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https://github.com/storytold/storyteller-ml.git
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Introduce code from Rust monorepo. This is a mess
This commit is contained in:
Brandon Thomas
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from nemo.collections.nlp.models.token_classification.punctuation_capitalization_model import (
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PunctuationCapitalizationModel,
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)
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from nemo.collections.nlp.models.token_classification.token_classification_model import TokenClassificationModel
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+490
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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from typing import Dict, List, Optional
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import onnx
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import torch
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from omegaconf import DictConfig, OmegaConf
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from pytorch_lightning import Trainer
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from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss
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from nemo.collections.nlp.data.token_classification.punctuation_capitalization_dataset import (
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BertPunctuationCapitalizationDataset,
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BertPunctuationCapitalizationInferDataset,
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)
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from nemo.collections.nlp.metrics.classification_report import ClassificationReport
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from nemo.collections.nlp.models.nlp_model import NLPModel
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from nemo.collections.nlp.modules.common import TokenClassifier
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from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
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from nemo.collections.nlp.parts.utils_funcs import tensor2list
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from nemo.core.classes.common import PretrainedModelInfo, typecheck
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from nemo.core.classes.exportable import Exportable, ExportFormat
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from nemo.core.neural_types import LogitsType, NeuralType
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from nemo.utils import logging
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__all__ = ['PunctuationCapitalizationModel']
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class PunctuationCapitalizationModel(NLPModel, Exportable):
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@property
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def input_types(self) -> Optional[Dict[str, NeuralType]]:
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return self.bert_model.input_types
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@property
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def output_types(self) -> Optional[Dict[str, NeuralType]]:
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return {
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"punct_logits": NeuralType(('B', 'T', 'C'), LogitsType()),
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"capit_logits": NeuralType(('B', 'T', 'C'), LogitsType()),
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}
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def __init__(self, cfg: DictConfig, trainer: Trainer = None):
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"""
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Initializes BERT Punctuation and Capitalization model.
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"""
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self.setup_tokenizer(cfg.tokenizer)
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super().__init__(cfg=cfg, trainer=trainer)
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self.bert_model = get_lm_model(
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pretrained_model_name=cfg.language_model.pretrained_model_name,
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config_file=self.register_artifact('language_model.config_file', cfg.language_model.config_file),
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config_dict=OmegaConf.to_container(cfg.language_model.config) if cfg.language_model.config else None,
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checkpoint_file=cfg.language_model.lm_checkpoint,
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vocab_file=self.register_artifact('tokenizer.vocab_file', cfg.tokenizer.vocab_file),
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)
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self.punct_classifier = TokenClassifier(
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hidden_size=self.bert_model.config.hidden_size,
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num_classes=len(self._cfg.punct_label_ids),
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activation=cfg.punct_head.activation,
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log_softmax=False,
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dropout=cfg.punct_head.fc_dropout,
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num_layers=cfg.punct_head.punct_num_fc_layers,
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use_transformer_init=cfg.punct_head.use_transformer_init,
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)
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self.capit_classifier = TokenClassifier(
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hidden_size=self.bert_model.config.hidden_size,
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num_classes=len(self._cfg.capit_label_ids),
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activation=cfg.capit_head.activation,
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log_softmax=False,
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dropout=cfg.capit_head.fc_dropout,
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num_layers=cfg.capit_head.capit_num_fc_layers,
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use_transformer_init=cfg.capit_head.use_transformer_init,
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)
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self.loss = CrossEntropyLoss(logits_ndim=3)
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self.agg_loss = AggregatorLoss(num_inputs=2)
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# setup to track metrics
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self.punct_class_report = ClassificationReport(
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num_classes=len(self._cfg.punct_label_ids),
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label_ids=self._cfg.punct_label_ids,
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mode='macro',
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dist_sync_on_step=True,
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)
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self.capit_class_report = ClassificationReport(
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num_classes=len(self._cfg.capit_label_ids),
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label_ids=self._cfg.capit_label_ids,
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mode='macro',
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dist_sync_on_step=True,
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)
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@typecheck()
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def forward(self, input_ids, attention_mask, token_type_ids=None):
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"""
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No special modification required for Lightning, define it as you normally would
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in the `nn.Module` in vanilla PyTorch.
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"""
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hidden_states = self.bert_model(
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input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask
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)
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punct_logits = self.punct_classifier(hidden_states=hidden_states)
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capit_logits = self.capit_classifier(hidden_states=hidden_states)
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return punct_logits, capit_logits
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def _make_step(self, batch):
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input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, punct_labels, capit_labels = batch
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punct_logits, capit_logits = self(
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input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask
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)
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punct_loss = self.loss(logits=punct_logits, labels=punct_labels, loss_mask=loss_mask)
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capit_loss = self.loss(logits=capit_logits, labels=capit_labels, loss_mask=loss_mask)
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loss = self.agg_loss(loss_1=punct_loss, loss_2=capit_loss)
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return loss, punct_logits, capit_logits
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def training_step(self, batch, batch_idx):
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"""
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Lightning calls this inside the training loop with the data from the training dataloader
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passed in as `batch`.
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"""
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loss, _, _ = self._make_step(batch)
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lr = self._optimizer.param_groups[0]['lr']
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self.log('lr', lr, prog_bar=True)
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self.log('train_loss', loss)
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return {'loss': loss, 'lr': lr}
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def validation_step(self, batch, batch_idx, dataloader_idx=0):
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"""
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Lightning calls this inside the validation loop with the data from the validation dataloader
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passed in as `batch`.
