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未验证 提交 4c01d6d5 编写于 作者: W whs 提交者： GitHub
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Enhance checking in some operator. (#24473)

上级 4a702ef3
隐藏空白更改
内联 并排
Showing with 269 addition and 113 deletion
paddle/fluid/operators/affine_grid_op.cc
浏览文件 @ 4c01d6d5
...@@ -42,29 +42,57 @@ class AffineGridOp : public framework::OperatorWithKernel { ...@@ -42,29 +42,57 @@ class AffineGridOp : public framework::OperatorWithKernel {
public: public:
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Theta"), PADDLE_ENFORCE_EQ(ctx->HasInput("Theta"), true,
"Input(Theta) of AffineGridOp should not be null."); platform::errors::NotFound(
PADDLE_ENFORCE(ctx->HasOutput("Output"), "The input 'Theta' of AffineGridOp is not found."));
"Output(Output) of AffineGridOp should not be null."); PADDLE_ENFORCE_EQ(ctx->HasOutput("Output"), true,
platform::errors::NotFound(
"The output 'Output' of AffineGridOp is not found."));
auto theta_dims = ctx->GetInputDim("Theta"); auto theta_dims = ctx->GetInputDim("Theta");
PADDLE_ENFORCE(theta_dims.size() == 3, PADDLE_ENFORCE_EQ(
"AffineGrid's Input(Theta) should be 3-D tensor."); theta_dims.size(), 3,
platform::errors::InvalidArgument(
"The input Theta's dimensions size should be 3. But received "
"Theta's demensions size=[%d], Theta's dimensions=[%s].",
theta_dims.size(), theta_dims));
auto output_shape = ctx->Attrs().Get<std::vector<int>>("output_shape"); auto output_shape = ctx->Attrs().Get<std::vector<int>>("output_shape");
if (output_shape.size() == 0) { if (output_shape.size() == 0) {
PADDLE_ENFORCE(ctx->HasInput("OutputShape"), PADDLE_ENFORCE_EQ(
"Input(OutputShape) of AffineGridOp should not be null if " ctx->HasInput("OutputShape"), true,
"attr(output_shape) is not configured."); platform::errors::NotFound(
"The input 'OutputShape' of AffineGridOp should not be null if "
"'output_shape' is not configured."));
auto output_shape_dims = ctx->GetInputDim("OutputShape"); auto output_shape_dims = ctx->GetInputDim("OutputShape");
PADDLE_ENFORCE(output_shape_dims.size() == 1, PADDLE_ENFORCE_EQ(
"AffineGrid's Input(OutputShape) should be 1-D tensor."); output_shape_dims.size(), 1,
platform::errors::InvalidArgument(
"The dimesions size of input OutputShape in AffineGridOp should "
"be 1. But received OutputShape's dimesions size=[%d], "
"OutputShape's dimesions=[%s]",
output_shape_dims.size(), output_shape_dims));
} else { } else {
PADDLE_ENFORCE(output_shape.size() == 4, PADDLE_ENFORCE_EQ(
"The size of attr(output_shape) should be 4."); output_shape.size(), 4,
platform::errors::InvalidArgument(
"The size of attribute 'output_shape' in AffineGridOp should be "
"4. But received output_shape's size=[%d].",
output_shape.size()));
} }
PADDLE_ENFORCE(theta_dims[1] == 2, "Input(theta) dims[1] should be 2."); PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE(theta_dims[2] == 3, "Input(theta) dims[2] should be 3."); theta_dims[1], 2,
platform::errors::InvalidArgument(
"The second dimesion of input 'theta' in AffineGridOp should be 2. "
"But received second dimesion=[%d], dimesions=[%s]",
theta_dims[1], theta_dims));
PADDLE_ENFORCE_EQ(
theta_dims[2], 3,
platform::errors::InvalidArgument(
"The third dimesion of input 'theta' in AffineGridOp should be 3. "
"But received third dimesion=[%d], dimesions=[%s]",
theta_dims[2], theta_dims));
// N * H * W * 2 // N * H * W * 2
ctx->SetOutputDim("Output", ctx->SetOutputDim("Output",
framework::make_ddim({theta_dims[0], -1, -1, 2})); framework::make_ddim({theta_dims[0], -1, -1, 2}));
......
