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import numpy as np
import torch
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
from data import load, load_multiple
from utils import compute_metrics_np
from contrastive import ContrastiveModule
def main(args):
# load real data
dataset_list = ['Opp_g','UCIHAR','MotionSense','w-HAR','Shoaib','har70plus','realworld','TNDA-HAR','PAMAP',\
'USCHAD','Mhealth','Harth','ut-complex','Wharf','WISDM','DSADS','UTD-MHAD','MMAct']
real_inputs_list, real_masks_list, real_labels_list, label_list_list, all_text_list, _ = load_multiple(dataset_list, args.padding_size, args.data_path)
test_real_dataloader_list = []
for real_inputs, real_masks, real_labels in zip(real_inputs_list, real_masks_list, real_labels_list):
real_dataset = TensorDataset(real_inputs, real_masks, real_labels)
test_real_dataloader_list.append(DataLoader(real_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}_" + f"{date}"
)
model = ContrastiveModule(args).cuda()
model.model.load_state_dict(torch.load(f'{args.checkpoint}'))
model.eval()
with torch.no_grad():
for ds, real_labels, test_real_dataloader, label_list, all_text in zip(dataset_list, real_labels_list, test_real_dataloader_list, label_list_list, all_text_list):
pred_whole, logits_whole = [], []
for input, mask, label in test_real_dataloader:
input = input.cuda()
mask = mask.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, logits_per_text = model(input, all_text)
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(real_labels, pred)
prec = precision_score(real_labels, pred, average='macro')
rec = recall_score(real_labels, pred, average='macro')
f1 = f1_score(real_labels, pred, average='macro')
print(f"{ds} acc: {acc}, {ds} prec: {prec}, {ds} rec: {rec}, {ds} f1: {f1}")
wandb.log({f"{ds} acc": acc, f"{ds} prec": prec, f"{ds} rec": rec, f"{ds} 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(), real_labels.numpy())
print(f"{ds} 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"{ds} R@1": r_at_1, f"{ds} R@2": r_at_2, f"{ds} R@3": r_at_3, f"{ds} R@4": r_at_4, f"{ds} R@5": r_at_5, f"{ds} MRR": mrr_score})
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Unified Pre-trained Motion Time Series Model')
# data
parser.add_argument('--padding_size', type=int, default='200', help='padding size (default: 200)')
parser.add_argument('--data_path', type=str, default='./data/', help='/path/to/data/')
# training
parser.add_argument('--run_tag', type=str, default='exp0', help='logging tag')
parser.add_argument('--stage', type=str, default='evaluation', help='training or evaluation stage')
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) |