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AWS SageMaker inference using PyTorchModel
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import torch | |
import torch.nn as nn | |
import json | |
from pathlib import Path | |
from transformers import AutoConfig, AutoTokenizer, AutoModel | |
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') | |
class Tagger(nn.Module): | |
""" | |
Minimal model class just for inference | |
""" | |
def __init__(self): | |
super().__init__() | |
self.bert_model_name='distilbert-base-uncased' | |
self.label_names=['ml', 'cs', 'ph', 'mth', 'bio', 'fin'] | |
config = AutoConfig.from_pretrained(self.bert_model_name) | |
self.bert = AutoModel.from_config(config) | |
self.classifier = nn.Linear(self.bert.config.hidden_size, len(self.label_names)) | |
self.sigmoid_fnc = nn.Sigmoid() | |
def forward(self, input_ids, attention_mask): | |
output = self.bert(input_ids, attention_mask=attention_mask) | |
output = self.classifier(output.last_hidden_state[:,0]) #taking the ouput from [CLS] token | |
output = self.sigmoid_fnc(output) | |
return output | |
def model_fn(model_dir): | |
model = Tagger() | |
with open(Path(model_dir) / 'model.pth', 'rb') as f: | |
model.load_state_dict(torch.load(f)) | |
return model | |
def input_fn(request_body, request_content_type): | |
if request_content_type == "application/json": | |
data = json.loads(request_body) | |
if isinstance(data['text'], str): | |
abstract = data['text'] | |
else: | |
raise ValueError("Unsupported input type. Input type can be a string or an non-empty list. I got {}".format(abstract)) | |
encoded_input = tokenizer(abstract, padding=True, truncation=True, return_tensors='pt') | |
return encoded_input['input_ids'].long(), encoded_input['attention_mask'].long() | |
raise ValueError("Unsupported content type: {}".format(request_content_type)) | |
def predict_fn(input_data, model): | |
# forward pass | |
model.eval() | |
with torch.no_grad(): | |
model_output = model(input_data[0], input_data[1]) | |
label_nms = ['Machine Learning', 'Computer Science', 'Physics', 'Mathematics', 'Biology', 'Finance-Economics'] | |
# results in dictionary | |
get_result = {} | |
for i in range(len(label_nms)): | |
get_result[label_nms[i]] = round(model_output.tolist()[0][i], 3) | |
# return dictonary, which will be json serializable | |
return get_result |
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