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fix error message in conv/conv_transpose. test=develop (#27464)

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* fix error message in conv/conv_transpose. test=develop
revert-27520-disable_pr
Kaipeng Deng 5 years ago committed by GitHub
parent 59c049995e
commit d7f422c984
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 70 additions and 40 deletions
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60
paddle/fluid/operators/conv_cudnn_op.cu
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@ -50,8 +50,9 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
"It must use CUDAPlace.");
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
const Tensor* input = ctx.Input<Tensor>("Input");
auto* filter = ctx.Input<Tensor>("Filter");
auto* output = ctx.Output<Tensor>("Output");
@ -60,14 +61,16 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
bool exhaustive_search =
FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
bool deterministic = FLAGS_cudnn_deterministic;
auto exhaustive_deterministic = exhaustive_search && deterministic;
PADDLE_ENFORCE_EQ(exhaustive_deterministic, false,
platform::errors::InvalidArgument(
"Cann't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time."));
if (exhaustive_search && FLAGS_cudnn_deterministic) {
PADDLE_THROW(
"Cann't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time.");
}
const std::string padding_algorithm =
ctx.Attr<std::string>("padding_algorithm");
const std::string data_format = ctx.Attr<std::string>("data_format");
@ -197,7 +200,8 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
&transformed_input);
} break;
default:
PADDLE_THROW("ConvOp only support tensors with 4 or 5 dimensions.");
PADDLE_THROW(platform::errors::InvalidArgument(
"ConvOp only support tensors with 4 or 5 dimensions."));
}
} else {
@ -317,8 +321,9 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
"It must use CUDAPlace.");
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto input = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
@ -337,14 +342,16 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
int groups = ctx.Attr<int>("groups");
bool exhaustive_search =
FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
bool deterministic = FLAGS_cudnn_deterministic;
if (exhaustive_search && deterministic) {
PADDLE_THROW(
"Can't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time.");
}
auto exhaustive_deterministic = exhaustive_search && deterministic;
PADDLE_ENFORCE_EQ(exhaustive_deterministic, false,
platform::errors::InvalidArgument(
"Cann't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time."));
const std::string data_format = ctx.Attr<std::string>("data_format");
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
@ -495,7 +502,8 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
&transformed_input);
} break;
default:
PADDLE_THROW("ConvOp only support tensors with 4 or 5 dimensions.");
PADDLE_THROW(platform::errors::InvalidArgument(
"ConvOp only support tensors with 4 or 5 dimensions."));
}
} else {
transformed_input.ShareDataWith(transformed_input_channel);
@ -701,8 +709,9 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
"It must use CUDAPlace.");
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto X = ctx.Input<Tensor>("Input");
auto W = ctx.Input<Tensor>("Filter");
auto dO = ctx.Input<Tensor>("DOutput");
@ -736,14 +745,16 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
const std::vector<int>& strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
bool exhaustive_search =
FLAGS_cudnn_exhaustive_search || ctx.Attr<bool>("exhaustive_search");
bool deterministic = FLAGS_cudnn_deterministic;
if (exhaustive_search && deterministic) {
PADDLE_THROW(
"Can't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time.");
}
auto exhaustive_deterministic = exhaustive_search && deterministic;
PADDLE_ENFORCE_EQ(exhaustive_deterministic, false,
platform::errors::InvalidArgument(
"Cann't set exhaustive_search True and "
"FLAGS_cudnn_deterministic True at same time."));
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
@ -878,7 +889,8 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
}
} break;
default:
PADDLE_THROW("ConvOp only support tensors with 4 or 5 dimensions.");
PADDLE_THROW(platform::errors::InvalidArgument(
"ConvOp only support tensors with 4 or 5 dimensions."));
}
} else {

18
paddle/fluid/operators/conv_op.h
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@ -685,8 +685,9 @@ class GemmConvDoubleGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
"It must use CPUPlace.");
PADDLE_ENFORCE_EQ(
platform::is_cpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CPUPlace."));
const Tensor* X = ctx.Input<Tensor>("Input");
const Tensor* dY = ctx.Input<Tensor>("DOutput");
const Tensor* ddX = ctx.Input<Tensor>("DDInput");
@ -982,11 +983,20 @@ class DepthwiseConvKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(
output->dims()[output->dims().size() - 1] %
input->dims()[input->dims().size() - 1],
0, "The output channels must be a multiple of the input channels");
0, platform::errors::InvalidArgument(
"ShapeError: The output channels must be a multiple of the "
"input channels. But receivced output channel number is %d "
"and input channel number is %d",
output->dims()[output->dims().size() - 1],
input->dims()[input->dims().size() - 1]));
} else {
PADDLE_ENFORCE_EQ(
output->dims()[1] % input->dims()[1], 0,
"The output channels must be a multiple of the input channels");
platform::errors::InvalidArgument(
"ShapeError: The output channels must be a multiple of the "
"input channels. But receivced output channel number is %d "
"and input channel number is %d",
output->dims()[1], input->dims()[1]));
}
// transform tensor
Tensor transformed_input(input->type());

20
paddle/fluid/operators/conv_transpose_cudnn_op.cu
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@ -51,8 +51,9 @@ template <typename T>
class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true,
"It must use CUDAPlace.");
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto* input = ctx.Input<Tensor>("Input");
auto* filter = ctx.Input<Tensor>("Filter");
auto* output = ctx.Output<Tensor>("Output");
@ -145,9 +146,8 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
ctx, input_pad, input_transpose, pad_value, &transformed_input);
} break;
default:
PADDLE_ENFORCE_EQ(
rank == 4 || rank == 5, true,
"Op(ConvTranspose) only supports 4-D or 5-D input Tensor.");
PADDLE_THROW(platform::errors::InvalidArgument(
"Op(ConvTranspose) only supports 4-D or 5-D input Tensor."));
}
} else {
transformed_input = input_transpose;
@ -290,8 +290,9 @@ template <typename T>
class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use CUDAPlace.");
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto input = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
@ -393,9 +394,8 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
&transformed_output_grad);
} break;
default:
PADDLE_ENFORCE_EQ(
rank == 4 || rank == 5, true,
"Op(ConvTranspose) only supports 4-D or 5-D input Tensor.");
PADDLE_THROW(platform::errors::InvalidArgument(
"Op(ConvTranspose) only supports 4-D or 5-D input Tensor."));
}
} else {
transformed_output_grad = output_grad_transpose;

12
paddle/fluid/operators/conv_transpose_op.h
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@ -580,7 +580,12 @@ class DepthwiseConvTransposeKernel : public framework::OpKernel<T> {
output->mutable_data<T>(context.GetPlace());
int groups = context.Attr<int>("groups");
PADDLE_ENFORCE_EQ(groups, filter.dims()[0]);
PADDLE_ENFORCE_EQ(
groups, filter.dims()[0],
platform::errors::InvalidArgument(
"groups should be error to the 1st dimension of filter. But "
"received groups is %d and filter dimension[0] is %d",
groups, filter.dims()[0]));
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
@ -588,7 +593,10 @@ class DepthwiseConvTransposeKernel : public framework::OpKernel<T> {
std::string padding_algorithm =
context.Attr<std::string>("padding_algorithm");
for (auto v : dilations) {
PADDLE_ENFORCE_EQ(v, 1);
PADDLE_ENFORCE_EQ(v, 1, platform::errors::InvalidArgument(
"dilations should be 1 in depthwise conv. "
"But received dilations is %d",
v));
}
auto in_dims = input->dims();

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