test train and eval pathways for residual vq

tests/test_readme.py

@@ -64,9 +64,11 @@ def test_vq_mask():
 
 @pytest.mark.parametrize('implicit_neural_codebook', (True, False))
 @pytest.mark.parametrize('use_cosine_sim', (True, False))
+@pytest.mark.parametrize('train', (True, False))
 def test_residual_vq(
     implicit_neural_codebook,
-    use_cosine_sim
+    use_cosine_sim,
+    train
 ):
     from vector_quantize_pytorch import ResidualVQ
 
@@ -80,14 +82,9 @@ def test_residual_vq(
 
     x = torch.randn(1, 256, 32)
 
-    quantized, indices, commit_loss = residual_vq(x)
-    quantized, indices, commit_loss, all_codes = residual_vq(x, return_all_codes = True)
-
-    # test eval mode and `get_output_from_indices`
-
-    residual_vq.eval()
-    quantized, indices, commit_loss = residual_vq(x)
+    residual_vq.train(train)
 
+    quantized, indices, commit_loss = residual_vq(x, freeze_codebook = train and not implicit_neural_codebook)
     quantized_out = residual_vq.get_output_from_indices(indices)
     assert torch.allclose(quantized, quantized_out, atol = 1e-6)