def __init__(self):
super(NetEqualCount, self).__init__()
self.equalcount = P.EqualCount()
x = Tensor(np.array([1, 20, 5]).astype(np.int32))
y = Tensor(np.array([2, 20, 5]).astype(np.int32))
self.x = Parameter(initializer(x, x.shape()), name='x')
self.y = Parameter(initializer(y, y.shape()), name='y')
def __init__( self):
super(NetSoftmaxWithCrossEntropy, self).__init__()
logits = Tensor(np.array([[1,1,10],
[1,10,1],
[10,1,1]]).astype(np.float32))
self.logits = Parameter(initializer(logits, logits.shape()), name ='logits')
labels = Tensor(np.array([2,1,0]).astype(np.int32))
self.labels = Parameter(initializer(labels, labels.shape()), name ='labels')
self.SoftmaxWithCrossEntropy = P.SparseSoftmaxCrossEntropyWithLogits(True)
def __init__(self):
super(NetArgmax, self).__init__()
self.argmax = P.Argmax(output_type=mstype.int32)
x = Tensor(
np.array([[1., 20., 5.], [67., 8., 9.], [130., 24.,
15.]]).astype(np.float32))
self.x = Parameter(initializer(x, x.shape()), name='x')
def __init__(self):
super(NetSoftmax, self).__init__()
self.softmax = P.Softmax()
x = Tensor(
np.array([[0.1, 0.3, 0.6], [0.2, -0.6, 0.8],
[0.6, 1, 0.4]]).astype(np.float32))
self.x = Parameter(initializer(x, x.shape()), name='x')
def test(name, file_path, batch_size):
""" test """
network = get_model(name)
batch = get_dataset(batch_size=batch_size)
data_list = []
for data in batch:
data_list.append(data.asnumpy())
batch_data = np.concatenate(data_list, axis=0).transpose((0, 3, 1, 2))
input_tensor = Tensor(batch_data)
print(input_tensor.shape())
network(input_tensor)
def test_check_layer_norm_2():
x = Tensor(np.ones([20, 5, 10, 10]), mstype.float32)
shape1 = x.shape()[1:]
m = nn.LayerNorm(shape1, begin_params_axis=1)
with pytest.raises(NotImplementedError):
m(x)
