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提交 f302c6a3 编写于 作者: C chengduoZH
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write conv2d and conv3d together

上级 ba7db29d
隐藏空白更改
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Showing with 145 addition and 294 deletion
paddle/operators/conv_cudnn_op.cc
浏览文件 @ f302c6a3
......@@ -41,8 +41,8 @@ namespace ops = paddle::operators;
REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(
conv_cudnn, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(conv_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv_cudnn_grad,
ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/conv_op.cc
浏览文件 @ f302c6a3
......@@ -198,12 +198,12 @@ namespace ops = paddle::operators;
REGISTER_OP(conv3d, ops::ConvOp, ops::Conv3DOpMaker, conv3d_grad,
ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv2d,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv2d, ops::GemmConv2DKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv2d_grad, ops::GemmConvGrad2DKernel<paddle::platform::CPUPlace, float>);
conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(conv3d,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv3d, ops::GemmConv3DKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv3d_grad, ops::GemmConvGrad3DKernel<paddle::platform::CPUPlace, float>);
conv3d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/conv_op.cu
浏览文件 @ f302c6a3
......@@ -16,12 +16,12 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d,
ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
conv2d, ops::GemmConv2DKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
conv2d_grad, ops::GemmConvGrad2DKernel<paddle::platform::GPUPlace, float>);
conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(conv3d,
ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
conv3d, ops::GemmConv3DKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
conv3d_grad, ops::GemmConvGrad3DKernel<paddle::platform::GPUPlace, float>);
conv3d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/conv_op.h
浏览文件 @ f302c6a3
......@@ -62,7 +62,7 @@ class ConvOpGrad : public framework::OperatorWithKernel {
};
template <typename Place, typename T>
class GemmConv2DKernel : public framework::OpKernel<T> {
class GemmConvKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* input = context.Input<Tensor>("Input");
......@@ -77,49 +77,78 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
int batch_size = input->dims()[0];
int input_channels = input->dims()[1];
int filter_height = filter.dims()[filter.dims().size() - 2];
int filter_width = filter.dims()[filter.dims().size() - 1];
int output_channels = output->dims()[1];
int output_height = output->dims()[2];
int output_width = output->dims()[3];
const int batch_size = static_cast<int>(input->dims()[0]);
// filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
filter_shape_vec.erase(filter_shape_vec.begin(),
filter_shape_vec.begin() + 2);
// output_shape_vec: {o_h, o_w} or {o_d, o_h, o_w}
std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
output_shape_vec.erase(output_shape_vec.begin(),
output_shape_vec.begin() + 2);
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
// use col_shape in the im2col calculation
framework::DDim col_shape = {input_channels / groups, filter_height,
filter_width, output_height, output_width};
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
// o_h, o_w}
std::vector<int64_t> col_shape_vec;
col_shape_vec.push_back(input->dims()[1] / groups);
col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
filter_shape_vec.end());
col_shape_vec.insert(col_shape_vec.end(), output_shape_vec.begin(),
output_shape_vec.end());
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
framework::DDim col_matrix_shape = {
input_channels / groups * filter_height * filter_width,
output_height * output_width};
// size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w, o_d *
// o_h * o_w)
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix = col;
Tensor col_matrix;
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
framework::DDim input_shape = {input->dims()[1], input->dims()[2],
input->dims()[3]};
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {output_channels,
output_height * output_width};
// convolution operator: im2col + gemm
int in_step = input_channels / groups;
int out_step = output_channels / groups;
framework::DDim output_matrix_shape = {
output->dims()[1],
output->numel() / (output->dims()[0] * output->dims()[1])};
// convolution operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
// im2col
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
im2col(context.device_context(), in_slice, col, strides[0], strides[1],
paddings[0], paddings[0], paddings[1], paddings[1]);
if (filter_shape_vec.size() == 2) {
// im2col
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
} else if (filter_shape_vec.size() == 3) {
// vol2col
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
}
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
......@@ -132,7 +161,7 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
};
template <typename Place, typename T>
class GemmConvGrad2DKernel : public framework::OpKernel<T> {
class GemmConvGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* input = context.Input<Tensor>("Input");
......@@ -142,267 +171,74 @@ class GemmConvGrad2DKernel : public framework::OpKernel<T> {
context.Output<Tensor>(framework::GradVarName("Input"));
Tensor* filter_grad =
context.Output<Tensor>(framework::GradVarName("Filter"));
// The filter and filter_grad will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *context.Input<Tensor>("Filter");
if (!input_grad && !filter_grad) return;
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
int batch_size = input->dims()[0];
int input_channels = input->dims()[1];
int filter_height = filter.dims()[filter.dims().size() - 2];
int filter_width = filter.dims()[filter.dims().size() - 1];
int output_channels = output_grad->dims()[1];
int output_height = output_grad->dims()[2];