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @ 4c01d6d5
...@@ -38,42 +38,64 @@ class GenerateProposalLabelsOp : public framework::OperatorWithKernel { ...@@ -38,42 +38,64 @@ class GenerateProposalLabelsOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("RpnRois"), PADDLE_ENFORCE_EQ(
"Input(RpnRois) shouldn't be null."); ctx->HasInput("RpnRois"), true,
PADDLE_ENFORCE(ctx->HasInput("GtClasses"), platform::errors::NotFound("Input(RpnRois) shouldn't be null."));
"Input(GtClasses) shouldn't be null."); PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE(ctx->HasInput("IsCrowd"), ctx->HasInput("GtClasses"), true,
"Input(IsCrowd) shouldn't be null."); platform::errors::NotFound("Input(GtClasses) shouldn't be null."));
PADDLE_ENFORCE(ctx->HasInput("GtBoxes"), PADDLE_ENFORCE_EQ(
"Input(GtBoxes) shouldn't be null."); ctx->HasInput("IsCrowd"), true,
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null."); platform::errors::NotFound("Input(IsCrowd) shouldn't be null."));
PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE( ctx->HasInput("GtBoxes"), true,
ctx->HasOutput("Rois"), platform::errors::NotFound("Input(GtBoxes) shouldn't be null."));
"Output(Rois) of GenerateProposalLabelsOp should not be null"); PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE( ctx->HasInput("ImInfo"), true,
ctx->HasOutput("LabelsInt32"), platform::errors::NotFound("Input(ImInfo) shouldn't be null."));
"Output(LabelsInt32) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE( PADDLE_ENFORCE_EQ(
ctx->HasOutput("BboxTargets"), ctx->HasOutput("Rois"), true,
"Output(BboxTargets) of GenerateProposalLabelsOp should not be null"); platform::errors::NotFound(
PADDLE_ENFORCE(ctx->HasOutput("BboxInsideWeights"), "Output(Rois) of GenerateProposalLabelsOp should not be null"));
"Output(BboxInsideWeights) of GenerateProposalLabelsOp " PADDLE_ENFORCE_EQ(ctx->HasOutput("LabelsInt32"), true,
"should not be null"); platform::errors::NotFound("Output(LabelsInt32) of "
PADDLE_ENFORCE(ctx->HasOutput("BboxOutsideWeights"), "GenerateProposalLabelsOp "
"Output(BboxOutsideWeights) of GenerateProposalLabelsOp " "should not be null"));
"should not be null"); PADDLE_ENFORCE_EQ(ctx->HasOutput("BboxTargets"), true,
platform::errors::NotFound("Output(BboxTargets) of "
"GenerateProposalLabelsOp "
"should not be null"));
PADDLE_ENFORCE_EQ(
ctx->HasOutput("BboxInsideWeights"), true,
platform::errors::NotFound(
"Output(BboxInsideWeights) of GenerateProposalLabelsOp "
"should not be null"));
PADDLE_ENFORCE_EQ(
ctx->HasOutput("BboxOutsideWeights"), true,
platform::errors::NotFound(
"Output(BboxOutsideWeights) of GenerateProposalLabelsOp "
"should not be null"));
auto rpn_rois_dims = ctx->GetInputDim("RpnRois"); auto rpn_rois_dims = ctx->GetInputDim("RpnRois");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes"); auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
auto im_info_dims = ctx->GetInputDim("ImInfo"); auto im_info_dims = ctx->GetInputDim("ImInfo");
PADDLE_ENFORCE_EQ(rpn_rois_dims.size(), 2, PADDLE_ENFORCE_EQ(rpn_rois_dims.size(), 2,
"The rank of Input(RpnRois) must be 2."); platform::errors::InvalidArgument(
