add dataset

classifier_training.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import models
+from omegaconf import DictConfig, OmegaConf
+from data.dataset import get_dataset
+import time
+
+import hydra
+
+
+def get_model(config):
+    if config.dataset == 'gender_dataset':
+        num_class=2
+    else:
+        num_class=307
+
+    # Changing number of model's output classes to 1
+    #for resnet18
+    if config.classifier.model == 'resnet18':
+        model = models.resnet18(pretrained=False)
+        model.fc = nn.Linear(512, num_class)
+
+
+
+    #for resnet50
+    elif config.classifier.model == 'resnet101':
+        model = models.resnet101(pretrained=False)
+        model.fc = nn.Linear(2048, num_class)
+
+        
+
+    # for densenet 121
+    elif config.classifier.model == 'mnasnet':
+        model = models.mnasnet1_0(pretrained=True)
+        num_features = model.classifier[1].in_features
+        model.classifier[1] = nn.Linear(num_features, num_class)
+        # model.classifier = nn.Linear(1024, 1)
+
+        
+
+    #for vgg19_bn
+    elif config.classifier.model == 'densenet121':
+        model = models.densenet121(pretrained=True)
+        num_features = model.classifier.in_features
+        model.fc = nn.Linear(num_features, num_class)
+
+        
+
+
+    # Transfer execution to GPU
+    model = model.to('cuda')
+    
+    return model 
+
+@hydra.main(version_base=None, config_path="./config", config_name="config")
+def main(cfg: DictConfig) -> None:
+    model=get_model(cfg)
+    train_dataloader,test_dataloader=get_dataset(cfg)
+    num_epochs = cfg.classifier.num_epochs
+    start_time = time.time()
+
+
+
+
+
+
+
+
+
+
+if __name__ == "__main__":
+    main()

config/config.yaml

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+dataset: gender_dataset
+classifier: 
+  model:  resnet18
+  lr: 0.01
+  momentum: 0.9
+  num_epochs: 200
+
+
+paths:
+  gender_dataset: ./CelebA_HQ_face_gender_dataset
+  identity_dataset: ./CelebA_HQ_face_identity_dataset
+  password: secret
+
+
+optim:
+  batch_size: 8
+  num_epochs: 200
+  num_workers : 4

data/dataset.py

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+import torch
+import torchvision
+from torchvision import datasets, models, transforms
+import torch.nn as nn
+import os
+
+transforms_train = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.RandomHorizontalFlip(), # data augmentation
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) # normalization
+])
+
+transforms_test = transforms.Compose([
+    transforms.Resize((256, 256)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
+])
+
+
+def get_dataset(config):
+    if config.dataset == 'gender_dataset':
+        path=config.paths.gender_dataset
+    else:
+        path=config.paths.identity_dataset
+    train_dataset = datasets.ImageFolder(os.path.join(path, 'train'), transforms_train)
+    test_dataset = datasets.ImageFolder(os.path.join(path, 'test'), transforms_test)
+
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=config.optim.batch_size, shuffle=True, num_workers=config.optim.num_workers)
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=config.optim.batch_size, shuffle=False, num_workers=config.optim.num_workers)
+    return train_dataloader,test_dataloader