def test_multiple_conv2d():
conv1 = P.Conv2D(out_channel=2, pad_mode="pad", kernel_size=(5, 5), pad=0, stride=2, dilation=1)
conv2 = P.Conv2D(out_channel=2, pad_mode="pad", kernel_size=(5, 5), pad=1, stride=2, dilation=1)
def get_conv(x, w1, w2):
return conv2(conv1(x, w1), w2)
return get_conv
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_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,
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_planes, kernel_size, stride):
super(DepthWiseConv, self).__init__()
platform = context.get_context("device_target")
weight_shape = [1, kernel_size, in_planes]
weight_init = _initialize_weight_goog(shape=weight_shape)
if platform == "GPU":
self.depthwise_conv = P.Conv2D(out_channel=in_planes*1,
kernel_size=kernel_size,
stride=stride,
pad=int(kernel_size/2),
pad_mode="pad",
group=in_planes)
self.weight = Parameter(initializer(weight_init,
[in_planes*1, 1, kernel_size, kernel_size]), name='depthwise_weight')
else:
self.depthwise_conv = P.DepthwiseConv2dNative(channel_multiplier=1,
kernel_size=kernel_size,
stride=stride, pad_mode='pad',
pad=int(kernel_size/2))
self.weight = Parameter(initializer(weight_init,
[1, in_planes, kernel_size, kernel_size]), name='depthwise_weight')
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(ConvBN, self).__init__()
self.conv = P.Conv2D(32, 3)
self.conv_weight = Tensor(np.ones([32, 32, 3, 3]), mindspore.float32)
self.scale = Tensor(np.ones([32]), mindspore.float32)
self.bias = Tensor(np.ones([32]), mindspore.float32)
self.mean = Tensor(np.ones([32]), mindspore.float32)
self.variance = Tensor(np.ones([32]), mindspore.float32)
self.bn = P.BatchNorm()
def __init__(self):
super(NetConv2d, self).__init__()
out_channel = 2
kernel_size = 1
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
def test_conv2d(out_channel, kernel_size, pad, stride, dilation):
conv = P.Conv2D(out_channel=out_channel,
kernel_size=kernel_size,
pad_mode="pad",
pad=pad,
stride=stride,
dilation=dilation)
def get_conv(x, w):
return conv(x, w)
return get_conv
def construct(self, x):
out_channel = 16
kernel_size = 3
conv2d = P.Conv2D(out_channel,
kernel_size,
1,
pad_mode='valid',
pad=0,
stride=1,
dilation=1,
group=1)
return conv2d(x, self.w)
def __init__(self):
super(Net, self).__init__()
out_channel = 512
kernel_size = 2048
self.conv = P.Conv2D(out_channel, (kernel_size, kernel_size),
mode=1,
pad_mode="same",
pad=3,
stride=2,
dilation=1,
group=1)
self.w = Parameter(initializer('normal', [512, 2048, 1, 1]), name='w')
def __init__(self, axis=0, out_nums=1):
super(NetConv2dDynamic, self).__init__()
self.dynshape = inner.GpuConvertToDynamicShape()
out_channel = 2
kernel_size = 1
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
def __init__(self):
super(ConvNet, self).__init__()
out_channel = 16
kernel_size = 3
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="pad",
pad=0,
stride=1,
dilation=2,
group=1)
self.w = Parameter(Tensor(np.ones([16, 16, 3, 3]).astype(np.float32)), name='w')
def __init__(self):
super(Net, self).__init__()
out_channel = 64
kernel_size = 7
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
self.w = Parameter(initializer('normal', [64, 3, 7, 7]), name='w')
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',
quant_delay=0,
num_bits=8,
per_channel=False,
symmetric=False,
narrow_range=False):
kernel_size = twice(kernel_size)
super(Conv2dQuant,
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\'.')