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"""
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_, _, _, subtokens_mask, _, punct_labels, capit_labels = batch
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val_loss, punct_logits, capit_logits = self._make_step(batch)
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subtokens_mask = subtokens_mask > 0.5
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punct_preds = torch.argmax(punct_logits, axis=-1)[subtokens_mask]
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punct_labels = punct_labels[subtokens_mask]
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self.punct_class_report.update(punct_preds, punct_labels)
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capit_preds = torch.argmax(capit_logits, axis=-1)[subtokens_mask]
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capit_labels = capit_labels[subtokens_mask]
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self.capit_class_report.update(capit_preds, capit_labels)
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return {
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'val_loss': val_loss,
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'punct_tp': self.punct_class_report.tp,
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'punct_fn': self.punct_class_report.fn,
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'punct_fp': self.punct_class_report.fp,
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'capit_tp': self.capit_class_report.tp,
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'capit_fn': self.capit_class_report.fn,
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'capit_fp': self.capit_class_report.fp,
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}
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def test_step(self, batch, batch_idx, dataloader_idx=0):
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"""
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Lightning calls this inside the validation loop with the data from the validation dataloader
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passed in as `batch`.
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"""
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_, _, _, subtokens_mask, _, punct_labels, capit_labels = batch
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test_loss, punct_logits, capit_logits = self._make_step(batch)
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subtokens_mask = subtokens_mask > 0.5
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punct_preds = torch.argmax(punct_logits, axis=-1)[subtokens_mask]
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punct_labels = punct_labels[subtokens_mask]
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self.punct_class_report.update(punct_preds, punct_labels)
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capit_preds = torch.argmax(capit_logits, axis=-1)[subtokens_mask]
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capit_labels = capit_labels[subtokens_mask]
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self.capit_class_report.update(capit_preds, capit_labels)
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return {
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'test_loss': test_loss,
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'punct_tp': self.punct_class_report.tp,
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'punct_fn': self.punct_class_report.fn,
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'punct_fp': self.punct_class_report.fp,
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'capit_tp': self.capit_class_report.tp,
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'capit_fn': self.capit_class_report.fn,
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'capit_fp': self.capit_class_report.fp,
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}
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def multi_validation_epoch_end(self, outputs, dataloader_idx: int = 0):
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"""
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Called at the end of validation to aggregate outputs.
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outputs: list of individual outputs of each validation step.
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"""
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avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
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# calculate metrics and log classification report for Punctuation task
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punct_precision, punct_recall, punct_f1, punct_report = self.punct_class_report.compute()
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logging.info(f'Punctuation report: {punct_report}')
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# calculate metrics and log classification report for Capitalization task
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capit_precision, capit_recall, capit_f1, capit_report = self.capit_class_report.compute()
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logging.info(f'Capitalization report: {capit_report}')
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self.log('val_loss', avg_loss, prog_bar=True)
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self.log('punct_precision', punct_precision)
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self.log('punct_f1', punct_f1)
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self.log('punct_recall', punct_recall)
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self.log('capit_precision', capit_precision)
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self.log('capit_f1', capit_f1)
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self.log('capit_recall', capit_recall)
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self.punct_class_report.reset()
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self.capit_class_report.reset()
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def multi_test_epoch_end(self, outputs, dataloader_idx: int = 0):
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"""
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Called at the end of test to aggregate outputs.
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outputs: list of individual outputs of each validation step.
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"""
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avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
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# calculate metrics and log classification report for Punctuation task
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punct_precision, punct_recall, punct_f1, punct_report = self.punct_class_report.compute()
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logging.info(f'Punctuation report: {punct_report}')
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# calculate metrics and log classification report for Capitalization task
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capit_precision, capit_recall, capit_f1, capit_report = self.capit_class_report.compute()
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logging.info(f'Capitalization report: {capit_report}')
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self.log('test_loss', avg_loss, prog_bar=True)
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self.log('punct_precision', punct_precision)
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self.log('punct_f1', punct_f1)
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self.log('punct_recall', punct_recall)
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self.log('capit_precision', capit_precision)
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self.log('capit_f1', capit_f1)
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self.log('capit_recall', capit_recall)
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def update_data_dir(self, data_dir: str) -> None:
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"""
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Update data directory
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Args:
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data_dir: path to data directory
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"""
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if os.path.exists(data_dir):
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logging.info(f'Setting model.dataset.data_dir to {data_dir}.')
