mirror of
https://github.com/ARM-software/workload-automation.git
synced 2025-02-21 04:18:58 +00:00
Move UxperfParser into utils
UxperfParser class is moved from the UxperfResultProcessor class into a new python module in utils. This will help to use the UxperfParser even when the result procesor is not configured.
This commit is contained in:
jummp01
parent
3feb702898
commit
c38dc287ab
@ -14,17 +14,13 @@
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#
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import os
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import re
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import logging
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from collections import defaultdict
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from distutils.version import LooseVersion
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from wlauto import ResultProcessor, Parameter
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from wlauto.instrumentation import instrument_is_enabled
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from wlauto.instrumentation.fps import VSYNC_INTERVAL
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from wlauto.exceptions import ResultProcessorError, ConfigError
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from wlauto.utils.fps import FpsProcessor, SurfaceFlingerFrame, GfxInfoFrame
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from wlauto.utils.types import numeric, boolean
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from wlauto.utils.uxperf import UxPerfParser
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try:
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import pandas as pd
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@ -77,6 +73,7 @@ class UxPerfResultProcessor(ResultProcessor):
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]
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def initialize(self, context):
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# needed for uxperf parser
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if not pd or LooseVersion(pd.__version__) < LooseVersion('0.13.1'):
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message = ('uxperf result processor requires pandas Python package '
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'(version 0.13.1 or higher) to be installed.\n'
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@ -101,161 +98,3 @@ class UxPerfResultProcessor(ResultProcessor):
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if self.add_frames:
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self.logger.debug('Adding per-action frame metrics')
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parser.add_action_frames(framelog, self.drop_threshold, self.generate_csv)
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class UxPerfParser(object):
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'''
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Parses logcat messages for UX Performance markers.
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UX Performance markers are output from logcat under a debug priority. The
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logcat tag for the marker messages is UX_PERF. The messages associated with
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this tag consist of a name for the action to be recorded and a timestamp.
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These fields are delimited by a single space. e.g.
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<TAG> : <MESSAGE>
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UX_PERF : gestures_swipe_left_start 861975087367
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...
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...
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UX_PERF : gestures_swipe_left_end 862132085804
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Timestamps are produced using the running Java Virtual Machine's
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high-resolution time source, in nanoseconds.
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'''
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def __init__(self, context):
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self.context = context
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self.actions = defaultdict(list)
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self.logger = logging.getLogger('UxPerfParser')
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# regex for matching logcat message format:
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self.regex = re.compile(r'UX_PERF.*?:\s*(?P<message>.*\d+$)')
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def parse(self, log):
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'''
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Opens log file and parses UX_PERF markers.
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Actions delimited by markers are captured in a dictionary with
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actions mapped to timestamps.
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'''
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loglines = self._read(log)
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self._gen_action_timestamps(loglines)
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def add_action_frames(self, frames, drop_threshold, generate_csv): # pylint: disable=too-many-locals
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'''
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Uses FpsProcessor to parse frame.csv extracting fps, frame count, jank
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and vsync metrics on a per action basis. Adds results to metrics.
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'''
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refresh_period = self._parse_refresh_peroid()
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for action in self.actions:
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# default values
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fps, frame_count, janks, not_at_vsync = float('nan'), 0, 0, 0
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p90, p95, p99 = [float('nan')] * 3
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metrics = (fps, frame_count, janks, not_at_vsync)
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df = self._create_sub_df(self.actions[action], frames)
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if not df.empty: # pylint: disable=maybe-no-member
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fp = FpsProcessor(df, action=action)
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try:
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per_frame_fps, metrics = fp.process(refresh_period, drop_threshold)
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fps, frame_count, janks, not_at_vsync = metrics
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if generate_csv:
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name = action + '_fps'
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filename = name + '.csv'
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fps_outfile = os.path.join(self.context.output_directory, filename)
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per_frame_fps.to_csv(fps_outfile, index=False, header=True)
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self.context.add_artifact(name, path=filename, kind='data')
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p90, p95, p99 = fp.percentiles()
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except AttributeError:
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self.logger.warning('Non-matched timestamps in dumpsys output: action={}'
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.format(action))
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self.context.result.add_metric(action + '_FPS', fps)
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self.context.result.add_metric(action + '_frame_count', frame_count)
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self.context.result.add_metric(action + '_janks', janks, lower_is_better=True)
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self.context.result.add_metric(action + '_not_at_vsync', not_at_vsync, lower_is_better=True)
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self.context.result.add_metric(action + '_frame_time_90percentile', p90, 'ms', lower_is_better=True)
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self.context.result.add_metric(action + '_frame_time_95percentile', p95, 'ms', lower_is_better=True)
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self.context.result.add_metric(action + '_frame_time_99percentile', p99, 'ms', lower_is_better=True)
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def add_action_timings(self):
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'''
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Add simple action timings in millisecond resolution to metrics
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'''
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for action, timestamps in self.actions.iteritems():
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# nanosecond precision, but not necessarily nanosecond resolution
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# truncate to guarantee millisecond precision
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ts_ms = tuple(int(ts) for ts in timestamps)
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if len(ts_ms) == 2:
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start, finish = ts_ms
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duration = finish - start
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result = self.context.result
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result.add_metric(action + "_start", start, units='ms')
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result.add_metric(action + "_finish", finish, units='ms')
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result.add_metric(action + "_duration", duration, units='ms', lower_is_better=True)
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else:
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self.logger.warning('Expected two timestamps. Received {}'.format(ts_ms))
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def _gen_action_timestamps(self, lines):
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'''
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Parses lines and matches against logcat tag.
