Changes

.gitignore

@@ -17,4 +17,5 @@ __pycache__
 *.pth
 
 # Ignore pickle files
-*.pkl
+*.pkldata/
+data/

config/default.yaml

@@ -7,10 +7,10 @@ diffusion_params:
   beta_end : 0.02
 
 model_params:
-  im_channels : 1
-  im_size : 28
-  down_channels : [32, 64, 128, 256]
-  mid_channels : [256, 256, 128]
+  im_channels : 3
+  im_size : 32
+  down_channels: [64, 128, 256, 512]
+  mid_channels: [512, 512, 256]
   down_sample : [True, True, False]
   time_emb_dim : 128
   num_down_layers : 2

tools/train_ddpm.py

@@ -5,12 +5,22 @@
 import numpy as np
 from tqdm import tqdm
 from torch.optim import Adam
-from dataset.mnist_dataset import MnistDataset
+
 from torch.utils.data import DataLoader
 from models.unet_base import Unet
 from scheduler.linear_noise_scheduler import LinearNoiseScheduler
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+from torchvision import datasets, transforms
+
+
+transform = transforms.Compose([
+    transforms.ToTensor(),
+    transforms.Resize((32, 32)),
+])
+
+dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
+train_loader = DataLoader(dataset, batch_size=64, shuffle=True)
 
 
 def train(args):
@@ -20,11 +30,10 @@ def train(args):
             config = yaml.safe_load(file)
         except yaml.YAMLError as exc:
             print(exc)
-    print(config)
+    #print(config)
     ########################
 
     diffusion_config = config['diffusion_params']
-    dataset_config = config['dataset_params']
     model_config = config['model_params']
     train_config = config['train_params']
 
@@ -33,10 +42,8 @@ def train(args):
                                      beta_start=diffusion_config['beta_start'],
                                      beta_end=diffusion_config['beta_end'])
 
-    # Create the dataset
-    mnist = MnistDataset('train', im_path=dataset_config['im_path'])
-    mnist_loader = DataLoader(mnist, batch_size=train_config['batch_size'], shuffle=True, num_workers=4)
 
+
     # Instantiate the model
     model = Unet(model_config).to(device)
     model.train()
@@ -58,7 +65,7 @@ def train(args):
     # Run training
     for epoch_idx in range(num_epochs):
         losses = []
-        for im in tqdm(mnist_loader):
+        for im, _ in train_loader:
             optimizer.zero_grad()
             im = im.float().to(device)
 
@@ -92,3 +99,4 @@ def train(args):
                         default='config/default.yaml', type=str)
     args = parser.parse_args()
     train(args)
+