Doing minor changes

tac_ssl.py

@@ -1,4 +1,5 @@
 import os
+import pickle
 from PIL import Image
 
 from train_mm_moco import evaluate_and_plot, compute_tsne, MultiModalMoCo
@@ -43,7 +44,8 @@ class CustomMultiModalDataset(Dataset):
 
 # Initialize augmentation
 simple_transforms = transforms.Compose([
-    transforms.CenterCrop(500),
+    transforms.Resize((275, 275)),
+    #transforms.CenterCrop(500),
     transforms.ToTensor(),
     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 ])
@@ -52,41 +54,66 @@ data_transforms = transforms.Compose([
     transforms.RandomApply([transforms.RandomRotation(150)], p=0.50),
     transforms.RandomResizedCrop(224, scale=(0.2, 1.0)),
     transforms.RandomApply([transforms.RandomHorizontalFlip()], p=0.50),
-    transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
+    #transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
     transforms.RandomGrayscale(p=0.2),
-    transforms.RandomApply([transforms.GaussianBlur(3, sigma=(0.1, 2.0))], p=0.5),
+    #transforms.RandomApply([transforms.GaussianBlur(3, sigma=(0.1, 2.0))], p=0.5),
 ])
 
 # Initialize dataset and dataloader
 vision_folder = "/home/vedant/Downloads/ssvtp_data/images_rgb"
 tactile_folder = "/home/vedant/Downloads/ssvtp_data/images_tac"
 dataset = CustomMultiModalDataset(vision_folder, tactile_folder, transform=simple_transforms)
-#dataloader = DataLoader(dataset, batch_size=128, shuffle=True)
 
-# Split the dataset into 80-20
+preload = True
-train_size = int(0.8 * len(dataset))
+if not preload:
-test_size = len(dataset) - train_size
+    # Split the dataset into 80-20
-train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
+    train_size = int(0.8 * len(dataset))
+    test_size = len(dataset) - train_size
+    train_dataset, test_dataset = random_split(dataset, [train_size, test_size])
 
-# Initialize dataloaders for train and test
+    # Get the indices of the training and test sets
-train_dataloader = DataLoader(train_dataset, batch_size=96, shuffle=True)
+    train_indices = train_dataset.indices
-test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
+    test_indices = test_dataset.indices
 
+    # Save these indices to disk
+    with open('indices/train_indices.pkl', 'wb') as f:
+        pickle.dump(train_indices, f)
+        
+    with open('indices/test_indices.pkl', 'wb') as f:
+        pickle.dump(test_indices, f)
+
+    # Initialize dataloaders for train and test
+    train_dataloader = DataLoader(train_dataset, batch_size=96, shuffle=True)
+    test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
+else:
+    # Load the indices from disk
+    with open('indices/train_indices.pkl', 'rb') as f:
+        train_indices = pickle.load(f)
+        
+    with open('indices/test_indices.pkl', 'rb') as f:
+        test_indices = pickle.load(f)
+
+    # Create subset datasets and DataLoaders
+    train_subset = torch.utils.data.Subset(dataset, train_indices)
+    test_subset = torch.utils.data.Subset(dataset, test_indices)
+
+    train_dataloader = DataLoader(train_subset, batch_size=96, shuffle=True)
+    test_dataloader = DataLoader(test_subset, batch_size=32, shuffle=False)
 
 # Initialize model
-model = MultiModalMoCo(writer, K=4096, m=0.999, T=0.07).to(device)
+model = MultiModalMoCo(writer, K=4096, m=0.99, T=0.07).to(device)
 
 # Initialize optimizer
 vision_module = list(model.vision_base_q.parameters()) + list(model.vision_head_intra_q.parameters()) + list(model.vision_head_inter_q.parameters())
 tactile_module = list(model.tactile_base_q.parameters()) + list(model.tactile_head_intra_q.parameters()) + list(model.tactile_head_inter_q.parameters())
-optim_vision = optim.Adam(vision_module, lr=0.0001)
+optim_vision = optim.Adam(vision_module, lr=0.1)
-optim_tactile = optim.Adam(tactile_module, lr=0.0001)
+optim_tactile = optim.Adam(tactile_module, lr=0.1)
 
