def testBoundary(self):
n_total = 450
batch_size = 451
self.assertEqual(
calc_n_batches_per_epoch(n_total, batch_size,
drop_remainder=False), 1)
n_total = 450
batch_size = 449
self.assertEqual(
calc_n_batches_per_epoch(n_total, batch_size,
drop_remainder=False), 2)
n_total = 0
batch_size = 10
self.assertEqual(
calc_n_batches_per_epoch(n_total, batch_size,
drop_remainder=False), 0)
print("stdevs: %s" % image_stdevs)
for j in range(0, data['label/0/class'].shape[0]):
print("Class: %s" % data['label/0/class'][j])
plt.imshow(data['images'][j,:,:,:])
plt.show()
image_means = [round(float(x), 4) for x in
list(np.mean(data['images'], axis=(0, 1, 2), dtype=np.float64))]
image_stdevs = [round(float(x), 4) for x in
list(np.std(data['images'], axis=(0, 1, 2), dtype=np.float64))]
n_records_train = n_records_in_tfr(tfr_file)
n_batches_per_epoch_train = calc_n_batches_per_epoch(
n_records_train, 64)
train_model, train_model_base = create_model(
model_name='cats_vs_dogs',
input_feeder=input_feeder_train,
target_labels=labels,
n_classes_per_label_type=n_classes_per_label,
n_gpus=1,
continue_training=False,
transfer_learning=False,
path_of_model_to_load=None)
val_model, val_model_base = create_model(
model_name='cats_vs_dogs',
output_labels=output_labels,
image_pre_processing_fun=preprocess_image,
image_pre_processing_args={
**image_processing, 'is_training': False
},
buffer_size=args['buffer_size'],
num_parallel_calls=args['n_cpus'],
drop_batch_remainder=False)
# Export Image Processing Settings
export_dict_to_json({
**image_processing, 'is_training': False
}, os.path.join(args['run_outputs_dir'], 'image_processing.json'))
logger.info("Calculating batches per epoch")
n_batches_per_epoch_train = calc_n_batches_per_epoch(
n_records_train, args['batch_size'])
n_records_val = n_records_in_tfr_parallel(tfr_val, args['n_cpus'])
n_batches_per_epoch_val = calc_n_batches_per_epoch(n_records_val,
args['batch_size'])
if TEST_SET:
n_records_test = n_records_in_tfr_parallel(tfr_test, args['n_cpus'])
n_batches_per_epoch_test = calc_n_batches_per_epoch(
n_records_test, args['batch_size'], drop_remainder=False)
###########################################
# CREATE MODEL ###########
###########################################
logger.info("Preparing Model")
def testBatchLarger(self):
n_total = 450
batch_size = 660
self.assertEqual(
calc_n_batches_per_epoch(n_total, batch_size,
drop_remainder=False), 1)
def testDropRemainderCaseNormal(self):
n_total = 26
batch_size = 5
self.assertEqual(calc_n_batches_per_epoch(n_total, batch_size), 5)
def testNormalCase(self):
n_total = 26
batch_size = 5
self.assertEqual(
calc_n_batches_per_epoch(n_total, batch_size,
drop_remainder=False), 6)