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31 行
1.1 KiB
31 行
1.1 KiB
import pytest
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from mlagents.torch_utils import torch
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from mlagents.trainers.torch.decoders import ValueHeads
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def test_valueheads():
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stream_names = [f"reward_signal_{num}" for num in range(5)]
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input_size = 5
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batch_size = 4
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# Test default 1 value per head
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value_heads = ValueHeads(stream_names, input_size)
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input_data = torch.ones((batch_size, input_size))
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value_out = value_heads(input_data) # Note: mean value will be removed shortly
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for stream_name in stream_names:
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assert value_out[stream_name].shape == (batch_size,)
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# Test that inputting the wrong size input will throw an error
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with pytest.raises(Exception):
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value_out = value_heads(torch.ones((batch_size, input_size + 2)))
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# Test multiple values per head (e.g. discrete Q function)
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output_size = 4
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value_heads = ValueHeads(stream_names, input_size, output_size)
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input_data = torch.ones((batch_size, input_size))
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value_out = value_heads(input_data)
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for stream_name in stream_names:
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assert value_out[stream_name].shape == (batch_size, output_size)
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