Clean up conversion warning language, and use the ability to distinguish info, warning, and errors better.
Also improves the conversion of some symbols (changes a warning to a conversion for more cases that is now possible). PiperOrigin-RevId: 231334490
This commit is contained in:
Martin Wicke
TensorFlower Gardener
parent
ad65038c90
commit
2ac6545b3a
@ -62,6 +62,45 @@ def full_name_node(name, ctx=ast.Load()):
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return node
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return node
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def get_arg_value(node, arg_name, arg_pos=None):
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"""Get the value of an argument from a ast.Call node.
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This function goes through the positional and keyword arguments to check
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whether a given argument was used, and if so, returns its value (the node
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representing its value).
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This cannot introspect *args or **args, but it safely handles *args in
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Python3.5+.
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Args:
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node: The ast.Call node to extract arg values from.
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arg_name: The name of the argument to extract.
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arg_pos: The position of the argument (in case it's passed as a positional
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argument).
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Returns:
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A tuple (arg_present, arg_value) containing a boolean indicating whether
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the argument is present, and its value in case it is.
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"""
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# Check keyword args
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if arg_name is not None:
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for kw in node.keywords:
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if kw.arg == arg_name:
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return (True, kw.value)
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# Check positional args
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if arg_pos is not None:
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idx = 0
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for arg in node.args:
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if sys.version_info[:2] >= (3, 5) and isinstance(arg, ast.Starred):
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continue # Can't parse Starred
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if idx == arg_pos:
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return (True, arg)
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idx += 1
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return (False, None)
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class APIChangeSpec(object):
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class APIChangeSpec(object):
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"""This class defines the transformations that need to happen.
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"""This class defines the transformations that need to happen.
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@ -195,11 +234,10 @@ class _PastaEditVisitor(ast.NodeVisitor):
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"""Adds an error to be printed about full_name at node."""
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"""Adds an error to be printed about full_name at node."""
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function_warnings = self._api_change_spec.function_warnings
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function_warnings = self._api_change_spec.function_warnings
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if full_name in function_warnings:
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if full_name in function_warnings:
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warning_message = function_warnings[full_name]
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level, message = function_warnings[full_name]
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warning_message = warning_message.replace("<function name>", full_name)
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message = message.replace("<function name>", full_name)
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self.add_log(WARNING, node.lineno, node.col_offset,
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self.add_log(level, node.lineno, node.col_offset,
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"%s requires manual check. %s" % (full_name,
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"%s requires manual check. %s" % (full_name, message))
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warning_message))
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return True
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return True
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else:
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else:
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return False
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return False
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@ -232,13 +270,13 @@ class _PastaEditVisitor(ast.NodeVisitor):
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arg_warnings = self._get_applicable_dict("function_arg_warnings",
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arg_warnings = self._get_applicable_dict("function_arg_warnings",
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full_name, name)
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full_name, name)
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used_args = [kw.arg for kw in node.keywords]
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for (kwarg, arg), (level, warning) in arg_warnings.items():
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for (kwarg, arg), warning in arg_warnings.items():
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present, _ = get_arg_value(node, kwarg, arg)
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if kwarg in used_args or len(node.args) > arg:
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if present:
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warned = True
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warned = True
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warning_message = warning.replace("<function name>", full_name or name)
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warning_message = warning.replace("<function name>", full_name or name)
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self.add_log(WARNING, node.lineno, node.col_offset,
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self.add_log(level, node.lineno, node.col_offset,
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"%s called with %s argument requires manual check: %s." %
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"%s called with %s argument requires manual check: %s" %
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(full_name or name, kwarg, warning_message))
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(full_name or name, kwarg, warning_message))
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return warned
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return warned
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@ -280,12 +318,14 @@ class _PastaEditVisitor(ast.NodeVisitor):
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if full_name in function_reorders:
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if full_name in function_reorders:
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reordered = function_reorders[full_name]
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reordered = function_reorders[full_name]
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new_keywords = []
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new_keywords = []
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for idx, arg in enumerate(node.args):
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idx = 0
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for arg in node.args:
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if sys.version_info[:2] >= (3, 5) and isinstance(arg, ast.Starred):
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if sys.version_info[:2] >= (3, 5) and isinstance(arg, ast.Starred):
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continue # Can't move Starred to keywords
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continue # Can't move Starred to keywords
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keyword_arg = reordered[idx]
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keyword_arg = reordered[idx]
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keyword = ast.keyword(arg=keyword_arg, value=arg)
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keyword = ast.keyword(arg=keyword_arg, value=arg)
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new_keywords.append(keyword)
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new_keywords.append(keyword)
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idx += 1
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if new_keywords:
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if new_keywords:
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self.add_log(INFO, node.lineno, node.col_offset,
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self.add_log(INFO, node.lineno, node.col_offset,
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@ -374,11 +414,10 @@ class _PastaEditVisitor(ast.NodeVisitor):
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logs = []
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logs = []
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new_node = transformer(parent, node, full_name, name, logs)
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new_node = transformer(parent, node, full_name, name, logs)
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self.add_logs(logs)
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self.add_logs(logs)
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if new_node:
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if new_node and new_node is not node:
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if new_node is not node:
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pasta.ast_utils.replace_child(parent, node, new_node)
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pasta.ast_utils.replace_child(parent, node, new_node)
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node = new_node
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node = new_node
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self._stack[-1] = node
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self._stack[-1] = node
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self.generic_visit(node)
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self.generic_visit(node)
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@ -417,7 +417,7 @@ class TestAstEdits(test_util.TensorFlowTestCase):
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def __init__(self):
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def __init__(self):
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NoUpdateSpec.__init__(self)
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NoUpdateSpec.__init__(self)
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self.function_warnings = {"*.foo": "not good"}
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self.function_warnings = {"*.foo": (ast_edits.WARNING, "not good")}
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texts = ["object.foo()", "get_object().foo()",
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texts = ["object.foo()", "get_object().foo()",
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"get_object().foo()", "object.foo().bar()"]
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"get_object().foo()", "object.foo().bar()"]
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@ -37,6 +37,7 @@ reorders = {
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'tf.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'],
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'tf.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'],
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'tf.depth_to_space': ['input', 'block_size', 'name', 'data_format'],
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'tf.depth_to_space': ['input', 'block_size', 'name', 'data_format'],
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'tf.feature_column.categorical_column_with_vocabulary_file': ['key', 'vocabulary_file', 'vocabulary_size', 'num_oov_buckets', 'default_value', 'dtype'],
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'tf.feature_column.categorical_column_with_vocabulary_file': ['key', 'vocabulary_file', 'vocabulary_size', 'num_oov_buckets', 'default_value', 'dtype'],
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'tf.image.sample_distorted_bounding_box': ['image_size', 'bounding_boxes', 'seed', 'seed2', 'min_object_covered', 'aspect_ratio_range', 'area_range', 'max_attempts', 'use_image_if_no_bounding_boxes', 'name'],
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'tf.io.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'],
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'tf.io.decode_csv': ['records', 'record_defaults', 'field_delim', 'use_quote_delim', 'name', 'na_value', 'select_cols'],
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'tf.io.parse_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.io.parse_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.io.parse_single_example': ['serialized', 'features', 'name', 'example_names'],
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'tf.io.parse_single_example': ['serialized', 'features', 'name', 'example_names'],
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@ -56,12 +57,19 @@ reorders = {
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'tf.math.reduce_prod': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_prod': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_sum': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.math.reduce_sum': ['input_tensor', 'axis', 'keepdims', 'name', 'reduction_indices', 'keep_dims'],
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'tf.multinomial': ['logits', 'num_samples', 'seed', 'name', 'output_dtype'],
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'tf.multinomial': ['logits', 'num_samples', 'seed', 'name', 'output_dtype'],
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'tf.nn.conv1d': ['value', 'filters', 'stride', 'padding', 'use_cudnn_on_gpu', 'data_format', 'name'],
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'tf.nn.conv2d': ['input', 'filter', 'strides', 'padding', 'use_cudnn_on_gpu', 'data_format', 'dilations', 'name'],
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'tf.nn.conv2d_backprop_filter': ['input', 'filter_sizes', 'out_backprop', 'strides', 'padding', 'use_cudnn_on_gpu', 'data_format', 'dilations', 'name'],
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'tf.nn.conv2d_backprop_input': ['input_sizes', 'filter', 'out_backprop', 'strides', 'padding', 'use_cudnn_on_gpu', 'data_format', 'dilations', 'name'],
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'tf.nn.convolution': ['input', 'filter', 'padding', 'strides', 'dilation_rate', 'name', 'data_format'],
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'tf.nn.convolution': ['input', 'filter', 'padding', 'strides', 'dilation_rate', 'name', 'data_format'],
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'tf.nn.crelu': ['features', 'name', 'axis'],
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'tf.nn.crelu': ['features', 'name', 'axis'],
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'tf.nn.ctc_beam_search_decoder': ['inputs', 'sequence_length', 'beam_width', 'top_paths', 'merge_repeated'],
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'tf.nn.depth_to_space': ['input', 'block_size', 'name', 'data_format'],
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'tf.nn.depth_to_space': ['input', 'block_size', 'name', 'data_format'],
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'tf.nn.depthwise_conv2d': ['input', 'filter', 'strides', 'padding', 'rate', 'name', 'data_format'],
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'tf.nn.depthwise_conv2d': ['input', 'filter', 'strides', 'padding', 'rate', 'name', 'data_format'],
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'tf.nn.embedding_lookup': ['params', 'ids', 'partition_strategy', 'name', 'validate_indices', 'max_norm'],
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'tf.nn.embedding_lookup': ['params', 'ids', 'partition_strategy', 'name', 'validate_indices', 'max_norm'],
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'tf.nn.embedding_lookup_sparse': ['params', 'sp_ids', 'sp_weights', 'partition_strategy', 'name', 'combiner', 'max_norm'],
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'tf.nn.embedding_lookup_sparse': ['params', 'sp_ids', 'sp_weights', 'partition_strategy', 'name', 'combiner', 'max_norm'],
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'tf.nn.fractional_avg_pool': ['value', 'pooling_ratio', 'pseudo_random', 'overlapping', 'deterministic', 'seed', 'seed2', 'name'],
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'tf.nn.fractional_max_pool': ['value', 'pooling_ratio', 'pseudo_random', 'overlapping', 'deterministic', 'seed', 'seed2', 'name'],
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'tf.nn.in_top_k': ['predictions', 'targets', 'k', 'name'],
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'tf.nn.in_top_k': ['predictions', 'targets', 'k', 'name'],
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'tf.nn.moments': ['x', 'axes', 'shift', 'name', 'keep_dims'],
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'tf.nn.moments': ['x', 'axes', 'shift', 'name', 'keep_dims'],
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'tf.nn.pool': ['input', 'window_shape', 'pooling_type', 'padding', 'dilation_rate', 'strides', 'name', 'data_format'],
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'tf.nn.pool': ['input', 'window_shape', 'pooling_type', 'padding', 'dilation_rate', 'strides', 'name', 'data_format'],
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@ -113,6 +121,7 @@ reorders = {
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'tf.strings.reduce_join': ['inputs', 'axis', 'keep_dims', 'separator', 'name', 'reduction_indices'],
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'tf.strings.reduce_join': ['inputs', 'axis', 'keep_dims', 'separator', 'name', 'reduction_indices'],
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'tf.strings.substr': ['input', 'pos', 'len', 'name', 'unit'],
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'tf.strings.substr': ['input', 'pos', 'len', 'name', 'unit'],
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'tf.substr': ['input', 'pos', 'len', 'name', 'unit'],
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'tf.substr': ['input', 'pos', 'len', 'name', 'unit'],
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'tf.test.assert_equal_graph_def': ['actual', 'expected', 'checkpoint_v2'],
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'tf.transpose': ['a', 'perm', 'name', 'conjugate'],
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'tf.transpose': ['a', 'perm', 'name', 'conjugate'],
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'tf.tuple': ['tensors', 'name', 'control_inputs'],
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'tf.tuple': ['tensors', 'name', 'control_inputs'],
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'tf.while_loop': ['cond', 'body', 'loop_vars', 'shape_invariants', 'parallel_iterations', 'back_prop', 'swap_memory', 'name', 'maximum_iterations', 'return_same_structure']
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'tf.while_loop': ['cond', 'body', 'loop_vars', 'shape_invariants', 'parallel_iterations', 'back_prop', 'swap_memory', 'name', 'maximum_iterations', 'return_same_structure']
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@ -177,10 +177,11 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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# Warnings that should be printed if corresponding functions are used.
