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| #!/usr/bin/env python3 | |
| # import models/encoder/decoder to be tested | |
| from examples.speech_recognition.models.vggtransformer import ( | |
| TransformerDecoder, | |
| VGGTransformerEncoder, | |
| VGGTransformerModel, | |
| vggtransformer_1, | |
| vggtransformer_2, | |
| vggtransformer_base, | |
| ) | |
| # import base test class | |
| from .asr_test_base import ( | |
| DEFAULT_TEST_VOCAB_SIZE, | |
| TestFairseqDecoderBase, | |
| TestFairseqEncoderBase, | |
| TestFairseqEncoderDecoderModelBase, | |
| get_dummy_dictionary, | |
| get_dummy_encoder_output, | |
| get_dummy_input, | |
| ) | |
| class VGGTransformerModelTest_mid(TestFairseqEncoderDecoderModelBase): | |
| def setUp(self): | |
| def override_config(args): | |
| """ | |
| vggtrasformer_1 use 14 layers of transformer, | |
| for testing purpose, it is too expensive. For fast turn-around | |
| test, reduce the number of layers to 3. | |
| """ | |
| args.transformer_enc_config = ( | |
| "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" | |
| ) | |
| super().setUp() | |
| extra_args_setter = [vggtransformer_1, override_config] | |
| self.setUpModel(VGGTransformerModel, extra_args_setter) | |
| self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) | |
| class VGGTransformerModelTest_big(TestFairseqEncoderDecoderModelBase): | |
| def setUp(self): | |
| def override_config(args): | |
| """ | |
| vggtrasformer_2 use 16 layers of transformer, | |
| for testing purpose, it is too expensive. For fast turn-around | |
| test, reduce the number of layers to 3. | |
| """ | |
| args.transformer_enc_config = ( | |
| "((1024, 16, 4096, True, 0.15, 0.15, 0.15),) * 3" | |
| ) | |
| super().setUp() | |
| extra_args_setter = [vggtransformer_2, override_config] | |
| self.setUpModel(VGGTransformerModel, extra_args_setter) | |
| self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) | |
| class VGGTransformerModelTest_base(TestFairseqEncoderDecoderModelBase): | |
| def setUp(self): | |
| def override_config(args): | |
| """ | |
| vggtrasformer_base use 12 layers of transformer, | |
| for testing purpose, it is too expensive. For fast turn-around | |
| test, reduce the number of layers to 3. | |
| """ | |
| args.transformer_enc_config = ( | |
| "((512, 8, 2048, True, 0.15, 0.15, 0.15),) * 3" | |
| ) | |
| super().setUp() | |
| extra_args_setter = [vggtransformer_base, override_config] | |
| self.setUpModel(VGGTransformerModel, extra_args_setter) | |
| self.setUpInput(get_dummy_input(T=50, D=80, B=5, K=DEFAULT_TEST_VOCAB_SIZE)) | |
| class VGGTransformerEncoderTest(TestFairseqEncoderBase): | |
| def setUp(self): | |
| super().setUp() | |
| self.setUpInput(get_dummy_input(T=50, D=80, B=5)) | |
| def test_forward(self): | |
| print("1. test standard vggtransformer") | |
| self.setUpEncoder(VGGTransformerEncoder(input_feat_per_channel=80)) | |
| super().test_forward() | |
| print("2. test vggtransformer with limited right context") | |
| self.setUpEncoder( | |
| VGGTransformerEncoder( | |
| input_feat_per_channel=80, transformer_context=(-1, 5) | |
| ) | |
| ) | |
| super().test_forward() | |
| print("3. test vggtransformer with limited left context") | |
| self.setUpEncoder( | |
| VGGTransformerEncoder( | |
| input_feat_per_channel=80, transformer_context=(5, -1) | |
| ) | |
| ) | |
| super().test_forward() | |
| print("4. test vggtransformer with limited right context and sampling") | |
| self.setUpEncoder( | |
| VGGTransformerEncoder( | |
| input_feat_per_channel=80, | |
| transformer_context=(-1, 12), | |
| transformer_sampling=(2, 2), | |
| ) | |
| ) | |
| super().test_forward() | |
| print("5. test vggtransformer with windowed context and sampling") | |
| self.setUpEncoder( | |
| VGGTransformerEncoder( | |
| input_feat_per_channel=80, | |
| transformer_context=(12, 12), | |
| transformer_sampling=(2, 2), | |
| ) | |
| ) | |
| class TransformerDecoderTest(TestFairseqDecoderBase): | |
| def setUp(self): | |
| super().setUp() | |
| dict = get_dummy_dictionary(vocab_size=DEFAULT_TEST_VOCAB_SIZE) | |
| decoder = TransformerDecoder(dict) | |
| dummy_encoder_output = get_dummy_encoder_output(encoder_out_shape=(50, 5, 256)) | |
| self.setUpDecoder(decoder) | |
| self.setUpInput(dummy_encoder_output) | |
| self.setUpPrevOutputTokens() | |