192 lines
8.5 KiB
Python
192 lines
8.5 KiB
Python
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import torch
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import torch.nn as nn
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from torchvision import models # For using the ResNet-50 model
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import torch.nn.functional as F
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import timm
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import random
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import numpy as np
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from PIL import Image
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import matplotlib.pyplot as plt
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from sklearn.manifold import TSNE
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class MultiModalMoCo(nn.Module):
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def __init__(self, writer, K=4096, m=0.99, T=1.0):
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super(MultiModalMoCo, self).__init__()
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self.writer = writer
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self.K = K
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self.m = m
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self.T = T
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self.intra_dim = 64
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self.inter_dim = 64
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# Initialize the queue
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self.queue = torch.zeros((self.K, self.intra_dim), dtype=torch.float).cuda()
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self.queue_ptr = 0
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def create_mlp_head(output_dim):
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return nn.Sequential(
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nn.Linear(2048, 2048),
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nn.ReLU(),
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nn.Linear(2048, output_dim)
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)
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def create_resnet_encoder():
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resnet = models.resnet50(weights='ResNet50_Weights.IMAGENET1K_V1')
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features = list(resnet.children())[:-2]
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features.append(nn.AdaptiveAvgPool2d((1, 1)))
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features.append(nn.Flatten())
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return nn.Sequential(*features)
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# Vision encoders
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self.vision_base_q = create_resnet_encoder()
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self.vision_head_intra_q = create_mlp_head(self.intra_dim)
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self.vision_head_inter_q = create_mlp_head(self.inter_dim)
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self.vision_base_k = create_resnet_encoder()
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self.vision_head_intra_k = create_mlp_head(self.intra_dim)
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self.vision_head_inter_k = create_mlp_head(self.inter_dim)
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# Tactile encoders
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self.tactile_base_q = create_resnet_encoder()
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self.tactile_head_intra_q = create_mlp_head(self.intra_dim)
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self.tactile_head_inter_q = create_mlp_head(self.inter_dim)
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self.tactile_base_k = create_resnet_encoder()
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self.tactile_head_intra_k = create_mlp_head(self.intra_dim)
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self.tactile_head_inter_k = create_mlp_head(self.inter_dim)
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# Initialize key encoders with query encoder weights
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self._momentum_update_key_encoder(self.vision_base_q, self.vision_base_k)
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self._momentum_update_key_encoder(self.tactile_base_q, self.tactile_base_k)
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@torch.no_grad()
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def concat_all_gather(self,tensor):
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tensors_gather = [torch.ones_like(tensor)
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for _ in range(torch.distributed.get_world_size())]
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torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
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output = torch.cat(tensors_gather, dim=0)
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return output
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def moco_contrastive_loss(self, q, k):
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q = nn.functional.normalize(q, dim=1)
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k = nn.functional.normalize(k, dim=1)
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logits = torch.mm(q, k.T.detach()) / self.T
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labels = torch.arange(logits.shape[0], dtype=torch.long).cuda()
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return nn.CrossEntropyLoss()(logits, labels)
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@torch.no_grad()
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def _momentum_update_key_encoder(self, base_q, base_k):
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for param_q, param_k in zip(base_q.parameters(), base_k.parameters()):
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param_k.data = param_k.data * self.m + param_q.data * (1. - self.m)
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def forward(self, x_vision_q, x_vision_k, x_tactile_q, x_tactile_k, epoch, i, len_train_dataloader):
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vision_base_q = self.vision_base_q(x_vision_q)
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vision_queries_intra = self.vision_head_intra_q(vision_base_q)
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vision_queries_inter = self.vision_head_inter_q(vision_base_q)
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with torch.no_grad():
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self._momentum_update_key_encoder(self.vision_base_q, self.vision_base_k)
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vision_base_k = self.vision_base_k(x_vision_k)
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vision_keys_intra = self.vision_head_intra_k(vision_base_k)
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vision_keys_inter = self.vision_head_inter_k(vision_base_k)
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tactile_base_q = self.tactile_base_q(x_tactile_q)
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tactile_queries_intra = self.tactile_head_intra_q(tactile_base_q)
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tactile_queries_inter = self.tactile_head_inter_q(tactile_base_q)
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with torch.no_grad():
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self._momentum_update_key_encoder(self.tactile_base_q, self.tactile_base_k)
