m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

update expand as op to use the shape of the target tensor instead of the target tensor itself. (#29020)

Browse Source 
* update, test=develop
musl/disable_test_yolov3_temporarily
lilong12 5 years ago committed by GitHub
parent f4c894a693
commit 7e5e9934fe
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 34 additions and 51 deletions
Show Stats Download Patch File Download Diff File

49
paddle/fluid/operators/expand_as_v2_op.cc
Unescape View File

@ -25,28 +25,22 @@ class ExpandAsV2Op : public framework::OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "ExpandAsV2");
OP_INOUT_CHECK(ctx->HasInput("target_tensor"), "Input", "target_tensor",
"ExpandAsV2");
OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "ExpandAsV2");
auto x_dims = ctx->GetInputDim("X");
auto target_tensor_dims = ctx->GetInputDim("target_tensor");
auto target_shape = ctx->Attrs().Get<std::vector<int>>("target_shape");
PADDLE_ENFORCE_GE(
target_tensor_dims.size(), static_cast<size_t>(x_dims.size()),
target_shape.size(), static_cast<size_t>(x_dims.size()),
platform::errors::InvalidArgument(
"The rank of Input(target_tensor) must be greater than or equal "
"The rank of target_shape must be greater than or equal "
"to the rank of Input(X). But received Input(X): input "
"rank %u, input shape [%s]; received Input(target_tensor): "
"input rank %u, input shape [%s].",
x_dims.size(), x_dims, target_tensor_dims.size(),
target_tensor_dims));
PADDLE_ENFORCE_LE(
target_tensor_dims.size(), MAX_RANK_SUPPORTED,
platform::errors::InvalidArgument(
"The rank of Input(target_tensor) must not be less than or equal "
"to %d. But received: input rank %u, input shape [%s].",
MAX_RANK_SUPPORTED, x_dims.size(), x_dims));
std::vector<int> out_shape = framework::vectorize<int>(target_tensor_dims);
ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
"rank %u; received target_shape: rank %u.",
x_dims.size(), target_shape.size()));
PADDLE_ENFORCE_LE(target_shape.size(), MAX_RANK_SUPPORTED,
platform::errors::InvalidArgument(
"The rank of target_shape must be less than or equal "
"to %d. But received: rank %u.",
MAX_RANK_SUPPORTED, target_shape.size()));
ctx->SetOutputDim("Out", framework::make_ddim(target_shape));
}
};
@ -62,23 +56,11 @@ class ExpandAsV2OpMaker : public framework::OpProtoAndCheckerMaker {
"After expanding, size of each dimension of Output(Out) is equal "
"to size of the corresponding dimension of Input(X) multiplying "
"the corresponding value given by Attr(expand_times).");
AddInput("target_tensor", "Expand tensor's shape for each dimension.");
AddAttr<std::vector<int>>("target_shape",
"Expand shape for each dimension.")
.SetDefault({});
AddComment(R"DOC(
Expand the input by given times number. You should set times
number for each dimension by providing tensor 'expend_tensor'. The rank of X
should be in [1, 6]. Please note that size of 'expend_tensor' must be the same
with X's rank. Following is a using case:
Input(X) is a 3-D tensor with shape [2, 3, 1]:
[
[[1], [2], [3]],
[[4], [5], [6]]
]
target_tensors'shape: [2, 6, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]:
[
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
]
Expand the input to the given shape.
)DOC");
}
};
@ -117,7 +99,6 @@ class ExpandAsV2GradOpMaker : public framework::SingleGradOpMaker<T> {
void Apply(GradOpPtr<T> op) const override {
op->SetType("expand_as_v2_grad");
op->SetInput("X", this->Input("X"));
op->SetInput("target_tensor", this->Input("target_tensor"));
op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
op->SetAttrMap(this->Attrs());

