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 wandb 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, m=0.99, T=1.0, nn_model=None): super(MultiModalMoCo, self).__init__() self.m = m self.T = T self.nn_model = nn_model self.intra_dim = 128 self.inter_dim = 128 def create_mlp_head(output_dim): if self.nn_model == 'resnet18': return nn.Sequential( nn.Linear(512, 2048), nn.ReLU(), nn.Linear(2048, output_dim) ) elif self.nn_model == 'resnet50': return nn.Sequential( nn.Linear(2048, 2048), nn.ReLU(), nn.Linear(2048, output_dim) ) def create_resnet_encoder(): if self.nn_model == 'resnet18': resnet = models.resnet18(weights='ResNet18_Weights.IMAGENET1K_V1') elif self.nn_model == 'resnet50': 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()) return nn.Sequential(*features) # Vision encoders self.vision_base_q = create_resnet_encoder() self.vision_base_k = create_resnet_encoder() self.tactile_base_q = create_resnet_encoder() self.tactile_base_k = create_resnet_encoder() # Projection heads self.phi_vision_q = create_mlp_head(self.intra_dim) self.phi_tactile_q = create_mlp_head(self.intra_dim) self.phi_vision_k = create_mlp_head(self.intra_dim) self.phi_tactile_k = create_mlp_head(self.intra_dim) self.Phi_vision_q = create_mlp_head(self.intra_dim) self.Phi_tactile_q = create_mlp_head(self.intra_dim) self.Phi_vision_k = create_mlp_head(self.intra_dim) self.Phi_tactile_k = create_mlp_head(self.intra_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) self._momentum_update_key_encoder(self.phi_vision_q, self.phi_vision_k) self._momentum_update_key_encoder(self.phi_tactile_q, self.phi_tactile_k) self._momentum_update_key_encoder(self.Phi_vision_q, self.Phi_vision_k) self._momentum_update_key_encoder(self.Phi_tactile_q, self.Phi_tactile_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) q_vv = self.phi_vision_q(vision_base_q) q_vt = self.phi_tactile_q(vision_base_q) with torch.no_grad(): # no gradient # Update key encoders self._momentum_update_key_encoder(self.vision_base_q, self.vision_base_k) self._momentum_update_key_encoder(self.phi_vision_q, self.phi_vision_k) self._momentum_update_key_encoder(self.phi_tactile_q, self.phi_tactile_k) # Compute key features vision_base_k = self.vision_base_k(x_vision_k) k_vv = self.phi_vision_k(vision_base_k) k_tv = self.phi_tactile_k(vision_base_k) tactile_base_q = self.tactile_base_q(x_tactile_q) q_tv = self.Phi_vision_q(tactile_base_q) q_tt = self.Phi_tactile_q(tactile_base_q) with torch.no_grad(): # no gradient # Update key encoders self._momentum_update_key_encoder(self.tactile_base_q, self.tactile_base_k) self._momentum_update_key_encoder(self.Phi_vision_q, self.Phi_vision_k) self._momentum_update_key_encoder(self.Phi_tactile_q, self.Phi_tactile_k) # Compute key features tactile_base_k = self.tactile_base_k(x_tactile_k) k_vt = self.Phi_vision_k(tactile_base_k) k_tt = self.Phi_tactile_k(tactile_base_k) # Compute the contrastive loss for each pair of queries and keys vision_vision_intra = self.moco_contrastive_loss(q_vv, k_vv) tactile_tactile_intra = self.moco_contrastive_loss(q_tt, k_tt) tactile_vision_inter = self.moco_contrastive_loss(q_vt, k_vt) vision_tactile_inter = self.moco_contrastive_loss(q_tv, k_tv) # Combine losses (you can use different strategies to combine these losses) weight_inter = 1 combined_loss = vision_vision_intra + tactile_tactile_intra + (tactile_vision_inter + vision_tactile_inter) * weight_inter if len_train_dataloader != 0: wandb.log({ 'module loss/vision intra loss': vision_vision_intra.item(), 'module loss/tactile intra loss': tactile_tactile_intra.item(), 'module loss/vision tactile inter loss': vision_tactile_inter.item() * weight_inter, 'module loss/tactile vision inter loss': tactile_vision_inter.item() * weight_inter }, step=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, 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) x_vision_test_denorm = x_vision_test_denorm.transpose(0, 2, 3, 1) x_tactile_test_denorm = x_tactile_test_denorm.transpose(0, 2, 3, 1) wandb.log({ "Vision_Images": [wandb.Image(img_tensor) for img_tensor in x_vision_test_denorm], "Tactile_Images": [wandb.Image(img_tensor) for img_tensor in x_tactile_test_denorm] }, commit=False) wandb.log({"testing loss": test_loss.item()}, step=epoch * len(test_dataloader)) print(f"Test Loss: {test_loss.item():.4f}") def compute_tsne(model, test_dataloader, 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]), 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) 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=2) # 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 wandb.log({"t-SNE": wandb.Image(image)}, commit=False) 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