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How to use _upgrade method in tempest

Best Python code snippet using tempest_python

tf_upgrade_v2_test.py

Source:tf_upgrade_v2_test.py

...87          for name in api_names_v1:88            cls.v1_symbols["tf." + name] = attr89      visitor = public_api.PublicAPIVisitor(symbol_collector_v1)90      traverse.traverse(tf.compat.v1, visitor)91  def _upgrade(self, old_file_text):92    in_file = six.StringIO(old_file_text)93    out_file = six.StringIO()94    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())95    count, report, errors = (96        upgrader.process_opened_file("test.py", in_file,97                                     "test_out.py", out_file))98    return count, report, errors, out_file.getvalue()99  def testParseError(self):100    _, report, unused_errors, unused_new_text = self._upgrade(101        "import tensorflow as tf\na + \n")102    self.assertTrue(report.find("Failed to parse") != -1)103  def testReport(self):104    text = "tf.angle(a)\n"105    _, report, unused_errors, unused_new_text = self._upgrade(text)106    # This is not a complete test, but it is a sanity test that a report107    # is generating information.108    self.assertTrue(report.find("Renamed function `tf.angle` to "109                                "`tf.math.angle`"))110  def testRename(self):111    text = "tf.conj(a)\n"112    _, unused_report, unused_errors, new_text = self._upgrade(text)113    self.assertEqual(new_text, "tf.math.conj(a)\n")114    text = "tf.rsqrt(tf.log_sigmoid(3.8))\n"115    _, unused_report, unused_errors, new_text = self._upgrade(text)116    self.assertEqual(new_text, "tf.math.rsqrt(tf.math.log_sigmoid(3.8))\n")117  def testAllAPI(self):118    if not hasattr(tf.compat, "v2"):119      return120    # Converts all symbols in the v1 namespace to the v2 namespace, raising121    # an error if the target of the conversion is not in the v2 namespace.122    # Please regenerate the renames file or edit any manual renames if this123    # test fails.124    def conversion_visitor(unused_path, unused_parent, children):125      for child in children:126        _, attr = tf_decorator.unwrap(child[1])127        api_names = tf_export.get_v1_names(attr)128        for name in api_names:129          _, _, _, text = self._upgrade("tf." + name)130          if (text and131              not text.startswith("tf.compat.v1") and132              not text.startswith("tf.compat.v2") and133              text not in self.v2_symbols and134              # Builds currently install old version of estimator that doesn't135              # have some 2.0 symbols.136              not text.startswith("tf.estimator")):137            self.assertFalse(138                True, "Symbol %s generated from %s not in v2 API" % (139                    text, name))140    visitor = public_api.PublicAPIVisitor(conversion_visitor)141    visitor.do_not_descend_map["tf"].append("contrib")142    visitor.private_map["tf.compat"] = ["v1", "v2"]143    traverse.traverse(tf.compat.v1, visitor)144  def testAllAPIV1(self):145    collect = True146    v1_symbols = set([])147    # Converts all symbols in the v1 namespace to the v2 namespace, raising148    # an error if the target of the conversion is not in the v1 namespace.149    def conversion_visitor(unused_path, unused_parent, children):150      for child in children:151        _, attr = tf_decorator.unwrap(child[1])152        api_names = tf_export.get_v1_names(attr)153        for name in api_names:154          if collect:155            v1_symbols.add("tf." + name)156          else:157            _, _, _, text = self._upgrade("tf." + name)158            if (text and159                not text.startswith("tf.compat.v1") and160                not text.startswith("tf.compat.v2") and161                not text.startswith("tf.estimator") and162                text not in v1_symbols):163              self.assertFalse(164                  True, "Symbol %s generated from %s not in v1 API" % (165                      text, name))166    visitor = public_api.PublicAPIVisitor(conversion_visitor)167    visitor.do_not_descend_map["tf"].append("contrib")168    visitor.private_map["tf.compat"] = ["v1", "v2"]169    traverse.traverse(tf.compat.v1, visitor)170    collect = False171    traverse.traverse(tf.compat.v1, visitor)172  def testV1KeywordArgNames(self):173    all_keyword_renames = (174        tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)175    # Visitor that verifies V1 argument names.176    def arg_test_visitor(unused_path, unused_parent, children):177      for child in children:178        _, attr = tf_decorator.unwrap(child[1])179        names_v1 = tf_export.get_v1_names(attr)180        for name in names_v1:181          name = "tf.%s" % name182          if name not in all_keyword_renames:183            continue184          arg_names_v1 = tf_inspect.getargspec(attr)[0]185          keyword_renames = all_keyword_renames[name]186          self.assertEqual(type(keyword_renames), dict)187          # Assert that v1 function has valid v1 argument names.188          for from_name, _ in keyword_renames.items():189            self.assertIn(190                from_name, arg_names_v1,191                "%s not found in %s arguments: %s" %192                (from_name, name, str(arg_names_v1)))193    visitor = public_api.PublicAPIVisitor(arg_test_visitor)194    visitor.do_not_descend_map["tf"].append("contrib")195    visitor.private_map["tf.compat"] = ["v1", "v2"]196    traverse.traverse(tf.compat.v1, visitor)197  def testV2KeywordArgNames(self):198    # This test converts a call of the form:199    # tf.foo(arg1=0, arg2=1, ...)200    # to 2.0. Then, checks that converted function has valid argument names.201    if not hasattr(tf.compat, "v2"):202      return203    v2_arg_exceptions = {204        "verify_shape_is_now_always_true",205        # These arguments should not be used, they just specify206        # that a function takes named arguments.207        "keyword_required",208        "_sentinel",209    }210    v1_name_exceptions = {211        "tf.print",  # requires print_function import212    }213    function_warnings = (214        tf_upgrade_v2.TFAPIChangeSpec().function_warnings)215    function_transformers = (216        tf_upgrade_v2.TFAPIChangeSpec().function_transformers)217    keyword_renames = (218        tf_upgrade_v2.TFAPIChangeSpec().function_keyword_renames)219    # Visitor that converts to V2 and checks V2 argument names.220    def conversion_visitor(unused_path, unused_parent, children):221      for child in children:222        _, attr = tf_decorator.unwrap(child[1])223        if not tf_inspect.isfunction(attr):224          continue225        names_v1 = tf_export.get_v1_names(attr)226        arg_names_v1 = get_args(attr)227        for name in names_v1:228          tf_name = "tf.%s" % name229          if tf_name in function_warnings or tf_name in function_transformers:230            continue  # These require manual change231          if tf_name in v1_name_exceptions:232            continue233          # Assert that arg names after converting to v2 are present in234          # v2 function.235          # 1. First, create an input of the form:236          #    tf.foo(arg1=val1, arg2=val2, ...)237          args = ",".join(238              ["%s=%d" % (from_name, from_index)239               for from_index, from_name in enumerate(arg_names_v1)])240          text_input = "%s(%s)" % (tf_name, args)241          # 2. Convert the input to V2.242          _, _, _, text = self._upgrade(text_input)243          new_function_name, new_args = get_func_and_args_from_str(text)244          if new_function_name == "tf.compat.v1.%s" % name:245            if tf_name in keyword_renames:246              # If we rename arguments, new function must be available in 2.0.247              # We should not be using compat.v1 in this case.248              self.assertFalse(249                  "Function '%s' is not in 2.0 when converting\n%s\nto\n%s" %250                  (new_function_name, text_input, text))251            continue252          if new_function_name.startswith("tf.compat.v2"):253            self.assertIn(new_function_name.replace("tf.compat.v2.", "tf."),254                          self.v2_symbols)255            continue256          # 3. Verify V2 function and arguments.257          args_v2 = get_args(self.v2_symbols[new_function_name])258          args_v2.extend(v2_arg_exceptions)259          for new_arg in new_args:260            self.assertIn(261                new_arg, args_v2,262                "Invalid argument '%s' in 2.0 when converting\n%s\nto\n%s.