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Modified pad validator of conv3d.

This commit is contained in:
liuxiao93 2021-03-30 19:07:01 +08:00
parent 0a870440e0
commit 57a75bb455
6 changed files with 39 additions and 45 deletions
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4
mindspore/_checkparam.py
View File

@ -100,7 +100,7 @@ def _check_3d_int_or_tuple(arg_name, arg_value, prim_name, allow_five=False, ret
def _raise_message(third_one_flag=False, three_input_flag=False):
if third_one_flag:
raise ValueError(f"For '{prim_name}' the depth of attr '{arg_name}' should be 1, but got {arg_value[-3]}")
raise ValueError(f"For '{prim_name}' the depth of attr '{arg_name}' should be 1, but got {ret_value[-3]}")
if three_input_flag:
raise ValueError(f"For '{prim_name}' attr '{arg_name}' should be an positive int number or a tuple of "
f"three positive int numbers, but got {arg_value}")
@ -110,8 +110,6 @@ def _check_3d_int_or_tuple(arg_name, arg_value, prim_name, allow_five=False, ret
def _get_return_value():
if isinstance(arg_value, int):
ret = (1, 1, arg_value, arg_value, arg_value) if ret_five else (arg_value, arg_value, arg_value)
if third_one:
ret = (1, 1, 1, arg_value, arg_value) if ret_five else (1, arg_value, arg_value)
elif len(arg_value) == 3:
ret = (1, 1, arg_value[0], arg_value[1], arg_value[2]) if ret_five else arg_value
elif len(arg_value) == 5:

6
mindspore/ccsrc/backend/optimizer/ascend/enhancer/split_n_optimizer.cc
View File

@ -24,7 +24,8 @@
#include "base/core_ops.h"
#include "backend/optimizer/common/helper.h"
namespace mindspore::opt {
namespace mindspore {
namespace opt {
namespace {
using KernelWithIndex = std::pair<AnfNodePtr, size_t>;
const std::set<std::string> InvalidOps = {kSplitOpName, kSplitVOpName, kConcatOpName};
@ -211,4 +212,5 @@ const AnfNodePtr SplitOpOptimizer::Process(const FuncGraphPtr &func_graph, const
}
return nullptr;
}
} // namespace mindspore::opt
} // namespace opt
} // namespace mindspore

1
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/dynamic_gru_v2_grad_fission.cc
View File

@ -23,7 +23,6 @@
namespace mindspore {
namespace opt {
namespace {
constexpr size_t kDynamicGRUV2GradInputNum = 12;
constexpr size_t kDynamicGRUV2GradOutputNum = 6;

6
mindspore/ccsrc/backend/optimizer/ascend/ir_fission/max_pool3d_grad_grad_fission.cc
View File

@ -21,7 +21,8 @@
#include "frontend/optimizer/opt.h"
#include "backend/optimizer/common/helper.h"
namespace mindspore::opt {
namespace mindspore {
namespace opt {
constexpr size_t kInputNum = 3;
constexpr size_t kFloat16Len = 2; // size of float16;
namespace {
@ -117,4 +118,5 @@ const AnfNodePtr MaxPool3DGradGradFission::Process(const FuncGraphPtr &graph, co
}
return new_cnode;
}
} // namespace mindspore::opt
} // namespace opt
} // namespace mindspore

