def _load_and_preprocess_datasets():
"""Load raw EMNIST data and preprocess images and labels."""
emnist_train, emnist_test = (
emnist_data_utils.create_real_images_tff_client_data())
# Raw image datasets.
train_dataset = emnist_train.create_tf_dataset_from_all_clients()
test_dataset = emnist_test.create_tf_dataset_from_all_clients()
# Preprocessed image datasets.
preprocessed_train_dataset = emnist_data_utils.preprocess_img_dataset(
train_dataset, include_label=True, batch_size=BATCH_SIZE, shuffle=True)
preprocessed_test_dataset = emnist_data_utils.preprocess_img_dataset(
test_dataset, include_label=True, batch_size=BATCH_SIZE, shuffle=False)
return preprocessed_train_dataset, preprocessed_test_dataset
def _get_client_ids_meeting_condition(train_tff_data, bad_accuracy_cutoff,
good_accuracy_cutoff,
invert_imagery_likelihood,
classifier_model):
"""Get clients that classify <bad_accuracy_cutoff or >good_accuracy_cutoff."""
bad_client_ids_inversion_map = {}
good_client_ids_inversion_map = {}
for client_id in train_tff_data.client_ids:
invert_imagery = (1 == np.random.binomial(n=1,
p=invert_imagery_likelihood))
# TF Dataset for particular client.
raw_images_ds = train_tff_data.create_tf_dataset_for_client(client_id)
# Preprocess into format expected by classifier.
images_ds = emnist_data_utils.preprocess_img_dataset(
raw_images_ds,
invert_imagery=invert_imagery,
include_label=True,
batch_size=None,
shuffle=False,
repeat=False)
# Run classifier on all data on client, compute % classified correctly.
total_count, correct_count = _analyze_classifier(
images_ds, classifier_model)
accuracy = float(correct_count) / float(total_count)
if accuracy < bad_accuracy_cutoff:
bad_client_ids_inversion_map[client_id] = invert_imagery
if accuracy > good_accuracy_cutoff:
good_client_ids_inversion_map[client_id] = invert_imagery
return bad_client_ids_inversion_map, good_client_ids_inversion_map
def test_preprocessed_img_labels_are_case_agnostic(self):
raw_images_ds = _get_example_client_dataset_containing_lowercase()
raw_ds_iterator = iter(raw_images_ds)
# The first element in the raw dataset is an uppercase 'I' (label is 18).
self.assertEqual(next(raw_ds_iterator)['label'].numpy(), 18)
# The second element in the raw dataset is an uppercase 'C' (label is 12).
self.assertEqual(next(raw_ds_iterator)['label'].numpy(), 12)
# The third element in the raw dataset is a lowercase 'd' (label is 39).
self.assertEqual(next(raw_ds_iterator)['label'].numpy(), 47)
processed_ds = emnist_data_utils.preprocess_img_dataset(
raw_images_ds, include_label=True, batch_size=BATCH_SIZE, shuffle=False)
_, label_batch = next(iter(processed_ds))
processed_label_iterator = iter(label_batch)
# The first element (in first batch) in the processed dataset has a case
# agnostic label of 18 (i.e., assert that value remains unchanged).
self.assertEqual(next(processed_label_iterator).numpy(), 18)
# The second element (in first batch) in the processed dataset has a case
# agnostic label of 12 (i.e., assert that value remains unchanged).
self.assertEqual(next(processed_label_iterator).numpy(), 12)
# The third element (in first batch) in the processed dataset should now
# have a case agnostic label of 47 - 26 = 21.
self.assertEqual(next(processed_label_iterator).numpy(), 47 - 26)
for _, label_batch in iter(processed_ds):
for label in label_batch:
self.assertGreaterEqual(label, 0)
self.assertLessEqual(label, 36)
def test_preprocessed_img_inversion(self):
raw_images_ds = _get_example_client_dataset()
# Inversion turned off, average pixel is dark.
standard_images_ds = emnist_data_utils.preprocess_img_dataset(
raw_images_ds, invert_imagery=False, batch_size=BATCH_SIZE)
for batch in iter(standard_images_ds):
for image in batch:
self.assertLessEqual(np.average(image), -0.7)
# Inversion turned on, average pixel is light.
inverted_images_ds = emnist_data_utils.preprocess_img_dataset(
raw_images_ds, invert_imagery=True, batch_size=BATCH_SIZE)
for batch in iter(inverted_images_ds):
for image in batch:
self.assertGreaterEqual(np.average(image), 0.7)
def _create_real_images_dataset_for_eval():
"""Returns a `tf.data.Dataset` of real images."""
eval_tff_data = emnist_data_utils.create_real_images_tff_client_data(
split='test')
raw_data = eval_tff_data.create_tf_dataset_from_all_clients()
return emnist_data_utils.preprocess_img_dataset(raw_data,
include_label=False,
batch_size=EVAL_BATCH_SIZE,
shuffle=True,
repeat=True)
def _create_real_images_dataset_for_central(batchsize, cache_dir):
"""Returns a `tf.data.Dataset` of real images."""
