model = Baseline(model='train', model_name=model_name, model_path=model_path) #model.load_param('models/model_1_180000.pth') model = model.cuda() optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) #exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) # kd_id = 0 # kd_num = 7 # batch_size = 48 # instance_num = 1 train_data, val_data, trains, vals = make_dataloader(kd_id, kd_num) train_loader = DataLoader(dataset=train_data, batch_size=batch_size, sampler=RandomSampler(trains, batch_size, instance_num), shuffle=False, num_workers=2, collate_fn=train_collate) #train_loader = DataLoader(dataset=train_data, batch_size=48, shuffle=False, num_workers=2, collate_fn=train_collate) val_loader = DataLoader(dataset=val_data, batch_size=64, shuffle=False, num_workers=2, collate_fn=train_collate) train_length = len(train_loader) val_length = len(val_loader) if __name__ == '__main__': max_epoch = 50 max_val_acc = 0
# model_name = 'MixNet' # model_path = ' ' model = Baseline(model='train',model_name = model_name, model_path=model_path) #model.load_param('models/model_1_180000.pth') model = model.cuda() optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) #exp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) # kd_id = 0 # kd_num = 7 # batch_size = 48 # instance_num = 1 train_data, val_data, trains, vals = make_dataloader(kd_id,kd_num) train_loader = DataLoader(dataset=train_data, batch_size=batch_size, sampler=RandomSampler(trains, batch_size, instance_num), shuffle=False, num_workers=2, collate_fn=train_collate) #train_loader = DataLoader(dataset=train_data, batch_size=48, shuffle=False, num_workers=2, collate_fn=train_collate) val_loader = DataLoader(dataset=val_data, batch_size=64, shuffle=False, num_workers=2, collate_fn=train_collate ) train_length = len(train_loader) val_length = len(val_loader) if __name__ == '__main__': max_epoch = 50 max_val_acc = 0 for epoch in range(0,max_epoch): adjust_lr(optimizer, epoch) train_fuc(model, epoch) val_acc = val_fuc(model, epoch) torch.save(model.state_dict(), 'models/'+ str(kd_id)+'_'+ model_name + '_'+ '%.5f'%(val_acc) +'_'+ str(epoch) +'.pth')