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LICENSE

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+MIT License
+
+Copyright (c) 2022 Xin Wen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Parametric Classification for Generalized Category Discovery: A Baseline Study
+
+
+<p align="center">
+    <a href="https://openaccess.thecvf.com/content/ICCV2023/html/Wen_Parametric_Classification_for_Generalized_Category_Discovery_A_Baseline_Study_ICCV_2023_paper.html"><img src="https://img.shields.io/badge/-ICCV%202023-68488b"></a>
+    <a href="https://arxiv.org/abs/2211.11727"><img src="https://img.shields.io/badge/arXiv-2211.11727-b31b1b"></a>
+    <a href="https://wen-xin.info/simgcd"><img src="https://img.shields.io/badge/Project-Website-blue"></a>
+  <a href="https://github.com/CVMI-Lab/SlotCon/blob/master/LICENSE"><img src="https://img.shields.io/badge/License-MIT-blue.svg"></a>
+</p>
+<p align="center">
+	Parametric Classification for Generalized Category Discovery: A Baseline Study (ICCV 2023)<br>
+  By
+  <a href="https://wen-xin.info">Xin Wen</a>*, 
+  <a href="https://bzhao.me/">Bingchen Zhao</a>*, and 
+  <a href="https://xjqi.github.io/">Xiaojuan Qi</a>.
+</p>
+
+![teaser](assets/teaser.jpg)
+
+Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples.
+Previous studies argued that parametric classifiers are prone to overfitting to seen categories, and endorsed using a non-parametric classifier formed with semi-supervised $k$-means.
+
+However, in this study, we investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We demonstrate that two prediction biases exist: the classifier tends to predict seen classes more often, and produces an imbalanced distribution across seen and novel categories. 
+Based on these findings, we propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
+We hope the investigation and proposed simple framework can serve as a strong baseline to facilitate future studies in this field.
+
+## Running
+
+### Dependencies
+
+```
+pip install -r requirements.txt
+```
+
+### Config
+
+Set paths to datasets and desired log directories in ```config.py```
+
+
+### Datasets
+
+We use fine-grained benchmarks in this paper, including:
+
+* [The Semantic Shift Benchmark (SSB)](https://github.com/sgvaze/osr_closed_set_all_you_need#ssb) and [Herbarium19](https://www.kaggle.com/c/herbarium-2019-fgvc6)
+
+We also use generic object recognition datasets, including:
+
+* [CIFAR-10/100](https://pytorch.org/vision/stable/datasets.html) and [ImageNet-100/1K](https://image-net.org/download.php)
+
+
+### Scripts
+
+**Train the model**:
+
+```
+bash scripts/run_${DATASET_NAME}.sh
+```
+
+We found picking the model according to 'Old' class performance could lead to possible over-fitting, and since 'New' class labels on the held-out validation set should be assumed unavailable, we suggest not to perform model selection, and simply use the last-epoch model.
+
+## Results
+Our results:
+
+<table><thead><tr><th>Source</th><th colspan="3">Paper (3 runs) </th><th colspan="3">Current Github (5 runs) </th></tr></thead><tbody><tr><td>Dataset</td><td>All</td><td>Old</td><td>New</td><td>All</td><td>Old</td><td>New</td></tr><tr><td>CIFAR10</td><td>97.1±0.0</td><td>95.1±0.1</td><td>98.1±0.1</td><td>97.0±0.1</td><td>93.9±0.1</td><td>98.5±0.1</td></tr><tr><td>CIFAR100</td><td>80.1±0.9</td><td>81.2±0.4</td><td>77.8±2.0</td><td>79.8±0.6</td><td>81.1±0.5</td><td>77.4±2.5</td></tr><tr><td>ImageNet-100</td><td>83.0±1.2</td><td>93.1±0.2</td><td>77.9±1.9</td><td>83.6±1.4</td><td>92.4±0.1</td><td>79.1±2.2</td></tr><tr><td>ImageNet-1K</td><td>57.1±0.1</td><td>77.3±0.1</td><td>46.9±0.2</td><td>57.0±0.4</td><td>77.1±0.1</td><td>46.9±0.5</td></tr><tr><td>CUB</td><td>60.3±0.1</td><td>65.6±0.9</td><td>57.7±0.4</td><td>61.5±0.5</td><td>65.7±0.5</td><td>59.4±0.8</td></tr><tr><td>Stanford Cars</td><td>53.8±2.2</td><td>71.9±1.7</td><td>45.0±2.4</td><td>53.4±1.6</td><td>71.5±1.6</td><td>44.6±1.7</td></tr><tr><td>FGVC-Aircraft</td><td>54.2±1.9</td><td>59.1±1.2</td><td>51.8±2.3</td><td>54.3±0.7</td><td>59.4±0.4</td><td>51.7±1.2</td></tr><tr><td>Herbarium 19</td><td>44.0±0.4</td><td>58.0±0.4</td><td>36.4±0.8</td><td>44.2±0.2</td><td>57.6±0.6</td><td>37.0±0.4</td></tr></tbody></table>
+
+## Citing this work
+
+If you find this repo useful for your research, please consider citing our paper:
+
+```
+@inproceedings{wen2023simgcd,
+    author    = {Wen, Xin and Zhao, Bingchen and Qi, Xiaojuan},
+    title     = {Parametric Classification for Generalized Category Discovery: A Baseline Study},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    year      = {2023},
+    pages     = {16590-16600}
+}
+```
+
+## Acknowledgements
+
+The codebase is largely built on this repo: https://github.com/sgvaze/generalized-category-discovery.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

