def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2VecEncoder,
classifier_feedforward: FeedForward,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
# raise ValueError(self.vocab.get_vocab_size("tokens"))
# raise ValueError(text_field_embedder.get_output_dim())
if text_field_embedder.get_output_dim() != encoder.get_input_dim():
raise ConfigurationError("The output dimension of the text_field_embedder must match the "
"input dimension of the title_encoder. Found {} and {}, "
"respectively.".format(text_field_embedder.get_output_dim(),
encoder.get_input_dim()))
self.text_field_embedder = text_field_embedder
self.num_classes = self.vocab.get_vocab_size("labels")
self.encoder = encoder
self.classifier_feedforward = classifier_feedforward
self.metrics = {
"multilabel-f1": MultiLabelF1Measure(),
'accuracy': BooleanAccuracy()
}
self.pearson_r = PearsonCorrelation()
self.loss = nn.MultiLabelSoftMarginLoss() #BCEWithLogitsLoss()
self._threshold = 0.5
initializer(self)
class WS353(Metric):
def __init__(self, sim_file_path: str) -> None:
self._sim_data = []
self._sim_gold = []
self._data_reader = KoWikiReader()
self._pearson = PearsonCorrelation()
with open(sim_file_path, 'r', encoding='utf-8') as f:
f.readline()
for line in f:
w1, w2, score = line.strip().split('\t')
self._sim_data.append((w1, w2))
self._sim_gold.append(float(score))
self._sim_gold = torch.tensor(self._sim_gold)
@overrides
def __call__(self,
vocab: Vocabulary,
embedder: SyllableEmbedder,
cuda_device: torch.device,
print_mode: bool = False) -> None:
preds = []
for i in range(len(self._sim_data)):
w1, w2 = self._sim_data[i]
w1 = self._data_reader.text_to_instance(source=Token(w1))['source']
w2 = self._data_reader.text_to_instance(source=Token(w2))['source']
w1.index(vocab)
w2.index(vocab)
w1 = w1.as_tensor(w1.get_padding_lengths())['syllables'].to(cuda_device)
w2 = w2.as_tensor(w2.get_padding_lengths())['syllables'].to(cuda_device)
e1, e2 = embedder(w1), embedder(w2)
preds.append(F.cosine_similarity(e1, e2))
self._pearson(torch.tensor(preds), self._sim_gold)
if print_mode:
print('w1\tw2\tgold\tpred')
for ((w1, w2), gold, pred) in zip(self._sim_data, self._sim_gold, preds):
print(f'{w1}\t{w2}\t{gold.item():.2f}\t{pred.item():.2f}')
print(f'pscore: {self.get_metric():.3f}')
@overrides
def get_metric(self, reset: bool = False):
score = self._pearson.get_metric(reset)
if reset:
self.reset()
return score
@overrides
def reset(self):
self._pearson.reset()
def forward(self,
sentence: Dict[str, torch.Tensor],
labels: torch.Tensor = None) -> Dict[str, torch.Tensor]:
# 接下来我们需要实现forward，这是实际计算发生的地方。数据集中的每个实例(Instance)都将（与其他实例(instances)一起批处理）输入forward。
# 张量的输入作为forward方法的输入，并且它们的名称应该是实例（Instances）中字段（fields）的名称。
# 在这种情况下，我们有一个句子字段(sentence field)和（可能）标签字段(labels field)，所以我们将相应地构建我们的forward：
mask = get_text_field_mask(sentence)
# AllenNLP设计用于批量输入，但不同的输入序列具有不同的长度。
# 因此，AllenNLP填充（padding）较短的输入，以便批处理具有统一的形状，这意味着我们的计算需要使用掩码(mask)来排除填充。
# 这里我们只使用效用函数(utility function) get_text_field_mask，它返回与填充和未填充位置相对应的0和1的张量。
embeddings = self.word_embeddings(sentence)
# 我们首先将句子张量（每个句子一系列tokens ID）传递给word_embeddings模块，该模块将每个句子转换为嵌入式张量序列(a sequence of embedded tensors)。
encoder_out = self.encoder(embeddings, mask)
# 接下来，我们将嵌入式张量(embedded tensors)（和掩码(mask)）传递给LSTM，LSTM产生一系列编码(encoded)输出。
tag_logits = self.hidden2tag(encoder_out)
output = {"tag_logits": tag_logits}
# 最后，我们将每个编码输出张量(encoded output tensor)传递给前馈层(feedforward)，以产生对应于各种标签(tags)的logits。
if labels is not None:
self.accuracy(tag_logits, labels, mask)
output["loss"] = sequence_cross_entropy_with_logits(
tag_logits, labels, mask)
logits_flat = tag_logits.view(-1, tag_logits.size(-1))
# shape : (batch * sequence_length, num_classes)
log_probs_flat = torch.nn.functional.log_softmax(logits_flat,
dim=-1)
# shape : (batch * max_len, 1)
targets_flat = labels.view(-1, 1).long()
negative_log_likelihood_flat = -torch.gather(
log_probs_flat, dim=1, index=targets_flat)
# shape : (batch, sequence_length)
negative_log_likelihood = negative_log_likelihood_flat.view(
*labels.size())
# shape : (batch, sequence_length)
negative_log_likelihood = negative_log_likelihood * mask.float()
from allennlp.training.metrics import PearsonCorrelation
self.m = PearsonCorrelation()
self.m(predictions=negative_log_likelihood,
gold_labels=labels.float())
# 和以前一样，标签是可选的，因为我们可能希望运行此模型来对未标记的数据进行预测。
# 如果我们有标签，那么我们使用它们来更新我们的准确度指标（accuracy metric）并计算输出中的“损失(loss)”。
return output
def __init__(self, sim_file_path: str) -> None:
self._sim_data = []
self._sim_gold = []
self._data_reader = KoWikiReader()
self._pearson = PearsonCorrelation()
with open(sim_file_path, 'r', encoding='utf-8') as f:
f.readline()
for line in f:
w1, w2, score = line.strip().split('\t')
self._sim_data.append((w1, w2))
self._sim_gold.append(float(score))
self._sim_gold = torch.tensor(self._sim_gold)
def test_pearson_correlation_unmasked_computation(self):
pearson_correlation = PearsonCorrelation()
batch_size = 100
num_labels = 10
predictions_1 = np.random.randn(batch_size, num_labels).astype("float32")
labels_1 = 0.5 * predictions_1 + np.random.randn(batch_size, num_labels).astype("float32")
predictions_2 = np.random.randn(1).repeat(num_labels).astype("float32")
predictions_2 = predictions_2[np.newaxis, :].repeat(batch_size, axis=0)
labels_2 = np.random.randn(1).repeat(num_labels).astype("float32")
labels_2 = 0.5 * predictions_2 + labels_2[np.newaxis, :].repeat(batch_size, axis=0)
