def tf_summary_to_dict(tf_summary_str_or_pb, namespace=""):
"""Convert a Tensorboard Summary to a dictionary
Accepts either a tensorflow.summary.Summary
or one encoded as a string.
"""
values = {}
if isinstance(tf_summary_str_or_pb, Summary):
summary_pb = tf_summary_str_or_pb
elif isinstance(tf_summary_str_or_pb, Event):
summary_pb = tf_summary_str_or_pb.summary
values["global_step"] = tf_summary_str_or_pb.step
values["_timestamp"] = tf_summary_str_or_pb.wall_time
else:
summary_pb = Summary()
summary_pb.ParseFromString(tf_summary_str_or_pb)
for value in summary_pb.value:
kind = value.WhichOneof("value")
if kind == "simple_value":
values[namespaced_tag(value.tag, namespace)] = value.simple_value
elif kind == "image":
from PIL import Image
image = wandb.Image(
Image.open(six.BytesIO(value.image.encoded_image_string)))
tag_idx = value.tag.rsplit('/', 1)
if len(tag_idx) > 1 and tag_idx[1].isdigit():
tag, idx = tag_idx
values.setdefault(history_image_key(tag), []).append(image)
else:
values[history_image_key(value.tag)] = image
# Coming soon...
# elif kind == "audio":
# audio = wandb.Audio(six.BytesIO(value.audio.encoded_audio_string),
# sample_rate=value.audio.sample_rate, content_type=value.audio.content_type)
elif kind == "histo":
first = value.histo.bucket_limit[0] + \
value.histo.bucket_limit[0] - value.histo.bucket_limit[1]
last = value.histo.bucket_limit[-2] + \
value.histo.bucket_limit[-2] - value.histo.bucket_limit[-3]
np_histogram = (list(value.histo.bucket),
[first] + value.histo.bucket_limit[:-1] + [last])
values[namespaced_tag(
value.tag)] = wandb.Histogram(np_histogram=np_histogram)
return values
def tf_summary_to_dict(tf_summary_str_or_pb, namespace=""):
"""Convert a Tensorboard Summary to a dictionary
Accepts either a tensorflow.summary.Summary
or one encoded as a string.
"""
values = {}
if hasattr(tf_summary_str_or_pb, "summary"):
summary_pb = tf_summary_str_or_pb.summary
values[namespaced_tag("global_step",
namespace)] = tf_summary_str_or_pb.step
values["_timestamp"] = tf_summary_str_or_pb.wall_time
elif isinstance(tf_summary_str_or_pb, (str, bytes, bytearray)):
summary_pb = Summary()
summary_pb.ParseFromString(tf_summary_str_or_pb)
else:
if not hasattr(tf_summary_str_or_pb, "value"):
raise ValueError(
"Can't log %s, only Event, Summary, or Summary proto buffer strings are accepted"
)
else:
summary_pb = tf_summary_str_or_pb
for value in summary_pb.value:
kind = value.WhichOneof("value")
if kind in IGNORE_KINDS:
continue
if kind == "simple_value":
values[namespaced_tag(value.tag, namespace)] = value.simple_value
elif kind == "tensor":
values[namespaced_tag(value.tag,
namespace)] = make_ndarray(value.tensor)
elif kind == "image":
from PIL import Image
img_str = value.image.encoded_image_string
# Supports gifs from TboardX
if img_str.startswith(b"GIF"):
image = wandb.Video(six.BytesIO(img_str), format="gif")
else:
image = wandb.Image(Image.open(six.BytesIO(img_str)))
tag_idx = value.tag.rsplit('/', 1)
if len(tag_idx) > 1 and tag_idx[1].isdigit():
tag, idx = tag_idx
values.setdefault(history_image_key(tag, namespace),
[]).append(image)
else:
values[history_image_key(value.tag, namespace)] = [image]
