def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True):
super(GNNFeatureTransform, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.has_bias = has_bias
if isinstance(weight_init, Tensor):
if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("weight_init shape error")
self.weight = Parameter(
initializer(weight_init, [out_channels, in_channels]))
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(bias_init, [out_channels]))
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True):
super(GNNFeatureTransform, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels)
self.out_channels = Validator.check_positive_int(out_channels)
self.has_bias = Validator.check_bool(has_bias)
if isinstance(weight_init, Tensor):
if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("weight_init shape error")
self.weight = Parameter(initializer(weight_init,
[out_channels, in_channels]),
name="weight")
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(bias_init, [out_channels]),
name="bias")
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
def __init__(self, input_channels, output_channels, weight_init):
super(DenseNoTranpose, self).__init__()
self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float32),
name="weight")
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float32), name="bias")
self.matmul = P.MatMul(transpose_b=False)
self.bias_add = P.BiasAdd()
def __init__(self,
in_planes,
kernel_size,
stride,
pad_mode,
pad,
channel_multiplier=1,
has_bias=False):
super(DepthwiseConv, self).__init__()
self.has_bias = has_bias
self.in_channels = in_planes
self.channel_multiplier = channel_multiplier
self.out_channels = in_planes * channel_multiplier
self.kernel_size = (kernel_size, kernel_size)
self.depthwise_conv = P.DepthwiseConv2dNative(
channel_multiplier=channel_multiplier,
kernel_size=self.kernel_size,
stride=stride,
pad_mode=pad_mode,
pad=pad)
self.bias_add = P.BiasAdd()
weight_shape = [channel_multiplier, in_planes, *self.kernel_size]
self.weight = Parameter(initializer('ones', weight_shape),
name='weight')
if has_bias:
bias_shape = [channel_multiplier * in_planes]
self.bias = Parameter(initializer('zeros', bias_shape),
name='bias')
else:
self.bias = None
def __init__(self,
input_dim,
output_dim,
weight_bias_init,
act_str,
keep_prob=0.8,
scale_coef=1.0,
use_activation=True,
convert_dtype=True,
drop_out=False):
super(DenseLayer, self).__init__()
weight_init, bias_init = weight_bias_init
self.weight = init_method(weight_init, [input_dim, output_dim],
name="weight")
self.bias = init_method(bias_init, [output_dim], name="bias")
self.act_func = self._init_activation(act_str)
self.matmul = P.MatMul(transpose_b=False)
self.bias_add = P.BiasAdd()
self.cast = P.Cast()
self.dropout = Dropout(keep_prob=keep_prob)
self.mul = P.Mul()
self.realDiv = P.RealDiv()
self.scale_coef = scale_coef
self.use_activation = use_activation
self.convert_dtype = convert_dtype
self.drop_out = drop_out
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
kernel_size = twice(kernel_size)
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
stride, pad_mode, padding, dilation,
group, has_bias, weight_init, bias_init)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group)
self.bias_add = P.BiasAdd()
if pad_mode not in ('valid', 'same', 'pad'):
raise ValueError(
'Attr \'pad_mode\' of \'Conv2d\' Op passed ' + str(pad_mode) +
', should be one of values in \'valid\', \'same\', \'pad\'.')
