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How to use to_module method in Testify

Best Python code snippet using Testify_python

test_feature_utils.py

Source:test_feature_utils.py

1import numpy as np2import pytest3import torch4from ludwig.features import feature_utils5def test_ludwig_feature_dict():6    feature_dict = feature_utils.LudwigFeatureDict()7    to_module = torch.nn.Module()8    type_module = torch.nn.Module()9    feature_dict["to"] = to_module10    feature_dict["type"] = type_module11    assert iter(feature_dict) is not None12    assert next(feature_dict) is not None13    assert len(feature_dict) == 214    assert feature_dict.keys() == ["to", "type"]15    assert feature_dict.items() == [("to", to_module), ("type", type_module)]16    assert feature_dict["to"] == to_module17    feature_dict.update({"to_empty": torch.nn.Module()})18    assert len(feature_dict) == 319    assert [key for key in feature_dict] == ["to", "type", "to_empty"]20def test_ludwig_feature_dict_with_periods():21    feature_dict = feature_utils.LudwigFeatureDict()22    to_module = torch.nn.Module()23    feature_dict["to."] = to_module24    assert feature_dict.keys() == ["to."]25    assert feature_dict.items() == [("to.", to_module)]26    assert feature_dict["to."] == to_module27@pytest.mark.parametrize("sequence_type", [list, tuple, np.array])28def test_compute_token_probabilities(sequence_type):29    inputs = sequence_type(30        [31            [0.1, 0.2, 0.7],32            [0.3, 0.4, 0.3],33            [0.6, 0.3, 0.2],34        ]35    )36    token_probabilities = feature_utils.compute_token_probabilities(inputs)37    assert np.allclose(token_probabilities, [0.7, 0.4, 0.6])38def test_compute_sequence_probability():39    inputs = np.array([0.7, 0.4, 0.6])40    sequence_probability = feature_utils.compute_sequence_probability(41        inputs, max_sequence_length=2, return_log_prob=False42    )...

utils.py

Source:utils.py

1import numpy as np2import torch3def assert_equal_model_outputs(input_var, model1, model2):4    model1.eval()5    model2.eval()6    model1_output = model1(input_var)7    model2_output = model2(input_var)8    assert torch.equal(model1_output, model2_output)9def assert_almost_equal_model_outputs(input_var, model1, model2):10    model1.eval()11    model2.eval()12    model1_output = model1(input_var)13    model2_output = model2(input_var)14    assert np.all(15        np.isclose(16            model1_output.data.numpy(),17            model2_output.data.numpy(),18            rtol=1e-04,19            atol=1e-06,20        )21    )22def copy_module_weights(from_module, to_module):23    to_module.weight.data.copy_(from_module.weight.data)24    to_module.bias.data.copy_(from_module.bias.data)25def assert_iterable_length_and_type(iterable, length, element_type):26    assert len(iterable) == length27    for element in iterable:28        assert isinstance(element, element_type)29def get_default_input_size_for_model(model_name):30    if (31        model_name == 'alexnet'32        or model_name.startswith('vgg')33        or model_name.startswith('squeezenet')34    ):35        return 224, 224...

connection.py

Source:connection.py

1from collections import namedtuple2import asyncio3import logging4class Connection(namedtuple('Connection', ['from_module', 'from_output',5                                           'to_module', 'to_input',6                                           'key'])):7    async def establish(self):8        from_node, to_node = self.from_module.node, self.to_module.node9        connect = from_node.connect(self.from_module, self.from_output,10                                    self.to_module, self.to_input)11        set_key_from = from_node.set_key(self.from_module, self.from_output,12                                         self.key)13        set_key_to = to_node.set_key(self.to_module, self.to_input, self.key)14        await asyncio.gather(connect, set_key_from, set_key_to)15        logging.info('Connection from %s:%s on %s to %s:%s on %s established',16                     self.from_module.name, self.from_output, from_node.name,...

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