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(NetConv2d, self).__init__()
out_channel = 2
kernel_size = 1
self.conv = P.Conv2D(out_channel,
kernel_size,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
self.w = Parameter(initializer(
Tensor(
np.arange(2 * 3 * 1 * 1).reshape(2, 3, 1,
1).astype(np.float32)),
[2, 3, 1, 1]),
name='w')
self.x = Parameter(initializer(
Tensor(
np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3,
3).astype(np.float32)),
[1, 3, 3, 3]),
name='x')
'desc_inputs':
[Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]
}),
('AvgPool2d_1', {
'block': nn.AvgPool2d(5, pad_mode='same'),
'desc_inputs':
[Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]
}),
('AvgPool2d_2', {
'block': nn.AvgPool2d(5, pad_mode='valid'),
'desc_inputs':
[Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]
}),
('Conv2D_1', {
'block':
P.Conv2D(1, 6, pad_mode='same', pad=0),
'desc_inputs': [
Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))
]
}),
('Conv2D_2', {
'block':
P.Conv2D(1, 6, pad_mode='valid', pad=0),
'desc_inputs': [
Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))
]
}),
('Conv2D_3', {
'block':
'block': PReLUGradNet(),
'desc_inputs': [Tensor(np.ones([1, 3, 4, 4], np.float32)),
Tensor(np.ones([1, 3, 4, 4], np.float32)),
Tensor(np.ones(3, np.float32))],
}),
]
test_cases_for_verify_exception = [
('ApplyMomentum_Error', {
'block': (P.ApplyMomentum(), {'exception': TypeError}),
'desc_inputs': [[2], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64], [128, 32, 32, 64]],
'desc_bprop': [[128, 32, 32, 64]],
'skip': ['backward']
}),
('Conv2d_ValueError_1', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {'exception': TypeError}),
'desc_inputs': [0],
}),
('Conv2d_ValueError_2', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2), {'exception': ValueError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_1', {
'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {'exception': ValueError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_2', {
'block': (lambda _: P.MaxPoolWithArgmax(ksize='1'), {'exception': TypeError}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_3', {
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
data_format='NCHW',
has_bias=False,
weight_init='normal',
damping=0.03,
loss_scale=1,
frequency=278,
bias_init='zeros'):
self.thor = True
ksizes = (1, kernel_size, kernel_size, 1)
self.hw = kernel_size * kernel_size
strides = (1, stride, stride, 1)
kernel_size = twice(kernel_size)
super(Conv2d_Thor, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
data_format,
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.img2col = P.CusImg2Col(ksizes=ksizes, strides=strides)
self.cube_matmul = P.CusMatMulCube(transpose_a=True)
self.matrix_combine = P.CusMatrixCombine()
self.cholesky = P.CusCholeskyTrsm()
self.transpose02314 = P.CusTranspose02314()
self.matrix_A_dim = self.in_channels * self.kernel_size[0] * self.kernel_size[1]
self.matrix_G_dim = self.out_channels
self.matrix_A_device_shape, self.matrix_A_device_dim = caculate_device_shape(self.matrix_A_dim,
self.in_channels, True)
self.matrix_G_device_shape, self.matrix_G_device_dim = caculate_device_shape(self.matrix_G_dim,
self.in_channels, False)
self.matrix_A_device_temp_shape = (
self.matrix_A_device_shape[0], self.matrix_A_device_shape[2], self.matrix_A_device_shape[1],
self.matrix_A_device_shape[3])
self.matrix_G_device_temp_shape = (
self.matrix_G_device_shape[0], self.matrix_G_device_shape[2], self.matrix_G_device_shape[1],
self.matrix_G_device_shape[3])
self.matrix_A_inv = Parameter(
Tensor(np.reshape(np.identity(self.matrix_A_device_dim).astype(np.float16), self.matrix_A_device_shape)),
name='matrix_A_inv', requires_grad=False)
self.A_inv_max = Parameter(initializer(0, [1], mstype.float32), name="A_inv_max", requires_grad=False)
self.matrix_G_inv = Parameter(
Tensor(np.reshape(np.identity(self.matrix_G_device_dim).astype(np.float16), self.matrix_G_device_shape)),
name="matrix_G_inv", requires_grad=False)
self.G_inv_max = Parameter(initializer(0, [1], mstype.float32), name="G_inv_max", requires_grad=False)
self.fake_G = Tensor(
np.reshape(np.identity(self.matrix_G_device_dim).astype(np.float16), self.matrix_G_device_shape))
