TVSSL/train_mm_moco.py

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