"The dimensions size of Input(RpnRois) must be 2. "
"But received dimensions size=[%d], dimensions=[%s].",
rpn_rois_dims.size(), rpn_rois_dims));
PADDLE_ENFORCE_EQ(gt_boxes_dims.size(), 2, PADDLE_ENFORCE_EQ(gt_boxes_dims.size(), 2,
"The rank of Input(GtBoxes) must be 2."); platform::errors::InvalidArgument(
"The dimensions size of Input(GtBoxes) must be 2. "
"But received dimensions size=[%d], dimensions=[%s].",
gt_boxes_dims.size(), gt_boxes_dims));
PADDLE_ENFORCE_EQ(im_info_dims.size(), 2, PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
"The rank of Input(ImInfo) must be 2."); platform::errors::InvalidArgument(
"The dimensions size of Input(ImInfo) must be 2. But "
"received dimensions size=[%d], dimensions=[%s].",
im_info_dims.size(), im_info_dims));
int class_nums = ctx->Attrs().Get<int>("class_nums"); int class_nums = ctx->Attrs().Get<int>("class_nums");
...@@ -399,15 +421,30 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> { ...@@ -399,15 +421,30 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
bool use_random = context.Attr<bool>("use_random"); bool use_random = context.Attr<bool>("use_random");
bool is_cascade_rcnn = context.Attr<bool>("is_cascade_rcnn"); bool is_cascade_rcnn = context.Attr<bool>("is_cascade_rcnn");
bool is_cls_agnostic = context.Attr<bool>("is_cls_agnostic"); bool is_cls_agnostic = context.Attr<bool>("is_cls_agnostic");
PADDLE_ENFORCE_EQ(rpn_rois->lod().size(), 1UL, PADDLE_ENFORCE_EQ(
"GenerateProposalLabelsOp rpn_rois needs 1 level of LoD"); rpn_rois->lod().size(), 1UL,
platform::errors::InvalidArgument(
"GenerateProposalLabelsOp rpn_rois needs 1 level of LoD. But "
"received level of LoD is [%d], LoD is [%s].",
rpn_rois->lod().size(), rpn_rois->lod()));
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
gt_classes->lod().size(), 1UL, gt_classes->lod().size(), 1UL,
"GenerateProposalLabelsOp gt_classes needs 1 level of LoD"); platform::errors::InvalidArgument(
PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL, "GenerateProposalLabelsOp gt_classes needs 1 level of LoD. But "
"GenerateProposalLabelsOp is_crowd needs 1 level of LoD"); "received level of LoD is [%d], LoD is [%s].",
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL, gt_classes->lod().size(), gt_classes->lod()));
"GenerateProposalLabelsOp gt_boxes needs 1 level of LoD"); PADDLE_ENFORCE_EQ(
is_crowd->lod().size(), 1UL,
platform::errors::InvalidArgument(
"GenerateProposalLabelsOp is_crowd needs 1 level of LoD. But "
"received level of LoD is [%d], LoD is [%s].",
is_crowd->lod().size(), is_crowd->lod()));
PADDLE_ENFORCE_EQ(
gt_boxes->lod().size(), 1UL,
platform::errors::InvalidArgument(
"GenerateProposalLabelsOp gt_boxes needs 1 level of LoD. But "
"received level of LoD is [%d], LoD is [%s].",
gt_boxes->lod().size(), gt_boxes->lod()));
int64_t n = static_cast<int64_t>(rpn_rois->lod().back().size() - 1); int64_t n = static_cast<int64_t>(rpn_rois->lod().back().size() - 1);
rois->mutable_data<T>({n * batch_size_per_im, kBoxDim}, context.GetPlace()); rois->mutable_data<T>({n * batch_size_per_im, kBoxDim}, context.GetPlace());
......