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init=None,
bias_init=None,
quant_delay=0,
num_bits=8,
per_channel=False,
symmetric=False,
narrow_range=False):
super(Conv2dQuant, self).__init__()
if isinstance(kernel_size, int):
self.kernel_size = (kernel_size, kernel_size)
else:
self.kernel_size = kernel_size
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.has_bias = has_bias
self.stride = twice(stride)
self.dilation = twice(dilation)
self.pad_mode = pad_mode
self.padding = padding
self.group = group
self.quant_delay = quant_delay
if weight_init is None:
weight_init = initializer(
'normal',
[out_channels, in_channels // group, *self.kernel_size])
self.weight = Parameter(weight_init, name='weight')
if bias_init is None:
bias_init = initializer('zeros', [out_channels])
if has_bias:
self.bias = Parameter(bias_init, name='bias')
self.bias_add = P.BiasAdd()
self.conv = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group)
self.fake_quant_weight = FakeQuantWithMinMax(min_init=-6,
max_init=6,
ema=False,
num_bits=num_bits,
quant_delay=quant_delay,
per_channel=per_channel,
out_channels=out_channels,
symmetric=symmetric,
narrow_range=narrow_range)
def __init__(self,
in_channel,
out_channel,
kernel_size,
mode=1,
pad=0,
stride=1,
dilation=1,
group=1,
output_padding=0,
data_format="NCDHW",
bias_init="zeros",
has_bias=True):
super().__init__()
self.weight_shape = (in_channel, out_channel, kernel_size[0],
kernel_size[1], kernel_size[2])
self.weight = weight_variable(self.weight_shape)
self.conv_transpose = nps.Conv3DTranspose(in_channel=in_channel, out_channel=out_channel,\
kernel_size=kernel_size, mode=mode, pad=pad, stride=stride, \
dilation=dilation, group=group, output_padding=output_padding, \
data_format=data_format)
self.bias_init = bias_init
self.has_bias = has_bias
self.bias_add = P.BiasAdd(data_format=data_format)
if self.has_bias:
self.bias = Parameter(initializer(self.bias_init, [out_channel]),
name='bias')
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
data_format='NCHW'):
kernel_size = twice(kernel_size)
stride = twice(stride)
self._dilation = dilation
dilation = twice(dilation)
super(Conv2d,
self).__init__(in_channels, out_channels, kernel_size, stride,
pad_mode, padding, dilation, group, has_bias,
weight_init, bias_init, data_format)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group,
data_format=self.format)
self._init_depthwise_conv2d()
self.bias_add = P.BiasAdd()
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
damping=0.03,
loss_scale=1,
frequency=278,
batch_size=32,
has_bias=True,
activation=None):
super(Dense_Thor_GPU, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels)
self.out_channels = Validator.check_positive_int(out_channels)
self.has_bias = Validator.check_bool(has_bias)
self.thor = True
if isinstance(weight_init, Tensor):
if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("weight_init shape error")
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]))
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(bias_init, [out_channels]))
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
self.activation = get_activation(activation)
self.activation_flag = self.activation is not None
split_dim = 128
matrix_A_shape, matrix_G_shape = caculate_matmul_shape(self.in_channels, self.out_channels, split_dim)
self.matrix_A_inv = Parameter(Tensor(np.zeros(matrix_A_shape).astype(np.float32)), requires_grad=False)
self.matrix_G_inv = Parameter(Tensor(np.zeros(matrix_G_shape).astype(np.float32)), requires_grad=False)
self.broadcast_to = P.BroadcastTo(matrix_A_shape)
self.cov_step = Parameter(initializer(0, [1], mstype.int32), requires_grad=False)
self.shape = P.Shape()
self.reshape = P.Reshape()
self.transpose = P.Transpose()
self.mul = P.Mul()
self.cube_matmul = P.MatMul(transpose_a=True)
self.loss_scale = Tensor(1 / loss_scale, mstype.float16)
self.batch_size = Tensor(batch_size, mstype.float16)
self.getG = P.InsertGradientOf(self.save_gradient)
self.damping = Parameter(Tensor(damping), requires_grad=False)
self.dampingA = Tensor(np.identity(in_channels), mstype.float32)
self.dampingG = Tensor(np.identity(out_channels), mstype.float32)
self.cast = P.Cast()
self.gather = P.Gather()
self.freq = Tensor(frequency, mstype.int32)
self.axis = 0
self.add = P.Add()
self.sqrt = P.Sqrt()
self.cholesky = P.CholeskyTrsm(split_dim=split_dim)
self.vector_matmul = P.BatchMatMul(transpose_a=True)
def __init__(self, in_channel, x):
super().__init__()
#self._save_graphs(save_graph_flag=True, save_graph_path=".")
