pipelines/components/PyTorch/pytorch-kfp-components/tests/iris/iris_handler.py

69 lines
2.0 KiB
Python

#!/usr/bin/env/python3
#
# Copyright (c) Facebook, Inc. and its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import logging
import numpy as np
import torch
from ts.torch_handler.base_handler import BaseHandler
logger = logging.getLogger(__name__)
class IRISClassifierHandler(BaseHandler):
"""
IRISClassifier handler class. This handler takes an input tensor and
output the type of iris based on the input
"""
def __init__(self):
super(IRISClassifierHandler, self).__init__()
def preprocess(self, data):
"""
preprocessing step - Reads the input array and converts it to tensor
:param data: Input to be passed through the layers for prediction
:return: output - Preprocessed input
"""
input_data_str = data[0].get("data")
if input_data_str is None:
input_data_str = data[0].get("body")
input_data = input_data_str.decode("utf-8")
input_tensor = torch.Tensor(ast.literal_eval(input_data))
return input_tensor
def postprocess(self, inference_output):
"""
Does postprocess after inference to be returned to user
:param inference_output: Output of inference
:return: output - Output after post processing
"""
predicted_idx = str(np.argmax(inference_output.cpu().detach().numpy()))
if self.mapping:
return [self.mapping[str(predicted_idx)]]
return [predicted_idx]
_service = IRISClassifierHandler()