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mindspore
提交 2604aced 编写于 作者: W wangnan39@huawei.com
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extend conv stride and dilation to 2d

上级 268d358a
展开全部 隐藏空白更改
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Showing with 362 addition and 290 deletion
mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_cpu_kernel.cc
浏览文件 @ 2604aced
......@@ -35,8 +35,22 @@ void Conv2dCPUKernel::InitKernel(const CNodePtr &kernel_node) {
dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
int kernel_size = SizeToInt(weight_shape[3]);
int stride = AnfAlgo::GetNodeAttr<int>(kernel_node, STRIDE);
int dilation = AnfAlgo::GetNodeAttr<int>(kernel_node, DILATION);
auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, STRIDE);
auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, DILATION);
if (stride_ori.size() != 4 || stride_ori[2] != stride_ori[3]) {
MS_LOG(EXCEPTION) << "conv2d only support equal stride, and stride must be 4d!";
}
if (stride_ori[0] != 1 || stride_ori[1] != 1) {
MS_LOG(EXCEPTION) << "conv2d stride only support 1 in N axis and C axis!";
}
if (dilation_ori.size() != 4 || dilation_ori[2] != 1 || dilation_ori[3] != 1) {
MS_LOG(EXCEPTION) << "conv2d dilation only support 1, and dilation must be 4d!";
}
if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
MS_LOG(EXCEPTION) << "conv2d dilation only support 1 in N axis and C axis!";
}
int stride = stride_ori[2];
int dilation = dilation_ori[2];
dnnl::memory::dims strides{stride, stride};
dnnl::memory::dims dilates{dilation - 1, dilation - 1};
......
mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_filter_cpu_kernel.cc
浏览文件 @ 2604aced
......@@ -35,8 +35,19 @@ void Conv2dGradFilterCPUKernel::InitKernel(const CNodePtr &kernel_node) {
dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
int kernel_size = SizeToInt(weight_shape[3]);
int stride = AnfAlgo::GetNodeAttr<int>(kernel_node, STRIDE);
int dilation = AnfAlgo::GetNodeAttr<int>(kernel_node, DILATION);
auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, STRIDE);
auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, DILATION);
if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
MS_LOG(EXCEPTION) << "Conv2dGradFilterCPUKernel only support equal stride, and stride must be 2d!";
}
if (dilation_ori.size() != 4 || dilation_ori[2] != 1 || dilation_ori[3] != 1) {
MS_LOG(EXCEPTION) << "Conv2dGradFilterCPUKernel dilation only support 1, and dilation must be 4d!";
}
if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
MS_LOG(EXCEPTION) << "Conv2dGradFilterCPUKernel dilation only support 1 in N axis and C axis!";
}
int stride = stride_ori[0];
int dilation = dilation_ori[2];
dnnl::memory::dims strides{stride, stride};
dnnl::memory::dims dilates{dilation - 1, dilation - 1};
......
mindspore/ccsrc/device/cpu/kernel/mkldnn/conv2d_grad_input_cpu_kernel.cc
浏览文件 @ 2604aced
......@@ -35,8 +35,19 @@ void Conv2dGradInputCPUKernel::InitKernel(const CNodePtr &kernel_node) {
dnnl::memory::desc dst_desc = GetDefaultMemDesc(dst_shape);
int kernel_size = SizeToInt(weight_shape[3]);
int stride = AnfAlgo::GetNodeAttr<int>(kernel_node, STRIDE);
int dilation = AnfAlgo::GetNodeAttr<int>(kernel_node, DILATION);
auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, STRIDE);
auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, DILATION);
if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
MS_LOG(EXCEPTION) << "Conv2dGradInputCPUKernel only support equal stride, and stride must be 2d!";
}
if (dilation_ori.size() != 4 || dilation_ori[2] != 1 || dilation_ori[3] != 1) {
MS_LOG(EXCEPTION) << "Conv2dGradInputCPUKernel dilation only support 1, and dilation must be 4d!";
}
if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
MS_LOG(EXCEPTION) << "Conv2dGradInputCPUKernel dilation only support 1 in N axis and C axis!";
}
int stride = stride_ori[0];
int dilation = dilation_ori[2];
dnnl::memory::dims strides{stride, stride};
dnnl::memory::dims dilates{dilation - 1, dilation - 1};
std::vector<int> int_padding_l;
......
