diff --git a/paddle/fluid/operators/detection/density_prior_box_op.cc b/paddle/fluid/operators/detection/density_prior_box_op.cc index 27f3d85056172d2f726e6da978d42b2322a3f13c..2b97506ff1ee77db94725a420a3170872fe51d1f 100644 --- a/paddle/fluid/operators/detection/density_prior_box_op.cc +++ b/paddle/fluid/operators/detection/density_prior_box_op.cc @@ -19,15 +19,27 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("Input"), - "Input(Input) of DensityPriorBoxOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Image"), - "Input(Image) of DensityPriorBoxOp should not be null."); + OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", + "DensityPriorBoxOp"); + OP_INOUT_CHECK(ctx->HasInput("Image"), "Input", "Image", + "DensityPriorBoxOp"); auto image_dims = ctx->GetInputDim("Image"); auto input_dims = ctx->GetInputDim("Input"); - PADDLE_ENFORCE(image_dims.size() == 4, "The layout of image is NCHW."); - PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW."); + PADDLE_ENFORCE_EQ( + image_dims.size(), 4, + platform::errors::InvalidArgument( + "The Input(Image) of Op(density_prior_box) should be a 4-D Tensor " + "and data format is NCHW. But received Image's dimensions = %d, " + "shape = [%s].", + image_dims.size(), image_dims)); + PADDLE_ENFORCE_EQ( + input_dims.size(), 4, + platform::errors::InvalidArgument( + "The Input(Input) of Op(density_prior_box) should be a 4-D Tensor " + "and data format is NCHW. But received Input's dimensions = %d, " + "shape = [%s].", + input_dims.size(), input_dims)); if (ctx->IsRuntime()) { PADDLE_ENFORCE_LT( @@ -53,8 +65,13 @@ class DensityPriorBoxOp : public framework::OperatorWithKernel { auto densities = ctx->Attrs().Get>("densities"); bool flatten = ctx->Attrs().Get("flatten_to_2d"); - PADDLE_ENFORCE_EQ(fixed_sizes.size(), densities.size(), - "The number of fixed_sizes and densities must be equal."); + PADDLE_ENFORCE_EQ( + fixed_sizes.size(), densities.size(), + platform::errors::InvalidArgument( + "The length of fixed_sizes and densities must be equal. " + "But received: fixed_sizes's length is %d, densities's length " + "is %d", + fixed_sizes.size(), densities.size())); size_t num_priors = 0; for (size_t i = 0; i < densities.size(); ++i) { num_priors += (fixed_ratios.size()) * (pow(densities[i], 2)); @@ -110,10 +127,16 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { "encoded in density prior boxes.") .AddCustomChecker([](const std::vector& variances) { PADDLE_ENFORCE_EQ(variances.size(), 4, - "Must and only provide 4 variance."); + platform::errors::InvalidArgument( + "The length of variance must " + "be 4. But received: variances' length is %d.", + variances.size())); for (size_t i = 0; i < variances.size(); ++i) { PADDLE_ENFORCE_GT(variances[i], 0.0, - "variance[%d] must be greater than 0.", i); + platform::errors::OutOfRange( + "variance[%d] must be greater " + "than 0. But received: variance[%d] = %f", + i, i, variances[i])); } }); AddAttr("clip", "(bool) Whether to clip out-of-boundary boxes.") @@ -127,14 +150,22 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { "Density prior boxes step across width, 0.0 for auto calculation.") .SetDefault(0.0) .AddCustomChecker([](const float& step_w) { - PADDLE_ENFORCE_GE(step_w, 0.0, "step_w should be larger than 0."); + PADDLE_ENFORCE_GE(step_w, 0.0, + platform::errors::InvalidArgument( + "step_w should be larger " + "than 0. But received: step_w = %f.", + step_w)); }); AddAttr( "step_h", "Density prior boxes step across height, 0.0 for auto calculation.") .SetDefault(0.0) .AddCustomChecker([](const float& step_h) { - PADDLE_ENFORCE_GE(step_h, 0.0, "step_h should be larger than 0."); + PADDLE_ENFORCE_GE(step_h, 0.0, + platform::errors::InvalidArgument( + "step_h should be larger " + "than 0. But received: step_h = %f.", + step_h)); }); AddAttr("offset", @@ -147,8 +178,12 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { .SetDefault(std::vector{}) .AddCustomChecker([](const std::vector& fixed_sizes) { for (size_t i = 0; i < fixed_sizes.size(); ++i) { - PADDLE_ENFORCE_GT(fixed_sizes[i], 0.0, - "fixed_sizes[%d] should be larger than 0.", i); + PADDLE_ENFORCE_GT( + fixed_sizes[i], 0.0, + platform::errors::OutOfRange( + "fixed_sizes[%d] should be " + "larger than 0. But received: fixed_sizes[%d] = %f", + i, i, fixed_sizes[i])); } }); @@ -158,8 +193,12 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { .SetDefault(std::vector{}) .AddCustomChecker([](const std::vector& fixed_ratios) { for (size_t i = 0; i < fixed_ratios.size(); ++i) { - PADDLE_ENFORCE_GT(fixed_ratios[i], 0.0, - "fixed_ratios[%d] should be larger than 0.", i); + PADDLE_ENFORCE_GT( + fixed_ratios[i], 0.0, + platform::errors::OutOfRange( + "fixed_ratios[%d] should be " + "larger than 0. But received: fixed_ratios[%d] = %f", + i, i, fixed_ratios[i])); } }); @@ -169,8 +208,12 @@ class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { .SetDefault(std::vector{}) .AddCustomChecker([](const std::vector& densities) { for (size_t i = 0; i < densities.size(); ++i) { - PADDLE_ENFORCE_GT(densities[i], 0, - "densities[%d] should be larger than 0.", i); + PADDLE_ENFORCE_GT( + densities[i], 0, + platform::errors::OutOfRange( + "densities[%d] should be " + "larger than 0. But received: densities[%d] = %f.", + i, i, densities[i])); } }); AddComment(R"DOC( diff --git a/paddle/fluid/operators/detection/prior_box_op.cc b/paddle/fluid/operators/detection/prior_box_op.cc index f4f3bcd667ad4d61228cc6481b40b51bf24faf18..bd584d660f7d4f76f9ea30354f3b0c2696b6d048 100644 --- a/paddle/fluid/operators/detection/prior_box_op.cc +++ b/paddle/fluid/operators/detection/prior_box_op.cc @@ -26,15 +26,26 @@ class PriorBoxOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("Input"), - "Input(Input) of PriorBoxOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Image"), - "Input(Image) of PriorBoxOp should not be null."); + OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "PriorBoxOp"); + OP_INOUT_CHECK(ctx->HasInput("Image"), "Input", "Image", "PriorBoxOp"); auto image_dims = ctx->GetInputDim("Image"); auto input_dims = ctx->GetInputDim("Input"); - PADDLE_ENFORCE(image_dims.size() == 4, "The layout of image is NCHW."); - PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW."); + + PADDLE_ENFORCE_EQ( + image_dims.size(), 4, + platform::errors::InvalidArgument( + "The Input(Image) of Op(PriorBoxOp) should be a 4-D Tensor " + "and data format is NCHW. But received Image's dimensions = %d, " + "shape = [%s].", + image_dims.size(), image_dims)); + PADDLE_ENFORCE_EQ( + input_dims.size(), 4, + platform::errors::InvalidArgument( + "The Input(Input) of Op(PriorBoxOp) should be a 4-D Tensor " + "and data format is NCHW. But received Input's dimensions = %d, " + "shape = [%s].", + input_dims.size(), input_dims)); auto min_sizes = ctx->Attrs().Get>("min_sizes"); auto max_sizes = ctx->Attrs().Get>("max_sizes"); @@ -47,13 +58,22 @@ class PriorBoxOp : public framework::OperatorWithKernel { size_t num_priors = aspect_ratios_vec.size() * min_sizes.size(); if (max_sizes.size() > 0) { - PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(), - "The number of min_size and max_size must be equal."); + PADDLE_ENFORCE_EQ( + max_sizes.size(), min_sizes.size(), + platform::errors::InvalidArgument( + "The length of min_size and " + "max_size must be equal. But received: min_size's length is %d, " + "max_size's length is %d.", + min_sizes.size(), max_sizes.size())); num_priors += max_sizes.size(); for (size_t i = 0; i < max_sizes.size(); ++i) { - PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i], - "max_size[%d] must be greater than min_size[%d].", i, - i); + PADDLE_ENFORCE_GT( + max_sizes[i], min_sizes[i], + platform::errors::InvalidArgument( + "max_size[%d] must be greater " + "than min_size[%d]. But received: max_size[%d] is %f, " + "min_size[%d] is %f.", + i, i, i, max_sizes[i], i, min_sizes[i])); } } @@ -121,11 +141,16 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { "(vector) List of min sizes " "of generated prior boxes.") .AddCustomChecker([](const std::vector& min_sizes) { - PADDLE_ENFORCE_GT(min_sizes.size(), 0, - "Size of min_sizes must be at least 1."); + PADDLE_ENFORCE_GT( + min_sizes.size(), 0, + platform::errors::InvalidArgument("Size of min_sizes must be " + "at least 1.")); for (size_t i = 0; i < min_sizes.size(); ++i) { PADDLE_ENFORCE_GT(min_sizes[i], 0.0, - "min_sizes[%d] must be positive.", i); + platform::errors::OutOfRange( + "min_sizes[%d] must be larger " + "than 0. But received: min_sizes[%d] is %f.", + i, i, min_sizes[i])); } }); AddAttr>( @@ -141,10 +166,16 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { "(vector) List of variances to be encoded in prior boxes.") .AddCustomChecker([](const std::vector& variances) { PADDLE_ENFORCE_EQ(variances.size(), 4, - "Must and only provide 4 variance."); + platform::errors::InvalidArgument( + "The length of variance must " + "be 4. But received: variances' length is %d.", + variances.size())); for (size_t i = 0; i < variances.size(); ++i) { PADDLE_ENFORCE_GT(variances[i], 0.0, - "variance[%d] must be greater than 0.", i); + platform::errors::OutOfRange( + "variance[%d] must be greater " + "than 0. But received: variance[%d] = %f", + i, i, variances[i])); } }); AddAttr("flip", "(bool) Whether to flip aspect ratios.") @@ -156,13 +187,21 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker { "Prior boxes step across width, 0.0 for auto calculation.") .SetDefault(0.0) .AddCustomChecker([](const float& step_w) { - PADDLE_ENFORCE_GE(step_w, 0.0, "step_w should be larger than 0."); + PADDLE_ENFORCE_GE(step_w, 0.0, + platform::errors::InvalidArgument( + "step_w should be larger " + "than 0. But received: step_w = %f.", + step_w)); }); AddAttr("step_h", "Prior boxes step across height, 0.0 for auto calculation.") .SetDefault(0.0) .AddCustomChecker([](const float& step_h) { - PADDLE_ENFORCE_GE(step_h, 0.0, "step_h should be larger than 0."); + PADDLE_ENFORCE_GE(step_h, 0.0, + platform::errors::InvalidArgument( + "step_h should be larger " + "than 0. But received: step_h = %f.", + step_h)); }); AddAttr("offset", diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index cd179f40f47d881267ac45302f8e8ef79a49784e..857b872ae47e294be98ca5336572f04fe24b6f7e 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -1754,6 +1754,8 @@ def prior_box(input, """ helper = LayerHelper("prior_box", **locals()) dtype = helper.input_dtype() + check_variable_and_dtype( + input, 'input', ['uint8', 'int8', 'float32', 'float64'], 'prior_box') def _is_list_or_tuple_(data): return (isinstance(data, list) or isinstance(data, tuple)) @@ -1932,18 +1934,18 @@ def density_prior_box(input, """ helper = LayerHelper("density_prior_box", **locals()) dtype = helper.input_dtype() + check_variable_and_dtype(input, 'input', ['float32', 'float64'], + 'density_prior_box') def _is_list_or_tuple_(data): return (isinstance(data, list) or isinstance(data, tuple)) - if not _is_list_or_tuple_(densities): - raise TypeError('densities should be a list or a tuple or None.') - if not _is_list_or_tuple_(fixed_sizes): - raise TypeError('fixed_sizes should be a list or a tuple or None.') - if not _is_list_or_tuple_(fixed_ratios): - raise TypeError('fixed_ratios should be a list or a tuple or None.') + check_type(densities, 'densities', (list, tuple), 'density_prior_box') + check_type(fixed_sizes, 'fixed_sizes', (list, tuple), 'density_prior_box') + check_type(fixed_ratios, 'fixed_ratios', (list, tuple), 'density_prior_box') if len(densities) != len(fixed_sizes): raise ValueError('densities and fixed_sizes length should be euqal.') + if not (_is_list_or_tuple_(steps) and len(steps) == 2): raise ValueError('steps should be a list or tuple ', 'with length 2, (step_width, step_height).')