From b465bb0de78e268fa3da11a3ae9c9c43921258f3 Mon Sep 17 00:00:00 2001 From: Kaipeng Deng Date: Thu, 9 Apr 2020 22:55:08 +0800 Subject: [PATCH] fix adaptive_pool2d/pool3d error message. test=develop (#23658) --- .../operators/grid_sampler_cudnn_op.cu.cc | 25 +++--- paddle/fluid/operators/grid_sampler_op.cc | 54 +++++++++---- paddle/fluid/operators/kldiv_loss_op.cc | 51 ++++++++---- paddle/fluid/operators/spectral_norm_op.cc | 79 +++++++++++++------ paddle/fluid/operators/temporal_shift_op.cc | 43 +++++++--- paddle/fluid/operators/temporal_shift_op.cu | 5 +- python/paddle/fluid/layers/loss.py | 6 +- python/paddle/fluid/layers/nn.py | 23 ++++++ 8 files changed, 207 insertions(+), 79 deletions(-) diff --git a/paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc b/paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc index 92f235c1d2..c266b0d32b 100644 --- a/paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc +++ b/paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc @@ -30,8 +30,9 @@ template class CUDNNGridSampleOpKernel : public framework::OpKernel { 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, + platform::errors::InvalidArgument( + "It must use CUDAPlace when using CUDA Kernel")); auto& dev_ctx = ctx.template device_context(); auto handle = dev_ctx.cudnn_handle(); auto* input = ctx.Input("X"); @@ -59,10 +60,13 @@ class CUDNNGridSampleOpKernel : public framework::OpKernel { cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor( DataLayout::kNCHW, framework::vectorize(output->dims())); - PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSpatialTfSamplerForward( - handle, cudnn_st_desc, CudnnDataType::kOne(), cudnn_input_desc, - input_data, grid_data, CudnnDataType::kZero(), cudnn_output_desc, - output_data)); + PADDLE_ENFORCE_CUDA_SUCCESS( + platform::dynload::cudnnSpatialTfSamplerForward( + handle, cudnn_st_desc, CudnnDataType::kOne(), cudnn_input_desc, + input_data, grid_data, CudnnDataType::kZero(), cudnn_output_desc, + output_data), + platform::errors::InvalidArgument( + "cudnnSpatialTfSamplerForward in Op(grid_sampler) failed")); } }; @@ -70,8 +74,9 @@ template class CUDNNGridSampleGradOpKernel : public framework::OpKernel { 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, + platform::errors::InvalidArgument( + "It must use CUDAPlace when using CUDA Kernel")); auto& dev_ctx = ctx.template device_context(); auto handle = dev_ctx.cudnn_handle(); auto* input = ctx.Input("X"); @@ -117,7 +122,9 @@ class CUDNNGridSampleGradOpKernel : public framework::OpKernel { input_data, CudnnDataType::kZero(), cudnn_input_grad_desc, input_grad_data, CudnnDataType::kOne(), cudnn_output_grad_desc, output_grad_data, grid_data, CudnnDataType::kZero(), - grid_grad_data)); + grid_grad_data), + platform::errors::InvalidArgument( + "cudnnSpatialTfSamplerBackward in Op(grid_sampler) failed")); } }; diff --git a/paddle/fluid/operators/grid_sampler_op.cc b/paddle/fluid/operators/grid_sampler_op.cc index 968b0710e0..ea0fc05bbd 100644 --- a/paddle/fluid/operators/grid_sampler_op.cc +++ b/paddle/fluid/operators/grid_sampler_op.cc @@ -28,31 +28,55 @@ class GridSampleOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of GridSampleOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Grid"), - "Input(Grid) of GridSampleOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Output"), - "Output(Output) of GridSampleOp should not be null."); + PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, + platform::errors::NotFound( + "Input(X) of GridSampleOp should not be null.")); + PADDLE_ENFORCE_EQ(ctx->HasInput("Grid"), true, + platform::errors::NotFound( + "Input(Grid) of GridSampleOp should not be null.")); + PADDLE_ENFORCE_EQ( + ctx->HasOutput("Output"), true, + platform::errors::NotFound( + "Output(Output) of GridSampleOp should not be null.")); auto x_dims = ctx->GetInputDim("X"); auto grid_dims = ctx->GetInputDim("Grid"); - PADDLE_ENFORCE(x_dims.size() == 