feature(whl): add ag_news dataset.

fling/dataset/__init__.py

@@ -4,4 +4,6 @@
 from .tiny_imagenet import TinyImagenetDataset
 from .mini_imagenet import MiniImagenetDataset
 from .imagenet import ImagenetDataset
+from .ag_news import AGNewsDataset
+from .sogou_news import SogouNews
 from .build_dataset import get_dataset

fling/dataset/ag_news.py

@@ -0,0 +1,68 @@
+import torch
+from torch.utils.data import Dataset
+from torchtext.datasets import AG_NEWS
+from torchtext.data.utils import get_tokenizer
+from torchtext.vocab import build_vocab_from_iterator
+
+from fling.utils.registry_utils import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register('ag_news')
+class AGNewsDataset(Dataset):
+    """
+    Implementation of AG news dataset. This dataset contains over 1 million of news articles with 4 categories.
+    For more information, please refer to the link: http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
+    """
+    vocab = None
+
+    def __init__(self, cfg: dict, train: bool):
+        super(AGNewsDataset, self).__init__()
+        self.train = train
+        self.cfg = cfg
+        split = 'train' if self.train else 'test'
+        self.dataset = list(AG_NEWS(cfg.data.data_path, split=split))
+        self.tokenizer = get_tokenizer("basic_english")
+        self.max_length = cfg.data.get('max_length', float('inf'))
+
+        def _yield_tokens(data_iter):
+            for _, text in data_iter:
+                dat = self.tokenizer(text)
+                yield dat
+
+        # Prepare vocabulary tabular.
+        if AGNewsDataset.vocab is None:
+            AGNewsDataset.vocab = build_vocab_from_iterator(
+                _yield_tokens(iter(self.dataset)), specials=['<unk>', '<pad>'], min_freq=5
+            )
+            AGNewsDataset.vocab.set_default_index(self.vocab["<unk>"])
+
+        real_max_len = max([len(self._process_text((self.dataset[i][1]))) for i in range(len(self.dataset))])
+        self.max_length = min(self.max_length, real_max_len)
+
+        print(
+            f'Dataset Generated. Total vocab size: {len(self.vocab)}; '
+            f'Max length of the input: {self.max_length}; '
+            f'Dataset length: {len(self.dataset)}.'
+        )
+
+    def _process_text(self, x):
+        return AGNewsDataset.vocab(self.tokenizer(x))
+
+    def _process_label(self, x):
+        return int(x) - 1
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, item):
+        label, text = self.dataset[item]
+        label = self._process_label(label)
+        text = self._process_text(text)
+
+        if len(text) > self.max_length:
+            text = text[:self.max_length]
+        else:
+            text += [self.vocab['<pad>']] * (self.max_length - len(text))
+
+        assert len(text) == self.max_length
+        return {'input': torch.LongTensor(text), 'class_id': label}

fling/dataset/sogou_news.py

@@ -0,0 +1,72 @@
+import csv
+
+import torch
+from torch.utils.data import Dataset
+from torchtext.datasets import SogouNews
+from torchtext.data.utils import get_tokenizer
+from torchtext.vocab import build_vocab_from_iterator
+
+from fling.utils.registry_utils import DATASET_REGISTRY
+
+
+@DATASET_REGISTRY.register('sogou_news')
+class SogouNewsDataset(Dataset):
+    """
+    Implementation of Sogou news dataset. The Sogou News dataset is a mixture of 2,909,551 news articles from the \
+    SogouCA and SogouCS news corpora, in 5 categories. For more information, please refer to the link: \
+    http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
+    """
+    vocab = None
+
+    def __init__(self, cfg: dict, train: bool):
+        super(SogouNewsDataset, self).__init__()
+        self.train = train
+        self.cfg = cfg
+        split = 'train' if self.train else 'test'
+        csv.field_size_limit(int(1e8))
+        self.dataset = list(SogouNews(cfg.data.data_path, split=split))
+        self.tokenizer = get_tokenizer("basic_english")
+        self.max_length = cfg.data.get('max_length', float('inf'))
+
+        def _yield_tokens(data_iter):
+            for _, text in data_iter:
+                dat = self.tokenizer(text)
+                yield dat
+
+        # Prepare vocabulary tabular.
+        if SogouNewsDataset.vocab is None:
+            SogouNewsDataset.vocab = build_vocab_from_iterator(
+                _yield_tokens(iter(self.dataset)), specials=['<unk>', '<pad>'], min_freq=5
+            )
+            SogouNewsDataset.vocab.set_default_index(self.vocab["<unk>"])
+
+        real_max_len = max([len(self._process_text((self.dataset[i][1]))) for i in range(len(self.dataset))])
+        self.max_length = min(self.max_length, real_max_len)
+
+        print(
+            f'Dataset Generated. Total vocab size: {len(self.vocab)}; '
+            f'Max length of the input: {self.max_length}; '
+            f'Dataset length: {len(self.dataset)}.'
+        )
+
+    def _process_text(self, x):
+        return SogouNewsDataset.vocab(self.tokenizer(x))
+
+    def _process_label(self, x):
+        return int(x) - 1
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, item):
+        label, text = self.dataset[item]
+        label = self._process_label(label)
+        text = self._process_text(text)
+
+        if len(text) > self.max_length:
+            text = text[:self.max_length]
+        else:
+            text += [self.vocab['<pad>']] * (self.max_length - len(text))
+
+        assert len(text) == self.max_length
+        return {'input': torch.LongTensor(text), 'class_id': label}

fling/model/__init__.py

@@ -5,9 +5,5 @@
 from .resnet import resnet4, resnet6, resnet8, resnet10, resnet18, resnet34, resnet50
 from .swin_transformer import SwinTransformer
 from .vit import ViT
+from .language_classifier import TransformerClassifier
 from .build_model import get_model
-
-# Algorithm specific models
-# FedRoD
-from .fedrod_resnet import fedrod_resnet4, fedrod_resnet6, fedrod_resnet8, fedrod_resnet10, fedrod_resnet18,\
-    fedrod_resnet34, fedrod_resnet50