From 5b3dd8063348b3a3950e619f39c5cfcf3c84084e Mon Sep 17 00:00:00 2001 From: zhupengyang Date: Fri, 10 Apr 2020 20:13:34 +0800 Subject: [PATCH] Op(prelu) error message enhancement (#23616) --- paddle/fluid/operators/prelu_op.cc | 55 +++++++++++-------- python/paddle/fluid/layers/nn.py | 2 + .../fluid/tests/unittests/test_prelu_op.py | 16 ++++++ 3 files changed, 50 insertions(+), 23 deletions(-) diff --git a/paddle/fluid/operators/prelu_op.cc b/paddle/fluid/operators/prelu_op.cc index 41769727e8a..66717298a28 100644 --- a/paddle/fluid/operators/prelu_op.cc +++ b/paddle/fluid/operators/prelu_op.cc @@ -24,40 +24,48 @@ class PReluOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} void InferShape(framework::InferShapeContext *ctx) const override { - std::string mode = ctx->Attrs().Get("mode"); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "prelu"); + OP_INOUT_CHECK(ctx->HasInput("Alpha"), "Input", "Alpha", "prelu"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "prelu"); auto x_dim = ctx->GetInputDim("X"); - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of PreluOp should not be null"); - PADDLE_ENFORCE(ctx->HasInput("Alpha"), - "Input(Alpha) of PreluOp should not be null"); - - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of PreluOp should not be null"); + std::string mode = ctx->Attrs().Get("mode"); if (mode == "all") { - PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1, - "For mode 'all', size of weight Alpha must be one."); + PADDLE_ENFORCE_EQ( + product(ctx->GetInputDim("Alpha")), 1, + platform::errors::InvalidArgument( + "For mode 'all', size of weight Alpha must be one.")); } else if (mode == "channel") { - PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == x_dim[1], - "For channel-wise mode, size of weight Alpha must be " - "equal to the number of channels, should be %d", - x_dim[1]); + PADDLE_ENFORCE_EQ(product(ctx->GetInputDim("Alpha")), x_dim[1], + platform::errors::InvalidArgument( + "For mode 'channel', size of weight Alpha must be " + "equal to the number of channels of input(x). But " + "recevied alpha's size: %d, x_dim[1]: %d", + product(ctx->GetInputDim("Alpha")), x_dim[1])); } else if (mode == "element") { auto alpha_dim = ctx->GetInputDim("Alpha"); auto alpha_rank = alpha_dim.size(); auto x_rank = x_dim.size(); + PADDLE_ENFORCE_EQ( + alpha_rank, x_rank, + platform::errors::InvalidArgument( + "For mode 'element', rank of weight Alpha must be ", + "equal to the rank of input(x). But recevied alpha's rank: %d, " + "x's rank: %d.", + alpha_rank, x_rank)); size_t x_product = 1; size_t alpha_product = 1; - PADDLE_ENFORCE_EQ(alpha_rank, x_rank, - "For element-wise mode, rank of weight Alpha must be ", - "equal to the rank of input."); for (int64_t i = x_rank - 1; i > 0; i--) { x_product *= x_dim[i]; alpha_product *= alpha_dim[i]; } - PADDLE_ENFORCE_EQ(x_product, alpha_product, - "For element-wise mode, size of weight Alpha must be " - "equal to the number of input."); + PADDLE_ENFORCE_EQ( + alpha_product, x_product, + platform::errors::InvalidArgument( + "For mode 'element', the size of weight Alpha must be " + "equal to the size of input(x). But recevied alpha's size: %d, " + "x's size: %d.", + alpha_product, x_product)); } else { PADDLE_THROW("Unkown mode %s", mode); } @@ -108,9 +116,10 @@ class PReluGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext *ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), - "Input(Out@GRAD) should not be null"); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "prelu"); + OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input", + "Out@GRAD", "prelu"); + auto x_grad_name = framework::GradVarName("X"); auto alpha_grad_name = framework::GradVarName("Alpha"); diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 758597c6e07..a0eb6c93bc4 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -9132,6 +9132,8 @@ def prelu(x, mode, param_attr=None, name=None): x,mode,param_attr=ParamAttr(name='alpha')) """ + check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'prelu') + helper = LayerHelper('prelu', **locals()) if mode not in ['all', 'channel', 'element']: raise ValueError('mode should be one of all, channel, element.') diff --git a/python/paddle/fluid/tests/unittests/test_prelu_op.py b/python/paddle/fluid/tests/unittests/test_prelu_op.py index ef984d80758..2676e036a2f 100644 --- a/python/paddle/fluid/tests/unittests/test_prelu_op.py +++ b/python/paddle/fluid/tests/unittests/test_prelu_op.py @@ -17,6 +17,8 @@ from __future__ import print_function import unittest import numpy as np import six +import paddle.fluid as fluid +from paddle.fluid import Program, program_guard from op_test import OpTest, skip_check_grad_ci @@ -80,5 +82,19 @@ if six.PY2: self.attrs = {'mode': "element"} +class TestPReluOpError(unittest.TestCase): + def test_errors(self): + with program_guard(Program()): + # The input type must be Variable. + self.assertRaises(TypeError, fluid.layers.prelu, 1, 'all') + # The input dtype must be float16, float32, float64. + x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32') + self.assertRaises(TypeError, fluid.layers.prelu, x_int32, 'all') + # support the input dtype is float32 + x_fp16 = fluid.layers.data( + name='x_fp16', shape=[12, 10], dtype='float32') + fluid.layers.prelu(x_fp16, 'all') + + if __name__ == "__main__": unittest.main() -- GitLab