# in most cases, the data is constructed like predictions_1, the data of such a batch different.
# but in a few cases, for example, predictions_2, the data of such a batch is exactly the same.
predictions_labels = [(predictions_1, labels_1), (predictions_2, labels_2)]
stride = 10
for predictions, labels in predictions_labels:
pearson_correlation.reset()
for i in range(batch_size // stride):
timestep_predictions = torch.FloatTensor(predictions[stride * i:stride * (i + 1), :])
timestep_labels = torch.FloatTensor(labels[stride * i:stride * (i + 1), :])
expected_pearson_correlation = pearson_corrcoef(predictions[:stride * (i + 1), :].reshape(-1),
labels[:stride * (i + 1), :].reshape(-1))
pearson_correlation(timestep_predictions, timestep_labels)
assert_allclose(expected_pearson_correlation, pearson_correlation.get_metric(), rtol=1e-5)
# Test reset
pearson_correlation.reset()
pearson_correlation(torch.FloatTensor(predictions), torch.FloatTensor(labels))
assert_allclose(pearson_corrcoef(predictions.reshape(-1), labels.reshape(-1)),
pearson_correlation.get_metric(), rtol=1e-5)
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
seq2vec_encoder: Seq2VecEncoder,
seq2seq_encoder: Seq2SeqEncoder = None,
dropout: float = None,
scale: float = 1,
label_namespace: str = "labels",
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
if seq2seq_encoder:
self._seq2seq_encoder = seq2seq_encoder
else:
self._seq2seq_encoder = None
self._seq2vec_encoder = seq2vec_encoder
self._classifier_input_dim = self._seq2vec_encoder.get_output_dim(
) * 2 # run encoder seperately and concat the result
if dropout:
self._dropout = torch.nn.Dropout(dropout)
self._dropout_a = torch.nn.Dropout(dropout)
self._dropout_b = torch.nn.Dropout(dropout)
else:
self._dropout = None
self._label_namespace = label_namespace
self._num_labels = 1 # because we're running a regression task
self._scale = scale
self.__first = True
self._mlp_dims = [self._classifier_input_dim] * 3
self._mlp_layers = torch.nn.ModuleList()
for i, j in zip(self._mlp_dims, self._mlp_dims[1:]):
self._mlp_layers.append(torch.nn.Linear(i, j))
self._mlp_layers.append(torch.nn.ReLU())
if dropout:
self._mlp_layers.append(torch.nn.Dropout(dropout))
self._classification_layer = torch.nn.Linear(
self._classifier_input_dim, self._num_labels)
self._metric = PearsonCorrelation()
self._similarity = torch.nn.CosineSimilarity()
self._loss = torch.nn.MSELoss()
initializer(self)
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
similarity_function: SimilarityFunction,
projection_feedforward: FeedForward,
inference_encoder: Seq2SeqEncoder,
output_feedforward: FeedForward,
output_logit: FeedForward,
dropout: float = 0.5,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
self._encoder = encoder
self._matrix_attention = LegacyMatrixAttention(similarity_function)
self._projection_feedforward = projection_feedforward
self._inference_encoder = inference_encoder
if dropout:
self.dropout = torch.nn.Dropout(dropout)
self.rnn_input_dropout = InputVariationalDropout(dropout)
else:
self.dropout = None
self.rnn_input_dropout = None
self._output_feedforward = output_feedforward
self._num_labels = 1
check_dimensions_match(text_field_embedder.get_output_dim(),
encoder.get_input_dim(),
"text field embedding dim", "encoder input dim")
check_dimensions_match(encoder.get_output_dim() * 4,
projection_feedforward.get_input_dim(),
"encoder output dim",
"projection feedforward input")
check_dimensions_match(projection_feedforward.get_output_dim(),
inference_encoder.get_input_dim(),
"proj feedforward output dim",
"inference lstm input dim")
self._metric = PearsonCorrelation()
self._loss = torch.nn.MSELoss()
initializer(self)
def __init__(self, word_embeddings: TextFieldEmbedder,
encoder: Seq2VecEncoder, vocab: Vocabulary) -> None:
super().__init__(vocab)
self.word_embeddings = word_embeddings
self.encoder = encoder
hidden_dim = 128
self.mlp = torch.nn.Sequential(
torch.nn.Linear(in_features=encoder.get_output_dim() * 4,
out_features=hidden_dim), torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_dim, out_features=hidden_dim),
torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_dim, out_features=1))
self.covar = Covariance()
self.pearson = PearsonCorrelation()
def test_pearson_correlation_masked_computation(self, device: str):
pearson_correlation = PearsonCorrelation()
batch_size = 100
num_labels = 10
predictions_1 = torch.randn(batch_size, num_labels, device=device)
labels_1 = 0.5 * predictions_1 + torch.randn(
batch_size, num_labels, device=device)
predictions_2 = torch.randn(1, device=device).expand(num_labels)
predictions_2 = predictions_2.unsqueeze(0).expand(batch_size, -1)
labels_2 = torch.randn(1, device=device).expand(num_labels)
labels_2 = 0.5 * predictions_2 + labels_2.unsqueeze(0).expand(
batch_size, -1)
predictions_labels = [(predictions_1, labels_1),
(predictions_2, labels_2)]
# Random binary mask
mask = torch.randint(0,
2,
size=(batch_size, num_labels),
device=device).bool()
stride = 10
for predictions, labels in predictions_labels:
pearson_correlation.reset()
for i in range(batch_size // stride):
timestep_predictions = predictions[stride * i:stride *
(i + 1), :]
timestep_labels = labels[stride * i:stride * (i + 1), :]
timestep_mask = mask[stride * i:stride * (i + 1), :]
expected_pearson_correlation = pearson_corrcoef(
predictions[:stride * (i + 1), :].view(-1).cpu().numpy(),
labels[:stride * (i + 1), :].view(-1).cpu().numpy(),
fweights=mask[:stride * (i + 1), :].view(-1).cpu().numpy(),
)
pearson_correlation(timestep_predictions, timestep_labels,
timestep_mask)
assert_allclose(expected_pearson_correlation,
pearson_correlation.get_metric())
# Test reset
pearson_correlation.reset()
pearson_correlation(predictions, labels, mask)
expected_pearson_correlation = pearson_corrcoef(
predictions.view(-1).cpu().numpy(),
labels.view(-1).cpu().numpy(),
fweights=mask.view(-1).cpu().numpy(),
)
assert_allclose(expected_pearson_correlation,
pearson_correlation.get_metric())
def test_pearson_correlation_unmasked_computation(self):
pearson_correlation = PearsonCorrelation()
batch_size = 100
num_labels = 10
predictions = np.random.randn(batch_size, num_labels).astype("float32")
labels = 0.5 * predictions + np.random.randn(
batch_size, num_labels).astype("float32")
stride = 10