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ 4c01d6d5
...@@ -43,14 +43,21 @@ class GenerateProposalsOp : public framework::OperatorWithKernel { ...@@ -43,14 +43,21 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override { void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Scores"), "Input(Scores) shouldn't be null."); PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE(ctx->HasInput("BboxDeltas"), ctx->HasInput("Scores"), true,
"Input(BboxDeltas) shouldn't be null."); platform::errors::NotFound("Input(Scores) shouldn't be null."));
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null."); PADDLE_ENFORCE_EQ(
PADDLE_ENFORCE(ctx->HasInput("Anchors"), ctx->HasInput("BboxDeltas"), true,
"Input(Anchors) shouldn't be null."); platform::errors::NotFound("Input(BboxDeltas) shouldn't be null."));
PADDLE_ENFORCE(ctx->HasInput("Variances"), PADDLE_ENFORCE_EQ(
"Input(Variances) shouldn't be null."); ctx->HasInput("ImInfo"), true,
platform::errors::NotFound("Input(ImInfo) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Anchors"), true,
platform::errors::NotFound("Input(Anchors) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Variances"), true,
platform::errors::NotFound("Input(Variances) shouldn't be null."));
ctx->SetOutputDim("RpnRois", {-1, 4}); ctx->SetOutputDim("RpnRois", {-1, 4});
ctx->SetOutputDim("RpnRoiProbs", {-1, 1}); ctx->SetOutputDim("RpnRoiProbs", {-1, 1});
...@@ -247,7 +254,6 @@ static inline Tensor VectorToTensor(const std::vector<T> &selected_indices, ...@@ -247,7 +254,6 @@ static inline Tensor VectorToTensor(const std::vector<T> &selected_indices,
template <class T> template <class T>
static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox, static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
Tensor *scores, T nms_threshold, float eta) { Tensor *scores, T nms_threshold, float eta) {
PADDLE_ENFORCE_NOT_NULL(bbox);
int64_t num_boxes = bbox->dims()[0]; int64_t num_boxes = bbox->dims()[0];
// 4: [xmin ymin xmax ymax] // 4: [xmin ymin xmax ymax]
int64_t box_size = bbox->dims()[1]; int64_t box_size = bbox->dims()[1];
......
paddle/fluid/operators/detection/generate_proposals_op.cu
浏览文件 @ 4c01d6d5
...@@ -379,7 +379,11 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> { ...@@ -379,7 +379,11 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
float nms_thresh = context.Attr<float>("nms_thresh"); float nms_thresh = context.Attr<float>("nms_thresh");
float min_size = context.Attr<float>("min_size"); float min_size = context.Attr<float>("min_size");
float eta = context.Attr<float>("eta"); float eta = context.Attr<float>("eta");
PADDLE_ENFORCE_GE(eta, 1., "Not support adaptive NMS."); PADDLE_ENFORCE_GE(eta, 1.,
platform::errors::InvalidArgument(
"Not support adaptive NMS. The attribute 'eta' "
"should not less than 1. But received eta=[%d]",
eta));
auto &dev_ctx = context.template device_context<DeviceContext>(); auto &dev_ctx = context.template device_context<DeviceContext>();
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ 4c01d6d5
...@@ -31,40 +31,44 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel { ...@@ -31,40 +31,44 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Anchor"), OP_INOUT_CHECK(ctx->HasInput("Anchor"), "Input", "Anchor",
"Input(Anchor) of RpnTargetAssignOp should not be null"); "rpn_target_assign");
PADDLE_ENFORCE(ctx->HasInput("GtBoxes"), OP_INOUT_CHECK(ctx->HasInput("GtBoxes"), "Input", "GtBoxes",
"Input(GtBoxes) of RpnTargetAssignOp should not be null"); "rpn_target_assign");
PADDLE_ENFORCE(ctx->HasInput("IsCrowd"), OP_INOUT_CHECK(ctx->HasInput("IsCrowd"), "Input", "IsCrowd",
"Input(Anchor) of RpnTargetAssignOp should not be null"); "rpn_target_assign");
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), OP_INOUT_CHECK(ctx->HasInput("ImInfo"), "Input", "ImInfo",
"Input(ImInfo) of RpnTargetAssignOp should not be null"); "rpn_target_assign");
PADDLE_ENFORCE( OP_INOUT_CHECK(ctx->HasOutput("LocationIndex"), "Output", "LocationIndex",
ctx->HasOutput("LocationIndex"), "rpn_target_assign");
"Output(LocationIndex) of RpnTargetAssignOp should not be null"); OP_INOUT_CHECK(ctx->HasOutput("ScoreIndex"), "Output", "ScoreIndex",