mindspore/ccsrc/kernel/gpu/nn/conv2d_gpu_kernel.h
浏览文件 @ 2604aced
......@@ -113,9 +113,24 @@ class Conv2dGpuFwdKernel : public GpuKernel {
CHECK_CUDNN_RET_WITH_EXCEPT(cudnnSetConvolutionGroupCount(conv_desc_, group_), "cudnnSetConvGroupCount failed");
pad_height_ = GetAttr<int>(kernel_node, "pad");
pad_width_ = pad_height_;
stride_ = GetAttr<int>(kernel_node, "stride");
dilation_ = GetAttr<int>(kernel_node, "dilation");
pad_mode_ = GetAttr<std::string>(kernel_node, "pad_mode");
auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "stride");
auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "dilation");
if (stride_ori.size() != 4 || stride_ori[2] != stride_ori[3]) {
MS_LOG(EXCEPTION) << "conv2d only support equal stride, and stride must be 4d!";
}
if (stride_ori[0] != 1 || stride_ori[1] != 1) {
MS_LOG(EXCEPTION) << "conv2d stride only support 1 in N axis and C axis!";
}
if (dilation_ori.size() != 4 || dilation_ori[2] != dilation_ori[3]) {
MS_LOG(EXCEPTION) << "conv2d only support equal dilation, and dilation must be 4d!";
}
if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
MS_LOG(EXCEPTION) << "conv2d dilation only support 1 in N axis and C axis!";
}
stride_ = stride_ori[2];
dilation_ = dilation_ori[2];
cudnnTensorDescriptor_t input_descriptor_real = nullptr;
if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
SetPad(in_shape, kernel_node);
......
mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_filter_gpu_kernel.h
浏览文件 @ 2604aced
......@@ -116,9 +116,20 @@ class ConvGradFilterGpuBkwKernel : public GpuKernel {
pad_height_ = GetAttr<int>(kernel_node, "pad");
pad_width_ = pad_height_;
stride_ = GetAttr<int>(kernel_node, "stride");
dilation_ = GetAttr<int>(kernel_node, "dilation");
pad_mode_ = GetAttr<std::string>(kernel_node, "pad_mode");
auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "stride");
auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "dilation");
if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
MS_LOG(EXCEPTION) << "ConvGradFilterGpuBkwKernel only support equal stride, and stride must be 2d!";
}
if (dilation_ori.size() != 4 || dilation_ori[2] != dilation_ori[3]) {
MS_LOG(EXCEPTION) << "ConvGradFilterGpuBkwKernel only support equal dilation, and dilation must be 4d!";
}
if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
MS_LOG(EXCEPTION) << "ConvGradFilterGpuBkwKernel dilation only support 1 in N axis and C axis!";
}
stride_ = stride_ori[0];
dilation_ = dilation_ori[2];
cudnnTensorDescriptor_t x_desc_real = nullptr;
if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
SetPad(in_shape, kernel_node);
......
mindspore/ccsrc/kernel/gpu/nn/conv2d_grad_input_gpu_kernel.h
浏览文件 @ 2604aced
......@@ -117,9 +117,20 @@ class ConvGradInputGpuBkwKernel : public GpuKernel {
pad_height_ = GetAttr<int>(kernel_node, "pad");
pad_width_ = pad_height_;
stride_ = GetAttr<int>(kernel_node, "stride");
dilation_ = GetAttr<int>(kernel_node, "dilation");
pad_mode_ = GetAttr<std::string>(kernel_node, "pad_mode");
auto stride_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "stride");
auto dilation_ori = AnfAlgo::GetNodeAttr<std::vector<int>>(kernel_node, "dilation");
if (stride_ori.size() != 2 || stride_ori[0] != stride_ori[1]) {
MS_LOG(EXCEPTION) << "ConvGradInputGpuBkwKernel only support equal stride, and stride must be 2d!";
}
if (dilation_ori.size() != 4 || dilation_ori[2] != dilation_ori[3]) {
MS_LOG(EXCEPTION) << "ConvGradInputGpuBkwKernel only support equal dilation, and dilation must be 4d!";
}
if (dilation_ori[0] != 1 || dilation_ori[1] != 1) {
MS_LOG(EXCEPTION) << "ConvGradInputGpuBkwKernel dilation only support 1 in N axis and C axis!";
}
stride_ = stride_ori[0];
dilation_ = dilation_ori[2];
cudnnTensorDescriptor_t dx_desc_real = nullptr;
if (pad_mode_ == kSamePadModeUpperCase || pad_mode_ == kSamePadModeLowerCase) {
SetPad(input_shape, kernel_node);
......
