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#!/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 os
import tensorflow as tf
from tlt import TLT_BASE_DIR
from tlt.datasets.tf_dataset import TFDataset
from tlt.datasets.text_classification.text_classification_dataset import TextClassificationDataset
from tlt.utils.dataset_utils import prepare_huggingface_input_data
from tlt.utils.file_utils import read_json_file
from tlt.utils.inc_utils import INCTFDataLoader
from downloader.datasets import DataDownloader
DATASET_CONFIG_DIR = os.path.join(TLT_BASE_DIR, "datasets/configs")
config_file = os.path.join(DATASET_CONFIG_DIR, "tf_text_classification_datasets.json")
config_dict = read_json_file(config_file)
DATASETS = list(config_dict.keys())
class TFDSTextClassificationDataset(TFDataset, TextClassificationDataset):
"""
A text classification dataset from the TensorFlow datasets catalog
"""
def __init__(self, dataset_dir, dataset_name, split=["train"], shuffle_files=True, **kwargs):
if not isinstance(split, list):
raise ValueError("Value of split argument must be a list.")
TextClassificationDataset.__init__(self, dataset_dir, dataset_name, "tf_datasets")
if dataset_name not in DATASETS:
raise ValueError("Dataset name is not supported. Choose from: {}".format(DATASETS))
# as_supervised gives us the (input, label) structure that the model expects
as_supervised = True
# Glue datasets don't support as_supervised=True, so we need to set as_supervised=False, and then fix
# the data format after loading
if "glue" in dataset_name:
as_supervised = False
downloader = DataDownloader(dataset_name, dataset_dir=dataset_dir, catalog='tfds', as_supervised=as_supervised,
shuffle_files=shuffle_files, with_info=True)
data, self._info = downloader.download(split=split)
# Since glue datasets don't support the supervised (input, label) structure, we have to manually format it
if "glue" in dataset_name:
for split_id in range(len(data)):
data[split_id] = data[split_id].map(lambda x: (x['sentence'], x['label']))
self._dataset = None
self._train_subset = None
self._validation_subset = None
self._test_subset = None
self._preprocessed = None
if len(split) == 1:
self._validation_type = None # Train & evaluate on the whole dataset
self._dataset = data[0]
else:
self._validation_type = 'defined_split' # Defined by user or TFDS
for i, s in enumerate(split):
if s == 'train':
self._train_subset = data[i]
elif s == 'validation':
self._validation_subset = data[i]
elif s == 'test':
self._test_subset = data[i]
self._dataset = data[i] if self._dataset is None else self._dataset.concatenate(data[i])
@property
def class_names(self):
if "label" in self._info.features.keys():
return self._info.features["label"].names
else:
return []
@property
def info(self):
return {'dataset_info': self._info, 'preprocessing_info': self._preprocessed}
@property
def dataset(self):
return self._dataset
def preprocess(self, batch_size):
"""
Batch the dataset
Args:
batch_size (int): desired batch size
Raises:
TypeError: if the batch_size is not a positive integer
ValueError: if the dataset is not defined or has already been processed
"""
if not isinstance(batch_size, int) or batch_size < 1:
raise ValueError("batch_size should be a positive integer")
if self._preprocessed:
raise ValueError("Data has already been preprocessed: {}".format(self._preprocessed))
# Get the non-None splits
split_list = ['_dataset', '_train_subset', '_validation_subset', '_test_subset']
subsets = [s for s in split_list if getattr(self, s, None)]
for subset in subsets:
setattr(self, subset, getattr(self, subset).cache())
setattr(self, subset, getattr(self, subset).batch(batch_size))
setattr(self, subset, getattr(self, subset).prefetch(tf.data.AUTOTUNE))
self._preprocessed = {'batch_size': batch_size}
def get_inc_dataloaders(self, hub_name, max_seq_length):
calib_data, calib_labels = prepare_huggingface_input_data(self.train_subset, hub_name, max_seq_length)
calib_data['label'] = tf.convert_to_tensor(calib_labels)
eval_data, eval_labels = prepare_huggingface_input_data(self.validation_subset, hub_name, max_seq_length)
eval_data['label'] = tf.convert_to_tensor(eval_labels)
calib_data.pop('token_type_ids')
eval_data.pop('token_type_ids')
calib_dataloader = INCTFDataLoader(calib_data, batch_size=self._preprocessed['batch_size'])
eval_dataloader = INCTFDataLoader(eval_data, batch_size=self._preprocessed['batch_size'])
return calib_dataloader, eval_dataloader
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