def testReadableStrIncludesBAtTheEndOnRequest(self):
self.assertEqual("0B",
cli_shared.bytes_to_readable_str(0, include_b=True))
self.assertEqual(
"1.00kB", cli_shared.bytes_to_readable_str(1024, include_b=True))
self.assertEqual(
"1.00MB", cli_shared.bytes_to_readable_str(1024**2,
include_b=True))
self.assertEqual(
"1.00GB", cli_shared.bytes_to_readable_str(1024**3,
include_b=True))
def testReadableStrIncludesBAtTheEndOnRequest(self):
self.assertEqual("0B", cli_shared.bytes_to_readable_str(0, include_b=True))
self.assertEqual(
"1.00kB", cli_shared.bytes_to_readable_str(
1024, include_b=True))
self.assertEqual(
"1.00MB", cli_shared.bytes_to_readable_str(
1024**2, include_b=True))
self.assertEqual(
"1.00GB", cli_shared.bytes_to_readable_str(
1024**3, include_b=True))
def _measure_tensor_list_column_widths(self, data):
"""Determine the maximum widths of the timestamp and op-type column.
This method assumes that data is sorted in the default order, i.e.,
by ascending timestamps.
Args:
data: (list of DebugTensorDaum) the data based on which the maximum
column widths will be determined.
Returns:
(int) maximum width of the timestamp column. 0 if data is empty.
(int) maximum width of the dump size column. 0 if data is empty.
(int) maximum width of the op type column. 0 if data is empty.
"""
max_timestamp_width = 0
if data:
max_rel_time_ms = (data[-1].timestamp - self._debug_dump.t0) / 1000.0
max_timestamp_width = len("[%.3f] " % max_rel_time_ms)
max_dump_size_width = 0
for dump in data:
dump_size_str = cli_shared.bytes_to_readable_str(dump.dump_size_bytes)
if len(dump_size_str) + 1 > max_dump_size_width:
max_dump_size_width = len(dump_size_str) + 1
max_op_type_width = 0
for dump in data:
op_type = self._debug_dump.node_op_type(dump.node_name)
if len(op_type) > max_op_type_width:
max_op_type_width = len(op_type)
return max_timestamp_width, max_dump_size_width, max_op_type_width
def testSizeAboveOneGigaByteWorks(self):
self.assertEqual("1.00G", cli_shared.bytes_to_readable_str(1024**3))
self.assertEqual("2000.00G",
cli_shared.bytes_to_readable_str(1024**3 * 2000))
def testSizesBetweenOneMegaByteandOneGigaByteWorks(self):
self.assertEqual("1.00M", cli_shared.bytes_to_readable_str(1024**2))
self.assertEqual("2.40M",
cli_shared.bytes_to_readable_str(int(1024**2 * 2.4)))
self.assertEqual("1023.00M",
cli_shared.bytes_to_readable_str(1024**2 * 1023))
def testSizesBelowOneKiloByteWorks(self):
self.assertEqual("0", cli_shared.bytes_to_readable_str(0))
self.assertEqual("500", cli_shared.bytes_to_readable_str(500))
self.assertEqual("1023", cli_shared.bytes_to_readable_str(1023))
def testNoneSizeWorks(self):
self.assertEqual(str(None), cli_shared.bytes_to_readable_str(None))
def testSizeAboveOneGigaByteWorks(self):
self.assertEqual("1.00G", cli_shared.bytes_to_readable_str(1024 ** 3))
self.assertEqual("2000.00G", cli_shared.bytes_to_readable_str(1024 ** 3 * 2000))
def testSizesBetweenOneMegaByteandOneGigaByteWorks(self):
self.assertEqual("1.00M", cli_shared.bytes_to_readable_str(1024 ** 2))
self.assertEqual("2.40M", cli_shared.bytes_to_readable_str(int(1024 ** 2 * 2.4)))
self.assertEqual("1023.00M", cli_shared.bytes_to_readable_str(1024 ** 2 * 1023))
def testSizesBelowOneKiloByteWorks(self):
self.assertEqual("0", cli_shared.bytes_to_readable_str(0))
self.assertEqual("500", cli_shared.bytes_to_readable_str(500))
self.assertEqual("1023", cli_shared.bytes_to_readable_str(1023))
def testNoneSizeWorks(self):
self.assertEqual(str(None), cli_shared.bytes_to_readable_str(None))
def list_tensors(self, args, screen_info=None):
"""Command handler for list_tensors.
