def _initialize_weight_default(shape=None, layer_type='conv', bias=False):
if layer_type not in ('conv', 'bn', 'fc'):
raise ValueError('The layer type is not known, the supported are conv, bn and fc')
if bias and layer_type == 'bn':
return Zero()
if layer_type == 'conv':
return One()
if layer_type == 'bn':
return One()
return One()
def _initialize_weight_goog(shape=None, layer_type='conv', bias=False):
if layer_type not in ('conv', 'bn', 'fc'):
raise ValueError('The layer type is not known, the supported are conv, bn and fc')
if bias:
return Zero()
if layer_type == 'conv':
assert isinstance(shape, (tuple, list)) and len(
shape) == 3, 'The shape must be 3 scalars, and are in_chs, ks, out_chs respectively'
n = shape[1] * shape[1] * shape[2]
return Normal(math.sqrt(2.0 / n))
if layer_type == 'bn':
return One()
assert isinstance(shape, (tuple, list)) and len(
shape) == 2, 'The shape must be 2 scalars, and are in_chs, out_chs respectively'
n = shape[1]
init_range = 1.0 / math.sqrt(n)
return Uniform(init_range)
def weight_variable(shape, factor=0.1):
return One()
def test_zero_dimension_with_zero_shape():
with pytest.raises(ValueError) as ex:
Tensor(shape=(1, 0, 3), dtype=mindspore.float32, init=One())
assert "Shape can not contain zero value." in str(ex.value)
def weight_variable():
return One()