 # Training loop
-n_epochs = 250  # Number of epochs
+n_epochs = 500  # Number of epochs
 for epoch in range(n_epochs):
     for i, (x_vision, x_tactile) in enumerate(train_dataloader):
-        
+
         # Augment images
         x_vision_q = data_transforms(x_vision).to(device)
         x_vision_k = data_transforms(x_vision).to(device)
@@ -110,10 +137,7 @@ for epoch in range(n_epochs):
             writer.add_scalar('training loss', loss.item(), epoch * len(train_dataloader) + i)
 
     # Evaluate and plot
-    compute_tsne(model, test_dataloader, writer, epoch)
+    #compute_tsne(model, test_dataloader, writer, epoch)
-    evaluate_and_plot(model, test_dataloader, epoch, writer, device)
+    #evaluate_and_plot(model, test_dataloader, epoch, writer, device)
     if epoch % 10 == 0:
-        torch.save(model.state_dict(), 'models/model.pth')
+        torch.save(model.state_dict(), 'models/model.pth')
-
-    
-plt.show()

tac_ssl_test.py

@@ -11,7 +11,7 @@ from PIL import Image
 import matplotlib.pyplot as plt
 from sklearn.manifold import TSNE
 from train_mm_moco import MultiModalMoCo
-from sklearn.neighbors import NearestNeighbors
+from sklearn.neighbors import NearestNeighbors, KNeighborsClassifier
 
 
 def denormalize(tensor, mean, std):
@@ -34,7 +34,11 @@ def compute_tsne(model, test_dataloader):
 
     vision_base_q = vision_base_q.cpu().numpy()
     tactile_base_q = tactile_base_q.cpu().numpy()
+    combined_data = np.concatenate((vision_base_q, tactile_base_q), axis=0)
 
+    nn_all = find_knn(combined_data, np.asarray(range(1,201)), n=8)
+    plot_images_by_labels(np.concatenate((x_vision_test.cpu().numpy(), x_tactile_test.cpu().numpy()), axis=0), nn_all[0])
+    
     image_data = np.concatenate((x_vision_test.cpu().numpy(), x_tactile_test.cpu().numpy()), axis=0)
 
     tsne = TSNE(n_components=2, random_state=0, perplexity=75,n_iter=50000)
@@ -47,13 +51,12 @@ def compute_tsne(model, test_dataloader):
     tsne_data = tsne.fit_transform(data)
     nn_all = find_knn(tsne_data, labels)
     plot_images_by_labels(image_data, nn_all[0])
-    print(nn_all[:5])
 
     fig = plt.figure(figsize=(10, 10))
     
     for i, (x, y) in enumerate(tsne_data):
-        plt.scatter(x, y, color='blue')
+        plt.scatter(x, y, color='blue' if labels[i] <= 100 else 'red')
-        plt.text(x, y, f"{labels[i]}", fontsize=12, ha='center', va='bottom')
+        #plt.text(x, y, f"{labels[i]}", fontsize=12, ha='center', va='bottom')
     plt.savefig('temp_figure.png')
     plt.close(fig)
 
@@ -65,18 +68,24 @@ def compute_tsne(model, test_dataloader):
     plt.axis('off')
     plt.show()
 
-def find_knn(tsne_data, labels):
+def find_knn(tsne_data, labels, n=6):
-    neigh = NearestNeighbors(n_neighbors=8)
+    neigh = KNeighborsClassifier(n_neighbors=n, weights='distance')
-    neigh.fit(tsne_data)
+    X_train = tsne_data[1:,:]
-    knn = neigh.kneighbors(tsne_data, return_distance=False)
+    y_train = labels[1:]
-    return labels[knn]
+    neigh.fit(X_train, y_train)
+    _, indices = neigh.kneighbors(tsne_data[0,:].reshape(1, -1))
+    return labels[indices]
 
