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)