PADDLE_ENFORCE( "rpn_target_assign");
ctx->HasOutput("ScoreIndex"), OP_INOUT_CHECK(ctx->HasOutput("TargetLabel"), "Output", "TargetLabel",
"Output(ScoreIndex) of RpnTargetAssignOp should not be null"); "rpn_target_assign");
PADDLE_ENFORCE( OP_INOUT_CHECK(ctx->HasOutput("TargetBBox"), "Output", "TargetBBox",
ctx->HasOutput("TargetLabel"), "rpn_target_assign");
"Output(TargetLabel) of RpnTargetAssignOp should not be null"); OP_INOUT_CHECK(ctx->HasOutput("BBoxInsideWeight"), "Output",
PADDLE_ENFORCE( "BBoxInsideWeight", "rpn_target_assign");
ctx->HasOutput("TargetBBox"),
"Output(TargetBBox) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BBoxInsideWeight"),
"Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
auto anchor_dims = ctx->GetInputDim("Anchor"); auto anchor_dims = ctx->GetInputDim("Anchor");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes"); auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
auto im_info_dims = ctx->GetInputDim("ImInfo"); auto im_info_dims = ctx->GetInputDim("ImInfo");
PADDLE_ENFORCE_EQ(anchor_dims.size(), 2, PADDLE_ENFORCE_EQ(anchor_dims.size(), 2,
"The rank of Input(Anchor) must be 2."); platform::errors::InvalidArgument(
"The dimensions size of Input(Anchor) must be 2. But "
"received dimensions size=[%d], dimensions=[%s].",
anchor_dims.size(), anchor_dims));
PADDLE_ENFORCE_EQ(gt_boxes_dims.size(), 2, PADDLE_ENFORCE_EQ(gt_boxes_dims.size(), 2,
"The rank of Input(GtBoxes) must be 2."); platform::errors::InvalidArgument(
"The dimensions size of Input(GtBoxes) must be 2. "
"But received dimensions size=[%d], dimensions=[%s].",
gt_boxes_dims.size(), gt_boxes_dims));
PADDLE_ENFORCE_EQ(im_info_dims.size(), 2, PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
"The rank of Input(ImInfo) must be 2."); platform::errors::InvalidArgument(
"The dimensions size of Input(ImInfo) must be 2. But "
"received dimensions size=[%d], dimensions=[%s].",
im_info_dims.size(), im_info_dims));
ctx->SetOutputDim("LocationIndex", {-1}); ctx->SetOutputDim("LocationIndex", {-1});
ctx->SetOutputDim("ScoreIndex", {-1}); ctx->SetOutputDim("ScoreIndex", {-1});
...@@ -357,9 +361,15 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> { ...@@ -357,9 +361,15 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight"); auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL, PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"RpnTargetAssignOp gt_boxes needs 1 level of LoD"); platform::errors::InvalidArgument(
"RpnTargetAssignOp gt_boxes needs 1 level of LoD. "
"But received level of LoD is [%d], LoD is [%s].",
gt_boxes->lod().size(), gt_boxes->lod()));
PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL, PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL,
"RpnTargetAssignOp is_crowd needs 1 level of LoD"); platform::errors::InvalidArgument(
"RpnTargetAssignOp is_crowd needs 1 level of LoD. "
"But received level of LoD is [%d], LoD is [%s].",
is_crowd->lod().size(), is_crowd->lod()));
int64_t anchor_num = static_cast<int64_t>(anchor->dims()[0]); int64_t anchor_num = static_cast<int64_t>(anchor->dims()[0]);
int64_t batch_num = static_cast<int64_t>(gt_boxes->lod().back().size() - 1); int64_t batch_num = static_cast<int64_t>(gt_boxes->lod().back().size() - 1);
...@@ -479,8 +489,20 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> { ...@@ -479,8 +489,20 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
lod0_score.emplace_back(total_score_num); lod0_score.emplace_back(total_score_num);
} }
PADDLE_ENFORCE_LE(total_loc_num, max_num); PADDLE_ENFORCE_LE(
PADDLE_ENFORCE_LE(total_score_num, max_num); total_loc_num, max_num,
platform::errors::InvalidArgument(
"The number of sampled bboxes should not be greater than the "
"number of all anchor boxes(%d), but the number of sampled "
"bboxes is :%d.",
max_num, total_loc_num));
PADDLE_ENFORCE_LE(
total_score_num, max_num,
platform::errors::InvalidArgument(
"The number of sampled scores should not be greater than the "
"number of all anchor boxes(%d), but the number of sampled "
"scores is :%d.",
max_num, total_score_num));
lod_loc.emplace_back(lod0_loc); lod_loc.emplace_back(lod0_loc);
loc_score.emplace_back(lod0_score); loc_score.emplace_back(lod0_score);
......