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self._cfg.dataset.data_dir = data_dir
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else:
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raise ValueError(f'{data_dir} not found')
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def setup_training_data(self, train_data_config: Optional[DictConfig] = None):
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"""Setup training data"""
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if train_data_config is None:
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train_data_config = self._cfg.train_ds
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# for older(pre - 1.0.0.b3) configs compatibility
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if not hasattr(self._cfg, "class_labels") or self._cfg.class_labels is None:
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OmegaConf.set_struct(self._cfg, False)
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self._cfg.class_labels = {}
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self._cfg.class_labels = OmegaConf.create(
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{'punct_labels_file': 'punct_label_ids.csv', 'capit_labels_file': 'capit_label_ids.csv'}
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)
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self._train_dl = self._setup_dataloader_from_config(cfg=train_data_config)
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if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
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self.register_artifact('class_labels.punct_labels_file', self._train_dl.dataset.punct_label_ids_file)
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self.register_artifact('class_labels.capit_labels_file', self._train_dl.dataset.capit_label_ids_file)
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# save label maps to the config
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self._cfg.punct_label_ids = OmegaConf.create(self._train_dl.dataset.punct_label_ids)
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self._cfg.capit_label_ids = OmegaConf.create(self._train_dl.dataset.capit_label_ids)
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def setup_validation_data(self, val_data_config: Optional[Dict] = None):
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"""
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Setup validaton data
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val_data_config: validation data config
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"""
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if val_data_config is None:
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val_data_config = self._cfg.validation_ds
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self._validation_dl = self._setup_dataloader_from_config(cfg=val_data_config)
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def setup_test_data(self, test_data_config: Optional[Dict] = None):
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if test_data_config is None:
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test_data_config = self._cfg.test_ds
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self._test_dl = self._setup_dataloader_from_config(cfg=test_data_config)
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def _setup_dataloader_from_config(self, cfg: DictConfig):
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# use data_dir specified in the ds_item to run evaluation on multiple datasets
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if 'ds_item' in cfg and cfg.ds_item is not None:
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data_dir = cfg.ds_item
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else:
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data_dir = self._cfg.dataset.data_dir
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text_file = os.path.join(data_dir, cfg.text_file)
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label_file = os.path.join(data_dir, cfg.labels_file)
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dataset = BertPunctuationCapitalizationDataset(
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tokenizer=self.tokenizer,
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text_file=text_file,
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label_file=label_file,
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pad_label=self._cfg.dataset.pad_label,
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punct_label_ids=self._cfg.punct_label_ids,
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capit_label_ids=self._cfg.capit_label_ids,
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max_seq_length=self._cfg.dataset.max_seq_length,
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ignore_extra_tokens=self._cfg.dataset.ignore_extra_tokens,
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ignore_start_end=self._cfg.dataset.ignore_start_end,
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use_cache=self._cfg.dataset.use_cache,
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num_samples=cfg.num_samples,
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punct_label_ids_file=self._cfg.class_labels.punct_labels_file
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if 'class_labels' in self._cfg
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else 'punct_label_ids.csv',
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capit_label_ids_file=self._cfg.class_labels.capit_labels_file
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if 'class_labels' in self._cfg
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else 'capit_label_ids.csv',
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)
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return torch.utils.data.DataLoader(
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dataset=dataset,
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collate_fn=dataset.collate_fn,
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batch_size=cfg.batch_size,
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shuffle=cfg.shuffle,
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num_workers=self._cfg.dataset.num_workers,
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pin_memory=self._cfg.dataset.pin_memory,
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drop_last=self._cfg.dataset.drop_last,
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)
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def _setup_infer_dataloader(
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self, queries: List[str], batch_size: int, max_seq_length: int = None
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) -> 'torch.utils.data.DataLoader':
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"""
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Setup function for a infer data loader.
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Args:
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queries: lower cased text without punctuation
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batch_size: batch size to use during inference
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max_seq_length: maximum sequence length after tokenization
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Returns:
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A pytorch DataLoader.
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"""
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if max_seq_length is None:
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max_seq_length = self._cfg.dataset.max_seq_length
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dataset = BertPunctuationCapitalizationInferDataset(
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tokenizer=self.tokenizer, queries=queries, max_seq_length=max_seq_length
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)
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return torch.utils.data.DataLoader(
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dataset=dataset,
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collate_fn=dataset.collate_fn,
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batch_size=batch_size,
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shuffle=False,
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num_workers=self._cfg.dataset.num_workers,
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pin_memory=self._cfg.dataset.pin_memory,
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drop_last=False,
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)
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def add_punctuation_capitalization(
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self, queries: List[str], batch_size: int = None, max_seq_length: int = 512
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) -> List[str]:
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"""
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Adds punctuation and capitalization to the queries. Use this method for debugging and prototyping.
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Args:
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queries: lower cased text without punctuation
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batch_size: batch size to use during inference
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max_seq_length: maximum sequence length after tokenization
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Returns:
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result: text with added capitalization and punctuation
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"""
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if queries is None or len(queries) == 0:
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return []
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if batch_size is None:
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batch_size = len(queries)
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logging.info(f'Using batch size {batch_size} for inference')
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# We will store the output here
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result = []
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# Model's mode and device
|
||||
mode = self.training
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
try:
|
||||
# Switch model to evaluation mode
|
||||
self.eval()
|
||||
self = self.to(device)
|
||||
|
||||
infer_datalayer = self._setup_infer_dataloader(queries, batch_size, max_seq_length)
|
||||
|
||||
# store predictions for all queries in a single list
|
||||
all_punct_preds = []
|
||||
all_capit_preds = []
|
||||
|
||||
for batch in infer_datalayer:
|
||||
input_ids, input_type_ids, input_mask, subtokens_mask = batch
|
||||
|
||||
punct_logits, capit_logits = self.forward(
|
||||
input_ids=input_ids.to(device),
|
||||
token_type_ids=input_type_ids.to(device),
|
||||
attention_mask=input_mask.to(device),
|
||||
)
|
||||
|
||||
subtokens_mask = subtokens_mask > 0.5
|
||||
|
||||
punct_preds = [
|
||||
tensor2list(p_l[subtokens_mask[i]]) for i, p_l in enumerate(torch.argmax(punct_logits, axis=-1))
|
||||
]
|
||||
capit_preds = [
|
||||
tensor2list(c_l[subtokens_mask[i]]) for i, c_l in enumerate(torch.argmax(capit_logits, axis=-1))
|
||||
]
|
||||
|
||||
all_punct_preds.extend(punct_preds)
|
||||
all_capit_preds.extend(capit_preds)
|
||||
|
||||
punct_ids_to_labels = {v: k for k, v in self._cfg.punct_label_ids.items()}
|
||||
capit_ids_to_labels = {v: k for k, v in self._cfg.capit_label_ids.items()}
|
||||
|
||||
queries = [q.strip().split() for q in queries]
|
||||
for i, query in enumerate(queries):
|
||||
punct_preds = all_punct_preds[i]
|
||||
capit_preds = all_capit_preds[i]
|
||||
if len(query) != len(punct_preds):
|
||||
logging.warning(
|
||||
f'Max sequence length of query {query} is set to {max_seq_length}. Truncating the input.'
