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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
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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 {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Theta"),
"Input(Theta) of AffineGridOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of AffineGridOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("Theta"), true,
platform::errors::NotFound(
"The input 'Theta' of AffineGridOp is not found."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Output"), true,
platform::errors::NotFound(
"The output 'Output' of AffineGridOp is not found."));
auto theta_dims = ctx->GetInputDim("Theta");
PADDLE_ENFORCE(theta_dims.size() == 3,
"AffineGrid's Input(Theta) should be 3-D tensor.");
PADDLE_ENFORCE_EQ(
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");
if (output_shape.size() == 0) {
PADDLE_ENFORCE(ctx->HasInput("OutputShape"),
"Input(OutputShape) of AffineGridOp should not be null if "
"attr(output_shape) is not configured.");
PADDLE_ENFORCE_EQ(
ctx->HasInput("OutputShape"), true,
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");
PADDLE_ENFORCE(output_shape_dims.size() == 1,
"AffineGrid's Input(OutputShape) should be 1-D tensor.");
PADDLE_ENFORCE_EQ(
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 {
PADDLE_ENFORCE(output_shape.size() == 4,
"The size of attr(output_shape) should be 4.");
PADDLE_ENFORCE_EQ(
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(theta_dims[2] == 3, "Input(theta) dims[2] should be 3.");
PADDLE_ENFORCE_EQ(
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
ctx->SetOutputDim("Output",
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 {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("RpnRois"),
"Input(RpnRois) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("GtClasses"),
"Input(GtClasses) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("IsCrowd"),
"Input(IsCrowd) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("GtBoxes"),
"Input(GtBoxes) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null.");
PADDLE_ENFORCE(
ctx->HasOutput("Rois"),
"Output(Rois) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("LabelsInt32"),
"Output(LabelsInt32) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BboxTargets"),
"Output(BboxTargets) of GenerateProposalLabelsOp should not be null");
PADDLE_ENFORCE(ctx->HasOutput("BboxInsideWeights"),
"Output(BboxInsideWeights) of GenerateProposalLabelsOp "
"should not be null");
PADDLE_ENFORCE(ctx->HasOutput("BboxOutsideWeights"),
"Output(BboxOutsideWeights) of GenerateProposalLabelsOp "
"should not be null");
PADDLE_ENFORCE_EQ(
ctx->HasInput("RpnRois"), true,
platform::errors::NotFound("Input(RpnRois) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("GtClasses"), true,
platform::errors::NotFound("Input(GtClasses) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("IsCrowd"), true,
platform::errors::NotFound("Input(IsCrowd) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("GtBoxes"), true,
platform::errors::NotFound("Input(GtBoxes) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("ImInfo"), true,
platform::errors::NotFound("Input(ImInfo) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasOutput("Rois"), true,
platform::errors::NotFound(
"Output(Rois) of GenerateProposalLabelsOp should not be null"));
PADDLE_ENFORCE_EQ(ctx->HasOutput("LabelsInt32"), true,
platform::errors::NotFound("Output(LabelsInt32) of "
"GenerateProposalLabelsOp "
"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 gt_boxes_dims = ctx->GetInputDim("GtBoxes");
auto im_info_dims = ctx->GetInputDim("ImInfo");
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,
"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,
"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");
......@@ -399,15 +421,30 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
bool use_random = context.Attr<bool>("use_random");
bool is_cascade_rcnn = context.Attr<bool>("is_cascade_rcnn");
bool is_cls_agnostic = context.Attr<bool>("is_cls_agnostic");
PADDLE_ENFORCE_EQ(rpn_rois->lod().size(), 1UL,
"GenerateProposalLabelsOp rpn_rois needs 1 level of LoD");
PADDLE_ENFORCE_EQ(
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(
gt_classes->lod().size(), 1UL,
"GenerateProposalLabelsOp gt_classes needs 1 level of LoD");
PADDLE_ENFORCE_EQ(is_crowd->lod().size(), 1UL,
"GenerateProposalLabelsOp is_crowd needs 1 level of LoD");
PADDLE_ENFORCE_EQ(gt_boxes->lod().size(), 1UL,
"GenerateProposalLabelsOp gt_boxes needs 1 level of LoD");
platform::errors::InvalidArgument(
"GenerateProposalLabelsOp gt_classes needs 1 level of LoD. But "
"received level of LoD is [%d], LoD is [%s].",
gt_classes->lod().size(), gt_classes->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);
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 {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Scores"), "Input(Scores) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("BboxDeltas"),
"Input(BboxDeltas) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("ImInfo"), "Input(ImInfo) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Anchors"),
"Input(Anchors) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Variances"),
"Input(Variances) shouldn't be null.");
PADDLE_ENFORCE_EQ(
ctx->HasInput("Scores"), true,
platform::errors::NotFound("Input(Scores) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("BboxDeltas"), true,
platform::errors::NotFound("Input(BboxDeltas) shouldn't be null."));
PADDLE_ENFORCE_EQ(
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("RpnRoiProbs", {-1, 1});
......@@ -247,7 +254,6 @@ static inline Tensor VectorToTensor(const std::vector<T> &selected_indices,
template <class T>
static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
Tensor *scores, T nms_threshold, float eta) {
PADDLE_ENFORCE_NOT_NULL(bbox);
int64_t num_boxes = bbox->dims()[0];
// 4: [xmin ymin xmax ymax]
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> {
float nms_thresh = context.Attr<float>("nms_thresh");
float min_size = context.Attr<float>("min_size");
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>();
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ 4c01d6d5
......@@ -31,40 +31,44 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Anchor"),
"Input(Anchor) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(ctx->HasInput("GtBoxes"),
"Input(GtBoxes) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(ctx->HasInput("IsCrowd"),
"Input(Anchor) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(ctx->HasInput("ImInfo"),
"Input(ImInfo) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("LocationIndex"),
"Output(LocationIndex) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("ScoreIndex"),
