From 9b0f12749ef0bbe1a744fadf75252405e12586f1 Mon Sep 17 00:00:00 2001 From: Your Name Date: Wed, 23 Feb 2022 15:41:19 +0800 Subject: [PATCH] update test Signed-off-by: Your Name --- test.py | 90 ++++++++++++++++++++++++++++----------------------------- 1 file changed, 44 insertions(+), 46 deletions(-) diff --git a/test.py b/test.py index 75b75ce..c4d2a6d 100644 --- a/test.py +++ b/test.py @@ -35,61 +35,59 @@ if __name__ == '__main__': # op.model_card = ModelCard(model_details="resnet test modelcard", training_data="use resnet test data") old_out = op(towhee_img) # print(old_out.feature_vector[0][:10]) - print(old_out.feature_vector[:10]) + # print(old_out.feature_vector[:10]) # print(old_out.feature_vector.shape) training_config = TrainingConfig() yaml_path = 'resnet_training_yaml.yaml' # dump_default_yaml(yaml_path=yaml_path) training_config.load_from_yaml(yaml_path) - # output_dir='./temp_output', - # overwrite_output_dir=True, - # epoch_num=2, - # per_gpu_train_batch_size=16, - # prediction_loss_only=True, - # metric='Accuracy' - # # device_str='cuda', - # # n_gpu=4 - # ) - # - mnist_transform = transforms.Compose([transforms.ToTensor(), - RandomResizedCrop(224), - Lambda(lambda x: x.repeat(3, 1, 1)), - transforms.Normalize(mean=[0.5], std=[0.5])]) - train_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=True) - eval_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=False) - # fake_transform = transforms.Compose([transforms.ToTensor(), - # RandomResizedCrop(224),]) - # train_data = get_dataset('fake', size=20, transform=fake_transform) - # - # op.change_before_train(num_classes=10) - # # trainer = op.setup_trainer() + + training_config.overwrite_output_dir=True + training_config.epoch_num=3 + training_config.batch_size=256 + training_config.device_str='cpu' + training_config.n_gpu=-1 + training_config.save_to_yaml(yaml_path) + + # mnist_transform = transforms.Compose([transforms.ToTensor(), + # RandomResizedCrop(224), + # Lambda(lambda x: x.repeat(3, 1, 1)), + # transforms.Normalize(mean=[0.5], std=[0.5])]) + # train_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=True) + # eval_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=False) + # training_config.output_dir = 'mnist_output' + fake_transform = transforms.Compose([transforms.ToTensor(), + RandomResizedCrop(224),]) + train_data = dataset('fake', size=1000, transform=fake_transform) + eval_data = dataset('fake', size=500, transform=fake_transform) + training_config.output_dir = 'fake_output' + + # trainer = op.setup_trainer() # print(op.get_model()) - # # my_optimimzer = AdamW(op.get_model().parameters(), lr=0.002, betas=(0.91, 0.98), eps=1e-08, weight_decay=0.01, amsgrad=False) - # # op.setup_trainer() + # my_optimimzer = AdamW(op.get_model().parameters(), lr=0.002, betas=(0.91, 0.98), eps=1e-08, weight_decay=0.01, amsgrad=False) + # op.setup_trainer() # - # # trainer.add_callback() - # # trainer.set_optimizer() + # trainer.add_callback() + # trainer.set_optimizer() # - # # op.trainer.set_optimizer(my_optimimzer) - # # trainer.configs.save_to_yaml('changed_optimizer_yaml.yaml') + # op.trainer.set_optimizer(my_optimimzer) + # trainer.configs.save_to_yaml('changed_optimizer_yaml.yaml') # - # # my_loss = nn.BCELoss() - # # trainer.set_loss(my_loss, 'my_loss111') - # # trainer.configs.save_to_yaml('chaned_loss_yaml.yaml') - # # op.trainer._create_optimizer() - # # op.trainer.set_optimizer() - # # trainer = op.setup_trainer(training_config, train_dataset=train_data, eval_dataset=eval_data) - # - # # freezer = LayerFreezer(op.get_model()) - # # freezer.by_idx([-1]) + # my_loss = nn.BCELoss() + # trainer.set_loss(my_loss, 'my_loss111') + # trainer.configs.save_to_yaml('chaned_loss_yaml.yaml') + # op.trainer._create_optimizer() + # op.trainer.set_optimizer() + # trainer = op.setup_trainer(training_config, train_dataset=train_data, eval_dataset=eval_data) + + freezer = LayerFreezer(op.get_model()) + freezer.set_slice(-1) op.train(training_config, train_dataset=train_data, eval_dataset=eval_data) - # # op.trainer.run_train() - # # training_config.num_epoch = 3 - # # op.train(training_config, train_dataset=train_data, resume_checkpoint_path=training_config.output_dir + '/epoch_2') - # - # # op.save('./test_save') - # # op.load('./test_save') - # # new_out = op(towhee_img) + # op.train(training_config, train_dataset=train_data, eval_dataset=eval_data, resume_checkpoint_path=training_config.output_dir + '/epoch_2') + + # op.save('./test_save') + # op.load('./test_save') + # new_out = op(towhee_img) # - # # assert (new_out[0]!=old_out[0]).all() + # assert (new_out[0]!=old_out[0]).all()