add custom optimizer and loss case

resnet_image_embedding.py

@@ -16,6 +16,7 @@ import numpy
 import torch
 import torchvision
 from PIL import Image
+from torch import nn as nn
 from torchvision import transforms
 from pathlib import Path
 from typing import NamedTuple
@@ -31,7 +32,7 @@ class ResnetImageEmbedding(NNOperator):
     PyTorch model for image embedding.
     """
     def __init__(self, model_name: str, framework: str = 'pytorch') -> None:
-        super().__init__()
+        super().__init__(framework=framework)
         if framework == 'pytorch':
             import importlib.util
             path = os.path.join(str(Path(__file__).parent), 'pytorch', 'model.py')
@@ -40,6 +41,7 @@ class ResnetImageEmbedding(NNOperator):
             module = importlib.util.module_from_spec(spec)
             spec.loader.exec_module(module)
         self.model = module.Model(model_name)
+
         self.tfms = transforms.Compose([transforms.Resize(235, interpolation=InterpolationMode.BICUBIC),
                                         transforms.CenterCrop(224),
                                         transforms.ToTensor(),
@@ -51,12 +53,9 @@ class ResnetImageEmbedding(NNOperator):
         Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
         return Outputs(embedding)
 
-    def get_model(self):
+    def get_model(self) -> nn.Module:
         return self.model._model
 
-    # def test(self):
-    #     return self.framework
-
     def change_before_train(self, num_classes: int = 0):
         if num_classes > 0:
             self.model.create_classifier(num_classes)

resnet_training_yaml.yaml

@@ -5,16 +5,17 @@ device:
 metrics:
   metric: Accuracy
 train:
-  batch_size: 16
+  batch_size: 32
+  overwrite_output_dir: true
+  epoch_num: 1
 learning:
   optimizer:
     name_: SGD
-    lr: 0.03
+    lr: 0.04
     momentum: 0.001
-    nesterov: 111
   loss:
     name_: CrossEntropyLoss
-    label_smoothing: 0.1
+    ignore_index: -1
 #learning:
 #  optimizer:
 #    name_: Adam

test.py

@@ -1,4 +1,5 @@
 import numpy as np
+from torch.optim import AdamW
 from torchvision import transforms
 from torchvision.transforms import RandomResizedCrop, Lambda
 from towhee.trainer.modelcard import ModelCard
@@ -10,8 +11,10 @@ from towhee.types import Image
 from towhee.trainer.training_config import dump_default_yaml
 from PIL import Image as PILImage
 from timm.models.resnet import ResNet
+from torch import nn
 
 if __name__ == '__main__':
+    dump_default_yaml(yaml_path='default_config.yaml')
     # img = torch.rand([1, 3, 224, 224])
     img_path = './ILSVRC2012_val_00049771.JPEG'
     # # logo_path = os.path.join(Path(__file__).parent.parent.parent.parent.resolve(), 'towhee_logo.png')
@@ -28,7 +31,6 @@ if __name__ == '__main__':
     op = ResnetImageEmbedding('resnet34')
     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])
 
     training_config = TrainingConfig()
@@ -45,7 +47,6 @@ if __name__ == '__main__':
     #     # n_gpu=4
     # )
 
-
     mnist_transform = transforms.Compose([transforms.ToTensor(),
                                           RandomResizedCrop(224),
                                           Lambda(lambda x: x.repeat(3, 1, 1)),
@@ -57,6 +58,23 @@ if __name__ == '__main__':
     # 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)
+    # op.setup_trainer()
+
+    # trainer.add_callback()
+    # trainer.set_optimizer()
+
+    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')