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import click
import collections
import logging
import numpy as np
import os
from caffe2.proto import caffe2_pb2
from caffe2.python import core
import caffe2.contrib.tensorboard.tensorboard_exporter as tb_exporter
try:
# tensorboard>=1.14.0
from tensorboard.compat.proto.summary_pb2 import Summary, HistogramProto
from tensorboard.compat.proto.event_pb2 import Event
from tensorboard.summary.writer.event_file_writer import EventFileWriter as FileWriter
except ImportError:
from tensorflow.core.framework.summary_pb2 import Summary, HistogramProto
from tensorflow.core.util.event_pb2 import Event
try:
# tensorflow>=1.0.0
from tensorflow.summary import FileWriter
except ImportError:
# tensorflow<=0.12.1
from tensorflow.train import SummaryWriter as FileWriter
class Config(object):
HEIGHT = 600
ASPECT_RATIO = 1.6
CODE_TEMPLATE = """
<script>
function load() {{
document.getElementById("{id}").pbtxt = {data};
}}
</script>
<link rel="import"
href="https://tensorboard.appspot.com/tf-graph-basic.build.html"
onload=load()
>
<div style="height:{height}px">
<tf-graph-basic id="{id}"></tf-graph-basic>
</div>
"""
IFRAME_TEMPLATE = """
<iframe
seamless
style="width:{width}px;height:{height}px;border:0"
srcdoc="{code}">
</iframe>
"""
def _show_graph(graph_def):
import IPython.display
code = CODE_TEMPLATE.format(
data=repr(str(graph_def)),
id='graph' + str(np.random.rand()),
height=Config.HEIGHT)
iframe = IFRAME_TEMPLATE.format(
code=code.replace('"', '"'),
width=Config.HEIGHT * Config.ASPECT_RATIO,
height=Config.HEIGHT + 20)
IPython.display.display(IPython.display.HTML(iframe))
def visualize_cnn(cnn, **kwargs):
g = tb_exporter.cnn_to_graph_def(cnn, **kwargs)
_show_graph(g)
def visualize_net(nets, **kwargs):
g = tb_exporter.nets_to_graph_def(nets, **kwargs)
_show_graph(g)
def visualize_ops(ops, **kwargs):
g = tb_exporter.ops_to_graph_def(ops, **kwargs)
_show_graph(g)
@click.group()
def cli():
pass
def write_events(tf_dir, events):
writer = FileWriter(tf_dir, len(events))
for event in events:
writer.add_event(event)
writer.flush()
writer.close()
def graph_def_to_event(step, graph_def):
return Event(
wall_time=step, step=step, graph_def=graph_def.SerializeToString())
@cli.command("tensorboard-graphs")
@click.option("--c2-netdef", type=click.Path(exists=True, dir_okay=False),
multiple=True)
@click.option("--tf-dir", type=click.Path(exists=True))
def tensorboard_graphs(c2_netdef, tf_dir):
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
def parse_net_def(path):
import google.protobuf.text_format # type: ignore[import]
net_def = caffe2_pb2.NetDef()
with open(path) as f:
google.protobuf.text_format.Merge(f.read(), net_def)
return core.Net(net_def)
graph_defs = [tb_exporter.nets_to_graph_def([parse_net_def(path)])
for path in c2_netdef]
events = [graph_def_to_event(i, graph_def)
for (i, graph_def) in enumerate(graph_defs, start=1)]
write_events(tf_dir, events)
log.info("Wrote %s graphs to logdir %s", len(events), tf_dir)
@cli.command("tensorboard-events")
@click.option("--c2-dir", type=click.Path(exists=True, file_okay=False),
help="Root directory of the Caffe2 run")
@click.option("--tf-dir", type=click.Path(writable=True),
help="Output path to the logdir used by TensorBoard")
def tensorboard_events(c2_dir, tf_dir):
np.random.seed(1701)
log = logging.getLogger(__name__)
log.setLevel(logging.INFO)
S = collections.namedtuple('S', ['min', 'max', 'mean', 'std'])
def parse_summary(filename):
try:
with open(filename) as f:
rows = [(float(el) for el in line.split()) for line in f]
return [S(*r) for r in rows]
except Exception as e:
log.exception(e)
return None
def get_named_summaries(root):
summaries = [
(fname, parse_summary(os.path.join(dirname, fname)))
for dirname, _, fnames in os.walk(root)
for fname in fnames
]
return [(n, s) for (n, s) in summaries if s]
def inferred_histo(summary, samples=1000):
np.random.seed(
hash(
summary.std + summary.mean + summary.min + summary.max
) % np.iinfo(np.int32).max
)
samples = np.random.randn(samples) * summary.std + summary.mean
samples = np.clip(samples, a_min=summary.min, a_max=summary.max)
(hist, edges) = np.histogram(samples)
upper_edges = edges[1:]
r = HistogramProto(
min=summary.min,
max=summary.max,
num=len(samples),
sum=samples.sum(),
sum_squares=(samples * samples).sum())
r.bucket_limit.extend(upper_edges)
r.bucket.extend(hist)
return r
def named_summaries_to_events(named_summaries):
names = [n for (n, _) in named_summaries]
summaries = [s for (_, s) in named_summaries]
summaries = list(zip(*summaries))
def event(step, values):
s = Summary()
scalar = [
Summary.Value(
tag="{}/{}".format(name, field),
simple_value=v)
for name, value in zip(names, values)
for field, v in value._asdict().items()]
hist = [
Summary.Value(
tag="{}/inferred_normal_hist".format(name),
histo=inferred_histo(value))
for name, value in zip(names, values)
]
s.value.extend(scalar + hist)
return Event(wall_time=int(step), step=step, summary=s)
return [event(step, values)
for step, values in enumerate(summaries, start=1)]
named_summaries = get_named_summaries(c2_dir)
events = named_summaries_to_events(named_summaries)
write_events(tf_dir, events)
log.info("Wrote %s events to logdir %s", len(events), tf_dir)
if __name__ == "__main__":
cli()
|