for i in range(batch_size // stride):
timestep_predictions = torch.FloatTensor(
predictions[stride * i:stride * (i + 1), :])
timestep_labels = torch.FloatTensor(labels[stride * i:stride *
(i + 1), :])
expected_pearson_correlation = np.corrcoef(
predictions[:stride * (i + 1), :].reshape(-1),
labels[:stride * (i + 1), :].reshape(-1))[0, 1]
pearson_correlation(timestep_predictions, timestep_labels)
assert_allclose(expected_pearson_correlation,
pearson_correlation.get_metric(),
rtol=1e-5)
# Test reset
pearson_correlation.reset()
pearson_correlation(torch.FloatTensor(predictions),
torch.FloatTensor(labels))
assert_allclose(np.corrcoef(predictions.reshape(-1),
labels.reshape(-1))[0, 1],
pearson_correlation.get_metric(),
rtol=1e-5)
class RuseModel(Model):
def __init__(self, word_embeddings: TextFieldEmbedder,
encoder: Seq2VecEncoder, vocab: Vocabulary) -> None:
super().__init__(vocab)
self.word_embeddings = word_embeddings
self.encoder = encoder
hidden_dim = 128
self.mlp = torch.nn.Sequential(
torch.nn.Linear(in_features=encoder.get_output_dim() * 4,
out_features=hidden_dim), torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_dim, out_features=hidden_dim),
torch.nn.Tanh(),
torch.nn.Linear(in_features=hidden_dim, out_features=1))
self.covar = Covariance()
self.pearson = PearsonCorrelation()
def forward(self, mt_sent: Dict[str, torch.Tensor],
ref_sent: Dict[str, torch.Tensor], human_score: np.ndarray,
origin: str) -> Dict[str, torch.Tensor]:
mt_mask = get_text_field_mask(mt_sent)
ref_mask = get_text_field_mask(ref_sent)
mt_embeddings = self.word_embeddings(mt_sent)
ref_embeddings = self.word_embeddings(ref_sent)
mt_encoder_out = self.encoder(mt_embeddings, mt_mask)
ref_encoder_out = self.encoder(ref_embeddings, ref_mask)
input = torch.cat((mt_encoder_out, ref_encoder_out,
torch.mul(mt_encoder_out, ref_encoder_out),
torch.abs(mt_encoder_out - ref_encoder_out)), 1)
reg = self.mlp(input)
output = {"reg": reg}
if human_score is not None:
# run metric calculation
self.covar(reg, human_score)
self.pearson(reg, human_score)
# calculate mean squared error
delta = reg - human_score
output["loss"] = torch.mul(delta, delta).sum()
return output
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
return {
"covar": self.covar.get_metric(reset),
"pearson": self.pearson.get_metric(reset)
}
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
seq2vec_encoder: Seq2VecEncoder,
seq2seq_encoder: Seq2SeqEncoder = None,
dropout: float = None,
scale: float = 1,
label_namespace: str = "labels",
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
if seq2seq_encoder:
self._seq2seq_encoder = seq2seq_encoder
else:
self._seq2seq_encoder = None
self._seq2vec_encoder = seq2vec_encoder
self._classifier_input_dim = self._seq2vec_encoder.get_output_dim()
if dropout:
self._dropout = torch.nn.Dropout(dropout)
else:
self._dropout = None
self._label_namespace = label_namespace
self._num_labels = 1 # because we're running a regression task
self._scale = scale
self._classification_layer = torch.nn.Linear(
self._classifier_input_dim, self._num_labels)
self._metric = PearsonCorrelation()
self._loss = torch.nn.MSELoss()
initializer(self)
def test_pearson_correlation_unmasked_computation(self, device: str):
pearson_correlation = PearsonCorrelation()
batch_size = 100
num_labels = 10
predictions_1 = torch.randn(batch_size, num_labels, device=device)
labels_1 = 0.5 * predictions_1 + torch.randn(
batch_size, num_labels, device=device)
predictions_2 = torch.randn(1, device=device).expand(num_labels)
predictions_2 = predictions_2.unsqueeze(0).expand(batch_size, -1)
labels_2 = torch.randn(1, device=device).expand(num_labels)
labels_2 = 0.5 * predictions_2 + labels_2.unsqueeze(0).expand(
batch_size, -1)
# in most cases, the data is constructed like predictions_1, the data of such a batch different.
# but in a few cases, for example, predictions_2, the data of such a batch is exactly the same.
predictions_labels = [(predictions_1, labels_1),
(predictions_2, labels_2)]
stride = 10
for predictions, labels in predictions_labels:
pearson_correlation.reset()
for i in range(batch_size // stride):
timestep_predictions = predictions[stride * i:stride *
(i + 1), :]
timestep_labels = labels[stride * i:stride * (i + 1), :]
expected_pearson_correlation = pearson_corrcoef(
predictions[:stride * (i + 1), :].view(-1).cpu().numpy(),
labels[:stride * (i + 1), :].view(-1).cpu().numpy(),
)
pearson_correlation(timestep_predictions, timestep_labels)
assert_allclose(expected_pearson_correlation,
pearson_correlation.get_metric())
# Test reset
pearson_correlation.reset()
pearson_correlation(predictions, labels)
assert_allclose(
pearson_corrcoef(
predictions.view(-1).cpu().numpy(),
labels.view(-1).cpu().numpy()),
pearson_correlation.get_metric(),
)
def test_pearson_correlation_masked_computation(self):
pearson_correlation = PearsonCorrelation()
batch_size = 100
num_labels = 10
predictions_1 = np.random.randn(batch_size, num_labels).astype("float32")
labels_1 = 0.5 * predictions_1 + np.random.randn(batch_size, num_labels).astype("float32")
predictions_2 = np.random.randn(1).repeat(num_labels).astype("float32")
predictions_2 = predictions_2[np.newaxis, :].repeat(batch_size, axis=0)
labels_2 = np.random.randn(1).repeat(num_labels).astype("float32")
labels_2 = 0.5 * predictions_2 + labels_2[np.newaxis, :].repeat(batch_size, axis=0)
predictions_labels = [(predictions_1, labels_1), (predictions_2, labels_2)]
# Random binary mask
mask = np.random.randint(0, 2, size=(batch_size, num_labels)).astype("float32")
stride = 10
for predictions, labels in predictions_labels:
pearson_correlation.reset()
for i in range(batch_size // stride):
timestep_predictions = torch.FloatTensor(predictions[stride * i:stride * (i + 1), :])
timestep_labels = torch.FloatTensor(labels[stride * i:stride * (i + 1), :])
timestep_mask = torch.FloatTensor(mask[stride * i:stride * (i + 1), :])
expected_pearson_correlation = pearson_corrcoef(predictions[:stride * (i + 1), :].reshape(-1),
labels[:stride * (i + 1), :].reshape(-1),
fweights=mask[:stride * (i + 1), :].reshape(-1))
pearson_correlation(timestep_predictions, timestep_labels, timestep_mask)
assert_allclose(expected_pearson_correlation, pearson_correlation.get_metric(), rtol=1e-5)
# Test reset
pearson_correlation.reset()
pearson_correlation(torch.FloatTensor(predictions),