self.shape = P.Shape()
self.reshape = P.Reshape()
self.transpose = P.Transpose()
self.cov_step = Parameter(initializer(0, [1], mstype.int32), name="cov_step", requires_grad=False)
self.mul = P.Mul()
self.cast = P.Cast()
self.damping = Tensor(damping)
self.vector_matmul = P.CusBatchMatMul()
self.diag_block_dim = 128
self.channels_slice_flag = False
if self.in_channels % C0 != 0:
self.channels_slice_flag = True
self.padA_flag = False
if (self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim \
and self.matrix_A_dim > self.diag_block_dim:
self.padA_flag = True
pad_dim = self.diag_block_dim - self.matrix_A_dim % self.diag_block_dim
self.padA = P.Pad(((0, pad_dim), (0, pad_dim)))
self.device_shape_pad_flag = False
if self.matrix_A_dim != self.matrix_A_device_dim:
self.device_shape_pad_flag = True
self.device_shape_pad = P.Pad(((0, 0), (0, C0 - self.in_channels), (0, 0), (0, C0 - self.in_channels)))
self.slice = P.Slice()
self.gather = P.GatherV2()
self.freq = Tensor(frequency, mstype.int32)
self.loss_scale = Tensor(1 / loss_scale, mstype.float16)
self.axis = 0
dampingA_dim = self.matrix_A_dim
if (self.matrix_A_dim % self.diag_block_dim) != 0 and self.matrix_A_dim > self.diag_block_dim:
dampingA_dim = (self.matrix_A_dim // self.diag_block_dim + 1) * self.diag_block_dim
dampingG_dim = self.matrix_G_dim
if (self.matrix_G_dim % self.diag_block_dim) != 0 and self.matrix_G_dim > self.diag_block_dim:
dampingG_dim = (self.matrix_G_dim // self.diag_block_dim + 1) * self.diag_block_dim
self.dampingA = Tensor(np.identity(dampingA_dim), mstype.float32)
self.dampingG = Tensor(np.identity(dampingG_dim), mstype.float32)
self.fused_abs_max1 = P.CusFusedAbsMax1([self.matrix_A_dim, self.matrix_A_dim])
self.fused_abs_max2 = P.CusFusedAbsMax1()
self.log = P.Log()
self.exp = P.Exp()
self.sqrt = P.Sqrt()
self.getG = P.InsertGradientOf(self.save_gradient)
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
eps=1e-5,
momentum=0.997,
weight_init=None,
beta_init=None,
gamma_init=None,
mean_init=None,
var_init=None,
quant_delay=0,
freeze_bn=100000,
fake=True,
num_bits=8,
per_channel=False,
symmetric=False,
narrow_range=False):
super(Conv2dBatchNormQuant, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.pad_mode = pad_mode
self.padding = padding
self.dilation = twice(dilation)
self.stride = twice(stride)
self.group = group
self.fake = fake
self.freeze_bn = freeze_bn
self.momentum = momentum
self.quant_delay = quant_delay
if isinstance(kernel_size, int):
self.kernel_size = (kernel_size, kernel_size)
else:
self.kernel_size = kernel_size
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 gamma_init is None:
gamma_init = initializer('ones', [out_channels])
self.gamma = Parameter(gamma_init, name='gamma')
if beta_init is None:
beta_init = initializer('zeros', [out_channels])
self.beta = Parameter(beta_init, name='beta')
if mean_init is None:
mean_init = initializer('zeros', [out_channels])
self.moving_mean = Parameter(mean_init,
name='moving_mean',
requires_grad=False)
if var_init is None:
var_init = initializer('ones', [out_channels])
self.moving_variance = Parameter(var_init,
name='moving_variance',
requires_grad=False)
self.step = Parameter(initializer('normal', [1], dtype=mstype.int32),
name='step',
requires_grad=False)
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)
self.batchnorm_fold_train = P.BatchNormFold(epsilon=eps,
momentum=momentum,
is_training=True,
freeze_bn=freeze_bn)
self.batchnorm_fold_infer = P.BatchNormFold(epsilon=eps,
momentum=momentum,
is_training=False,
freeze_bn=freeze_bn)
self.correct_mul = P.CorrectionMul()
self.relu = P.ReLU()
self.batchnorm_fold2 = P.BatchNormFold2(freeze_bn=freeze_bn)
self.batchnorm_fold2_infer = P.BatchNormFold2(freeze_bn=0)
self.one = Tensor(1, mstype.int32)
self.assignadd = P.AssignAdd()
'block': nn.Dense(1, 6, has_bias=False, bias_init=Tensor(np.ones([6]).astype(np.float32))),
'desc_inputs': [Tensor(np.ones(shape=[6, 1]).astype(np.float32))]}),
('MaxPool2d_1', {
'block': nn.MaxPool2d(5, pad_mode='same', padding=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
('MaxPool2d_2', {
'block': nn.MaxPool2d(5, pad_mode='valid', padding=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
('AvgPool2d_1', {