mindspore/ccsrc/kernel/tbe/tbe_adapter.cc
浏览文件 @ 2604aced
......@@ -148,9 +148,6 @@ void TbeAdapter::InputOrderPass(const std::string &op_name, std::vector<std::vec
}
std::map<std::string, FAttrsPass> TbeAdapter::build_json_attr_pass_map_ = {
{"Conv2D", TbeAdapter::Conv2DAttrJsonPass},
{"Conv2DBackpropFilter", TbeAdapter::Conv2DBackpropFilterAttrJsonPass},
{"Conv2DBackpropInput", TbeAdapter::Conv2DBackpropInputAttrJsonPass},
{"MaximumGrad", TbeAdapter::MaximumGradAttrJsonPass},
{"MinimumGrad", TbeAdapter::MinimumGradAttrJsonPass},
{"Cast", TbeAdapter::CastAttrJsonPass}};
......@@ -168,135 +165,6 @@ bool TbeAdapter::RunAttrPass(const mindspore::AnfNodePtr &anf_node,
return false;
}
void TbeAdapter::Conv2DAttrJsonPass(const mindspore::AnfNodePtr &anf_node,
const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
nlohmann::json *attrs_json) {
MS_EXCEPTION_IF_NULL(anf_node);
MS_EXCEPTION_IF_NULL(attrs_json);
auto attr_num = op_info_attrs.size();
auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
MS_EXCEPTION_IF_NULL(primitive);
for (size_t i = 0; i < attr_num; i++) {
nlohmann::json attr_obj;
MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
std::string attr_name = op_info_attrs[i]->name();
std::vector<int> attr_value;
if (primitive->GetAttr(attr_name) != nullptr) {
auto value = primitive->GetAttr(attr_name);
int data = GetValue<int>(value);
size_t list_int_size = 0;
if (attr_name == "stride") {
list_int_size = 4;
} else if (attr_name == "dilation") {
list_int_size = 4;
} else if (attr_name == "pad") {
value = primitive->GetAttr("pad_list");
attr_value = GetValue<std::vector<int>>(value);
}
for (size_t j = 0; j < list_int_size; j++) {
attr_value.push_back(data);
}
attr_obj["value"] = attr_value;
} else {
attr_obj["value"] = 0;
}
attr_obj["name"] = attr_name;
attr_obj["valid"] = true;
(*attrs_json).push_back(attr_obj);
}
MS_LOG(INFO) << "Conv2DAttrPass done.";
}
void TbeAdapter::Conv2DBackpropFilterAttrJsonPass(
const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
nlohmann::json *attrs_json) {
MS_EXCEPTION_IF_NULL(anf_node);
MS_EXCEPTION_IF_NULL(attrs_json);
auto attr_num = op_info_attrs.size();
auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
MS_EXCEPTION_IF_NULL(primitive);
for (size_t i = 0; i < attr_num; i++) {
nlohmann::json attr_obj;
MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
std::string attr_name = op_info_attrs[i]->name();
if (primitive->GetAttr(attr_name) != nullptr) {
auto value = primitive->GetAttr(attr_name);
if (attr_name == "pad_mode") {
std::string attr_value = GetValue<std::string>(value);
(void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper);
attr_obj["value"] = attr_value;
} else if (attr_name == "filter_sizes") {
std::vector<int> attr_value = GetValue<std::vector<int>>(value);
attr_obj["value"] = attr_value;
} else {
std::vector<int> attr_value;
int data = GetValue<int>(value);
size_t list_int_size = 0;
if (attr_name == "stride") {
list_int_size = 2;
} else if (attr_name == "dilation") {
