def _CompressHistogram(self, histo_ev): """Callback for _ProcessHistogram.""" return CompressedHistogramEvent( histo_ev.wall_time, histo_ev.step, compressor.compress_histogram_proto( histo_ev.histogram_value, self._compression_bps))
def _CompressHistogram(self, histo_ev): """Callback for _ProcessHistogram.""" return CompressedHistogramEvent( histo_ev.wall_time, histo_ev.step, compressor.compress_histogram_proto( histo_ev.histogram_value, self._compression_bps))
def aggregate(path, run_wanted=0): keys = [ #'Eval/Reward', # 'Eval/Length', # 'Eval/MCTS_Confidence', #'Train/AvgLoss' # 'Actor/Sample_length', # 'Train/Samples' 'Train/Shark_Speed_H' ] agg_keys = {k: [] for k in keys} path = glob.glob(os.path.join(path, "events.out.tfevents.*")) if not path: return None, None path = path[0] import tensorflow as tf i = 0 j = 0 str_ = '' for event in my_summary_iterator(path): val = event.summary.value if not val: continue tag = event.summary.value[0].tag for key in keys: if tag.startswith(key): # run = tag[tag.rfind('/')+1:] # if int(run) != run_wanted: # continue x = compress_histogram_proto(event.summary.value[0].histo) # import pdb; pdb.set_trace() agg_keys[key].append([y.value for y in x]) if i in [1, 744, 988, 2288, 2971]: arr = tf.make_ndarray(event.summary.value[0].tensor) X = (arr[:, 0] + arr[:, 1]) / 2. Y = np.vstack([X, arr[:, 2]]).T.tolist() str_ += '\n\\addplot3 [area plot] coordinates {' for x_ in Y: str_ += str((x_[0], j, x_[1])) str_ += '};\n' j += 1 i += 1 print(str_) import pdb pdb.set_trace() return agg_keys
def _read_tb_data(self): """Read data from a tensorboard file""" files = self._get_tb_files() x, y = [], [] scalar = None # Read TB file for file in files: # Traverse events/summaries for e in tf.train.summary_iterator(file): # Traverse every value in the summary for v in e.summary.value: if self.tb_tag == v.tag: if scalar is None: if v.HasField("simple_value"): scalar = True elif v.HasField("histo"): scalar = False else: raise ValueError( "Only simple_value and histo data can be processed from TB file" ) x.append(e.step) if scalar: y.append(v.simple_value) else: # Convert to compressed histogram y.append([ chv.value for chv in compress_histogram_proto(v.histo) ]) # Check for correct parsing assert len(x) > 0 and len(y) > 0, "Parsing TB incorrect:" "len(x)=%d, len(y)=%d\nx: %s\ny: %s" % (len(x), len(y), x, y) # Sort the lists by step x, y = (list(t) for t in zip(*sorted(zip(x, y), key=lambda t: t[0]))) return dict(x=x, y=y, i=None)
def aggregate(path, run_wanted=0): keys = [ #'Eval/Reward', # 'Eval/Length', # 'Eval/MCTS_Confidence', #'Train/AvgLoss' 'Actor/Sample_length', 'Train/Samples' ] agg_keys = {k: [] for k in keys} path = os.path.join(path, "event.tfevents") for event in my_summary_iterator(path): tag = event.summary.value[0].tag for key in keys: if tag.startswith(key): run = tag[tag.rfind('/') + 1:] if int(run) != run_wanted: continue x = compress_histogram_proto(event.summary.value[0].histo) agg_keys[key].append([y.value for y in x]) return agg_keys