| 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(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')) |
|
|
| |
| 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') |
|
|
| |
| parser.add_argument('--mode', type=str, default='full', choices=['random','probe','full'], help='full fine-tuning, linear probe, random init') |
|
|
| |
| 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') |
|
|
| |
| 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) |
|
|