list_int_size = 4;
}
for (size_t j = 0; j < list_int_size; j++) {
attr_value.push_back(data);
}
attr_obj["value"] = attr_value;
}
attr_obj["valid"] = true;
} else {
attr_obj["valid"] = false;
}
attr_obj["name"] = attr_name;
attrs_json->push_back(attr_obj);
}
MS_LOG(INFO) << "Conv2DBackpropFilterAttrJsonPass done.";
}
void TbeAdapter::Conv2DBackpropInputAttrJsonPass(
const mindspore::AnfNodePtr &anf_node, const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
nlohmann::json *attrs_json) {
MS_EXCEPTION_IF_NULL(anf_node);
MS_EXCEPTION_IF_NULL(attrs_json);
auto attr_num = op_info_attrs.size();
auto primitive = AnfAlgo::GetCNodePrimitive(anf_node);
MS_EXCEPTION_IF_NULL(primitive);
for (size_t i = 0; i < attr_num; i++) {
nlohmann::json attr_obj;
MS_EXCEPTION_IF_NULL(op_info_attrs[i]);
std::string attr_name = op_info_attrs[i]->name();
if (primitive->GetAttr(attr_name) != nullptr) {
auto value = primitive->GetAttr(attr_name);
if (attr_name == "pad_mode") {
std::string attr_value = GetValue<std::string>(value);
(void)transform(attr_value.begin(), attr_value.end(), attr_value.begin(), ::toupper);
attr_obj["value"] = attr_value;
} else if (attr_name == "input_sizes") {
std::vector<int> attr_value = GetValue<std::vector<int>>(value);
attr_obj["value"] = attr_value;
} else {
std::vector<int> attr_value;
int data = GetValue<int>(value);
size_t list_int_size = 0;
if (attr_name == "stride") {
list_int_size = 2;
} else if (attr_name == "dilation") {
list_int_size = 4;
}
for (size_t j = 0; j < list_int_size; j++) {
attr_value.push_back(data);
}
attr_obj["value"] = attr_value;
}
attr_obj["valid"] = true;
} else {
attr_obj["valid"] = false;
}
attr_obj["name"] = attr_name;
attrs_json->push_back(attr_obj);
}
MS_LOG(INFO) << "Conv2DBackpropInputAttrJsonPass done.";
}
void TbeAdapter::MaximumGradAttrJsonPass(const mindspore::AnfNodePtr &anf_node,
const std::vector<std::shared_ptr<mindspore::kernel::OpAttr>> &op_info_attrs,
nlohmann::json *attrs_json) {
......
mindspore/ccsrc/onnx/onnx_exporter.cc
浏览文件 @ 2604aced
......@@ -179,7 +179,7 @@ OPERATOR_ONNX_CONVERT_DEFINE(Squeeze, Squeeze,
OPERATOR_ONNX_CONVERT_DEFINE(
Conv2D, Conv,
OpNameInfo()
.Attr("dilation", "dilations", onnx::AttributeProto_AttributeType_INTS, SetAttrValueToProto<Int32Imm, 2>)
.Attr("dilation", "dilations", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>)
.Attr("group", "group", onnx::AttributeProto_AttributeType_INT, SetAttrValueToProto<Int32Imm>)
.Attr("kernel_size", "kernel_shape", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto)
.Attr("pad_mode", "auto_pad", onnx::AttributeProto_AttributeType_STRING,
......@@ -197,8 +197,7 @@ OPERATOR_ONNX_CONVERT_DEFINE(
prim);
}
})
.Attr("stride", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrValueToProto<Int32Imm, 2>))
.Attr("stride", "strides", onnx::AttributeProto_AttributeType_INTS, SetAttrTupleValueToProto<2>))
OPERATOR_ONNX_CONVERT_DEFINE(BiasAdd, Add, OpNameInfo())
OPERATOR_ONNX_CONVERT_DEFINE(MatMul, Gemm,
OpNameInfo()
......