|
||||
)
|
||||
|
||||
# removing the end of phrase punctuation of the truncated segment
|
||||
punct_preds[-1] = 0
|
||||
max_len = len(punct_preds)
|
||||
query = query[:max_len]
|
||||
|
||||
query_with_punct_and_capit = ''
|
||||
for j, word in enumerate(query):
|
||||
punct_label = punct_ids_to_labels[punct_preds[j]]
|
||||
capit_label = capit_ids_to_labels[capit_preds[j]]
|
||||
|
||||
if capit_label != self._cfg.dataset.pad_label:
|
||||
word = word.capitalize()
|
||||
query_with_punct_and_capit += word
|
||||
if punct_label != self._cfg.dataset.pad_label:
|
||||
query_with_punct_and_capit += punct_label
|
||||
query_with_punct_and_capit += ' '
|
||||
|
||||
result.append(query_with_punct_and_capit.strip())
|
||||
finally:
|
||||
# set mode back to its original value
|
||||
self.train(mode=mode)
|
||||
return result
|
||||
|
||||
@classmethod
|
||||
def list_available_models(cls) -> Optional[Dict[str, str]]:
|
||||
"""
|
||||
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
|
||||
|
||||
Returns:
|
||||
List of available pre-trained models.
|
||||
"""
|
||||
result = []
|
||||
result.append(
|
||||
PretrainedModelInfo(
|
||||
pretrained_model_name="punctuation_en_bert",
|
||||
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/punctuation_en_bert/versions/1.0.0rc1/files/punctuation_en_bert.nemo",
|
||||
description="The model was trained with NeMo BERT base uncased checkpoint on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.",
|
||||
)
|
||||
)
|
||||
result.append(
|
||||
PretrainedModelInfo(
|
||||
pretrained_model_name="punctuation_en_distilbert",
|
||||
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/punctuation_en_distilbert/versions/1.0.0rc1/files/punctuation_en_distilbert.nemo",
|
||||
description="The model was trained with DiltilBERT base uncased checkpoint from HuggingFace on a subset of data from the following sources: Tatoeba sentences, books from Project Gutenberg, Fisher transcripts.",
|
||||
)
|
||||
)
|
||||
return result
|
||||
|
||||
@property
|
||||
def input_module(self):
|
||||
return self.bert_model
|
||||
|
||||
@property
|
||||
def output_module(self):
|
||||
return self
|
||||
@@ -0,0 +1,522 @@
|
||||
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
from typing import Dict, List, Optional, Union
|
||||
|
||||
import onnx
|
||||
import torch
|
||||
from omegaconf import DictConfig, OmegaConf
|
||||
from pytorch_lightning import Trainer
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from nemo.collections.common.losses import CrossEntropyLoss
|
||||
from nemo.collections.nlp.data.data_utils.data_preprocessing import get_labels_to_labels_id_mapping
|
||||
from nemo.collections.nlp.data.token_classification.token_classification_dataset import (
|
||||
BertTokenClassificationDataset,
|
||||
BertTokenClassificationInferDataset,
|
||||
)
|
||||
from nemo.collections.nlp.data.token_classification.token_classification_utils import get_label_ids
|
||||
from nemo.collections.nlp.metrics.classification_report import ClassificationReport
|
||||
from nemo.collections.nlp.models.nlp_model import NLPModel
|
||||
from nemo.collections.nlp.modules.common import TokenClassifier
|
||||
from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
|
||||
from nemo.collections.nlp.parts.utils_funcs import get_classification_report, plot_confusion_matrix, tensor2list
|
||||
from nemo.core.classes.common import PretrainedModelInfo, typecheck
|
||||
from nemo.core.neural_types import NeuralType
|
||||
from nemo.utils import logging
|
||||
|
||||
__all__ = ['TokenClassificationModel']
|
||||
|
||||
|
||||
class TokenClassificationModel(NLPModel):
|
||||
"""Token Classification Model with BERT, applicable for tasks such as Named Entity Recognition"""
|
||||
|
||||
@property
|
||||
def input_types(self) -> Optional[Dict[str, NeuralType]]:
|
||||
return self.bert_model.input_types
|
||||
|
||||
@property
|
||||
def output_types(self) -> Optional[Dict[str, NeuralType]]:
|
||||
return self.classifier.output_types
|
||||
|
||||
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
|
||||
"""Initializes Token Classification Model."""
