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import os
from PIL import Image
from train_mm_moco import evaluate_and_plot, compute_tsne, MultiModalMoCo
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
from torchvision import transforms
from torch.utils.data import random_split
from torch.utils.data import DataLoader, Dataset
from torch.utils.tensorboard import SummaryWriter
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
writer = SummaryWriter('runs/mmssl')
# Custom dataset
class CustomMultiModalDataset(Dataset):
def __init__(self, vision_folder, tactile_folder, transform=None):
self.vision_folder = vision_folder
self.tactile_folder = tactile_folder
self.transform = transform
self.vision_files = sorted(os.listdir(vision_folder))
self.tactile_files = sorted(os.listdir(tactile_folder))
def __len__(self):
return len(self.vision_files)
def __getitem__(self, idx):
vision_path = os.path.join(self.vision_folder, self.vision_files[idx])
tactile_path = os.path.join(self.tactile_folder, self.tactile_files[idx])
vision_image = Image.open(vision_path).convert("RGB")
tactile_image = Image.open(tactile_path).convert("RGB")
if self.transform:
vision_image = self.transform(vision_image)
tactile_image = self.transform(tactile_image)
return vision_image, tactile_image
# Initialize augmentation
simple_transforms = transforms.Compose([
transforms.CenterCrop(500),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
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.RandomGrayscale(p=0.2),
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
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
train_dataloader = DataLoader(train_dataset, batch_size=96, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# Initialize model
model = MultiModalMoCo(writer, K=4096, m=0.999, 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_tactile = optim.Adam(tactile_module, lr=0.0001)
# Training loop
n_epochs = 250 # 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)
x_tactile_q = data_transforms(x_tactile).to(device)
x_tactile_k = data_transforms(x_tactile).to(device)
# Forward pass to get the loss
loss = model(x_vision_q, x_vision_k, x_tactile_q, x_tactile_k, epoch, i, len(train_dataloader))
# Backward pass and optimization
optim_vision.zero_grad()
optim_tactile.zero_grad()
loss.backward()
optim_vision.step()
optim_tactile.step()
# Logging
if i % 10 == 0:
print(f"Epoch [{epoch+1}/{n_epochs}], Step [{i+1}/{len(train_dataloader)}], Loss: {loss.item():.4f}")
writer.add_scalar('training loss', loss.item(), epoch * len(train_dataloader) + i)
# Evaluate and plot
compute_tsne(model, test_dataloader, writer, epoch)
evaluate_and_plot(model, test_dataloader, epoch, writer, device)
if epoch % 10 == 0:
torch.save(model.state_dict(), 'models/model.pth')
plt.show()

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import torch
import torch.nn as nn
from torchvision import models # For using the ResNet-50 model
import torch.nn.functional as F
import timm
import random
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
class MultiModalMoCo(nn.Module):
def __init__(self, writer, K=4096, m=0.99, T=1.0):
super(MultiModalMoCo, self).__init__()
self.writer = writer
self.K = K
self.m = m
self.T = T
self.intra_dim = 64
self.inter_dim = 64
# Initialize the queue
self.queue = torch.zeros((self.K, self.intra_dim), dtype=torch.float).cuda()
self.queue_ptr = 0
def create_mlp_head(output_dim):
return nn.Sequential(
nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048, output_dim)
)
def create_resnet_encoder():
resnet = models.resnet50(weights='ResNet50_Weights.IMAGENET1K_V1')
features = list(resnet.children())[:-2]
features.append(nn.AdaptiveAvgPool2d((1, 1)))
features.append(nn.Flatten())
return nn.Sequential(*features)
# Vision encoders
self.vision_base_q = create_resnet_encoder()
self.vision_head_intra_q = create_mlp_head(self.intra_dim)
self.vision_head_inter_q = create_mlp_head(self.inter_dim)
self.vision_base_k = create_resnet_encoder()
self.vision_head_intra_k = create_mlp_head(self.intra_dim)
self.vision_head_inter_k = create_mlp_head(self.inter_dim)
# Tactile encoders
self.tactile_base_q = create_resnet_encoder()
self.tactile_head_intra_q = create_mlp_head(self.intra_dim)
self.tactile_head_inter_q = create_mlp_head(self.inter_dim)
self.tactile_base_k = create_resnet_encoder()
self.tactile_head_intra_k = create_mlp_head(self.intra_dim)
self.tactile_head_inter_k = create_mlp_head(self.inter_dim)
# Initialize key encoders with query encoder weights
self._momentum_update_key_encoder(self.vision_base_q, self.vision_base_k)
self._momentum_update_key_encoder(self.tactile_base_q, self.tactile_base_k)
@torch.no_grad()
def concat_all_gather(self,tensor):
tensors_gather = [torch.ones_like(tensor)