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# Warnings that should be printed if corresponding functions are used.
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self.function_warnings = {
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self.function_warnings = {
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"tf.reverse":
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"tf.reverse": (
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"ERROR: tf.reverse has had its argument semantics changed "
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ast_edits.ERROR,
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"tf.reverse has had its argument semantics changed "
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"significantly. The converter cannot detect this reliably, so "
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"significantly. The converter cannot detect this reliably, so "
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"you need to inspect this usage manually.\n",
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"you need to inspect this usage manually.\n"),
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}
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}
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@ -19,6 +19,8 @@ from __future__ import division
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from __future__ import print_function
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from __future__ import print_function
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import ast
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import ast
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import functools
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import sys
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import pasta
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import pasta
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import six
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import six
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@ -27,6 +29,9 @@ from tensorflow.tools.compatibility import ast_edits
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from tensorflow.tools.compatibility import renames_v2
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from tensorflow.tools.compatibility import renames_v2
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from tensorflow.tools.compatibility import reorders_v2
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from tensorflow.tools.compatibility import reorders_v2
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# These pylint warnings are a mistake.
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# pylint: disable=g-explicit-bool-comparison,g-bool-id-comparison
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class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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"""List of maps that describe what changed in the API."""
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"""List of maps that describe what changed in the API."""
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@ -38,6 +43,15 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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# Only keyword args are handled, so make sure to also put any function in
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# Only keyword args are handled, so make sure to also put any function in
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# function_reorders to ensure that all args are made into keywords first.
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# function_reorders to ensure that all args are made into keywords first.
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self.function_keyword_renames = {
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self.function_keyword_renames = {
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"tf.test.assert_equal_graph_def": {
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"checkpoint_v2": None,
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},
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"tf.nn.embedding_lookup": {
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"validate_indices": None,
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},
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"tf.image.sample_distorted_bounding_box": {
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"seed2": None,
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},
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"tf.gradients": {
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"tf.gradients": {
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"colocate_gradients_with_ops": None,
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"colocate_gradients_with_ops": None,
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},
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},
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@ -380,6 +394,21 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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"tf.nn.weighted_moments": {
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"tf.nn.weighted_moments": {
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"keep_dims": "keepdims"
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"keep_dims": "keepdims"
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},
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},
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"tf.nn.conv1d": {
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"value": "input",
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"use_cudnn_on_gpu": None,
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},
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"tf.nn.conv2d": {
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"filter": "filters",
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"use_cudnn_on_gpu": None,
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},
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"tf.nn.conv2d_backprop_filter": {
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"use_cudnn_on_gpu": None,
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},
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"tf.nn.conv2d_backprop_input": {
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"filter": "filters",
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"use_cudnn_on_gpu": None,
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},
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}
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}
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# pylint: disable=line-too-long
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# pylint: disable=line-too-long
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@ -662,6 +691,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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# Mapping from function to the new name of the function
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# Mapping from function to the new name of the function
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self.symbol_renames = renames_v2.renames
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self.symbol_renames = renames_v2.renames
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self.symbol_renames.update(self.manual_symbol_renames)
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self.symbol_renames.update(self.manual_symbol_renames)
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self.symbol_renames = {
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name: new_name
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for name, new_name in self.symbol_renames.items()
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}
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# Variables that should be changed to functions.
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# Variables that should be changed to functions.
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self.change_to_function = {}
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self.change_to_function = {}
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@ -684,6 +717,11 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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"tf.nn.space_to_batch",
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"tf.nn.space_to_batch",
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"tf.boolean_mask",
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"tf.boolean_mask",
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"tf.convert_to_tensor",
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"tf.convert_to_tensor",
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"tf.nn.conv1d",
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"tf.nn.conv2d",
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"tf.nn.conv2d_backprop_filter",
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"tf.nn.conv2d_backprop_input",
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"tf.nn.ctc_beam_search_decoder",
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"tf.nn.moments",
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"tf.nn.moments",
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"tf.nn.convolution",
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"tf.nn.convolution",
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"tf.nn.crelu",
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"tf.nn.crelu",
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@ -746,6 +784,7 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
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"tf.nn.embedding_lookup_sparse",
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"tf.nn.embedding_lookup_sparse",