torch.FloatTensor(labels), torch.FloatTensor(mask))
expected_pearson_correlation = pearson_corrcoef(predictions.reshape(-1), labels.reshape(-1),
fweights=mask.reshape(-1))
assert_allclose(expected_pearson_correlation, pearson_correlation.get_metric(), rtol=1e-5)
def test_distributed_pearson(self):
batch_size = 10
num_labels = 10
predictions = torch.randn(batch_size, num_labels)
labels = 0.5 * predictions + torch.randn(batch_size, num_labels)
expected_pearson_correlation = pearson_corrcoef(
predictions.view(-1).cpu().numpy(),
labels.view(-1).cpu().numpy(),
)
predictions = [predictions[:5], predictions[5:]]
labels = [labels[:5], labels[5:]]
metric_kwargs = {"predictions": predictions, "gold_labels": labels}
run_distributed_test(
[-1, -1],
global_distributed_metric,
PearsonCorrelation(),
metric_kwargs,
expected_pearson_correlation,
exact=(0.0001, 1e-01),
)
def test_pearson_correlation_masked_computation(self):
pearson_correlation = PearsonCorrelation()
batch_size = 100
num_labels = 10
predictions = np.random.randn(batch_size, num_labels).astype("float32")
labels = 0.5 * predictions + np.random.randn(
batch_size, num_labels).astype("float32")
# Random binary mask
mask = np.random.randint(0, 2, size=(batch_size,
num_labels)).astype("float32")
stride = 10
for i in range(batch_size // stride):
timestep_predictions = torch.FloatTensor(
predictions[stride * i:stride * (i + 1), :])
timestep_labels = torch.FloatTensor(labels[stride * i:stride *
(i + 1), :])
timestep_mask = torch.FloatTensor(mask[stride * i:stride *
(i + 1), :])
covariance_matrices = np.cov(
predictions[:stride * (i + 1), :].reshape(-1),
labels[:stride * (i + 1), :].reshape(-1),
fweights=mask[:stride * (i + 1), :].reshape(-1))
expected_pearson_correlation = covariance_matrices[0, 1] / np.sqrt(
covariance_matrices[0, 0] * covariance_matrices[1, 1])
pearson_correlation(timestep_predictions, timestep_labels,
timestep_mask)
assert_allclose(expected_pearson_correlation,
pearson_correlation.get_metric(),
rtol=1e-5)
# Test reset
pearson_correlation.reset()
pearson_correlation(torch.FloatTensor(predictions),
torch.FloatTensor(labels), torch.FloatTensor(mask))
covariance_matrices = np.cov(predictions.reshape(-1),
labels.reshape(-1),
fweights=mask.reshape(-1))
expected_pearson_correlation = covariance_matrices[0, 1] / np.sqrt(
covariance_matrices[0, 0] * covariance_matrices[1, 1])
assert_allclose(expected_pearson_correlation,
pearson_correlation.get_metric(),
rtol=1e-5)
class AttMT(Model):
"""
This ``Model`` implements the baseline model with attention
Parameters
----------
vocab : ``Vocabulary``
text_field_embedder : ``TextFieldEmbedder``
Used to embed the ``mtref`` and ``mtsys`` ``TextFields`` we get as input to the
model.
encoder : ``Seq2SeqEncoder``
Used to encode the mtref and mtsys.
similarity_function : ``SimilarityFunction``
This is the similarity function used when computing the similarity matrix between encoded
words in the mtref and words in the mtsys.
output_feedforward : ``FeedForward``
Used to prepare the concatenated mtref and mtsys for prediction.
output_logit: FeedForward,
legacy input that does nothing
dropout : ``float``, optional (default=0.5)
Dropout percentage to use.
initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
Used to initialize the model parameters.
regularizer : ``RegularizerApplicator``, optional (default=``None``)
If provided, will be used to calculate the regularization penalty during training.
"""
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
similarity_function: SimilarityFunction,
output_feedforward: FeedForward,
output_logit: FeedForward,
dropout: float = 0.5,
aggr_type: str = "both",
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
self._encoder = encoder
self._matrix_attention = LegacyMatrixAttention(similarity_function)
if dropout:
self.dropout = torch.nn.Dropout(dropout)
self.rnn_input_dropout = InputVariationalDropout(dropout)
else:
self.dropout = None
self.rnn_input_dropout = None
self._output_feedforward = output_feedforward
self._output_logit = output_logit
self._num_labels = 1
check_dimensions_match(text_field_embedder.get_output_dim(),
encoder.get_input_dim(),
"text field embedding dim", "encoder input dim")
# check_dimensions_match(encoder.get_output_dim() * 4, projection_feedforward.get_input_dim(),
# "encoder output dim", "projection feedforward input")
# check_dimensions_match(projection_feedforward.get_output_dim(), inference_encoder.get_input_dim(),
# "proj feedforward output dim", "inference lstm input dim")
self._aggr_type = aggr_type
self._metric = PearsonCorrelation()
self._loss = torch.nn.MSELoss()
initializer(self)
def forward(
self, # type: ignore
ref: Dict[str, torch.LongTensor],
mt: Dict[str, torch.LongTensor],
score: torch.IntTensor = None,
# pylint:disable=unused-argument
) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
ref : Dict[str, torch.LongTensor]
From a ``TextField``
mt : Dict[str, torch.LongTensor]
From a ``TextField``
score : torch.IntTensor, optional (default = None)
From a ``NumericField``
Returns
-------
An output dictionary consisting of:
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
embedded_mtref = self._text_field_embedder(ref)
embedded_mtsys = self._text_field_embedder(mt)
mtref_mask = get_text_field_mask(ref).float()
mtsys_mask = get_text_field_mask(mt).float()
# apply dropout for LSTM
if self.rnn_input_dropout:
embedded_mtref = self.rnn_input_dropout(embedded_mtref)
embedded_mtsys = self.rnn_input_dropout(embedded_mtsys)
# encode mtref and mtsys
# Shape: (batch_size, mtref/sys_length, modeldim*bi? =600)
encoded_mtref = self._encoder(embedded_mtref, mtref_mask)
encoded_mtsys = self._encoder(embedded_mtsys, mtsys_mask)
# Shape: (batch_size, mtref_length, mtsys_length)
similarity_matrix = self._matrix_attention(encoded_mtref,
encoded_mtsys)
# Shape: (batch_size, mtref_length, mtsys_length)
p2h_attention = masked_softmax(similarity_matrix, mtsys_mask)
# Shape: (batch_size, mtref_length, modeldim*2)
attended_mtref = weighted_sum(encoded_mtsys, p2h_attention)
# Shape: (batch_size, mtsys_length, mtref_length)
h2p_attention = masked_softmax(
similarity_matrix.transpose(1, 2).contiguous(), mtref_mask)
# Shape: (batch_size, mtsys_length, modeldim*2)
attended_mtsys = weighted_sum(encoded_mtref, h2p_attention)
