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import numpy as np
import transformers
from onnxruntime.quantization import quantize_dynamic,QuantType
import transformers.convert_graph_to_onnx as onnx_convert
from pathlib import Path
import os
import torch
import yaml
def read_yaml(file_path):
with open(file_path, "r") as f:
return yaml.safe_load(f)
config = read_yaml('config.yaml')
sent_chkpt=config['SENTIMENT_CLF']['sent_chkpt']
sent_mdl_dir=config['SENTIMENT_CLF']['sent_mdl_dir']
sent_onnx_mdl_dir=config['SENTIMENT_CLF']['sent_onnx_mdl_dir']
sent_onnx_mdl_name=config['SENTIMENT_CLF']['sent_onnx_mdl_name']
sent_onnx_quant_mdl_name=config['SENTIMENT_CLF']['sent_onnx_quant_mdl_name']
def classify_sentiment(texts,model,tokenizer):
"""
user will pass texts separated by comma
"""
try:
texts=texts.split(',')
except:
pass
input = tokenizer(texts, padding=True, truncation=True,
return_tensors="pt")
logits = model(**input)['logits'].softmax(dim=1)
logits = torch.argmax(logits, dim=1)
output = ['Positive' if i == 1 else 'Negative' for i in logits]
return output
def create_onnx_model_sentiment(_model, _tokenizer,sent_onnx_mdl_dir=sent_onnx_mdl_dir):
"""
Args:
_model: model checkpoint with AutoModelForSequenceClassification
_tokenizer: model checkpoint with AutoTokenizer
Returns:
Creates a simple ONNX model & int8 Quantized Model in the directory "sent_clf_onnx/" if directory not present
"""
if not os.path.exists(sent_onnx_mdl_dir):
try:
os.mkdir(sent_onnx_mdl_dir)
except:
pass
pipeline=transformers.pipeline("text-classification", model=_model, tokenizer=_tokenizer)
onnx_convert.convert_pytorch(pipeline,
opset=11,
output=Path(f"{sent_onnx_mdl_dir}/{sent_onnx_mdl_name}"),
use_external_format=False
)
# quantize_dynamic(f"{sent_onnx_mdl_dir}/{sent_onnx_mdl_name}",
# f"{sent_onnx_mdl_dir}/{sent_onnx_quant_mdl_name}",
# weight_type=QuantType.QUInt8)
else:
pass
def classify_sentiment_onnx(texts, _session, _tokenizer):
"""
Args:
texts: input texts from user
_session: pass ONNX runtime session
_tokenizer: Relevant Tokenizer e.g. AutoTokenizer.from_pretrained("same checkpoint as the model")
Returns:
list of Positve and Negative texts
"""
try:
texts=texts.split(',')
except:
pass
_inputs = _tokenizer(texts, padding=True, truncation=True,
return_tensors="np")
input_feed={
"input_ids":np.array(_inputs['input_ids']),
"attention_mask":np.array((_inputs['attention_mask']))
}
output = _session.run(input_feed=input_feed, output_names=['output_0'])[0]
output=np.argmax(output,axis=1)
output = ['Positive' if i == 1 else 'Negative' for i in output]
return output
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