|
||||
# extract str to int labels mapping if a mapping file provided
|
||||
if isinstance(cfg.label_ids, str):
|
||||
if os.path.exists(cfg.label_ids):
|
||||
logging.info(f'Reusing label_ids file found at {cfg.label_ids}.')
|
||||
label_ids = get_labels_to_labels_id_mapping(cfg.label_ids)
|
||||
# update the config to store name to id mapping
|
||||
cfg.label_ids = OmegaConf.create(label_ids)
|
||||
else:
|
||||
raise ValueError(f'{cfg.label_ids} not found.')
|
||||
|
||||
self.setup_tokenizer(cfg.tokenizer)
|
||||
self.class_weights = None
|
||||
super().__init__(cfg=cfg, trainer=trainer)
|
||||
|
||||
self.bert_model = get_lm_model(
|
||||
pretrained_model_name=cfg.language_model.pretrained_model_name,
|
||||
config_file=self.register_artifact('language_model.config_file', cfg.language_model.config_file),
|
||||
config_dict=OmegaConf.to_container(cfg.language_model.config) if cfg.language_model.config else None,
|
||||
checkpoint_file=cfg.language_model.lm_checkpoint,
|
||||
vocab_file=self.register_artifact('tokenizer.vocab_file', cfg.tokenizer.vocab_file),
|
||||
)
|
||||
|
||||
self.classifier = TokenClassifier(
|
||||
hidden_size=self.bert_model.config.hidden_size,
|
||||
num_classes=len(self._cfg.label_ids),
|
||||
num_layers=self._cfg.head.num_fc_layers,
|
||||
activation=self._cfg.head.activation,
|
||||
log_softmax=False,
|
||||
dropout=self._cfg.head.fc_dropout,
|
||||
use_transformer_init=self._cfg.head.use_transformer_init,
|
||||
)
|
||||
|
||||
self.loss = self.setup_loss(class_balancing=self._cfg.dataset.class_balancing)
|
||||
|
||||
# setup to track metrics
|
||||
self.classification_report = ClassificationReport(
|
||||
len(self._cfg.label_ids), label_ids=self._cfg.label_ids, dist_sync_on_step=True
|
||||
)
|
||||
|
||||
def update_data_dir(self, data_dir: str) -> None:
|
||||
"""
|
||||
Update data directory and get data stats with Data Descriptor
|
||||
Weights are later used to setup loss
|
||||
|
||||
Args:
|
||||
data_dir: path to data directory
|
||||
"""
|
||||
self._cfg.dataset.data_dir = data_dir
|
||||
logging.info(f'Setting model.dataset.data_dir to {data_dir}.')
|
||||
|
||||
def setup_loss(self, class_balancing: str = None):
|
||||
"""Setup loss
|
||||
Setup or update loss.
|
||||
|
||||
Args:
|
||||
class_balancing: whether to use class weights during training
|
||||
"""
|
||||
if class_balancing not in ['weighted_loss', None]:
|
||||
raise ValueError(f'Class balancing {class_balancing} is not supported. Choose from: [null, weighted_loss]')
|
||||
if class_balancing == 'weighted_loss' and self.class_weights:
|
||||
# you may need to increase the number of epochs for convergence when using weighted_loss
|
||||
loss = CrossEntropyLoss(logits_ndim=3, weight=self.class_weights)
|
||||
logging.debug(f'Using {class_balancing} class balancing.')
|
||||
else:
|
||||
loss = CrossEntropyLoss(logits_ndim=3)
|
||||
logging.debug(f'Using CrossEntropyLoss class balancing.')
|
||||
return loss
|
||||
|
||||
@typecheck()
|
||||
def forward(self, input_ids, token_type_ids, attention_mask):
|
||||
hidden_states = self.bert_model(
|
||||
input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask
|
||||
)
|
||||
logits = self.classifier(hidden_states=hidden_states)
|
||||
return logits
|
||||
|
||||
def training_step(self, batch, batch_idx):
|
||||
"""
|
||||
Lightning calls this inside the training loop with the data from the training dataloader
|
||||
passed in as `batch`.
|
||||
"""
|
||||
input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, labels = batch
|
||||
logits = self(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)
|
||||
loss = self.loss(logits=logits, labels=labels, loss_mask=loss_mask)
|
||||
lr = self._optimizer.param_groups[0]['lr']
|
||||
|
||||
self.log('train_loss', loss)
|
||||
self.log('lr', lr, prog_bar=True)
|
||||
|
||||
return {
|
||||
'loss': loss,
|
||||
'lr': lr,
|
||||
}
|
||||
|
||||
def validation_step(self, batch, batch_idx):
|
||||
"""
|
||||
Lightning calls this inside the validation loop with the data from the validation dataloader
|
||||
passed in as `batch`.
|
||||
"""
|
||||
input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, labels = batch
|
||||
logits = self(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)
|
||||
val_loss = self.loss(logits=logits, labels=labels, loss_mask=loss_mask)
|
||||
|
||||
subtokens_mask = subtokens_mask > 0.5
|
||||
|
||||
preds = torch.argmax(logits, axis=-1)[subtokens_mask]
|
||||
labels = labels[subtokens_mask]
|
||||
tp, fn, fp, _ = self.classification_report(preds, labels)
|
||||
|
||||
return {'val_loss': val_loss, 'tp': tp, 'fn': fn, 'fp': fp}
|
||||
|
||||
def validation_epoch_end(self, outputs):
|
||||
"""
|
||||
Called at the end of validation to aggregate outputs.