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"tf.nn.in_top_k",
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"tf.nn.in_top_k",
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"tf.nn.space_to_depth",
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"tf.nn.space_to_depth",
|
||||||
|
"tf.test.assert_equal_graph_def",
|
||||||
"tf.linalg.norm",
|
"tf.linalg.norm",
|
||||||
"tf.norm",
|
"tf.norm",
|
||||||
"tf.reverse_sequence",
|
"tf.reverse_sequence",
|
||||||
@ -754,6 +793,9 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
|
|||||||
# keyword arguments. Add keyword arguments in rare case when they
|
# keyword arguments. Add keyword arguments in rare case when they
|
||||||
# are not specified.
|
# are not specified.
|
||||||
"tf.nn.softmax_cross_entropy_with_logits",
|
"tf.nn.softmax_cross_entropy_with_logits",
|
||||||
|
"tf.nn.fractional_avg_pool",
|
||||||
|
"tf.nn.fractional_max_pool",
|
||||||
|
"tf.image.sample_distorted_bounding_box",
|
||||||
}
|
}
|
||||||
|
|
||||||
# Functions that were reordered should be changed to the new keyword args
|
# Functions that were reordered should be changed to the new keyword args
|
||||||
@ -761,43 +803,8 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
|
|||||||
# positional arguments yourself, this could do the wrong thing.
|
# positional arguments yourself, this could do the wrong thing.
|
||||||
self.function_reorders = reorders_v2.reorders
|
self.function_reorders = reorders_v2.reorders
|
||||||
|
|
||||||
# Specially handled functions (pasta version)
|
|
||||||
# Each transformer is a callable which will be called with the arguments
|
|
||||||
# transformer(parent, node, full_name, name, logs, errors)
|
|
||||||
# Where logs and errors are lists to which (line, col, msg) tuples can be
|
|
||||||
# appended, full_name is the FQN of the function called (or None if that is
|
|
||||||
# unknown), name is the name of the function called (or None is that is
|
|
||||||
# unknown). node is an ast.Call node representing this function call, and
|
|
||||||
# parent is its parent in the AST.
|
|
||||||
# The function may modify node (but not parent), and must return
|
|
||||||
# - none, if nothing was modified
|
|
||||||
# - node, if node was modified in place (make sure to use
|
|
||||||
# pasta.ast_utils.replace_child to swap out children, otherwise formatting
|
|
||||||
# may get messy)
|
|
||||||
# - a replacement for node, if the whole call node was replaced. The caller
|
|
||||||
# will take care of changing parent.
|
|
||||||
self.function_transformers = {
|
|
||||||
"*.make_initializable_iterator": self._iterator_transformer,
|
|
||||||
"*.make_one_shot_iterator": self._iterator_transformer,
|
|
||||||
"tf.nn.dropout": self._dropout_transformer,
|
|
||||||
"tf.batch_gather": self._batch_gather_transformer,
|
|
||||||
"tf.to_bfloat16": self._cast_transformer,
|
|
||||||
"tf.to_complex128": self._cast_transformer,
|
|
||||||
"tf.to_complex64": self._cast_transformer,
|
|
||||||
"tf.to_double": self._cast_transformer,
|
|
||||||
"tf.to_float": self._cast_transformer,
|
|
||||||
"tf.to_int32": self._cast_transformer,
|
|
||||||
"tf.to_int64": self._cast_transformer,
|
|
||||||
"tf.nn.softmax_cross_entropy_with_logits":
|
|
||||||
self._softmax_cross_entropy_with_logits_transformer,
|
|
||||||
"tf.image.resize_area": self._image_resize_transformer,
|
|
||||||
"tf.image.resize_bicubic": self._image_resize_transformer,
|
|
||||||
"tf.image.resize_bilinear": self._image_resize_transformer,
|
|
||||||
"tf.image.resize_nearest_neighbor": self._image_resize_transformer,
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
decay_function_comment = (
|
decay_function_comment = (
|
||||||
|
ast_edits.INFO,
|
||||||
"<function name> has been changed to return a callable instead "
|
"<function name> has been changed to return a callable instead "
|
||||||
"of a tensor when graph building, but its functionality remains "
|
"of a tensor when graph building, but its functionality remains "
|
||||||
"unchanged during eager execution (returns a callable like "
|
"unchanged during eager execution (returns a callable like "
|
||||||
@ -806,64 +813,66 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
|
|||||||
" be correct).\n"
|
" be correct).\n"
|
||||||
)
|
)
|
||||||
|
|
||||||
# TODO(b/118888586): add default value change to update script.
|
|
||||||
default_loss_reduction_changed = (
|
|
||||||
"default value of loss_reduction has been changed to "
|
|
||||||
"SUM_OVER_BATCH_SIZE.\n"
|
|
||||||
)
|
|
||||||
|
|
||||||
assert_return_type_comment = (
|
assert_return_type_comment = (
|
||||||
"assert_* functions have been changed to return None, the "
|
ast_edits.INFO,
|
||||||
|
"<function name> has been changed to return None, the "
|
||||||
"data argument has been removed, and arguments have been reordered."
|
"data argument has been removed, and arguments have been reordered."
|
||||||
"\nThe calls have been converted to compat.v1 for safety (even though "
|
"\nThe calls have been converted to compat.v1 for safety (even though "
|
||||||
" they may already have been correct)."
|
" they may already have been correct)."
|
||||||
)
|
)
|
||||||
|
|
||||||
assert_rank_comment = (
|
assert_rank_comment = (
|
||||||
"assert_rank_* functions have been changed to return None, and"
|
ast_edits.INFO,
|
||||||
|
"<function name> has been changed to return None, and"
|
||||||
" the data and summarize arguments have been removed."
|
" the data and summarize arguments have been removed."
|
||||||
"\nThe calls have been converted to compat.v1 for safety (even though "
|
"\nThe calls have been converted to compat.v1 for safety (even though "
|
||||||
" they may already have been correct)."
|
" they may already have been correct)."
|
||||||
)
|
)
|
||||||
|
|
||||||
tf_01s_like_no_optimize_comment = (
|
|
||||||
"tf.zeros_like and tf.ones_like no longer have the optimize "
|
|
||||||
"argument in TF 2.0 or after (also, `tensor' argument is renamed to "
|
|
||||||
"`input')."
|
|
||||||
"\nThe calls have been converted to compat.v1 for safety (even though "
|
|
||||||
" they may already have been correct)."
|
|
||||||
)
|
|
||||||
|
|
||||||
deprecate_partition_strategy_comment = (
|
|
||||||
"`partition_strategy` has been removed from `%s` "
|
|
||||||
" The 'div' strategy is used by default.")
|
|
||||||
|
|
||||||
initializers_no_dtype_comment = (
|
initializers_no_dtype_comment = (
|
||||||
"tf.initializers and tf.keras.initializers no longer have the "
|
ast_edits.INFO,
|
||||||
|
"Initializers no longer have the "
|
||||||
"dtype argument in the constructor or partition_info argument in the "
|
"dtype argument in the constructor or partition_info argument in the "
|
||||||
"call method in TF 2.0 and after. The only API symbols are now "
|
"__call__ method.\nThe calls have been converted to compat.v1 for"
|
||||||
"tf.keras.initializers.* or tf.initializers.*."
|
"safety (even though they may already have been correct).")
|
||||||
"\nThe calls have been converted to compat.v1 for safety (even though "
|
|
||||||
"they may already have been correct).")
|
|
||||||
|
|
||||||
uniform_unit_scaling_initializer_comment = (
|
|
||||||
"uniform_unit_scaling_initializer has been removed. Please use"
|
|
||||||
" tf.initializers.variance_scaling instead with distribution=uniform "
|
|
||||||
"to get equivalent behaviour.")
|
|
||||||
|
|
||||||
metrics_comment = (
|
metrics_comment = (
|
||||||
"tf.metrics have been converted to object oriented versions in"
|
ast_edits.INFO,
|
||||||
|
"tf.metrics have been replaced with object oriented versions in"
|
||||||
" TF 2.0 and after. The metric function calls have been converted to "
|
" TF 2.0 and after. The metric function calls have been converted to "
|
||||||
"compat.v1 for backward compatibility. Please update these calls to "
|
"compat.v1 for backward compatibility. Please update these calls to "
|
||||||
"the TF 2.0 versions.")
|
"the TF 2.0 versions.")
|
||||||
|
|
||||||
losses_comment = (
|
losses_comment = (
|
||||||
"tf.losses have been converted to object oriented versions in"
|
ast_edits.INFO,
|
||||||
|
"tf.losses have been replaced with object oriented versions in"
|
||||||
" TF 2.0 and after. The loss function calls have been converted to "
|
" TF 2.0 and after. The loss function calls have been converted to "
|
||||||
"compat.v1 for backward compatibility. Please update these calls to "
|
"compat.v1 for backward compatibility. Please update these calls to "
|
||||||
"the TF 2.0 versions.")
|
"the TF 2.0 versions.")
|
||||||
|
|
||||||
|
# This could be done with a _rename_if_arg_not_found_transformer
|
||||||
|
deprecate_partition_strategy_comment = (
|
||||||
|
ast_edits.WARNING,
|
||||||
|
"`partition_strategy` has been removed from <function name>. "
|
||||||
|
" The 'div' strategy will be used by default.")
|
||||||
|
|
||||||
|
# TODO(b/118888586): add default value change to update script.
|
||||||
|
default_loss_reduction_changed = (
|
||||||
|
ast_edits.WARNING,
|
||||||
|
"default value of loss_reduction has been changed to "
|
||||||
|
"SUM_OVER_BATCH_SIZE.\n"
|
||||||
|
)
|
||||||
|
|
||||||
|
# make change instead
|
||||||
|
uniform_unit_scaling_initializer_comment = (
|
||||||
|
ast_edits.ERROR,
|
||||||
|
"uniform_unit_scaling_initializer has been removed. Please use"
|
||||||
|
" tf.initializers.variance_scaling instead with distribution=uniform "
|
||||||
|
"to get equivalent behaviour.")
|
||||||
|
|
||||||
|
# Make change instead (issue warning about strip_...)
|
||||||
export_saved_model_renamed = (
|
export_saved_model_renamed = (
|
||||||
|
ast_edits.ERROR,
|
||||||
"(Manual edit required) Please rename the method export_savedmodel() "
|
"(Manual edit required) Please rename the method export_savedmodel() "
|
||||||
"to export_saved_model(). Two things to note:\n\t(1) The argument "
|
"to export_saved_model(). Two things to note:\n\t(1) The argument "
|
||||||
"strip_default_attributes has been removed. The function will always "
|
"strip_default_attributes has been removed. The function will always "
|
||||||
@ -947,13 +956,10 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
|
|||||||
assert_rank_comment,
|
assert_rank_comment,
|
||||||
"tf.debugging.assert_rank_in":
|
"tf.debugging.assert_rank_in":
|
||||||
assert_rank_comment,
|
assert_rank_comment,
|
||||||
"tf.device":
|
"tf.flags": (
|
||||||
"tf.device no longer takes function as an argument. "
|
ast_edits.ERROR,
|
||||||
"'device_name_or_function' argument has been renamed to "
|
|
||||||
"'device_name'.",
|
|
||||||
"tf.flags":
|
|
||||||
"tf.flags has been removed, please use the argparse or absl"
|
"tf.flags has been removed, please use the argparse or absl"
|
||||||
" module if you need command line parsing.",
|
" modules if you need command line parsing."),
|
||||||
"tf.train.exponential_decay":
|
"tf.train.exponential_decay":
|
||||||
decay_function_comment,
|
decay_function_comment,
|
||||||
"tf.train.piecewise_constant_decay":
|
"tf.train.piecewise_constant_decay":
|
||||||
@ -988,55 +994,12 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
|
|||||||
default_loss_reduction_changed,
|
default_loss_reduction_changed,
|
||||||
"tf.estimator.BaselineRegressor":
|
"tf.estimator.BaselineRegressor":
|
||||||
default_loss_reduction_changed,
|
default_loss_reduction_changed,
|
||||||
"tf.nn.conv1d":
|
|
||||||
"WARNING: use_cudnn_on_gpu argument has been removed and \"value\""
|
|
||||||
" was renamed to \"input\"",
|
|
||||||
"tf.nn.conv2d":
|
|
||||||
"WARNING: use_cudnn_on_gpu argument has been removed and "
|
|
||||||
"\"filter\" was renamed to \"filters\"",
|
|
||||||
"tf.nn.conv2d_backprop_filter":
|
|
||||||
"WARNING: use_cudnn_on_gpu argument has been removed",
|
|
||||||
"tf.nn.conv2d_backprop_input":
|
|
||||||
"WARNING: use_cudnn_on_gpu argument has been removed and "
|
|
||||||
"\"filter\" was renamed to \"filters\"",
|
|
||||||
"tf.nn.erosion2d":
|
|
||||||
"WARNING: <function name> now requires a data_format argument",
|
|
||||||
"tf.nn.nce_loss":
|
"tf.nn.nce_loss":
|
||||||
deprecate_partition_strategy_comment % "tf.nn.nce_loss",
|
deprecate_partition_strategy_comment,
|
||||||
"tf.nn.safe_embedding_lookup_sparse":
|
"tf.nn.safe_embedding_lookup_sparse":
|
||||||
deprecate_partition_strategy_comment %
|
deprecate_partition_strategy_comment,
|
||||||
"tf.nn.safe_embedding_lookup_sparse",
|
|
||||||
"tf.nn.sampled_softmax_loss":
|
"tf.nn.sampled_softmax_loss":
|
||||||
deprecate_partition_strategy_comment % "tf.nn.sampled_softmax_loss",
|
deprecate_partition_strategy_comment,
|
||||||
"tf.zeros_like":
|
|
||||||
tf_01s_like_no_optimize_comment,
|
|
||||||
"tf.ones_like":
|
|
||||||
tf_01s_like_no_optimize_comment,
|
|
||||||
"tf.nn.embedding_lookup":
|
|
||||||
"WARNING: validate_indices argument has been removed.",
|
|
||||||
"tf.while_loop":
|
|
||||||
"tf.while_loop no longer takes 'return_same_structure' argument. "
|
|
||||||
"'return_same_structure' now defaults to True. Also, 'name'"
|
|
||||||
"argument is now the last argument.",
|
|
||||||
"tf.image.sample_distorted_bounding_box":
|
|
||||||
"tf.image.sample_distorted_bounding_box no longer takes 'seed2' "
|
|
||||||
"argument.",
|
|
||||||
"tf.nn.ctc_beam_search_decoder":
|
|
||||||
"tf.nn.ctc_beam_search_decoder no longer takes 'merge_repeated' "
|
|
||||||
"argument. 'merge_repeated' now defaults to False.",
|
|
||||||
"tf.nn.fractional_avg_pool":
|
|
||||||
"tf.nn.fractional_avg_pool no longer takes 'seed2' and "
|
|
||||||
"'deterministic' arguments. Now it takes a single 'seed' arg. If "
|
|
||||||
"'seed' is zero, the execution is random and deterministic "
|
|
||||||
"otherwise",
|
|
||||||
"tf.nn.fractional_max_pool":
|
|
||||||
"tf.nn.fractional_max_pool no longer takes 'seed2' and "
|
|
||||||
"'deterministic' arguments. Now it takes a single 'seed' arg. If "
|
|
||||||
"'seed' is zero, the execution is random and deterministic "
|
|
||||||
"otherwise",
|
|
||||||
"tf.test.assert_equal_graph_def":
|
|
||||||
"tf.assert_equal_graph_def no longer takes 'checkpoint_v2' "
|
|
||||||
"argument. 'checkpoint_v2' now defaults to True.",
|
|
||||||
"tf.keras.initializers.Zeros":
|
"tf.keras.initializers.Zeros":
|
||||||
initializers_no_dtype_comment,
|
initializers_no_dtype_comment,
|
||||||
"tf.keras.initializers.zeros":
|
"tf.keras.initializers.zeros":
|
||||||
@ -1211,227 +1174,489 @@ class TFAPIChangeSpec(ast_edits.APIChangeSpec):
