#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2022 Intel Corporation
#
# 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.
#
# SPDX-License-Identifier: Apache-2.0
#
import abc
from tlt.models.model import BaseModel
from tlt.utils.types import FrameworkType, UseCaseType
[docs]class TextClassificationModel(BaseModel):
"""
Class to represent a pretrained model for text classification
"""
[docs] def __init__(self, model_name: str, framework: FrameworkType, use_case: UseCaseType, dropout_layer_rate: float):
self._dropout_layer_rate = dropout_layer_rate
BaseModel.__init__(self, model_name, framework, use_case)
# Default learning rate for text models
self._learning_rate = 3e-5
if framework == FrameworkType.TENSORFLOW:
self._quantization_approach = 'static'
else:
self._quantization_approach = 'dynamic'
@property
@abc.abstractmethod
def num_classes(self):
"""
The number of output neurons in the model; equal to the number of classes in the dataset
"""
pass
@abc.abstractmethod
def predict(self, input_samples):
"""
Generates predictions for the input samples.
The input samples can be a BaseDataset type of object or a numpy array.
Returns a numpy array of predictions.
"""
pass
@property
def dropout_layer_rate(self):
"""
The probability of any one node being dropped when a dropout layer is used
"""
return self._dropout_layer_rate