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https://github.com/clearml/clearml
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77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
# ClearML - Example of tensorboard with tensorflow (without any actual training)
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#
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import os
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import tensorflow as tf
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import numpy as np
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from tempfile import gettempdir
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from PIL import Image
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from clearml import Task
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def generate_summary(k, step):
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# Make a normal distribution, with a shifting mean
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mean_moving_normal = tf.random.normal(shape=[1000], mean=(5 * k), stddev=1)
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# Record that distribution into a histogram summary
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tf.summary.histogram("normal/moving_mean", mean_moving_normal, step=step)
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tf.summary.scalar("normal/value", mean_moving_normal[-1], step=step)
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# Make a normal distribution with shrinking variance
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variance_shrinking_normal = tf.random.normal(shape=[1000], mean=0, stddev=1-k)
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# Record that distribution too
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tf.summary.histogram("normal/shrinking_variance", variance_shrinking_normal, step=step)
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tf.summary.scalar("normal/variance_shrinking_normal", variance_shrinking_normal[-1], step=step)
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# Let's combine both of those distributions into one dataset
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normal_combined = tf.concat([mean_moving_normal, variance_shrinking_normal], 0)
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# We add another histogram summary to record the combined distribution
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tf.summary.histogram("normal/bimodal", normal_combined, step=step)
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tf.summary.scalar("normal/normal_combined", normal_combined[0], step=step)
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# Add a gamma distribution
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gamma = tf.random.gamma(shape=[1000], alpha=k)
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tf.summary.histogram("gamma", gamma, step=step)
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# And a poisson distribution
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poisson = tf.random.poisson(shape=[1000], lam=k)
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tf.summary.histogram("poisson", poisson, step=step)
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# And a uniform distribution
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uniform = tf.random.uniform(shape=[1000], maxval=k*10)
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tf.summary.histogram("uniform", uniform, step=step)
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# Finally, combine everything together!
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all_distributions = [mean_moving_normal, variance_shrinking_normal, gamma, poisson, uniform]
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all_combined = tf.concat(all_distributions, 0)
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tf.summary.histogram("all_combined", all_combined, step=step)
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# Log text value
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tf.summary.text("this is a test", "This is the content", step=step)
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# convert to 4d [batch, col, row, RGB-channels]
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image_open = Image.open(os.path.join('..', '..', 'reporting', 'data_samples', 'picasso.jpg'))
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image = np.asarray(image_open)
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image_gray = image[:, :, 0][np.newaxis, :, :, np.newaxis]
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image_rgba = np.concatenate((image, 255*np.atleast_3d(np.ones(shape=image.shape[:2], dtype=np.uint8))), axis=2)
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image_rgba = image_rgba[np.newaxis, :, :, :]
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image = image[np.newaxis, :, :, :]
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tf.summary.image("test", image, max_outputs=10, step=step)
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tf.summary.image("test_gray", image_gray, max_outputs=10, step=step)
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tf.summary.image("test_rgba", image_rgba, max_outputs=10, step=step)
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task = Task.init(project_name='examples', task_name='TensorbBoard toy example')
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# create the tensorboard file writer in a temp folder
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writer = tf.summary.create_file_writer(os.path.join(gettempdir(), "toy_tb_example"))
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# Setup a loop and write the summaries to disk
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N = 40
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for step in range(N):
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k_val = step/float(N)
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with writer.as_default():
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generate_summary(k_val, tf.cast(step, tf.int64))
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print('Tensorboard toy example done')
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