def __init__(self):
super(ParameterNet, self).__init__()
self.para_xavier_uniform = Parameter(init.initializer('xavier_uniform', parameter_shape), name="xavier_uniform")
self.para_he_uniform = Parameter(init.initializer('he_uniform', parameter_shape), name="he_uniform")
self.para_xavier_uniform2 = Parameter(init.initializer(init.XavierUniform(), parameter_shape), name="xavier_uniform2")
self.para_he_uniform2 = Parameter(init.initializer(init.HeUniform(), parameter_shape), name="he_uniform2")
self.para_truncated_normal = Parameter(init.initializer(init.TruncatedNormal(), parameter_shape), name="truncated_normal")
self.para_normal = Parameter(init.initializer(init.Normal(), parameter_shape), name="normal")
self.para_uniform = Parameter(init.initializer(init.Uniform(), parameter_shape), name="uniform")
def test_init_he_uniform():
""" test_init_he_uniform """
tensor1 = init.initializer(init.HeUniform(), [20, 22], ms.float32)
tensor2 = init.initializer(init.HeUniform(), [20, 22, 5, 5], ms.float32)
tensor3 = init.initializer('he_uniform', [20, 22, 5, 5], ms.float32)
tensor4 = init.initializer('he_uniform', [20, 22], ms.float32)
tensors = [tensor1, tensor2, tensor3, tensor4]
for tensor in tensors:
shape = tensor.asnumpy().shape
if len(shape) > 2:
s = reduce(lambda x, y: x * y, shape[2:])
else:
s = 1
n_in = shape[1] * s
std = math.sqrt(2 / n_in)
boundary = std * math.sqrt(3)
assert _check_uniform(tensor, -boundary, boundary)
def test_init_he_uniform_error():
with py.raises(ValueError):
init.initializer(init.HeUniform(), [6], ms.float32)
step_size = dataset.get_dataset_size()
print("step" + str(step_size))
# define net
net = resnet(class_num=config.class_num)
# init weight
if args_opt.pre_trained:
param_dict = load_checkpoint(args_opt.pre_trained)
load_param_into_net(net, param_dict)
else:
for _, cell in net.cells_and_names():
if isinstance(cell, nn.Conv2d):
cell.weight.set_data(
weight_init.initializer(weight_init.HeUniform(),
cell.weight.shape,
cell.weight.dtype))
if isinstance(cell, nn.Dense):
cell.weight.set_data(
weight_init.initializer(weight_init.HeNormal(),
cell.weight.shape,
cell.weight.dtype))
# init lr
lr = get_lr(lr_init=config.lr_init,
lr_end=config.lr_end,
lr_max=config.lr_max,
warmup_epochs=config.warmup_epochs,
total_epochs=config.epoch_size,
steps_per_epoch=step_size,