self.biasadd = P.BiasAdd()
self.equal = P.Equal()
self.addn = P.AddN()
self.conv = Conv2d(in_channels=in_channel,
out_channels=in_channel,
kernel_size=1,
stride=1,
has_bias=False,
weight_init='ones',
pad_mode='same')
self.bn = BatchNorm2d(num_features=in_channel)
self.assignadd = P.AssignAdd()
self.assign = P.Assign()
self.relu = ReLU()
self.mean = P.ReduceMean(keep_dims=False)
self.bias = Parameter(Tensor(
np.random.randint(2, size=(3, )).astype((np.float32))),
name="bias")
self.bias2 = Parameter(Tensor(np.ones([3]).astype(np.float32)),
name="bias2")
self.parameterupdate = ParameterUpdate(self.bias)
self.value = Tensor(np.random.randn(*(3, )), ms.float32)
self.x = x
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros',
strategy=None):
kernel_size = twice(kernel_size)
super(Conv2d, self).__init__(in_channels, out_channels, kernel_size,
stride, pad_mode, padding, dilation,
group, has_bias, weight_init, bias_init)
self.add = P.TensorAdd(strategy)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group,
strategy=None)
self.bias_add = P.BiasAdd()
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None):
super(Dense, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels)
self.out_channels = Validator.check_positive_int(out_channels)
self.has_bias = Validator.check_bool(has_bias)
if isinstance(weight_init, Tensor):
if weight_init.dim() != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("Weight init shape error.")
self.weight = Parameter(initializer(weight_init,
[out_channels, in_channels]),
name="weight")
self.bias = None
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.dim() != 1 or bias_init.shape[0] != out_channels:
raise ValueError("Bias init shape error.")
self.bias = Parameter(initializer(bias_init, [out_channels]),
name="bias")
self.bias_add = P.BiasAdd()
self.matmul = P.MatMul(transpose_b=True)
self.activation = get_activation(activation)
self.activation_flag = self.activation is not None
def __init__(self,
in_planes,
kernel_size,
stride,
pad_mode,
pad,
channel_multiplier=1,
has_bias=False):
super(DepthWiseConv, self).__init__()
self.has_bias = has_bias
self.depthwise_conv = P.DepthwiseConv2dNative(
channel_multiplier=channel_multiplier,
kernel_size=kernel_size,
stride=stride,
pad_mode=pad_mode,
pad=pad)
self.bias_add = P.BiasAdd()
weight_shape = [
channel_multiplier, in_planes, kernel_size[0], kernel_size[1]
]
self.weight = Parameter(initializer('ones', weight_shape))
if has_bias:
bias_shape = [channel_multiplier * in_planes]
self.bias = Parameter(initializer('zeros', bias_shape))
else:
self.bias = None
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None):
super(Dense, self).__init__()
self.in_channels = Validator.check_positive_int(in_channels)
self.out_channels = Validator.check_positive_int(out_channels)
self.has_bias = Validator.check_bool(has_bias)
self.reshape = P.Reshape()
self.shape_op = P.Shape()
if isinstance(weight_init, Tensor):
if weight_init.ndim != 2 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in_channels:
raise ValueError("Weight init shape error.")
