def generate_event(self, values):
"""
Method for generating scalar event.
Args:
values (dict): A dict contains:
{
wall_time (float): Timestamp.
step (int): Train step.
value (float): Scalar value.
tag (str): Tag name.
}
Returns:
summary_pb2.Event.
"""
scalar_event = summary_pb2.Event()
scalar_event.wall_time = values.get('wall_time')
scalar_event.step = values.get('step')
value = scalar_event.summary.value.add()
value.tag = values.get('tag')
value.scalar_value = values.get('value')
return scalar_event
def generate_event(self, values):
"""
Method for generating tensor event.
Args:
values (dict): A dict contains:
{
wall_time (float): Timestamp.
step (int): Train step.
value (float): Tensor value.
tag (str): Tag name.
}
Returns:
summary_pb2.Event.
"""
tensor_event = summary_pb2.Event()
tensor_event.wall_time = values.get('wall_time')
tensor_event.step = values.get('step')
value = tensor_event.summary.value.add()
value.tag = values.get('tag')
tensor = values.get('value')
value.tensor.dims[:] = tensor.get('dims')
value.tensor.data_type = tensor.get('data_type')
value.tensor.float_data[:] = tensor.get('float_data')
print(tensor.get('float_data'))
return tensor_event
def generate_event(self, values):
"""
Method for generating image event.
Args:
values (dict): A dict contains:
{
wall_time (float): Timestamp.
step (int): Train step.
image (np.array): Pixels tensor.
tag (str): Tag name.
}
Returns:
summary_pb2.Event.
"""
image_event = summary_pb2.Event()
image_event.wall_time = values.get('wall_time')
image_event.step = values.get('step')
height, width, channel, image_string = self._get_image_string(
values.get('image'))
value = image_event.summary.value.add()
value.tag = values.get('tag')
value.image.height = height
value.image.width = width
value.image.colorspace = channel
value.image.encoded_image = image_string
return image_event
def create_lineage_info(train_event_dict, eval_event_dict, dataset_event_dict):
"""
Create parsed lineage info tuple.
Args:
train_event_dict (Union[dict, None]): The dict of train event.
eval_event_dict (Union[dict, None]): The dict of evaluation event.
dataset_event_dict (Union[dict, None]): The dict of dataset graph event.
Returns:
namedtuple, parsed lineage info.
"""
if train_event_dict is not None:
train_event = summary_pb2.Event()
ParseDict(train_event_dict, train_event)
else:
train_event = None
if eval_event_dict is not None:
eval_event = summary_pb2.Event()
ParseDict(eval_event_dict, eval_event)
else:
eval_event = None
if dataset_event_dict is not None:
dataset_event = summary_pb2.Event()
ParseDict(dataset_event_dict, dataset_event)
else:
dataset_event = None
lineage_info = LineageInfo(
train_lineage=train_event,
eval_lineage=eval_event,
dataset_graph=dataset_event,
)
return lineage_info
def generate_event(self, values):
"""
Method for generating graph event.
Args:
values (dict): Graph values. e.g. {'graph': graph_dict}.
Returns:
summary_pb2.Event.
"""
graph_json = {
'wall_time': time.time(),
'graph_def': values.get('graph'),
}
graph_event = json_format.Parse(json.dumps(graph_json), summary_pb2.Event())
return graph_event
def generate_event(self, values):
"""
Method for generating histogram event.
Args:
values (dict): A dict contains:
{
wall_time (float): Timestamp.
step (int): Train step.
value (float): Histogram value.
tag (str): Tag name.
}
Returns:
summary_pb2.Event.
"""
histogram_event = summary_pb2.Event()
histogram_event.wall_time = values.get('wall_time')
histogram_event.step = values.get('step')
value = histogram_event.summary.value.add()
value.tag = values.get('tag')
buckets = values.get('buckets')
for bucket in buckets:
left, width, count = bucket
bucket = value.histogram.buckets.add()
bucket.left = left
bucket.width = width
bucket.count = count
value.histogram.min = values.get("min", -1)
value.histogram.max = values.get("max", -1)
return histogram_event
