Created
September 9, 2019 06:03
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Seldon Core Python Wrapper for Emotion Detections
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from ngraph_onnx.onnx_importer.importer import import_onnx_file | |
import ngraph as ng | |
import numpy as np | |
class EmotionModel(object): | |
def __init__(self): | |
model = import_onnx_file("model/model.onnx") | |
runtime = ng.runtime(backend_name="CPU") | |
self.inference = runtime.computation(model) | |
self.emotion_table = { | |
"0": "neutral", | |
"1": "happiness", | |
"2": "surprise", | |
"3": "sadness", | |
"4": "anger", | |
"5": "disgust", | |
"6": "fear", | |
"7": "contempt", | |
} | |
def _softmax(self, x): | |
e_x = np.exp(x - np.max(x)) | |
return e_x / np.sum(e_x) | |
def _postprocess(self, x): | |
prob = self._softmax(x) | |
prob = np.squeeze(prob) | |
classes = np.argsort(prob)[::-1] | |
return {self.emotion_table[str(c)]: str(prob[c]) for c in classes} | |
def predict(self, X, feature_names): | |
return self._postprocess(self.inference(X)) |
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