def test_log_custom_chart(wandb_init_run):
custom_chart = create_custom_chart(
"test_spec", wandb.Table(data=[[1, 2], [3, 4]], columns=["A", "B"]),
{}, {})
wandb.log({"my_custom_chart": custom_chart})
assert wandb.run._backend.history[0].get("my_custom_chart_table")
wandb.finish()
def tf_summary_to_dict(tf_summary_str_or_pb, namespace=""): # noqa: C901
"""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:
summary_pb = tf_summary_str_or_pb
if not hasattr(summary_pb, "value") or len(summary_pb.value) == 0:
# Ignore these, caller is responsible for handling None
return None
def encode_images(img_strs, value):
try:
from PIL import Image
except ImportError:
wandb.termwarn(
'Install pillow if you are logging images with Tensorboard. To install, run "pip install pillow".',
repeat=False,
)
return
if len(img_strs) == 0:
return
images = []
for img_str in img_strs:
# Supports gifs from TboardX
if img_str.startswith(b"GIF"):
images.append(wandb.Video(six.BytesIO(img_str), format="gif"))
else:
images.append(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),
[]).extend(images)
else:
values[history_image_key(value.tag, namespace)] = images
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":
plugin_name = value.metadata.plugin_data.plugin_name
if plugin_name == "scalars" or plugin_name == "":
values[namespaced_tag(value.tag,
namespace)] = make_ndarray(value.tensor)
elif plugin_name == "images":
img_strs = value.tensor.string_val[
2:] # First two items are dims.
encode_images(img_strs, value)
elif plugin_name == "histograms":
# https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/summary_v2.py#L15-L26
ndarray = make_ndarray(value.tensor)
shape = ndarray.shape
counts = []
bins = []
if shape[0] > 1:
bins.append(ndarray[0][0]) # Add the left most edge
for v in ndarray:
counts.append(v[2])
bins.append(v[1]) # Add the right most edges
elif shape[0] == 1:
counts = [ndarray[0][2]]
bins = ndarray[0][:2]
if len(counts) > 0:
values[namespaced_tag(
value.tag,
namespace)] = wandb.Histogram(np_histogram=(counts,
bins))
elif plugin_name == "pr_curves":
pr_curve_data = make_ndarray(value.tensor)
precision = pr_curve_data[-2, :].tolist()
recall = pr_curve_data[-1, :].tolist()
# TODO: (kdg) implement spec for showing additional info in tool tips
# true_pos = pr_curve_data[1,:]
# false_pos = pr_curve_data[2,:]
# true_neg = pr_curve_data[1,:]
# false_neg = pr_curve_data[1,:]
# threshold = [1.0 / n for n in range(len(true_pos), 0, -1)]
# min of each in case tensorboard ever changes their pr_curve
# to allow for different length outputs
data = []
for i in range(min(len((precision)), len(recall))):
# drop additional threshold values if they exist
if precision[i] != 0 or recall[i] != 0:
data.append((recall[i], precision[i]))
# sort data so custom chart looks the same as tb generated pr curve
# ascending recall, descending precision for the same recall values
data = sorted(data, key=lambda x: (x[0], -x[1]))
data_table = wandb.Table(data=data,
columns=["recall", "precision"])
values[namespaced_tag(value.tag,
namespace)] = create_custom_chart(
"wandb/line/v0",
data_table,
{
"x": "recall",
"y": "precision"
},
{"title": "Precision v. Recall"},
)
elif kind == "image":
img_str = value.image.encoded_image_string
encode_images([img_str], value)
# 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] # noqa: W503
- value.histo.bucket_limit[1] # noqa: W503
)
last = (
value.histo.bucket_limit[-2] +
value.histo.bucket_limit[-2] # noqa: W503
- value.histo.bucket_limit[-3] # noqa: W503
)
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,
)
# TODO(jhr): figure out how to share this between userspace and internal process or dont
# 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