提交 4520afcf 编写于 作者: Y yangyaming

Consider corner case.

上级 f2d596d4
...@@ -109,11 +109,23 @@ class ExpandGradKernel : public framework::OpKernel { ...@@ -109,11 +109,23 @@ class ExpandGradKernel : public framework::OpKernel {
} }
int dims = reshape_dims_vec.size() * 6 + reduce_dims_vec.size() - 7; int dims = reshape_dims_vec.size() * 6 + reduce_dims_vec.size() - 7;
switch (dims) { // no need reduce, just copy
REP_EXPAND_GRAD_TEMPLATE(72) if (reduce_dims_vec.size() == 0) {
default: auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
PADDLE_ENFORCE(false, "Only support tensor whose rank in [1, 6]."); auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
}; out0->mutable_data<T>(context.GetPlace());
if (platform::is_cpu_place(context.GetPlace())) {
out0->CopyFrom<T>(*in0, platform::CPUPlace());
} else {
out0->CopyFrom<T>(*in0, platform::GPUPlace());
}
} else {
switch (dims) {
REP_EXPAND_GRAD_TEMPLATE(72)
default:
PADDLE_ENFORCE(false, "Only support tensor whose rank in [1, 6].");
};
}
} }
protected: protected:
......
...@@ -22,8 +22,8 @@ class TestExpandOpRank2(OpTest): ...@@ -22,8 +22,8 @@ class TestExpandOpRank2(OpTest):
def setUp(self): def setUp(self):
self.op_type = "expand" self.op_type = "expand"
self.inputs = {'X': np.random.random((12, 14)).astype("float32")} self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
self.attrs = {'expandTimes': [3, 4]} self.attrs = {'expandTimes': [1, 1]}
output = np.tile(self.inputs['X'], (3, 4)) output = np.tile(self.inputs['X'], (1, 1))
self.outputs = {'Out': output} self.outputs = {'Out': output}
def test_check_output(self): def test_check_output(self):
...@@ -37,8 +37,8 @@ class TestExpandOpRank3(OpTest): ...@@ -37,8 +37,8 @@ class TestExpandOpRank3(OpTest):
def setUp(self): def setUp(self):
self.op_type = "expand" self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")} self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
self.attrs = {'expandTimes': [3, 2, 1]} self.attrs = {'expandTimes': [1, 1, 1]}
output = np.tile(self.inputs['X'], (3, 2, 1)) output = np.tile(self.inputs['X'], (1, 1, 1))
self.outputs = {'Out': output} self.outputs = {'Out': output}
def test_check_output(self): def test_check_output(self):
......
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