def test_conv2d_abnormal_kernel_truncated_normal(): input_data = init.initializer(init.TruncatedNormal(), [64, 3, 7, 7], ms.float32).to_tensor() context.set_context(mode=context.GRAPH_MODE) model = ms.Model( Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=3, padding=0, weight_init="truncatednormal")) model.predict(input_data)
def __init__(self, model=None): """Initializing the trainer with the provided model. Arguments: client_id: The ID of the client using this trainer (optional). model: The model to train. """ super().__init__() if hasattr(Config().trainer, 'cpuonly') and Config().trainer.cpuonly: mindspore.context.set_context(mode=mindspore.context.PYNATIVE_MODE, device_target='CPU') else: mindspore.context.set_context(mode=mindspore.context.PYNATIVE_MODE, device_target='GPU') if model is None: self.model = models_registry.get() # Initializing the loss criterion loss_criterion = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') # Initializing the optimizer optimizer = nn.Momentum(self.model.trainable_params(), Config().trainer.learning_rate, Config().trainer.momentum) self.mindspore_model = mindspore.Model( self.model, loss_criterion, optimizer, metrics={"Accuracy": Accuracy()})
def test_conv2d_abnormal_kernel_normal(): kernel = np.random.randn(64, 3, 7, 7).astype(np.float32) input_data = np.random.randn(32, 3, 224, 112).astype(np.float32) context.set_context(mode=context.GRAPH_MODE) model = ms.Model( Conv2d(in_channels=3, out_channels=64, kernel_size=7, stride=3, padding=0, weight_init=ms.Tensor(kernel))) model.predict(ms.Tensor(input_data))
def test_conv2d_abnormal_kernel_negative(): kernel = np.random.randn(64, 3, 7, 7).astype(np.float32) with py.raises(ValueError): ms.Model( Conv2d(in_channels=3, out_channels=64, kernel_size=-7, stride=3, padding=0, weight_init=ms.Tensor(kernel)))