16
mindspore/nn/layer/conv.py
View File

@ -21,7 +21,7 @@ from mindspore.ops.primitive import constexpr
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator, Rel, twice, triple
from mindspore._checkparam import Validator, Rel, twice, _check_3d_int_or_tuple
from mindspore._extends import cell_attr_register
from ..cell import Cell
@ -594,9 +594,9 @@ class Conv3d(_Conv):
weight_init='normal',
bias_init='zeros',
data_format='NCDHW'):
kernel_size = triple(kernel_size)
stride = triple(stride)
dilation = triple(dilation)
kernel_size = _check_3d_int_or_tuple("kernel_size", kernel_size, self.cls_name)
stride = _check_3d_int_or_tuple("stride", stride, self.cls_name)
dilation = _check_3d_int_or_tuple("dilation", dilation, self.cls_name)
Validator.check_value_type('padding', padding, (int, tuple), self.cls_name)
if isinstance(padding, tuple):
Validator.check_equal_int(len(padding), 6, 'padding size', self.cls_name)
@ -765,13 +765,13 @@ class Conv3dTranspose(_Conv):
weight_init='normal',
bias_init='zeros',
data_format='NCDHW'):
kernel_size = triple(kernel_size)
stride = triple(stride)
dilation = triple(dilation)
kernel_size = _check_3d_int_or_tuple("kernel_size", kernel_size, self.cls_name)
stride = _check_3d_int_or_tuple("stride", stride, self.cls_name)
dilation = _check_3d_int_or_tuple("dilation", dilation, self.cls_name)
Validator.check_value_type('padding', padding, (int, tuple), self.cls_name)
if isinstance(padding, tuple):
Validator.check_equal_int(len(padding), 6, 'padding size', self.cls_name)
output_padding = triple(output_padding)
output_padding = _check_3d_int_or_tuple("output_padding", output_padding, self.cls_name, greater_zero=False)
super(Conv3dTranspose, self).__init__(
in_channels,
out_channels,

