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# coding=utf-8

# Copyright 2019 The Google Research Authors.

#

# 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.

"""Unprocessing model function and train and eval specs for Estimator.

Unprocessing Images for Learned Raw Denoising

http://timothybrooks.com/tech/unprocessing

"""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import tensorflow as tf

from unprocessing import process

def psnr(labels, predictions):

"""Computes average peak signal-to-noise ratio of `predictions`.

Here PSNR is defined with respect to the maximum value of 1. All image tensors

must be within the range [0, 1].

Args:

labels: Tensor of shape [B, H, W, N].

predictions: Tensor of shape [B, H, W, N].

Returns:

Tuple of (psnr, update_op) as returned by tf.metrics.

"""

predictions.shape.assert_is_compatible_with(labels.shape)

with tf.control_dependencies([tf.assert_greater_equal(labels, 0.0),

tf.assert_less_equal(labels, 1.0)]):

psnrs = tf.image.psnr(labels, predictions, max_val=1.0)

psnrs = tf.boolean_mask(psnrs, tf.logical_not(tf.is_inf(psnrs)))

return tf.metrics.mean(psnrs, name='psnr')

def create_model_fn(inference_fn, hparams):

"""Creates a model function for Estimator.

Args:

inference_fn: Model inference function with specification:

Args -

noisy_img - Tensor of shape [B, H, W, 4].

variance - Tensor of shape [B, H, W, 4].

Returns -

Tensor of shape [B, H, W, 4].

hparams: Hyperparameters for model as a tf.contrib.training.HParams object.

Returns:

`_model_fn`.

"""

def _model_fn(features, labels, mode, params):

"""Constructs the model function.

Args:

features: Dictionary of input features.

labels: Tensor of labels if mode is `TRAIN` or `EVAL`, otherwise `None`.

mode: ModeKey object (`TRAIN` or `EVAL`).

params: Parameter dictionary passed from the Estimator object.

Returns:

An EstimatorSpec object that encapsulates the model and its serving

configurations.

"""

del params # Unused.

def process_images(images):

"""Closure for processing images with fixed metadata."""

return process.process(images, features['red_gain'],

features['blue_gain'], features['cam2rgb'])

denoised_img = inference_fn(features['noisy_img'], features['variance'])

noisy_img = process_images(features['noisy_img'])

denoised_img = process_images(denoised_img)

truth_img = process_images(labels)

if mode in [tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL]:

loss = tf.losses.absolute_difference(truth_img, denoised_img)

else:

loss = None

if mode == tf.estimator.ModeKeys.TRAIN:

optimizer = tf.train.AdamOptimizer(learning_rate=hparams.learning_rate)

train_op = tf.contrib.layers.optimize_loss(

loss=loss,

global_step=tf.train.get_global_step(),

learning_rate=None,

optimizer=optimizer,

name='') # Prevents scope prefix.

else:

train_op = None

if mode == tf.estimator.ModeKeys.EVAL:

eval_metric_ops = {'PSNR': psnr(truth_img, denoised_img)}

def summary(images, name):

"""As a hack, saves image summaries by adding to `eval_metric_ops`."""

images = tf.saturate_cast(images * 255 + 0.5, tf.uint8)

eval_metric_ops[name] = (tf.summary.image(name, images, max_outputs=2),

tf.no_op())

summary(noisy_img, 'Noisy')

summary(denoised_img, 'Denoised')

summary(truth_img, 'Truth')

diffs = (denoised_img - truth_img + 1.0) / 2.0

summary(diffs, 'Diffs')

else:

eval_metric_ops = None

return tf.estimator.EstimatorSpec(

mode=mode,

loss=loss,

train_op=train_op,

eval_metric_ops=eval_metric_ops)

return _model_fn

def create_train_and_eval_specs(train_dataset_fn,

eval_dataset_fn,

eval_steps=250):

"""Creates a TrainSpec and EvalSpec.

Args:

train_dataset_fn: Function returning a Dataset of training data.

eval_dataset_fn: Function returning a Dataset of evaluation data.

eval_steps: Number of steps for evaluating model.

Returns:

Tuple of (TrainSpec, EvalSpec).

"""

train_spec = tf.estimator.TrainSpec(input_fn=train_dataset_fn, max_steps=None)

eval_spec = tf.estimator.EvalSpec(

input_fn=eval_dataset_fn, steps=eval_steps, name='')

return train_spec, eval_spec