|
||||
outputs: list of individual outputs of each validation step.
|
||||
"""
|
||||
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
|
||||
|
||||
# calculate metrics and classification report
|
||||
precision, recall, f1, report = self.classification_report.compute()
|
||||
|
||||
logging.info(report)
|
||||
|
||||
self.log('val_loss', avg_loss, prog_bar=True)
|
||||
self.log('precision', precision)
|
||||
self.log('f1', f1)
|
||||
self.log('recall', recall)
|
||||
|
||||
self.classification_report.reset()
|
||||
|
||||
def test_step(self, batch, batch_idx):
|
||||
input_ids, input_type_ids, input_mask, subtokens_mask, loss_mask, labels = batch
|
||||
logits = self(input_ids=input_ids, token_type_ids=input_type_ids, attention_mask=input_mask)
|
||||
val_loss = self.loss(logits=logits, labels=labels, loss_mask=loss_mask)
|
||||
|
||||
subtokens_mask = subtokens_mask > 0.5
|
||||
|
||||
preds = torch.argmax(logits, axis=-1)[subtokens_mask]
|
||||
labels = labels[subtokens_mask]
|
||||
tp, fn, fp, _ = self.classification_report(preds, labels)
|
||||
|
||||
return {'test_loss': val_loss, 'tp': tp, 'fn': fn, 'fp': fp}
|
||||
|
||||
def test_epoch_end(self, outputs):
|
||||
avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()
|
||||
# calculate metrics and classification report
|
||||
precision, recall, f1, report = self.classification_report.compute()
|
||||
logging.info(report)
|
||||
|
||||
self.log('test_loss', avg_loss, prog_bar=True)
|
||||
self.log('precision', precision)
|
||||
self.log('f1', f1)
|
||||
self.log('recall', recall)
|
||||
|
||||
def setup_training_data(self, train_data_config: Optional[DictConfig] = None):
|
||||
if train_data_config is None:
|
||||
train_data_config = self._cfg.train_ds
|
||||
|
||||
labels_file = os.path.join(self._cfg.dataset.data_dir, train_data_config.labels_file)
|
||||
|
||||
# for older(pre - 1.0.0.b3) configs compatibility
|
||||
if not hasattr(self._cfg, "class_labels") or self._cfg.class_labels is None:
|
||||
OmegaConf.set_struct(self._cfg, False)
|
||||
self._cfg.class_labels = {}
|
||||
self._cfg.class_labels = OmegaConf.create({'class_labels_file': 'label_ids.csv'})
|
||||
OmegaConf.set_struct(self._cfg, True)
|
||||
|
||||
label_ids, label_ids_filename, self.class_weights = get_label_ids(
|
||||
label_file=labels_file,
|
||||
is_training=True,
|
||||
pad_label=self._cfg.dataset.pad_label,
|
||||
label_ids_dict=self._cfg.label_ids,
|
||||
get_weights=True,
|
||||
class_labels_file_artifact=self._cfg.class_labels.class_labels_file,
|
||||
)
|
||||
# save label maps to the config
|
||||
self._cfg.label_ids = OmegaConf.create(label_ids)
|
||||
|
||||
self.register_artifact('class_labels.class_labels_file', label_ids_filename)
|
||||
self._train_dl = self._setup_dataloader_from_config(cfg=train_data_config)
|
||||
|
||||
def setup_validation_data(self, val_data_config: Optional[DictConfig] = None):
|
||||
if val_data_config is None:
|
||||
val_data_config = self._cfg.validation_ds
|
||||
|
||||
labels_file = os.path.join(self._cfg.dataset.data_dir, val_data_config.labels_file)
|
||||
get_label_ids(
|
||||
label_file=labels_file,
|
||||
is_training=False,
|
||||
pad_label=self._cfg.dataset.pad_label,
|
||||
label_ids_dict=self._cfg.label_ids,
|
||||
get_weights=False,
|
||||
)
|
||||
|
||||
self._validation_dl = self._setup_dataloader_from_config(cfg=val_data_config)
|
||||
|
||||
def setup_test_data(self, test_data_config: Optional[DictConfig] = None):
|
||||
if test_data_config is None:
|
||||
test_data_config = self._cfg.test_ds
|
||||
|
||||
labels_file = os.path.join(self._cfg.dataset.data_dir, test_data_config.labels_file)
|
||||
get_label_ids(
|
||||
label_file=labels_file,
|
||||
is_training=False,
|
||||
pad_label=self._cfg.dataset.pad_label,
|
||||
label_ids_dict=self._cfg.label_ids,
|
||||
get_weights=False,
|
||||
)
|
||||
|
||||
self._test_dl = self._setup_dataloader_from_config(cfg=test_data_config)
|
||||
|
||||
def _setup_dataloader_from_config(self, cfg: DictConfig) -> DataLoader:
|
||||
"""
|
||||
Setup dataloader from config
|
||||
Args:
|
||||
cfg: config for the dataloader
|
||||
Return:
|
||||
Pytorch Dataloader
|
||||
"""
|
||||
dataset_cfg = self._cfg.dataset
|
||||
data_dir = dataset_cfg.data_dir
|
||||
|
||||
if not os.path.exists(data_dir):
|
||||
raise FileNotFoundError(f"Data directory is not found at: {data_dir}.")
