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# ------------------------------------------------------------------------
# Copyright (c) 2023-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Tensorboard application."""
import time
import numpy as np
try:
import tensorflow as tf
except ImportError:
tf = None
class TensorBoard(object):
"""TensorBoard application."""
def __init__(self, log_dir=None):
"""Create a summary writer logging to log_dir."""
if tf is None:
raise ImportError("Failed to import ``tensorflow`` package.")
tf.config.set_visible_devices([], "GPU")
if log_dir is None:
log_dir = "./logs/" + time.strftime("%Y%m%d_%H%M%S", time.localtime(time.time()))
self.writer = tf.summary.create_file_writer(log_dir)
@staticmethod
def is_available():
"""Return if tensor board is available."""
return tf is not None
def close(self):
"""Close board and apply all cached summaries."""
self.writer.close()
def histogram_summary(self, tag, values, step, buckets=10):
"""Write a histogram of values."""
with self.writer.as_default():
tf.summary.histogram(tag, values, step, buckets=buckets)
def image_summary(self, tag, images, step, order="BGR"):
"""Write a list of images."""
if isinstance(images, (tuple, list)):
images = np.stack(images)
if len(images.shape) != 4:
raise ValueError("Images can not be packed to (N, H, W, C).")
if order == "BGR":
images = images[:, :, :, ::-1]
with self.writer.as_default():
tf.summary.image(tag, images, step, max_outputs=images.shape[0])
def scalar_summary(self, tag, value, step):
"""Write a scalar."""
with self.writer.as_default():
tf.summary.scalar(tag, value, step)
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