def _register_this_run_info(self, curses_cli):
curses_cli.register_command_handler(
"run",
self._run_handler,
self._argparsers["run"].format_help(),
prefix_aliases=["r"])
curses_cli.register_command_handler(
"invoke_stepper",
self._on_run_start_step_handler,
self._argparsers["invoke_stepper"].format_help(),
prefix_aliases=["s"])
curses_cli.register_command_handler(
"run_info",
self._run_info_handler,
self._argparsers["run_info"].format_help(),
prefix_aliases=["ri"])
curses_cli.register_command_handler(
"print_feed",
self._print_feed_handler,
self._argparsers["print_feed"].format_help(),
prefix_aliases=["pf"])
if self._tensor_filters:
# Register tab completion for the filter names.
curses_cli.register_tab_comp_context(["run", "r"],
list(self._tensor_filters.keys()))
if self._feed_dict:
# Register tab completion for feed_dict keys.
feed_keys = [common.get_graph_element_name(key)
for key in self._feed_dict.keys()]
curses_cli.register_tab_comp_context(["print_feed", "pf"], feed_keys)
def _register_this_run_info(self, curses_cli):
curses_cli.register_command_handler(
"run",
self._run_handler,
self._argparsers["run"].format_help(),
prefix_aliases=["r"])
curses_cli.register_command_handler(
"invoke_stepper",
self._on_run_start_step_handler,
self._argparsers["invoke_stepper"].format_help(),
prefix_aliases=["s"])
curses_cli.register_command_handler(
"run_info",
self._run_info_handler,
self._argparsers["run_info"].format_help(),
prefix_aliases=["ri"])
curses_cli.register_command_handler(
"print_feed",
self._print_feed_handler,
self._argparsers["print_feed"].format_help(),
prefix_aliases=["pf"])
if self._tensor_filters:
# Register tab completion for the filter names.
curses_cli.register_tab_comp_context(["run", "r"],
list(self._tensor_filters.keys()))
if self._feed_dict:
# Register tab completion for feed_dict keys.
feed_keys = [common.get_graph_element_name(key)
for key in self._feed_dict.keys()]
curses_cli.register_tab_comp_context(["print_feed", "pf"], feed_keys)
def _print_feed_handler(self, args, screen_info=None):
np_printoptions = cli_shared.numpy_printoptions_from_screen_info(
screen_info)
if not self._feed_dict:
return cli_shared.error(
"The feed_dict of the current run is None or empty.")
parsed = self._argparsers["print_feed"].parse_args(args)
tensor_name, tensor_slicing = (
command_parser.parse_tensor_name_with_slicing(parsed.tensor_name))
feed_key = None
feed_value = None
for key in self._feed_dict:
key_name = common.get_graph_element_name(key)
if key_name == tensor_name:
feed_key = key_name
feed_value = self._feed_dict[key]
break
if feed_key is None:
return cli_shared.error(
"The feed_dict of the current run does not contain the key %s" %
tensor_name)
else:
return cli_shared.format_tensor(
feed_value,
feed_key + " (feed)",
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=cli_shared.parse_ranges_highlight(parsed.ranges),
include_numeric_summary=parsed.numeric_summary)
def _print_feed_handler(self, args, screen_info=None):
np_printoptions = cli_shared.numpy_printoptions_from_screen_info(
screen_info)
if not self._feed_dict:
return cli_shared.error(
"The feed_dict of the current run is None or empty.")
parsed = self._argparsers["print_feed"].parse_args(args)
tensor_name, tensor_slicing = (
command_parser.parse_tensor_name_with_slicing(parsed.tensor_name))
feed_key = None
feed_value = None
for key in self._feed_dict:
key_name = common.get_graph_element_name(key)
if key_name == tensor_name:
feed_key = key_name
feed_value = self._feed_dict[key]
break
if feed_key is None:
return cli_shared.error(
"The feed_dict of the current run does not contain the key %s" %
tensor_name)
else:
return cli_shared.format_tensor(
feed_value,
feed_key + " (feed)",
np_printoptions,
print_all=parsed.print_all,
tensor_slicing=tensor_slicing,
highlight_options=cli_shared.parse_ranges_highlight(parsed.ranges),
include_numeric_summary=parsed.numeric_summary)
def get_run_short_description(run_call_count,
fetches,
feed_dict,
is_callable_runner=False):
"""Get a short description of the run() call.
Args:
run_call_count: (int) Run call counter.
fetches: Fetches of the `Session.run()` call. See doc of `Session.run()`
for more details.
feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
for more details.
is_callable_runner: (bool) whether a runner returned by
Session.make_callable is being run.
