From b7a00908e4642c90f4d79e3fae6315e97e38a4ad Mon Sep 17 00:00:00 2001 From: Vedant Dave Date: Wed, 30 Aug 2023 13:39:44 +0200 Subject: [PATCH] Adding train file --- train_mm.py | 191 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 191 insertions(+) create mode 100644 train_mm.py diff --git a/train_mm.py b/train_mm.py new file mode 100644 index 0000000..e1337c4 --- /dev/null +++ b/train_mm.py @@ -0,0 +1,191 @@ +import torch +import torch.nn as nn +from torchvision import models # For using the ResNet-50 model +import torch.nn.functional as F + +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=0.07): + super(MultiModalMoCo, self).__init__() + + self.writer = writer + self.K = K + self.m = m + self.T = T + + self.intra_dim = 64 + self.inter_dim = 64 + + self.W_vision_intra = nn.Parameter(torch.randn(self.intra_dim, self.intra_dim)) + self.W_tactile_intra = nn.Parameter(torch.randn(self.intra_dim, self.intra_dim)) + self.W_vision_inter = nn.Parameter(torch.randn(self.inter_dim, self.inter_dim)) + self.W_tactile_inter = nn.Parameter(torch.randn(self.inter_dim, self.inter_dim)) + + 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 _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 compute_logits(self, z_a, z_pos, W): + Wz = torch.matmul(W, z_pos.T) # (z_dim, B) + logits = torch.matmul(z_a, Wz) # (B, B) + logits = logits - torch.max(logits, 1)[0][:, None] + return logits + + 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 = nn.CrossEntropyLoss()(self.compute_logits(vision_queries_intra, vision_keys_intra, self.W_vision_intra), + torch.arange(x_vision_q.size(0)).to(x_vision_q.device)) + + tactile_loss_intra = nn.CrossEntropyLoss()(self.compute_logits(tactile_queries_intra, tactile_keys_intra, self.W_tactile_intra), + torch.arange(x_tactile_q.size(0)).to(x_tactile_q.device)) + + vision_tactile_inter = nn.CrossEntropyLoss()(self.compute_logits(vision_queries_inter, tactile_keys_inter, self.W_vision_inter), + torch.arange(x_vision_q.size(0)).to(x_vision_q.device)) + + tactile_vision_inter = nn.CrossEntropyLoss()(self.compute_logits(tactile_queries_inter, vision_keys_inter, self.W_tactile_inter), + torch.arange(x_tactile_q.size(0)).to(x_tactile_q.device)) + + # 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) \ No newline at end of file