mirror of
https://github.com/google-deepmind/deepmind-research.git
synced 2026-05-09 12:37:43 +08:00
Yan Wu
235 lines
7.1 KiB
Python
235 lines
7.1 KiB
Python
# python3
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# Copyright 2019 DeepMind Technologies Limited and Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tools for latent optimisation."""
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import collections
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import os
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from absl import logging
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import numpy as np
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import tensorflow.compat.v1 as tf
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import tensorflow_probability as tfp
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from cs_gan import nets
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tfd = tfp.distributions
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class ModelOutputs(
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collections.namedtuple('AdversarialModelOutputs',
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['optimization_components', 'debug_ops'])):
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"""All the information produced by the adversarial module.
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Fields:
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* `optimization_components`: A dictionary. Each entry in this dictionary
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corresponds to a module to train using their own optimizer. The keys are
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names of the components, and the values are `common.OptimizationComponent`
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instances. The keys of this dict can be made keys of the configuration
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used by the main train loop, to define the configuration of the
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optimization details for each module.
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* `debug_ops`: A dictionary, from string to a scalar `tf.Tensor`. Quantities
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used for tracking training.
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"""
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class OptimizationComponent(
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collections.namedtuple('OptimizationComponent', ['loss', 'vars'])):
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"""Information needed by the optimizer to train modules.
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Usage:
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`optimizer.minimize(
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opt_compoment.loss, var_list=opt_component.vars)`
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Fields:
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* `loss`: A `tf.Tensor` the loss of the module.
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* `vars`: A list of variables, the ones which will be used to minimize the
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loss.
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"""
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def cross_entropy_loss(logits, expected):
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"""The cross entropy classification loss between logits and expected values.
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The loss proposed by the original GAN paper: https://arxiv.org/abs/1406.2661.
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Args:
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logits: a `tf.Tensor`, the model produced logits.
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expected: a `tf.Tensor`, the expected output.
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Returns:
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A scalar `tf.Tensor`, the average loss obtained on the given inputs.
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Raises:
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ValueError: if the logits do not have shape [batch_size, 2].
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"""
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num_logits = logits.get_shape()[1]
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if num_logits != 2:
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raise ValueError(('Invalid number of logits for cross_entropy_loss! '
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'cross_entropy_loss supports only 2 output logits!'))
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return tf.reduce_mean(
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tf.nn.sparse_softmax_cross_entropy_with_logits(
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logits=logits, labels=expected))
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def optimise_and_sample(init_z, module, data, is_training):
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"""Optimising generator latent variables and sample."""
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if module.num_z_iters is None or module.num_z_iters == 0:
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z_final = init_z
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else:
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init_loop_vars = (0, _project_z(init_z, module.z_project_method))
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loop_cond = lambda i, _: i < module.num_z_iters
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def loop_body(i, z):
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loop_samples = module.generator(z, is_training)
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gen_loss = module.gen_loss_fn(data, loop_samples)
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z_grad = tf.gradients(gen_loss, z)[0]
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z -= module.z_step_size * z_grad
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z = _project_z(z, module.z_project_method)
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return i + 1, z
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# Use the following static loop for debugging
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# z = init_z
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# for _ in xrange(num_z_iters):
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# _, z = loop_body(0, z)
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# z_final = z
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_, z_final = tf.while_loop(loop_cond,
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loop_body,
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init_loop_vars)
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return module.generator(z_final, is_training), z_final
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def get_optimisation_cost(initial_z, optimised_z):
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optimisation_cost = tf.reduce_mean(
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tf.reduce_sum((optimised_z - initial_z)**2, -1))
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return optimisation_cost
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def _project_z(z, project_method='clip'):
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"""To be used for projected gradient descent over z."""
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if project_method == 'norm':
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z_p = tf.nn.l2_normalize(z, axis=-1)
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elif project_method == 'clip':
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z_p = tf.clip_by_value(z, -1, 1)
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else:
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raise ValueError('Unknown project_method: {}'.format(project_method))
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return z_p
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class DataProcessor(object):
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def preprocess(self, x):
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return x * 2 - 1
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def postprocess(self, x):
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return (x + 1) / 2.
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def _get_np_data(data_processor, dataset, split='train'):
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"""Get the dataset as numpy arrays."""
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index = 0 if split == 'train' else 1
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if dataset == 'mnist':
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# Construct the dataset.
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x, _ = tf.keras.datasets.mnist.load_data()[index]
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# Note: tf dataset is binary so we convert it to float.
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x = x.astype(np.float32)
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x = x / 255.
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x = x.reshape((-1, 28, 28, 1))
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if dataset == 'cifar':
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x, _ = tf.keras.datasets.cifar10.load_data()[index]
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x = x.astype(np.float32)
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x = x / 255.
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if data_processor:
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# Normalize data if a processor is given.
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x = data_processor.preprocess(x)
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return x
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def make_output_dir(output_dir):
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logging.info('Creating output dir %s', output_dir)
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if not tf.gfile.IsDirectory(output_dir):
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tf.gfile.MakeDirs(output_dir)
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def get_ckpt_dir(output_dir):
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ckpt_dir = os.path.join(output_dir, 'ckpt')
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if not tf.gfile.IsDirectory(ckpt_dir):
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tf.gfile.MakeDirs(ckpt_dir)
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return ckpt_dir
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def get_real_data_for_eval(num_eval_samples, dataset, split='valid'):
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data = _get_np_data(data_processor=None, dataset=dataset, split=split)
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data = data[:num_eval_samples]
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return tf.constant(data)
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def get_summaries(ops):
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summaries = []
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for name, op in ops.items():
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# Ensure to log the value ops before writing them in the summary.
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# We do this instead of a hook to ensure IS/FID are never computed twice.
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print_op = tf.print(name, [op], output_stream=tf.logging.info)
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with tf.control_dependencies([print_op]):
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summary = tf.summary.scalar(name, op)
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summaries.append(summary)
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return summaries
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def get_train_dataset(data_processor, dataset, batch_size):
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"""Creates the training data tensors."""
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x_train = _get_np_data(data_processor, dataset, split='train')
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# Create the TF dataset.
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dataset = tf.data.Dataset.from_tensor_slices(x_train)
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# Shuffle and repeat the dataset for training.
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# This is required because we want to do multiple passes through the entire
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# dataset when training.
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dataset = dataset.shuffle(100000).repeat()
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# Batch the data and return the data batch.
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one_shot_iterator = dataset.batch(batch_size).make_one_shot_iterator()
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data_batch = one_shot_iterator.get_next()
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return data_batch
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def get_generator(dataset):
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if dataset == 'mnist':
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return nets.MLPGeneratorNet()
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if dataset == 'cifar':
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return nets.ConvGenNet()
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def get_metric_net(dataset, num_outputs=2, use_sn=True):
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if dataset == 'mnist':
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return nets.MLPMetricNet(num_outputs)
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if dataset == 'cifar':
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return nets.ConvMetricNet(num_outputs, use_sn)
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def make_prior(num_latents):
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# Zero mean, unit variance prior.
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prior_mean = tf.zeros(shape=(num_latents), dtype=tf.float32)
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prior_scale = tf.ones(shape=(num_latents), dtype=tf.float32)
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return tfd.Normal(loc=prior_mean, scale=prior_scale)
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