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Groups timestamps by action name.
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Creates a dictionary of lists with actions mapped to timestamps.
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'''
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for line in lines:
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match = self.regex.search(line)
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if match:
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message = match.group('message')
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action_with_suffix, timestamp = message.rsplit(' ', 1)
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action, _ = action_with_suffix.rsplit('_', 1)
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self.actions[action].append(timestamp)
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def _parse_refresh_peroid(self):
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'''
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Reads the first line of the raw dumpsys output for the refresh period.
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'''
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raw_path = os.path.join(self.context.output_directory, 'surfaceflinger.raw')
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if os.path.isfile(raw_path):
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raw_lines = self._read(raw_path)
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refresh_period = int(raw_lines.next())
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else:
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refresh_period = VSYNC_INTERVAL
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return refresh_period
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def _create_sub_df(self, action, frames):
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'''
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Creates a data frame containing fps metrics for a captured action.
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'''
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if len(action) == 2:
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start, end = map(int, action)
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df = pd.read_csv(frames)
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# SurfaceFlinger Algorithm
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if df.columns.tolist() == list(SurfaceFlingerFrame._fields): # pylint: disable=maybe-no-member
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field = 'actual_present_time'
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# GfxInfo Algorithm
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elif df.columns.tolist() == list(GfxInfoFrame._fields): # pylint: disable=maybe-no-member
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field = 'FrameCompleted'
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else:
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field = ''
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self.logger.error('frames.csv not in a recognised format. Cannot parse.')
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if field:
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df = df[start < df[field]]
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df = df[df[field] <= end]
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else:
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self.logger.warning('Discarding action. Expected 2 timestamps, got {}!'.format(len(action)))
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df = pd.DataFrame()
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return df
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def _read(self, log):
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'''
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Opens a file a yields the lines with whitespace stripped.
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'''
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try:
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with open(log, 'r') as rfh:
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for line in rfh:
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yield line.strip()
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except IOError:
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self.logger.error('Could not open {}'.format(log))
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170
wlauto/utils/uxperf.py
Normal file
170
wlauto/utils/uxperf.py
Normal file
@ -0,0 +1,170 @@
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import os
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import re
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import logging
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from collections import defaultdict
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from wlauto.utils.fps import FpsProcessor, SurfaceFlingerFrame, GfxInfoFrame, VSYNC_INTERVAL
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try:
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import pandas as pd
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except ImportError:
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pd = None
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class UxPerfParser(object):
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'''
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Parses logcat messages for UX Performance markers.
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UX Performance markers are output from logcat under a debug priority. The
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logcat tag for the marker messages is UX_PERF. The messages associated with
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this tag consist of a name for the action to be recorded and a timestamp.
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These fields are delimited by a single space. e.g.
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<TAG> : <MESSAGE>
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UX_PERF : gestures_swipe_left_start 861975087367
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...
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...
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UX_PERF : gestures_swipe_left_end 862132085804
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Timestamps are produced using the running Java Virtual Machine's
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high-resolution time source, in nanoseconds.
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'''
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def __init__(self, context, prefix=''):
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self.context = context
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self.prefix = prefix
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self.actions = defaultdict(list)
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self.logger = logging.getLogger('UxPerfParser')
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# regex for matching logcat message format:
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self.regex = re.compile(r'UX_PERF.*?:\s*(?P<message>.*\d+$)')
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def parse(self, log):
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'''
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Opens log file and parses UX_PERF markers.
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Actions delimited by markers are captured in a dictionary with
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actions mapped to timestamps.
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'''
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loglines = self._read(log)
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self._gen_action_timestamps(loglines)
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def add_action_frames(self, frames, drop_threshold, generate_csv): # pylint: disable=too-many-locals
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'''
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Uses FpsProcessor to parse frame.csv extracting fps, frame count, jank
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and vsync metrics on a per action basis. Adds results to metrics.
