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masked loss
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@@ -37,16 +37,12 @@ class DecoderTests(unittest.TestCase):
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dummy_memory = T.autograd.Variable(T.rand(4, 120, 32))
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print(layer)
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output, alignment, stop_output = layer(dummy_input, dummy_memory)
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output, alignment = layer(dummy_input, dummy_memory)
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print(output.shape)
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print(" > Stop ", stop_output.shape)
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assert output.shape[0] == 4
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assert output.shape[1] == 120 / 5
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assert output.shape[2] == 32 * 5
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assert stop_output.shape[0] == 4
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assert stop_output.shape[1] == 120 / 5
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assert stop_output.shape[2] == 5
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class EncoderTests(unittest.TestCase):
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+11
-5
@@ -43,9 +43,10 @@ class TestDataset(unittest.TestCase):
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text_lengths = data[1]
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linear_input = data[2]
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mel_input = data[3]
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stop_targets = data[4]
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item_idx = data[5]
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mel_lengths = data[4]
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stop_target = data[5]
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item_idx = data[6]
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neg_values = text_input[text_input < 0]
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check_count = len(neg_values)
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assert check_count == 0, \
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@@ -82,8 +83,9 @@ class TestDataset(unittest.TestCase):
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text_lengths = data[1]
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linear_input = data[2]
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mel_input = data[3]
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stop_target = data[4]
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item_idx = data[5]
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mel_lengths = data[4]
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stop_target = data[5]
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item_idx = data[6]
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# check the last time step to be zero padded
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assert mel_input[0, -1].sum() == 0
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@@ -92,6 +94,10 @@ class TestDataset(unittest.TestCase):
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assert linear_input[0, -2].sum() != 0
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assert stop_target[0, -1] == 1
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assert stop_target.sum() == 1
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assert len(mel_lengths.shape) == 1
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print(mel_lengths)
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print(mel_input)
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assert mel_lengths[0] == mel_input[0].shape[0]
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