|
||||
|
||||
text_file = os.path.join(data_dir, cfg.text_file)
|
||||
labels_file = os.path.join(data_dir, cfg.labels_file)
|
||||
|
||||
if not (os.path.exists(text_file) and os.path.exists(labels_file)):
|
||||
raise FileNotFoundError(
|
||||
f'{text_file} or {labels_file} not found. The data should be split into 2 files: text.txt and \
|
||||
labels.txt. Each line of the text.txt file contains text sequences, where words are separated with \
|
||||
spaces. The labels.txt file contains corresponding labels for each word in text.txt, the labels are \
|
||||
separated with spaces. Each line of the files should follow the format: \
|
||||
[WORD] [SPACE] [WORD] [SPACE] [WORD] (for text.txt) and \
|
||||
[LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt).'
|
||||
)
|
||||
dataset = BertTokenClassificationDataset(
|
||||
text_file=text_file,
|
||||
label_file=labels_file,
|
||||
max_seq_length=dataset_cfg.max_seq_length,
|
||||
tokenizer=self.tokenizer,
|
||||
num_samples=cfg.num_samples,
|
||||
pad_label=dataset_cfg.pad_label,
|
||||
label_ids=self._cfg.label_ids,
|
||||
ignore_extra_tokens=dataset_cfg.ignore_extra_tokens,
|
||||
ignore_start_end=dataset_cfg.ignore_start_end,
|
||||
use_cache=dataset_cfg.use_cache,
|
||||
)
|
||||
return DataLoader(
|
||||
dataset=dataset,
|
||||
collate_fn=dataset.collate_fn,
|
||||
batch_size=cfg.batch_size,
|
||||
shuffle=cfg.shuffle,
|
||||
num_workers=dataset_cfg.num_workers,
|
||||
pin_memory=dataset_cfg.pin_memory,
|
||||
drop_last=dataset_cfg.drop_last,
|
||||
)
|
||||
|
||||
def _setup_infer_dataloader(self, queries: List[str], batch_size: int) -> 'torch.utils.data.DataLoader':
|
||||
"""
|
||||
Setup function for a infer data loader.
|
||||
|
||||
Args:
|
||||
queries: text
|
||||
batch_size: batch size to use during inference
|
||||
|
||||
Returns:
|
||||
A pytorch DataLoader.
|
||||
"""
|
||||
|
||||
dataset = BertTokenClassificationInferDataset(tokenizer=self.tokenizer, queries=queries, max_seq_length=-1)
|
||||
|
||||
return torch.utils.data.DataLoader(
|
||||
dataset=dataset,
|
||||
collate_fn=dataset.collate_fn,
|
||||
batch_size=batch_size,
|
||||
shuffle=False,
|
||||
num_workers=self._cfg.dataset.num_workers,
|
||||
pin_memory=self._cfg.dataset.pin_memory,
|
||||
drop_last=False,
|
||||
)
|
||||
|
||||
@torch.no_grad()
|
||||
def _infer(self, queries: List[str], batch_size: int = None) -> List[int]:
|
||||
"""
|
||||
Get prediction for the queries
|
||||
Args:
|
||||
queries: text sequences
|
||||
batch_size: batch size to use during inference.
|
||||
Returns:
|
||||
all_preds: model predictions
|
||||
"""
|
||||
# store predictions for all queries in a single list
|
||||
all_preds = []
|
||||
mode = self.training
|
||||
try:
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
# Switch model to evaluation mode
|
||||
self.eval()
|
||||
self.to(device)
|
||||
infer_datalayer = self._setup_infer_dataloader(queries, batch_size)
|
||||
|
||||
for batch in infer_datalayer:
|
||||
input_ids, input_type_ids, input_mask, subtokens_mask = batch
|
||||
|
||||
logits = self.forward(
|
||||
input_ids=input_ids.to(device),
|
||||
token_type_ids=input_type_ids.to(device),
|
||||
attention_mask=input_mask.to(device),
|
||||
)
|
||||
|
||||
subtokens_mask = subtokens_mask > 0.5
|
||||
preds = tensor2list(torch.argmax(logits, axis=-1)[subtokens_mask])
|
||||
all_preds.extend(preds)
|
||||
finally:
|
||||
# set mode back to its original value
|
||||
self.train(mode=mode)
|
||||
return all_preds
|
||||
|
||||
def add_predictions(
|
||||
self, queries: Union[List[str], str], batch_size: int = 32, output_file: Optional[str] = None
|
||||
) -> List[str]:
|
||||
"""
|
||||
Add predicted token labels to the queries. Use this method for debugging and prototyping.
|
||||
Args:
|
||||
queries: text
|
||||
batch_size: batch size to use during inference.
