ludwig-ai--ludwig
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92 行
3.2 KiB
Python
92 行
3.2 KiB
Python
import pytest
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import torch
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from ludwig.constants import ENCODER, ENCODER_OUTPUT
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from ludwig.features.binary_feature import BinaryInputFeature, BinaryOutputFeature
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from ludwig.schema.features.binary_feature import BinaryInputFeatureConfig, BinaryOutputFeatureConfig
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from ludwig.schema.utils import load_config_with_kwargs
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from ludwig.utils.torch_utils import get_torch_device
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BATCH_SIZE = 2
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BINARY_W_SIZE = 1
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DEVICE = get_torch_device()
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@pytest.fixture(scope="module")
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def binary_config():
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return {
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"name": "binary_feature",
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"type": "binary",
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}
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@pytest.mark.parametrize("encoder", ["passthrough", "dense"])
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def test_binary_input_feature(binary_config: dict, encoder: str):
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binary_config.update({ENCODER: {"type": encoder}})
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binary_config, _ = load_config_with_kwargs(BinaryInputFeatureConfig, binary_config)
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binary_input_feature = BinaryInputFeature(binary_config).to(DEVICE)
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binary_tensor = binary_input_feature.create_sample_input(batch_size=BATCH_SIZE).to(DEVICE)
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assert binary_tensor.shape == torch.Size([BATCH_SIZE])
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assert binary_tensor.dtype == torch.bool
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encoder_output = binary_input_feature(binary_tensor)
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assert encoder_output[ENCODER_OUTPUT].shape[1:] == binary_input_feature.output_shape
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def test_binary_output_feature():
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binary_output_config = {
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"name": "binary_feature",
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"type": "binary",
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"input_size": BINARY_W_SIZE,
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"decoder": {
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"type": "regressor",
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"input_size": 1,
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},
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"loss": {
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"type": "binary_weighted_cross_entropy",
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"positive_class_weight": 1,
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"robust_lambda": 0,
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"confidence_penalty": 0,
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},
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}
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binary_output_config, _ = load_config_with_kwargs(BinaryOutputFeatureConfig, binary_output_config)
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binary_output_feature = BinaryOutputFeature(binary_output_config, {}).to(DEVICE)
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combiner_outputs = dict()
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combiner_outputs["combiner_output"] = torch.randn([BATCH_SIZE, BINARY_W_SIZE], dtype=torch.float32).to(DEVICE)
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binary_output = binary_output_feature(combiner_outputs, {})
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assert "last_hidden" in binary_output
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assert "logits" in binary_output
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assert binary_output["logits"].size() == torch.Size([BATCH_SIZE])
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def test_binary_output_feature_without_positive_class_weight():
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binary_output_config = {
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"name": "binary_feature",
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"type": "binary",
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"input_size": BINARY_W_SIZE,
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"decoder": {
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"type": "regressor",
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"input_size": 1,
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},
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"loss": {
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"type": "binary_weighted_cross_entropy",
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"positive_class_weight": None,
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"robust_lambda": 0,
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"confidence_penalty": 0,
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},
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}
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binary_output_config, _ = load_config_with_kwargs(BinaryOutputFeatureConfig, binary_output_config)
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binary_output_feature = BinaryOutputFeature(binary_output_config, {}).to(DEVICE)
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combiner_outputs = {}
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combiner_outputs["combiner_output"] = torch.randn([BATCH_SIZE, BINARY_W_SIZE], dtype=torch.float32).to(DEVICE)
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binary_output = binary_output_feature(combiner_outputs, {})
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assert "last_hidden" in binary_output
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assert "logits" in binary_output
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assert binary_output["logits"].size() == torch.Size([BATCH_SIZE])
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