Delete handler.py

handler.py

@@ -1,259 +0,0 @@
-import torch
-import numpy as np
-import transformers
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-from typing import List, Optional, Tuple, Union, Dict, Any
-
-from transformers import BertTokenizer, Pipeline
-from transformers import models, DataCollatorWithPadding, AutoTokenizer
-from transformers.modeling_outputs import SequenceClassifierOutput
-from transformers.pipelines import PIPELINE_REGISTRY
-
-from transformers.models.bert.configuration_bert import BertConfig
-from transformers.models.bert.modeling_bert import (
-    BertPreTrainedModel,
-    BERT_INPUTS_DOCSTRING,
-    _TOKENIZER_FOR_DOC,
-    _CHECKPOINT_FOR_DOC,
-    BERT_START_DOCSTRING,
-    _CONFIG_FOR_DOC,
-    _SEQ_CLASS_EXPECTED_OUTPUT,
-    _SEQ_CLASS_EXPECTED_LOSS,
-    BertModel,
-)
-
-from transformers.file_utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings_to_model_forward,
-    add_start_docstrings
-)
-
-@dataclass
-class Task:
-  id: int
-  name: str
-  type: str
-  num_labels: int
-
-
-@add_start_docstrings(
-    """
-    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForSequenceClassification(BertPreTrainedModel):
-  def __init__(self, config, **kwargs):
-    super().__init__(transformers.PretrainedConfig())
-    #task_labels_map={"binary_classification": 2, "label_classification": 5}
-    self.tasks = kwargs.get("tasks_map", {})
-    self.config = config
-
-    self.bert = BertModel(config)
-    classifier_dropout = (
-        config.classifier_dropout
-        if config.classifier_dropout is not None
-        else config.hidden_dropout_prob
-    )
-    self.dropout = nn.Dropout(classifier_dropout)
-    ## add task specific output heads
-    self.classifier1 = nn.Linear(
-        config.hidden_size, self.tasks[0].num_labels
-    )
-    self.classifier2 = nn.Linear(
-        config.hidden_size, self.tasks[1].num_labels
-    )
-
-    self.init_weights()
-
-  @add_start_docstrings_to_model_forward(
-  BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-  )
-  @add_code_sample_docstrings(
-      processor_class=_TOKENIZER_FOR_DOC,
-      checkpoint=_CHECKPOINT_FOR_DOC,
-      output_type=SequenceClassifierOutput,
-      config_class=_CONFIG_FOR_DOC,
-      expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
-      expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
-  )
-  def forward(
-    self,
-    input_ids: Optional[torch.Tensor] = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    token_type_ids: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.Tensor] = None,
-    head_mask: Optional[torch.Tensor] = None,
-    inputs_embeds: Optional[torch.Tensor] = None,
-    labels: Optional[torch.Tensor] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-    task_ids=None,
-) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
-    r"""
-    labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
-        Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
-        config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
-        If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-    """
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    outputs = self.bert(
-        input_ids,
-        attention_mask=attention_mask,
-        token_type_ids=token_type_ids,
-        position_ids=position_ids,
-        head_mask=head_mask,
-        inputs_embeds=inputs_embeds,
-        output_attentions=output_attentions,
-        output_hidden_states=output_hidden_states,
-        return_dict=return_dict,
-    )
-
-    pooled_output = outputs[1]
-
-    pooled_output = self.dropout(pooled_output)
-  
-    unique_task_ids_list = torch.unique(task_ids).tolist()
-    loss_list = []
-    logits = None
-    for unique_task_id in unique_task_ids_list:
-
-      loss = None
-      task_id_filter = task_ids == unique_task_id 
-
-      if unique_task_id == 0:
-        logits = self.classifier1(pooled_output[task_id_filter])
-      elif unique_task_id == 1:
-        logits = self.classifier2(pooled_output[task_id_filter])
-
-      
-      if labels is not None: 
-        loss_fct = CrossEntropyLoss()