for _ in range(torch.distributed.get_world_size())]
torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
output = torch.cat(tensors_gather, dim=0)
return output
def moco_contrastive_loss(self, q, k):
q = nn.functional.normalize(q, dim=1)
k = nn.functional.normalize(k, dim=1)
logits = torch.mm(q, k.T.detach()) / self.T
labels = torch.arange(logits.shape[0], dtype=torch.long).cuda()
return nn.CrossEntropyLoss()(logits, labels)
@torch.no_grad()
def _momentum_update_key_encoder(self, base_q, base_k):
for param_q, param_k in zip(base_q.parameters(), base_k.parameters()):
param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)
def forward(self, x_vision_q, x_vision_k, x_tactile_q, x_tactile_k, epoch, i, len_train_dataloader):
vision_base_q = self.vision_base_q(x_vision_q)
vision_queries_intra = self.vision_head_intra_q(vision_base_q)
vision_queries_inter = self.vision_head_inter_q(vision_base_q)
with torch.no_grad():
self._momentum_update_key_encoder(self.vision_base_q, self.vision_base_k)
vision_base_k = self.vision_base_k(x_vision_k)
vision_keys_intra = self.vision_head_intra_k(vision_base_k)
vision_keys_inter = self.vision_head_inter_k(vision_base_k)
tactile_base_q = self.tactile_base_q(x_tactile_q)
tactile_queries_intra = self.tactile_head_intra_q(tactile_base_q)
tactile_queries_inter = self.tactile_head_inter_q(tactile_base_q)
with torch.no_grad():
self._momentum_update_key_encoder(self.tactile_base_q, self.tactile_base_k)
tactile_base_k = self.tactile_base_k(x_tactile_k)
tactile_keys_intra = self.tactile_head_intra_k(tactile_base_k)
tactile_keys_inter = self.tactile_head_inter_k(tactile_base_k)
# Compute the contrastive loss for each pair of queries and keys
vision_loss_intra = self.moco_contrastive_loss(vision_queries_intra, vision_keys_intra)
tactile_loss_intra = self.moco_contrastive_loss(tactile_queries_intra, tactile_keys_intra)
vision_tactile_inter = self.moco_contrastive_loss(vision_queries_inter, tactile_keys_inter)
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
combined_loss = vision_loss_intra + tactile_loss_intra + (vision_tactile_inter + tactile_vision_inter) * weight_inter
if len_train_dataloader != 0:
self.writer.add_scalar('module loss/vision intra loss', vision_loss_intra.item(), epoch * len_train_dataloader + i)
self.writer.add_scalar('module loss/tactile intra loss', tactile_loss_intra.item(), epoch * len_train_dataloader + i)
self.writer.add_scalar('module loss/vision tactile inter loss', vision_tactile_inter.item() * weight_inter, epoch * len_train_dataloader + i)
self.writer.add_scalar('module loss/tactile vision inter loss', tactile_vision_inter.item() * weight_inter, epoch * len_train_dataloader + i)
return combined_loss
def denormalize(tensor, mean, std):
for t, m, s in zip(tensor, mean, std):
t.mul_(s).add_(m)
return tensor
def evaluate_and_plot(model, test_dataloader, epoch, writer, device):
model.eval()
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]), 4)
x_vision_test = x_vision_test[random_indices].to(device)
x_tactile_test = x_tactile_test[random_indices].to(device)
with torch.no_grad():
test_loss = model(x_vision_test, x_vision_test, x_tactile_test, x_tactile_test, epoch, 0, 0)
# Denormalize vision images
x_vision_test_denorm = denormalize(x_vision_test.clone(), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
x_vision_test_denorm = x_vision_test_denorm.cpu().numpy()
x_vision_test_denorm = np.clip(x_vision_test_denorm, 0, 1)
# Denormalize tactile images
x_tactile_test_denorm = denormalize(x_tactile_test.clone(), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
x_tactile_test_denorm = x_tactile_test_denorm.cpu().numpy()
x_tactile_test_denorm = np.clip(x_tactile_test_denorm, 0, 1)
writer.add_images('Vision_Images', x_vision_test_denorm, epoch)
writer.add_images('Tactile_Images', x_tactile_test_denorm, epoch)
writer.add_scalar('testing loss', test_loss.item(), epoch * len(test_dataloader))
print(f"Test Loss: {test_loss.item():.4f}")
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)
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)
tactile_base_q = model.tactile_base_q(x_tactile_test)
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)
# Create pairs of corresponding representations and labels
num_samples = min(vision_base_q.shape[0], tactile_base_q.shape[0])
data = np.concatenate((vision_base_q[:num_samples], tactile_base_q[:num_samples]), axis=0)
labels = np.arange(1, num_samples+1).repeat(2)
tsne_data = tsne.fit_transform(data)
fig = plt.figure(figsize=(10, 10))
for i, (x, y) in enumerate(tsne_data):
plt.scatter(x, y, color='blue')
plt.text(x, y, f"{labels[i]}", fontsize=12, ha='center', va='bottom')
plt.savefig('temp_figure.png')
plt.close(fig)
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)