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evaluator.py
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evaluator.py
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# Copyright 2021 DeepMind Technologies Limited.
#
#
# 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
#
# https://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.
"""Evaluation runner."""
import collections
from absl import logging
import tensorflow.compat.v2 as tf
from galaxy_mergers import config as tp_config
from galaxy_mergers import helpers
from galaxy_mergers import losses
from galaxy_mergers import model
from galaxy_mergers import preprocessing
class GalaxyMergeClassifierEvaluator():
"""Galaxy Merge Rate Prediction Evaluation Runner."""
def __init__(self, strategy, optimizer_config, total_train_batch_size,
train_net_args, eval_batch_size, eval_net_args,
l2_regularization, data_config, resnet_kwargs, n_train_epochs):
"""Initializes evaluator/experiment."""
logging.info('Initializing evaluator...')
self._strategy = strategy
self._data_config = data_config
self._use_additional_features = bool(data_config['additional_features'])
self._eval_batch_size = eval_batch_size
self._eval_net_args = eval_net_args
self._num_buckets = data_config['num_eval_buckets']
self._n_repeats = data_config['n_crop_repeat']
self._image_size = data_config['image_size']
self._task_type = data_config['task']
self._loss_config = data_config['loss_config']
self._model_uncertainty = data_config['model_uncertainty']
del l2_regularization, optimizer_config, train_net_args
del total_train_batch_size, n_train_epochs
logging.info('Creating model...')
num_classes = 2 if self._model_uncertainty else 1
if self._task_type == losses.TASK_CLASSIFICATION:
num_classes = len(self._data_config['class_boundaries'])
self.model = model.ResNet(
n_repeats=self._data_config['n_crop_repeat'], num_classes=num_classes,
use_additional_features=self._use_additional_features, **resnet_kwargs)
self._eval_input = None
def build_eval_input(self, additional_lambdas=None):
"""Create the galaxy merger evaluation dataset."""
def decode_fn(record_bytes):
parsed_example = tf.io.parse_single_example(
record_bytes,
{
'image':
tf.io.VarLenFeature(tf.float32),
'image_shape':
tf.io.FixedLenFeature([3], dtype=tf.int64),
'axis':
tf.io.FixedLenFeature([], dtype=tf.int64),
'proposed_crop':
tf.io.FixedLenFeature([2, 2], dtype=tf.int64),
'normalized_time':
tf.io.FixedLenFeature([], dtype=tf.float32),
'unnormalized_time':
tf.io.FixedLenFeature([], dtype=tf.float32),
'grounded_normalized_time':
tf.io.FixedLenFeature([], dtype=tf.float32),
'redshift':
tf.io.FixedLenFeature([], dtype=tf.float32),
'sequence_average_redshift':
tf.io.FixedLenFeature([], dtype=tf.float32),
'mass':
tf.io.FixedLenFeature([], dtype=tf.float32),
'time_index':
tf.io.FixedLenFeature([], dtype=tf.int64),
'sequence_id':
tf.io.FixedLenFeature([], dtype=tf.string),
})
parsed_example['image'] = tf.sparse.to_dense(
parsed_example['image'], default_value=0)
dataset_row = parsed_example
return dataset_row
def build_eval_pipeline(_):
"""Generate the processed input evaluation data."""
logging.info('Building evaluation input pipeline...')
ds_path = self._data_config['dataset_path']
ds = tf.data.TFRecordDataset([ds_path]).map(decode_fn)
augmentations = dict(
rotation_and_flip=False,
rescaling=False,
translation=False
)
ds = preprocessing.prepare_dataset(
ds=ds, target_size=self._image_size,
crop_type=self._data_config['test_crop_type'],
n_repeats=self._n_repeats,
augmentations=augmentations,
task_type=self._task_type,
additional_features=self._data_config['additional_features'],
class_boundaries=self._data_config['class_boundaries'],
time_intervals=self._data_config['time_filter_intervals'],
frequencies_to_use=self._data_config['frequencies_to_use'],
additional_lambdas=additional_lambdas)
batched_ds = ds.cache().batch(self._eval_batch_size).prefetch(128)
logging.info('Finished building input pipeline...')
return batched_ds
return self._strategy.experimental_distribute_datasets_from_function(
build_eval_pipeline)
def run_test_model_ensemble(self, images, physical_features, augmentations):
"""Run evaluation on input images."""