\n"263                "Supported arguments: %s" % (264                    new_arg, text_input, text, str(args_v2)))265          # 4. Verify that the argument exists in v1 as well.266          if new_function_name in set(["tf.nn.ctc_loss",267                                       "tf.saved_model.save"]):268            continue269          args_v1 = get_args(self.v1_symbols[new_function_name])270          args_v1.extend(v2_arg_exceptions)271          for new_arg in new_args:272            self.assertIn(273                new_arg, args_v1,274                "Invalid argument '%s' in 1.0 when converting\n%s\nto\n%s.\n"275                "Supported arguments: %s" % (276                    new_arg, text_input, text, str(args_v1)))277    visitor = public_api.PublicAPIVisitor(conversion_visitor)278    visitor.do_not_descend_map["tf"].append("contrib")279    visitor.private_map["tf.compat"] = ["v1", "v2"]280    traverse.traverse(tf.compat.v1, visitor)281  def testPositionsMatchArgGiven(self):282    full_dict = tf_upgrade_v2.TFAPIChangeSpec().function_arg_warnings283    method_names = full_dict.keys()284    for method_name in method_names:285      args = full_dict[method_name].keys()286      # special case for optimizer methods287      if method_name.startswith("*."):288        method = method_name.replace("*", "tf.train.Optimizer")289      else:290        method = method_name291      method = get_symbol_for_name(tf, method)292      arg_spec = tf_inspect.getfullargspec(method)293      for (arg, pos) in args:294        # to deal with the self argument on methods on objects295        if method_name.startswith("*."):296          pos += 1297        self.assertEqual(arg_spec[0][pos], arg)298  def testReorderFileNeedsUpdate(self):299    reordered_function_names = (300        tf_upgrade_v2.TFAPIChangeSpec().reordered_function_names)301    function_reorders = (302        tf_upgrade_v2.TFAPIChangeSpec().function_reorders)303    manual_function_reorders = (304        tf_upgrade_v2.TFAPIChangeSpec().manual_function_reorders)305    added_names_message = """Some function names in306self.reordered_function_names are not in reorders_v2.py.307Please run the following commands to update reorders_v2.py:308bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map309bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map310"""311    removed_names_message = """%s in self.reorders_v2 does not match312any name in self.reordered_function_names.313Please run the following commands to update reorders_v2.py:314bazel build tensorflow/tools/compatibility/update:generate_v2_reorders_map315bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map316"""317    self.assertTrue(318        reordered_function_names.issubset(function_reorders),319        added_names_message)320    # function_reorders should contain reordered_function_names321    # and their TensorFlow V1 aliases.322    for name in function_reorders:323      if name in manual_function_reorders:324        continue325      # get other names for this function326      attr = get_symbol_for_name(tf.compat.v1, name)327      _, attr = tf_decorator.unwrap(attr)328      v1_names = tf_export.get_v1_names(attr)329      self.assertTrue(v1_names)330      v1_names = ["tf.%s" % n for n in v1_names]331      # check if any other name is in332      self.assertTrue(333          any(n in reordered_function_names for n in v1_names),334          removed_names_message % name)335  def testRenameConstant(self):336    text = "tf.MONOLITHIC_BUILD\n"337    _, unused_report, unused_errors, new_text = self._upgrade(text)338    self.assertEqual(new_text, "tf.sysconfig.MONOLITHIC_BUILD\n")339    text = "some_call(tf.MONOLITHIC_BUILD)\n"340    _, unused_report, unused_errors, new_text = self._upgrade(text)341    self.assertEqual(new_text, "some_call(tf.sysconfig.MONOLITHIC_BUILD)\n")342  def testRenameArgs(self):343    text = ("tf.nn.pool(input_a, window_shape_a, pooling_type_a, padding_a, "344            "dilation_rate_a, strides_a, name_a, data_format_a)\n")345    _, unused_report, unused_errors, new_text = self._upgrade(text)346    self.assertEqual(new_text,347                     ("tf.nn.pool(input=input_a, window_shape=window_shape_a,"348                      " pooling_type=pooling_type_a, padding=padding_a, "349                      "dilations=dilation_rate_a, strides=strides_a, "350                      "name=name_a, data_format=data_format_a)\n"))351  def testReorder(self):352    text = "tf.boolean_mask(a, b, c, d)\n"353    _, unused_report, unused_errors, new_text = self._upgrade(text)354    self.assertEqual(new_text,355                     "tf.boolean_mask(tensor=a, mask=b, name=c, axis=d)\n")356  def testLearningRateDecay(self):357    for decay in ["tf.train.exponential_decay",358                  "tf.train.polynomial_decay", "tf.train.natural_exp_decay",359                  "tf.train.inverse_time_decay", "tf.train.cosine_decay",360                  "tf.train.cosine_decay_restarts",361                  "tf.train.linear_cosine_decay",362                  "tf.train.noisy_linear_cosine_decay",363                  "tf.train.piecewise_constant_decay",364                 ]:365      text = "%s(a, b)\n" % decay366      _, report, unused_errors, _ = self._upgrade(text)367      self.assertIn("switch to the schedules in "368                    "`tf.keras.optimizers.schedules`", report)369  def testMetrics(self):370    metrics = [371        "accuracy",372        "auc",373        "average_precision_at_k",374        "false_negatives",375        "false_negatives_at_thresholds",376        "false_positives",377        "false_positives_at_thresholds",378        "mean",379        "mean_absolute_error",380        "mean_cosine_distance",381        "mean_iou",382        "mean_per_class_accuracy",383        "mean_relative_error",384        "mean_squared_error",385        "mean_tensor",386        "percentage_below",387        "precision",388        "precision_at_k",389        "precision_at_thresholds",390        "precision_at_top_k",391        "recall",392        "recall_at_k",393        "recall_at_thresholds",394        "recall_at_top_k",395        "root_mean_squared_error",396        "sensitivity_at_specificity",397        "sparse_average_precision_at_k",398        "sparse_precision_at_k",399        "specificity_at_sensitivity",400        "true_negatives",401        "true_negatives_at_thresholds",402        "true_positives",403        "true_positives_at_thresholds",404    ]405    for m in metrics:406      text = "tf.metrics." + m + "(a, b)"407      _, report, unused_errors, new_text = self._upgrade(text)408      self.assertEqual("tf.compat.v1.metrics." + m + "(a, b)", new_text)409      self.assertIn(410          "tf.metrics have been replaced with object oriented versions", report)411  def testLosses(self):412    losses = [413        "absolute_difference",414        "add_loss",415        "compute_weighted_loss",416        "cosine_distance",417        "get_losses",418        "get_regularization_loss",419        "get_regularization_losses",420        "get_total_loss",421        "hinge_loss",422        "huber_loss",423        "log_loss",424        "mean_pairwise_squared_error",425        "mean_squared_error",426        "sigmoid_cross_entropy",427        "softmax_cross_entropy",428        "sparse_softmax_cross_entropy",429    ]430    for l in losses:431      text = "tf.losses." + l + "(a, b)"432      _, report, unused_errors, new_text = self._upgrade(text)433      self.assertEqual("tf.compat.v1.losses." + l + "(a, b)", new_text)434      self.assertIn(435          "tf.losses have been replaced with object oriented versions", report)436  def testEstimatorLossReductionChange(self):437    classes = [438        "LinearClassifier", "LinearRegressor", "DNNLinearCombinedClassifier",439        "DNNLinearCombinedRegressor", "DNNRegressor", "DNNClassifier",440        "BaselineClassifier", "BaselineRegressor"441    ]442    for c in classes:443      ns = "tf.estimator." + c444      text = ns + "()"445      expected_text = ns + "(loss_reduction=tf.compat.v1.losses.Reduction.SUM)"446      _, report, errors, new_text = self._upgrade(text)447      self.assertEqual(expected_text, new_text)448      text = ns + "(loss_reduction=TEST)"449      expected_text = ns + "(loss_reduction=TEST)"450      _, report, errors, new_text = self._upgrade(text)451      self.assertEqual(text, new_text)452    text = "tf.estimator.BaselineClassifier(m, c, w, v, o, c, lr)"453    expected_text = (454        "tf.estimator.BaselineClassifier(" +455        "model_dir=m, n_classes=c, weight_column=w, label_vocabulary=v, "456        "optimizer=o, config=c, loss_reduction=lr)")457    _, report, errors, new_text = self._upgrade(text)458    self.assertEqual(expected_text, new_text)459    text = "tf.estimator.BaselineClassifier(model_dir=model_dir)"460    expected_text = ("tf.estimator.BaselineClassifier(" +461                     "model_dir=model_dir, "462                     "loss_reduction=tf.compat.v1.losses.Reduction.SUM)")463    _, report, errors, new_text = self._upgrade(text)464    self.assertEqual(expected_text, new_text)465  def testGetVariableWithUseResource(self):466    text = "tf.get_variable(name=\"a\")"467    expected_text = "tf.compat.v1.get_variable(name=\"a\", use_resource=False)"468    _, unused_report, unused_errors, new_text = self._upgrade(text)469    self.assertEqual(expected_text, new_text)470    text = "tf.get_variable(name=\"a\", use_resource=None)"471    expected_text = "tf.compat.v1.get_variable(name=\"a\", use_resource=None)"472    _, unused_report, unused_errors, new_text = self._upgrade(text)473    self.assertEqual(expected_text, new_text)474  def testExtractGlimpse(self):475    text = ("tf.image.extract_glimpse(x, size, off, False, "476            "False, False, name=\"foo\")\n")477    _, unused_report, unused_errors, new_text = self._upgrade(text)478    self.assertEqual(479        new_text,480        "tf.image.extract_glimpse(x, size, off, False, "481        "False, 'uniform' if (False) else 'gaussian', name=\"foo\")\n",482    )483    text = ("tf.image.extract_glimpse(x, size, off, centered=False, "484            "normalized=False, uniform_noise=True if uniform_noise else "485            "False, name=\"foo\")\n")486    _, unused_report, unused_errors, new_text = self._upgrade(text)487    self.assertEqual(488        new_text,489        "tf.image.extract_glimpse(x, size, off, centered=False, "490        "normalized=False, noise='uniform' if (True if uniform_noise else "491        "False) else 'gaussian', name=\"foo\")\n",492    )493    text = ("tf.image.extract_glimpse(x,\n"494            "                         size,\n"495            "                         off,\n"496            "                         centered=True,\n"497            "                         normalized=True, # Stuff before\n"498            "                         uniform_noise=False,\n"499            "                         name=\"foo\")# Stuff after\n")500    _, unused_report, unused_errors, new_text = self._upgrade(text)501    self.assertEqual(502        new_text, "tf.image.extract_glimpse(x,\n"503        "                         size,\n"504        "                         off,\n"505        "                         centered=True,\n"506        "                         normalized=True, # Stuff before\n"507        "                         noise='uniform' if (False) else 'gaussian',\n"508        "                         name=\"foo\")# Stuff after\n")509    text = "tf.image.extract_glimpse(x)\n"510    _, unused_report, errors, new_text = self._upgrade(text)511    self.assertEqual(new_text, text)512    self.assertEqual(errors, [])513  def testDropout(self):514    text = "tf.nn.dropout(x, keep_prob, name=\"foo\")\n"515    _, unused_report, unused_errors, new_text = self._upgrade(text)516    self.assertEqual(517        new_text,518        "tf.nn.dropout(x, 1 - (keep_prob), name=\"foo\")\n",519    )520    text = "tf.nn.dropout(x, keep_prob=.4, name=\"foo\")\n"521    _, unused_report, unused_errors, new_text = self._upgrade(text)522    self.assertEqual(523        new_text,524        "tf.nn.dropout(x, rate=1 - (.4), name=\"foo\")\n",525    )526    text = (527        "tf.nn.dropout(x,  # Stuff before\n"528        "              keep_prob=.4,  # Stuff after\n"529        "              name=\"foo\")\n"530    )531    _, unused_report, unused_errors, new_text = self._upgrade(text)532    self.assertEqual(533        new_text,534        "tf.nn.dropout(x,  # Stuff before\n"535        "              rate=1 - (.4),  # Stuff after\n"536        "              name=\"foo\")\n",537    )538    text = "tf.nn.dropout(x)\n"539    _, unused_report, errors, new_text = self._upgrade(text)540    self.assertEqual(new_text, text)541    self.assertIn("tf.nn.dropout called without arguments", errors[0])542  def testDropoutExpr(self):543    text = "tf.nn.dropout(x, 1 - func(3 + 4.), name=\"foo\")\n"544    _, unused_report, unused_errors, new_text = self._upgrade(text)545    self.assertEqual(546        new_text,547        "tf.nn.dropout(x, 1 - (1 - func(3 + 4.)), name=\"foo\")\n",548    )549  def testMathCountNonZeroChanges(self):550    text = (551        "tf.math.count_nonzero(input_tensor=input, dtype=dtype, name=name, "552        "reduction_indices=axis, keep_dims=keepdims)\n"553        )554    _, unused_report, unused_errors, new_text = self._upgrade(text)555    expected_text = (556        "tf.math.count_nonzero(input=input, dtype=dtype, name=name, "557        "axis=axis, keepdims=keepdims)\n"558        )559    self.assertEqual(new_text, expected_text)560  def testCountNonZeroChanges(self):561    text = (562        "tf.count_nonzero(input_tensor=input, dtype=dtype, name=name, "563        "reduction_indices=axis, keep_dims=keepdims)\n"564        )565    _, unused_report, unused_errors, new_text = self._upgrade(text)566    expected_text = (567        "tf.math.count_nonzero(input=input, dtype=dtype, name=name, "568        "axis=axis, keepdims=keepdims)\n"569        )570    self.assertEqual(new_text, expected_text)571  def testRandomMultinomialToRandomCategorical(self):572    text = (573        "tf.random.multinomial(logits, samples, seed, name, output_dtype)\n"574        )575    _, unused_report, unused_errors, new_text = self._upgrade(text)576    expected_text = (577        "tf.random.categorical(logits=logits, num_samples=samples, seed=seed, "578        "name=name, dtype=output_dtype)\n"579        )580    self.assertEqual(new_text, expected_text)581    text = (582        "tf.multinomial(logits, samples, seed, name, output_dtype)\n"583        )584    _, unused_report, unused_errors, new_text = self._upgrade(text)585    expected_text = (586        "tf.random.categorical(logits=logits, num_samples=samples, seed=seed, "587        "name=name, dtype=output_dtype)\n"588        )589    self.assertEqual(new_text, expected_text)590  def testRandomPoissonConversion(self):591    text1 = "tf.random_poisson(lam, shape, dtype)"592    text2 = "tf.random.poisson(lam, shape, dtype)"593    expected_text = "tf.random.poisson(lam=lam, shape=shape, dtype=dtype)"594    _, unused_report, unused_errors, new_text1 = self._upgrade(text1)595    self.assertEqual(new_text1, expected_text)596    _, unused_report, unused_errors, new_text2 = self._upgrade(text2)597    self.assertEqual(new_text2, expected_text)598  def testConvolutionOpUpdate(self):599    text = (600        "tf.nn.convolution(input, filter, padding, strides, dilation_rate, "601        "name, data_format)"602    )603    _, unused_report, unused_errors, new_text = self._upgrade(text)604    expected_text = (605        "tf.nn.convolution(input=input, filters=filter, padding=padding, "606        "strides=strides, dilations=dilation_rate, name=name, "607        "data_format=data_format)"608    )609    self.assertEqual(new_text, expected_text)610  def test_substr(self):611    text = "tf.substr(input, pos, len, name, unit)\n"612    _, unused_report, errors, new_text = self._upgrade(text)613    self.assertEqual("tf.strings.substr(input=input, pos=pos, len=len, "614                     "name=name, unit=unit)\n", new_text)615    self.assertEqual(errors, [])616  def testColocateGradientsWithOps(self):617    text = "tf.gradients(yx=a, foo=False)\n"618    _, unused_report, errors, new_text = self._upgrade(text)619    self.assertEqual(text, new_text)620    self.assertEqual(errors, [])621    text = "tf.gradients(yx=a, colocate_gradients_with_ops=False)\n"622    _, report, unused_errors, new_text = self._upgrade(text)623    self.assertEqual("tf.gradients(yx=a)\n", new_text)624    self.assertIn("tf.gradients no longer takes", report)625    text = "tf.gradients(y, x, grad_ys, name, colocate, gate)\n"626    expected = ("tf.gradients(ys=y, xs=x, grad_ys=grad_ys, name=name, "627                "gate_gradients=gate)\n")628    _, unused_report, errors, new_text = self._upgrade(text)629    self.assertEqual(expected, new_text)630  def testColocateGradientsWithOpsMinimize(self):631    text = "optimizer.minimize(a, foo=False)\n"632    _, unused_report, errors, new_text = self._upgrade(text)633    self.assertEqual(text, new_text)634    self.assertEqual(errors, [])635    text = "optimizer.minimize(a, colocate_gradients_with_ops=False)\n"636    _, report, unused_errors, new_text = self._upgrade(text)637    self.assertEqual("optimizer.minimize(a)\n", new_text)638    self.assertIn("Optimizer.minimize no longer takes", report)639  def testColocateGradientsWithOpsComputeGradients(self):640    text = "optimizer.compute_gradients(a, foo=False)\n"641    _, unused_report, errors, new_text = self._upgrade(text)642    self.assertEqual(text, new_text)643    self.assertEqual(errors, [])644    text = "optimizer.compute_gradients(a, colocate_gradients_with_ops=False)\n"645    _, report, unused_errors, new_text = self._upgrade(text)646    self.assertEqual("optimizer.compute_gradients(a)\n", new_text)647    self.assertIn("Optimizer.compute_gradients no longer takes", report)648  def testExportSavedModelRename(self):649    text = "self.est.export_savedmodel(path)"650    _, report, unused_errors, unused_new_text = self._upgrade(text)651    self.assertIn(652        "rename the method export_savedmodel() to export_saved_model()",653        report)654  def testArgmin(self):655    text = "tf.argmin(input, name=n, dimension=1, output_type=type)"656    expected_text = "tf.argmin(input=input, name=n, axis=1, output_type=type)"657    _, unused_report, unused_errors, new_text = self._upgrade(text)658    self.assertEqual(new_text, expected_text)659    text = "tf.argmin(input, 0)"660    expected_text = "tf.argmin(input=input, axis=0)"661    _, unused_report, unused_errors, new_text = self._upgrade(text)662    self.assertEqual(new_text, expected_text)663    text = "tf.arg_min(input, 0)"664    expected_text = "tf.argmin(input, 0)"665    _, unused_report, unused_errors, new_text = self._upgrade(text)666    self.assertEqual(new_text, expected_text)667  def testArgmax(self):668    text = "tf.argmax(input, name=n, dimension=1, output_type=type)"669    expected_text = "tf.argmax(input=input, name=n, axis=1, output_type=type)"670    _, unused_report, unused_errors, new_text = self._upgrade(text)671    self.assertEqual(new_text, expected_text)672    text = "tf.argmax(input, 0)"673    expected_text = "tf.argmax(input=input, axis=0)"674    _, unused_report, unused_errors, new_text = self._upgrade(text)675    self.assertEqual(new_text, expected_text)676    text = "tf.arg_max(input, 0)"677    expected_text = "tf.argmax(input, 0)"678    _, unused_report, unused_errors, new_text = self._upgrade(text)679    self.assertEqual(new_text, expected_text)680  def testEstimatorInputs(self):681    text = "tf.estimator.inputs.numpy_input_fn(0)"682    expected_text = "tf.compat.v1.estimator.inputs.numpy_input_fn(0)"683    _, unused_report, unused_errors, new_text = self._upgrade(text)684    self.assertEqual(new_text, expected_text)685    text = "tf.estimator.inputs.pandas_input_fn(0)"686    expected_text = "tf.compat.v1.estimator.inputs.pandas_input_fn(0)"687    _, unused_report, unused_errors, new_text = self._upgrade(text)688    self.assertEqual(new_text, expected_text)689  def testBatchToSpace(self):690    text = "tf.batch_to_space_nd(input, block_shape, crops, name)"691    expected_text = "tf.batch_to_space(input, block_shape, crops, name)"692    _, unused_report, unused_errors, new_text = self._upgrade(text)693    self.assertEqual(new_text, expected_text)694    text = "tf.batch_to_space(input, crops, block_size, name)"695    expected_text = (696        "tf.batch_to_space(input=input, crops=crops, block_shape=block_size, "697        "name=name)")698    _, unused_report, unused_errors, new_text = self._upgrade(text)699    self.assertEqual(new_text, expected_text)700    text = "tf.manip.batch_to_space_nd(input, block_shape, crops, name)"701    expected_text = "tf.batch_to_space(input, block_shape, crops, name)"702    _, unused_report, unused_errors, new_text = self._upgrade(text)703    self.assertEqual(new_text, expected_text)704  def testExtractImagePatches(self):705    text = (706        "tf.extract_image_patches(images, ksizes=ksizes, strides=strides,"707        "rates=rates, padding=padding, name=name)")708    expected_text = (709        "tf.image.extract_image_patches(images, sizes=ksizes, strides=strides,"710        "rates=rates, padding=padding, name=name)")711    _, unused_report, unused_errors, new_text = self._upgrade(text)712    self.assertEqual(new_text, expected_text)713  def testKerasSavedModel(self):714    text = (715        "tf.contrib.saved_model.save_keras_model(model, './saved_models')\n"716        "tf.contrib.saved_model.load_keras_model(saved_model_path)\n")717    expected_text = (718        "tf.keras.experimental.export_saved_model(model, './saved_models')\n"719        "tf.keras.experimental.load_from_saved_model(saved_model_path)\n")720    _, unused_report, unused_errors, new_text = self._upgrade(text)721    self.assertEqual(new_text, expected_text)722  def testStatelessMultinomial(self):723    text = (724        "tf.random.stateless_multinomial(logits, num_samples, seed, "725        "output_dtype=dtype, name=name)")726    expected_text = (727        "tf.random.stateless_categorical(logits, num_samples, seed, "728        "dtype=dtype, name=name)")729    _, unused_report, unused_errors, new_text = self._upgrade(text)730    self.assertEqual(new_text, expected_text)731  def testSoftMaxCrossEntropyWithLogitsV2(self):732    text = (733        "tf.nn.softmax_cross_entropy_with_logits_v2("734        "labels=labels, logits=logits, dim=2)")735    expected_text = (736        "tf.nn.softmax_cross_entropy_with_logits("737        "labels=labels, logits=logits, axis=2)")738    _, unused_report, errors, new_text = self._upgrade(text)739    self.assertEqual(new_text, expected_text)740    self.assertFalse(errors)741  def testSoftMaxCrossEntropyWithLogits(self):742    text = ("tf.nn.softmax_cross_entropy_with_logits("743            "labels=labels, logits=logits, dim=2)")744    expected_text = (745        "tf.nn.softmax_cross_entropy_with_logits("746        "labels=tf.stop_gradient(labels), logits=logits, axis=2)")747    _, unused_report, unused_errors, new_text = self._upgrade(text)748    self.assertEqual(new_text, expected_text)749    text = ("tf.nn.softmax_cross_entropy_with_logits("750            "labels=foo(bar))")751    expected_text = ("tf.nn.softmax_cross_entropy_with_logits("752                     "labels=tf.stop_gradient(foo(bar)))")753    _, unused_report, unused_errors, new_text = self._upgrade(text)754    self.assertEqual(expected_text, new_text)755  def testSoftMaxCrossEntropyWithLogitsDoesntNest(self):756    text = ("tf.nn.softmax_cross_entropy_with_logits("757            "labels=tf.stop_gradient(labels), logits=logits, dim=2)")758    expected_text = (759        "tf.nn.softmax_cross_entropy_with_logits("760        "labels=tf.stop_gradient(labels), logits=logits, axis=2)")761    _, unused_report, unused_errors, new_text = self._upgrade(text)762    self.assertEqual(new_text, expected_text)763    text = ("tf.nn.softmax_cross_entropy_with_logits("764            "labels=tf.stop_gradient(foo(bar)))")765    expected_text = ("tf.nn.softmax_cross_entropy_with_logits("766                     "labels=tf.stop_gradient(foo(bar)))")767    _, unused_report, unused_errors, new_text = self._upgrade(text)768    self.assertEqual(expected_text, new_text)769    text = ("tf.nn.softmax_cross_entropy_with_logits("770            "labels=foo())")771    expected_text = ("tf.nn.softmax_cross_entropy_with_logits("772                     "labels=tf.stop_gradient(foo()))")773    _, unused_report, unused_errors, new_text = self._upgrade(text)774    self.assertEqual(expected_text, new_text)775    text = ("tf.nn.softmax_cross_entropy_with_logits("776            "labels=foo().zz())")777    expected_text = ("tf.nn.softmax_cross_entropy_with_logits("778                     "labels=tf.stop_gradient(foo().zz()))")779    _, unused_report, unused_errors, new_text = self._upgrade(text)780    self.assertEqual(expected_text, new_text)781  def testSparseMatmul(self):782    text = ("tf.sparse_matmul(a, b, c, d, e, f, g)\n")783    expected_text = ("tf.linalg.matmul(a=a, b=b, transpose_a=c, transpose_b=d, "784                     "a_is_sparse=e, b_is_sparse=f, name=g)\n")785    _, unused_report, unused_errors, new_text = self._upgrade(text)786    self.assertEqual(new_text, expected_text)787  def testWeightedMoments(self):788    text = "tf.nn.weighted_moments(x, axes, freq, name, kd)"789    expected_text = (790        "tf.nn.weighted_moments(x=x, axes=axes, frequency_weights=freq, "791        "name=name, keepdims=kd)")792    _, unused_report, unused_errors, new_text = self._upgrade(text)793    self.assertEqual(new_text, expected_text)794  def testSparseAdd(self):795    text = "tf.sparse.add(a, b, t)"796    expected_text = "tf.sparse.add(a=a, b=b, threshold=t)"797    _, unused_report, unused_errors, new_text = self._upgrade(text)798    self.assertEqual(new_text, expected_text)799  def testSparseConcat(self):800    text = "tf.sparse.concat(ax, inp, name, exp, concat)"801    expected_text = (802        "tf.sparse.concat(axis=ax, sp_inputs=inp, name=name, "803        "expand_nonconcat_dims=exp, axis=concat)")804    _, unused_report, unused_errors, new_text = self._upgrade(text)805    self.assertEqual(new_text, expected_text)806  def testSeparableConv2D(self):807    text = "tf.nn.separable_conv2d(inp, d, pt, strides, pad, rate, name, fmt)"808    expected_text = (809        "tf.nn.separable_conv2d(input=inp, depthwise_filter=d, "810        "pointwise_filter=pt, strides=strides, padding=pad, "811        "dilations=rate, name=name, data_format=fmt)")812    _, unused_report, unused_errors, new_text = self._upgrade(text)813    self.assertEqual(new_text, expected_text)814  def testConv2D(self):815    text = (816        "tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu, "817        "data_format)")818    expected_text = (819        "tf.nn.conv2d(input=input, filters=filter, strides=strides, "820        "padding=padding, data_format=data_format)")821    _, unused_report, unused_errors, new_text = self._upgrade(text)822    self.assertEqual(new_text, expected_text)823    text = (824        "tf.nn.conv2d(input, filter=filter, strides=strides, padding=padding, "825        "use_cudnn_on_gpu=use_cudnn_on_gpu)")826    expected_text = ("tf.nn.conv2d(input=input, filters=filter, "827                     "strides=strides, padding=padding)")828    _, unused_report, unused_errors, new_text = self._upgrade(text)829    self.assertEqual(new_text, expected_text)830  def testConv2DBackpropFilter(self):831    text = (832        "tf.nn.conv2d_backprop_filter(input, filter_sizes, out_backprop, "833        "strides, padding, use_cudnn_on_gpu, data_format)")834    expected_text = (835        "tf.compat.v1.nn.conv2d_backprop_filter(input, filter_sizes, "836        "out_backprop, strides, padding, use_cudnn_on_gpu, data_format)")837    _, unused_report, unused_errors, new_text = self._upgrade(text)838    self.assertEqual(new_text, expected_text)839  def testConv2DBackpropInput(self):840    text = (841        "tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, "842        "strides, padding, use_cudnn_on_gpu, data_format)")843    expected_text = (844        "tf.nn.conv2d_transpose(output_shape=input_sizes, filters=filter, "845        "input=out_backprop, strides=strides, padding=padding, "846        "data_format=data_format)")847    _, unused_report, unused_errors, new_text = self._upgrade(text)848    self.assertEqual(new_text, expected_text)849  def testSpacetoBatch(self):850    text = "tf.space_to_batch_nd(input, shape, paddings, name)"851    expected_text = "tf.space_to_batch(input, shape, paddings, name)"852    _, unused_report, unused_errors, new_text = self._upgrade(text)853    self.assertEqual(new_text, expected_text)854    text = "tf.nn.space_to_batch(input, paddings, block_size, name)"855    expected_text = (856        "tf.space_to_batch(input=input, paddings=paddings, "857        "block_shape=block_size, name=name)")858    _, unused_report, unused_errors, new_text = self._upgrade(text)859    self.assertEqual(new_text, expected_text)860  def testInTopK(self):861    text = "tf.math.in_top_k(a, b, c, n)"862    expected_text = (863        "tf.math.in_top_k(predictions=a, targets=b, k=c, name=n)")864    _, unused_report, unused_errors, new_text = self._upgrade(text)865    self.assertEqual(new_text, expected_text)866  def testDepthToSpace(self):867    text = "tf.nn.depth_to_space(input, block_size, name, data_format)"868    expected_text = (869        "tf.nn.depth_to_space(input=input, block_size=block_size, "870        "name=name, data_format=data_format)")871    _, unused_report, unused_errors, new_text = self._upgrade(text)872    self.assertEqual(new_text, expected_text)873  def testEmbeddingLookup(self):874    text = ("tf.nn.embedding_lookup(params, ids, partition_strategy, name, "875            "validate_indices, max_norm)")876    expected_text = ("tf.nn.embedding_lookup(params=params, ids=ids, "877                     "partition_strategy=partition_strategy, name=name, "878                     "max_norm=max_norm)")879    _, unused_report, unused_errors, new_text = self._upgrade(text)880    self.assertEqual(new_text, expected_text)881  def testEmbeddingLookupSparse(self):882    text = ("tf.nn.embedding_lookup_sparse(params, sp_ids, sp_weights, "883            "partition_strategy, name, combiner, max_norm)")884    expected_text = ("tf.nn.embedding_lookup_sparse(params=params, "885                     "sp_ids=sp_ids, sp_weights=sp_weights, "886                     "partition_strategy=partition_strategy, name=name, "887                     "combiner=combiner, max_norm=max_norm)")888    _, unused_report, unused_errors, new_text = self._upgrade(text)889    self.assertEqual(new_text, expected_text)890  def testNnInTopK(self):891    text = "tf.nn.in_top_k(predictions, targets, k, name)"892    expected_text = ("tf.nn.in_top_k(predictions=predictions, "893                     "targets=targets, k=k, name=name)")894    _, unused_report, unused_errors, new_text = self._upgrade(text)895    self.assertEqual(new_text, expected_text)896  def testSpaceToDepth(self):897    text = "tf.nn.space_to_depth(input, block_size, name, data_format)"898    expected_text = ("tf.nn.space_to_depth(input=input, block_size=block_size, "899                     "name=name, data_format=data_format)")900    _, unused_report, unused_errors, new_text = self._upgrade(text)901    self.assertEqual(new_text, expected_text)902  def testPrint(self):903    # tf.print() cannot be parsed unless we import print_function904    text = """from __future__ import print_function905tf.print()906tf.print('abc')907"""908    _, unused_report, unused_errors, new_text = self._upgrade(text)909    self.assertEqual(new_text, text)  # Text should stay the same910  def testSparseSplit(self):911    text = (912        "tf.sparse_split(sp_input=sp_input, num_split=num_split, axis=axis, "913        "name=name)")914    expected_text = (915        "tf.sparse.split(sp_input=sp_input, num_split=num_split, axis=axis, "916        "name=name)")917    _, unused_report, unused_errors, new_text = self._upgrade(text)918    self.assertEqual(new_text, expected_text)919    text = (920        "tf.sparse_split(sp_input=sp_input, num_split=num_split, "921        "name=name, split_dim=axis)")922    expected_text = (923        "tf.sparse.split(sp_input=sp_input, num_split=num_split, "924        "name=name, axis=axis)")925    _, unused_report, unused_errors, new_text = self._upgrade(text)926    self.assertEqual(new_text, expected_text)927    text = (928        "tf.sparse.split(sp_input=sp_input, num_split=num_split, "929        "name=name, split_dim=axis)")930    expected_text = (931        "tf.sparse.split(sp_input=sp_input, num_split=num_split, "932        "name=name, axis=axis)")933    _, unused_report, unused_errors, new_text = self._upgrade(text)934    self.assertEqual(new_text, expected_text)935  def testIterators(self):936    for (text, expected) in [937        ("(expr + yielding(data)).make_one_shot_iterator()",938         "tf.compat.v1.data.make_one_shot_iterator((expr + yielding(data)))"),939        ("dataset.make_one_shot_iterator()",940         "tf.compat.v1.data.make_one_shot_iterator(dataset)"),941        ("dataset.make_one_shot_iterator(shared_name=foo)",942         "tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)"),943        ("dataset.make_one_shot_iterator(x, y, z)",944         "tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)"),945        ("dataset.make_initializable_iterator()",946         "tf.compat.v1.data.make_initializable_iterator(dataset)"),947        ("ds.make_initializable_iterator(shared_name=foo)",948         "tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)"),949        ("dataset.make_initializable_iterator(x, y, z)",950         "tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)"),951        ("tf.data.make_one_shot_iterator(dataset)",952         "tf.compat.v1.data.make_one_shot_iterator(dataset)"),953        ("tf.data.make_one_shot_iterator(dataset, shared_name=foo)",954         "tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)"),955        ("tf.data.make_one_shot_iterator(dataset, x, y, z)",956         "tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)"),957        ("tf.data.make_initializable_iterator(dataset)",958         "tf.compat.v1.data.make_initializable_iterator(dataset)"),959        ("tf.data.make_initializable_iterator(ds, shared_name=foo)",960         "tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)"),961        ("tf.data.make_initializable_iterator(dataset, x, y, z)",962         "tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)"),963        ("tf.compat.v1.data.make_one_shot_iterator(dataset)",964         "tf.compat.v1.data.make_one_shot_iterator(dataset)"),965        ("tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)",966         "tf.compat.v1.data.make_one_shot_iterator(dataset, shared_name=foo)"),967        ("tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)",968         "tf.compat.v1.data.make_one_shot_iterator(dataset, x, y, z)"),969        ("tf.compat.v1.data.make_initializable_iterator(dataset)",970         "tf.compat.v1.data.make_initializable_iterator(dataset)"),971        ("tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)",972         "tf.compat.v1.data.make_initializable_iterator(ds, shared_name=foo)"),973        ("tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)",974         "tf.compat.v1.data.make_initializable_iterator(dataset, x, y, z)")]:975      _, unused_report, unused_errors, actual = self._upgrade(text)976      self.assertEqual(actual, expected)977  def testMapAndBatch(self):978    suffix = ".data.experimental.map_and_batch_with_legacy_function(args)"979    text = "tf" + suffix980    expected = "tf.compat.v1" + suffix981    _, unused_report, unused_errors, actual = self._upgrade(text)982    self.assertEqual(actual, expected)983  def testCast(self):984    for (name, dtype) in [("int32", "int32"),985                          ("int64", "int64"),986                          ("float", "float32"),987                          ("double", "float64"),988                          ("complex64", "complex64"),989                          ("complex128", "complex128"),990                          ("bfloat16", "bfloat16")]:991      text = "tf.to_%s(x, name='test')" % name992      expected_text = "tf.cast(x, name='test', dtype=tf.%s)" % dtype993      _, unused_report, unused_errors, new_text = self._upgrade(text)994      self.assertEqual(expected_text, new_text)995  def testCastPositionalSecondArgument(self):996    for (name, dtype) in [("int32", "int32"),997                          ("int64", "int64"),998                          ("float", "float32"),999                          ("double", "float64"),1000                          ("complex64", "complex64"),1001                          ("complex128", "complex128"),1002                          ("bfloat16", "bfloat16")]:1003      text = "tf.to_%s(x, 'test')" % name1004      expected_text = "tf.cast(x, name='test', dtype=tf.%s)" % dtype1005      _, unused_report, unused_errors, new_text = self._upgrade(text)1006      self.assertEqual(expected_text, new_text)1007  def testImageResize(self):1008    for method in ["bilinear", "area", "bicubic", "nearest_neighbor"]:1009      text = "tf.image.resize_%s(i, s)" % method1010      expected_text = ("tf.image.resize(i, s, "1011                       "method=tf.image.ResizeMethod.%s)" % method.upper())1012      _, unused_report, unused_errors, new_text = self._upgrade(text)1013      self.assertEqual(expected_text, new_text)1014  def testImageResizeExtraPositionalArgs(self):1015    for method in ["bilinear", "area", "bicubic", "nearest_neighbor"]:1016      text = "tf.image.resize_%s(i, s, a, p)" % method1017      expected_text = [1018          "tf.image.resize(i, s, ", "preserve_aspect_ratio=p, ",1019          "method=tf.image.ResizeMethod.%s)" % method.upper()1020      ]1021      _, unused_report, unused_errors, new_text = self._upgrade(text)1022      for s in expected_text:1023        self.assertIn(s, new_text)1024  def testCond(self):1025    text = "tf.cond(a, b, c, True)"1026    expected_text = "tf.cond(pred=a, true_fn=b, false_fn=c)"1027    _, unused_report, errors, new_text = self._upgrade(text)1028    self.assertEqual(expected_text, new_text)1029    self.assertIn("tf.cond", errors[0])1030    self.assertIn("requires manual check", errors[0])1031  def testParens(self):1032    text = """1033def _log_prob(self, x):1034  return tf.reduce_logsumexp(1035      (self.mixture_distribution.logits + self.distribution.log_prob(1036          x[..., tf.newaxis])),1037          axis=-1)"""1038    expected_text = """1039def _log_prob(self, x):1040  return tf.reduce_logsumexp(1041      input_tensor=(self.mixture_distribution.logits + self.distribution.log_prob(1042          x[..., tf.newaxis])),1043          axis=-1)"""1044    _, unused_report, unused_errors, new_text = self._upgrade(text)1045    self.assertEqual(expected_text, new_text)1046  def testAssertStatements(self):1047    for name in ["assert_greater", "assert_equal", "assert_none_equal",1048                 "assert_less", "assert_negative", "assert_positive",1049                 "assert_non_negative", "assert_non_positive", "assert_near",1050                 "assert_less", "assert_less_equal", "assert_greater",1051                 "assert_greater_equal", "assert_integer", "assert_type",1052                 "assert_scalar"]:1053      text = "tf.%s(a)" % name1054      expected_text = "tf.compat.v1.%s(a)" % name1055      _, report, unused_errors, new_text = self._upgrade(text)1056      self.assertEqual(expected_text, new_text)1057      self.assertIn("%s has been" % name, report)1058      text = "tf.debugging.%s(a)" % name1059      expected_text = "tf.compat.v1.debugging.%s(a)" % name1060      _, report, unused_errors, new_text = self._upgrade(text)1061      self.assertEqual(expected_text, new_text)1062      self.assertIn("%s has been" % name, report)1063  def testAssertRankStatements(self):1064    for name in ["assert_rank", "assert_rank_at_least", "assert_rank_in"]:1065      text = "tf.%s(a)" % name1066      expected_text = "tf.compat.v1.%s(a)" % name1067      _, report, unused_errors, new_text = self._upgrade(text)1068      self.assertEqual(expected_text, new_text)1069      self.assertIn("%s has been" % name, report)1070      text = "tf.debugging.%s(a)" % name1071      expected_text = "tf.compat.v1.debugging.%s(a)" % name1072      _, report, unused_errors, new_text = self._upgrade(text)1073      self.assertEqual(expected_text, new_text)1074      self.assertIn("%s has been" % name, report)1075  def test_assert_equal_graph_def(self):1076    text = "tf.test.assert_equal_graph_def(a, b, checkpoint_v2=x)"1077    expected = "tf.test.assert_equal_graph_def(actual=a, expected=b)"1078    _, _, _, new_text = self._upgrade(text)1079    self.assertEqual(expected, new_text)1080  def test_is_tensor_upgrade(self):1081    text = "tf.contrib.framework.is_tensor(x)"1082    expected = "tf.is_tensor(x)"1083    _, _, _, new_text = self._upgrade(text)1084    self.assertEqual(expected, new_text)1085  def test_CriticalSection_upgrade(self):1086    text = "tf.contrib.framework.CriticalSection(shared_name='blah')"1087    expected = "tf.CriticalSection(shared_name='blah')"1088    _, _, _, new_text = self._upgrade(text)1089    self.assertEqual(expected, new_text)1090  def test_sample_distorted_bounding_box(self):1091    # pylint: disable=line-too-long1092    text = "tf.image.sample_distorted_bounding_box(a, b, c, d, e, f, g, h, i, j)"1093    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)"1094    # pylint: enable=line-too-long1095    _, _, _, new_text = self._upgrade(text)1096    self.assertEqual(expected, new_text)1097  def test_contrib_initialize(self):1098    text = "tf.contrib.summary.initialize"1099    expected = "tf.compat.v1.summary.initialize"1100    _, _, _, new_text = self._upgrade(text)1101    self.assertEqual(expected, new_text)1102  def