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!275 fix Conv2d 

Merge pull request !275 from jiangjinsheng/inner_ge_conv1d
This commit is contained in:
mindspore-ci-bot 2020-07-15 17:58:27 +08:00 committed by Gitee
parent 8cc51969f3 8ee091fb92
commit aee14e34bf
2 changed files with 34 additions and 10 deletions
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40
mindspore/nn/layer/conv.py
View File

@ -19,7 +19,7 @@ from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore._checkparam import ParamValidator as validator, Rel
from mindspore._checkparam import Validator
from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative
from mindspore._checkparam import check_bool, twice, check_int_positive
from mindspore._extends import cell_attr_register
from ..cell import Cell
@ -48,7 +48,16 @@ class _Conv(Cell):
self.kernel_size = kernel_size
self.stride = stride
self.pad_mode = pad_mode
self.padding = check_int_non_negative(padding)
if isinstance(padding, int):
Validator.check_integer('padding', padding, 0, Rel.GE, self.cls_name)
self.padding = padding
elif isinstance(padding, tuple):
for pad in padding:
Validator.check_integer('padding item', pad, 0, Rel.GE, self.cls_name)
self.padding = padding
else:
raise TypeError("padding type must be int/tuple(int) cannot be {}!".format(type(padding)))
self.dilation = dilation
self.group = check_int_positive(group)
self.has_bias = has_bias
@ -142,7 +151,10 @@ class Conv2d(_Conv):
- 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 same, equal to padding. If `padding` is tuple with
four integer, the padding of top, bottom, left and right equal to padding[0], padding[1],
padding[2], padding[3] with corresponding. Default: 0.
dilation (Union[int, tuple[int]]): The data type is int or tuple with 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
@ -437,7 +449,10 @@ class Conv2dTranspose(_Conv):
- same: Adopted the way of completion.
- valid: Adopted the way of discarding.
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 same, equal to padding. If `padding` is tuple with
four integer, the padding of top, bottom, left and right equal to padding[0], padding[1],
padding[2], padding[3] with corresponding. Default: 0.
dilation (Union[int, tuple[int]]): The data type is int or tuple with 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
@ -482,6 +497,9 @@ class Conv2dTranspose(_Conv):
kernel_size = twice(kernel_size)
stride = twice(stride)
dilation = twice(dilation)
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)
# 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.
@ -521,12 +539,16 @@ class Conv2dTranspose(_Conv):
dilation=dilation,
group=group)
self.bias_add = P.BiasAdd()
if isinstance(self.padding, int):
self.padding_top, self.padding_bottom, self.padding_left, self.padding_right = (self.padding,) * 4
else:
self.padding_top, self.padding_bottom, self.padding_left, self.padding_right = self.padding
def set_strategy(self, strategy):
self.conv2d_transpose.set_strategy(strategy)
return self
def _deconv_output_length(self, input_length, filter_size, stride_size, dilation_size):
def _deconv_output_length(self, input_length, filter_size, stride_size, dilation_size, padding):
"""Calculate the width and height of output."""
length = 0
filter_size = filter_size + (filter_size - 1) * (dilation_size - 1)
@ -538,14 +560,16 @@ class Conv2dTranspose(_Conv):
elif self.is_same:
length = input_length * stride_size
elif self.is_pad:
length = input_length * stride_size - 2 * self.padding + filter_size - stride_size
length = input_length * stride_size - padding + filter_size - stride_size
return length
def construct(self, x):
n, _, h, w = self.shape(x)
h_out = self._deconv_output_length(h, self.kernel_size[0], self.stride[0], self.dilation[0])
w_out = self._deconv_output_length(w, self.kernel_size[1], self.stride[1], self.dilation[1])
h_out = self._deconv_output_length(h, self.kernel_size[0], self.stride[0], self.dilation[0],
self.padding_top + self.padding_bottom)
w_out = self._deconv_output_length(w, self.kernel_size[1], self.stride[1], self.dilation[1],
self.padding_left + self.padding_right)
if self.has_bias:
return self.bias_add(self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out)),
self.bias)

4
mindspore/ops/operations/nn_ops.py
View File

@ -1330,7 +1330,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
validator.check_value_type('pad', pad, (int, tuple), self.name)
if isinstance(pad, int):
pad = (pad,) * 4
self.pad = pad
self.padding = pad
else:
validator.check_integer('pad size', len(pad), 4, Rel.EQ, self.name)
@ -1382,7 +1382,7 @@ class Conv2DBackpropInput(PrimitiveWithInfer):
pad_right = pad_needed_w - pad_left
pad_list = (pad_top, pad_bottom, pad_left, pad_right)
elif self.pad_mode == 'PAD':
pad_list = self.pad
pad_list = self.padding
self.add_prim_attr('pad_list', pad_list)
out = {
'value': None,