List tensors dumped during debugged Session.run() call.
Args:
args: Command-line arguments, excluding the command prefix, as a list of
str.
screen_info: Optional dict input containing screen information such as
cols.
Returns:
Output text lines as a RichTextLines object.
"""
# TODO(cais): Add annotations of substrings for dumped tensor names, to
# facilitate on-screen highlighting/selection of node names.
_ = screen_info
parsed = self._arg_parsers["list_tensors"].parse_args(args)
output = []
filter_strs = []
if parsed.op_type_filter:
op_type_regex = re.compile(parsed.op_type_filter)
filter_strs.append("Op type regex filter: \"%s\"" % parsed.op_type_filter)
else:
op_type_regex = None
if parsed.node_name_filter:
node_name_regex = re.compile(parsed.node_name_filter)
filter_strs.append("Node name regex filter: \"%s\"" %
parsed.node_name_filter)
else:
node_name_regex = None
output = debugger_cli_common.RichTextLines(filter_strs)
output.append("")
if parsed.tensor_filter:
try:
filter_callable = self.get_tensor_filter(parsed.tensor_filter)
except ValueError:
output = cli_shared.error("There is no tensor filter named \"%s\"." %
parsed.tensor_filter)
_add_main_menu(output, node_name=None, enable_list_tensors=False)
return output
data_to_show = self._debug_dump.find(filter_callable)
else:
data_to_show = self._debug_dump.dumped_tensor_data
# TODO(cais): Implement filter by lambda on tensor value.
max_timestamp_width, max_dump_size_width, max_op_type_width = (
self._measure_tensor_list_column_widths(data_to_show))
# Sort the data.
data_to_show = self._sort_dump_data_by(
data_to_show, parsed.sort_by, parsed.reverse)
output.extend(
self._tensor_list_column_heads(parsed, max_timestamp_width,
max_dump_size_width, max_op_type_width))
dump_count = 0
for dump in data_to_show:
if node_name_regex and not node_name_regex.match(dump.node_name):
continue
if op_type_regex:
op_type = self._debug_dump.node_op_type(dump.node_name)
if not op_type_regex.match(op_type):
continue
rel_time = (dump.timestamp - self._debug_dump.t0) / 1000.0
dump_size_str = cli_shared.bytes_to_readable_str(dump.dump_size_bytes)
dumped_tensor_name = "%s:%d" % (dump.node_name, dump.output_slot)
op_type = self._debug_dump.node_op_type(dump.node_name)
line = "[%.3f]" % rel_time
line += " " * (max_timestamp_width - len(line))
line += dump_size_str
line += " " * (max_timestamp_width + max_dump_size_width - len(line))
line += op_type
line += " " * (max_timestamp_width + max_dump_size_width +
max_op_type_width - len(line))
line += " %s" % dumped_tensor_name
output.append(
line,
font_attr_segs=[(
len(line) - len(dumped_tensor_name), len(line),
debugger_cli_common.MenuItem("", "pt %s" % dumped_tensor_name))])
dump_count += 1
if parsed.tensor_filter:
output.prepend([
"%d dumped tensor(s) passing filter \"%s\":" %
(dump_count, parsed.tensor_filter)
])
else:
output.prepend(["%d dumped tensor(s):" % dump_count])
_add_main_menu(output, node_name=None, enable_list_tensors=False)
return output