10
paddle/fluid/operators/expand_as_v2_op.h
Unescape View File

@ -59,8 +59,8 @@ class ExpandAsV2Kernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto rank = context.Input<Tensor>("X")->dims().size();
auto* target_tensor = context.Input<Tensor>("target_tensor");
auto target_rank = target_tensor->dims().size();
auto target_shape = context.Attr<std::vector<int>>("target_shape");
auto target_rank = target_shape.size();
PADDLE_ENFORCE_GE(target_rank, rank,
platform::errors::InvalidArgument(
"The rank (%d) of the input 'target_tensor' for "
@ -85,9 +85,8 @@ class ExpandAsV2Kernel : public framework::OpKernel<T> {
void ExpandAs(const framework::ExecutionContext& context) const {
auto* in0 = context.Input<Tensor>("X");
auto in_dims = in0->dims();
auto* target_tensor = context.Input<Tensor>("target_tensor");
auto target_shape = context.Attr<std::vector<int>>("target_shape");
auto vec_in_dims = framework::vectorize<int>(in_dims);
auto target_shape = framework::vectorize<int>(target_tensor->dims());
auto diff = target_shape.size() - vec_in_dims.size();
vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
std::vector<int> repeat_times(vec_in_dims.size());
@ -132,9 +131,8 @@ class ExpandAsV2GradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in0 = context.Input<Tensor>("X");
auto* target_tensor = context.Input<Tensor>("target_tensor");
auto target_shape = context.Attr<std::vector<int>>("target_shape");
auto x_dims = in0->dims();
auto target_shape = target_tensor->dims();
auto vec_in_dims = framework::vectorize<int>(x_dims);
auto diff = target_shape.size() - vec_in_dims.size();
vec_in_dims.insert(vec_in_dims.begin(), diff, 1);

16
python/paddle/fluid/tests/unittests/test_expand_as_v2_op.py
Unescape View File

@ -26,8 +26,8 @@ class TestExpandAsOpRank1(OpTest):
self.op_type = "expand_as_v2"
x = np.random.rand(100).astype("float64")
target_tensor = np.random.rand(2, 100).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
self.inputs = {'X': x}
self.attrs = {'target_shape': target_tensor.shape}
bcast_dims = [2, 1]
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
@ -44,8 +44,8 @@ class TestExpandAsOpRank2(OpTest):
self.op_type = "expand_as_v2"
x = np.random.rand(10, 12).astype("float64")
target_tensor = np.random.rand(10, 12).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
self.inputs = {'X': x}
self.attrs = {'target_shape': target_tensor.shape}
bcast_dims = [1, 1]
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
@ -62,8 +62,8 @@ class TestExpandAsOpRank3(OpTest):
self.op_type = "expand_as_v2"
x = np.random.rand(2, 3, 20).astype("float64")
target_tensor = np.random.rand(2, 3, 20).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
self.inputs = {'X': x}
self.attrs = {'target_shape': target_tensor.shape}
bcast_dims = [1, 1, 1]
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}
@ -80,8 +80,8 @@ class TestExpandAsOpRank4(OpTest):
self.op_type = "expand_as_v2"
x = np.random.rand(1, 1, 7, 16).astype("float64")
target_tensor = np.random.rand(4, 6, 7, 16).astype("float64")
self.inputs = {'X': x, 'target_tensor': target_tensor}
self.attrs = {}
self.inputs = {'X': x}
self.attrs = {'target_shape': target_tensor.shape}
bcast_dims = [4, 6, 1, 1]
output = np.tile(self.inputs['X'], bcast_dims)
self.outputs = {'Out': output}

10
python/paddle/tensor/manipulation.py
Unescape View File

@ -1183,7 +1183,7 @@ def expand_as(x, y, name=None):
# [[1, 2, 3], [1, 2, 3]]
"""
if in_dygraph_mode():
return core.ops.expand_as_v2(x, y)
return core.ops.expand_as_v2(x, 'target_shape', y.shape)
check_variable_and_dtype(
x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'expand_as')
@ -1195,12 +1195,16 @@ def expand_as(x, y, name=None):
"you must set its stop_gradient to be False by "
"some_var.stop_gradient = True, supporting "
"some_var as the input 'x'.")
inputs = {"X": [x], "target_tensor": [y]}
inputs = {"X": [x]}
helper = LayerHelper('expand_as', **locals())
dtype = helper.input_dtype(input_param_name='x')
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(type='expand_as_v2', inputs=inputs, outputs={'Out': out})
helper.append_op(
type='expand_as_v2',
inputs=inputs,
attrs={'target_shape': y.shape},
outputs={'Out': out})
return out

Write Preview
Loading…
Cancel
Save