test_contrib_framework_argsort(self):1103    text = "tf.contrib.framework.argsort"1104    expected = "tf.argsort"1105    # pylint: enable=line-too-long1106    _, _, _, new_text = self._upgrade(text)1107    self.assertEqual(expected, new_text)1108  def test_contrib_rnn_cell(self):1109    text = "tf.contrib.rnn.RNNCell"1110    expected = "tf.compat.v1.nn.rnn_cell.RNNCell"1111    # pylint: enable=line-too-long1112    _, _, _, new_text = self._upgrade(text)1113    self.assertEqual(expected, new_text)1114  def test_flags_bare(self):1115    _, _, errors, _ = self._upgrade("tf.flags")1116    self.assertIn("tf.flags has been removed", errors[0])1117  def test_flags_flags(self):1118    _, _, errors, _ = self._upgrade("tf.flags.FLAGS")1119    self.assertIn("tf.flags has been removed", errors[0])1120  def test_max_pool_2d(self):1121    text = "tf.nn.max_pool(value=4)"1122    expected_text = "tf.nn.max_pool2d(input=4)"1123    _, _, _, new_text = self._upgrade(text)1124    self.assertEqual(expected_text, new_text)1125  def test_contrib_estimator_early_stopping(self):1126    api_symbols = [1127        "make_early_stopping_hook", "stop_if_higher_hook", "stop_if_lower_hook",1128        "stop_if_no_decrease_hook", "stop_if_no_increase_hook"1129    ]1130    for symbol in api_symbols:1131      text = "tf.contrib.estimator." + symbol1132      expected_text = "tf.estimator.experimental." + symbol1133      _, _, _, new_text = self._upgrade(text)1134      self.assertEqual(expected_text, new_text)1135  def test_contrib_rnn(self):1136    api_symbols = ["BasicLSTMCell", "BasicRNNCell", "GRUCell", "LSTMCell",1137                   "MultiRNNCell"]1138    for symbol in api_symbols:1139      text = "tf.contrib.rnn." + symbol1140      expected_text = "tf.compat.v1.nn.rnn_cell." + symbol1141      _, _, _, new_text = self._upgrade(text)1142      self.assertEqual(expected_text, new_text)1143  def test_contrib_summary_audio(self):1144    text = "tf.contrib.summary.audio('foo', myval, 44100, 3, 'fam', 42)"1145    expected = ("tf.compat.v2.summary.audio(name='foo', data=myval, "1146                "sample_rate=44100, max_outputs=3, step=42)")1147    _, _, errors, new_text = self._upgrade(text)1148    self.assertEqual(expected, new_text)1149    self.assertIn("'family' argument", errors[0])1150    self.assertIn("Manual check required", errors[1])1151  def test_contrib_summary_histogram(self):1152    text = "tf.contrib.summary.histogram('foo', myval, 'fam', 42)"1153    expected = ("tf.compat.v2.summary.histogram(name='foo', data=myval, "1154                "step=42)")1155    _, _, errors, new_text = self._upgrade(text)1156    self.assertEqual(expected, new_text)1157    self.assertIn("'family' argument", errors[0])1158    self.assertIn("Manual check required", errors[1])1159  def test_contrib_summary_image(self):1160    text = "tf.contrib.summary.image('foo', myval, red, 3, 'fam', 42)"1161    expected = ("tf.compat.v2.summary.image(name='foo', data=myval, "1162                "max_outputs=3, step=42)")1163    _, _, errors, new_text = self._upgrade(text)1164    self.assertEqual(expected, new_text)1165    self.assertIn("'bad_color' argument", errors[0])1166    self.assertIn("'family' argument", errors[1])1167    self.assertIn("Manual check required", errors[2])1168  def test_contrib_summary_scalar(self):1169    text = "tf.contrib.summary.scalar('foo', myval, 'fam', 42)"1170    expected = ("tf.compat.v2.summary.scalar(name='foo', data=myval, "1171                "step=42)")1172    _, _, errors, new_text = self._upgrade(text)1173    self.assertEqual(expected, new_text)1174    self.assertIn("'family' argument", errors[0])1175    self.assertIn("Manual check required", errors[1])1176  def test_contrib_summary_audio_nostep(self):1177    text = "tf.contrib.summary.audio('foo', myval, 44100)"1178    expected = ("tf.compat.v2.summary.audio(name='foo', data=myval, "1179                "sample_rate=44100, "1180                "step=tf.compat.v1.train.get_or_create_global_step())")1181    _, _, errors, new_text = self._upgrade(text)1182    self.assertEqual(expected, new_text)1183    self.assertIn("'step' argument", errors[0])1184    self.assertIn("Manual check required", errors[1])1185  def test_contrib_summary_histogram_nostep(self):1186    text = "tf.contrib.summary.histogram('foo', myval)"1187    expected = ("tf.compat.v2.summary.histogram(name='foo', data=myval, "1188                "step=tf.compat.v1.train.get_or_create_global_step())")1189    _, _, errors, new_text = self._upgrade(text)1190    self.assertEqual(expected, new_text)1191    self.assertIn("'step' argument", errors[0])1192    self.assertIn("Manual check required", errors[1])1193  def test_contrib_summary_image_nostep(self):1194    text = "tf.contrib.summary.image('foo', myval)"1195    expected = ("tf.compat.v2.summary.image(name='foo', data=myval, "1196                "step=tf.compat.v1.train.get_or_create_global_step())")1197    _, _, errors, new_text = self._upgrade(text)1198    self.assertEqual(expected, new_text)1199    self.assertIn("'step' argument", errors[0])1200    self.assertIn("Manual check required", errors[1])1201  def test_contrib_summary_scalar_nostep(self):1202    text = "tf.contrib.summary.scalar('foo', myval)"1203    expected = ("tf.compat.v2.summary.scalar(name='foo', data=myval, "1204                "step=tf.compat.v1.train.get_or_create_global_step())")1205    _, _, errors, new_text = self._upgrade(text)1206    self.assertEqual(expected, new_text)1207    self.assertIn("'step' argument", errors[0])1208    self.assertIn("Manual check required", errors[1])1209  def test_avg_pool_2d(self):1210    text = "tf.nn.avg_pool(value=4)"1211    expected_text = "tf.nn.avg_pool2d(input=4)"1212    _, _, _, new_text = self._upgrade(text)1213    self.assertEqual(expected_text, new_text)1214  def test_saved_model_load(self):1215    text = "tf.saved_model.load(sess, ['foo_graph'])"1216    expected = "tf.compat.v1.saved_model.load(sess, ['foo_graph'])"1217    _, _, _, new_text = self._upgrade(text)1218    self.assertEqual(expected, new_text)1219  def test_saved_model_load_v2(self):1220    text = "tf.saved_model.load_v2('/tmp/blah')"1221    expected = "tf.compat.v2.saved_model.load('/tmp/blah')"1222    _, _, _, new_text = self._upgrade(text)1223    self.assertEqual(expected, new_text)1224  def test_uniform_unit_scaling_initializer(self):1225    text = "tf.uniform_unit_scaling_initializer(0.5)"1226    expected_text = (1227        "tf.keras.initializers.VarianceScaling(" +1228        "scale=0.5, distribution=\"uniform\")")1229    _, _, _, new_text = self._upgrade(text)1230    self.assertEqual(expected_text, new_text)1231    text = "tf.initializers.uniform_unit_scaling(0.5)"1232    expected_text = (1233        "tf.keras.initializers.VarianceScaling(" +1234        "scale=0.5, distribution=\"uniform\")")1235    _, _, _, new_text = self._upgrade(text)1236    self.assertEqual(expected_text, new_text)1237  def test_name_scope(self):1238    text = "tf.name_scope(None, default_name, [some, values])"1239    expected_text = "tf.name_scope(name=default_name)"1240    _, _, _, new_text = self._upgrade(text)1241    self.assertEqual(expected_text, new_text)1242    text = "tf.name_scope(default_name=default_name, values=stuff)"1243    expected_text = "tf.name_scope(name=default_name)"1244    _, _, _, new_text = self._upgrade(text)1245    self.assertEqual(expected_text, new_text)1246    text = "tf.name_scope(name=n, default_name=d, values=s)"1247    expected_text = "tf.compat.v1.name_scope(name=n, default_name=d, values=s)"1248    _, report, _, new_text = self._upgrade(text)1249    self.assertEqual(expected_text, new_text)1250    self.assertIn("`name` passed to `name_scope`", report)1251    text = "tf.name_scope(name=None, values=stuff)"1252    _, _, errors, _ = self._upgrade(text)1253    self.assertIn("name_scope call with neither name nor default_name",1254                  errors[0])1255class TestUpgradeFiles(test_util.TensorFlowTestCase):1256  def testInplace(self):1257    """Check to make sure we don't have a file system race."""1258    temp_file = tempfile.NamedTemporaryFile("w", delete=False)1259    original = "tf.conj(a)\n"1260    upgraded = "tf.math.conj(a)\n"1261    temp_file.write(original)1262    temp_file.close()1263    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec())1264    upgrader.process_file(temp_file.name, temp_file.name)1265    self.assertAllEqual(open(temp_file.name).read(), upgraded)1266    os.unlink(temp_file.name)...

