227 lines
9.6 KiB
Python
Executable File
227 lines
9.6 KiB
Python
Executable File
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 wandb
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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, m=0.99, T=1.0, nn_model=None):
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super(MultiModalMoCo, self).__init__()
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self.m = m
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self.T = T
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self.nn_model = nn_model
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self.intra_dim = 128
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self.inter_dim = 128
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def create_mlp_head(output_dim):
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if self.nn_model == 'resnet18':
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return nn.Sequential(
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nn.Linear(512, 2048),
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nn.ReLU(),
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nn.Linear(2048, output_dim)
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)
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elif self.nn_model == 'resnet50':
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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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if self.nn_model == 'resnet18':
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resnet = models.resnet18(weights='ResNet18_Weights.IMAGENET1K_V1')
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elif self.nn_model == 'resnet50':
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resnet = models.resnet50(weights='ResNet50_Weights.IMAGENET1K_V1')
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#resnet = models.regnet_x_800mf(weights='RegNet_X_800MF_Weights')
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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_base_k = create_resnet_encoder()
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self.tactile_base_q = create_resnet_encoder()
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self.tactile_base_k = create_resnet_encoder()
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# Projection heads
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self.phi_vision_q = create_mlp_head(self.intra_dim)
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self.phi_tactile_q = create_mlp_head(self.intra_dim)
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self.phi_vision_k = create_mlp_head(self.intra_dim)
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self.phi_tactile_k = create_mlp_head(self.intra_dim)
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self.Phi_vision_q = create_mlp_head(self.intra_dim)
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self.Phi_tactile_q = create_mlp_head(self.intra_dim)
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self.Phi_vision_k = create_mlp_head(self.intra_dim)
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self.Phi_tactile_k = create_mlp_head(self.intra_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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self._momentum_update_key_encoder(self.phi_vision_q, self.phi_vision_k)
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self._momentum_update_key_encoder(self.phi_tactile_q, self.phi_tactile_k)
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self._momentum_update_key_encoder(self.Phi_vision_q, self.Phi_vision_k)
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self._momentum_update_key_encoder(self.Phi_tactile_q, self.Phi_tactile_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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q_vv = self.phi_vision_q(vision_base_q)
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q_vt = self.phi_tactile_q(vision_base_q)
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vision_base_k = self.vision_base_k(x_vision_k)
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k_vv = self.phi_vision_k(vision_base_k)
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k_tv = self.phi_tactile_k(vision_base_k)
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tactile_base_q = self.tactile_base_q(x_tactile_q)
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q_tv = self.Phi_vision_q(tactile_base_q)
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q_tt = self.Phi_tactile_q(tactile_base_q)
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tactile_base_k = self.tactile_base_k(x_tactile_k)
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k_vt = self.Phi_vision_k(tactile_base_k)
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k_tt = self.Phi_tactile_k(tactile_base_k)
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# Update key encoders
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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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self._momentum_update_key_encoder(self.phi_vision_q, self.phi_vision_k)
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self._momentum_update_key_encoder(self.phi_tactile_q, self.phi_tactile_k)
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self._momentum_update_key_encoder(self.Phi_vision_q, self.Phi_vision_k)
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self._momentum_update_key_encoder(self.Phi_tactile_q, self.Phi_tactile_k)
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# Compute the contrastive loss for each pair of queries and keys
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vision_vision_intra = self.moco_contrastive_loss(q_vv, k_vv)
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tactile_tactile_intra = self.moco_contrastive_loss(q_tt, k_tt)
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tactile_vision_inter = self.moco_contrastive_loss(q_vt, k_vt)
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vision_tactile_inter = self.moco_contrastive_loss(q_tv, k_tv)
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# Combine losses (you can use different strategies to combine these losses)
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weight_inter = 1
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combined_loss = vision_vision_intra + tactile_tactile_intra + (tactile_vision_inter + vision_tactile_inter) * weight_inter
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if len_train_dataloader != 0:
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wandb.log({
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'module loss/vision intra loss': vision_vision_intra.item(),
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'module loss/tactile intra loss': tactile_tactile_intra.item(),
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'module loss/vision tactile inter loss': vision_tactile_inter.item() * weight_inter,
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'module loss/tactile vision inter loss': tactile_vision_inter.item() * weight_inter
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}, step=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, 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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x_vision_test_denorm = x_vision_test_denorm.transpose(0, 2, 3, 1)
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x_tactile_test_denorm = x_tactile_test_denorm.transpose(0, 2, 3, 1)
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wandb.log({
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"Vision_Images": [wandb.Image(img_tensor) for img_tensor in x_vision_test_denorm],
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"Tactile_Images": [wandb.Image(img_tensor) for img_tensor in x_tactile_test_denorm]
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}, commit=False)
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wandb.log({"testing loss": test_loss.item()}, step=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, 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]), 20)
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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=2)
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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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wandb.log({"t-SNE": wandb.Image(image)}, commit=False)
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def find_knn(query_point, data_points, k=5):
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# Calculate the Euclidean distances
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distances = torch.norm(data_points - query_point, dim=1)
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# Find the indices of the k smallest distances
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knn_indices = torch.topk(distances, k, largest=False, sorted=True)[1]
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# Get the k smallest distances
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knn_distances = distances[knn_indices]
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return knn_indices, knn_distances |