-        loss = loss_fct(logits.view(-1, self.tasks[unique_task_id].num_labels), labels[task_id_filter].view(-1))
-        loss_list.append(loss)
-    
-    # logits are only used for eval. and in case of eval the batch is not multi task
-    # For training only the loss is used
-
-    if loss_list:
-      loss = torch.stack(loss_list).mean()
-    if not return_dict:
-      output = (logits,) + outputs[2:]
-      return ((loss,) + output) if loss is not None else output
-    
-    return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-def softmax(_outputs):
-  maxes = np.max(_outputs, axis=-1, keepdims=True)
-  shifted_exp = np.exp(_outputs - maxes)
-  return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
-
-class BiBert_MultiTaskPipeline(Pipeline):
-
-
-  def _sanitize_parameters(self, **kwargs):
-
-    preprocess_kwargs = {}
-    if "task_id" in kwargs:
-      preprocess_kwargs["task_id"] = kwargs["task_id"]
-    
-    forward_kwargs = {}
-    if "task_id" in kwargs:
-      forward_kwargs["task_id"] = kwargs["task_id"]
-
-    postprocess_kwargs = {}
-    if "top_k" in kwargs:
-      postprocess_kwargs["top_k"] = kwargs["top_k"]
-      postprocess_kwargs["_legacy"] = False
-    return preprocess_kwargs, forward_kwargs, postprocess_kwargs
-
-    
-
-  def preprocess(self, inputs, task_id):
-    return_tensors = self.framework
-    feature = self.tokenizer(inputs, padding = True, return_tensors=return_tensors).to(self.device)
-    task_ids = np.full(shape=1,fill_value=task_id, dtype=int)
-    feature["task_ids"] = torch.IntTensor(task_ids)
-    return feature
-  
-  def _forward(self, model_inputs, task_id):
-    return self.model(**model_inputs)
-
-  def postprocess(self, model_outputs, top_k=1, _legacy=True):
-    outputs = model_outputs["logits"][0]
-    outputs = outputs.numpy()
-    scores = softmax(outputs)
-
-    if top_k == 1 and _legacy:
-      return {"label": self.model.config.id2label[scores.argmax().item()], "score": scores.max().item()}
-
-    dict_scores = [
-        {"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
-    ]
-    if not _legacy:
-      dict_scores.sort(key=lambda x: x["score"], reverse=True)
-      if top_k is not None:
-          dict_scores = dict_scores[:top_k]
-    return dict_scores
-
-class EndpointHandler():
-  def __init__(self, path=""):
-    # Preload all the elements you are going to need at inference.
-    PIPELINE_REGISTRY.register_pipeline(
-    "bibert-multitask-classification",
-    pipeline_class=BiBert_MultiTaskPipeline,
-    pt_model=BertForSequenceClassification
-    )
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    tokenizer = AutoTokenizer.from_pretrained(path)
-
-    tasks = [
-        Task(id=0, name='label_classification', type='seq_classification', num_labels=5),
-        Task(id=1, name='binary_classification', type='seq_classification', num_labels=2)
-        ]
-
-    multitask_model = BertForSequenceClassification.from_pretrained(
-            path,
-            tasks_map=tasks
-        ).to(device)
-    self.classifier_s = pipeline("bibert-multitask-classification", model = multitask_model, task_id="0", tokenizer=tokenizer, device = device)
-    self.classifier_p = pipeline("bibert-multitask-classification", model = multitask_model, task_id="1", tokenizer=tokenizer, device = device)
-
-  def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
-    """
-    data args:
-        inputs (:obj: `str` | `PIL.Image` | `np.array`)
-        kwargs
-    Return:
-        A :obj:`list` | `dict`: will be serialized and returned
-    """
-    inputs = data.pop("text", data)
-    lang = data.pop("lang", None)
-    if isinstance(inputs, str):
-      inputs = [inputs]
-    
-    prediction_p = self.classifier_p(inputs) 
-    print(prediction_p)
-    label = prediction_p[0]['label']
-    score = prediction_p[0]['score']
-    
-    if label == '0' and score >= 0.75:
-      label = 2
-      return {"label":label, "score": score}
-    else:
-      prediction_s = self.classifier_s(inputs)
-      label = prediction_s[0]['label']
-      score = prediction_s[0]['score']
-    return prediction_s