mindspore/ccsrc/transform/op_declare.cc
浏览文件 @ 2604aced
......@@ -754,9 +754,9 @@ OUTPUT_MAP(MaxPoolGradWithArgmax) = {{0, OUTPUT_DESC(y)}};
// Conv2D
INPUT_MAP(Conv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
ATTR_MAP(Conv2D) = {
{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
};
OUTPUT_MAP(Conv2D) = {{0, OUTPUT_DESC(y)}};
......@@ -766,8 +766,8 @@ INPUT_ATTR_MAP(Conv2DBackpropInputD) = {
{3, ATTR_DESC(input_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
ATTR_MAP(Conv2DBackpropInputD) = {
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, "strides", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
};
OUTPUT_MAP(Conv2DBackpropInputD) = {{0, OUTPUT_DESC(y)}};
......@@ -777,17 +777,17 @@ INPUT_ATTR_MAP(Conv2DBackpropFilterD) = {
{3, ATTR_DESC(filter_sizes, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
ATTR_MAP(Conv2DBackpropFilterD) = {
{"pad_list", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, "strides", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
};
OUTPUT_MAP(Conv2DBackpropFilterD) = {{0, OUTPUT_DESC(y)}};
// DepthwiseConv2D
INPUT_MAP(DepthwiseConv2D) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(filter)}};
ATTR_MAP(DepthwiseConv2D) = {
{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
};
OUTPUT_MAP(DepthwiseConv2D) = {{0, OUTPUT_DESC(y)}};
......@@ -797,9 +797,9 @@ INPUT_MAP(DepthwiseConv2DBackpropInputD) = {{2, INPUT_DESC(filter)}, {3, INPUT_D
INPUT_ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
{1, ATTR_DESC(input_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
ATTR_MAP(DepthwiseConv2DBackpropInputD) = {
{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
};
OUTPUT_MAP(DepthwiseConv2DBackpropInputD) = {{0, OUTPUT_DESC(input_grad)}};
......@@ -808,9 +808,9 @@ INPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{1, INPUT_DESC(input)}, {3, INPUT_D
INPUT_ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
{2, ATTR_DESC(filter_size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
ATTR_MAP(DepthwiseConv2DBackpropFilterD) = {
{"stride", ATTR_DESC(strides, "pad", AnyTraits<std::vector<int64_t>>())},
{"stride", ATTR_DESC(strides, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"pads", ATTR_DESC(pads, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, "pad", AnyTraits<std::vector<int64_t>>())},
{"dilation", ATTR_DESC(dilations, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
};
OUTPUT_MAP(DepthwiseConv2DBackpropFilterD) = {{0, OUTPUT_DESC(filter_grad)}};
......
mindspore/nn/layer/conv.py
浏览文件 @ 2604aced
......@@ -17,7 +17,7 @@ from mindspore import log as logger
from mindspore.ops import operations as P
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer
from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative, check_int
from mindspore._checkparam import check_bool, twice, check_int_positive, check_int_non_negative
from mindspore._extends import cell_attr_register
from ..cell import Cell
......@@ -42,17 +42,23 @@ class _Conv(Cell):
self.in_channels = check_int_positive(in_channels)
self.out_channels = check_int_positive(out_channels)
self.kernel_size = kernel_size
self.stride = check_int_positive(stride)
self.stride = stride
self.pad_mode = pad_mode
self.padding = check_int_non_negative(padding)
self.dilation = check_int(dilation)
self.dilation = dilation
self.group = check_int_positive(group)
self.has_bias = has_bias
if (not isinstance(kernel_size, tuple)) or len(kernel_size) != 2 or \
(not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) or \
kernel_size[0] < 1 or kernel_size[1] < 1:
if (not isinstance(kernel_size[0], int)) or (not isinstance(kernel_size[1], int)) 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.")
if (not isinstance(stride[0], int)) or (not isinstance(stride[1], int)) or stride[0] < 1 or stride[1] < 1:
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:
raise ValueError("Attr 'dilation' of 'Conv2D' Op passed "
+ str(self.dilation) + ", should 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.")
......@@ -107,12 +113,13 @@ class Conv2d(_Conv):
Args:
in_channels (int): The number of input channel :math:`C_{in}`.
out_channels (int): The number of output channel :math:`C_{out}`.
kernel_size (Union[int, tuple]): The data type is int or tuple with 2 integers. Specifies the height
kernel_size (Union[int, tuple[int]]): The data type is int or tuple with 2 integers. Specifies the height
and width of the 2D convolution window. Single int means the value if for both height and width of
the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
width of the kernel.
stride (int): Specifies stride for all spatial dimensions with the same value. Value of stride should be
greater or equal to 1 but bounded by the height and width of the input. Default: 1.
stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
the height and width of movement are both strides, or a tuple of two int numbers that
represent height and width of movement respectively. Default: 1.
pad_mode (str): Specifies padding mode. The optional values are
"same", "valid", "pad". Default: "same".
......@@ -130,9 +137,11 @@ class Conv2d(_Conv):
Tensor borders. `padding` should be greater than or equal to 0.
padding (int): Implicit paddings on both sides of the input. Default: 0.
dilation (int): Specifying 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 be greater
or equal to 1 and bounded by the height and width of the input. Default: 1.