|
|||||||
|
|
||||||
# Warnings that are emitted only if a specific arg is found.
|
# Warnings that are emitted only if a specific arg is found.
|
||||||
self.function_arg_warnings = {
|
self.function_arg_warnings = {
|
||||||
|
"tf.nn.conv1d": {
|
||||||
|
("use_cudnn_on_gpu", 4): (
|
||||||
|
ast_edits.WARNING,
|
||||||
|
"use_cudnn_on_gpu has been removed, behavior is now equivalent"
|
||||||
|
"to setting it to True."),
|
||||||
|
},
|
||||||
|
"tf.nn.conv2d": {
|
||||||
|
("use_cudnn_on_gpu", 4): (
|
||||||
|
ast_edits.WARNING,
|
||||||
|
"use_cudnn_on_gpu has been removed, behavior is now equivalent"
|
||||||
|
"to setting it to True."),
|
||||||
|
},
|
||||||
|
"tf.nn.conv2d_backprop_filter": {
|
||||||
|
("use_cudnn_on_gpu", 5): (
|
||||||
|
ast_edits.WARNING,
|
||||||
|
"use_cudnn_on_gpu has been removed, behavior is now equivalent"
|
||||||
|
"to setting it to True."),
|
||||||
|
},
|
||||||
|
"tf.nn.conv2d_backprop_input": {
|
||||||
|
("use_cudnn_on_gpu", 5): (
|
||||||
|
ast_edits.WARNING,
|
||||||
|
"use_cudnn_on_gpu has been removed, behavior is now equivalent"
|
||||||
|
"to setting it to True."),
|
||||||
|
},
|
||||||
"tf.gradients": {
|
"tf.gradients": {
|
||||||
("colocate_gradients_with_ops", 4):
|
("colocate_gradients_with_ops", 4): (
|
||||||
|
ast_edits.INFO,
|
||||||
"tf.gradients no longer takes "
|
"tf.gradients no longer takes "
|
||||||
"'colocate_gradients_with_ops' argument, it behaves as if it "
|
"'colocate_gradients_with_ops' argument, it behaves as if it "
|
||||||
"was set to True.",
|
"was set to True."),
|
||||||
},
|
},
|
||||||
"*.minimize": {
|
"*.minimize": {
|
||||||
("colocate_gradients_with_ops", 5):
|
("colocate_gradients_with_ops", 5): (
|
||||||
|
ast_edits.INFO,
|
||||||
"Optimizer.minimize no longer takes "
|
"Optimizer.minimize no longer takes "
|
||||||
"'colocate_gradients_with_ops' argument, it behaves as if it "
|
"'colocate_gradients_with_ops' argument, it behaves as if it "
|
||||||
"was set to True.",
|
"was set to True."),
|
||||||
},
|
},
|
||||||
"*.compute_gradients": {
|
"*.compute_gradients": {
|
||||||
("colocate_gradients_with_ops", 4):
|
("colocate_gradients_with_ops", 4): (
|
||||||
|
ast_edits.INFO,
|
||||||
"Optimizer.compute_gradients no "
|
"Optimizer.compute_gradients no "
|
||||||
"longer takes 'colocate_gradients_with_ops' argument, it "
|
"longer takes 'colocate_gradients_with_ops' argument, it "
|
||||||
"behaves as if it was set to True.",
|
"behaves as if it was set to True."),
|
||||||
},
|
},
|
||||||
"tf.cond": {
|
"tf.cond": {
|
||||||
("strict", 3):
|
("strict", 3): (
|
||||||
|
ast_edits.WARNING,
|
||||||
"tf.cond no longer takes 'strict' argument, it behaves as "
|
"tf.cond no longer takes 'strict' argument, it behaves as "
|
||||||
"if was set to True."
|
"if was set to True.")
|
||||||
},
|
},
|
||||||
}
|
}
|
||||||
|
|
||||||
self.symbol_renames = {
|
# Specially handled functions
|
||||||
name: new_name
|
# Each transformer is a callable which will be called with the arguments
|
||||||
for name, new_name in self.symbol_renames.items()
|
# transformer(parent, node, full_name, name, logs, errors)
|
||||||
|
# Where logs is a list to which (level, line, col, msg) tuples can be
|
||||||
|
# appended, full_name is the FQN of the function called (or None if that is
|
||||||
|
# unknown), name is the name of the function called (or None is that is
|
||||||
|
# unknown). node is an ast.Call node representing this function call, and
|
||||||
|
# parent is its parent in the AST.
|
||||||
|
# The function may modify node (but not parent), and must return
|
||||||
|
# - none, if nothing was modified
|
||||||
|
# - node, if node was modified in place (make sure to use
|
||||||
|
# pasta.ast_utils.replace_child to swap out children, otherwise formatting
|
||||||
|
# may get messy)
|
||||||
|
# - a replacement for node, if the whole call node was replaced. The caller
|
||||||
|
# will take care of changing parent.
|
||||||
|
self.function_transformers = {
|
||||||
|
"*.make_initializable_iterator": _iterator_transformer,
|
||||||
|
"*.make_one_shot_iterator": _iterator_transformer,
|
||||||
|
"tf.nn.dropout": _dropout_transformer,
|
||||||
|
"tf.batch_gather": _batch_gather_transformer,
|
||||||
|
"tf.to_bfloat16": _cast_transformer,
|
||||||
|
"tf.to_complex128": _cast_transformer,
|
||||||
|
"tf.to_complex64": _cast_transformer,
|
||||||
|
"tf.to_double": _cast_transformer,
|
||||||
|
"tf.to_float": _cast_transformer,
|
||||||
|
"tf.to_int32": _cast_transformer,
|
||||||
|
"tf.to_int64": _cast_transformer,
|
||||||
|
"tf.nn.softmax_cross_entropy_with_logits":
|
||||||
|
_softmax_cross_entropy_with_logits_transformer,
|
||||||
|
"tf.image.resize_area": _image_resize_transformer,
|
||||||
|
"tf.image.resize_bicubic": _image_resize_transformer,
|
||||||
|
"tf.image.resize_bilinear": _image_resize_transformer,
|
||||||
|
"tf.image.resize_nearest_neighbor": _image_resize_transformer,
|
||||||
|
"tf.nn.fractional_avg_pool": _pool_seed_transformer,
|
||||||
|
"tf.nn.fractional_max_pool": _pool_seed_transformer,
|
||||||
|
"tf.device": functools.partial(
|
||||||
|
_rename_if_arg_found_transformer, arg_name="device_name",
|
||||||
|
arg_ok_predicate=_is_ast_str, remove_if_ok=False,
|
||||||
|
message="tf.device no longer takes functions as an argument. "
|
||||||
|
"We could not determine that the argument value is a string, so "
|
||||||
|
"the call was converted to compat.v1."),
|
||||||
|
"tf.zeros_like": functools.partial(
|
||||||
|
_rename_if_arg_found_transformer, arg_name="optimize",
|
||||||
|
arg_ok_predicate=_is_ast_true, remove_if_ok=True,
|
||||||
|
message="tf.zeros_like no longer takes an optimize argument, and "
|
||||||
|
"behaves as if optimize=True. This call site specifies something "
|
||||||
|
"other than optimize=True, so it was converted to compat.v1."),
|
||||||
|
"tf.ones_like": functools.partial(
|
||||||
|
_rename_if_arg_found_transformer, arg_name="optimize",
|
||||||
|
arg_ok_predicate=_is_ast_true, remove_if_ok=True,
|
||||||
|
message="tf.ones_like no longer takes an optimize argument, and "
|
||||||
|
"behaves as if optimize=True. This call site specifies something "
|
||||||
|
"other than optimize=True, so it was converted to compat.v1."),
|
||||||
|
"tf.while_loop": functools.partial(
|
||||||
|
_rename_if_arg_found_transformer,
|
||||||
|
arg_name="return_same_structure",
|
||||||
|
arg_ok_predicate=_is_ast_true, remove_if_ok=True,
|
||||||
|
message="tf.while_loop no longer takes 'return_same_structure' "
|
||||||
|
"argument and behaves as if return_same_structure=True. This call "
|
||||||
|
"site specifies something other than return_same_structure=True, "
|
||||||
|
"so it was converted to compat.v1."),
|
||||||
|
"tf.nn.ctc_beam_search_decoder": functools.partial(
|
||||||
|
_rename_if_arg_found_transformer,
|
||||||
|
arg_name="merge_repeated",
|
||||||
|
arg_ok_predicate=_is_ast_false, remove_if_ok=True,
|
||||||
|
message="tf.nn.ctc_beam_search_decoder no longer takes the "
|
||||||
|
"'merge_repeated' argument and behaves as if merge_repeated=False. "
|
||||||
|
"This call site specifies something other than "
|
||||||
|
"merge_repeated=False, so it was converted to compat.v1."),
|
||||||
|
"tf.nn.erosion2d": functools.partial(
|
||||||
|
_add_argument_transformer,
|
||||||
|
arg_name="data_format",
|
||||||
|
arg_value_ast=ast.Str("NHWC")),
|
||||||
}
|
}
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _iterator_transformer(parent, node, full_name, name, logs):