# The pooling layer -- max and avg pooling.
# (batch_size, model_dim *2 = 600)
v_a_max, _ = replace_masked_values(attended_mtref,
mtref_mask.unsqueeze(-1),
-1e7).max(dim=1)
# (batch_size, model_dim *2 = 600)
v_b_max, _ = replace_masked_values(attended_mtsys,
mtsys_mask.unsqueeze(-1),
-1e7).max(dim=1)
v_a_avg = torch.sum(attended_mtref * mtref_mask.unsqueeze(-1),
dim=1) / torch.sum(mtref_mask, 1, keepdim=True)
v_b_avg = torch.sum(attended_mtsys * mtsys_mask.unsqueeze(-1),
dim=1) / torch.sum(mtsys_mask, 1, keepdim=True)
if self._aggr_type == 'both':
# Now concat
# (batch_size, model_dim *2* 2 * 4)
v_all = torch.cat([
v_a_avg, v_b_avg, v_a_avg - v_b_avg, v_a_avg * v_b_avg,
v_a_max, v_b_max, v_a_max - v_b_max, v_a_max * v_b_max
],
dim=1)
elif self._aggr_type == 'max':
# (batch_size, model_dim *2* 4)
v_all = torch.cat(
[v_a_max, v_b_max, v_a_max - v_b_max, v_a_max * v_b_max],
dim=1)
elif self._aggr_type == 'avg':
v_all = torch.cat(
[v_a_avg, v_b_avg, v_a_avg - v_b_avg, v_a_avg * v_b_avg],
dim=1)
# the final MLP -- apply dropout to input, and MLP applies to output & hidden
if self.dropout:
v_all = self.dropout(v_all)
pred = self._output_feedforward(v_all)
output_dict = {'pred': pred}
if score is not None:
loss = self._loss(pred, score)
self._metric(pred, score)
output_dict["loss"] = loss
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
return {'pearson': self._metric.get_metric(reset)}
def __init__(self,
vocab: Vocabulary,
token_representation_dim: int,
encoder: Optional[Seq2SeqEncoder] = None,
decoder: Optional[Union[FeedForward, str]] = None,
contextualizer: Optional[Contextualizer] = None,
pretrained_file: Optional[str] = None,
transfer_contextualizer_from_pretrained_file: bool = False,
transfer_encoder_from_pretrained_file: bool = False,
freeze_encoder: bool = False,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super(SelectiveRegressor, self).__init__(vocab, regularizer)
self._token_representation_dim = token_representation_dim
self._contextualizer = contextualizer
if encoder is None:
encoder = PassThroughEncoder(
input_dim=self._token_representation_dim)
self._encoder = encoder
# Load the contextualizer and encoder weights from the
# pretrained_file if applicable
if pretrained_file:
archive = None
if self._contextualizer and transfer_contextualizer_from_pretrained_file:
logger.info("Attempting to load contextualizer weights from "
"pretrained_file at {}".format(pretrained_file))
archive = load_archive(cached_path(pretrained_file))
contextualizer_state = archive.model._contextualizer.state_dict(
)
contextualizer_layer_num = self._contextualizer._layer_num
self._contextualizer.load_state_dict(contextualizer_state)
if contextualizer_layer_num is not None:
logger.info("Setting layer num to {}".format(
contextualizer_layer_num))
self._contextualizer.set_layer_num(
contextualizer_layer_num)
else:
self._contextualizer.reset_layer_num()
logger.info("Successfully loaded contextualizer weights!")
if transfer_encoder_from_pretrained_file:
logger.info("Attempting to load encoder weights from "
"pretrained_file at {}".format(pretrained_file))
if archive is None:
archive = load_archive(cached_path(pretrained_file))
encoder_state = archive.model._encoder.state_dict()
self._encoder.load_state_dict(encoder_state)
logger.info("Successfully loaded encoder weights!")
self._freeze_encoder = freeze_encoder
for parameter in self._encoder.parameters():
# If freeze is true, requires_grad should be false and vice versa.
parameter.requires_grad_(not self._freeze_encoder)
if decoder is None or decoder == "linear":
# Create the default decoder (logistic regression) if it is not provided.
decoder = FeedForward.from_params(
Params({
"input_dim": self._encoder.get_output_dim(),
"num_layers": 1,
"hidden_dims": 1,
"activations": "linear"
}))
logger.info("No decoder provided to model, using default "
"decoder: {}".format(decoder))
elif decoder == "mlp":
# Create the MLP decoder
decoder = FeedForward.from_params(
Params({
"input_dim": self._encoder.get_output_dim(),
"num_layers": 2,
"hidden_dims": [1024, 1],
"activations": ["relu", "linear"]
}))
logger.info("Using MLP decoder: {}".format(decoder))
self._decoder = decoder
check_dimensions_match(self._token_representation_dim,
self._encoder.get_input_dim(),
"token representation dim", "encoder input dim")
check_dimensions_match(self._encoder.get_output_dim(),
self._decoder.get_input_dim(),
"encoder output dim", "decoder input dim")
check_dimensions_match(self._decoder.get_output_dim(), 1,
"decoder output dim",
"1, since we're predicting a real value")
# SmoothL1Loss as described in "Neural Models of Factuality" (NAACL 2018)
self.loss = torch.nn.SmoothL1Loss(reduction="none")
self.metrics = {
"mae": MeanAbsoluteError(),
"pearson_r": PearsonCorrelation()
}
# Whether to run in error analysis mode or not, see commands.error_analysis
self.error_analysis = False
logger.info("Applying initializer...")
initializer(self)
class BERTMoji(Model):
"""
This ``Model`` performs text classification for an academic paper. We assume we're given a
title and an abstract, and we predict some output label.
The basic model structure: we'll embed the title and the abstract, and encode each of them with
separate Seq2VecEncoders, getting a single vector representing the content of each. We'll then
concatenate those two vectors, and pass the result through a feedforward network, the output of
which we'll use as our scores for each label.
Parameters
----------
vocab : ``Vocabulary``, required
A Vocabulary, required in order to compute sizes for input/output projections.
text_field_embedder : ``TextFieldEmbedder``, required
Used to embed the ``tokens`` ``TextField`` we get as input to the model.
encoder : ``Seq2VecEncoder``
The encoder that we will use to convert the text to a vector.
classifier_feedforward : ``FeedForward``
initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
Used to initialize the model parameters.
regularizer : ``RegularizerApplicator``, optional (default=``None``)
If provided, will be used to calculate the regularization penalty during training.
"""
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2VecEncoder,
classifier_feedforward: FeedForward,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
# raise ValueError(self.vocab.get_vocab_size("tokens"))
# raise ValueError(text_field_embedder.get_output_dim())
if text_field_embedder.get_output_dim() != encoder.get_input_dim():
raise ConfigurationError("The output dimension of the text_field_embedder must match the "
"input dimension of the title_encoder. Found {} and {}, "
"respectively.".format(text_field_embedder.get_output_dim(),
encoder.get_input_dim()))
self.text_field_embedder = text_field_embedder
self.num_classes = self.vocab.get_vocab_size("labels")
self.encoder = encoder
self.classifier_feedforward = classifier_feedforward
self.metrics = {
"multilabel-f1": MultiLabelF1Measure(),
'accuracy': BooleanAccuracy()
}
self.pearson_r = PearsonCorrelation()
self.loss = nn.MultiLabelSoftMarginLoss() #BCEWithLogitsLoss()
self._threshold = 0.5
initializer(self)
@overrides
def forward(self, # type: ignore
tokens: Dict[str, torch.LongTensor],
label: torch.LongTensor = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, Variable], required
The output of ``TextField.as_array()``.
label : Variable, optional (default = None)
A variable representing the label for each instance in the batch.