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
be greater or equal to 1 and bounded by the height and width of the
input. Default: 1.
group (int): Split filter into groups, `in_ channels` and `out_channels` should be
divisible by the number of groups. Default: 1.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
......@@ -172,6 +181,8 @@ class Conv2d(_Conv):
weight_init='normal',
bias_init='zeros'):
kernel_size = twice(kernel_size)
stride = twice(stride)
dilation = twice(dilation)
super(Conv2d, self).__init__(
in_channels,
out_channels,
......@@ -241,7 +252,9 @@ class Conv2dTranspose(_Conv):
and width of the 2D convolution window. Single int means the value is for both height and width of
the kernel. A tuple of 2 ints means the first value is for the height and the other is for the
width of the kernel.
stride (int): Specifies the same value for all spatial dimensions. Default: 1.
stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
the height and width of movement are both strides, or a tuple of two int numbers that
represent height and width of movement respectively. Default: 1.
pad_mode (str): Select the mode of the pad. The optional values are
"pad", "same", "valid". Default: "same".
......@@ -251,8 +264,11 @@ class Conv2dTranspose(_Conv):
- valid: Adopted the way of discarding.
padding (int): Implicit paddings on both sides of the input. Default: 0.
dilation (int): Specifies the dilation rate to use for dilated
convolution. Default: 1.
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
be greater or equal to 1 and bounded by the height and width of the
input. Default: 1.
group (int): Split filter into groups, `in_channels` and `out_channels` should be
divisible by the number of groups. Default: 1.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
......@@ -290,6 +306,8 @@ class Conv2dTranspose(_Conv):
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.
......@@ -333,26 +351,26 @@ class Conv2dTranspose(_Conv):
self.conv2d_transpose.set_strategy(strategy)
return self
def _deconv_output_length(self, input_length, filter_size):
def _deconv_output_length(self, input_length, filter_size, stride_size, dilation_size):
"""Calculate the width and height of output."""
length = 0
if self.is_valid:
if filter_size - self.stride > 0:
length = input_length * self.stride + filter_size - self.stride
if filter_size - stride_size > 0:
length = input_length * stride_size + filter_size - stride_size
else:
length = input_length * self.stride
length = input_length * stride_size
elif self.is_same:
length = input_length * self.stride
length = input_length * stride_size
elif self.is_pad:
length = input_length * self.stride - 2 * self.padding + filter_size + \
(filter_size - 1) * (self.dilation - 1) - self.stride
length = input_length * stride_size - 2 * self.padding + filter_size + \
(filter_size - 1) * (dilation_size - 1) - 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])
w_out = self._deconv_output_length(w, self.kernel_size[1])
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])
if self.has_bias:
return self.bias_add(self.conv2d_transpose(x, self.weight, (n, self.out_channels, h_out, w_out)),
self.bias)
......
mindspore/ops/_op_impl/tbe/conv2d.py
浏览文件 @ 2604aced
......@@ -34,7 +34,7 @@ from mindspore.ops.op_info_register import op_info_register
"value": "all"
},
{
"name": "pad",
"name": "pad_list",
"param_type": "required",
"type": "listInt",
"value": "all"
......
mindspore/ops/operations/_grad_ops.py
浏览文件 @ 2604aced
......@@ -119,8 +119,8 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
pad (int): The pad value to fill. Default: 0.
mode (int): 0 Math convolutiuon, 1 cross-correlation convolution ,
2 deconvolution, 3 depthwise convolution. Default: 1.
stride (int): The stride to apply conv filter. Default: 1.
dilation (int): Specifies the dilation rate to use for dilated convolution. Default: 1.
stride (tuple): The stride to apply conv filter. Default: (1, 1).
dilation (tuple): Specifies the dilation rate to use for dilated convolution. Default: (1, 1, 1, 1).
group (int): Splits input into groups. Default: 1.
Returns:
......@@ -135,8 +135,8 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
pad=0,
pad_list=(0, 0, 0, 0),
mode=1,
stride=1,
dilation=1,
stride=(1, 1),
dilation=(1, 1, 1, 1),
group=1):
"""init Convolution"""
self.init_prim_io_names(inputs=['out_backprop', 'input', 'filter_sizes'], outputs=['output'])
......@@ -146,7 +146,9 @@ class Conv2DBackpropFilter(PrimitiveWithInfer):
pad_mode = pad_mode.upper()
self.add_prim_attr('pad_mode', pad_mode)
self.pad = pad
self.stride = stride
if isinstance(stride, tuple) and len(stride) == 4:
self.stride = (stride[2], stride[3])
self.add_prim_attr('stride', self.stride)
self.dilation = dilation
self.group = group
self.add_prim_attr('data_format', "NCHW")
......