int output_width = output_grad->dims()[3];
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
// use col_shape in the im2col and col2im calculation
framework::DDim col_shape = {input_channels / groups, filter_height,
filter_width, output_height, output_width};
// use col_matrix_shape in the gemm calculation
framework::DDim col_matrix_shape = {
input_channels / groups * filter_height * filter_width,
output_height * output_width};
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix = col;
col_matrix.Resize(col_matrix_shape);
framework::DDim input_shape = {input->dims()[1], input->dims()[2],
input->dims()[3]};
framework::DDim output_matrix_shape = {
output_grad->dims()[1],
output_grad->dims()[2] * output_grad->dims()[3]};
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
// convolution backward input operator: gemm + col2im
// convolution backward weight operator: im2col + gemm
int in_step = input_channels / groups;
int out_step = output_channels / groups;
math::SetConstant<Place, T> set_zero;
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), input_grad, static_cast<T>(0));
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
Tensor in_grad_batch = input_grad->Slice(i, i + 1).Resize(input_shape);
for (int g = 0; g < groups; g++) {
// gemm
Tensor out_grad_slice =
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
// col2im
Tensor in_grad_slice =
in_grad_batch.Slice(g * in_step, (g + 1) * in_step);
col2im(context.device_context(), in_grad_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
}
}
}
if (filter_grad) {
filter_grad->mutable_data<T>(context.GetPlace());
Tensor filter_grad_ = *filter_grad;
filter_grad_.Resize(filter_matrix_shape);
set_zero(context.device_context(), filter_grad, static_cast<T>(0));
const int batch_size = static_cast<int>(input->dims()[0]);
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
for (int g = 0; g < groups; g++) {
// im2col
Tensor out_grad_slice =
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
// gemm
Tensor filter_grad_slice =
filter_grad_.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), out_grad_slice,
false, col_matrix, true, T(1.0),
&filter_grad_slice, T(1.0));
}
}
}
}
};
// filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
filter_shape_vec.erase(filter_shape_vec.begin(),
filter_shape_vec.begin() + 2);
template <typename Place, typename T>
class GemmConv3DKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* input = context.Input<Tensor>("Input");
// The filter will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *context.Input<Tensor>("Filter");
Tensor* output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
// output_shape_vec: {o_h, o_w} or {o_d, o_h, o_w}
std::vector<int64_t> output_shape_vec(
framework::vectorize(output_grad->dims()));
output_shape_vec.erase(output_shape_vec.begin(),
output_shape_vec.begin() + 2);
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
// use col_shape in the im2col calculation
// col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
// o_h, o_w}
std::vector<int64_t> col_shape_vec;
col_shape_vec.push_back(input->dims()[1] / groups);
col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
filter_shape_vec.end());
col_shape_vec.insert(col_shape_vec.end(), output_shape_vec.begin(),
output_shape_vec.end());
framework::DDim col_shape(framework::make_ddim(col_shape_vec));
int batch_size = input->dims()[0];
int input_channels = input->dims()[1];
int filter_depth = filter.dims()[filter.dims().size() - 3];
int filter_height = filter.dims()[filter.dims().size() - 2];
int filter_width = filter.dims()[filter.dims().size() - 1];
int output_channels = output->dims()[1];
int output_depth = output->dims()[2];
int output_height = output->dims()[3];
int output_width = output->dims()[4];
math::Vol2ColFunctor<Place, T> vol2col;
// use col_shape in the vol2col calculation
framework::DDim col_shape = {input_channels / groups,
filter_depth,
filter_height,
filter_width,
output_depth,
output_height,
output_width};
// use col_matrix_shape in the gemm calculation
framework::DDim col_matrix_shape = {
input_channels / groups * filter_depth * filter_height * filter_width,
output_depth * output_height * output_width};
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix = col;
col_matrix.Resize(col_matrix_shape);
// size: (i_c/g * k_h * k_w, o_h * o_w)
// or
// (i_c/g * k_d * k_h * k_w, o_d * o_h * o_w)
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
framework::DDim input_shape = {
input->dims()[1], input->dims()[2], input->dims()[3],
input->dims()[4]}; // channel, depth, height, width
framework::DDim filter_matrix_shape = {
filter.dims()[0],
filter.numel() / filter.dims()[0]}; // filter_out_channel,
// filter_in_channel*filter_depth*filter_height*filter_width
framework::DDim filter_matrix_shape = {filter.dims()[0],
filter.numel() / filter.dims()[0]};
filter.Resize(filter_matrix_shape);
framework::DDim output_matrix_shape = {
output_channels, output_depth * output_height * output_width};
// convolution operator: vol2col + gemm
int in_step = input_channels / groups;
int out_step = output_channels / groups;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
// vol2col
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
vol2col(context.device_context(), in_slice, col, strides[0], strides[1],
strides[2], paddings[0], paddings[1], paddings[2]);
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), filter_slice, false,
col_matrix, false, T(1.0), &out_slice, T(0.0));
}
}
}
};
template <typename Place, typename T>
class GemmConvGrad3DKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* input = context.Input<Tensor>("Input");
const Tensor* output_grad =
context.Input<Tensor>(framework::GradVarName("Output"));
Tensor* input_grad =
context.Output<Tensor>(framework::GradVarName("Input"));
Tensor* filter_grad =
context.Output<Tensor>(framework::GradVarName("Filter"));
// The filter and filter_grad will be reshaped in the calculations,
// so here use an assignment operation,
// that avoids modifying the variable in the Scope.