paddle/fluid/operators/im2sequence_op.cc
浏览文件 @ 4c01d6d5
...@@ -26,14 +26,20 @@ class Im2SequenceOp : public framework::OperatorWithKernel { ...@@ -26,14 +26,20 @@ class Im2SequenceOp : public framework::OperatorWithKernel {
protected: protected:
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
"Input(X) of Im2SequenceOp should not be null."); platform::errors::NotFound(
PADDLE_ENFORCE(ctx->HasOutput("Out"), "The input 'X' of Im2SequenceOp is not found."));
"Output(Out) of Im2SequenceOp op should not be null."); PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::NotFound(
"The output 'Out' of Im2SequenceOp is not found."));
auto in_dim = ctx->GetInputDim("X"); auto in_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(in_dim.size(), 4, PADDLE_ENFORCE_EQ(
"Input(X) format must be 4D tensor, eg., NCHW."); in_dim.size(), 4,
platform::errors::InvalidArgument(
"The dimesions size of input 'X' in Im2SequenceOp should be 4. But "
"received dimesions size=[%d], dimesions=[%s].",
in_dim.size(), in_dim));
auto img_channels = in_dim[1]; auto img_channels = in_dim[1];
auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels"); auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
...@@ -146,9 +152,13 @@ class Im2SequenceGradOp : public framework::OperatorWithKernel { ...@@ -146,9 +152,13 @@ class Im2SequenceGradOp : public framework::OperatorWithKernel {
protected: protected:
void InferShape(framework::InferShapeContext* ctx) const override { void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), platform::errors::NotFound(
"Input(Out@GRAD) shouldn't be null."); "The input 'X' of Im2SequenceGradOp is not found."));
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
platform::errors::NotFound(
"The input %s of Im2SequenceGradOp is not found.",
framework::GradVarName("Out")));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
} }
}; };
......
paddle/fluid/operators/label_smooth_op.cc
浏览文件 @ 4c01d6d5
...@@ -28,18 +28,24 @@ class LabelSmoothOp : public framework::OperatorWithKernel { ...@@ -28,18 +28,24 @@ class LabelSmoothOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {} : OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override { void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
"Input(X) of LabelSmoothOp should not be null."); platform::errors::NotFound(
PADDLE_ENFORCE(ctx->HasOutput("Out"), "The input 'X' of LabelSmoothOp is not found."));
"Output(Out) of LabelSmoothOp should not be null."); PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::NotFound(
"The output 'Out' of LabelSmoothOp is not found."));
auto in_dims = ctx->GetInputDim("X"); auto in_dims = ctx->GetInputDim("X");
if (ctx->HasInput("PriorDist")) { if (ctx->HasInput("PriorDist")) {
auto noise_dims = ctx->GetInputDim("PriorDist"); auto noise_dims = ctx->GetInputDim("PriorDist");
auto noise_numel = paddle::framework::product(noise_dims); auto noise_numel = paddle::framework::product(noise_dims);
PADDLE_ENFORCE( PADDLE_ENFORCE_EQ(
in_dims[in_dims.size() - 1] == noise_numel, in_dims[in_dims.size() - 1], noise_numel,
"The number of elements in Input(PriorDist) must be equal to the " platform::errors::InvalidArgument(
"dimension of each label."); "The number of elements in input 'PriorDist' must be equal to "
"the "
"dimension of each label. But received each label's "
"dimension=[%d], number of elements in input 'PriorDist' is [%d]",
in_dims[in_dims.size() - 1], noise_numel));
} }
ctx->ShareLoD("X", /*->*/ "Out"); ctx->ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", in_dims); ctx->SetOutputDim("Out", in_dims);
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 4c01d6d5
...@@ -406,6 +406,22 @@ def rpn_target_assign(bbox_pred, ...@@ -406,6 +406,22 @@ def rpn_target_assign(bbox_pred,
""" """
helper = LayerHelper('rpn_target_assign', **locals()) helper = LayerHelper('rpn_target_assign', **locals())