image_variations = [images]
image_shape = images.shape.as_list()
if augmentations['rotation_and_flip']:
image_variations = preprocessing.get_all_rotations_and_flips(
image_variations)
if augmentations['rescaling']:
image_variations = preprocessing.get_all_rescalings(
image_variations, image_shape[1], augmentations['translation'])
# Put all augmented images into the batch: batch * num_augmented
augmented_images = tf.stack(image_variations, axis=0)
augmented_images = tf.reshape(augmented_images, [-1] + image_shape[1:])
if self._use_additional_features:
physical_features = tf.concat(
[physical_features] * len(image_variations), axis=0)
n_reps = self._data_config['n_crop_repeat']
augmented_images = preprocessing.move_repeats_to_batch(augmented_images,
n_reps)
logits_or_times = self.model(augmented_images, physical_features,
**self._eval_net_args)
if self._task_type == losses.TASK_CLASSIFICATION:
mu, log_sigma_sq = helpers.aggregate_classification_ensemble(
logits_or_times, len(image_variations),
self._data_config['test_time_ensembling'])
else:
assert self._task_type in losses.REGRESSION_TASKS
mu, log_sigma_sq = helpers.aggregate_regression_ensemble(
logits_or_times, len(image_variations),
self._model_uncertainty,
self._data_config['test_time_ensembling'])
return mu, log_sigma_sq
@property
def checkpoint_items(self):
return {'model': self.model}
def run_model_on_dataset(evaluator, dataset, config, n_batches=16):
"""Runs the model against a dataset, aggregates model output."""
scalar_metrics_to_log = collections.defaultdict(list)
model_outputs_to_log = collections.defaultdict(list)
dataset_features_to_log = collections.defaultdict(list)
batch_count = 1
for all_inputs in dataset:
if config.experiment_kwargs.data_config['additional_features']:
images = all_inputs[0]
physical_features = all_inputs[1]
labels, regression_targets, _ = all_inputs[2:5]
other_dataset_features = all_inputs[5:]
else:
images, physical_features = all_inputs[0], None
labels, regression_targets, _ = all_inputs[1:4]
other_dataset_features = all_inputs[4:]
mu, log_sigma_sq = evaluator.run_test_model_ensemble(
images, physical_features,
config.experiment_kwargs.data_config['test_augmentations'])
loss_config = config.experiment_kwargs.data_config['loss_config']
task_type = config.experiment_kwargs.data_config['task']
uncertainty = config.experiment_kwargs.data_config['model_uncertainty']
conf = config.experiment_kwargs.data_config['eval_confidence_interval']
scalar_metrics, vector_metrics = losses.compute_loss_and_metrics(
mu, log_sigma_sq, regression_targets, labels,
task_type, uncertainty, loss_config, 0, conf, mode='eval')
for i, dataset_feature in enumerate(other_dataset_features):
dataset_features_to_log[i].append(dataset_feature.numpy())
for scalar_metric in scalar_metrics:
v = scalar_metrics[scalar_metric]
val = v if isinstance(v, int) or isinstance(v, float) else v.numpy()
scalar_metrics_to_log[scalar_metric].append(val)
for vector_metric in vector_metrics:
val = vector_metrics[vector_metric].numpy()
model_outputs_to_log[vector_metric].append(val)
regression_targets_np = regression_targets.numpy()
labels_np = labels.numpy()
model_outputs_to_log['regression_targets'].append(regression_targets_np)
model_outputs_to_log['labels'].append(labels_np)
model_outputs_to_log['model_input_images'].append(images.numpy())
if n_batches and batch_count >= n_batches:
break
batch_count += 1
return scalar_metrics_to_log, model_outputs_to_log, dataset_features_to_log
def get_config_dataset_evaluator(filter_time_intervals,
ckpt_path,
config_override=None,
setup_dataset=True):
"""Set-up a default config, evaluation dataset, and evaluator."""
config = tp_config.get_config(filter_time_intervals=filter_time_intervals)
if config_override:
with config.ignore_type():
config.update_from_flattened_dict(config_override)
strategy = tf.distribute.OneDeviceStrategy(device='/gpu:0')
experiment = GalaxyMergeClassifierEvaluator(
strategy=strategy, **config.experiment_kwargs)
helpers.restore_checkpoint(ckpt_path, experiment)
if setup_dataset:
additional_lambdas = [
lambda ds: ds['sequence_id'],
lambda ds: ds['time_index'],
lambda ds: ds['axis'],
lambda ds: ds['normalized_time'],
lambda ds: ds['grounded_normalized_time'],
lambda ds: ds['unnormalized_time'],
lambda ds: ds['redshift'],
lambda ds: ds['mass']
]
ds = experiment.build_eval_input(additional_lambdas=additional_lambdas)
else:
ds = None
return config, ds, experiment