'block': nn.AvgPool2d(5, pad_mode='same', padding=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
('AvgPool2d_2', {
'block': nn.AvgPool2d(5, pad_mode='valid', padding=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32))]}),
('Conv2D_1', {
'block': P.Conv2D(1, 6, pad_mode='same', pad=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
('Conv2D_2', {
'block': P.Conv2D(1, 6, pad_mode='valid', pad=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
('Conv2D_3', {
'block': P.Conv2D(1, 6, pad_mode='pad', pad=0),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
('Conv2D_4', {
'block': P.Conv2D(1, 6, pad_mode='pad', pad=7),
'desc_inputs': [Tensor(np.ones(shape=[5, 5, 8, 8]).astype(np.float32)),
Tensor(np.ones(shape=[1, 5, 6, 6]).astype(np.float32))]}),
('MatMul_1', {
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)
self._dilation = dilation
dilation = twice(dilation)
super(Conv2d_Thor,
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._init_depthwise_conv2d(weight_init)
self.bias_add = P.BiasAdd()
self.thor = True
self.hw = kernel_size[0] * kernel_size[1]
self.matrix_A_dim = self.in_channels * self.kernel_size[
0] * self.kernel_size[1]
self.matrix_G_dim = self.out_channels
self.shape = P.Shape()
self.reshape = P.Reshape()
self.mul = P.Mul()
self.cast = P.Cast()
self.A_normalizer = Parameter(initializer(0, [1], mstype.float32),
name="A_normalizer",
requires_grad=False)
self.G_normalizer = Parameter(initializer(0, [1], mstype.float32),
name="G_normalizer",
requires_grad=False)
self.is_Ascend = True
if context.get_context("device_target") == "Ascend":
ksizes = (1, kernel_size[0], kernel_size[1], 1)
strides = (1, stride[0], stride[1], 1)
self.img2col = P.CusImg2Col(ksizes=ksizes, strides=strides)
self.cube_matmul = P.CusMatMulCube(transpose_a=True)
self.transpose02314 = P.CusTranspose02314()
dampingA_dim = self.matrix_A_dim
self.diag_block_dim = 128
if (self.matrix_A_dim % self.diag_block_dim
) != 0 and self.matrix_A_dim > self.diag_block_dim:
dampingA_dim = (self.matrix_A_dim // self.diag_block_dim +
1) * self.diag_block_dim
dampingG_dim = self.matrix_G_dim
if (self.matrix_G_dim % self.diag_block_dim
) != 0 and self.matrix_G_dim > self.diag_block_dim:
dampingG_dim = (self.matrix_G_dim // self.diag_block_dim +
1) * self.diag_block_dim
self.matrix_A_cov = Parameter(Tensor(
np.zeros([dampingA_dim, dampingA_dim]).astype(np.float32)),
name='matrix_A',
requires_grad=False)
self.matrix_G_cov = Parameter(Tensor(
np.zeros([dampingG_dim, dampingG_dim]).astype(np.float32)),
name='matrix_G',
requires_grad=False)
self.channels_slice_flag = False
self.C0 = 16
if self.in_channels % self.C0 != 0:
self.channels_slice_flag = True
self.padA_flag = False
if (self.matrix_A_dim // self.diag_block_dim) * self.diag_block_dim != self.matrix_A_dim \
and self.matrix_A_dim > self.diag_block_dim:
self.padA_flag = True
pad_dim = self.diag_block_dim - self.matrix_A_dim % self.diag_block_dim
self.padA = P.Pad(((0, pad_dim), (0, pad_dim)))
self.slice = P.Slice()
else:
self.is_Ascend = False
self.img2col = P.Im2Col(kernel_size=kernel_size,
stride=stride,
pad_mode="same")
self.matmul = P.MatMul(transpose_b=True)
self.reduce_mean = P.ReduceMean(keep_dims=False)
self.matrix_A_cov = Parameter(Tensor(
np.zeros([self.matrix_A_dim,
self.matrix_A_dim]).astype(np.float32)),
name='matrix_A',
requires_grad=False)
self.matrix_G_cov = Parameter(Tensor(
np.zeros([self.matrix_G_dim,
self.matrix_G_dim]).astype(np.float32)),
name='matrix_G',
requires_grad=False)
self.getG = P.InsertGradientOf(self.save_gradient)
'exception': ValueError,
'error_keywords': ['BatchNorm']
}),
'desc_inputs': [
Tensor(np.ones([5, 3]).astype(np.float32)),
Tensor(np.ones([3]).astype(np.float32)),
Tensor(np.ones([3]).astype(np.float32)),
Tensor(np.ones([5]).astype(np.float32)),
Tensor(np.ones([5]).astype(np.float32))
],
'skip': ['backward']
}),
# input is scalar
('Conv2D0', {
'block': (P.Conv2D(2, (5, 5)), {
'exception': TypeError,
'error_keywords': ['Conv2D']
}),
'desc_inputs': [5.0, 5.0],
'skip': ['backward']
}),
# input is Tensor(bool)
('Conv2D1', {
'block': (P.Conv2D(2, (5, 5)), {
'exception': TypeError,
'error_keywords': ['Conv2D']
}),
'desc_inputs': [
Tensor(np.ones([5]).astype(np.bool_)),