Returns
-------
An output dictionary consisting of:
class_probabilities : torch.FloatTensor
A tensor of shape ``(batch_size, num_classes)`` representing a distribution over the
label classes for each instance.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
# print(tokens)
embedded = self.text_field_embedder(tokens)
mask = util.get_text_field_mask(tokens)
encoded = self.encoder(embedded, mask)
logits = self.classifier_feedforward(encoded)
output_dict = {'logits': torch.sigmoid(logits)}
if label is None: # inference
decoded = self.decode(output_dict)
output_dict['decoded'] = decoded
else:
loss = self.loss(logits, label.float())
loss = loss + (1-rsq_loss(logits, label.float()))
self.pearson_r(logits, label.float())
preds = (logits > self._threshold).long()
for metric in self.metrics.values():
metric(preds, label)
output_dict["loss"] = loss
return output_dict
# @overrides
def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does a simple argmax over the class probabilities, converts indices to string labels, and
adds a ``"label"`` key to the dictionary with the result.
"""
def get_scores(row):
scores = ((self.vocab.get_token_from_index(i, namespace="labels"), s) for (i, s) in enumerate(row))
return sorted(scores, key=lambda x: x[1], reverse=True)
class_probabilities = output_dict['logits']
predictions = class_probabilities.cpu().data.numpy()
output_dict['scores'] = list(map(get_scores, predictions))
return output_dict
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
def unpack(m):
if isinstance(m, tuple):
return m[-1]
return m
metrics = {metric_name: unpack(metric.get_metric(reset)) for metric_name, metric in self.metrics.items()}
metrics['pearson_r'] = self.pearson_r.get_metric(reset)
return metrics
class ESIM(Model):
"""
This ``Model`` implements the ESIM sequence model described in `"Enhanced LSTM for Natural Language Inference"
<https://www.semanticscholar.org/paper/Enhanced-LSTM-for-Natural-Language-Inference-Chen-Zhu/83e7654d545fbbaaf2328df365a781fb67b841b4>`_
by Chen et al., 2017.
This code was taken from the AllenNLP repo, and modified for predicting (continuous) scores for MT system outputs
Parameters
----------
vocab : ``Vocabulary``
text_field_embedder : ``TextFieldEmbedder``
Used to embed the ``mtref`` and ``mtsys`` ``TextFields`` we get as input to the
model.
encoder : ``Seq2SeqEncoder``
Used to encode the mtref and mtsys.
similarity_function : ``SimilarityFunction``
This is the similarity function used when computing the similarity matrix between encoded
words in the mtref and words in the mtsys.
projection_feedforward : ``FeedForward``
The feedforward network used to project down the encoded and enhanced mtref and mtsys.
inference_encoder : ``Seq2SeqEncoder``
Used to encode the projected mtref and mtsys for prediction.
output_feedforward : ``FeedForward``
Used to prepare the concatenated mtref and mtsys for prediction.
output_logit: FeedForward,
legacy input that does nothing
dropout : ``float``, optional (default=0.5)
Dropout percentage to use.
initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
Used to initialize the model parameters.
regularizer : ``RegularizerApplicator``, optional (default=``None``)
If provided, will be used to calculate the regularization penalty during training.
"""
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
similarity_function: SimilarityFunction,
projection_feedforward: FeedForward,
inference_encoder: Seq2SeqEncoder,
output_feedforward: FeedForward,
output_logit: FeedForward,
dropout: float = 0.5,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
self._encoder = encoder
self._matrix_attention = LegacyMatrixAttention(similarity_function)
self._projection_feedforward = projection_feedforward
self._inference_encoder = inference_encoder
if dropout:
self.dropout = torch.nn.Dropout(dropout)
self.rnn_input_dropout = InputVariationalDropout(dropout)
else:
self.dropout = None
self.rnn_input_dropout = None
self._output_feedforward = output_feedforward
self._num_labels = 1
check_dimensions_match(text_field_embedder.get_output_dim(),
encoder.get_input_dim(),
"text field embedding dim", "encoder input dim")
check_dimensions_match(encoder.get_output_dim() * 4,
projection_feedforward.get_input_dim(),
"encoder output dim",
"projection feedforward input")
check_dimensions_match(projection_feedforward.get_output_dim(),
inference_encoder.get_input_dim(),
"proj feedforward output dim",
"inference lstm input dim")
self._metric = PearsonCorrelation()
self._loss = torch.nn.MSELoss()
initializer(self)
def forward(
self, # type: ignore
ref: Dict[str, torch.LongTensor],
mt: Dict[str, torch.LongTensor],
score: torch.IntTensor = None # pylint:disable=unused-argument
) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
ref : Dict[str, torch.LongTensor]
From a ``TextField``
mt : Dict[str, torch.LongTensor]
From a ``TextField``
score : torch.IntTensor, optional (default = None)
From a ``NumericField``
Returns
-------
An output dictionary consisting of:
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
# print(worker)
embedded_mtref = self._text_field_embedder(ref)
embedded_mtsys = self._text_field_embedder(mt)
mtref_mask = get_text_field_mask(ref).float()
mtsys_mask = get_text_field_mask(mt).float()
# apply dropout for LSTM
if self.rnn_input_dropout:
embedded_mtref = self.rnn_input_dropout(embedded_mtref)
embedded_mtsys = self.rnn_input_dropout(embedded_mtsys)
# encode mtref and mtsys
# Shape: (batch_size, mtref/sys_length, modeldim*2 =600)
encoded_mtref = self._encoder(embedded_mtref, mtref_mask)
encoded_mtsys = self._encoder(embedded_mtsys, mtsys_mask)
# Shape: (batch_size, mtref_length, mtsys_length)
similarity_matrix = self._matrix_attention(encoded_mtref,
encoded_mtsys)
# Shape: (batch_size, mtref_length, mtsys_length)
p2h_attention = masked_softmax(similarity_matrix, mtsys_mask)
# Shape: (batch_size, mtref_length, embedding_dim)
attended_mtsys = weighted_sum(encoded_mtsys, p2h_attention)
# Shape: (batch_size, mtsys_length, mtref_length)
h2p_attention = masked_softmax(
similarity_matrix.transpose(1, 2).contiguous(), mtref_mask)
# Shape: (batch_size, mtsys_length, embedding_dim)
attended_mtref = weighted_sum(encoded_mtref, h2p_attention)
# the "enhancement" layer
# Shape: (batch_size, mtref/sys_length, modeldim *2 * 4=2400)
mtref_enhanced = torch.cat([
encoded_mtref, attended_mtsys, encoded_mtref - attended_mtsys,
encoded_mtref * attended_mtsys
],
dim=-1)
mtsys_enhanced = torch.cat([
encoded_mtsys, attended_mtref, encoded_mtsys - attended_mtref,
encoded_mtsys * attended_mtref
],
dim=-1)
