def model_fn(features, labels, mode, params):
"""Contrastive model function."""
model_mode = utils.estimator_mode_to_model_mode(mode)
hparams = params['hparams']
trainer = ContrastiveTrainer(
model_inputs=features,
labels=labels,
train_global_batch_size=hparams.bs,
hparams=hparams,
mode=model_mode,
num_classes=inputs.get_num_classes(hparams),
training_set_size=inputs.get_num_train_images(hparams),
is_tpu=params['use_tpu'])
if mode == tf.estimator.ModeKeys.PREDICT:
predictions_map = trainer.signature_def_map()
exports = {
k: tf.estimator.export.PredictOutput(v)
for k, v in predictions_map.items()
}
# Export a default SignatureDef to keep the API happy.
exports[tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = (
exports['contrastive_eval'])
spec = tf.estimator.tpu.TPUEstimatorSpec(
mode=mode,
predictions=predictions_map['contrastive_eval'],
export_outputs=exports)
return spec
# We directly write summaries for the relevant losses, so just hard-code a
# dummy value to keep the Estimator API happy.
loss = tf.constant(0.)
if mode == tf.estimator.ModeKeys.EVAL:
spec = tf.estimator.tpu.TPUEstimatorSpec(
mode=mode, loss=loss, eval_metrics=trainer.eval_metrics())
return spec
else: # TRAIN
spec = tf.estimator.tpu.TPUEstimatorSpec(
mode=mode,
train_op=trainer.train_op(),
loss=loss,
scaffold_fn=trainer.scaffold_fn(),
host_call=trainer.host_call(FLAGS.model_dir))
return spec
def test_get_num_classes(self, input_fn, expected_classes):
params = make_params(input_fn)
self.assertEqual(inputs.get_num_classes(params['hparams']),
expected_classes)