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142
train_compare.py
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142
train_compare.py
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import os
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import os.path as osp
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from tqdm import tqdm
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from model.loss import FocalLoss
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from tools.dataset import load_data
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import matplotlib.pyplot as plt
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from configs import trainer_tools
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import yaml
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with open('configs/scatter.yml', 'r') as f:
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conf = yaml.load(f, Loader=yaml.FullLoader)
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# Data Setup
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train_dataloader, class_num = load_data(training=True, cfg=conf)
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val_dataloader, _ = load_data(training=False, cfg=conf)
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tr_tools = trainer_tools(conf)
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backbone_mapping = tr_tools.get_backbone()
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metric_mapping = tr_tools.get_metric(class_num)
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if conf['models']['backbone'] in backbone_mapping:
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model = backbone_mapping[conf['models']['backbone']]().to(conf['base']['device'])
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else:
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raise ValueError('不支持该模型: {}'.format({conf['models']['backbone']}))
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if conf['training']['metric'] in metric_mapping:
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metric = metric_mapping[conf['training']['metric']]()
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else:
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raise ValueError('不支持的metric类型: {}'.format(conf['training']['metric']))
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if torch.cuda.device_count() > 1 and conf['base']['distributed']:
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print("Let's use", torch.cuda.device_count(), "GPUs!")
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model = nn.DataParallel(model)
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metric = nn.DataParallel(metric)
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# Training Setup
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if conf['training']['loss'] == 'focal_loss':
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criterion = FocalLoss(gamma=2)
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else:
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criterion = nn.CrossEntropyLoss()
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optimizer_mapping = tr_tools.get_optimizer(model, metric)
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if conf['training']['optimizer'] in optimizer_mapping:
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optimizer = optimizer_mapping[conf['training']['optimizer']]()
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scheduler = optim.lr_scheduler.StepLR(
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optimizer,
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step_size=conf['training']['lr_step'],
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gamma=conf['training']['lr_decay']
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)
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else:
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raise ValueError('不支持的优化器类型: {}'.format(conf['training']['optimizer']))
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# Checkpoints Setup
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checkpoints = conf['training']['checkpoints']
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os.makedirs(checkpoints, exist_ok=True)
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if __name__ == '__main__':
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print('backbone>{} '.format(conf['models']['backbone']),
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'metric>{} '.format(conf['training']['metric']),
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'checkpoints>{} '.format(conf['training']['checkpoints']),
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)
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train_losses = []
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val_losses = []
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epochs = []
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temp_loss = 100
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if conf['training']['restore']:
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print('load pretrain model: {}'.format(conf['training']['restore_model']))
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model.load_state_dict(torch.load(conf['training']['restore_model'],
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map_location=conf['base']['device']))
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for e in range(conf['training']['epochs']):
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train_loss = 0
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model.train()
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for train_data, train_labels in tqdm(train_dataloader,
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desc="Epoch {}/{}"
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.format(e, conf['training']['epochs']),
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ascii=True,
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total=len(train_dataloader)):
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train_data = train_data.to(conf['base']['device'])
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train_labels = train_labels.to(conf['base']['device'])
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train_embeddings = model(train_data).to(conf['base']['device']) # [256,512]
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# pdb.set_trace()
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if not conf['training']['metric'] == 'softmax':
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thetas = metric(train_embeddings, train_labels) # [256,357]
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else:
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thetas = metric(train_embeddings)
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tloss = criterion(thetas, train_labels)
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optimizer.zero_grad()
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tloss.backward()
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optimizer.step()
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train_loss += tloss.item()
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train_lossAvg = train_loss / len(train_dataloader)
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train_losses.append(train_lossAvg)
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epochs.append(e)
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val_loss = 0
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model.eval()
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with torch.no_grad():
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for val_data, val_labels in tqdm(val_dataloader, desc="val",
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ascii=True, total=len(val_dataloader)):
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val_data = val_data.to(conf['base']['device'])
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val_labels = val_labels.to(conf['base']['device'])
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val_embeddings = model(val_data).to(conf['base']['device'])
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if not conf['training']['metric'] == 'softmax':
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thetas = metric(val_embeddings, val_labels)
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else:
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thetas = metric(val_embeddings)
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vloss = criterion(thetas, val_labels)
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val_loss += vloss.item()
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val_lossAvg = val_loss / len(val_dataloader)
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val_losses.append(val_lossAvg)
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if val_lossAvg < temp_loss:
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if torch.cuda.device_count() > 1:
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torch.save(model.state_dict(), osp.join(checkpoints, 'best.pth'))
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else:
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torch.save(model.state_dict(), osp.join(checkpoints, 'best.pth'))
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temp_loss = val_lossAvg
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scheduler.step()
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current_lr = optimizer.param_groups[0]['lr']
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log_info = ("Epoch {}/{}, train_loss: {}, val_loss: {} lr:{}"
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.format(e, conf['training']['epochs'], train_lossAvg, val_lossAvg, current_lr))
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print(log_info)
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# 写入日志文件
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with open(osp.join(conf['logging']['logging_dir']), 'a') as f:
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f.write(log_info + '\n')
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print("第%d个epoch的学习率：%f" % (e, current_lr))
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if torch.cuda.device_count() > 1 and conf['base']['distributed']:
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torch.save(model.module.state_dict(), osp.join(checkpoints, 'last.pth'))
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else:
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torch.save(model.state_dict(), osp.join(checkpoints, 'last.pth'))
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plt.plot(epochs, train_losses, color='blue')
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plt.plot(epochs, val_losses, color='red')
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# plt.savefig('lossMobilenetv3.png')
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plt.savefig('loss/mobilenetv3Large_2250_0316.png')
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