check_variable_and_dtype(bbox_pred, 'bbox_pred', ['float32', 'float64'],
'rpn_target_assign')
check_variable_and_dtype(cls_logits, 'cls_logits', ['float32', 'float64'],
'rpn_target_assign')
check_variable_and_dtype(anchor_box, 'anchor_box', ['float32', 'float64'],
'rpn_target_assign')
check_variable_and_dtype(anchor_var, 'anchor_var', ['float32', 'float64'],
'rpn_target_assign')
check_variable_and_dtype(gt_boxes, 'gt_boxes', ['float32', 'float64'],
'rpn_target_assign')
check_variable_and_dtype(is_crowd, 'is_crowd', ['int32'],
'rpn_target_assign')
check_variable_and_dtype(im_info, 'im_info', ['float32', 'float64'],
'rpn_target_assign')
# Assign target label to anchors # Assign target label to anchors
loc_index = helper.create_variable_for_type_inference(dtype='int32') loc_index = helper.create_variable_for_type_inference(dtype='int32')
score_index = helper.create_variable_for_type_inference(dtype='int32') score_index = helper.create_variable_for_type_inference(dtype='int32')
...@@ -2662,6 +2678,13 @@ def generate_proposal_labels(rpn_rois, ...@@ -2662,6 +2678,13 @@ def generate_proposal_labels(rpn_rois,
helper = LayerHelper('generate_proposal_labels', **locals()) helper = LayerHelper('generate_proposal_labels', **locals())
check_variable_and_dtype(rpn_rois, 'rpn_rois', ['float32', 'float64'],
'generate_proposal_labels')
check_variable_and_dtype(gt_classes, 'gt_classes', ['int32'],
'generate_proposal_labels')
check_variable_and_dtype(is_crowd, 'is_crowd', ['int32'],
'generate_proposal_labels')
rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype) rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
labels_int32 = helper.create_variable_for_type_inference( labels_int32 = helper.create_variable_for_type_inference(
dtype=gt_classes.dtype) dtype=gt_classes.dtype)
...@@ -2904,7 +2927,7 @@ def generate_proposals(scores, ...@@ -2904,7 +2927,7 @@ def generate_proposals(scores,
im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin
image information for N batch. Height and width are the input sizes image information for N batch. Height and width are the input sizes
and scale is the ratio of network input size and original size. and scale is the ratio of network input size and original size.
The data type must be int32. The data type can be float32 or float64.
anchors(Variable): A 4-D Tensor represents the anchors with a layout anchors(Variable): A 4-D Tensor represents the anchors with a layout
of [H, W, A, 4]. H and W are height and width of the feature map, of [H, W, A, 4]. H and W are height and width of the feature map,
num_anchors is the box count of each position. Each anchor is num_anchors is the box count of each position. Each anchor is
...@@ -2947,6 +2970,17 @@ def generate_proposals(scores, ...@@ -2947,6 +2970,17 @@ def generate_proposals(scores,
""" """
helper = LayerHelper('generate_proposals', **locals()) helper = LayerHelper('generate_proposals', **locals())
check_variable_and_dtype(scores, 'scores', ['float32'],
'generate_proposals')
check_variable_and_dtype(bbox_deltas, 'bbox_deltas', ['float32'],
'generate_proposals')
check_variable_and_dtype(im_info, 'im_info', ['float32', 'float64'],
'generate_proposals')
check_variable_and_dtype(anchors, 'anchors', ['float32'],
'generate_proposals')
check_variable_and_dtype(variances, 'variances', ['float32'],
'generate_proposals')
rpn_rois = helper.create_variable_for_type_inference( rpn_rois = helper.create_variable_for_type_inference(
dtype=bbox_deltas.dtype) dtype=bbox_deltas.dtype)
rpn_roi_probs = helper.create_variable_for_type_inference( rpn_roi_probs = helper.create_variable_for_type_inference(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 4c01d6d5
...@@ -5582,6 +5582,8 @@ def im2sequence(input, ...@@ -5582,6 +5582,8 @@ def im2sequence(input,
assert not in_dygraph_mode(), ( assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.") "sequence layer is not supported in dygraph mode yet.")
