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import numpy as np |
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import torch |
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import argparse |
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import os |
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import numpy as np |
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from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score |
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import wandb |
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import datetime |
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from torch.utils.data import DataLoader, TensorDataset |
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from data import load, load_multiple, load_custom_data |
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from utils import compute_metrics_np |
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from contrastive import ContrastiveModule |
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def main(args): |
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real_inputs, real_masks, real_labels, label_list, all_text = load_custom_data( |
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args.X_path, args.y_path, args.config_path, args.joint_list, args.original_sampling_rate, padding_size=args.padding_size, split='test' |
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) |
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real_dataset = TensorDataset(real_inputs, real_masks, real_labels) |
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test_real_dataloader = DataLoader(real_dataset, batch_size=args.batch_size, shuffle=False) |
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date = datetime.datetime.now().strftime("%d-%m-%y_%H:%M") |
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wandb.init( |
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project='UniMTS', |
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name=f"{args.run_tag}_{args.stage}_" + f"{date}" |
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) |
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model = ContrastiveModule(args).cuda() |
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model.model.load_state_dict(torch.load(f'{args.checkpoint}')) |
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model.eval() |
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with torch.no_grad(): |
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pred_whole, logits_whole = [], [] |
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for input, mask, label in test_real_dataloader: |
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input = input.cuda() |
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mask = mask.cuda() |
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label = label.cuda() |
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if not args.gyro: |
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b, t, c = input.shape |
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indices = np.array([range(i, i+3) for i in range(0, c, 6)]).flatten() |
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input = input[:,:,indices] |
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b, t, c = input.shape |
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if args.stft: |
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input_stft = input.permute(0,2,1).reshape(b * c,t) |
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input_stft = torch.abs(torch.stft(input_stft, n_fft = 25, hop_length = 28, onesided = False, center = True, return_complex = True)) |
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input_stft = input_stft.reshape(b, c, input_stft.shape[-2], input_stft.shape[-1]).reshape(b, c, t).permute(0,2,1) |
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input = torch.cat((input, input_stft), dim=-1) |
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input = input.reshape(b, t, 22, -1).permute(0, 3, 1, 2).unsqueeze(-1) |
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logits_per_imu, logits_per_text = model(input, all_text) |
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logits_whole.append(logits_per_imu) |
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pred = torch.argmax(logits_per_imu, dim=-1).detach().cpu().numpy() |
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pred_whole.append(pred) |
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pred = np.concatenate(pred_whole) |
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acc = accuracy_score(real_labels, pred) |
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prec = precision_score(real_labels, pred, average='macro') |
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rec = recall_score(real_labels, pred, average='macro') |
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f1 = f1_score(real_labels, pred, average='macro') |
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print(f"acc: {acc}, prec: {prec}, rec: {rec}, f1: {f1}") |
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wandb.log({f"acc": acc, f"prec": prec, f"rec": rec, f"f1": f1}) |
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logits_whole = torch.cat(logits_whole) |
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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(), real_labels.numpy()) |
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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}") |
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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}) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description='Unified Pre-trained Motion Time Series Model') |
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parser.add_argument('--padding_size', type=int, default='200', help='padding size (default: 200)') |
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parser.add_argument('--X_path', type=str, required=True, help='/path/to/data/') |
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parser.add_argument('--y_path', type=str, required=True, help='/path/to/label/') |
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parser.add_argument('--config_path', type=str, required=True, help='/path/to/config/') |
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parser.add_argument('--joint_list', nargs='+', type=int, required=True, help='List of joint indices') |
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parser.add_argument('--original_sampling_rate', type=int, required=True, help='original sampling rate') |
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parser.add_argument('--run_tag', type=str, default='exp0', help='logging tag') |
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parser.add_argument('--stage', type=str, default='evaluation', help='training or evaluation stage') |
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parser.add_argument('--gyro', type=int, default=0, help='using gyro or not') |
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parser.add_argument('--stft', type=int, default=0, help='using stft or not') |
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parser.add_argument('--batch_size', type=int, default=64, help='batch size') |
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parser.add_argument('--checkpoint', type=str, default='./checkpoint/', help='/path/to/checkpoint/') |
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args = parser.parse_args() |
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main(args) |