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tactile_base_k = self.tactile_base_k(x_tactile_k)
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tactile_keys_intra = self.tactile_head_intra_k(tactile_base_k)
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tactile_keys_inter = self.tactile_head_inter_k(tactile_base_k)
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# Compute the contrastive loss for each pair of queries and keys
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vision_loss_intra = self.moco_contrastive_loss(vision_queries_intra, vision_keys_intra)
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tactile_loss_intra = self.moco_contrastive_loss(tactile_queries_intra, tactile_keys_intra)
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vision_tactile_inter = self.moco_contrastive_loss(vision_queries_inter, tactile_keys_inter)
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tactile_vision_inter = self.moco_contrastive_loss(tactile_queries_inter, vision_keys_inter)
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# Combine losses (you can use different strategies to combine these losses)
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weight_inter = 0.1
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combined_loss = vision_loss_intra + tactile_loss_intra + (vision_tactile_inter + tactile_vision_inter) * weight_inter
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if len_train_dataloader != 0:
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self.writer.add_scalar('module loss/vision intra loss', vision_loss_intra.item(), epoch * len_train_dataloader + i)
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self.writer.add_scalar('module loss/tactile intra loss', tactile_loss_intra.item(), epoch * len_train_dataloader + i)
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self.writer.add_scalar('module loss/vision tactile inter loss', vision_tactile_inter.item() * weight_inter, epoch * len_train_dataloader + i)
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self.writer.add_scalar('module loss/tactile vision inter loss', tactile_vision_inter.item() * weight_inter, epoch * len_train_dataloader + i)
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return combined_loss
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def denormalize(tensor, mean, std):
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for t, m, s in zip(tensor, mean, std):
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t.mul_(s).add_(m)
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return tensor
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def evaluate_and_plot(model, test_dataloader, epoch, writer, device):
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model.eval()
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with torch.no_grad():
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test_data_list = list(test_dataloader)
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x_vision_test, x_tactile_test = random.choice(test_data_list)
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random_indices = random.sample(range(x_vision_test.shape[0]), 4)
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x_vision_test = x_vision_test[random_indices].to(device)
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x_tactile_test = x_tactile_test[random_indices].to(device)
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with torch.no_grad():
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test_loss = model(x_vision_test, x_vision_test, x_tactile_test, x_tactile_test, epoch, 0, 0)
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# Denormalize vision images
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x_vision_test_denorm = denormalize(x_vision_test.clone(), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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x_vision_test_denorm = x_vision_test_denorm.cpu().numpy()
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x_vision_test_denorm = np.clip(x_vision_test_denorm, 0, 1)
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# Denormalize tactile images
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x_tactile_test_denorm = denormalize(x_tactile_test.clone(), [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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x_tactile_test_denorm = x_tactile_test_denorm.cpu().numpy()
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x_tactile_test_denorm = np.clip(x_tactile_test_denorm, 0, 1)
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writer.add_images('Vision_Images', x_vision_test_denorm, epoch)
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writer.add_images('Tactile_Images', x_tactile_test_denorm, epoch)
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writer.add_scalar('testing loss', test_loss.item(), epoch * len(test_dataloader))
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print(f"Test Loss: {test_loss.item():.4f}")
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def compute_tsne(model, test_dataloader, writer, epoch):
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with torch.no_grad():
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test_data_list = list(test_dataloader)
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x_vision_test, x_tactile_test = random.choice(test_data_list)
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random_indices = random.sample(range(x_vision_test.shape[0]), 10)
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x_vision_test = x_vision_test[random_indices].to('cuda')
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x_tactile_test = x_tactile_test[random_indices].to('cuda')
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vision_base_q = model.vision_base_q(x_vision_test)
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tactile_base_q = model.tactile_base_q(x_tactile_test)
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vision_base_q = vision_base_q.cpu().numpy()
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tactile_base_q = tactile_base_q.cpu().numpy()
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tsne = TSNE(n_components=2, random_state=0, perplexity=5)
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# Create pairs of corresponding representations and labels
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num_samples = min(vision_base_q.shape[0], tactile_base_q.shape[0])
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data = np.concatenate((vision_base_q[:num_samples], tactile_base_q[:num_samples]), axis=0)
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labels = np.arange(1, num_samples+1).repeat(2)
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tsne_data = tsne.fit_transform(data)
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fig = plt.figure(figsize=(10, 10))
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for i, (x, y) in enumerate(tsne_data):
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plt.scatter(x, y, color='blue')
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plt.text(x, y, f"{labels[i]}", fontsize=12, ha='center', va='bottom')
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plt.savefig('temp_figure.png')
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plt.close(fig)
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image = Image.open('temp_figure.png')
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image = np.array(image) # Convert image to a NumPy array
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image = image[:, :, :3].transpose(2, 0, 1) # Extract RGB channels and change format to CHW
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writer.add_image('t-SNE', image, global_step=epoch)
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