4, - "Input(X) of GridSampleOp should be 4-D Tensor."); - PADDLE_ENFORCE(grid_dims.size() == 4, - "Input(Grid) of GridSampleOp should be 4-D Tensor."); + PADDLE_ENFORCE_EQ(x_dims.size(), 4, + platform::errors::InvalidArgument( + "Input(X) of GridSampleOp should be 4-D Tensor, but " + "received X dimension size(%d)", + x_dims.size())); + PADDLE_ENFORCE_EQ(grid_dims.size(), 4, + platform::errors::InvalidArgument( + "Input(Grid) of GridSampleOp should be 4-D Tensor, " + "but received X dimension size(%d)", + grid_dims.size())); if (ctx->IsRuntime() || grid_dims[3] > 0) { - PADDLE_ENFORCE(grid_dims[3] == 2, "Input(Grid) dims[3] should be 2."); + PADDLE_ENFORCE_EQ( + grid_dims[3], 2, + platform::errors::InvalidArgument( + "Input(Grid) dimension[3] should be 2, but received %d", + grid_dims[3])); } if (ctx->IsRuntime()) { - PADDLE_ENFORCE_EQ(grid_dims[0], x_dims[0], - "Input(X) and Input(Grid) dims[0] should be equal."); + PADDLE_ENFORCE_EQ( + grid_dims[0], x_dims[0], + platform::errors::InvalidArgument( + "Input(X) and Input(Grid) dimension[0] should be equal, but " + "received X dimension[0](%d) != Grid dimension[0](%d)", + x_dims[0], grid_dims[0])); PADDLE_ENFORCE_EQ( grid_dims[1], x_dims[2], - "Input(X) dims[2] and Input(Grid) dims[1] should be equal."); + platform::errors::InvalidArgument( + "Input(X) dims[2] and Input(Grid) dims[1] should be equal, but " + "received X dimension[2](%d) != Grid dimension[1](%d)", + x_dims[2], grid_dims[1])); PADDLE_ENFORCE_EQ( grid_dims[2], x_dims[3], - "Input(X) dims[3] and Input(Grid) dims[2] should be equal."); + platform::errors::InvalidArgument( + "Input(X) dims[3] and Input(Grid) dims[2] should be equal, but " + "received X dimension[3](%d) != Grid dimension[2](%d)", + x_dims[3], grid_dims[2])); } ctx->SetOutputDim("Output", x_dims); diff --git a/paddle/fluid/operators/kldiv_loss_op.cc b/paddle/fluid/operators/kldiv_loss_op.cc index 3e9138fa56..ad33324239 100644 --- a/paddle/fluid/operators/kldiv_loss_op.cc +++ b/paddle/fluid/operators/kldiv_loss_op.cc @@ -23,30 +23,42 @@ class KLDivLossOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of KLDivLossOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Target"), - "Input(Target) of KLDivLossOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Loss"), - "Output(Loss) of KLDivLossOp should not be null."); + PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, + platform::errors::NotFound( + "Input(X) of KLDivLossOp should not be null.")); + PADDLE_ENFORCE_EQ(ctx->HasInput("Target"), true, + platform::errors::NotFound( + "Input(Target) of KLDivLossOp should not be null.")); + PADDLE_ENFORCE_EQ(ctx->HasOutput("Loss"), true, + platform::errors::NotFound( + "Output(Loss) of KLDivLossOp should not be null.")); auto dim_x = ctx->GetInputDim("X"); auto dim_target = ctx->GetInputDim("Target"); PADDLE_ENFORCE_EQ(dim_x.size(), dim_target.size(), - "Input(X) rank and Input(Target) rank should be same."); + platform::errors::InvalidArgument( + "Input(X) rank and Input(Target) rank should be " + "same, but received X rank(%d) != Target rank(%d)", + dim_x.size(), dim_target.size())); for (int i = 0; i < dim_x.size(); i++) { if (ctx->IsRuntime() || (dim_x[i] > 0 && dim_target[i] > 0)) { - PADDLE_ENFORCE_EQ(dim_x[i], dim_target[i], - "Input(X) and Input(Target) should in same shape."); + PADDLE_ENFORCE_EQ( + dim_x[i], dim_target[i], + platform::errors::InvalidArgument( + "Input(X) and Input(Target) should in same shape. but received " + "X dimension[%d](%d) != Target dimension[%d](%d)", + i, dim_x[i], i, dim_target[i])); } } auto reduction = ctx->Attrs().Get("reduction"); - PADDLE_ENFORCE( - "mean" == reduction || "sum" == reduction || "batchmean" == reduction || - "none" == reduction, - "Attr(reduction) can only be 'none'|'batchmean'|'sum'|'mean'."); + auto reduction_valid = "mean" == reduction || "sum" == reduction || + "batchmean" == reduction || "none" == reduction; + PADDLE_ENFORCE_EQ( + reduction_valid, true, + platform::errors::InvalidArgument( + "Attr(reduction) can only be 'none'|'batchmean'|'sum'|'mean'.")); if ("none" == reduction) { ctx->SetOutputDim("Loss", dim_x); @@ -123,10 +135,15 @@ class KLDivLossOpGrad : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("Target"), "Input(Target) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")), - "Input(Loss@GRAD) should not be null"); + PADDLE_ENFORCE_EQ( + ctx->HasInput("X"), true, + platform::errors::NotFound("Input(X) should not be null")); + PADDLE_ENFORCE_EQ( + ctx->HasInput("Target"), true, + platform::errors::NotFound("Input(Target) should not be null")); + PADDLE_ENFORCE_EQ( + ctx->HasInput(framework::GradVarName("Loss")), true, + platform::errors::NotFound("Input(Loss@GRAD) should not be null")); auto dim_x = ctx->GetInputDim("X"); if (ctx->HasOutput(framework::GradVarName("X"))) { ctx->SetOutputDim(framework::GradVarName("X"), dim_x); diff --git a/paddle/fluid/operators/spectral_norm_op.cc b/paddle/fluid/operators/spectral_norm_op.cc index ccad6d7890..a49c25a8fc 100644 --- a/paddle/fluid/operators/spectral_norm_op.cc +++ b/paddle/fluid/operators/spectral_norm_op.cc @@ -26,26 +26,45 @@ class SpectralNormOp : public framework::OperatorWithKernel { protected: void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("Weight"), - "Input(Weight) of SpectralNormOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("U"), - "Input(U) of SpectralNormOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("V"), - "Input(V) of SpectralNormOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of SpectralNormOp should not be null."); + PADDLE_ENFORCE_EQ( + ctx->HasInput("Weight"), true, + platform::errors::NotFound( + "Input(Weight) of SpectralNormOp should not be null.")); + PADDLE_ENFORCE_EQ(ctx->HasInput("U"), true, + platform::errors::NotFound( + "Input(U) of SpectralNormOp should not be null.")); + PADDLE_ENFORCE_EQ(ctx->HasInput("V"), true, + platform::errors::NotFound( + "Input(V) of SpectralNormOp should not be null.")); + PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true, + platform::errors::NotFound( + "Output(Out) of SpectralNormOp should not be null.")); auto dim_weight = ctx->GetInputDim("Weight"); auto rank_weight = dim_weight.size(); - PADDLE_ENFORCE(rank_weight >= 2 && rank_weight <= 5, - "The rank of Input(Weights) can only be 2, 3," - "4, 5 for fc, conv1d, conv2d, conv3d layers."); + PADDLE_ENFORCE_GE(rank_weight, 2, + platform::errors::InvalidArgument( + "The rank of Input(Weights) should be greater equal " + "than 2, but received Weight rank(%d)", + rank_weight)); + PADDLE_ENFORCE_LE(rank_weight, 5, + platform::errors::InvalidArgument( + "The rank of Input(Weights) should be less equal " + "than 5, but received Weight rank(%d)", + rank_weight)); int dim = ctx->Attrs().Get("dim"); int power_iters = ctx->Attrs().Get("power_iters"); - PADDLE_ENFORCE(dim == 0 || dim == 1, "Attr(dim) can only be 0 or 1"); - PADDLE_ENFORCE(power_iters >= 0, - "Attr(power_iters) should be larger equal then 0"); + auto dim_valid = dim == 0 || dim == 1; + PADDLE_ENFORCE_EQ( + dim_valid, true, + platform::errors::InvalidArgument( + "Attr(dim) can only be 0 or 1, but received %d", dim)); + PADDLE_ENFORCE_GE( + power_iters, 0, + platform::errors::InvalidArgument( + "Attr(power_iters) should be greater equal then 0, but received %d", + power_iters)); int h = dim_weight[dim]; int w = 1; @@ -59,15 +78,22 @@ class SpectralNormOp : public framework::OperatorWithKernel { if (ctx->IsRuntime() || (dim_u[0] > 0 && h > 0)) { PADDLE_ENFORCE_EQ(dim_u[0], h, - "Input(U) dims[0] should be equal to " - "Input(Weight) dims[Attr(dim)]"); + platform::errors::InvalidArgument( + "Input(U) dimension[0] should be equal to " + "Input(Weight) dimension[Attr(dim)], but received " + "U dimension[0](%d) != Weight dimension[%d](%d)", + dim_u[0], dim, h)); } if (ctx->IsRuntime() || (dim_v[0] > 0 && w > 0)) { PADDLE_ENFORCE_EQ( dim_v[0], w, - "Input(V) dims[0] should