import numpy as np import torch import torch.nn.functional as F import argparse import os import numpy as np from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score import wandb import datetime from torch.utils.data import DataLoader, TensorDataset import torch.optim as optim from data import load_multiple, load_custom_data from utils import compute_metrics_np from contrastive import ContrastiveModule def main(args): train_inputs, train_masks, train_labels, _, _ = load_custom_data( args.X_train_path, args.y_train_path, args.config_path, args.joint_list, args.original_sampling_rate, padding_size=args.padding_size, split='train', k=args.k, few_shot_path=None ) train_dataset = TensorDataset(train_inputs, train_masks, train_labels) train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True) test_inputs, test_masks, test_labels, _, _ = load_custom_data( args.X_test_path, args.y_test_path, args.config_path, args.joint_list, args.original_sampling_rate, padding_size=args.padding_size, split='test' ) test_dataset = TensorDataset(test_inputs, test_masks, test_labels) test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size, shuffle=False) date = datetime.datetime.now().strftime("%d-%m-%y_%H:%M") wandb.init( project='UniMTS', name=f"{args.run_tag}_{args.stage}_{args.mode}_k={args.k}_" + f"{date}" ) save_path = './checkpoint/%s/' % args.run_tag model = ContrastiveModule(args).cuda() optimizer = optim.Adam(model.parameters(), lr=1e-4) if args.mode == 'full' or args.mode == 'probe': model.model.load_state_dict(torch.load(f'{args.checkpoint}')) if args.mode == 'probe': for name, param in model.model.named_parameters(): param.requires_grad = False best_loss = None for epoch in range(args.num_epochs): tol_loss = 0 model.train() for i, (input, mask, label) in enumerate(train_dataloader): input = input.cuda() labels = label.cuda() if not args.gyro: b, t, c = input.shape indices = np.array([range(i, i+3) for i in range(0, c, 6)]).flatten() input = input[:,:,indices] b, t, c = input.shape if args.stft: input_stft = input.permute(0,2,1).reshape(b * c,t) input_stft = torch.abs(torch.stft(input_stft, n_fft = 25, hop_length = 28, onesided = False, center = True, return_complex = True)) input_stft = input_stft.reshape(b, c, input_stft.shape[-2], input_stft.shape[-1]).reshape(b, c, t).permute(0,2,1) input = torch.cat((input, input_stft), dim=-1) input = input.reshape(b, t, 22, -1).permute(0, 3, 1, 2).unsqueeze(-1) output = model.classifier(input) loss = F.cross_entropy(output.float(), labels.long(), reduction="mean") optimizer.zero_grad() loss.backward() optimizer.step() tol_loss += len(input) * loss.item() # print(epoch, i, loss.item()) print(f'Epoch [{epoch+1}/{args.num_epochs}], Loss: {tol_loss / len(train_dataset):.4f}') wandb.log({' loss': tol_loss / len(train_dataset)}) if best_loss is None or tol_loss < best_loss: best_loss = tol_loss torch.save(model.state_dict(), os.path.join(save_path, f'k={args.k}_best_loss.pth')) # evaluation model.load_state_dict(torch.load(os.path.join(save_path, f'k={args.k}_best_loss.pth'))) model.eval() with torch.no_grad(): pred_whole, logits_whole = [], [] for input, mask, label in test_dataloader: input = input.cuda() label = label.cuda() if not args.gyro: b, t, c = input.shape indices = np.array([range(i, i+3) for i in range(0, c, 6)]).flatten() input = input[:,:,indices] b, t, c = input.shape if args.stft: input_stft = input.permute(0,2,1).reshape(b * c,t) input_stft = torch.abs(torch.stft(input_stft, n_fft = 25, hop_length = 28, onesided = False, center = True, return_complex = True)) input_stft = input_stft.reshape(b, c, input_stft.shape[-2], input_stft.shape[-1]).reshape(b, c, t).permute(0,2,1) input = torch.cat((input, input_stft), dim=-1) input = input.reshape(b, t, 22, -1).permute(0, 3, 1, 2).unsqueeze(-1) logits_per_imu = model.classifier(input) logits_whole.append(logits_per_imu) pred = torch.argmax(logits_per_imu, dim=-1).detach().cpu().numpy() pred_whole.append(pred) pred = np.concatenate(pred_whole) acc = accuracy_score(test_labels, pred) prec = precision_score(test_labels, pred, average='macro') rec = recall_score(test_labels, pred, average='macro') f1 = f1_score(test_labels, pred, average='macro') print(f"acc: {acc}, prec: {prec}, rec: {rec}, f1: {f1}") wandb.log({f"acc": acc, f"prec": prec, f"rec": rec, f"f1": f1}) logits_whole = torch.cat(logits_whole) r_at_1, r_at_2, r_at_3, r_at_4, r_at_5, mrr_score = compute_metrics_np(logits_whole.detach().cpu().numpy(), test_labels.numpy()) print(f"R@1: {r_at_1}, R@2: {r_at_2}, R@3: {r_at_3}, R@4: {r_at_4}, R@5: {r_at_5}, MRR: {mrr_score}") wandb.log({f"R@1": r_at_1, f"R@2": r_at_2, f"R@3": r_at_3, f"R@4": r_at_4, f"R@5": r_at_5, f"MRR": mrr_score}) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Unified Pre-trained Motion Time Series Model') # model parser.add_argument('--mode', type=str, default='full', choices=['random','probe','full'], help='full fine-tuning, linear probe, random init') # data parser.add_argument('--padding_size', type=int, default='200', help='padding size (default: 200)') parser.add_argument('--k', type=int, help='few shot samples per class (default: None)') parser.add_argument('--X_train_path', type=str, required=True, help='/path/to/train/data/') parser.add_argument('--X_test_path', type=str, required=True, help='/path/to/test/data/') parser.add_argument('--y_train_path', type=str, required=True, help='/path/to/train/label/') parser.add_argument('--y_test_path', type=str, required=True, help='/path/to/test/label/') parser.add_argument('--config_path', type=str, required=True, help='/path/to/config/') parser.add_argument('--few_shot_path', type=str, help='/path/to/few/shot/indices/') parser.add_argument('--joint_list', nargs='+', type=int, required=True, help='List of joint indices') parser.add_argument('--original_sampling_rate', type=int, required=True, help='original sampling rate') parser.add_argument('--num_class', type=int, required=True, help='number of classes') # training parser.add_argument('--stage', type=str, default='finetune', help='training stage') parser.add_argument('--num_epochs', type=int, default=200, help='number of fine-tuning epochs (default: 200)') parser.add_argument('--run_tag', type=str, default='exp0', help='logging tag') parser.add_argument('--gyro', type=int, default=0, help='using gyro or not') parser.add_argument('--stft', type=int, default=0, help='using stft or not') parser.add_argument('--batch_size', type=int, default=64, help='batch size') parser.add_argument('--checkpoint', type=str, default='./checkpoint/', help='/path/to/checkpoint/') args = parser.parse_args() main(args)