eval_tff_data = emnist_data_utils.create_real_images_tff_client_data(
split='train', cache_dir = cache_dir)
raw_data = eval_tff_data.create_tf_dataset_from_all_clients()
return emnist_data_utils.preprocess_img_dataset(
raw_data,
include_label=False,
batch_size=batchsize,
shuffle=True,
repeat=True)
def setUp(self):
super().setUp()
client_data = emnist_data_utils.create_real_images_tff_client_data(
split='synthetic')
images_ds = client_data.create_tf_dataset_for_client(
client_data.client_ids[0])
images_ds = emnist_data_utils.preprocess_img_dataset(
images_ds, shuffle=False)
images_ds_iterator = iter(images_ds)
self.real_images = next(images_ds_iterator)
np.random.seed(seed=123456)
self.fake_images = tf.constant(
np.random.random((32, 28, 28, 1)), dtype=tf.float32)
def _get_dataset(client_id):
"""Retrieve/preprocess a tf.data.Dataset for a given client_id."""
raw_ds = raw_client_data.create_tf_dataset_for_client(client_id)
invert_imagery = False
if selected_client_ids_inversion_map:
invert_imagery = selected_client_ids_inversion_map[client_id]
# If filter-by-example, do it here.
if client_ids_example_indices_map:
raw_ds = _filter_by_example(raw_ds, client_ids_example_indices_map,
client_id)
return emnist_data_utils.preprocess_img_dataset(
raw_ds,
invert_imagery=invert_imagery,
include_label=False,
batch_size=batch_size,
shuffle=True,
repeat=False)
def _get_client_ids_and_examples_based_on_classification(
train_tff_data, min_num_examples, invert_imagery_likelihood,
classifier_model):
"""Get maps storing whether imagery inverted and how examples classified."""
client_ids_with_correct_examples_map = {}
client_ids_with_incorrect_examples_map = {}
client_ids_correct_example_indices_map = {}
client_ids_incorrect_example_indices_map = {}
for client_id in train_tff_data.client_ids:
invert_imagery = (1 == np.random.binomial(n=1,
p=invert_imagery_likelihood))
# TF Dataset for particular client.
raw_images_ds = train_tff_data.create_tf_dataset_for_client(client_id)
# Preprocess into format expected by classifier.
images_ds = emnist_data_utils.preprocess_img_dataset(
raw_images_ds,
invert_imagery=invert_imagery,
include_label=True,
batch_size=None,
shuffle=False,
repeat=False)
# Run classifier on all data on client, return lists of indices of examples
# classified correctly and incorrectly.
correct_indices, incorrect_indices = _analyze_classifier(
images_ds, classifier_model)
if len(correct_indices) >= min_num_examples:
client_ids_with_correct_examples_map[client_id] = invert_imagery
client_ids_correct_example_indices_map[client_id] = correct_indices
if len(incorrect_indices) >= min_num_examples:
client_ids_with_incorrect_examples_map[client_id] = invert_imagery
client_ids_incorrect_example_indices_map[
client_id] = incorrect_indices
return (client_ids_with_correct_examples_map,
client_ids_with_incorrect_examples_map,
client_ids_correct_example_indices_map,
client_ids_incorrect_example_indices_map)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
invert_imagery_likelihood = FLAGS.invert_imagery_likelihood
print('invert_imagery_likelihood is %s' % invert_imagery_likelihood)
if invert_imagery_likelihood > 1.0:
raise ValueError(
'invert_imagery_likelihood cannot be greater than 1.0')
# TFF Dataset.
client_real_images_tff_data = (
emnist_data_utils.create_real_images_tff_client_data(split='train'))
print('There are %d unique clients.' %
len(client_real_images_tff_data.client_ids))
# EMNIST Classifier.
classifier_model = ecm.get_trained_emnist_classifier_model()
accuracy_list = []
overall_total_count = 0
overall_correct_count = 0
for client_id in client_real_images_tff_data.client_ids:
invert_imagery = (1 == np.random.binomial(n=1,
p=invert_imagery_likelihood))
# TF Dataset for particular client.
raw_images_ds = client_real_images_tff_data.create_tf_dataset_for_client(
client_id)
# Preprocess into format expected by classifier.
images_ds = emnist_data_utils.preprocess_img_dataset(
raw_images_ds,
invert_imagery=invert_imagery,
include_label=True,
batch_size=None,
shuffle=False,
repeat=False)
# Run classifier on all data on client, compute % classified correctly.
total_count, correct_count = _analyze_classifier(
images_ds, classifier_model)
accuracy = float(correct_count) / float(total_count)
accuracy_list.append(accuracy)
overall_total_count += total_count
overall_correct_count += correct_count
# Calculate histogram.
bin_width = 1
histogram = _compute_histogram(accuracy_list, bin_width)
print('\nHistogram:')
print(histogram.numpy())
# Sanity check (should be 3400)
print('(Histogram sum):')
print(sum(histogram.numpy()))
# Calculate percentile values.
percentile_25, percentile_75 = np.percentile(accuracy_list, q=(25, 75))
print('\nPercentiles...')
print('25th Percentile : %f' % percentile_25)
print('75th Percentile : %f' % percentile_75)
overall_accuracy = (float(overall_correct_count) /
float(overall_total_count))
print('\nOverall classification success percentage: %d / %d (%f)' %
(overall_correct_count, overall_total_count, overall_accuracy))