tf_upgrade_v2_safety_test.py

Source:tf_upgrade_v2_safety_test.py

...21from tensorflow.python.platform import test as test_lib22from tensorflow.tools.compatibility import ast_edits23from tensorflow.tools.compatibility import tf_upgrade_v2_safety24class TfUpgradeV2SafetyTest(test_util.TensorFlowTestCase):25  def _upgrade(self, old_file_text):26    in_file = six.StringIO(old_file_text)27    out_file = six.StringIO()28    upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2_safety.TFAPIChangeSpec())29    count, report, errors = (30        upgrader.process_opened_file("test.py", in_file,31                                     "test_out.py", out_file))32    return count, report, errors, out_file.getvalue()33  def testContribWarning(self):34    text = "tf.contrib.foo()"35    _, report, _, _ = self._upgrade(text)36    expected_info = "tf.contrib will not be distributed"37    self.assertIn(expected_info, report)38  def testTensorFlowImport(self):39    text = "import tensorflow as tf"40    expected_text = ("import tensorflow.compat.v1 as tf")41    _, _, _, new_text = self._upgrade(text)42    self.assertEqual(expected_text, new_text)43    text = "import tensorflow as tf, other_import as y"44    expected_text = ("import tensorflow.compat.v1 as tf, other_import as y")45    _, _, _, new_text = self._upgrade(text)46    self.assertEqual(expected_text, new_text)47    text = "import tensorflow"48    expected_text = ("import tensorflow.compat.v1 as tensorflow")49    _, _, _, new_text = self._upgrade(text)50    self.assertEqual(expected_text, new_text)51    text = "import tensorflow.foo"52    expected_text = "import tensorflow.compat.v1.foo"53    _, _, _, new_text = self._upgrade(text)54    self.assertEqual(expected_text, new_text)55    text = "import tensorflow.foo as bar"56    expected_text = "import tensorflow.compat.v1.foo as bar"57    _, _, _, new_text = self._upgrade(text)58    self.assertEqual(expected_text, new_text)59  def testTensorFlowGoogleImport(self):60    text = "import tensorflow.google as tf"61    expected_text = "import tensorflow.google.compat.v1 as tf"62    _, _, _, new_text = self._upgrade(text)63    self.assertEqual(expected_text, new_text)64    text = "import tensorflow.google"65    expected_text = "import tensorflow.google.compat.v1"66    _, _, _, new_text = self._upgrade(text)67    self.assertEqual(expected_text, new_text)68    text = "import tensorflow.google.compat.v1 as tf"69    expected_text = "import tensorflow.google.compat.v1 as tf"70    _, _, _, new_text = self._upgrade(text)71    self.assertEqual(expected_text, new_text)72    text = "import tensorflow.google.compat.v2 as tf"73    expected_text = "import tensorflow.google.compat.v2 as tf"74    _, _, _, new_text = self._upgrade(text)75    self.assertEqual(expected_text, new_text)76  def testTensorFlowImportInIndent(self):77    text = """78try:79  import tensorflow as tf  # import line80  tf.ones([4, 5])81except AttributeError:82  pass83"""84    expected_text = """85try:86  import tensorflow.compat.v1 as tf  # import line87  tf.ones([4, 5])88except AttributeError:89  pass90"""91    _, _, _, new_text = self._upgrade(text)92    self.assertEqual(expected_text, new_text)93  def testTensorFlowFromImport(self):94    text = "from tensorflow import foo"95    expected_text = "from tensorflow.compat.v1 import foo"96    _, _, _, new_text = self._upgrade(text)97    self.assertEqual(expected_text, new_text)98    text = "from tensorflow.foo import bar"99    expected_text = "from tensorflow.compat.v1.foo import bar"100    _, _, _, new_text = self._upgrade(text)101    self.assertEqual(expected_text, new_text)102    text = "from tensorflow import *"103    expected_text = "from tensorflow.compat.v1 import *"104    _, _, _, new_text = self._upgrade(text)105    self.assertEqual(expected_text, new_text)106  def testTensorFlowImportAlreadyHasCompat(self):107    text = "import tensorflow.compat.v1 as tf"108    _, _, _, new_text = self._upgrade(text)109    self.assertEqual(text, new_text)110    text = "import tensorflow.compat.v2 as tf"111    _, _, _, new_text = self._upgrade(text)112    self.assertEqual(text, new_text)113    text = "from tensorflow.compat import v2 as tf"114    _, _, _, new_text = self._upgrade(text)115    self.assertEqual(text, new_text)116  def testTensorFlowGoogleFromImport(self):117    text = "from tensorflow.google.compat import v1 as tf"118    _, _, _, new_text = self._upgrade(text)119    self.assertEqual(text, new_text)120    text = "from tensorflow.google.compat import v2 as tf"121    _, _, _, new_text = self._upgrade(text)122    self.assertEqual(text, new_text)123  def testTensorFlowDontChangeContrib(self):124    text = "import tensorflow.contrib as foo"125    _, _, _, new_text = self._upgrade(text)126    self.assertEqual(text, new_text)127    text = "from tensorflow import contrib"128    _, _, _, new_text = self._upgrade(text)129    self.assertEqual(text, new_text)130  def test_contrib_to_addons_move(self):131    small_mapping = {132        "tf.contrib.layers.poincare_normalize":133            "tfa.layers.PoincareNormalize",134        "tf.contrib.layers.maxout":135            "tfa.layers.Maxout",136        "tf.contrib.layers.group_norm":137            "tfa.layers.GroupNormalization",138        "tf.contrib.layers.instance_norm":139            "tfa.layers.InstanceNormalization",140    }141    for symbol, replacement in small_mapping.items():142      text = "{}('stuff', *args, **kwargs)".format(symbol)143      _, report, _, _ = self._upgrade(text)144      self.assertIn(replacement, report)145if __name__ == "__main__":146  test_lib.main()147  def testTensorFlowDontChangeContrib(self):148    text = "import tensorflow.contrib as foo"149    _, _, _, new_text = self._upgrade(text)150    self.assertEqual(text, new_text)151    text = "from tensorflow import contrib"152    _, _, _, new_text = self._upgrade(text)153    self.assertEqual(text, new_text)154  def test_contrib_to_addons_move(self):155    small_mapping = {156        "tf.contrib.layers.poincare_normalize":157            "tfa.layers.PoincareNormalize",158        "tf.contrib.layers.maxout":159            "tfa.layers.Maxout",160        "tf.contrib.layers.group_norm":161            "tfa.layers.GroupNormalization",162        "tf.contrib.layers.instance_norm":163            "tfa.layers.InstanceNormalization",164    }165    for symbol, replacement in small_mapping.items():166      text = "{}('stuff', *args, **kwargs)".format(symbol)167      _, report, _, _ = self._upgrade(text)168      self.assertIn(replacement, report)169if __name__ == "__main__":...

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