self.weight = Parameter(initializer(weight_init, [out_channels, in_channels]), name="weight")
self.bias = None
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError("Bias init shape error.")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.bias_add = P.BiasAdd()
self.matmul = P.MatMul(transpose_b=True)
self.activation = get_activation(activation) if isinstance(activation, str) else activation
if activation is not None and not isinstance(self.activation, (Cell, Primitive)):
raise TypeError("The activation must be str or Cell or Primitive,"" but got {}.".format(activation))
self.activation_flag = self.activation is not None
def __init__(
self,
in_channels,
out_channels,
activation=None,
has_bias=True,
weight_prior_fn=NormalPrior,
weight_posterior_fn=lambda name, shape: NormalPosterior(name=name, shape=shape),
bias_prior_fn=NormalPrior,
bias_posterior_fn=lambda name, shape: NormalPosterior(name=name, shape=shape)):
super(_DenseVariational, self).__init__()
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.has_bias = check_bool(has_bias)
if isinstance(weight_prior_fn, Cell):
if weight_prior_fn.__class__.__name__ != 'NormalPrior':
raise TypeError('The type of `weight_prior_fn` should be `NormalPrior`')
self.weight_prior = weight_prior_fn
else:
if weight_prior_fn.__name__ != 'NormalPrior':
raise TypeError('The type of `weight_prior_fn` should be `NormalPrior`')
self.weight_prior = weight_prior_fn()
try:
self.weight_posterior = weight_posterior_fn(shape=[self.out_channels, self.in_channels], name='bnn_weight')
except TypeError:
raise TypeError('The type of `weight_posterior_fn` should be `NormalPosterior`')
if self.has_bias:
if isinstance(bias_prior_fn, Cell):
if bias_prior_fn.__class__.__name__ != 'NormalPrior':
raise TypeError('The type of `bias_prior_fn` should be `NormalPrior`')
self.bias_prior = bias_prior_fn
else:
if bias_prior_fn.__name__ != 'NormalPrior':
raise TypeError('The type of `bias_prior_fn` should be `NormalPrior`')
self.bias_prior = bias_prior_fn()
try:
self.bias_posterior = bias_posterior_fn(shape=[self.out_channels], name='bnn_bias')
except TypeError:
raise TypeError('The type of `bias_posterior_fn` should be `NormalPosterior`')
self.activation = activation
if not self.activation:
self.activation_flag = False
else:
self.activation_flag = True
if isinstance(self.activation, str):
self.activation = get_activation(activation)
elif isinstance(self.activation, Cell):
self.activation = activation
else:
raise ValueError('The type of `activation` is wrong.')
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
self.sum = P.ReduceSum()
def __init__(self):
super(Net, self).__init__()
self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)),
name="weight")
self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))),
name="bias")
self.matmul = P.MatMul()
self.biasAdd = P.BiasAdd()
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
Validator.check_value_type("kernel_size", kernel_size, [int],
self.cls_name)
Validator.check_value_type("stride", stride, [int], self.cls_name)
Validator.check_value_type("padding", padding, [int], self.cls_name)
Validator.check_value_type("dilation", dilation, [int], self.cls_name)
Validator.check_integer('kernel_size', kernel_size, 1, Rel.GE,
self.cls_name)
Validator.check_integer('stride', stride, 1, Rel.GE, self.cls_name)
Validator.check_integer('padding', padding, 0, Rel.GE, self.cls_name)
Validator.check_integer('dilation', dilation, 1, Rel.GE, self.cls_name)
kernel_size = (1, kernel_size)
stride = (1, stride)
dilation = (1, dilation)
get_shape = P.Shape()
get_dtype = P.DType()
if isinstance(weight_init, Tensor):
weight_init_shape = get_shape(weight_init)
Validator.check_integer('weight_init_shape',
len(weight_init_shape), 3, Rel.EQ,
self.cls_name)
weight_init_dtype = get_dtype(weight_init)
weight_init_value = weight_init.asnumpy()
weight_init_value = np.expand_dims(weight_init_value, 2)
weight_init = Tensor(weight_init_value, weight_init_dtype)
super(Conv1d, self).__init__(in_channels, out_channels, kernel_size,
stride, pad_mode, padding, dilation,
group, has_bias, weight_init, bias_init)
self.padding = (0, 0, padding, padding)
self.conv2d = P.Conv2D(out_channel=self.out_channels,
kernel_size=self.kernel_size,
mode=1,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation,
group=self.group)
self.bias_add = P.BiasAdd()
if pad_mode not in ('valid', 'same', 'pad'):
raise ValueError(
'Attr \'pad_mode\' of \'Conv1d\' Op passed ' + str(pad_mode) +
', should be one of values in \'valid\', \'same\', \'pad\'.')