Returns:
(str) A short description of the run() call, including information about
the fetche(s) and feed(s).
"""
if is_callable_runner:
return "runner from make_callable()"
description = "run #%d: " % run_call_count
if isinstance(fetches, (ops.Tensor, ops.Operation, variables.Variable)):
description += "1 fetch (%s); " % common.get_graph_element_name(
fetches)
else:
# Could be (nested) list, tuple, dict or namedtuple.
num_fetches = len(common.get_flattened_names(fetches))
if num_fetches > 1:
description += "%d fetches; " % num_fetches
else:
description += "%d fetch; " % num_fetches
if not feed_dict:
description += "0 feeds"
else:
if len(feed_dict) == 1:
for key in feed_dict:
description += "1 feed (%s)" % (
key if isinstance(key, six.string_types)
or not hasattr(key, "name") else key.name)
else:
description += "%d feeds" % len(feed_dict)
return description
def get_run_short_description(run_call_count,
fetches,
feed_dict,
is_callable_runner=False):
"""Get a short description of the run() call.
Args:
run_call_count: (int) Run call counter.
fetches: Fetches of the `Session.run()` call. See doc of `Session.run()`
for more details.
feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
for more details.
is_callable_runner: (bool) whether a runner returned by
Session.make_callable is being run.
Returns:
(str) A short description of the run() call, including information about
the fetche(s) and feed(s).
"""
if is_callable_runner:
return "runner from make_callable()"
description = "run #%d: " % run_call_count
if isinstance(fetches, (ops.Tensor, ops.Operation, variables.Variable)):
description += "1 fetch (%s); " % common.get_graph_element_name(fetches)
else:
# Could be (nested) list, tuple, dict or namedtuple.
num_fetches = len(common.get_flattened_names(fetches))
if num_fetches > 1:
description += "%d fetches; " % num_fetches
else:
description += "%d fetch; " % num_fetches
if not feed_dict:
description += "0 feeds"
else:
if len(feed_dict) == 1:
for key in feed_dict:
description += "1 feed (%s)" % (
key if isinstance(key, six.string_types) or not hasattr(key, "name")
else key.name)
else:
description += "%d feeds" % len(feed_dict)
return description
def get_run_start_intro(run_call_count,
fetches,
feed_dict,
tensor_filters,
is_callable_runner=False):
"""Generate formatted intro for run-start UI.
Args:
run_call_count: (int) Run call counter.
fetches: Fetches of the `Session.run()` call. See doc of `Session.run()`
for more details.
feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
for more details.
tensor_filters: (dict) A dict from tensor-filter name to tensor-filter
callable.
is_callable_runner: (bool) whether a runner returned by
Session.make_callable is being run.
Returns:
(RichTextLines) Formatted intro message about the `Session.run()` call.
"""
fetch_lines = common.get_flattened_names(fetches)
if not feed_dict:
feed_dict_lines = [debugger_cli_common.RichLine(" (Empty)")]
else:
feed_dict_lines = []
for feed_key in feed_dict:
feed_key_name = common.get_graph_element_name(feed_key)
feed_dict_line = debugger_cli_common.RichLine(" ")
feed_dict_line += debugger_cli_common.RichLine(
feed_key_name,
debugger_cli_common.MenuItem(None, "pf '%s'" % feed_key_name))
# Surround the name string with quotes, because feed_key_name may contain
# spaces in some cases, e.g., SparseTensors.
feed_dict_lines.append(feed_dict_line)
feed_dict_lines = debugger_cli_common.rich_text_lines_from_rich_line_list(
feed_dict_lines)
out = debugger_cli_common.RichTextLines(_HORIZONTAL_BAR)
if is_callable_runner:
out.append("Running a runner returned by Session.make_callable()")
else:
out.append("Session.run() call #%d:" % run_call_count)
out.append("")
out.append("Fetch(es):")
out.extend(debugger_cli_common.RichTextLines(
[" " + line for line in fetch_lines]))
out.append("")
out.append("Feed dict:")
out.extend(feed_dict_lines)
out.append(_HORIZONTAL_BAR)
out.append("")
out.append("Select one of the following commands to proceed ---->")
out.extend(
_recommend_command(
"run",
"Execute the run() call with debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -n",
"Execute the run() call without debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -t <T>",
"Execute run() calls (T - 1) times without debugging, then "
"execute run() once more with debugging and drop back to the CLI"))
out.extend(
_recommend_command(
"run -f <filter_name>",
"Keep executing run() calls until a dumped tensor passes a given, "
"registered filter (conditional breakpoint mode)"))
more_lines = [" Registered filter(s):"]
if tensor_filters:
filter_names = []
for filter_name in tensor_filters:
filter_names.append(filter_name)
command_menu_node = debugger_cli_common.MenuItem(
"", "run -f %s" % filter_name)
more_lines.append(RL(" * ") + RL(filter_name, command_menu_node))
else:
more_lines.append(" (None)")
out.extend(
debugger_cli_common.rich_text_lines_from_rich_line_list(more_lines))
out.append("")
out.append_rich_line(RL("For more details, see ") +
RL("help.", debugger_cli_common.MenuItem("", "help")) +
".")
