2020-10-10 15:51:54 +08:00
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import unittest
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import numpy as np
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import paddle
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paddle.set_default_dtype("float64")
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paddle.disable_static(paddle.CPUPlace())
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from parakeet.models import transformer_tts as tts
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class TestMultiheadAttention(unittest.TestCase):
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def test_io_same_qk(self):
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net = tts.MultiheadAttention(64, 8)
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q = paddle.randn([4, 12, 64])
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mask = paddle.fluid.layers.sequence_mask(
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paddle.to_tensor([12, 10, 8, 9]), dtype=q.dtype)
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mask = paddle.unsqueeze(mask, 1) # unsqueeze for time_steps_q
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context_vector, attention_weights = net(q, q, q, mask, drop_n_heads=2)
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self.assertTupleEqual(context_vector.numpy().shape, (4, 12, 64))
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self.assertTupleEqual(attention_weights.numpy().shape, (4, 8, 12, 12))
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def test_io(self):
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net = tts.MultiheadAttention(64, 8, k_dim=12, v_dim=6)
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q = paddle.randn([4, 12, 64])
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mask = paddle.fluid.layers.sequence_mask(
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paddle.to_tensor([12, 10, 8, 9]), dtype=q.dtype)
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mask = paddle.unsqueeze(mask, 1) # unsqueeze for time_steps_q
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context_vector, attention_weights = net(q, q, q, mask, drop_n_heads=2)
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self.assertTupleEqual(context_vector.numpy().shape, (4, 12, 64))
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self.assertTupleEqual(attention_weights.numpy().shape, (4, 8, 12, 12))
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class TestTransformerEncoderLayer(unittest.TestCase):
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def test_io(self):
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net = tts.TransformerEncoderLayer(64, 8, 128)
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x = paddle.randn([4, 12, 64])
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mask = paddle.fluid.layers.sequence_mask(
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paddle.to_tensor([12, 10, 8, 9]), dtype=x.dtype)
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context_vector, attention_weights = net(x, mask)
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self.assertTupleEqual(context_vector.numpy().shape, (4, 12, 64))
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self.assertTupleEqual(attention_weights.numpy().shape, (4, 8, 12, 12))
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class TestTransformerDecoderLayer(unittest.TestCase):
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def test_io(self):
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net = tts.TransformerDecoderLayer(64, 8, 128, 0.5)
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q = paddle.randn([4, 32, 64])
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k = paddle.randn([4, 24, 64])
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v = paddle.randn([4, 24, 64])
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enc_lengths = paddle.to_tensor([24, 18, 20, 22])
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dec_lengths = paddle.to_tensor([32, 28, 30, 31])
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enc_mask = paddle.fluid.layers.sequence_mask(enc_lengths, dtype=k.dtype)
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dec_mask = paddle.fluid.layers.sequence_mask(dec_lengths, dtype=q.dtype)
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y, self_attn_weights, cross_attn_weights = net(q, k, v, enc_mask, dec_mask)
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self.assertTupleEqual(y.numpy().shape, (4, 32, 64))
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self.assertTupleEqual(self_attn_weights.numpy().shape, (4, 8, 32, 32))
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2020-10-14 10:05:26 +08:00
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self.assertTupleEqual(cross_attn_weights.numpy().shape, (4, 8, 32, 24))
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class TestTransformerTTS(unittest.TestCase):
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def test_io(self):
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return
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net = tts.TransformerTTS(vocab_size, padding_idx, d_model, d_mel, n_heads, d_ffn, positional_encoding_scalar,
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encoder_layers, decoder_layers, d_prenet, d_postnet, postnet_layers,
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postnet_kernel_size, max_reduction_factor, dropout)
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