mindspore/ops/operations/nn_ops.py
浏览文件 @ 2604aced
此差异已折叠。
tests/st/ops/cpu/test_conv2d_backprop_filter_op.py
浏览文件 @ 2604aced
......@@ -35,8 +35,8 @@ class Net4(nn.Cell):
pad_mode="valid",
pad=0,
mode=1,
stride=1,
dilation=1,
stride=(1, 1),
dilation=(1, 1, 1, 1),
group=1)
self.w = Parameter(initializer(Tensor(np.array([[[[1, 0, -1], [1, 0, -1], [1, 0, -1]]]]).astype(np.float32)), [1, 1, 3, 3]), name='w')
self.x = Parameter(initializer(Tensor(np.array([[[
......
tests/st/ops/gpu/test_conv2d_backprop_filter_op.py
浏览文件 @ 2604aced
......@@ -35,8 +35,8 @@ class Conv2dFilter(nn.Cell):
pad_mode="valid",
pad=0,
mode=1,
stride=1,
dilation=1,
stride=(1, 1),
dilation=(1, 1, 1, 1),
group=1)
self.get_shape = P.Shape()
......
tests/ut/cpp/python_input/gtest_input/pynative/ops_test.py
浏览文件 @ 2604aced
......@@ -21,17 +21,17 @@ from mindspore.common.tensor import Tensor
def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
"""Rearranges an image to row vector"""
batch_num, channel, height, width = img.shape
out_h = (height + 2*pad - filter_h - (filter_h - 1) * (dilation - 1))//stride + 1
out_w = (width + 2*pad - filter_w - (filter_w - 1) * (dilation - 1))//stride + 1
out_h = (height + 2*pad - filter_h - (filter_h - 1) * (dilation[2] - 1))//stride[2] + 1
out_w = (width + 2*pad - filter_w - (filter_w - 1) * (dilation[3] - 1))//stride[3] + 1
img = np.pad(img, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
col = np.zeros((batch_num, channel, filter_h, filter_w, out_h, out_w)).astype(img.dtype)
for y in range(filter_h):
y_max = y + stride*out_h
y_max = y + stride[2]*out_h
for x in range(filter_w):
x_max = x + stride*out_w
col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
x_max = x + stride[2]*out_w
col[:, :, y, x, :, :] = img[:, :, y:y_max:stride[2], x:x_max:stride[2]]
col = col.transpose(0, 4, 5, 1, 2, 3).reshape(batch_num*out_h*out_w, -1)
return col
......@@ -42,8 +42,8 @@ def conv2d(x, weight, bias=None, stride=1, pad=0,
"""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 - 1)) / stride)
out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation - 1)) / stride)
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)
......
tests/vm_impl/vm_me.py
浏览文件 @ 2604aced
......@@ -155,23 +155,35 @@ def batch_norm_grad(dy, x, scale, save_mean, save_inv_variance):
def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
"""Rearranges a row vector to an image."""
validator.check_integer("stride", stride, 0, Rel.GT)
if isinstance(stride, int):
stride_h = stride
stride_w = stride
elif isinstance(stride, tuple) and len(stride) == 2:
stride_h = stride[0]
stride_w = stride[1]
elif isinstance(stride, tuple) and len(stride) == 3:
stride_h = stride[2]
stride_w = stride[3]
else:
raise ValueError(f"The \'stride\' should be an int number or "
f"a tuple of two or four int numbers, but got {stride}")
batch_num, channel, height, width = input_shape
out_h = (height + 2*pad - filter_h)//stride + 1
out_w = (width + 2*pad - filter_w)//stride + 1
out_h = (height + 2*pad - filter_h)//stride_h + 1
out_w = (width + 2*pad - filter_w)//stride_w + 1
col = col.reshape(batch_num, out_h, out_w, channel, filter_h, filter_w) \
.transpose(0, 3, 4, 5, 1, 2)
img = np.zeros((batch_num,
channel,
height + 2*pad + stride - 1,
width + 2*pad + stride - 1)) \
height + 2*pad + stride_h - 1,
width + 2*pad + stride_w - 1)) \
.astype(col.dtype)
for y in range(filter_h):
y_max = y + stride*out_h
y_max = y + stride_h*out_h
for x in range(filter_w):
x_max = x + stride*out_w
img[:, :, y:y_max:stride, x:x_max:stride] += col[:, :, y, x, :, :]
x_max = x + stride_h*out_w
img[:, :, y:y_max:stride_h, x:x_max:stride_h] += col[:, :, y, x, :, :]
return img[:, :, pad:height + pad, pad:width + pad]
......@@ -205,11 +217,35 @@ def conv2d(x, weight, bias=None, stride=1, pad=0,
dilation=1, groups=1, padding_mode='zeros'):
"""Convolution 2D."""