# The projection layer down to the model dimension. Dropout is not applied before
# projection.
# Shape: (batch_size, mtref/sys_length, modeldim =300)
projected_enhanced_mtref = self._projection_feedforward(mtref_enhanced)
projected_enhanced_mtsys = self._projection_feedforward(mtsys_enhanced)
# Run the inference layer
if self.rnn_input_dropout:
projected_enhanced_mtref = self.rnn_input_dropout(
projected_enhanced_mtref)
projected_enhanced_mtsys = self.rnn_input_dropout(
projected_enhanced_mtsys)
# Shape: (batch_size, mtref/sys_length, modeldim*2 =600)
v_ai = self._inference_encoder(projected_enhanced_mtref, mtref_mask)
v_bi = self._inference_encoder(projected_enhanced_mtsys, mtsys_mask)
# The pooling layer -- max and avg pooling.
# (batch_size, model_dim*2 = 600)
v_a_max, _ = replace_masked_values(v_ai, mtref_mask.unsqueeze(-1),
-1e7).max(dim=1)
# (batch_size, model_dim * 2 = 600)
v_b_max, _ = replace_masked_values(v_bi, mtsys_mask.unsqueeze(-1),
-1e7).max(dim=1)
# (batch_size, model_dim * 2 = 600)
v_a_avg = torch.sum(v_ai * mtref_mask.unsqueeze(-1),
dim=1) / torch.sum(mtref_mask, 1, keepdim=True)
# (batch_size, model_dim * 2 = 600)
v_b_avg = torch.sum(v_bi * mtsys_mask.unsqueeze(-1),
dim=1) / torch.sum(mtsys_mask, 1, keepdim=True)
# Now concat
# (batch_size, model_dim * 2 * 4)
v_all = torch.cat([v_a_avg, v_a_max, v_b_avg, v_b_max], dim=1)
# the final MLP -- apply dropout to input, and MLP applies to output & hidden
if self.dropout:
v_all = self.dropout(v_all)
pred = self._output_feedforward(v_all)
output_dict = {'pred': pred}
if score is not None:
loss = self._loss(pred, score)
self._metric(pred, score)
output_dict["loss"] = loss
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
return {'pearson': self._metric.get_metric(reset)}
class LstmTagger(Model):
'''
您基本上必须实现的另一个类是Model，它是torch.nn.Module的子类。
它的工作原理在很大程度上取决于你，它主要只是需要一个前向方法(forward method)，它接受张量输入并产生一个张量输出的字典，
其中包括你用来训练模型的损失(losss)。
如上所述，我们的模型将包括嵌入层(embedding layer)，序列编码器(sequence encoder)和前馈网络(feedforward network)。
'''
'''
可能看似不寻常的一件事是我们将嵌入器（embedder）和序列编码器(sequence encoder)作为构造函数参数(constructor parameters)传递。
这使我们可以尝试不同的嵌入器(embedders)和编码器(encoders)，而无需更改模型代码。
'''
def __init__(
self,
word_embeddings: TextFieldEmbedder,
# 嵌入层(embedding layer)被指定为AllenNLP TextFieldEmbedder，它表示将tokens转换为张量(tensors)的一般方法。
# （这里我们知道我们想要用学习的张量来表示每个唯一的单词，但是使用通用类(general class)可以让我们轻松地尝试不同类型的嵌入，例如ELMo。）
encoder: Seq2SeqEncoder,
# 类似地，编码器(encoder)被指定为通用Seq2SeqEncoder，即使我们知道我们想要使用LSTM。
# 同样，这使得可以很容易地尝试其他序列编码器(sequence encoders)，例如Transformer。
vocab: Vocabulary
) -> None:
# 每个AllenNLP模型还需要一个词汇表(Vocabulary)，其中包含tokens到索引(indices)和索引标签(labels to indices)的命名空间映射。
super().__init__(vocab)
self.word_embeddings = word_embeddings
self.encoder = encoder
# 请注意，我们必须将vocab传递给基类构造函数(base class constructor)。
self.hidden2tag = torch.nn.Linear(
in_features=encoder.get_output_dim(),
out_features=vocab.get_vocab_size('labels'))
# 前馈层(feed forward layer)不作为参数传入，而是由我们构造。
# 请注意，它会查看编码器(encoder)以查找正确的输入维度并查看词汇表(vocabulary)（特别是在 label->index 映射处）以查找正确的输出维度。
self.accuracy = CategoricalAccuracy()
# 最后要注意的是我们还实例化了一个CategoricalAccuracy指标，我们将用它来跟踪每个训练(training)和验证(validation)epoch的准确性。
def forward(self,
sentence: Dict[str, torch.Tensor],
labels: torch.Tensor = None) -> Dict[str, torch.Tensor]:
# 接下来我们需要实现forward，这是实际计算发生的地方。数据集中的每个实例(Instance)都将（与其他实例(instances)一起批处理）输入forward。
# 张量的输入作为forward方法的输入，并且它们的名称应该是实例（Instances）中字段（fields）的名称。
# 在这种情况下，我们有一个句子字段(sentence field)和（可能）标签字段(labels field)，所以我们将相应地构建我们的forward：
mask = get_text_field_mask(sentence)
# AllenNLP设计用于批量输入，但不同的输入序列具有不同的长度。
# 因此，AllenNLP填充（padding）较短的输入，以便批处理具有统一的形状，这意味着我们的计算需要使用掩码(mask)来排除填充。
# 这里我们只使用效用函数(utility function) get_text_field_mask，它返回与填充和未填充位置相对应的0和1的张量。
embeddings = self.word_embeddings(sentence)
# 我们首先将句子张量（每个句子一系列tokens ID）传递给word_embeddings模块，该模块将每个句子转换为嵌入式张量序列(a sequence of embedded tensors)。
encoder_out = self.encoder(embeddings, mask)
# 接下来，我们将嵌入式张量(embedded tensors)（和掩码(mask)）传递给LSTM，LSTM产生一系列编码(encoded)输出。
tag_logits = self.hidden2tag(encoder_out)
output = {"tag_logits": tag_logits}
# 最后，我们将每个编码输出张量(encoded output tensor)传递给前馈层(feedforward)，以产生对应于各种标签(tags)的logits。
if labels is not None:
self.accuracy(tag_logits, labels, mask)
output["loss"] = sequence_cross_entropy_with_logits(
tag_logits, labels, mask)
logits_flat = tag_logits.view(-1, tag_logits.size(-1))
# shape : (batch * sequence_length, num_classes)
log_probs_flat = torch.nn.functional.log_softmax(logits_flat,
dim=-1)
# shape : (batch * max_len, 1)
targets_flat = labels.view(-1, 1).long()
negative_log_likelihood_flat = -torch.gather(
log_probs_flat, dim=1, index=targets_flat)
# shape : (batch, sequence_length)
negative_log_likelihood = negative_log_likelihood_flat.view(
*labels.size())
# shape : (batch, sequence_length)
negative_log_likelihood = negative_log_likelihood * mask.float()
from allennlp.training.metrics import PearsonCorrelation
self.m = PearsonCorrelation()
self.m(predictions=negative_log_likelihood,
gold_labels=labels.float())
# 和以前一样，标签是可选的，因为我们可能希望运行此模型来对未标记的数据进行预测。
# 如果我们有标签，那么我们使用它们来更新我们的准确度指标（accuracy metric）并计算输出中的“损失(loss)”。
return output
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
return {
"accuracy": self.accuracy.get_metric(reset),
"pearson_correlation": self.m.get_metric(reset)
}
class STSBRegressor(Model):
"""
This ``Model`` implements a basic text regressor. After embedding the text into
a text field, we will optionally encode the embeddings with a ``Seq2SeqEncoder``. The
resulting sequence is pooled using a ``Seq2VecEncoder`` and then passed to
a linear regression layer, which projects into a single value. If a
``Seq2SeqEncoder`` is not provided, we will pass the embedded text directly to the
``Seq2VecEncoder``.