|
|
||||||
# First, check that node.func.value is not already something we like
|
|
||||||
# (tf.compat.v1.data), or something which is handled in the rename
|
|
||||||
# (tf.data). This transformer only handles the method call to function call
|
|
||||||
# conversion.
|
|
||||||
if full_name and (full_name.startswith("tf.compat.v1.data") or
|
|
||||||
full_name.startswith("tf.data")):
|
|
||||||
return
|
|
||||||
|
|
||||||
# This should never happen, since we're only called for Attribute nodes.
|
def _is_ast_str(node):
|
||||||
if not isinstance(node.func, ast.Attribute):
|
"""Determine whether this node represents a string."""
|
||||||
return
|
allowed_types = [ast.Str]
|
||||||
|
if hasattr(ast, "Bytes"):
|
||||||
|
allowed_types += [ast.Bytes]
|
||||||
|
if hasattr(ast, "JoinedStr"):
|
||||||
|
allowed_types += [ast.JoinedStr]
|
||||||
|
if hasattr(ast, "FormattedValue"):
|
||||||
|
allowed_types += [ast.FormattedValue]
|
||||||
|
return isinstance(node, allowed_types)
|
||||||
|
|
||||||
# Transform from x.f(y) to tf.compat.v1.data.f(x, y)
|
|
||||||
# Fortunately, node.func.value should already have valid position info
|
|
||||||
node.args = [node.func.value] + node.args
|
|
||||||
node.func.value = ast_edits.full_name_node("tf.compat.v1.data")
|
|
||||||
|
|
||||||
logs.append((ast_edits.WARNING, node.lineno, node.col_offset,
|
def _is_ast_true(node):
|
||||||
"Changing dataset.%s() to tf.compat.v1.data.%s(dataset). "
|
if hasattr(ast, "NameConstant"):
|
||||||
"Please check this transformation.\n" % (name, name)))
|
return isinstance(node, ast.NameConstant) and node.value is True
|
||||||
|
else:
|
||||||
|
return isinstance(node, ast.Name) and node.id == "True"
|
||||||
|
|
||||||
return node
|
|
||||||
|
|
||||||
@staticmethod
|
def _is_ast_false(node):
|
||||||
def _dropout_transformer(parent, node, full_name, name, logs):
|
if hasattr(ast, "NameConstant"):
|
||||||
def _replace_keep_prob_node(parent, old_value):
|
return isinstance(node, ast.NameConstant) and node.value is False
|
||||||
"""Replaces old_value with 1-(old_value)."""
|
else:
|
||||||
one = ast.Num(n=1)
|
return isinstance(node, ast.Name) and node.id == "False"
|
||||||
one.lineno = 0
|
|
||||||
one.col_offset = 0
|
|
||||||
new_value = ast.BinOp(left=one, op=ast.Sub(),
|
|
||||||
right=old_value)
|
|
||||||
# This copies the prefix and suffix on old_value to new_value.
|
|
||||||
pasta.ast_utils.replace_child(parent, old_value, new_value)
|
|
||||||
ast.copy_location(new_value, old_value)
|
|
||||||
# Put parentheses around keep_prob.value (and remove the old prefix/
|
|
||||||
# suffix, they should only be around new_value).
|
|
||||||
pasta.base.formatting.set(old_value, "prefix", "(")
|
|
||||||
pasta.base.formatting.set(old_value, "suffix", ")")
|
|
||||||
|
|
||||||
# Check if we have a keep_prob keyword arg
|
|
||||||
for keep_prob in node.keywords:
|
|
||||||
if keep_prob.arg == "keep_prob":
|
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
|
||||||
"Changing keep_prob arg of tf.nn.dropout to rate\n"))
|
|
||||||
keep_prob.arg = "rate"
|
|
||||||
_replace_keep_prob_node(keep_prob, keep_prob.value)
|
|
||||||
return node
|
|
||||||
|
|
||||||
# Maybe it was a positional arg
|
# Lots of unused arguments below, since these are called in a standard manner.
|
||||||
if len(node.args) < 2:
|
# pylint: disable=unused-argument
|
||||||
logs.append((ast_edits.ERROR, node.lineno, node.col_offset,
|
|
||||||
"tf.nn.dropout called without arguments, so "
|
|
||||||
"automatic fix was disabled. tf.nn.dropout has changed "
|
def _rename_if_arg_found_transformer(parent, node, full_name, name, logs,
|
||||||
"the semantics of the second argument."))
|
arg_name=None,
|
||||||
|
arg_ok_predicate=None,
|
||||||
|
remove_if_ok=False,
|
||||||
|
message=None):
|
||||||
|
"""Replaces the given call with tf.compat.v1 if the given arg is found.
|
||||||
|
|
||||||
|
This requires the function to be called with all named args, so for using
|
||||||
|
this transformer, the function should also be added to renames.
|
||||||
|
|
||||||
|
If the arg is not found, the call site is left alone.
|
||||||
|
|
||||||
|
If the arg is found, and if arg_ok_predicate is given, it is called with
|
||||||
|
the ast Expression representing the argument value found. If it returns
|
||||||
|
True, the function is left alone.
|
||||||
|
|
||||||
|
If the arg is found, arg_ok_predicate is not None and returns ok, and
|
||||||
|
remove_if_ok is True, the argument is removed from the call.
|
||||||
|
|
||||||
|
Otherwise, `compat.v1` is inserted between tf and the function name.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
parent: Parent of node.
|
||||||
|
node: ast.Call node to maybe modify.
|
||||||
|
full_name: full name of function to modify
|
||||||
|
name: name of function to modify
|
||||||
|
logs: list of logs to append to
|
||||||
|
arg_name: name of the argument to look for
|
||||||
|
arg_ok_predicate: predicate callable with the ast of the argument value,
|
||||||
|
returns whether the argument value is allowed.
|
||||||
|
remove_if_ok: remove the argument if present and ok as determined by
|
||||||
|
arg_ok_predicate.
|
||||||
|
message: message to print if a non-ok arg is found (and hence, the function
|
||||||
|
is renamed to its compat.v1 version).
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
node, if it was modified, else None.
|
||||||
|
"""
|
||||||
|
# Check whether arg is there.
|
||||||
|
arg_present, arg_value = ast_edits.get_arg_value(node, arg_name)
|
||||||
|
if not arg_present:
|
||||||
|
return
|
||||||
|
|
||||||
|
# Check whether arg is problematic (and if not, maybe remove it).
|
||||||
|
if arg_ok_predicate and arg_ok_predicate(arg_value):
|
||||||
|
if remove_if_ok:
|
||||||
|
for i, kw in enumerate(node.keywords):
|
||||||
|
if kw.arg == arg_name:
|
||||||
|
node.keywords.pop(i)
|
||||||
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Removed argument %s for function %s" % (
|
||||||
|
arg_name, full_name or name)))
|
||||||
|
break
|
||||||
|
return node
|
||||||
else:
|
else:
|
||||||
_replace_keep_prob_node(node, node.args[1])
|
return
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
|
||||||
"Changing keep_prob arg of tf.nn.dropout to rate, and "
|
|
||||||
"recomputing value.\n"))
|
|
||||||
|
|
||||||
|
# All conditions met, insert v1 and log what we did.
|
||||||
|
# We must have a full name, so the func is an attribute.
|
||||||
|
new_name = full_name.replace("tf.", "tf.compat.v1.", 1)
|
||||||
|
node.func = ast_edits.full_name_node(new_name)
|
||||||
|
logs.append((
|
||||||
|
ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Renaming %s to %s because argument %s is present. %s" %
|
||||||
|
(full_name, new_name, arg_name, message if message is not None else "")
|
||||||
|
))
|
||||||
|
return node
|
||||||
|
|
||||||
|
|
||||||
|
def _add_argument_transformer(parent, node, full_name, name, logs,
|
||||||
|
arg_name, arg_value_ast):
|
||||||
|
"""Adds an argument (as a final kwarg arg_name=arg_value_ast)."""
