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)))