|
||||
output_file: file to save models predictions
|
||||
Returns:
|
||||
result: text with added entities
|
||||
"""
|
||||
if queries is None or len(queries) == 0:
|
||||
return []
|
||||
|
||||
if isinstance(queries, str):
|
||||
logging.info(f'Reading from {queries} file')
|
||||
with open(queries, 'r') as f:
|
||||
queries = f.readlines()
|
||||
|
||||
result = []
|
||||
all_preds = self._infer(queries, batch_size)
|
||||
|
||||
queries = [q.strip().split() for q in queries]
|
||||
num_words = [len(q) for q in queries]
|
||||
if sum(num_words) != len(all_preds):
|
||||
raise ValueError('Pred and words must have the same length')
|
||||
|
||||
ids_to_labels = {v: k for k, v in self._cfg.label_ids.items()}
|
||||
start_idx = 0
|
||||
end_idx = 0
|
||||
for query in queries:
|
||||
end_idx += len(query)
|
||||
|
||||
# extract predictions for the current query from the list of all predictions
|
||||
preds = all_preds[start_idx:end_idx]
|
||||
start_idx = end_idx
|
||||
|
||||
query_with_entities = ''
|
||||
for j, word in enumerate(query):
|
||||
# strip out the punctuation to attach the entity tag to the word not to a punctuation mark
|
||||
# that follows the word
|
||||
if word[-1].isalpha():
|
||||
punct = ''
|
||||
else:
|
||||
punct = word[-1]
|
||||
word = word[:-1]
|
||||
|
||||
query_with_entities += word
|
||||
label = ids_to_labels[preds[j]]
|
||||
|
||||
if label != self._cfg.dataset.pad_label:
|
||||
query_with_entities += '[' + label + ']'
|
||||
query_with_entities += punct + ' '
|
||||
result.append(query_with_entities.strip())
|
||||
|
||||
if output_file is not None:
|
||||
with open(output_file, 'w') as f:
|
||||
for r in result:
|
||||
f.write(r + '\n')
|
||||
logging.info(f'Predictions saved to {output_file}')
|
||||
return result
|
||||
|
||||
def evaluate_from_file(
|
||||
self,
|
||||
output_dir: str,
|
||||
text_file: str,
|
||||
labels_file: Optional[str] = None,
|
||||
add_confusion_matrix: Optional[bool] = False,
|
||||
normalize_confusion_matrix: Optional[bool] = True,
|
||||
batch_size: int = 1,
|
||||
) -> None:
|
||||
"""
|
||||
Run inference on data from a file, plot confusion matrix and calculate classification report.
|
||||
Use this method for final evaluation.
|
||||
|
||||
Args:
|
||||
output_dir: path to output directory to store model predictions, confusion matrix plot (if set to True)
|
||||
text_file: path to file with text. Each line of the text.txt file contains text sequences, where words
|
||||
are separated with spaces: [WORD] [SPACE] [WORD] [SPACE] [WORD]
|
||||
labels_file (Optional): path to file with labels. Each line of the labels_file should contain
|
||||
labels corresponding to each word in the text_file, the labels are separated with spaces:
|
||||
[LABEL] [SPACE] [LABEL] [SPACE] [LABEL] (for labels.txt).'
|
||||
add_confusion_matrix: whether to generate confusion matrix
|
||||
normalize_confusion_matrix: whether to normalize confusion matrix
|
||||
batch_size: batch size to use during inference.
|
||||
"""
|
||||
output_dir = os.path.abspath(output_dir)
|
||||
|
||||
with open(text_file, 'r') as f:
|
||||
queries = f.readlines()
|
||||
|
||||
all_preds = self._infer(queries, batch_size)
|
||||
with_labels = labels_file is not None
|
||||
if with_labels:
|
||||
with open(labels_file, 'r') as f:
|
||||
all_labels_str = f.readlines()
|
||||
all_labels_str = ' '.join([labels.strip() for labels in all_labels_str])
|
||||
|
||||
# writing labels and predictions to a file in output_dir is specified in the config
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
filename = os.path.join(output_dir, 'infer_' + os.path.basename(text_file))
|
||||
try:
|
||||
with open(filename, 'w') as f:
|
||||
if with_labels:
|
||||
f.write('labels\t' + all_labels_str + '\n')
|
||||
logging.info(f'Labels save to {filename}')
|
||||
|
||||
# convert labels from string label to ids
|
||||
ids_to_labels = {v: k for k, v in self._cfg.label_ids.items()}
|
||||
all_preds_str = [ids_to_labels[pred] for pred in all_preds]
|
||||
f.write('preds\t' + ' '.join(all_preds_str) + '\n')
|
||||
logging.info(f'Predictions saved to {filename}')
|
||||
|
||||
if with_labels and add_confusion_matrix:
|
||||
all_labels = all_labels_str.split()
|
||||
# convert labels from string label to ids
|
||||
label_ids = self._cfg.label_ids
|
||||
all_labels = [label_ids[label] for label in all_labels]
|
||||
|
||||
plot_confusion_matrix(
|
||||
all_labels, all_preds, output_dir, label_ids=label_ids, normalize=normalize_confusion_matrix
|
||||
)
|
||||
logging.info(get_classification_report(all_labels, all_preds, label_ids))
|
||||
except Exception:
|
||||
logging.error(
|
||||
f'When providing a file with labels, check that all labels in {labels_file} were'
|
||||
f'seen during training.'
|
||||
)
|
||||
raise
|
||||
|
||||
@classmethod
|
||||
def list_available_models(cls) -> Optional[PretrainedModelInfo]:
|
||||
"""
|
||||
This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud.
|
||||
|
||||
Returns:
|
||||
List of available pre-trained models.
|
||||
"""
|
||||
result = []
|
||||
model = PretrainedModelInfo(
|
||||
pretrained_model_name="ner_en_bert",
|
||||
location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ner_en_bert/versions/1.0.0rc1/files/ner_en_bert.nemo",
|
||||
description="The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition and achieves 74.61 F1 Macro score.",
|
||||
)
|
||||
result.append(model)
|
||||
return result
|
||||
Reference in New Issue
Block a user