# Coming soon...
# elif kind == "audio":
# audio = wandb.Audio(six.BytesIO(value.audio.encoded_audio_string),
# sample_rate=value.audio.sample_rate, content_type=value.audio.content_type)
elif kind == "histo":
tag = namespaced_tag(value.tag, namespace)
if len(value.histo.bucket_limit) >= 3:
first = value.histo.bucket_limit[0] + \
value.histo.bucket_limit[0] - value.histo.bucket_limit[1]
last = value.histo.bucket_limit[-2] + \
value.histo.bucket_limit[-2] - value.histo.bucket_limit[-3]
np_histogram = (list(value.histo.bucket), [first] +
value.histo.bucket_limit[:-1] + [last])
try:
#TODO: we should just re-bin if there are too many buckets
values[tag] = wandb.Histogram(np_histogram=np_histogram)
except ValueError:
wandb.termwarn(
"Not logging key \"{}\". Histograms must have fewer than {} bins"
.format(tag, wandb.Histogram.MAX_LENGTH),
repeat=False)
else:
#TODO: is there a case where we can render this?
wandb.termwarn(
"Not logging key \"{}\". Found a histogram with only 2 bins."
.format(tag),
repeat=False)
elif value.tag == "_hparams_/session_start_info":
if wandb.util.get_module("tensorboard.plugins.hparams"):
from tensorboard.plugins.hparams import plugin_data_pb2
plugin_data = plugin_data_pb2.HParamsPluginData()
plugin_data.ParseFromString(value.metadata.plugin_data.content)
for key, param in six.iteritems(
plugin_data.session_start_info.hparams):
if not wandb.run.config.get(key):
wandb.run.config[
key] = param.number_value or param.string_value or param.bool_value
else:
wandb.termerror(
"Received hparams tf.summary, but could not import the hparams plugin from tensorboard"
)
return values
def tf_summary_to_dict(tf_summary_str_or_pb, namespace=""):
"""Convert a Tensorboard Summary to a dictionary
Accepts either a tensorflow.summary.Summary
or one encoded as a string.
"""
values = {}
if hasattr(tf_summary_str_or_pb, "summary"):
summary_pb = tf_summary_str_or_pb.summary
values[namespaced_tag("global_step",
namespace)] = tf_summary_str_or_pb.step
values["_timestamp"] = tf_summary_str_or_pb.wall_time
elif isinstance(tf_summary_str_or_pb, (str, bytes, bytearray)):
summary_pb = Summary()
summary_pb.ParseFromString(tf_summary_str_or_pb)
else:
if not hasattr(tf_summary_str_or_pb, "value"):
raise ValueError(
"Can't log %s, only Event, Summary, or Summary proto buffer strings are accepted"
)
else:
summary_pb = tf_summary_str_or_pb
for value in summary_pb.value:
kind = value.WhichOneof("value")
if kind == "simple_value":
values[namespaced_tag(value.tag, namespace)] = value.simple_value
elif kind == "image":
from PIL import Image
image = wandb.Image(
Image.open(six.BytesIO(value.image.encoded_image_string)))
tag_idx = value.tag.rsplit('/', 1)
if len(tag_idx) > 1 and tag_idx[1].isdigit():
tag, idx = tag_idx
values.setdefault(history_image_key(tag, namespace),
[]).append(image)
else:
values[history_image_key(value.tag, namespace)] = image
# Coming soon...
# elif kind == "audio":
# audio = wandb.Audio(six.BytesIO(value.audio.encoded_audio_string),
# sample_rate=value.audio.sample_rate, content_type=value.audio.content_type)
elif kind == "histo":
first = value.histo.bucket_limit[0] + \
value.histo.bucket_limit[0] - value.histo.bucket_limit[1]
last = value.histo.bucket_limit[-2] + \
value.histo.bucket_limit[-2] - value.histo.bucket_limit[-3]
np_histogram = (list(value.histo.bucket),
[first] + value.histo.bucket_limit[:-1] + [last])
values[namespaced_tag(
value.tag,
namespace)] = wandb.Histogram(np_histogram=np_histogram)
elif value.tag == "_hparams_/session_start_info":
if wandb.util.get_module("tensorboard.plugins.hparams"):
from tensorboard.plugins.hparams import plugin_data_pb2
plugin_data = plugin_data_pb2.HParamsPluginData()
plugin_data.ParseFromString(value.metadata.plugin_data.content)
for key, param in six.iteritems(
plugin_data.session_start_info.hparams):
if not wandb.run.config.get(key):
wandb.run.config[
key] = param.number_value or param.string_value or param.bool_value
else:
wandb.termerror(
"Received hparams tf.summary, but could not import the hparams plugin from tensorboard"
)
return values