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'''
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refresh_period = self._parse_refresh_peroid()
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for action in self.actions:
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# default values
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fps, frame_count, janks, not_at_vsync = float('nan'), 0, 0, 0
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p90, p95, p99 = [float('nan')] * 3
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metrics = (fps, frame_count, janks, not_at_vsync)
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df = self._create_sub_df(self.actions[action], frames)
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if not df.empty: # pylint: disable=maybe-no-member
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fp = FpsProcessor(df, action=action)
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try:
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per_frame_fps, metrics = fp.process(refresh_period, drop_threshold)
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fps, frame_count, janks, not_at_vsync = metrics
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if generate_csv:
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name = action + '_fps'
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filename = name + '.csv'
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fps_outfile = os.path.join(self.context.output_directory, filename)
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per_frame_fps.to_csv(fps_outfile, index=False, header=True)
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self.context.add_artifact(name, path=filename, kind='data')
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p90, p95, p99 = fp.percentiles()
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except AttributeError:
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self.logger.warning('Non-matched timestamps in dumpsys output: action={}'
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.format(action))
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self.context.result.add_metric(self.prefix + action + '_FPS', fps)
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self.context.result.add_metric(self.prefix + action + '_frame_count', frame_count)
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self.context.result.add_metric(self.prefix + action + '_janks', janks, lower_is_better=True)
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self.context.result.add_metric(self.prefix + action + '_not_at_vsync', not_at_vsync, lower_is_better=True)
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self.context.result.add_metric(self.prefix + action + '_frame_time_90percentile', p90, 'ms', lower_is_better=True)
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self.context.result.add_metric(self.prefix + action + '_frame_time_95percentile', p95, 'ms', lower_is_better=True)
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self.context.result.add_metric(self.prefix + action + '_frame_time_99percentile', p99, 'ms', lower_is_better=True)
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def add_action_timings(self):
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'''
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Add simple action timings in millisecond resolution to metrics
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'''
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for action, timestamps in self.actions.iteritems():
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# nanosecond precision, but not necessarily nanosecond resolution
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# truncate to guarantee millisecond precision
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ts_ms = tuple(int(ts) for ts in timestamps)
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if len(ts_ms) == 2:
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start, finish = ts_ms
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duration = finish - start
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result = self.context.result
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result.add_metric(self.prefix + action + "_start", start, units='ms')
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result.add_metric(self.prefix + action + "_finish", finish, units='ms')
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result.add_metric(self.prefix + action + "_duration", duration, units='ms', lower_is_better=True)
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else:
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self.logger.warning('Expected two timestamps. Received {}'.format(ts_ms))
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def _gen_action_timestamps(self, lines):
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'''
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Parses lines and matches against logcat tag.
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Groups timestamps by action name.
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Creates a dictionary of lists with actions mapped to timestamps.
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'''
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for line in lines:
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match = self.regex.search(line)
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if match:
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message = match.group('message')
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action_with_suffix, timestamp = message.rsplit(' ', 1)
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action, _ = action_with_suffix.rsplit('_', 1)
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self.actions[action].append(timestamp)
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def _parse_refresh_peroid(self):
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'''
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Reads the first line of the raw dumpsys output for the refresh period.
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'''
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raw_path = os.path.join(self.context.output_directory, 'surfaceflinger.raw')
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if os.path.isfile(raw_path):
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raw_lines = self._read(raw_path)
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refresh_period = int(raw_lines.next())
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else:
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refresh_period = VSYNC_INTERVAL
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return refresh_period
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def _create_sub_df(self, action, frames):
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'''
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Creates a data frame containing fps metrics for a captured action.
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'''
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if len(action) == 2:
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start, end = map(int, action)
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df = pd.read_csv(frames)
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# SurfaceFlinger Algorithm
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if df.columns.tolist() == list(SurfaceFlingerFrame._fields): # pylint: disable=maybe-no-member
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field = 'actual_present_time'
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# GfxInfo Algorithm
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elif df.columns.tolist() == list(GfxInfoFrame._fields): # pylint: disable=maybe-no-member
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field = 'FrameCompleted'
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else:
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field = ''
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self.logger.error('frames.csv not in a recognised format. Cannot parse.')
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if field:
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df = df[start < df[field]]
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df = df[df[field] <= end]
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else:
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self.logger.warning('Discarding action. Expected 2 timestamps, got {}!'.format(len(action)))
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df = pd.DataFrame()
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return df
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def _read(self, log):
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'''
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Opens a file a yields the lines with whitespace stripped.
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'''
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try:
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with open(log, 'r') as rfh:
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for line in rfh:
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yield line.strip()
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except IOError:
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self.logger.error('Could not open {}'.format(log))
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