Tensor filter = *context.Input<Tensor>("Filter");
output_grad->dims()[1],
output_grad->numel() /
(output_grad->dims()[0] * output_grad->dims()[1])};
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
// convolution backward input operator: gemm + col2im(or col2vol)
// convolution backward weight operator: im2col(or vol2col) + gemm
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output_grad->dims()[1]) / groups;
int batch_size = input->dims()[0];
int input_channels = input->dims()[1];
int filter_depth = filter.dims()[filter.dims().size() - 3];
int filter_height = filter.dims()[filter.dims().size() - 2];
int filter_width = filter.dims()[filter.dims().size() - 1];
int output_channels = output_grad->dims()[1];
int output_depth = output_grad->dims()[2];
int output_height = output_grad->dims()[3];
int output_width = output_grad->dims()[4];
math::Col2VolFunctor<Place, T> col2vol;
math::Vol2ColFunctor<Place, T> vol2col;
// use col_shape in the vol2col and col2vol calculation
framework::DDim col_shape = {input_channels / groups,
filter_depth,
filter_height,
filter_width,
output_depth,
output_height,
output_width};
// use col_matrix_shape in the gemm calculation
framework::DDim col_matrix_shape = {
input_channels / groups * filter_depth * filter_height * filter_width,
output_depth * output_height * output_width};
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix = col;
Tensor col_matrix;
col.mutable_data<T>(col_shape, context.GetPlace());
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
framework::DDim input_shape = {
input->dims()[1], input->dims()[2], input->dims()[3],
input->dims()[4]}; // channel, depth, height, width
framework::DDim output_matrix_shape = {output_grad->dims()[1],
output_grad->dims()[2] *
output_grad->dims()[3] *
output_grad->dims()[4]};
framework::DDim filter_matrix_shape = {
filter.dims()[0],
filter.numel() / filter.dims()[0]}; // filter_out_channel,
// filter_in_channel*filter_depth*filter_height*filter_width
filter.Resize(filter_matrix_shape);
// convolution backward input operator: gemm + col2vol
// convolution backward weight operator: vol2col + gemm
int in_step = input_channels / groups;
int out_step = output_channels / groups;
math::SetConstant<Place, T> set_zero;
if (input_grad) {
......@@ -421,13 +257,22 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
// col2vol
// col2im
Tensor in_grad_slice =
in_grad_batch.Slice(g * in_step, (g + 1) * in_step);
col2vol(context.device_context(), in_grad_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
if (filter_shape_vec.size() == 2) {
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
col2im(context.device_context(), in_grad_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
} else if (filter_shape_vec.size() == 3) {
math::Col2VolFunctor<Place, T> col2vol;
col2vol(context.device_context(), in_grad_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
}
}
}
}
......@@ -443,13 +288,22 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
for (int g = 0; g < groups; g++) {
// vol2col
// im2col
Tensor out_grad_slice =
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
if (filter_shape_vec.size() == 2) {
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
} else if (filter_shape_vec.size() == 3) {
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
}
// gemm
Tensor filter_grad_slice =
......@@ -462,6 +316,5 @@ class GemmConvGrad3DKernel : public framework::OpKernel<T> {
}
}
};
} // namespace operators
} // namespace paddle
python/paddle/v2/framework/tests/test_conv2d_op.py
浏览文件 @ f302c6a3
......@@ -61,25 +61,23 @@ class TestConv2dOp(OpTest):
def test_check_grad(self):
self.check_grad(
set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
def test_check_grad_no_filter(self):
self.check_grad(
['Input'],
'Output',
max_relative_error=0.05,
max_relative_error=0.02,
no_grad_set=set(['Filter']))
def test_check_grad_no_input(self):
self.check_grad(
['Filter'],
'Output',
max_relative_error=0.05,
max_relative_error=0.02,
no_grad_set=set(['Input']))
def init_test_case(self):
# self.groups = 1
# self.op_type = "conv2d"
self.pad = [0, 0]
self.stride = [1, 1]
self.dilations = [1, 1]
......
python/paddle/v2/framework/tests/test_conv3d_op.py
浏览文件 @ f302c6a3
......@@ -64,20 +64,20 @@ class TestConv3dOp(OpTest):
def test_check_grad(self):
self.check_grad(
set(['Input', 'Filter']), 'Output', max_relative_error=0.05)
set(['Input', 'Filter']), 'Output', max_relative_error=0.03)
def test_check_grad_no_filter(self):
self.check_grad(
['Input'],
'Output',
max_relative_error=0.05,
max_relative_error=0.03,
no_grad_set=set(['Filter']))
def test_check_grad_no_input(self):
self.check_grad(
['Filter'],
'Output',
max_relative_error=0.05,
max_relative_error=0.03,
no_grad_set=set(['Input']))
def init_test_case(self):
......
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