51
mindspore/ops/operations/nn_ops.py
View File

@ -7759,8 +7759,8 @@ class Conv3D(PrimitiveWithInfer):
Inputs:
- **input** (Tensor) - Tensor of shape :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`.
Currently input data type only support float16 and float32.
- **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is
:math:`(C_{out}, C_{in}, D_{in}, K_h, K_w)`. Currently weight data type only support float16 and float32.
- **weight** (Tensor) - Set size of kernel is :math:`(k_d, K_h, K_w)`, then the shape is
:math:`(C_{out}, C_{in}//groups, k_d, K_h, K_w)`. Currently weight data type only support float16 and float32.
- **bias** (Tensor) - Tensor of shape :math:`C_{in}`. Currently, only support none.
Outputs:
@ -7815,18 +7815,7 @@ class Conv3D(PrimitiveWithInfer):
f"six positive int numbers, but got `{len(pad)}`.")
self.add_prim_attr("pad", pad)
self.padding = pad
validator.check_int_range(self.padding[0], 0, self.kernel_size[0], Rel.INC_LEFT,
'pad_d belonging [0, kernel_size_d)', self.name)
validator.check_int_range(self.padding[1], 0, self.kernel_size[0], Rel.INC_LEFT,
'pad_d belonging [0, kernel_size_d)', self.name)
validator.check_int_range(self.padding[2], 0, self.kernel_size[1], Rel.INC_LEFT,
'pad_h belonging [0, kernel_size_h)', self.name)
validator.check_int_range(self.padding[3], 0, self.kernel_size[1], Rel.INC_LEFT,
'pad_h belonging [0, kernel_size_h)', self.name)
validator.check_int_range(self.padding[4], 0, self.kernel_size[2], Rel.INC_LEFT,
'pad_w belonging [0, kernel_size_w)', self.name)
validator.check_int_range(self.padding[5], 0, self.kernel_size[2], Rel.INC_LEFT,
'pad_w belonging [0, kernel_size_w)', self.name)
validator.check_value_type('pad_mode', pad_mode, [str], self.name)
self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name)
self.add_prim_attr('pad_mode', self.pad_mode)
@ -7902,6 +7891,21 @@ class Conv3D(PrimitiveWithInfer):
w_out = math.floor(w_out)
self.pad_list = [pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right]
filter_d = (self.kernel_size[0] - 1) * dilation_d + 1
filter_h = (self.kernel_size[1] - 1) * dilation_h + 1
filter_w = (self.kernel_size[2] - 1) * dilation_w + 1
validator.check_int_range(self.pad_list[0], 0, filter_d, Rel.INC_LEFT,
'pad_d belonging [0, filter_d)', self.name)
validator.check_int_range(self.pad_list[1], 0, filter_d, Rel.INC_LEFT,
'pad_d belonging [0, filter_d)', self.name)
validator.check_int_range(self.pad_list[2], 0, filter_h, Rel.INC_LEFT,
'pad_h belonging [0, filter_h)', self.name)
validator.check_int_range(self.pad_list[3], 0, filter_h, Rel.INC_LEFT,
'pad_h belonging [0, filter_h)', self.name)
validator.check_int_range(self.pad_list[4], 0, filter_w, Rel.INC_LEFT,
'pad_w belonging [0, filter_w)', self.name)
validator.check_int_range(self.pad_list[5], 0, filter_w, Rel.INC_LEFT,
'pad_w belonging [0, filter_w)', self.name)
self.add_prim_attr('pad_list', (pad_head, pad_tail, pad_top, pad_bottom, pad_left, pad_right))
out_channel = self.out_channel
out_shape = [x_shape[0], out_channel, d_out, h_out, w_out]
@ -8124,8 +8128,8 @@ class Conv3DTranspose(PrimitiveWithInfer):
- **dout** (Tensor) - the gradients w.r.t the output of the convolution. The shape conforms to the default
data_format :math:`(N, C_{in}, D_{out}, H_{out}, W_{out})`. Currently dout data type only support float16
and float32.
- **weight** (Tensor) - Set size of kernel is :math:`(D_in, K_h, K_w)`, then the shape is
:math:`(C_{in}//groups, C_{out}, D_{in}, K_h, K_w)`. Currently weight data type only support float16
- **weight** (Tensor) - Set size of kernel is :math:`(k_d, K_h, K_w)`, then the shape is
:math:`(C_{in}, C_{out}//groups, k_d, K_h, K_w)`. Currently weight data type only support float16
and float32.
- **bias** (Tensor) - Tensor of shape :math:`C_{out}`. Currently, only support none.
@ -8189,6 +8193,7 @@ class Conv3DTranspose(PrimitiveWithInfer):
raise ValueError(f"For `conv3d` attr 'pad' should be an positive int number or a tuple of "
f"six positive int numbers, but got `{len(pad)}`.")
self.pad_list = pad
validator.check_value_type('pad_mode', pad_mode, [str], self.name)
self.pad_mode = validator.check_string(pad_mode.lower(), ['valid', 'same', 'pad'], 'pad_mode', self.name)
self.add_prim_attr('pad_mode', self.pad_mode)
@ -8198,21 +8203,9 @@ class Conv3DTranspose(PrimitiveWithInfer):
if self.pad_mode == 'pad':
for item in self.pad_list:
validator.check_non_negative_int(item, 'pad item', self.name)
validator.check_int_range(self.pad_list[0], 0, self.kernel_size[0], Rel.INC_LEFT,
'pad_d belonging [0, kernel_size_d)', self.name)
validator.check_int_range(self.pad_list[1], 0, self.kernel_size[0], Rel.INC_LEFT,
'pad_d belonging [0, kernel_size_d)', self.name)
validator.check_int_range(self.pad_list[2], 0, self.kernel_size[1], Rel.INC_LEFT,
'pad_h belonging [0, kernel_size_h)', self.name)
validator.check_int_range(self.pad_list[3], 0, self.kernel_size[1], Rel.INC_LEFT,
'pad_h belonging [0, kernel_size_h)', self.name)
validator.check_int_range(self.pad_list[4], 0, self.kernel_size[2], Rel.INC_LEFT,
'pad_w belonging [0, kernel_size_w)', self.name)
validator.check_int_range(self.pad_list[5], 0, self.kernel_size[2], Rel.INC_LEFT,
'pad_w belonging [0, kernel_size_w)', self.name)
self.mode = validator.check_equal_int(mode, 1, 'mode', self.name)
self.add_prim_attr('mode', self.mode)
self.mode = validator.check_equal_int(group, 1, 'group', self.name)
self.group = validator.check_equal_int(group, 1, 'group', self.name)
self.add_prim_attr('groups', self.group)
self.format = validator.check_string(data_format, ['NCDHW'], 'format', self.name)
self.add_prim_attr('data_format', self.format)