def test_regex_matches_are_initialized_correctly(self):
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.linear_1_with_funky_name = torch.nn.Linear(5, 10)
self.linear_2 = torch.nn.Linear(10, 5)
self.conv = torch.nn.Conv1d(5, 5, 5)
def forward(self, inputs): # pylint: disable=arguments-differ
pass
# Make sure we handle regexes properly
json_params = """{"initializer": [
["conv", {"type": "constant", "val": 5}],
["funky_na.*bi", {"type": "constant", "val": 7}]
]}
"""
params = Params(json.loads(_jsonnet.evaluate_snippet("", json_params)))
initializers = InitializerApplicator.from_params(params['initializer'])
model = Net()
initializers(model)
for parameter in model.conv.parameters():
assert torch.equal(parameter.data,
torch.ones(parameter.size()) * 5)
parameter = model.linear_1_with_funky_name.bias
assert torch.equal(parameter.data, torch.ones(parameter.size()) * 7)
def test_regex_match_prevention_prevents_and_overrides(self):
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.linear_1 = torch.nn.Linear(5, 10)
self.linear_2 = torch.nn.Linear(10, 5)
# typical actual usage: modules loaded from allenlp.model.load(..)
self.linear_3_transfer = torch.nn.Linear(5, 10)
self.linear_4_transfer = torch.nn.Linear(10, 5)
self.pretrained_conv = torch.nn.Conv1d(5, 5, 5)
def forward(self, inputs): # pylint: disable=arguments-differ
pass
json_params = """{"initializer": [
[".*linear.*", {"type": "constant", "val": 10}],
[".*conv.*", {"type": "constant", "val": 10}],
[".*_transfer.*", "prevent"],
[".*pretrained.*",{"type": "prevent"}]
]}
"""
params = Params(json.loads(_jsonnet.evaluate_snippet("", json_params)))
initializers = InitializerApplicator.from_params(params['initializer'])
model = Net()
initializers(model)
for module in [model.linear_1, model.linear_2]:
for parameter in module.parameters():
assert torch.equal(parameter.data,
torch.ones(parameter.size()) * 10)
transfered_modules = [
model.linear_3_transfer, model.linear_4_transfer,
model.pretrained_conv
]
for module in transfered_modules:
for parameter in module.parameters():
assert not torch.equal(parameter.data,
torch.ones(parameter.size()) * 10)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'BiattentiveClassificationNetwork': # type: ignore
# pylint: disable=arguments-differ
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab=vocab, params=embedder_params)
embedding_dropout = params.pop("embedding_dropout")
pre_encode_feedforward = FeedForward.from_params(params.pop("pre_encode_feedforward"))
encoder = Seq2SeqEncoder.from_params(params.pop("encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop("integrator"))
integrator_dropout = params.pop("integrator_dropout")
output_layer_params = params.pop("output_layer")
if "activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
elmo = params.pop("elmo", None)
if elmo is not None:
elmo = Elmo.from_params(elmo)
use_input_elmo = params.pop_bool("use_input_elmo", False)
use_integrator_output_elmo = params.pop_bool("use_integrator_output_elmo", False)
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
params.assert_empty(cls.__name__)
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
pre_encode_feedforward=pre_encode_feedforward,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
elmo=elmo,
use_input_elmo=use_input_elmo,
use_integrator_output_elmo=use_integrator_output_elmo,
initializer=initializer,
regularizer=regularizer)