflow.compat import v1 as tf\n"
text = import_header + old_symbol
expected_text = import_header + old_symbol
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertIn("`tensorflow.compat.v1` was directly imported as `tf`",
report)
self.assertEmpty(errors)
import_header = "from tensorflow.compat import v1 as tf, v2 as tf2\n"
text = import_header + old_symbol
expected_text = import_header + old_symbol
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertIn("`tensorflow.compat.v1` was directly imported as `tf`",
report)
self.assertEmpty(errors)
import_header = "import tensorflow.compat.v2 as tf\n"
text = import_header + old_symbol
expected_text = import_header + old_symbol
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertIn("`tensorflow.compat.v2` was directly imported as `tf`",
report)
self.assertEmpty(errors)
import_header = "from tensorflow.compat import v1 as tf1, v2 as tf\n"
text = import_header + old_symbol
expected_text = import_header + old_symbol
_, report, errors, new_text = self._upgrade(text)
self.assertEqual(new_text, expected_text)
self.assertIn("`tensorflow.compat.v2` was directly imported as `tf`",
report)
self.assertEmpty(errors)
def test_api_spec_reset_between_files(self):
for old_symbol, new_symbol in [
("tf.conj(a)", "tf.math.conj(a)"),
("tf.to_int32(x)", "tf.cast(x, dtype=tf.int32)")]:
## Test that the api spec is reset in between files:
import_header = "import tensorflow.compat.v2 as tf\n"
text_a = import_header + old_symbol
expected_text_a = import_header + old_symbol
text_b = old_symbol
expected_text_b = new_symbol
results = self._upgrade_multiple([text_a, text_b])
result_a, result_b = results[0], results[1]
self.assertEqual(result_a[3], expected_text_a)
self.assertEqual(result_b[3], expected_text_b)
def test_model_to_estimator_checkpoint_warning(self):
text = "tf.keras.estimator.model_to_estimator(model)"
_, report, _, _ = self._upgrade(text)
expected_info = "will save object-based checkpoints"
self.assertIn(expected_info, report)
class TestUpgradeFiles(test_util.TensorFlowTestCase):
def testInplace(self):
"""Check to make sure we don't have a file system race."""
temp_file = tempfile.NamedTemporaryFile("w", delete=False)
original = "tf.conj(a)\n"
upgraded = "tf.math.conj(a)\n"
temp_file.write(original)
temp_file.close()
upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())
upgrader.process_file(temp_file.name, temp_file.name)
self.assertAllEqual(open(temp_file.name).read(), upgraded)
os.unlink(temp_file.name)
if __name__ == "__main__":
test_lib.main()