|
||||||
|
node.keywords.append(ast.keyword(arg=arg_name, value=arg_value_ast))
|
||||||
|
logs.add((
|
||||||
|
ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Adding argument '%s' to call to %s." % (pasta.dump(node.keywords[-1],
|
||||||
|
full_name or name))
|
||||||
|
))
|
||||||
|
return node
|
||||||
|
|
||||||
|
|
||||||
|
def _iterator_transformer(parent, node, full_name, name, logs):
|
||||||
|
"""Transform iterator methods to compat function calls."""
|
||||||
|
# First, check that node.func.value is not already something we like
|
||||||
|
# (tf.compat.v1.data), or something which is handled in the rename
|
||||||
|
# (tf.data). This transformer only handles the method call to function call
|
||||||
|
# conversion.
|
||||||
|
if full_name and (full_name.startswith("tf.compat.v1.data") or
|
||||||
|
full_name.startswith("tf.data")):
|
||||||
|
return
|
||||||
|
|
||||||
|
# This should never happen, since we're only called for Attribute nodes.
|
||||||
|
if not isinstance(node.func, ast.Attribute):
|
||||||
|
return
|
||||||
|
|
||||||
|
# Transform from x.f(y) to tf.compat.v1.data.f(x, y)
|
||||||
|
# Fortunately, node.func.value should already have valid position info
|
||||||
|
node.args = [node.func.value] + node.args
|
||||||
|
node.func.value = ast_edits.full_name_node("tf.compat.v1.data")
|
||||||
|
|
||||||
|
logs.append((ast_edits.WARNING, node.lineno, node.col_offset,
|
||||||
|
"Changing dataset.%s() to tf.compat.v1.data.%s(dataset). "
|
||||||
|
"Please check this transformation.\n" % (name, name)))
|
||||||
|
|
||||||
|
return node
|
||||||
|
|
||||||
|
|
||||||
|
def _dropout_transformer(parent, node, full_name, name, logs):
|
||||||
|
"""Replace keep_prob with 1-rate."""
|
||||||
|
def _replace_keep_prob_node(parent, old_value):
|
||||||
|
"""Replaces old_value with 1-(old_value)."""
|
||||||
|
one = ast.Num(n=1)
|
||||||
|
one.lineno = 0
|
||||||
|
one.col_offset = 0
|
||||||
|
new_value = ast.BinOp(left=one, op=ast.Sub(),
|
||||||
|
right=old_value)
|
||||||
|
# This copies the prefix and suffix on old_value to new_value.
|
||||||
|
pasta.ast_utils.replace_child(parent, old_value, new_value)
|
||||||
|
ast.copy_location(new_value, old_value)
|
||||||
|
# Put parentheses around keep_prob.value (and remove the old prefix/
|
||||||
|
# suffix, they should only be around new_value).
|
||||||
|
pasta.base.formatting.set(old_value, "prefix", "(")
|
||||||
|
pasta.base.formatting.set(old_value, "suffix", ")")
|
||||||
|
|
||||||
|
# Check if we have a keep_prob keyword arg
|
||||||
|
for keep_prob in node.keywords:
|
||||||
|
if keep_prob.arg == "keep_prob":
|
||||||
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Changing keep_prob arg of tf.nn.dropout to rate\n"))
|
||||||
|
keep_prob.arg = "rate"
|
||||||
|
_replace_keep_prob_node(keep_prob, keep_prob.value)
|
||||||
return node
|
return node
|
||||||
|
|
||||||
@staticmethod
|
# Maybe it was a positional arg
|
||||||
def _cast_transformer(parent, node, full_name, name, logs):
|
if len(node.args) < 2:
|
||||||
"""Transforms to_int and to_float to cast(..., dtype=...)."""
|
logs.append((ast_edits.ERROR, node.lineno, node.col_offset,
|
||||||
|
"tf.nn.dropout called without arguments, so "
|
||||||
# Find out the dtype to cast to from the function name
|
"automatic fix was disabled. tf.nn.dropout has changed "
|
||||||
dtype_str = name[3:]
|
"the semantics of the second argument."))
|
||||||
# Special cases where the full dtype is not given
|
else:
|
||||||
if dtype_str == "float":
|
_replace_keep_prob_node(node, node.args[1])
|
||||||
dtype_str = "float32"
|
|
||||||
elif dtype_str == "double":
|
|
||||||
dtype_str = "float64"
|
|
||||||
new_arg = ast.keyword(arg="dtype",
|
|
||||||
value=ast.Attribute(value=ast.Name(id="tf",
|
|
||||||
ctx=ast.Load()),
|
|
||||||
attr=dtype_str, ctx=ast.Load()))
|
|
||||||
# Ensures a valid transformation when a positional name arg is given
|
|
||||||
if len(node.args) == 2:
|
|
||||||
name_arg = ast.keyword(arg="name",
|
|
||||||
value=node.args[-1])
|
|
||||||
node.args = node.args[:-1]
|
|
||||||
node.keywords.append(name_arg)
|
|
||||||
|
|
||||||
# Python3 ast requires the args for the Attribute, but codegen will mess up
|
|
||||||
# the arg order if we just set them to 0.
|
|
||||||
new_arg.value.lineno = node.lineno
|
|
||||||
new_arg.value.col_offset = node.col_offset+100
|
|
||||||
|
|
||||||
node.keywords.append(new_arg)
|
|
||||||
if isinstance(node.func, ast.Attribute):
|
|
||||||
node.func.attr = "cast"
|
|
||||||
else:
|
|
||||||
assert isinstance(node.func, ast.Name)
|
|
||||||
node.func.id = "cast"
|
|
||||||
|
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
"Changed %s call to tf.cast(..., dtype=tf.%s)." % (full_name,
|
"Changing keep_prob arg of tf.nn.dropout to rate, and "
|
||||||
dtype_str)))
|
"recomputing value.\n"))
|
||||||
|
|
||||||
return node
|
return node
|
||||||
|
|
||||||
@staticmethod
|
|
||||||
def _softmax_cross_entropy_with_logits_transformer(
|
|
||||||
parent, node, full_name, name, logs):
|
|
||||||
def _wrap_label(parent, old_value):
|
|
||||||
"""Wrap labels with tf.stop_gradient."""
|
|
||||||
if six.PY3:
|
|
||||||
new_value = ast.Call(
|
|
||||||
ast.Name(id="tf.stop_gradient", ctx=ast.Load()),
|
|
||||||
[old_value], [])
|
|
||||||
else:
|
|
||||||
new_value = ast.Call(
|
|
||||||
ast.Name(id="tf.stop_gradient", ctx=ast.Load()),
|
|
||||||
[old_value], [], None, None)
|
|
||||||
|
|
||||||
# This copies the prefix and suffix on old_value to new_value.
|
def _cast_transformer(parent, node, full_name, name, logs):
|
||||||
pasta.ast_utils.replace_child(parent, old_value, new_value)
|
"""Transforms to_int and to_float to cast(..., dtype=...)."""
|
||||||
ast.copy_location(new_value, old_value)
|
|
||||||
|
|
||||||
# Check if we have a labels keyword arg
|
# Find out the dtype to cast to from the function name
|
||||||
for karg in node.keywords:
|
dtype_str = name[3:]
|
||||||
if karg.arg == "labels":
|
# Special cases where the full dtype is not given
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
if dtype_str == "float":
|
||||||
"Changing labels arg of "
|
dtype_str = "float32"
|
||||||
"tf.nn.softmax_cross_entropy_with_logits to "
|
elif dtype_str == "double":
|
||||||
"tf.stop_gradient(labels). Please check this "
|
dtype_str = "float64"
|
||||||
"transformation.\n"))
|
new_arg = ast.keyword(arg="dtype",
|
||||||
_wrap_label(karg, karg.value)
|
value=ast.Attribute(value=ast.Name(id="tf",
|
||||||
return node
|
ctx=ast.Load()),
|
||||||
return node
|
attr=dtype_str, ctx=ast.Load()))
|
||||||
|
# Ensures a valid transformation when a positional name arg is given
|
||||||
|
if len(node.args) == 2:
|
||||||
|
name_arg = ast.keyword(arg="name",
|
||||||
|
value=node.args[-1])
|
||||||
|
node.args = node.args[:-1]
|
||||||
|
node.keywords.append(name_arg)
|
||||||
|
|
||||||
@staticmethod
|
# Python3 ast requires the args for the Attribute, but codegen will mess up
|
||||||
def _batch_gather_transformer(parent, node, full_name, name, logs):
|
# the arg order if we just set them to 0.
|
||||||
# Check if the call already has a batch_dims argument
|
new_arg.value.lineno = node.lineno
|
||||||
if any([kw.arg == "batch_dims" for kw in node.keywords]):
|
new_arg.value.col_offset = node.col_offset+100
|
||||||
|
|
||||||
|
node.keywords.append(new_arg)
|
||||||
|
if isinstance(node.func, ast.Attribute):
|
||||||
|
node.func.attr = "cast"
|
||||||
|
else:
|
||||||
|
assert isinstance(node.func, ast.Name)
|
||||||
|
node.func.id = "cast"
|
||||||
|
|
||||||
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Changed %s call to tf.cast(..., dtype=tf.%s)." % (full_name,
|
||||||
|
dtype_str)))
|
||||||
|
return node
|
||||||
|
|
||||||
|
|
||||||
|
def _softmax_cross_entropy_with_logits_transformer(
|
||||||
|
parent, node, full_name, name, logs):
|
||||||
|
"""Wrap labels argument with stop_gradients."""
|
||||||
|
def _wrap_label(parent, old_value):
|
||||||
|
"""Wrap labels with tf.stop_gradient."""
|
||||||
|
if six.PY3:
|
||||||
|
new_value = ast.Call(
|
||||||
|
ast.Name(id="tf.stop_gradient", ctx=ast.Load()),
|
||||||
|
[old_value], [])
|
||||||
|
else:
|
||||||
|
new_value = ast.Call(
|
||||||
|
ast.Name(id="tf.stop_gradient", ctx=ast.Load()),
|
||||||
|
[old_value], [], None, None)
|
||||||
|
|
||||||
|
# This copies the prefix and suffix on old_value to new_value.
|
||||||
|
pasta.ast_utils.replace_child(parent, old_value, new_value)
|
||||||
|
ast.copy_location(new_value, old_value)
|
||||||
|
|
||||||
|
# Check if we have a labels keyword arg
|
||||||
|
for karg in node.keywords:
|
||||||
|
if karg.arg == "labels":
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
"tf.batch_gather already has batch_dims argument. Neat."))
