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Make bilinear_interp_op support attrs from input. (#11041)

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* Make bilinear_interp_op support attrs from input.

* Fix python api.
wangkuiyi-patch-1
whs 7 years ago committed by GitHub
parent bfecb57282
commit 85c203b117
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 111 additions and 15 deletions
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23
paddle/fluid/operators/bilinear_interp_op.cc
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@ -34,9 +34,22 @@ class BilinearInterpOp : public framework::OperatorWithKernel {
int out_w = ctx->Attrs().Get<int>("out_w");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
if (ctx->HasInput("OutSize")) {
auto out_size_dim = ctx->GetInputDim("OutSize");
PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
"OutSize's dimension size must be 1");
PADDLE_ENFORCE_EQ(out_size_dim[0], 2, "OutSize's dim[0] must be 2");
}
std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
}
};
class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
@ -45,6 +58,10 @@ class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("X",
"(Tensor) The input tensor of bilinear interpolation, "
"This is a 4-D tensor with shape of (N x C x h x w)");
AddInput("OutSize",
"(Tensor) This is a 1-D tensor with two number. "
"The first number is height and the second number is width.")
.AsDispensable();
AddOutput("Out",
"(Tensor) The dimension of output is (N x C x out_h x out_w]");
@ -78,6 +95,12 @@ class BilinearInterpOpGrad : public framework::OperatorWithKernel {
ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
}
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
}
};
} // namespace operators

25
paddle/fluid/operators/bilinear_interp_op.cu
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@ -102,10 +102,21 @@ class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
auto* input_t = ctx.Input<Tensor>("X"); // float tensor
auto* output_t = ctx.Output<Tensor>("Out"); // float tensor
auto* input = input_t->data<T>();
auto* output = output_t->mutable_data<T>(ctx.GetPlace());
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_dims = output_t->dims();
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
Tensor sizes;
framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
auto size_data = sizes.data<int>();
out_h = size_data[0];
out_w = size_data[1];
}
auto* output = output_t->mutable_data<T>(
{out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
int batch_size = input_t->dims()[0];
int channels = input_t->dims()[1];
int in_h = input_t->dims()[2];
@ -139,8 +150,8 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto* d_output = d_output_t->data<T>();
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
@ -149,6 +160,16 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
Tensor sizes;
framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
auto size_data = sizes.data<int>();
out_h = size_data[0];
out_w = size_data[1];
}
int batch_size = d_input_t->dims()[0];
int channels = d_input_t->dims()[1];
int in_h = d_input_t->dims()[2];

22
paddle/fluid/operators/bilinear_interp_op.h
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@ -24,11 +24,18 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input_t = ctx.Input<Tensor>("X"); // float tensor
auto* output_t = ctx.Output<Tensor>("Out"); // float tensor
auto out_dims = output_t->dims();
auto* input = input_t->data<T>();
auto* output = output_t->mutable_data<T>(ctx.GetPlace());
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
auto out_size_data = out_size_t->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
auto* output = output_t->mutable_data<T>(
{out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
int batch_size = input_t->dims()[0];
int channels = input_t->dims()[1];
int in_h = input_t->dims()[2];
@ -83,9 +90,8 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto* d_output = d_output_t->data<T>();
auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, T> zero;
@ -93,6 +99,14 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size_t = ctx.Input<Tensor>("OutSize");
if (out_size_t != nullptr) {
auto out_size_data = out_size_t->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
int batch_size = d_input_t->dims()[0];
int channels = d_input_t->dims()[1];
int in_h = d_input_t->dims()[2];

13
python/paddle/fluid/layers/nn.py
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@ -3944,7 +3944,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
input (Variable): The input tensor of bilinear interpolation,
This is a 4-D tensor of the shape
(num_batches, channels, in_h, in_w).
out_shape(list|tuple|None): Output shape of bilinear interpolation
out_shape(list|tuple|Variable|None): Output shape of bilinear interpolation
layer, the shape is (out_h, out_w).
Default: None
scale(int|None): The multiplier for the input height or width.
@ -3971,13 +3971,20 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
def _is_list_or_turple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
out_h = 0
out_w = 0
inputs = {"X": input}
if out_shape is not None:
if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2):
if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2) and (
out_shape is not Variable):
raise ValueError('out_shape should be a list or tuple ',
'with length 2, (out_h, out_w).')
if _is_list_or_turple_(out_shape):
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
else:
inputs['OutSize'] = out_shape
else:
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
@ -3985,7 +3992,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
out = helper.create_tmp_variable(dtype)
helper.append_op(
type="bilinear_interp",
inputs={"X": input},
inputs=inputs,
outputs={"Out": out},
attrs={"out_h": out_h,
"out_w": out_w})

37
python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py
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@ -17,7 +17,10 @@ import numpy as np
from op_test import OpTest
def bilinear_interp_np(input, out_h, out_w):
def bilinear_interp_np(input, out_h, out_w, out_size):
if out_size is not None:
out_h = out_size[0]
out_w = out_size[1]
batch_size, channel, in_h, in_w = input.shape
if out_h > 1:
ratio_h = (in_h - 1.0) / (out_h - 1.0)
@ -49,12 +52,15 @@ def bilinear_interp_np(input, out_h, out_w):
class TestBilinearInterpOp(OpTest):
def setUp(self):
self.out_size = None
self.init_test_case()
self.op_type = "bilinear_interp"
input_np = np.random.random(self.input_shape).astype("float32")
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w)
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
self.out_size)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
self.outputs = {'Out': output_np}
@ -68,6 +74,7 @@ class TestBilinearInterpOp(OpTest):
self.input_shape = [2, 3, 4, 4]
self.out_h = 2
self.out_w = 2
self.out_size = np.array([3, 3]).astype("int32")
class TestCase1(TestBilinearInterpOp):
@ -91,5 +98,29 @@ class TestCase3(TestBilinearInterpOp):
self.out_w = 128
class TestCase4(TestBilinearInterpOp):
def init_test_case(self):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.out_size = np.array([2, 2]).astype("int32")
class TestCase5(TestBilinearInterpOp):
def init_test_case(self):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.out_size = np.array([11, 11]).astype("int32")
class TestCase6(TestBilinearInterpOp):
def init_test_case(self):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.out_size = np.array([65, 129]).astype("int32")
if __name__ == "__main__":
unittest.main()

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