config.py

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+# -----------------
+# DATASET ROOTS
+# -----------------
+cifar_10_root = '${DATASET_DIR}/cifar10'
+cifar_100_root = '${DATASET_DIR}/cifar100'
+cub_root = '${DATASET_DIR}/cub'
+aircraft_root = '${DATASET_DIR}/fgvc-aircraft-2013b'
+car_root = '${DATASET_DIR}/cars'
+herbarium_dataroot = '${DATASET_DIR}/herbarium_19'
+imagenet_root = '${DATASET_DIR}/ImageNet'
+
+# OSR Split dir
+osr_split_dir = 'data/ssb_splits'
+
+# -----------------
+# OTHER PATHS
+# -----------------
+exp_root = 'dev_outputs' # All logs and checkpoints will be saved here

data/augmentations/__init__.py

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+from torchvision import transforms
+
+import torch
+
+def get_transform(transform_type='imagenet', image_size=32, args=None):
+
+    if transform_type == 'imagenet':
+
+        mean = (0.485, 0.456, 0.406)
+        std = (0.229, 0.224, 0.225)
+        interpolation = args.interpolation
+        crop_pct = args.crop_pct
+
+        train_transform = transforms.Compose([
+            transforms.Resize(int(image_size / crop_pct), interpolation),
+            transforms.RandomCrop(image_size),
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.ColorJitter(),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=torch.tensor(mean),
+                std=torch.tensor(std))
+        ])
+
+        test_transform = transforms.Compose([
+            transforms.Resize(int(image_size / crop_pct), interpolation),
+            transforms.CenterCrop(image_size),
+            transforms.ToTensor(),
+            transforms.Normalize(
+                mean=torch.tensor(mean),
+                std=torch.tensor(std))
+        ])
+
+    else:
+
+        raise NotImplementedError
+
+    return (train_transform, test_transform)