self.expand_dims = P.ExpandDims()
self.squeeze = P.Squeeze(2)
self.shape = P.Shape()
def __init__(self):
super(BatchnormNet, self).__init__()
self.conv1 = nn.Conv2d(3, 4, kernel_size=8, stride=2, pad_mode="pad", padding=3)
self.bn1 = nn.BatchNorm2d(4)
self.flatten = P.Flatten()
self.weight = Parameter(Tensor(np.ones([64, 10], np.float32)), name="weight")
self.bias = Parameter(Tensor(np.ones([10], np.float32)), name="bias")
self.fc = P.MatMul()
self.biasAdd = P.BiasAdd()
def __init__(self):
super(Net, self).__init__()
self.weight = Parameter(Tensor(np.ones([64, 10]).astype(np.float32)), name="weight")
self.bias = Parameter(Tensor(np.ones([10]).astype(np.float32)), name="bias")
self.fc = P.MatMul()
self.fc2 = nn.Dense(10, 10)
self.biasAdd = P.BiasAdd()
self.relu = nn.ReLU()
self.cast = P.Cast()
def __init__(self):
super(Net, self).__init__()
self.mul1 = P.Mul().shard(((2, 4), (2, 4)))
self.mul2 = P.Mul()
self.ba1 = P.BiasAdd()
self.weight = Parameter(Tensor(np.ones([128, 1000]), dtype=ms.float32), name="weight")
self.bias = Parameter(Tensor(np.ones([1000]), dtype=ms.float32), name="bias")
self.add = P.TensorAdd().shard(((1, 8), (1, 8)))
self.relu = P.ReLU()
def __init__(self, input_channels, output_channels, weight_init):
super(DenseNoTranpose, self).__init__()
self.weight = Parameter(initializer(weight_init, [input_channels, output_channels], mstype.float32))
self.bias = Parameter(initializer("zeros", [output_channels], mstype.float32))
self.matmul = P.MatMul(transpose_b=False)
self.bias_add = P.BiasAdd()
self.cast = P.Cast()
self.device_type = "Ascend" if context.get_context("device_target") == "Ascend" else "Others"
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
kernel_size = twice(kernel_size)
stride = twice(stride)
dilation = twice(dilation)
# out_channels and in_channels swap.
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel,
# then Conv2dTranspose's out_channel refers to Conv2DBackpropInput's in_channel.
super(Conv2dTranspose, self).__init__(in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init,
bias_init,
transposed=True)
self.in_channels = in_channels
self.out_channels = out_channels
self.shape = P.Shape()
if pad_mode not in ('valid', 'same', 'pad'):
raise ValueError(
'Attr \'pad_mode\' of \'Conv2dTranspose\' Op passed ' +
str(pad_mode) +
', should be one of values in \'valid\', \'same\', \'pad\'.')
self.is_valid = self.pad_mode == 'valid'
self.is_same = self.pad_mode == 'same'
self.is_pad = self.pad_mode == 'pad'
if check_bool(has_bias):
self.bias = Parameter(initializer(bias_init, [out_channels]),
name='bias')
