ludwig-ai--ludwig
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24 行
753 B
Python
24 行
753 B
Python
import logging
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import pytest
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import torch
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from ludwig.modules import recurrent_modules
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logger = logging.getLogger(__name__)
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@pytest.mark.parametrize("max_sequence_length,expected_output_shape", [(19, [19, 256]), (None, [256])])
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def test_recurrent_stack(max_sequence_length, expected_output_shape):
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recurrent_stack = recurrent_modules.RecurrentStack(
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input_size=10, max_sequence_length=max_sequence_length, hidden_size=256
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)
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assert recurrent_stack.output_shape == torch.Size(expected_output_shape)
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# Batch (N), Length (L), Input (H)
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inputs = torch.rand(2, 19, 10)
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hidden, final_state = recurrent_stack(inputs)
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assert hidden.shape == torch.Size([2, 19, 256])
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assert final_state.shape == torch.Size([2, 256])
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