# pylint: disable=unused-argument
validator.check_integer("stride", stride, 0, Rel.GT)
validator.check_type('stride', stride, (int, tuple))
if isinstance(stride, int):
stride = (stride, stride)
elif len(stride) == 4:
stride = (stride[2], stride[3])
if len(stride) != 2 or (not isinstance(stride[0], int)) or \
(not isinstance(stride[1], int)) or \
stride[0] < 1 or stride[1] < 1:
raise ValueError(f"The \'stride\' of \'conv2d\' should be an positive int number or "
f"a tuple of two positive int numbers, but got {stride}")
stride_h = stride[0]
stride_w = stride[1]
validator.check_type('dilation', dilation, (int, tuple))
if isinstance(dilation, int):
dilation = (dilation, dilation)
elif len(dilation) == 4:
dilation = (dilation[2], dilation[3])
if len(dilation) != 2 or (not isinstance(dilation[0], int)) or \
(not isinstance(dilation[1], int)) or \
dilation[0] < 1 or dilation[1] < 1:
raise ValueError(f"The \'dilation\' of \'conv2d\' should be an positive int number or "
f"a tuple of two positive int numbers, but got {dilation}")
dilation_h = dilation[0]
dilation_w = dilation[1]
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 - 1)) / stride)
out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation - 1)) / stride)
out_h = 1 + int((x_h + 2 * pad - filter_h - (filter_h - 1) * (dilation_h - 1)) / stride_h)
out_w = 1 + int((x_w + 2 * pad - filter_w - (filter_w - 1) * (dilation_w - 1)) / stride_w)
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)
......@@ -286,19 +322,43 @@ def flatten_grad(dout, x):
def im2col(img, filter_h, filter_w, stride=1, pad=0, dilation=1):
"""Rearranges an image to row vector."""
validator.check_integer("stride", stride, 0, Rel.GT)
if isinstance(stride, int):
stride_h = stride
stride_w = stride
elif isinstance(stride, tuple) and len(stride) == 2:
stride_h = stride[0]
stride_w = stride[1]
elif isinstance(stride, tuple) and len(stride) == 3:
stride_h = stride[2]
stride_w = stride[3]
else:
raise ValueError(f"The \'stride\' should be an int number or "
f"a tuple of two or four int numbers, but got {stride}")
if isinstance(dilation, int):
dilation_h = dilation
dilation_w = dilation
elif isinstance(dilation, tuple) and len(dilation) == 2:
dilation_h = dilation[0]
dilation_w = dilation[1]
elif isinstance(dilation, tuple) and len(dilation) == 3:
dilation_h = dilation[2]
dilation_w = dilation[3]
else:
raise ValueError(f"The \'dilation\' should be an int number or "
f"a tuple of two or four int numbers, but got {dilation}")
batch_num, channel, height, width = img.shape
out_h = (height + 2*pad - filter_h- (filter_h - 1) * (dilation - 1))//stride + 1
out_w = (width + 2*pad - filter_w- (filter_w - 1) * (dilation - 1))//stride + 1
out_h = (height + 2*pad - filter_h- (filter_h - 1) * (dilation_h - 1))//stride_h + 1
out_w = (width + 2*pad - filter_w- (filter_w - 1) * (dilation_w - 1))//stride_w + 1
img = np.pad(img, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
col = np.zeros((batch_num, channel, filter_h, filter_w, out_h, out_w)).astype(img.dtype)
for y in range(filter_h):
y_max = y + stride*out_h
y_max = y + stride_h*out_h
for x in range(filter_w):
x_max = x + stride*out_w
col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
x_max = x + stride_h*out_w
col[:, :, y, x, :, :] = img[:, :, y:y_max:stride_h, x:x_max:stride_h]
col = col.transpose(0, 4, 5, 1, 2, 3).reshape(batch_num*out_h*out_w, -1)
return col
......
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