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fix the error message for reduce_mean and reduce_sum op (#20063)

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* fix the error message for reduce_mean and reduce_sum op test=develop

* fix typo test=develop

* fix according review advice test=develop

* fix the test test=develop

* fix test=develop
fix-python-transpose
wangchaochaohu 5 years ago committed by GitHub
parent 9a76f3f916
commit 6e73e90bfb
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 63 additions and 9 deletions
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25
paddle/fluid/operators/reduce_ops/reduce_op.h
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@ -165,13 +165,20 @@ class ReduceOp : public framework::OperatorWithKernel {
"Output(Out) of ReduceOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto x_rank = x_dims.size();
PADDLE_ENFORCE_LE(x_rank, 6, "Tensors with rank at most 6 are supported.");
PADDLE_ENFORCE_LE(x_rank, 6,
"ShapeError: The input tensor X's dimensions of Reduce "
"should be less equal than 6. But received X's "
"dimensions = %d, X's shape = [%s].",
x_rank, x_dims);
auto dims = ctx->Attrs().Get<std::vector<int>>("dim");
for (size_t i = 0; i < dims.size(); ++i) {
PADDLE_ENFORCE_LT(dims[i], x_rank,
"ShapeError: The reduce dim index %d should be in the "
"range [-dimension(X), dimension(X)]."
"which dimesion = %d, But received dim index = %d",
i, x_rank, dims[i]);
if (dims[i] < 0) dims[i] = x_rank + dims[i];
PADDLE_ENFORCE_LT(
dims[i], x_rank,
"The dim should be in the range [-rank(input), rank(input)).");
}
sort(dims.begin(), dims.end());
bool reduce_all = ctx->Attrs().Get<bool>("reduce_all");
@ -202,7 +209,7 @@ class ReduceOp : public framework::OperatorWithKernel {
}
auto out_dims = framework::make_ddim(dims_vector);
ctx->SetOutputDim("Out", out_dims);
if (dims[0] != 0) {
if (dims.size() > 0 && dims[0] != 0) {
// Only pass LoD when not reducing on the first dim.
ctx->ShareLoD("X", /*->*/ "Out");
}
@ -223,10 +230,12 @@ class ReduceGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_LE(x_rank, 6, "Tensors with rank at most 6 are supported.");
auto dims = ctx->Attrs().Get<std::vector<int>>("dim");
for (size_t i = 0; i < dims.size(); ++i) {
PADDLE_ENFORCE_LT(dims[i], x_rank,
"ShapeError: The reduce dim index %d should be in the "
"range [-dimension(X), dimension(X)]."
"which dimesion = %d, But received dim index = %d",
i, x_rank, dims[i]);
if (dims[i] < 0) dims[i] = x_rank + dims[i];
PADDLE_ENFORCE_LT(
dims[i], x_rank,
"The dim should be in the range [-rank(input), rank(input)).");
}
sort(dims.begin(), dims.end());
auto x_grad_name = framework::GradVarName("X");

18
python/paddle/fluid/layers/nn.py
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@ -5364,6 +5364,15 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
"""
helper = LayerHelper('reduce_sum', **locals())
if not isinstance(input, Variable):
raise TypeError(
"The type of 'input' in reduce_sum must be Variable, but received %s"
% (type(input)))
if convert_dtype(
input.dtype) not in ['float32', 'float64', 'int32', 'int64']:
raise TypeError(
"The data type of 'input' in reduce_sum must be float32 or float64 or int32 or int64, but received %s."
% (convert_dtype(input.dtype)))
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]
@ -5423,6 +5432,15 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_mean(y, dim=[0, 1]) # [4.0, 5.0]
"""
helper = LayerHelper('reduce_mean', **locals())
if not isinstance(input, Variable):
raise TypeError(
"The type of 'input' in reduce_mean must be Variable, but received %s"
% (type(input)))
if convert_dtype(
input.dtype) not in ['float32', 'float64', 'int32', 'int64']:
raise TypeError(
"The data type of 'input' in reduce_mean must be float32 or float64 or int32 or int64, but received %s."
% (convert_dtype(input.dtype)))
out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
if dim is not None and not isinstance(dim, list):
dim = [dim]

2
python/paddle/fluid/tests/test_if_else_op.py
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@ -183,7 +183,7 @@ class TestIfElse(unittest.TestCase):
false_target = fluid.layers.tanh(false_target)
ie.output(false_target)
if_out = ie()
out = layers.reduce_sum(if_out)
out = layers.reduce_sum(if_out[0])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

27
python/paddle/fluid/tests/unittests/test_reduce_op.py
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@ -17,6 +17,9 @@ from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid.core as core
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
class TestSumOp(OpTest):
@ -411,5 +414,29 @@ class Test1DReduceWithAxes1(OpTest):
self.check_grad(['X'], 'Out')
class TestReduceSumOpError(OpTest):
def test_errors(self):
with program_guard(Program(), Program()):
# The input type of reduce_sum_op must be Variable.
x1 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace())
self.assertRaises(TypeError, fluid.layers.reduce_sum, x1)
# The input dtype of reduce_sum_op must be float32 or float64 or int32 or int64.
x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
self.assertRaises(TypeError, fluid.layers.reduce_sum, x2)
class TestReduceMeanOpError(OpTest):
def test_errors(self):
with program_guard(Program(), Program()):
# The input type of reduce_mean_op must be Variable.
x1 = fluid.create_lod_tensor(
np.array([[-1]]), [[1]], fluid.CPUPlace())
self.assertRaises(TypeError, fluid.layers.reduce_mean, x1)
# The input dtype of reduce_mean_op must be float32 or float64 or int32 or int64.
x2 = fluid.layers.data(name='x2', shape=[4], dtype="uint8")
self.assertRaises(TypeError, fluid.layers.reduce_mean, x2)
if __name__ == '__main__':
unittest.main()

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