Tensor(np.ones([5]).astype(np.bool_))
def infer_shape(self, logits_shape, label_shape):
return []
def infer_dtype(self, logits_type, labels_type):
# pylint: disable=unused-argument
return dtype.float64
tensorToScalar = TensorToScalar()
def get_tensor_to_scalar(logits, labels):
return tensorToScalar(logits, labels)
conv2d = P.Conv2D(64, (3, 3), pad_mode="pad", pad=1, stride=2)
def get_conv2d(x, w):
return conv2d(x, w)
conv2dNative = P.DepthwiseConv2dNative(3, (3, 3),
pad_mode="pad",
pad=1,
stride=2)
def get_conv2d_native(x, w):
return conv2dNative(x, w)
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
data_format='NCHW',
has_bias=False,
weight_init='normal',
damping=0.03,
loss_scale=1,
frequency=278,
batch_size=32,
bias_init='zeros'):
self.skfac = True
self.hw = kernel_size * kernel_size
kernel_size = twice(kernel_size)
super(Conv2d_SKFAC_GPU, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
data_format,
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.matrix_A_dim = self.in_channels * self.kernel_size[
0] * self.kernel_size[1]
self.matrix_G_dim = self.out_channels
split_dim = 128
self.matrix_A_inv = Parameter(np.zeros(
(self.matrix_A_dim, self.matrix_A_dim)).astype(np.float32),
requires_grad=False)
self.matrix_G_inv = Parameter(np.zeros(
(self.matrix_G_dim, self.matrix_G_dim)).astype(np.float32),
requires_grad=False)
self.cov_step = Parameter(initializer(0, [1], mstype.int32),
requires_grad=False)
self.img2col = P.Im2Col(kernel_size=kernel_size,
stride=stride,
pad_mode="same")
self.matmul = P.MatMul(transpose_a=True)
self.matmul_ = P.MatMul()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.mul = P.Mul()
self.getG = P.InsertGradientOf(self.save_gradient)
self.loss_scale = Tensor(1 / loss_scale, mstype.float16)
self.batch_size = Tensor(batch_size, mstype.float16)
self.transpose = P.Transpose()
self.cast = P.Cast()
self.gather = P.Gather()
self.freq = Tensor(frequency, mstype.int32)
self.axis = 0
self.sqrt = P.Sqrt()
self.reduce_mean = P.ReduceMean(keep_dims=False)
self.damping = Parameter(Tensor(damping), requires_grad=False)
self.dampingA = Tensor(np.identity(batch_size), mstype.float32)
self.dampingG = Tensor(np.identity(batch_size), mstype.float32)
self.I_G = Tensor(np.identity(out_channels), mstype.float32)
self.I_A = Tensor(np.identity(self.matrix_A_dim), mstype.float32)
self.cholesky = P.CholeskyTrsm(split_dim=split_dim)
self.vector_matmul = P.BatchMatMul(transpose_a=True)
self.batch_coefficient = Tensor((1 / 32)**0.5, mstype.float32)
'skip': ['backward']
}),
]
test_cases_for_verify_exception = [
('ApplyMomentum_Error', {
'block': (P.ApplyMomentum(), {
'exception': TypeError
}),
'desc_inputs': [[2], [128, 32, 32, 64], [128, 32, 32, 64],
[128, 32, 32, 64], [128, 32, 32, 64]],
'desc_bprop': [[128, 32, 32, 64]],
'skip': ['backward']
}),
('Conv2d_ValueError_1', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2.0), {
'exception': TypeError
}),
'desc_inputs': [0],
}),
('Conv2d_ValueError_2', {
'block': (lambda _: P.Conv2D(3, 4, mode=-2), {
'exception': ValueError
}),
'desc_inputs': [0],
}),
('MaxPoolWithArgmax_ValueError_1', {
'block': (lambda _: P.MaxPoolWithArgmax(padding='sane'), {
'exception': ValueError
}),
'desc_inputs': [0],
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from mindspore.ops import operations as P
from mindspore.ops import Primitive
make_tuple = Primitive('make_tuple')
tuple_getitem = Primitive('tuple_getitem')
conv = P.Conv2D(out_channel=64,
kernel_size=7,
mode=1,
pad_mode="valid",
pad=0,
stride=1,
dilation=1,
group=1)
bn = P.FusedBatchNorm()
relu = P.ReLU()
conv_bn1 = Primitive('ConvBN1')
bn2_relu = Primitive('BN2Relu')
class FnDict:
def __init__(self):
self.fnDict = {}
def __call__(self, fn):
self.fnDict[fn.__name__] = fn
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
pad_mode='same',
padding=0,
dilation=1,
group=1,
has_bias=False,
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)):
kernel_size = twice(kernel_size)
stride = twice(stride)
dilation = twice(dilation)
super(_ConvVariational, self).__init__(
in_channels,
out_channels,
kernel_size,
stride,
pad_mode,
padding,
dilation,
group,
has_bias,
weight_init='normal',
bias_init='zeros'
)
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\'.')