# cause Conv2DBackpropInput's out_channel refers to Conv2D's out_channel.
self.conv2d_transpose = P.Conv2DBackpropInput(out_channel=in_channels,
kernel_size=kernel_size,
mode=1,
pad_mode=pad_mode,
pad=padding,
stride=stride,
dilation=dilation,
group=group)
self.bias_add = P.BiasAdd()
def __init__(self, nx, nf):
super(Conv1D, self).__init__()
self.nx = nx
self.nf = nf
self.weight = Parameter(normal_weight([nx, nf], nf),
name='projection_weight')
self.bias = Parameter(zero_weight(nf), name='projection_bias')
self.matmul = P.MatMul()
# self.add = P.TensorAdd()
self.bias_add = P.BiasAdd()
def __init__(self):
super().__init__()
inputs = np.array([[1, 1], [1, 1]])
self.parameter1 = Parameter(Tensor(inputs, ms.float32),
name="parameter1")
biasadd = np.array([0, -1])
self.parameter2 = Parameter(Tensor(biasadd, ms.float32),
name="biasadd")
self.assign = P.Assign()
self.matmul = P.MatMul()
self.biasadd = P.BiasAdd()
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
weight_init='normal',
bias_init='zeros'):
super(DepthwiseConv2d, self).__init__()
self.kernel_size = twice(kernel_size)
self.stride = twice(stride)
self.dilation = twice(dilation)
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
validator.check_integer('group', group, in_channels, Rel.EQ)
validator.check_integer('group', group, out_channels, Rel.EQ)
validator.check_integer('group', group, 1, Rel.GE)
self.pad_mode = pad_mode
self.dilation = dilation
self.group = group
self.has_bias = has_bias
self.weight_init = weight_init
self.bias_init = bias_init
Validator.check_value_type('padding', padding, (int, tuple),
self.cls_name)
if isinstance(padding, tuple):
Validator.check_integer('padding size', len(padding), 4, Rel.EQ,
self.cls_name)
self.padding = padding
self.conv = P.DepthwiseConv2dNative(channel_multiplier=1,
kernel_size=self.kernel_size,
pad_mode=self.pad_mode,
pad=self.padding,
stride=self.stride,
dilation=self.dilation)
self.bias_add = P.BiasAdd()
weight_shape = [1, in_channels, *self.kernel_size]
self.weight = Parameter(initializer(weight_init, weight_shape),
name='weight')
if check_bool(has_bias):
self.bias = Parameter(initializer(bias_init, [out_channels]),
name='bias')
else:
if bias_init != 'zeros':
logger.warning(
"value of `has_bias` is False, value of `bias_init` will be ignore."
)
self.bias = None
def __init__(self, *args, **kwargs):
super(Conv1d, self).__init__(*args, **kwargs)
self.clear_buffer()
self._linearized_weight = None
self.transpose_op = P.Transpose()
self.reshape_op = P.Reshape()
self.squeeze_op = P.Squeeze(-2)
self.zeros = P.Zeros()
self.concat_op = P.Concat(axis=1)
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
self.get_weight = None
self.get_bias = None
def __init__(self,
output_channels,
bias_init='zeros',
):
super(Net, self).__init__()
self.biasAdd = P.BiasAdd()
if isinstance(bias_init, Tensor):
if bias_init.dim() != 1 or bias_init.shape()[0] != output_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(
bias_init, [output_channels]), name="bias")
def __init__(self,
in_channels,
out_channels,
weight_init='normal',
bias_init='zeros',
has_bias=True,
activation=None,
num_bits=8,
quant_delay=0,
per_channel=False,
symmetric=False,
narrow_range=False):
super(DenseQuant, self).__init__()
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.has_bias = check_bool(has_bias)
if isinstance(weight_init, Tensor):
if weight_init.dim() != 2 or weight_init.shape()[0] != out_channels or \
weight_init.shape()[1] != in_channels:
raise ValueError("weight_init shape error")
self.weight = Parameter(initializer(weight_init,
[out_channels, in_channels]),
name="weight")
if self.has_bias:
if isinstance(bias_init, Tensor):
if bias_init.dim() != 1 or bias_init.shape(
)[0] != out_channels:
raise ValueError("bias_init shape error")
self.bias = Parameter(initializer(bias_init, [out_channels]),
name="bias")
self.matmul = P.MatMul(transpose_b=True)
self.bias_add = P.BiasAdd()
self.activation = get_activation(activation)
self.activation_flag = self.activation is not None
self.fake_quant_weight = nn.FakeQuantWithMinMax(
min_init=-6,
max_init=6,
ema=False,
num_bits=num_bits,
quant_delay=quant_delay,
per_channel=per_channel,
channel_size=out_channels,
symmetric=symmetric,
narrow_range=narrow_range)
def __init__(self):
super(BlockNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, pad_mode="pad", padding=3)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
self.block_down_sample = ResidualBlock(
64, 256, stride=1, down_sample=True
)
self.flatten = P.Flatten()
self.weight = Parameter(Tensor(np.ones([1024, 10]).astype(np.float32)), name="weight")
self.bias = Parameter(Tensor(np.ones([10]).astype((np.float32))), name="bias")
self.fc = P.MatMul()
self.biasAdd = P.BiasAdd()