out.append("")
# Make main menu for the run-start intro.
menu = debugger_cli_common.Menu()
menu.append(debugger_cli_common.MenuItem("run", "run"))
menu.append(debugger_cli_common.MenuItem("exit", "exit"))
out.annotations[debugger_cli_common.MAIN_MENU_KEY] = menu
return out
def get_run_start_intro(run_call_count,
fetches,
feed_dict,
tensor_filters,
is_callable_runner=False):
"""Generate formatted intro for run-start UI.
Args:
run_call_count: (int) Run call counter.
fetches: Fetches of the `Session.run()` call. See doc of `Session.run()`
for more details.
feed_dict: Feeds to the `Session.run()` call. See doc of `Session.run()`
for more details.
tensor_filters: (dict) A dict from tensor-filter name to tensor-filter
callable.
is_callable_runner: (bool) whether a runner returned by
Session.make_callable is being run.
Returns:
(RichTextLines) Formatted intro message about the `Session.run()` call.
"""
fetch_lines = common.get_flattened_names(fetches)
if not feed_dict:
feed_dict_lines = [debugger_cli_common.RichLine(" (Empty)")]
else:
feed_dict_lines = []
for feed_key in feed_dict:
feed_key_name = common.get_graph_element_name(feed_key)
feed_dict_line = debugger_cli_common.RichLine(" ")
feed_dict_line += debugger_cli_common.RichLine(
feed_key_name,
debugger_cli_common.MenuItem(None, "pf '%s'" % feed_key_name))
# Surround the name string with quotes, because feed_key_name may contain
# spaces in some cases, e.g., SparseTensors.
feed_dict_lines.append(feed_dict_line)
feed_dict_lines = debugger_cli_common.rich_text_lines_from_rich_line_list(
feed_dict_lines)
out = debugger_cli_common.RichTextLines(_HORIZONTAL_BAR)
if is_callable_runner:
out.append("Running a runner returned by Session.make_callable()")
else:
out.append("Session.run() call #%d:" % run_call_count)
out.append("")
out.append("Fetch(es):")
out.extend(debugger_cli_common.RichTextLines(
[" " + line for line in fetch_lines]))
out.append("")
out.append("Feed dict:")
out.extend(feed_dict_lines)
out.append(_HORIZONTAL_BAR)
out.append("")
out.append("Select one of the following commands to proceed ---->")
out.extend(
_recommend_command(
"run",
"Execute the run() call with debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -n",
"Execute the run() call without debug tensor-watching",
create_link=True))
out.extend(
_recommend_command(
"run -t <T>",
"Execute run() calls (T - 1) times without debugging, then "
"execute run() once more with debugging and drop back to the CLI"))
out.extend(
_recommend_command(
"run -f <filter_name>",
"Keep executing run() calls until a dumped tensor passes a given, "
"registered filter (conditional breakpoint mode)"))
more_lines = [" Registered filter(s):"]
if tensor_filters:
filter_names = []
for filter_name in tensor_filters:
filter_names.append(filter_name)
command_menu_node = debugger_cli_common.MenuItem(
"", "run -f %s" % filter_name)
more_lines.append(RL(" * ") + RL(filter_name, command_menu_node))
else:
more_lines.append(" (None)")
out.extend(
debugger_cli_common.rich_text_lines_from_rich_line_list(more_lines))
out.extend(
_recommend_command(
"invoke_stepper",
"Use the node-stepper interface, which allows you to interactively "
"step through nodes involved in the graph run() call and "
"inspect/modify their values", create_link=True))
out.append("")
out.append_rich_line(RL("For more details, see ") +
RL("help.", debugger_cli_common.MenuItem("", "help")) +
".")
out.append("")
# Make main menu for the run-start intro.
menu = debugger_cli_common.Menu()
menu.append(debugger_cli_common.MenuItem("run", "run"))
menu.append(debugger_cli_common.MenuItem(
"invoke_stepper", "invoke_stepper"))
menu.append(debugger_cli_common.MenuItem("exit", "exit"))
out.annotations[debugger_cli_common.MAIN_MENU_KEY] = menu
return out