be equal to " - "the product of Input(Weight) dims except dims[Attr(dim)]"); + platform::errors::InvalidArgument( + "Input(V) dimension[0] should be equal to the product of " + "Input(Weight) dimension except dimension[Attr(dim)], but " + "received V dimension[0](%d) != product of Input(Weight) " + "dimension(%d)", + dim_v[0], w)); } ctx->SetOutputDim("Out", dim_weight); @@ -194,11 +220,18 @@ class SpectralNormOpGrad : public framework::OperatorWithKernel { protected: void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("Weight"), "Input(Weight) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("U"), "Input(U) should not be null"); - PADDLE_ENFORCE(ctx->HasInput("V"), "Input(V) should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), - "Input(Out@GRAD) should not be null"); + PADDLE_ENFORCE_EQ( + ctx->HasInput("Weight"), true, + platform::errors::NotFound("Input(Weight) should not be null")); + PADDLE_ENFORCE_EQ( + ctx->HasInput("U"), true, + platform::errors::NotFound("Input(U) should not be null")); + PADDLE_ENFORCE_EQ( + ctx->HasInput("V"), true, + platform::errors::NotFound("Input(V) should not be null")); + PADDLE_ENFORCE_EQ( + ctx->HasInput(framework::GradVarName("Out")), true, + platform::errors::NotFound("Input(Out@GRAD) should not be null")); auto dim_x = ctx->GetInputDim("Weight"); if (ctx->HasOutput(framework::GradVarName("Weight"))) { ctx->SetOutputDim(framework::GradVarName("Weight"), dim_x); diff --git a/paddle/fluid/operators/temporal_shift_op.cc b/paddle/fluid/operators/temporal_shift_op.cc index 9828d56679..819cac3ee4 100644 --- a/paddle/fluid/operators/temporal_shift_op.cc +++ b/paddle/fluid/operators/temporal_shift_op.cc @@ -27,26 +27,45 @@ class TemporalShiftOp : public framework::OperatorWithKernel { protected: void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, - "Input(X) of TemporalShiftOp should not be null."); - PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true, - "Output(Out) of TemporalShiftOp should not be null."); + platform::errors::NotFound( + "Input(X) of TemporalShiftOp should not be null.")); + PADDLE_ENFORCE_EQ( + ctx->HasOutput("Out"), true, + platform::errors::NotFound( + "Output(Out) of TemporalShiftOp should not be null.")); auto dim_x = ctx->GetInputDim("X"); PADDLE_ENFORCE_EQ(dim_x.size(), 4, - "Input(X) rank should be 4 in shape of [N*T, C, H, W]."); + platform::errors::InvalidArgument( + "Input(X) rank should be 4 in shape of [N*T, C, H, " + "W], but received X rank(%d)", + dim_x.size())); int seg_num = ctx->Attrs().Get("seg_num"); float shift_ratio = ctx->Attrs().Get("shift_ratio"); - PADDLE_ENFORCE_GT(seg_num, 0, "Attr(seg_num) should be greater than 0."); - PADDLE_ENFORCE_GT(shift_ratio, 0., - "Attr(shift_ratio) should be greater than 0"); - PADDLE_ENFORCE_LT(shift_ratio, 0.5, - "Attr(shift_ratio) should be less than 0.5"); + PADDLE_ENFORCE_GT( + seg_num, 0, + platform::errors::InvalidArgument( + "Attr(seg_num) should be greater than 0, but received %d", + seg_num)); + PADDLE_ENFORCE_GT( + shift_ratio, 0., + platform::errors::InvalidArgument( + "Attr(shift_ratio) should be greater than 0, but received %d", + shift_ratio)); + PADDLE_ENFORCE_LT( + shift_ratio, 0.5, + platform::errors::InvalidArgument( + "Attr(shift_ratio) should be less than 0.5, but received %d", + shift_ratio)); if (ctx->IsRuntime()) { - PADDLE_ENFORCE_EQ( - dim_x[0] % seg_num, 0, - "Input(X) dims[0] should be divided exactly by Attr(seg_num)."); + PADDLE_ENFORCE_EQ(dim_x[0] % seg_num, 0, + platform::errors::InvalidArgument( + "Input(X) dimension[0] should be divided exactly " + "by Attr(seg_num), but received X dimension[0](%d) " + "mod seg_num(%d) != 0", + dim_x[0], seg_num)); } ctx->SetOutputDim("Out", dim_x); diff --git a/paddle/fluid/operators/temporal_shift_op.cu b/paddle/fluid/operators/temporal_shift_op.cu index 24f1f8e178..a292f16fe2 100644 --- a/paddle/fluid/operators/temporal_shift_op.cu +++ b/paddle/fluid/operators/temporal_shift_op.cu @@ -90,8 +90,9 @@ template class TemporalShiftOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), - "This kernel only runs on GPU device."); + PADDLE_ENFORCE_EQ(platform::is_gpu_place(ctx.GetPlace()), true, + platform::errors::InvalidArgument( + "This kernel only runs on GPU device.")); auto* input = ctx.Input("X"); auto* output = ctx.Output("Out"); int t = ctx.Attr("seg_num"); diff --git a/python/paddle/fluid/layers/loss.py b/python/paddle/fluid/layers/loss.py index 5eda55d76a..94c14b194c 100644 --- a/python/paddle/fluid/layers/loss.py +++ b/python/paddle/fluid/layers/loss.py @@ -21,7 +21,7 @@ from .layer_function_generator import templatedoc from ..layer_helper import LayerHelper from ..framework import Variable, in_dygraph_mode from .. import core -from ..data_feeder import check_variable_and_dtype +from ..data_feeder import check_variable_and_dtype, check_type from ..param_attr import ParamAttr from ..initializer import NumpyArrayInitializer, Constant from .. import core @@ -1580,6 +1580,10 @@ def kldiv_loss(x, target, reduction='mean', name=None): loss = fluid.layers.kldiv_loss(x=x, target=target, reduction='batchmean') """ helper = LayerHelper('kldiv_loss', **locals()) + check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'kldiv_loss') + check_variable_and_dtype(target, 'target', ['float32', 'float64'], + 'kldiv_loss') + check_type(reduction, 'reduction', str, 'kldiv_loss') loss = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type='kldiv_loss', diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index f0d741a8cd..0da5b06127 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -2361,6 +2361,12 @@ def adaptive_pool2d(input, pool_size=[3, 3], pool_type='max') """ + check_variable_and_dtype( + input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'], + 'adaptive_pool2d') + check_type(pool_type, 'pool_type', str, 'adaptive_pool2d') + check_type(pool_size, 'pool_size', (int, list, tuple), 'adaptive_pool2d') + check_type(require_index, 'require_index', bool, 'adaptive_pool2d') if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", @@ -2516,6 +2522,12 @@ def adaptive_pool3d(input, pool_size=[3, 3, 3], pool_type='max') """ + check_variable_and_dtype( + input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'], + 'adaptive_pool3d') + check_type(pool_type, 'pool_type', str, 'adaptive_pool3d') + check_type(pool_size, 'pool_size', (int, list, tuple), 'adaptive_pool3d') + check_type(require_index, 'require_index', bool, 'adaptive_pool3d') if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", @@ -3568,6 +3580,11 @@ def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None): x = fluid.layers.spectral_norm(weight=weight, dim=1, power_iters=2) """ helper = LayerHelper('spectral_norm', **locals()) + check_variable_and_dtype(weight, 'weight', ['float32', 'float64'], + 'spectral_norm') + check_type(dim, 'dim', int, 'spectral_norm') + check_type(power_iters, 'power_iters', int, 'spectral_norm') + check_type(eps, 'eps', float, 'spectral_norm') dtype = weight.dtype # create intput and parameters @@ -12246,6 +12263,9 @@ def grid_sampler(x, grid, name=None): """ helper = LayerHelper("grid_sampler", **locals()) + check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'grid_sampler') + check_variable_and_dtype(grid, 'grid', ['float32', 'float64'], + 'grid_sampler') if not isinstance(x, Variable): return ValueError("The x should be a Variable") @@ -12601,6 +12621,9 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None): out = fluid.layers.temporal_shift(x=input, seg_num=2, shift_ratio=0.2) """ helper = LayerHelper("temporal_shift", **locals()) + check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'temporal_shift') + check_type(seg_num, 'seg_num', int, 'temporal_shift') + check_type(shift_ratio, 'shift_ratio', float, 'temporal_shift') out = helper.create_variable_for_type_inference(dtype=x.dtype) -- GitLab