 def plot_images_by_labels(image_data, labels_to_plot):
-    fig, axes = plt.subplots(1, len(labels_to_plot), figsize=(15, 5))
+    fig, axes = plt.subplots(2, len(labels_to_plot)//2, figsize=(15, 5))
+    axes = axes.flatten()
     for i, label in enumerate(labels_to_plot):
-        axes[i].imshow(image_data[label].transpose(1, 2, 0))
+        img = image_data[label]
+        normalized_image_data = (img - np.min(img)) / (np.max(img) - np.min(img))
+        axes[i].imshow(normalized_image_data.transpose(1, 2, 0))
         axes[i].set_title(label)
         axes[i].axis('off')
+
     plt.show()
 
 if __name__ == "__main__":

train_mm_moco.py

@@ -18,8 +18,8 @@ class MultiModalMoCo(nn.Module):
         self.m = m
         self.T = T
 
-        self.intra_dim = 64
+        self.intra_dim = 128
-        self.inter_dim = 64
+        self.inter_dim = 128
 
         # Initialize the queue
         self.queue = torch.zeros((self.K, self.intra_dim), dtype=torch.float).cuda()
@@ -34,6 +34,7 @@ class MultiModalMoCo(nn.Module):
 
         def create_resnet_encoder():
             resnet = models.resnet50(weights='ResNet50_Weights.IMAGENET1K_V1')
+            #resnet = models.regnet_x_800mf(weights='RegNet_X_800MF_Weights')
             features = list(resnet.children())[:-2]
             features.append(nn.AdaptiveAvgPool2d((1, 1)))
             features.append(nn.Flatten())
@@ -109,7 +110,7 @@ class MultiModalMoCo(nn.Module):
         tactile_vision_inter = self.moco_contrastive_loss(tactile_queries_inter, vision_keys_inter)
         
         # Combine losses (you can use different strategies to combine these losses)
-        weight_inter = 0.1
+        weight_inter = 1
         combined_loss = vision_loss_intra + tactile_loss_intra  + (vision_tactile_inter + tactile_vision_inter) * weight_inter
 
         if len_train_dataloader != 0:
@@ -160,7 +161,7 @@ def compute_tsne(model, test_dataloader, writer, epoch):
     with torch.no_grad():
         test_data_list = list(test_dataloader)
         x_vision_test, x_tactile_test = random.choice(test_data_list)
-        random_indices = random.sample(range(x_vision_test.shape[0]), 10)
+        random_indices = random.sample(range(x_vision_test.shape[0]), 20)
         x_vision_test = x_vision_test[random_indices].to('cuda')
         x_tactile_test = x_tactile_test[random_indices].to('cuda')
         vision_base_q = model.vision_base_q(x_vision_test)
@@ -169,7 +170,7 @@ def compute_tsne(model, test_dataloader, writer, epoch):
     vision_base_q = vision_base_q.cpu().numpy()
     tactile_base_q = tactile_base_q.cpu().numpy()
 
-    tsne = TSNE(n_components=2, random_state=0, perplexity=5)
+    tsne = TSNE(n_components=2, random_state=0, perplexity=2)
 
     # Create pairs of corresponding representations and labels
     num_samples = min(vision_base_q.shape[0], tactile_base_q.shape[0])
@@ -189,4 +190,17 @@ def compute_tsne(model, test_dataloader, writer, epoch):
     image = Image.open('temp_figure.png')
     image = np.array(image)  # Convert image to a NumPy array
     image = image[:, :, :3].transpose(2, 0, 1)  # Extract RGB channels and change format to CHW
-    writer.add_image('t-SNE', image, global_step=epoch)
+    writer.add_image('t-SNE', image, global_step=epoch)
+
+
+def find_knn(query_point, data_points, k=5):
+    # Calculate the Euclidean distances
+    distances = torch.norm(data_points - query_point, dim=1)
+    
+    # Find the indices of the k smallest distances
+    knn_indices = torch.topk(distances, k, largest=False, sorted=True)[1]
+    
+    # Get the k smallest distances
+    knn_distances = distances[knn_indices]
+    
+    return knn_indices, knn_distances