# convolution args
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
self.pad_mode = pad_mode
self.padding = padding
self.dilation = dilation
self.group = group
self.has_bias = has_bias
# distribution trainable parameters
self.shape = [self.out_channels,
self.in_channels // self.group, *self.kernel_size]
self.weight.requires_grad = False
if isinstance(weight_prior_fn, Cell):
self.weight_prior = weight_prior_fn
else:
self.weight_prior = weight_prior_fn()
for prior_name, prior_dist in self.weight_prior.name_cells().items():
if prior_name != 'normal':
raise TypeError("The type of distribution of `weight_prior_fn` should be `normal`")
if not (isinstance(getattr(prior_dist, '_mean_value'), Tensor) and
isinstance(getattr(prior_dist, '_sd_value'), Tensor)):
raise TypeError("The input form of `weight_prior_fn` is incorrect")
try:
self.weight_posterior = weight_posterior_fn(shape=self.shape, name='bnn_weight')
except TypeError:
raise TypeError('The input form of `weight_posterior_fn` is incorrect')
for posterior_name, _ in self.weight_posterior.name_cells().items():
if posterior_name != 'normal':
raise TypeError("The type of distribution of `weight_posterior_fn` should be `normal`")
if self.has_bias:
self.bias.requires_grad = False
if isinstance(bias_prior_fn, Cell):
self.bias_prior = bias_prior_fn
else:
self.bias_prior = bias_prior_fn()
for prior_name, prior_dist in self.bias_prior.name_cells().items():
if prior_name != 'normal':
raise TypeError("The type of distribution of `bias_prior_fn` should be `normal`")
if not (isinstance(getattr(prior_dist, '_mean_value'), Tensor) and
isinstance(getattr(prior_dist, '_sd_value'), Tensor)):
raise TypeError("The input form of `bias_prior_fn` is incorrect")
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`')
for posterior_name, _ in self.bias_posterior.name_cells().items():
if posterior_name != 'normal':
raise TypeError("The type of distribution of `bias_posterior_fn` should be `normal`")
# mindspore operations
self.bias_add = P.BiasAdd()
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.log = P.Log()
self.sum = P.ReduceSum()
"""Convolution 2D"""
batch_num, _, x_h, x_w = x.shape
filter_num, _, filter_h, filter_w = weight.shape
out_h = 1 + int((x_h + 2 * pad - filter_h - (filter_h - 1) *
(dilation[2] - 1)) / stride[2])
out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) *
(dilation[3] - 1)) / stride[3])
col = im2col(x, filter_h, filter_w, stride, pad, dilation)
col_w = np.reshape(weight, (filter_num, -1)).T
out = np.dot(col, col_w)
out = out.reshape(batch_num, out_h, out_w, -1).transpose(0, 3, 1, 2)
if bias is not None:
out += bias
return out
@vm_impl_getters.register(P.Conv2D)
def vm_impl_conv2d(self):
"""Generate vm_impl function for Conv2D"""
def vm_impl(x, w):
x = x.asnumpy()
weight = w.asnumpy()
bias = None
out = conv2d(x, weight, bias, self.stride, self.pad, self.dilation)
return Tensor(out)
return vm_impl
conv2d_prim = P.Conv2D(64, (3, 3), pad_mode='pad', pad=1, stride=2)