From 84cb8cf779a7beb6974cbdf065be52c81286a53f Mon Sep 17 00:00:00 2001 From: Joonas Roos Date: Mon, 7 Jun 2021 15:20:20 +0300 Subject: [PATCH] Change target class to Category --- .../edgespeechnets/edgespeechnets_learner.py | 4 ++-- .../edgespeechnets/test_edgespeechnets.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/opendr/perception/speech_recognition/edgespeechnets/edgespeechnets_learner.py b/src/opendr/perception/speech_recognition/edgespeechnets/edgespeechnets_learner.py index 49132ca7d7..14b60faee7 100644 --- a/src/opendr/perception/speech_recognition/edgespeechnets/edgespeechnets_learner.py +++ b/src/opendr/perception/speech_recognition/edgespeechnets/edgespeechnets_learner.py @@ -24,7 +24,7 @@ from opendr.engine.data import Timeseries from opendr.engine.learners import Learner -from opendr.engine.target import SpeechCommand +from opendr.engine.target import Category from opendr.perception.speech_recognition.edgespeechnets.algorithm.audioutils import get_mfcc import opendr.perception.speech_recognition.edgespeechnets.algorithm.models as models @@ -218,7 +218,7 @@ def infer(self, batch): prediction = output.max(1, keepdim=True) batch_predictions = [] for target, confidence in zip(prediction[1], prediction[0].exp()): - batch_predictions.append(SpeechCommand(target.item(), confidence=confidence.item())) + batch_predictions.append(Category(target.item(), confidence=confidence.item())) return batch_predictions[0] if len(batch_predictions) == 1 else batch_predictions def save(self, path): diff --git a/tests/sources/tools/perception/speech_recognition/edgespeechnets/test_edgespeechnets.py b/tests/sources/tools/perception/speech_recognition/edgespeechnets/test_edgespeechnets.py index e90b8c856c..462d9bcb14 100644 --- a/tests/sources/tools/perception/speech_recognition/edgespeechnets/test_edgespeechnets.py +++ b/tests/sources/tools/perception/speech_recognition/edgespeechnets/test_edgespeechnets.py @@ -23,7 +23,7 @@ from opendr.perception.speech_recognition.edgespeechnets.edgespeechnets_learner import EdgeSpeechNetsLearner from opendr.engine.data import Timeseries from opendr.engine.datasets import DatasetIterator -from opendr.engine.target import SpeechCommand +from opendr.engine.target import Category TEST_BATCH_SIZE = 2 TEST_EPOCHS = 1 @@ -78,12 +78,12 @@ def test_infer_batch(self): batch = [Timeseries(np.ones((1, TEST_SIGNAL_LENGTH))) for _ in range(TEST_INFER_LENGTH)] results = self.learner.infer(batch) self.assertTrue(len(results) == TEST_INFER_LENGTH) - self.assertTrue(all([isinstance(x, SpeechCommand) for x in results])) + self.assertTrue(all([isinstance(x, Category) for x in results])) def test_infer_pure_signal(self): signal = Timeseries(np.ones((1, TEST_SIGNAL_LENGTH))) result = self.learner.infer(signal) - self.assertTrue(isinstance(result, SpeechCommand)) + self.assertTrue(isinstance(result, Category)) def test_reset(self): weights_before_reset = list(self.learner.model.parameters())[0].clone()