在 Matterport 上设置张量板 - Mask RCNN

Question

I am following this tutorial for image detection using Matterport repo. I tried following this guide and edited the code to

How can I edit the following code to visualize the tensorboard ?

import tensorflow as tf
import datetime
%load_ext tensorboard

sess = tf.Session()

file_writer = tf.summary.FileWriter('/path/to/logs', sess.graph)

And then in the model area

# prepare config
config = KangarooConfig()
config.display()

# define the model
model = MaskRCNN(mode='training', model_dir='./', config=config)
model.keras_model.metrics_tensors = []


# Tensorflow board
logdir = os.path.join(
    "logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)

# load weights (mscoco) and exclude the output layers
model.load_weights('mask_rcnn_coco.h5',
                   by_name=True,
                   exclude=[
                       "mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox",
                       "mrcnn_mask"
                   ])

# train weights (output layers or 'heads')
model.train(train_set,
            test_set,
            learning_rate=config.LEARNING_RATE,
            epochs=5,
            layers='heads')

I am not sure where to callbacks=[tensorboard_callback] ?

Answer 1

In your model.train, if you look closely in the source code documentation, there is parameter called custom_callbacks , which defaults to None .

It is there where you need to write your code, so to train with a custom callback, you will need to add this line of code:

model.train(train_set,
            test_set,
            learning_rate=config.LEARNING_RATE,
            custom_callbacks = [tensorboard_callback],
            epochs=5,
            layers='heads')

Answer 2

You only have to open Anaconda Prompt and write tensorboard --logdir= yourlogdirectory , where yourlogdirectory is the directory containing the model checkpoint.

It should look something like this: logs\\xxxxxx20200528T1755, where xxxx stands for the name you give to your configuration.

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