@app.route("/")
def home():
return render_template("index.html")
@app.route("/predict")
def predict():
sentence = request.args.get("sentence")
start_time = time.time()
positive_prediction = sentence_prediction(sentence)
negative_prediction = 1 - positive_prediction
response = {}
response["response"] = {
"positive": str(positive_prediction),
"negative": str(negative_prediction),
"sentence": str(sentence),
"time_taken": str(time.time() - start_time),
}
return flask.jsonify(response)
if __name__ == "__main__":
MODEL = BERTBaseUncased()
# MODEL = nn.DataParallel(MODEL)
MODEL.load_state_dict(torch.load(config.MODEL_PATH))
MODEL.to(DEVICE)
MODEL.eval()
app.run()
if __name__ == '__main__':
device='cuda'
review = ['this is an amzaing place']
dataset = BERTDataset(
review = review,target=[0]
)
model = BERTBaseUncased()
#model = nn.DataParallel(model) #if you have used model as DataParallel-model then inside torch.onnx.export use 'model.module' instead of model
#====>>> question is from where 'module' arises ??=>>>> print model(uncomment line 25) here .You will output as Dataparallel({module:BERTBaseUncased())
# ====> that means 'module' is key of 'BERTBaseUncased' model's value
model.load_state_dict(torch.load(config.MODEL_PATH))
model.eval()
#print(model)
ids = dataset[0]['ids'].unsqueeze(0)
mask = dataset[0]['mask'].unsqueeze(0)
token_type_ids = dataset[0][''token_type_ids].unsqueeze(0)
torch.onnx.export(
model,
#model.module [===>> if dataparallel-model ==>> see above commented line 20]
#we have 3 inputs so name them here -- ordering is important
(ids,mask,token_type_ids),
#export it to model.onnx
'model.onnx',