|
"Changing labels arg of "
|
||||||
return None
|
"tf.nn.softmax_cross_entropy_with_logits to "
|
||||||
|
"tf.stop_gradient(labels). Please check this "
|
||||||
|
"transformation.\n"))
|
||||||
|
_wrap_label(karg, karg.value)
|
||||||
|
return node
|
||||||
|
return node
|
||||||
|
|
||||||
minus_one = ast.Num(n=-1)
|
|
||||||
minus_one.lineno = 0
|
def _batch_gather_transformer(parent, node, full_name, name, logs):
|
||||||
minus_one.col_offset = 0
|
"""Add batch_dims argument for gather calls."""
|
||||||
new_arg = ast.keyword("batch_dims", minus_one)
|
# Check if the call already has a batch_dims argument
|
||||||
node.keywords.append(new_arg)
|
if any([kw.arg == "batch_dims" for kw in node.keywords]):
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
"Added keyword argument batch_dims=-1 to tf.batch_gather."))
|
"tf.batch_gather already has batch_dims argument. Neat."))
|
||||||
return node
|
return None
|
||||||
|
|
||||||
@staticmethod
|
minus_one = ast.Num(n=-1)
|
||||||
def _image_resize_transformer(parent, node, full_name, name, logs):
|
minus_one.lineno = 0
|
||||||
"""Transforms image.resize_* to image.resize(..., method=*, ...)."""
|
minus_one.col_offset = 0
|
||||||
|
new_arg = ast.keyword("batch_dims", minus_one)
|
||||||
|
node.keywords.append(new_arg)
|
||||||
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Added keyword argument batch_dims=-1 to tf.batch_gather."))
|
||||||
|
return node
|
||||||
|
|
||||||
resize_method = name[7:].upper()
|
|
||||||
new_arg = ast.keyword(arg="method",
|
|
||||||
value=ast.Attribute(
|
|
||||||
value=ast.Attribute(
|
|
||||||
value=ast.Attribute(
|
|
||||||
value=ast.Name(id="tf", ctx=ast.Load()),
|
|
||||||
attr="image", ctx=ast.Load()),
|
|
||||||
attr="ResizeMethod", ctx=ast.Load()),
|
|
||||||
attr=resize_method, ctx=ast.Load()))
|
|
||||||
|
|
||||||
# Ensures a valid transformation when a positional name arg is given
|
def _image_resize_transformer(parent, node, full_name, name, logs):
|
||||||
if len(node.args) == 4:
|
"""Transforms image.resize_* to image.resize(..., method=*, ...)."""
|
||||||
pos_arg = ast.keyword(arg="preserve_aspect_ratio",
|
resize_method = name[7:].upper()
|
||||||
value=node.args[-1])
|
new_arg = ast.keyword(arg="method",
|
||||||
node.args = node.args[:-1]
|
value=ast.Attribute(
|
||||||
node.keywords.append(pos_arg)
|
value=ast.Attribute(
|
||||||
if len(node.args) == 3:
|
value=ast.Attribute(
|
||||||
pos_arg = ast.keyword(arg="align_corners",
|
value=ast.Name(id="tf", ctx=ast.Load()),
|
||||||
value=node.args[-1])
|
attr="image", ctx=ast.Load()),
|
||||||
node.args = node.args[:-1]
|
attr="ResizeMethod", ctx=ast.Load()),
|
||||||
node.keywords.append(pos_arg)
|
attr=resize_method, ctx=ast.Load()))
|
||||||
|
|
||||||
# Python3 ast requires the args for the Attribute, but codegen will mess up
|
# Ensures a valid transformation when a positional name arg is given
|
||||||
# the arg order if we just set them to 0.
|
if len(node.args) == 4:
|
||||||
new_arg.value.lineno = node.lineno
|
pos_arg = ast.keyword(arg="preserve_aspect_ratio",
|
||||||
new_arg.value.col_offset = node.col_offset+100
|
value=node.args[-1])
|
||||||
|
node.args = node.args[:-1]
|
||||||
|
node.keywords.append(pos_arg)
|
||||||
|
if len(node.args) == 3:
|
||||||
|
pos_arg = ast.keyword(arg="align_corners",
|
||||||
|
value=node.args[-1])
|
||||||
|
node.args = node.args[:-1]
|
||||||
|
node.keywords.append(pos_arg)
|
||||||
|
|
||||||
node.keywords.append(new_arg)
|
# Python3 ast requires the args for the Attribute, but codegen will mess up
|
||||||
if isinstance(node.func, ast.Attribute):
|
# the arg order if we just set them to 0.
|
||||||
node.func.attr = "resize"
|
new_arg.value.lineno = node.lineno
|
||||||
|
new_arg.value.col_offset = node.col_offset+100
|
||||||
|
|
||||||
|
node.keywords.append(new_arg)
|
||||||
|
if isinstance(node.func, ast.Attribute):
|
||||||
|
node.func.attr = "resize"
|
||||||
|
else:
|
||||||
|
assert isinstance(node.func, ast.Name)
|
||||||
|
node.func.id = "resize"
|
||||||
|
|
||||||
|
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Changed %s call to tf.image.resize(..., "
|
||||||
|
"method=tf.image.ResizeMethod.%s)." % (full_name,
|
||||||
|
resize_method)))
|
||||||
|
return node
|
||||||
|
|
||||||
|
|
||||||
|
def _pool_seed_transformer(parent, node, full_name, name, logs):
|
||||||
|
"""Removes seed2 and deterministic, and adds non-zero seed if needed."""
|
||||||
|
# This requires that this function uses all kwargs (add to renames!).
|
||||||
|
seed_arg = None
|
||||||
|
deterministic = False
|
||||||
|
modified = False
|
||||||
|
new_keywords = []
|
||||||
|
|
||||||
|
for kw in node.keywords:
|
||||||
|
if sys.version_info[:2] >= (3, 5) and isinstance(kw, ast.Starred):
|
||||||
|
pass
|
||||||
|
elif kw.arg == "seed":
|
||||||
|
seed_arg = kw
|
||||||
|
elif kw.arg == "seed2" or kw.arg == "deterministic":
|
||||||
|
lineno = getattr(kw, "lineno", node.lineno)
|
||||||
|
col_offset = getattr(kw, "col_offset", node.col_offset)
|
||||||
|
logs.append((ast_edits.INFO, lineno, col_offset,
|
||||||
|
"Removed argument %s for function %s" % (
|
||||||
|
kw.arg, full_name or name)))
|
||||||
|
if kw.arg == "deterministic":
|
||||||
|
if not _is_ast_false(kw.value):
|
||||||
|
deterministic = True
|
||||||
|
modified = True
|
||||||
|
continue
|
||||||
|
new_keywords.append(kw)
|
||||||
|
|
||||||
|
if deterministic:
|
||||||
|
if seed_arg is None:
|
||||||
|
new_keywords.append(ast.keyword(arg="seed", value=ast.Num(42)))
|
||||||
|
logs.add((
|
||||||
|
ast_edits.INFO, node.lineno, node.col_offset,
|
||||||
|
"Adding seed=42 to call to %s since determinism was requested" % (
|
||||||
|
full_name or name)
|
||||||
|
))
|
||||||
else:
|
else:
|
||||||
assert isinstance(node.func, ast.Name)
|
logs.add((
|
||||||
node.func.id = "resize"
|
ast_edits.WARNING, node.lineno, node.col_offset,
|
||||||
|
"The deterministic argument is deprecated for %s, pass a "
|
||||||
|
"non-zero seed for determinism. The deterministic argument is "
|
||||||
|
"present, possibly not False, and the seed is already set. The "
|
||||||
|
"converter cannot determine whether it is nonzero, please check."