data/cifar.py

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+from torchvision.datasets import CIFAR10, CIFAR100
+from copy import deepcopy
+import numpy as np
+
+from data.data_utils import subsample_instances
+from config import cifar_10_root, cifar_100_root
+
+
+class CustomCIFAR10(CIFAR10):
+
+    def __init__(self, *args, **kwargs):
+
+        super(CustomCIFAR10, self).__init__(*args, **kwargs)
+
+        self.uq_idxs = np.array(range(len(self)))
+
+    def __getitem__(self, item):
+
+        img, label = super().__getitem__(item)
+        uq_idx = self.uq_idxs[item]
+
+        return img, label, uq_idx
+
+    def __len__(self):
+        return len(self.targets)
+
+
+class CustomCIFAR100(CIFAR100):
+
+    def __init__(self, *args, **kwargs):
+        super(CustomCIFAR100, self).__init__(*args, **kwargs)
+
+        self.uq_idxs = np.array(range(len(self)))
+
+    def __getitem__(self, item):
+        img, label = super().__getitem__(item)
+        uq_idx = self.uq_idxs[item]
+
+        return img, label, uq_idx
+
+    def __len__(self):
+        return len(self.targets)
+
+
+def subsample_dataset(dataset, idxs):
+
+    # Allow for setting in which all empty set of indices is passed
+
+    if len(idxs) > 0:
+
+        dataset.data = dataset.data[idxs]
+        dataset.targets = np.array(dataset.targets)[idxs].tolist()
+        dataset.uq_idxs = dataset.uq_idxs[idxs]
+
+        return dataset
+
+    else:
+
+        return None
+
+
+def subsample_classes(dataset, include_classes=(0, 1, 8, 9)):
+
+    cls_idxs = [x for x, t in enumerate(dataset.targets) if t in include_classes]
+
+    target_xform_dict = {}
+    for i, k in enumerate(include_classes):
+        target_xform_dict[k] = i
+
+    dataset = subsample_dataset(dataset, cls_idxs)
+
+    # dataset.target_transform = lambda x: target_xform_dict[x]
+
+    return dataset
+
+
+def get_train_val_indices(train_dataset, val_split=0.2):
+
+    train_classes = np.unique(train_dataset.targets)
+
+    # Get train/test indices
+    train_idxs = []
+    val_idxs = []
+    for cls in train_classes:
+
+        cls_idxs = np.where(train_dataset.targets == cls)[0]
+
+        v_ = np.random.choice(cls_idxs, replace=False, size=((int(val_split * len(cls_idxs))),))
+        t_ = [x for x in cls_idxs if x not in v_]
+
+        train_idxs.extend(t_)
+        val_idxs.extend(v_)
+
+    return train_idxs, val_idxs
+
+
+def get_cifar_10_datasets(train_transform, test_transform, train_classes=(0, 1, 8, 9),
+                       prop_train_labels=0.8, split_train_val=False, seed=0):
+
+    np.random.seed(seed)
+
+    # Init entire training set
+    whole_training_set = CustomCIFAR10(root=cifar_10_root, transform=train_transform, train=True)
+
+    # Get labelled training set which has subsampled classes, then subsample some indices from that
+    train_dataset_labelled = subsample_classes(deepcopy(whole_training_set), include_classes=train_classes)
+    subsample_indices = subsample_instances(train_dataset_labelled, prop_indices_to_subsample=prop_train_labels)
+    train_dataset_labelled = subsample_dataset(train_dataset_labelled, subsample_indices)
+
+    # Split into training and validation sets
+    train_idxs, val_idxs = get_train_val_indices(train_dataset_labelled)
+    train_dataset_labelled_split = subsample_dataset(deepcopy(train_dataset_labelled), train_idxs)
+    val_dataset_labelled_split = subsample_dataset(deepcopy(train_dataset_labelled), val_idxs)
+    val_dataset_labelled_split.transform = test_transform
+
+    # Get unlabelled data
+    unlabelled_indices = set(whole_training_set.uq_idxs) - set(train_dataset_labelled.uq_idxs)
+    train_dataset_unlabelled = subsample_dataset(deepcopy(whole_training_set), np.array(list(unlabelled_indices)))
+
+    # Get test set for all classes
+    test_dataset = CustomCIFAR10(root=cifar_10_root, transform=test_transform, train=False)
+
+    # Either split train into train and val or use test set as val
+    train_dataset_labelled = train_dataset_labelled_split if split_train_val else train_dataset_labelled
+    val_dataset_labelled = val_dataset_labelled_split if split_train_val else None
+
+    all_datasets = {
+        'train_labelled': train_dataset_labelled,
+        'train_unlabelled': train_dataset_unlabelled,
+        'val': val_dataset_labelled,
+        'test': test_dataset,
+    }
+
+    return all_datasets
+
+
+def get_cifar_100_datasets(train_transform, test_transform, train_classes=range(80),
+                       prop_train_labels=0.8, split_train_val=False, seed=0):
+
+    np.random.seed(seed)
+
+    # Init entire training set
+    whole_training_set = CustomCIFAR100(root=cifar_100_root, transform=train_transform, train=True, download=True)
+
+    # Get labelled training set which has subsampled classes, then subsample some indices from that
+    train_dataset_labelled = subsample_classes(deepcopy(whole_training_set), include_classes=train_classes)
+    subsample_indices = subsample_instances(train_dataset_labelled, prop_indices_to_subsample=prop_train_labels)
+    train_dataset_labelled = subsample_dataset(train_dataset_labelled, subsample_indices)
+
+    # Split into training and validation sets
+    train_idxs, val_idxs = get_train_val_indices(train_dataset_labelled)
+    train_dataset_labelled_split = subsample_dataset(deepcopy(train_dataset_labelled), train_idxs)
+    val_dataset_labelled_split = subsample_dataset(deepcopy(train_dataset_labelled), val_idxs)
+    val_dataset_labelled_split.transform = test_transform
+
+    # Get unlabelled data
+    unlabelled_indices = set(whole_training_set.uq_idxs) - set(train_dataset_labelled.uq_idxs)
+    train_dataset_unlabelled = subsample_dataset(deepcopy(whole_training_set), np.array(list(unlabelled_indices)))
+
+    # Get test set for all classes
+    test_dataset = CustomCIFAR100(root=cifar_100_root, transform=test_transform, train=False, download=True)
+
+    # Either split train into train and val or use test set as val
+    train_dataset_labelled = train_dataset_labelled_split if split_train_val else train_dataset_labelled
+    val_dataset_labelled = val_dataset_labelled_split if split_train_val else None
+
+    all_datasets = {
+        'train_labelled': train_dataset_labelled,
+        'train_unlabelled': train_dataset_unlabelled,
+        'val': val_dataset_labelled,
+        'test': test_dataset,
+    }
+
+    return all_datasets
+
+
+if __name__ == '__main__':
+
+    x = get_cifar_100_datasets(None, None, split_train_val=False,
+                         train_classes=range(80), prop_train_labels=0.5)
+
+    print('Printing lens...')
+    for k, v in x.items():
+        if v is not None:
+            print(f'{k}: {len(v)}')
+
+    print('Printing labelled and unlabelled overlap...')
+    print(set.intersection(set(x['train_labelled'].uq_idxs), set(x['train_unlabelled'].uq_idxs)))
+    print('Printing total instances in train...')
+    print(len(set(x['train_labelled'].uq_idxs)) + len(set(x['train_unlabelled'].uq_idxs)))
+
+    print(f'Num Labelled Classes: {len(set(x["train_labelled"].targets))}')
+    print(f'Num Unabelled Classes: {len(set(x["train_unlabelled"].targets))}')
+    print(f'Len labelled set: {len(x["train_labelled"])}')
+    print(f'Len unlabelled set: {len(x["train_unlabelled"])}')