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For nn.DepthwiseConv2d, padding support tuple.

This commit is contained in:
liuxiao93 2020-08-24 11:55:15 +08:00
parent 287a23445a
commit d8773caffc
2 changed files with 28 additions and 10 deletions
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15
mindspore/nn/layer/conv.py
View File

@ -68,6 +68,7 @@ class _Conv(Cell):
self.group = check_int_positive(group)
self.has_bias = has_bias
if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
isinstance(kernel_size[0], bool) or isinstance(kernel_size[1], bool) or \
kernel_size[0] < 1 or kernel_size[1] < 1:
raise ValueError("Attr 'kernel_size' of 'Conv2D' Op passed "
+ str(self.kernel_size) + ", should be a int or tuple and equal to or greater than 1.")
@ -76,9 +77,9 @@ class _Conv(Cell):
raise ValueError("Attr 'stride' of 'Conv2D' Op passed "
+ str(self.stride) + ", should be a int or tuple and equal to or greater than 1.")
if (not isinstance(dilation[0], int)) or (not isinstance(dilation[1], int)) or \
dilation[0] < 1 or dilation[1] < 1:
isinstance(dilation[0], bool) or isinstance(dilation[1], bool) or dilation[0] < 1 or dilation[1] < 1:
raise ValueError("Attr 'dilation' of 'Conv2D' Op passed "
+ str(self.dilation) + ", should equal to or greater than 1.")
+ str(self.dilation) + ", should be a int or tuple and equal to or greater than 1.")
if in_channels % group != 0:
raise ValueError("Attr 'in_channels' of 'Conv2D' Op must be divisible by "
"attr 'group' of 'Conv2D' Op.")
@ -845,7 +846,10 @@ class DepthwiseConv2d(Cell):
- pad: Implicit paddings on both sides of the input. The number of `padding` will be padded to the input
Tensor borders. `padding` should be greater than or equal to 0.
padding (int): Implicit paddings on both sides of the input. Default: 0.
padding (Union[int, tuple[int]]): Implicit paddings on both sides of the input. If `padding` is one integer,
the padding of top, bottom, left and right is the same, equal to padding. If `padding` is a tuple
with four integers, the padding of top, bottom, left and right will be equal to padding[0],
padding[1], padding[2], and padding[3] accordingly. Default: 0.
dilation (Union[int, tuple[int]]): The data type is int or a tuple of 2 integers. Specifies the dilation rate
to use for dilated convolution. If set to be :math:`k > 1`, there will
be :math:`k - 1` pixels skipped for each sampling location. Its value should
@ -892,11 +896,14 @@ class DepthwiseConv2d(Cell):
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.pad_mode = pad_mode
self.padding = padding
self.dilation = dilation
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,

23
mindspore/ops/operations/nn_ops.py
View File

@ -983,7 +983,9 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
mode (int): 0 Math convolution, 1 cross-correlation convolution ,
2 deconvolution, 3 depthwise convolution. Default: 3.
pad_mode (str): "valid", "same", "pad" the mode to fill padding. Default: "valid".
pad (int): The pad value to fill. Default: 0.
pad (Union[int, tuple[int]]): The pad value to fill. Default: 0. If `pad` is one integer, the padding of
top, bottom, left and right is same, equal to pad. If `pad` is tuple with four integer, the padding
of top, bottom, left and right equal to pad[0], pad[1], pad[2], pad[3] with corresponding.
stride (Union[int, tuple[int]]): The stride to apply conv filter. Default: 1.
dilation (Union[int, tuple[int]]): Specifies the dilation rate to use for dilated convolution. Default: 1.
group (int): Splits input into groups. Default: 1.
@ -1028,9 +1030,18 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
raise ValueError("The height and width of dilation should be equal,"
f"but got height:{self.dilation[0]}, width:{self.dilation[1]}")
self.add_prim_attr('dilation', (1, 1, self.dilation[0], self.dilation[1]))
validator.check_value_type('pad', pad, (int,), self.name)
validator.check_value_type('pad', pad, (int, tuple), self.name)
if isinstance(pad, int):
pad = (pad,) * 4
else:
validator.check_integer('pad size', len(pad), 4, Rel.EQ, self.name)
self.padding = pad
self.pad_mode = validator.check_string('pad_mode', pad_mode, ['valid', 'same', 'pad'], self.name)
self.pad = validator.check_pad_value_by_mode(pad_mode, pad, self.name)
if pad_mode != 'pad' and pad != (0, 0, 0, 0):
raise ValueError(f"For '{self.name}', padding must be zero when pad_mode is '{pad_mode}'.")
if self.pad_mode == 'pad':
for item in pad:
validator.check_integer('pad item', item, 0, Rel.GE, self.name)
self.mode = validator.check_integer("mode", mode, 3, Rel.EQ, self.name)
self.add_prim_attr('data_format', "NCHW")
self.channel_multiplier = validator.check_integer("channel_multiplier", channel_multiplier, 0, Rel.GT,
@ -1065,11 +1076,11 @@ class DepthwiseConv2dNative(PrimitiveWithInfer):
pad_left = math.floor(pad_needed_w / 2)
pad_right = pad_needed_w - pad_left
elif self.pad_mode == 'pad':
pad_top, pad_bottom, pad_left, pad_right = self.pad, self.pad, self.pad, self.pad
pad_top, pad_bottom, pad_left, pad_right = self.padding
h_out = 1 + (x_shape[2] + 2 * self.pad - kernel_size_h - (kernel_size_h - 1) * (dilation_h - 1)) \
h_out = 1 + (x_shape[2] + pad_top + pad_bottom - kernel_size_h - (kernel_size_h - 1) * (dilation_h - 1)) \
/ stride_h
w_out = 1 + (x_shape[3] + 2 * self.pad - kernel_size_w - (kernel_size_w - 1) * (dilation_w - 1)) \
w_out = 1 + (x_shape[3] + pad_left + pad_right - kernel_size_w - (kernel_size_w - 1) * (dilation_w - 1)) \
/ stride_w
h_out = math.floor(h_out)
w_out = math.floor(w_out)