def test_native_module_library_with_in_out(self): @add_module() class Test(object): def __init__(self): pass test = Module.create("Test", **{"in": "foo", "out": "bar"}) self.assertIsInstance(test, Test)
def test_native_module_library(self): @add_module() class Test(object): def __init__(self): pass test = Module.create("Test") self.assertIsInstance(test, Test)
def test_native_module_library_with_params(self): @add_module() class TestWithParams(object): def __init__(self, my_param): self.my_param = my_param test = Module.create("TestWithParams", my_param="foobar") self.assertIsInstance(test, TestWithParams) self.assertEqual(test.my_param, "foobar")
def __init__(self, training_name, data_path, training_results_path): super().__init__(training_name, data_path, training_results_path) # Config of the data self.data_dataset = FashionMNISTDataset # Config of the model self.model_model = lambda config: Module.create_from_file( "deeptech/examples/mnist_model.json", "MNISTModel", num_classes=10, logits=True) # Config for training self.training_loss = SparseCrossEntropyLossFromLogits self.training_optimizer = smart_optimizer(SGD) self.training_trainer = SupervisedTrainer self.training_epochs = 10 self.training_batch_size = 32
def __init__(self, training_name, data_path, training_results_path): super().__init__(training_name, data_path, training_results_path) # Config of the data self.data_dataset = lambda split: COCODataset( split, COCODataset.InputType, FasterRCNNOutput) self.data_version = 2014 self.data_image_size = (800, 600) # Config of the model self.model_categories = [] # Fill from dataset. self.model_log_delta_preds = False self.model_model = lambda: Module.create( "FasterRCNN", num_classes=len(self.model_categories), log_deltas=self.model_log_delta_preds) # Config for training self.training_loss = self.create_loss self.training_optimizer = smart_optimizer(SGD, momentum=0.9) self.training_trainer = SupervisedTrainer self.training_epochs = 10 self.training_batch_size = 1 self.training_initial_lr = 0.001
def setUp(self) -> None: add_lib_from_json("tests/json_nets/vgg16_bn.jsonc") self.module = Module.create("VGG16_bn", logits=True) self.input_data = torch.from_numpy( np.zeros((1, 128, 128, 3), dtype=np.float32)) self.result = self.module(self.input_data)