"Output(ScoreIndex) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("TargetLabel"),
"Output(TargetLabel) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("TargetBBox"),
"Output(TargetBBox) of RpnTargetAssignOp should not be null");
PADDLE_ENFORCE(
ctx->HasOutput("BBoxInsideWeight"),
"Output(BBoxInsideWeight) of RpnTargetAssignOp should not be null");
OP_INOUT_CHECK(ctx->HasInput("Anchor"), "Input", "Anchor",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasInput("GtBoxes"), "Input", "GtBoxes",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasInput("IsCrowd"), "Input", "IsCrowd",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasInput("ImInfo"), "Input", "ImInfo",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasOutput("LocationIndex"), "Output", "LocationIndex",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasOutput("ScoreIndex"), "Output", "ScoreIndex",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasOutput("TargetLabel"), "Output", "TargetLabel",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasOutput("TargetBBox"), "Output", "TargetBBox",
"rpn_target_assign");
OP_INOUT_CHECK(ctx->HasOutput("BBoxInsideWeight"), "Output",
"BBoxInsideWeight", "rpn_target_assign");
auto anchor_dims = ctx->GetInputDim("Anchor");
auto gt_boxes_dims = ctx->GetInputDim("GtBoxes");
auto im_info_dims = ctx->GetInputDim("ImInfo");
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,
"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,
"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("ScoreIndex", {-1});
......@@ -357,9 +361,15 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto* bbox_inside_weight = context.Output<LoDTensor>("BBoxInsideWeight");
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,
"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 batch_num = static_cast<int64_t>(gt_boxes->lod().back().size() - 1);
......@@ -479,8 +489,20 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
lod0_score.emplace_back(total_score_num);
}
PADDLE_ENFORCE_LE(total_loc_num, max_num);
PADDLE_ENFORCE_LE(total_score_num, max_num);
PADDLE_ENFORCE_LE(
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);
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 {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of Im2SequenceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of Im2SequenceOp op should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
platform::errors::NotFound(
"The input 'X' of Im2SequenceOp is not found."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::NotFound(
"The output 'Out' of Im2SequenceOp is not found."));
auto in_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(in_dim.size(), 4,
"Input(X) format must be 4D tensor, eg., NCHW.");
PADDLE_ENFORCE_EQ(
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 kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
......@@ -146,9 +152,13 @@ class Im2SequenceGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
platform::errors::NotFound(
"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"));
}
};
......
paddle/fluid/operators/label_smooth_op.cc
浏览文件 @ 4c01d6d5
......@@ -28,18 +28,24 @@ class LabelSmoothOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of LabelSmoothOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of LabelSmoothOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
platform::errors::NotFound(
"The input 'X' of LabelSmoothOp is not found."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
platform::errors::NotFound(
"The output 'Out' of LabelSmoothOp is not found."));
auto in_dims = ctx->GetInputDim("X");
if (ctx->HasInput("PriorDist")) {
auto noise_dims = ctx->GetInputDim("PriorDist");
auto noise_numel = paddle::framework::product(noise_dims);
PADDLE_ENFORCE(
in_dims[in_dims.size() - 1] == noise_numel,
"The number of elements in Input(PriorDist) must be equal to the "
"dimension of each label.");
PADDLE_ENFORCE_EQ(
in_dims[in_dims.size() - 1], noise_numel,
platform::errors::InvalidArgument(
"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->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,
"""
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
loc_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,
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)
labels_int32 = helper.create_variable_for_type_inference(
dtype=gt_classes.dtype)
......@@ -2904,7 +2927,7 @@ def generate_proposals(scores,
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
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
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
......@@ -2947,6 +2970,17 @@ def generate_proposals(scores,
"""
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(
dtype=bbox_deltas.dtype)
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,
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
check_variable_and_dtype(input, 'input', ['float32'], 'im2sequence')
if isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
if isinstance(stride, int):
......@@ -6727,7 +6729,7 @@ def label_smooth(label,
label(Variable): The input variable containing the label data. The
label data should use one-hot representation. It's
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
labels. If not provided, an uniform distribution
is used. It's a multidimensional tensor with a shape of
......@@ -6750,7 +6752,7 @@ def label_smooth(label,
import paddle.fluid as fluid
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)
smooth_label = layers.label_smooth(
label=one_hot_label, epsilon=0.1, dtype="float32")
......@@ -6762,6 +6764,9 @@ def label_smooth(label,
return core.ops.label_smooth(label, prior_dist, 'epsilon',
float(epsilon))
check_variable_and_dtype(label, 'label', ['float32', 'float64'],
'label_smooth')
helper = LayerHelper("label_smooth", **locals())
label.stop_gradient = True
smooth_label = helper.create_variable_for_type_inference(dtype)
......@@ -9121,6 +9126,9 @@ def affine_grid(theta, out_shape, name=None):
"""
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 \
isinstance(out_shape, 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):
attrs = {}
if isinstance(out_shape, Variable):
ipts['OutputShape'] = out_shape
check_variable_and_dtype(out_shape, 'out_shape', ['int32'],
'affine_grid')
else:
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):
out, ids = layers.argsort(input=data, axis=1)
theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
out_shape = layers.data(
name="out_shape", shape=[-1], dtype="float32")
out_shape = layers.data(name="out_shape", shape=[-1], dtype="int32")
data_0 = layers.affine_grid(theta, out_shape)
data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
......
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