|
||||||
|
))
|
||||||
|
|
||||||
logs.append((ast_edits.INFO, node.lineno, node.col_offset,
|
if modified:
|
||||||
"Changed %s call to tf.image.resize(..., "
|
node.keywords = new_keywords
|
||||||
"method=tf.image.ResizeMethod.%s)." % (full_name,
|
|
||||||
resize_method)))
|
|
||||||
return node
|
return node
|
||||||
|
else:
|
||||||
|
return
|
||||||
|
|||||||
@ -112,7 +112,7 @@ Simple usage:
|
|||||||
report = ("TensorFlow 2.0 Upgrade Script\n"
|
report = ("TensorFlow 2.0 Upgrade Script\n"
|
||||||
"-----------------------------\n"
|
"-----------------------------\n"
|
||||||
"Converted %d files\n" % files_processed +
|
"Converted %d files\n" % files_processed +
|
||||||
"Detected %d errors that require attention" % num_errors + "\n" +
|
"Detected %d issues that require attention" % num_errors + "\n" +
|
||||||
"-" * 80 + "\n") + "".join(report)
|
"-" * 80 + "\n") + "".join(report)
|
||||||
with open(report_filename, "w") as report_file:
|
with open(report_filename, "w") as report_file:
|
||||||
report_file.write(report)
|
report_file.write(report)
|
||||||
|
|||||||
@ -376,19 +376,13 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
"tf.train.inverse_time_decay", "tf.train.cosine_decay",
|
"tf.train.inverse_time_decay", "tf.train.cosine_decay",
|
||||||
"tf.train.cosine_decay_restarts",
|
"tf.train.cosine_decay_restarts",
|
||||||
"tf.train.linear_cosine_decay",
|
"tf.train.linear_cosine_decay",
|
||||||
"tf.train.noisy_linear_cosine_decay"]:
|
"tf.train.noisy_linear_cosine_decay",
|
||||||
|
"tf.train.piecewise_constant_decay",
|
||||||
|
]:
|
||||||
|
|
||||||
text = "%s(a, b)\n" % decay
|
text = "%s(a, b)\n" % decay
|
||||||
_, report, errors, _ = self._upgrade(text)
|
_, report, unused_errors, _ = self._upgrade(text)
|
||||||
self.assertIn("%s requires manual check" % decay, errors[0])
|
self.assertIn("%s has been changed to return a callable" % decay, report)
|
||||||
self.assertIn("%s has been changed" % decay, report)
|
|
||||||
|
|
||||||
def testPiecewiseDecay(self):
|
|
||||||
text = "tf.train.piecewise_constant_decay(a, b)\n"
|
|
||||||
_, report, errors, _ = self._upgrade(text)
|
|
||||||
self.assertIn("tf.train.piecewise_constant_decay requires manual check",
|
|
||||||
errors[0])
|
|
||||||
self.assertIn("tf.train.piecewise_constant_decay has been changed", report)
|
|
||||||
|
|
||||||
def testMetrics(self):
|
def testMetrics(self):
|
||||||
metrics = [
|
metrics = [
|
||||||
@ -427,16 +421,11 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
"true_positives_at_thresholds",
|
"true_positives_at_thresholds",
|
||||||
]
|
]
|
||||||
for m in metrics:
|
for m in metrics:
|
||||||
ns = "tf.metrics." + m
|
text = "tf.metrics." + m + "(a, b)"
|
||||||
text = ns + "(a, b)"
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
_, report, errors, new_text = self._upgrade(text)
|
|
||||||
self.assertEqual("tf.compat.v1.metrics." + m + "(a, b)", new_text)
|
self.assertEqual("tf.compat.v1.metrics." + m + "(a, b)", new_text)
|
||||||
self.assertIn("%s requires manual check" % ns, errors[0])
|
|
||||||
self.assertIn(
|
self.assertIn(
|
||||||
"tf.metrics have been converted to object oriented"
|
"tf.metrics have been replaced with object oriented versions", report)
|
||||||
" versions in TF 2.0 and after. The metric function calls have been "
|
|
||||||
"converted to compat.v1 for backward compatibility. Please update "
|
|
||||||
"these calls to the TF 2.0 versions.", report)
|
|
||||||
|
|
||||||
def testLosses(self):
|
def testLosses(self):
|
||||||
losses = [
|
losses = [
|
||||||
@ -458,16 +447,11 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
"sparse_softmax_cross_entropy",
|
"sparse_softmax_cross_entropy",
|
||||||
]
|
]
|
||||||
for l in losses:
|
for l in losses:
|
||||||
ns = "tf.losses." + l
|
text = "tf.losses." + l + "(a, b)"
|
||||||
text = ns + "(a, b)"
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
_, report, errors, new_text = self._upgrade(text)
|
|
||||||
self.assertEqual("tf.compat.v1.losses." + l + "(a, b)", new_text)
|
self.assertEqual("tf.compat.v1.losses." + l + "(a, b)", new_text)
|
||||||
self.assertIn("%s requires manual check" % ns, errors[0])
|
|
||||||
self.assertIn(
|
self.assertIn(
|
||||||
"tf.losses have been converted to object oriented"
|
"tf.losses have been replaced with object oriented versions", report)
|
||||||
" versions in TF 2.0 and after. The loss function calls have been "
|
|
||||||
"converted to compat.v1 for backward compatibility. Please update "
|
|
||||||
"these calls to the TF 2.0 versions.", report)
|
|
||||||
|
|
||||||
def testEstimatorLossReductionChange(self):
|
def testEstimatorLossReductionChange(self):
|
||||||
classes = [
|
classes = [
|
||||||
@ -605,10 +589,9 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
self.assertEqual(errors, [])
|
self.assertEqual(errors, [])
|
||||||
|
|
||||||
text = "tf.gradients(a, colocate_gradients_with_ops=False)\n"
|
text = "tf.gradients(a, colocate_gradients_with_ops=False)\n"
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual("tf.gradients(a)\n", new_text)
|
self.assertEqual("tf.gradients(a)\n", new_text)
|
||||||
self.assertIn("tf.gradients", errors[0])
|
self.assertIn("tf.gradients no longer takes", report)
|
||||||
self.assertIn("requires manual check", errors[0])
|
|
||||||
|
|
||||||
def testColocateGradientsWithOpsMinimize(self):
|
def testColocateGradientsWithOpsMinimize(self):
|
||||||
text = "optimizer.minimize(a, foo=False)\n"
|
text = "optimizer.minimize(a, foo=False)\n"
|
||||||
@ -617,10 +600,9 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
self.assertEqual(errors, [])
|
self.assertEqual(errors, [])
|
||||||
|
|
||||||
text = "optimizer.minimize(a, colocate_gradients_with_ops=False)\n"
|
text = "optimizer.minimize(a, colocate_gradients_with_ops=False)\n"
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual("optimizer.minimize(a)\n", new_text)
|
self.assertEqual("optimizer.minimize(a)\n", new_text)
|
||||||
self.assertIn("requires manual check", errors[0])
|
self.assertIn("Optimizer.minimize no longer takes", report)
|
||||||
self.assertIn("minimize", errors[0])
|
|
||||||
|
|
||||||
def testColocateGradientsWithOpsComputeGradients(self):
|
def testColocateGradientsWithOpsComputeGradients(self):
|
||||||
text = "optimizer.compute_gradients(a, foo=False)\n"
|
text = "optimizer.compute_gradients(a, foo=False)\n"
|
||||||
@ -629,10 +611,9 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
self.assertEqual(errors, [])
|
self.assertEqual(errors, [])
|
||||||
|
|
||||||
text = "optimizer.compute_gradients(a, colocate_gradients_with_ops=False)\n"
|
text = "optimizer.compute_gradients(a, colocate_gradients_with_ops=False)\n"
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual("optimizer.compute_gradients(a)\n", new_text)
|
self.assertEqual("optimizer.compute_gradients(a)\n", new_text)
|
||||||
self.assertIn("requires manual check", errors[0])
|
self.assertIn("Optimizer.compute_gradients no longer takes", report)
|
||||||
self.assertIn("compute_gradients", errors[0])
|
|
||||||
|
|
||||||
def testExportSavedModelRename(self):
|
def testExportSavedModelRename(self):
|
||||||
text = "self.est.export_savedmodel(path)"
|
text = "self.est.export_savedmodel(path)"
|
||||||
@ -831,7 +812,7 @@ bazel-bin/tensorflow/tools/compatibility/update/generate_v2_reorders_map
|
|||||||
"validate_indices, max_norm)")
|
"validate_indices, max_norm)")
|
||||||
expected_text = ("tf.nn.embedding_lookup(params=params, ids=ids, "
|
expected_text = ("tf.nn.embedding_lookup(params=params, ids=ids, "
|
||||||
"partition_strategy=partition_strategy, name=name, "
|
"partition_strategy=partition_strategy, name=name, "
|
||||||
"validate_indices=validate_indices, max_norm=max_norm)")
|
"max_norm=max_norm)")
|
||||||
_, unused_report, unused_errors, new_text = self._upgrade(text)
|
_, unused_report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual(new_text, expected_text)
|
self.assertEqual(new_text, expected_text)
|
||||||
|
|
||||||
@ -1029,29 +1010,43 @@ def _log_prob(self, x):
|
|||||||
"assert_scalar"]:
|
"assert_scalar"]:
|
||||||
text = "tf.%s(a)" % name
|
text = "tf.%s(a)" % name
|
||||||
expected_text = "tf.compat.v1.%s(a)" % name
|
expected_text = "tf.compat.v1.%s(a)" % name
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual(expected_text, new_text)
|
self.assertEqual(expected_text, new_text)
|
||||||
self.assertIn("assert_* functions", errors[0])
|
self.assertIn("%s has been" % name, report)
|
||||||
|
|
||||||
text = "tf.debugging.%s(a)" % name
|
text = "tf.debugging.%s(a)" % name
|
||||||
expected_text = "tf.compat.v1.debugging.%s(a)" % name
|
expected_text = "tf.compat.v1.debugging.%s(a)" % name
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual(expected_text, new_text)
|
self.assertEqual(expected_text, new_text)
|
||||||
self.assertIn("assert_* functions", errors[0])
|
self.assertIn("%s has been" % name, report)
|
||||||
|
|
||||||
def testAssertRankStatements(self):
|
def testAssertRankStatements(self):
|
||||||
for name in ["assert_rank", "assert_rank_at_least", "assert_rank_in"]:
|
for name in ["assert_rank", "assert_rank_at_least", "assert_rank_in"]:
|
||||||
text = "tf.%s(a)" % name
|
text = "tf.%s(a)" % name
|
||||||
expected_text = "tf.compat.v1.%s(a)" % name
|
expected_text = "tf.compat.v1.%s(a)" % name
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual(expected_text, new_text)
|
self.assertEqual(expected_text, new_text)
|
||||||
self.assertIn("assert_rank_* functions", errors[0])
|
self.assertIn("%s has been" % name, report)
|
||||||
|
|
||||||
text = "tf.debugging.%s(a)" % name
|
text = "tf.debugging.%s(a)" % name
|
||||||
expected_text = "tf.compat.v1.debugging.%s(a)" % name
|
expected_text = "tf.compat.v1.debugging.%s(a)" % name
|
||||||
_, unused_report, errors, new_text = self._upgrade(text)
|
_, report, unused_errors, new_text = self._upgrade(text)
|
||||||
self.assertEqual(expected_text, new_text)
|
self.assertEqual(expected_text, new_text)
|
||||||
self.assertIn("assert_rank_* functions", errors[0])
|
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)"
|
||||||
|
expected = "tf.test.assert_equal_graph_def(actual=a, expected=b)"
|
||||||
|
_, _, _, 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)
|
||||||
|
|
||||||
|
|
||||||
class TestUpgradeFiles(test_util.TensorFlowTestCase):
|
class TestUpgradeFiles(test_util.TensorFlowTestCase):
|
||||||
|
|||||||
Loading…
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Reference in New Issue
Block a user