115 lines
4.3 KiB
Python
115 lines
4.3 KiB
Python
import random
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import time
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from torch.utils.data import DataLoader
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import torch
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from dataset import VideoPlayDataset, collate_fn
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from pred.model import CompactPredictor
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def asymmetricHuberLoss(delta=1.0, beta=1.3):
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"""
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创建一个可调用的非对称 Huber 损失函数。
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参数：
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delta (float): Huber 损失的 delta 参数。
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beta (float): 控制负误差惩罚的系数。
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返回：
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callable: 可调用的损失函数。
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"""
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def loss_function(input, target):
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error = input - target
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abs_error = torch.abs(error)
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linear_loss = abs_error - 0.5 * delta
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quadratic_loss = 0.5 * error**2
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loss = torch.where(abs_error < delta, quadratic_loss, linear_loss)
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loss = torch.where(error < 0, beta * loss, loss)
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return torch.mean(loss)
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return loss_function
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def train(model, dataloader, device, epochs=100):
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writer = SummaryWriter(f'./pred/runs/play_predictor_{time.strftime("%Y%m%d_%H%M")}')
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optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.01)
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scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3,
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total_steps=len(dataloader)*30)
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# Huber loss
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criterion = asymmetricHuberLoss(delta=1.0, beta=2.1)
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model.train()
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global_step = 0
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for epoch in range(epochs):
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total_loss = 0.0
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for batch_idx, batch in enumerate(dataloader):
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features = batch['features'].to(device)
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targets = batch['targets'].to(device)
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optimizer.zero_grad()
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outputs = model(features)
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loss = criterion(outputs, targets)
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loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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scheduler.step()
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total_loss += loss.item()
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global_step += 1
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if global_step % 100 == 0:
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writer.add_scalar('Loss/train', loss.item(), global_step)
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writer.add_scalar('LR', scheduler.get_last_lr()[0], global_step)
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if batch_idx % 50 == 0:
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# Monitor gradients
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grad_norms = [
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torch.norm(p.grad).item()
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for p in model.parameters() if p.grad is not None
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]
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writer.add_scalar('Grad/Norm', sum(grad_norms)/len(grad_norms), global_step)
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# Monitor parameter values
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param_means = [torch.mean(p.data).item() for p in model.parameters()]
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writer.add_scalar('Params/Mean', sum(param_means)/len(param_means), global_step)
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samples_count = len(targets)
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good = 0
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for r in range(samples_count):
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r = random.randint(0, samples_count-1)
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t = float(torch.exp2(targets[r])) - 1
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o = float(torch.exp2(outputs[r])) - 1
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d = features[r].cpu().numpy()[0]
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speed = np.exp2(features[r].cpu().numpy()[8]) / 6
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time_diff = np.exp2(d) / 3600
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inc = speed * time_diff
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model_error = abs(t - o)
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reg_error = abs(inc - t)
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if model_error < reg_error:
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good += 1
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#print(f"{t:07.1f} | {o:07.1f} | {d:07.1f} | {inc:07.1f} | {good/samples_count*100:.1f}%")
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writer.add_scalar('Train/WinRate', good/samples_count, global_step)
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print(f"Epoch {epoch+1} | Avg Loss: {total_loss/len(dataloader):.4f}")
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writer.close()
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return model
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if __name__ == "__main__":
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device = 'mps'
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# Initialize dataset and model
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dataset = VideoPlayDataset('./data/pred', './data/pred/publish_time.csv', 'short')
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dataloader = DataLoader(dataset, batch_size=128, shuffle=True, collate_fn=collate_fn)
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# Get feature dimension
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sample = next(iter(dataloader))
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input_size = sample['features'].shape[1]
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model = CompactPredictor(input_size).to(device)
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trained_model = train(model, dataloader, device, epochs=18)
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# Save model
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torch.save(trained_model.state_dict(), f"./pred/checkpoints/model_{time.strftime('%Y%m%d_%H%M')}.pt")
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