Parameters
----------
vocab : ``Vocabulary``
text_field_embedder : ``TextFieldEmbedder``
Used to embed the input text into a ``TextField``
seq2seq_encoder : ``Seq2SeqEncoder``, optional (default=``None``)
Optional Seq2Seq encoder layer for the input text.
seq2vec_encoder : ``Seq2VecEncoder``
Required Seq2Vec encoder layer. If `seq2seq_encoder` is provided, this encoder
will pool its output. Otherwise, this encoder will operate directly on the output
of the `text_field_embedder`.
dropout : ``float``, optional (default = ``None``)
Dropout percentage to use.
scale : ``float``, optional (default = 1)
Scale regression result is between 0 ~ scale
label_namespace: ``str``, optional (default = "labels")
Vocabulary namespace corresponding to labels. By default, we use the "labels" namespace.
initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
If provided, will be used to initialize the model parameters.
regularizer : ``RegularizerApplicator``, optional (default=``None``)
If provided, will be used to calculate the regularization penalty during training.
"""
def __init__(self,
vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
seq2vec_encoder: Seq2VecEncoder,
seq2seq_encoder: Seq2SeqEncoder = None,
dropout: float = None,
scale: float = 1,
label_namespace: str = "labels",
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
self._text_field_embedder = text_field_embedder
if seq2seq_encoder:
self._seq2seq_encoder = seq2seq_encoder
else:
self._seq2seq_encoder = None
self._seq2vec_encoder = seq2vec_encoder
self._classifier_input_dim = self._seq2vec_encoder.get_output_dim(
) * 2 # run encoder seperately and concat the result
if dropout:
self._dropout = torch.nn.Dropout(dropout)
self._dropout_a = torch.nn.Dropout(dropout)
self._dropout_b = torch.nn.Dropout(dropout)
else:
self._dropout = None
self._label_namespace = label_namespace
self._num_labels = 1 # because we're running a regression task
self._scale = scale
self.__first = True
self._mlp_dims = [self._classifier_input_dim] * 3
self._mlp_layers = torch.nn.ModuleList()
for i, j in zip(self._mlp_dims, self._mlp_dims[1:]):
self._mlp_layers.append(torch.nn.Linear(i, j))
self._mlp_layers.append(torch.nn.ReLU())
if dropout:
self._mlp_layers.append(torch.nn.Dropout(dropout))
self._classification_layer = torch.nn.Linear(
self._classifier_input_dim, self._num_labels)
self._metric = PearsonCorrelation()
self._similarity = torch.nn.CosineSimilarity()
self._loss = torch.nn.MSELoss()
initializer(self)
def forward(
self, # type: ignore
tokens_a: Dict[str, torch.LongTensor],
tokens_b: Dict[str, torch.LongTensor],
label: torch.IntTensor = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, torch.LongTensor]
From a ``TextField``
label : torch.IntTensor, optional (default = None)
From a ``LabelField``
Returns
-------
An output dictionary consisting of:
logits : torch.FloatTensor
A tensor of shape ``(batch_size, 1)`` representing
unnormalized log probabilities of the label.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
tokens = {
"tokens_a": tokens_a["tokens_a"],
"tokens_b": tokens_b["tokens_b"]
}
if self.__first:
self.__first = False
print("tokens: \n")
print(tokens)
# I don't know why tokens_a and tokens_b both includes keys named by each other
tokens_a = {"tokens_a": tokens_a["tokens_a"]}
tokens_b = {"tokens_b": tokens_b["tokens_b"]}
embedded_text = self._text_field_embedder(tokens)
embedded_text_a = embedded_text[
"tokens_a"] # TODO: check the shape for this
mask_a = get_text_field_mask(tokens_a).float()
embedded_text_b = embedded_text["tokens_b"]
mask_b = get_text_field_mask(tokens_b).float()
if self._seq2seq_encoder:
embedded_text_a = self._seq2seq_encoder(embedded_text_a,
mask=mask_a)
embedded_text_b = self._seq2seq_encoder(embedded_text_b,
mask=mask_b)
embedded_text_a = self._seq2vec_encoder(embedded_text_a, mask=mask_a)
embedded_text_b = self._seq2vec_encoder(embedded_text_b, mask=mask_b)
# embedded_text = torch.cat([embedded_text_a, embedded_text_b], dim=-1)
if self._dropout:
embedded_text_a = self._dropout_a(embedded_text_a)
embedded_text_b = self._dropout_b(embedded_text_b)
'''
if self._mlp_layers:
for l in self._mlp_layers:
embedded_text = l(embedded_text)
logits = self._classification_layer(embedded_text)
'''
logits = self._similarity(embedded_text_a, embedded_text_b) * 5
output_dict = {"logits": logits}
if label is not None: # convert the label into a float number and update the metric
label_to_str = lambda l: self.vocab.get_index_to_token_vocabulary(
self._label_namespace).get(l)
label_tensor = torch.tensor(
[
float(label_to_str(int(label[i])))
for i in range(label.shape[0])
],
device=logits.device,
requires_grad=True
) # make sure loss.backward have something to update
loss = self._loss(logits.view(-1), label_tensor)
output_dict["loss"] = loss
self._metric(logits, label_tensor)
return output_dict
@overrides
def decode(
self, output_dict: Dict[str,
torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does a simple argmax over the probabilities, converts index to string label, and
add ``"label"`` key to the dictionary with the result.
"""
# update this part to generate a float number result as similarity score
predictions = output_dict["logits"]
if predictions.dim() == 2:
predictions_list = [
predictions[i] for i in range(predictions.shape[0])
]
else:
predictions_list = [predictions]
classes = []
for prediction in predictions_list:
label_idx = "{:.1f}".format(prediction.long())
label_str = (self.vocab.get_index_to_token_vocabulary(
self._label_namespace).get(label_idx, str(label_idx)))
classes.append(label_str)
output_dict["label"] = classes
return output_dict
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
metrics = {'PearsonCorrelation': self._metric.get_metric(reset)}
return metrics
