From 5341779d1637cc5fac311fd64e99ebcdee7e3ffb Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Fri, 25 Feb 2022 10:09:30 +0800 Subject: [PATCH] update Signed-off-by: Jael Gu --- resnet_training_yaml.yaml | 3 +-- test.py | 17 +++++++++++------ 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/resnet_training_yaml.yaml b/resnet_training_yaml.yaml index b02ff26..8b3843b 100644 --- a/resnet_training_yaml.yaml +++ b/resnet_training_yaml.yaml @@ -4,7 +4,7 @@ callback: monitor: eval_epoch_metric patience: 2 model_checkpoint: - every_n_epoch: 1 + every_n_epoch: 2 tensorboard: comment: '' log_dir: null @@ -40,4 +40,3 @@ train: resume_from_checkpoint: null seed: 42 val_batch_size: -1 - freeze_bn: true diff --git a/test.py b/test.py index ee41a10..7fed63c 100644 --- a/test.py +++ b/test.py @@ -32,7 +32,7 @@ if __name__ == '__main__': towhee_img = Image(img_bytes, img_width, img_height, img_channel, img_mode, img_array) op = ResnetImageEmbedding('resnet50', num_classes=10) - # 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]) @@ -57,11 +57,14 @@ if __name__ == '__main__': # 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' + # op.model_card = ModelCard(datasets='mnist dataset') + fake_transform = transforms.Compose([transforms.ToTensor(), - RandomResizedCrop(224),]) - train_data = dataset('fake', size=100, transform=fake_transform) + RandomResizedCrop(224)]) + train_data = dataset('fake', size=20, transform=fake_transform) eval_data = dataset('fake', size=10, transform=fake_transform) training_config.output_dir = 'fake_output' + op.model_card = ModelCard(datasets='fake dataset') # trainer = op.setup_trainer() # print(op.get_model()) @@ -83,11 +86,13 @@ if __name__ == '__main__': freezer = LayerFreezer(op.get_model()) freezer.set_slice(-1) - op.train(training_config, train_dataset=train_data, eval_dataset=eval_data) - # op.train(training_config, train_dataset=train_data, eval_dataset=eval_data, resume_checkpoint_path=training_config.output_dir + '/epoch_2') + + # op.train(training_config, train_dataset=train_data, eval_dataset=eval_data) + 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.feature_vector==old_out.feature_vector).all() + assert (new_out.feature_vector == old_out.feature_vector).all()