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Fix requirements for cs_gan. Update to new TFGAN code.
PiperOrigin-RevId: 277907210
This commit is contained in:
Mihaela Rosca
committed by
Diego de Las Casas
Diego de Las Casas
parent
82063f13bc
commit
9cf75990be
+23
-125
@@ -16,135 +16,33 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import tensorflow as tf
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import tensorflow_gan as tfgan
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def compute_inception_score(
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images, max_classifier_batch_size=16, assert_data_ranges=True):
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"""Computes the classifier score, using the given model.
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Note that this metric is highly sensitive to the number of images used
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to compute it: more is better. A standard number is 50K images.
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For more details see: https://arxiv.org/abs/1606.03498.
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Args:
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images: A 4D tensor.
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max_classifier_batch_size: An integer. The maximum batch size size to pass
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through the classifier used to compute the metric.
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assert_data_ranges: Whether or not to assert the input is in a valid
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data range.
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Returns:
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A scalar `tf.Tensor` with the Inception score.
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"""
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num_images = images.shape.as_list()[0]
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def _choose_batch_size(num_images, max_batch_size):
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for size in range(max_batch_size, 0, -1):
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if num_images % size == 0:
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return size
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batch_size = _choose_batch_size(num_images, max_classifier_batch_size)
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tf.logging.debug('Using batch_size=%s for score classifier', batch_size)
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num_batches = num_images // batch_size
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if assert_data_ranges:
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images_batch = images[0:batch_size]
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min_data_range_check = tf.assert_greater_equal(images_batch, 0.0)
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max_data_range_check = tf.assert_less_equal(images_batch, 1.0)
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control_deps = [min_data_range_check, max_data_range_check]
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else:
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control_deps = []
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# Inception module does resizing, if necessary.
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with tf.control_dependencies(control_deps):
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return tfgan.eval.run_inception(images, num_batches=num_batches)
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def compute_fid(
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real_images, other, max_classifier_batch_size=16,
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assert_data_ranges=True):
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"""Computes the classifier score, using the given model.
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Note that this metric is highly sensitive to the number of images used
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to compute it: more is better. A standard number is 50K images.
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For more details see: https://arxiv.org/abs/1606.03498.
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Args:
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real_images: A 4D tensor. Samples from the true data distribution.
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other: A 4D tensor. Data for which to compute the FID.
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max_classifier_batch_size: An integer. The maximum batch size size to pass
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through the classifier used to compute the metric.
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assert_data_ranges: Whether or not to assert the input is in a valid
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data range.
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Returns:
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A scalar `tf.Tensor` with the Inception score.
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"""
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num_images = real_images.shape.as_list()[0]
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def _choose_batch_size(num_images, max_batch_size):
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for size in range(max_batch_size, 0, -1):
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if num_images % size == 0:
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return size
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batch_size = _choose_batch_size(num_images, max_classifier_batch_size)
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tf.logging.debug('Using batch_size=%s for score classifier', batch_size)
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num_batches = num_images // batch_size
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if assert_data_ranges:
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images_batch = tf.concat(
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[real_images[0:batch_size], other[0:batch_size]], axis=0)
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min_data_range_check = tf.assert_greater_equal(images_batch, 0.0)
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max_data_range_check = tf.assert_less_equal(images_batch, 1.0)
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control_deps = [min_data_range_check, max_data_range_check]
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else:
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control_deps = []
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# Inception module does resizing, if necessary.
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with tf.control_dependencies(control_deps):
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return tfgan.eval.frechet_inception_distance(
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real_images, other, num_batches=num_batches)
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def generate_big_batch(generator, generator_inputs, max_num_samples=100):
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"""Generate samples when the number of samples is too big to do one pass."""
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num_samples = generator_inputs.shape.as_list()[0]
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max_num_samples = min(max_num_samples, num_samples)
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batched_shape = [num_samples // max_num_samples, max_num_samples]
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batched_shape += generator_inputs.shape.as_list()[1:]
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batched_generator_inputs = tf.reshape(generator_inputs, batched_shape)
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# Create the samples by sequentially doing forward passes through the
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# generator. This ensures we do not run out of memory.
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output_samples = tf.map_fn(
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fn=generator,
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elems=batched_generator_inputs,
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parallel_iterations=1,
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back_prop=False,
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swap_memory=True)
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samples = tf.reshape(output_samples,
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[num_samples] + output_samples.shape.as_list()[2:])
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return samples
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def get_image_metrics(real_images, other):
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return {
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'inception_score': compute_inception_score(other),
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'fid': compute_fid(real_images, other)}
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def get_image_metrics_for_samples(
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real_images, generator, prior, data_processor, num_eval_samples):
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generator_inputs = prior.sample(num_eval_samples)
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samples = generate_big_batch(generator, generator_inputs)
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# Ensure the samples are in [0, 1].
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samples = data_processor.postprocess(samples)
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"""Compute inception score and FID."""
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max_classifier_batch = 10
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num_batches = num_eval_samples // max_classifier_batch
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def sample_fn(arg):
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del arg
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samples = generator(prior.sample(max_classifier_batch))
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# Ensure data is in [0, 1], as expected by TFGAN.
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# Resizing to appropriate size is done by TFGAN.
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return data_processor.postprocess(samples)
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fake_outputs = tfgan.eval.sample_and_run_inception(
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sample_fn,
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sample_inputs=[1.0] * num_batches) # Dummy inputs.
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fake_logits = fake_outputs['logits']
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inception_score = tfgan.eval.classifier_score_from_logits(fake_logits)
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real_outputs = tfgan.eval.run_inception(real_images, num_batches=num_batches)
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fid = tfgan.eval.frechet_classifier_distance_from_activations(
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real_outputs['pool_3'], fake_outputs['pool_3'])
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return {
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'inception_score': compute_inception_score(samples),
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'fid': compute_fid(real_images, samples)}
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'inception_score': inception_score,
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'fid': fid}
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@@ -1,8 +1,7 @@
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absl-py==0.7.1
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dm-sonnet==1.34
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numpy==1.16.4
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pillow==4.3.0
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tensorflow==1.14
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Pillow
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tensorflow==1.15rc2
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tensorflow-probability==0.7.0
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tensorflow-gan==1.0.0.dev0
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tensorflow-gpu >= 1.11.0 # GPU version of TensorFlow.
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tensorflow-gan==2.0.0
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@@ -15,6 +15,7 @@
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python3 -m venv cs_gan_venv
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source cs_gan_venv/bin/activate
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pip install --upgrade pip
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pip install -r cs_gan/requirements.txt
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python -m cs_gan.main_cs
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