check_variable_and_dtype(input, 'input', ['float32'], 'im2sequence')
if isinstance(filter_size, int): if isinstance(filter_size, int):
filter_size = [filter_size, filter_size] filter_size = [filter_size, filter_size]
if isinstance(stride, int): if isinstance(stride, int):
...@@ -6727,7 +6729,7 @@ def label_smooth(label, ...@@ -6727,7 +6729,7 @@ def label_smooth(label,
label(Variable): The input variable containing the label data. The label(Variable): The input variable containing the label data. The
label data should use one-hot representation. It's label data should use one-hot representation. It's
a multidimensional tensor with a shape of a multidimensional tensor with a shape of
:math:`[N_1, ..., Depth]`, where Depth is class number. :math:`[N_1, ..., Depth]`, where Depth is class number. The dtype can be "float32" and "float64".
prior_dist(Variable, optional): The prior distribution to be used to smooth prior_dist(Variable, optional): The prior distribution to be used to smooth
labels. If not provided, an uniform distribution labels. If not provided, an uniform distribution
is used. It's a multidimensional tensor with a shape of is used. It's a multidimensional tensor with a shape of
...@@ -6750,7 +6752,7 @@ def label_smooth(label, ...@@ -6750,7 +6752,7 @@ def label_smooth(label,
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
label = layers.data(name="label", shape=[1], dtype="float32") label = layers.data(name="label", shape=[1], dtype="int32")
one_hot_label = layers.one_hot(input=label, depth=10) one_hot_label = layers.one_hot(input=label, depth=10)
smooth_label = layers.label_smooth( smooth_label = layers.label_smooth(
label=one_hot_label, epsilon=0.1, dtype="float32") label=one_hot_label, epsilon=0.1, dtype="float32")
...@@ -6762,6 +6764,9 @@ def label_smooth(label, ...@@ -6762,6 +6764,9 @@ def label_smooth(label,
return core.ops.label_smooth(label, prior_dist, 'epsilon', return core.ops.label_smooth(label, prior_dist, 'epsilon',
float(epsilon)) float(epsilon))
check_variable_and_dtype(label, 'label', ['float32', 'float64'],
'label_smooth')
helper = LayerHelper("label_smooth", **locals()) helper = LayerHelper("label_smooth", **locals())
label.stop_gradient = True label.stop_gradient = True
smooth_label = helper.create_variable_for_type_inference(dtype) smooth_label = helper.create_variable_for_type_inference(dtype)
...@@ -9121,6 +9126,9 @@ def affine_grid(theta, out_shape, name=None): ...@@ -9121,6 +9126,9 @@ def affine_grid(theta, out_shape, name=None):
""" """
helper = LayerHelper('affine_grid') helper = LayerHelper('affine_grid')
check_variable_and_dtype(theta, 'theta', ['float32', 'float64'],
'affine_grid')
if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \ if not (isinstance(out_shape, list) or isinstance(out_shape, tuple) or \
isinstance(out_shape, Variable)): isinstance(out_shape, Variable)):
raise ValueError("The out_shape should be a list, tuple or Variable.") raise ValueError("The out_shape should be a list, tuple or Variable.")
...@@ -9133,6 +9141,8 @@ def affine_grid(theta, out_shape, name=None): ...@@ -9133,6 +9141,8 @@ def affine_grid(theta, out_shape, name=None):
attrs = {} attrs = {}
if isinstance(out_shape, Variable): if isinstance(out_shape, Variable):
ipts['OutputShape'] = out_shape ipts['OutputShape'] = out_shape
check_variable_and_dtype(out_shape, 'out_shape', ['int32'],
'affine_grid')
else: else:
attrs['output_shape'] = out_shape attrs['output_shape'] = out_shape
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ 4c01d6d5
...@@ -3055,8 +3055,7 @@ class TestBook(LayerTest): ...@@ -3055,8 +3055,7 @@ class TestBook(LayerTest):
out, ids = layers.argsort(input=data, axis=1) out, ids = layers.argsort(input=data, axis=1)
theta = layers.data(name="theta", shape=[2, 3], dtype="float32") theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
out_shape = layers.data( out_shape = layers.data(name="out_shape", shape=[-1], dtype="int32")
name="out_shape", shape=[-1], dtype="float32")
data_0 = layers.affine_grid(theta, out_shape) data_0 = layers.affine_grid(theta, out_shape)
data_1 = layers.affine_grid(theta, [5, 3, 28, 28]) data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
......
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