From b475ecfa28c603010f550b0a8ad9204a5840b65f Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Fri, 18 Mar 2022 15:31:08 +0800
Subject: Add CIFAR-100

---
 readme.md              |  2 +-
 supervised/baseline.py | 22 ++++++++++++++++------
 supervised/models.py   |  4 ++--
 3 files changed, 19 insertions(+), 9 deletions(-)

diff --git a/readme.md b/readme.md
index 252abfd..c309e9b 100644
--- a/readme.md
+++ b/readme.md
@@ -27,7 +27,7 @@
 	- [x] ResNet
 	- [ ] ViT
 	- [x] CIFAR-10
-	- [ ] CIFAR-100
+	- [x] CIFAR-100
 	- [x] ImageNet-1k
 
 - Self-supervised baseline
diff --git a/supervised/baseline.py b/supervised/baseline.py
index 15bb716..0217866 100644
--- a/supervised/baseline.py
+++ b/supervised/baseline.py
@@ -14,7 +14,7 @@ from torch.backends import cudnn
 from torch.nn import CrossEntropyLoss
 from torch.utils.data import DataLoader
 from torch.utils.tensorboard import SummaryWriter
-from torchvision.datasets import CIFAR10, ImageNet
+from torchvision.datasets import CIFAR10, CIFAR100, ImageNet
 from torchvision.transforms import transforms, InterpolationMode
 
 from libs.datautils import color_distortion, Clip, RandomGaussianBlur
@@ -135,7 +135,8 @@ def init_logging(args):
 
 
 def prepare_dataset(args):
-    if args.dataset == 'cifar10' or args.dataset == 'cifar':
+    if args.dataset == 'cifar10' or args.dataset == 'cifar100' \
+            or args.dataset == 'cifar':
         train_transform = transforms.Compose([
             transforms.RandomResizedCrop(
                 args.crop_size,
@@ -151,9 +152,16 @@ def prepare_dataset(args):
             transforms.ToTensor()
         ])
 
-        train_set = CIFAR10(args.dataset_dir, train=True, transform=train_transform,
-                            download=True)
-        test_set = CIFAR10(args.dataset_dir, train=False, transform=test_transform)
+        if args.dataset == 'cifar10' or args.dataset == 'cifar':
+            train_set = CIFAR10(args.dataset_dir, train=True,
+                                transform=train_transform, download=True)
+            test_set = CIFAR10(args.dataset_dir, train=False,
+                               transform=test_transform)
+        else:  # CIFAR-100
+            train_set = CIFAR100(args.dataset_dir, train=True,
+                                 transform=train_transform, download=True)
+            test_set = CIFAR100(args.dataset_dir, train=False,
+                                transform=test_transform)
     elif args.dataset == 'imagenet1k' or args.dataset == 'imagenet1k':
         train_transform = transforms.Compose([
             transforms.RandomResizedCrop(
@@ -199,7 +207,9 @@ def create_dataloader(args, train_set, test_set):
 
 def init_model(args):
     if args.dataset == 'cifar10' or args.dataset == 'cifar':
-        model = CIFARResNet50()
+        model = CIFARResNet50(num_classes=10)
+    elif args.dataset == 'cifar100':
+        model = CIFARResNet50(num_classes=100)
     elif args.dataset == 'imagenet1k' or args.dataset == 'imagenet1k':
         model = ImageNetResNet50()
     else:
diff --git a/supervised/models.py b/supervised/models.py
index f3ba35a..eb75790 100644
--- a/supervised/models.py
+++ b/supervised/models.py
@@ -5,9 +5,9 @@ from torchvision.models.resnet import BasicBlock
 
 
 class CIFARResNet50(ResNet):
-    def __init__(self):
+    def __init__(self, num_classes):
         super(CIFARResNet50, self).__init__(
-            block=BasicBlock, layers=[3, 4, 6, 3], num_classes=10
+            block=BasicBlock, layers=[3, 4, 6, 3], num_classes=num_classes
         )
         self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                                stride=1, padding=1, bias=False)
-- 
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