add eval ability

resnet_training_yaml.yaml

@@ -7,7 +7,7 @@ metrics:
 train:
   batch_size: 32
   overwrite_output_dir: true
-  epoch_num: 1
+  epoch_num: 2
 learning:
   optimizer:
     name_: SGD
@@ -16,6 +16,8 @@ learning:
   loss:
     name_: CrossEntropyLoss
     ignore_index: -1
+logging:
+  print_steps: 2
 #learning:
 #  optimizer:
 #    name_: Adam

test.py

@@ -29,7 +29,7 @@ if __name__ == '__main__':
     towhee_img = Image(img_bytes, img_width, img_height, img_channel, img_mode, img_array)
 
     op = ResnetImageEmbedding('resnet34')
-    op.model_card = ModelCard(model_details="resnet test modelcard", training_data="use resnet test data")
+    # 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])
 
@@ -51,33 +51,31 @@ if __name__ == '__main__':
                                           RandomResizedCrop(224),
                                           Lambda(lambda x: x.repeat(3, 1, 1)),
                                           transforms.Normalize(mean=[0.5], std=[0.5])])
-    train_data = get_dataset('mnist', transform=mnist_transform, download=True, root='data')
-
+    train_data = get_dataset('mnist', transform=mnist_transform, download=True, root='data', train=True)
+    eval_data = get_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(10)
     trainer = 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)
+    # 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()
 
-    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()
-    op.train(training_config, train_dataset=train_data)
-    training_config.num_epoch = 3
-    op.train(training_config, train_dataset=train_data, resume_checkpoint_path=training_config.output_dir + '/epoch_2')
+    op.train(training_config, train_dataset=train_data, eval_dataset=eval_data)
+    # 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')