Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 ed971564 编写于 作者: K Kaipeng Deng 提交者： GitHub
浏览文件
操作

Merge pull request #16439 from heavengate/resize_scale 

add attr scale. test=develop
上级 1a21d08f c7876e8c
显示空白变更内容
内联 并排
Showing with 236 addition and 65 deletion
paddle/fluid/API.spec
浏览文件 @ ed971564
......@@ -155,10 +155,10 @@ paddle.fluid.layers.label_smooth (ArgSpec(args=['label', 'prior_dist', 'epsilon'
paddle.fluid.layers.roi_pool (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0)), ('document', 'c317aa595deb31649083c8faa91cdb97'))
paddle.fluid.layers.roi_align (ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None)), ('document', '12c5bbb8b38c42e623fbc47611d766e1'))
paddle.fluid.layers.dice_loss (ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,)), ('document', '1ba0508d573f65feecf3564dce22aa1d'))
paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', '7a1966d7c3a48f1fc0881cdaf5d83b0b'))
paddle.fluid.layers.image_resize (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR', None, True, 1)), ('document', 'd1b08c11bb9277386fcf6ae70b6622d1'))
paddle.fluid.layers.image_resize_short (ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',)), ('document', '06211aefc50c5a3e940d7204d859cdf7'))
paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', 'e4fb4ed511b2293b8f04f7e872afbfd7'))
paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', '735fa9758a6d7ff3b47d7b827f961c1d'))
paddle.fluid.layers.resize_bilinear (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners', 'align_mode'], varargs=None, keywords=None, defaults=(None, None, None, None, True, 1)), ('document', 'c45591fbc4f64a178fbca219e1546a58'))
paddle.fluid.layers.resize_nearest (ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'actual_shape', 'align_corners'], varargs=None, keywords=None, defaults=(None, None, None, None, True)), ('document', 'ae6d73cdc7f3a138d8a338ecdb33c1ae'))
paddle.fluid.layers.gather (ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None), ('document', '98f1c86716b9b7f4dda83f20e2adeee2'))
paddle.fluid.layers.scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65f8e9d8ddfd0b412f940579c4faa342'))
paddle.fluid.layers.sequence_scatter (ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '15b522457dfef103f0c20ca9d397678b'))
......
paddle/fluid/operators/interpolate_op.cc
浏览文件 @ ed971564
......@@ -37,10 +37,19 @@ class InterpolateOp : public framework::OperatorWithKernel {
"Interpolation method can only be \"bilinear\" or \"nearest\".");
auto dim_x = ctx->GetInputDim("X"); // NCHW format
int out_h = ctx->Attrs().Get<int>("out_h");
int out_w = ctx->Attrs().Get<int>("out_w");
PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
int out_h, out_w;
float scale = ctx->Attrs().Get<float>("scale");
if (scale > 0) {
// round down
out_h = static_cast<int>(dim_x[2] * scale);
out_w = static_cast<int>(dim_x[3] * scale);
} else {
out_h = ctx->Attrs().Get<int>("out_h");
out_w = ctx->Attrs().Get<int>("out_w");
}
if (ctx->HasInput("OutSize") && ctx->IsRuntime()) {
auto out_size_dim = ctx->GetInputDim("OutSize");
PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
......@@ -77,6 +86,7 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("out_h", "output height of interpolate op.");
AddAttr<int>("out_w", "output width of interpolate op.");
AddAttr<float>("scale", "scale factor of interpolate op.").SetDefault(0.);
AddAttr<std::string>("interp_method",
"(string, default \"bilinear\"), interpolation "
"method, can be \"bilinear\" for "
......
paddle/fluid/operators/interpolate_op.cu
浏览文件 @ ed971564
......@@ -192,9 +192,21 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
auto* output = ctx.Output<Tensor>("Out");
auto* input_data = input->data<T>();
int n = input->dims()[0];
int c = input->dims()[1];
int in_h = input->dims()[2];
int in_w = input->dims()[3];
auto interp_method = ctx.Attr<std::string>("interp_method");
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
float scale = ctx.Attr<float>("scale");
if (scale > 0) {
out_h = in_h * scale;
out_w = in_w * scale;
}
auto out_size = ctx.Input<Tensor>("OutSize");
if (out_size != nullptr) {
Tensor sizes;
......@@ -207,11 +219,6 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
int n = input->dims()[0];
int c = input->dims()[1];
int in_h = input->dims()[2];
int in_w = input->dims()[3];
auto* output_data =
output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
......@@ -268,14 +275,20 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
math::SetConstant<platform::CUDADeviceContext, T> zero;
zero(device_ctx, input_grad, static_cast<T>(0.0));
int n = input_grad->dims()[0];
int c = input_grad->dims()[1];
int in_h = input_grad->dims()[2];
int in_w = input_grad->dims()[3];
auto interp_method = ctx.Attr<std::string>("interp_method");
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
float scale = ctx.Attr<float>("scale");
if (scale > 0) {
out_h = in_h * scale;
out_w - in_w* scale;
}
auto out_size = ctx.Input<Tensor>("OutSize");
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
if (out_size != nullptr) {
Tensor sizes;
framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
......@@ -284,10 +297,8 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
out_w = size_data[1];
}
int n = input_grad->dims()[0];
int c = input_grad->dims()[1];
int in_h = input_grad->dims()[2];
int in_w = input_grad->dims()[3];
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
int in_hw = in_h * in_w;
int out_hw = out_h * out_w;
......
paddle/fluid/operators/interpolate_op.h
浏览文件 @ ed971564
......@@ -163,9 +163,21 @@ class InterpolateKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
const int n = input->dims()[0];
const int c = input->dims()[1];
const int in_h = input->dims()[2];
const int in_w = input->dims()[3];
std::string interp_method = ctx.Attr<std::string>("interp_method");
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
float scale = ctx.Attr<float>("scale");
if (scale > 0) {
out_h = static_cast<int>(in_h * scale);
out_w = static_cast<int>(in_w * scale);
}
auto out_size = ctx.Input<Tensor>("OutSize");
if (out_size != nullptr) {
auto out_size_data = out_size->data<int>();
......@@ -175,11 +187,6 @@ class InterpolateKernel : public framework::OpKernel<T> {
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
const int n = input->dims()[0];
const int c = input->dims()[1];
const int in_h = input->dims()[2];
const int in_w = input->dims()[3];
output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CPUDeviceContext>();
......@@ -221,23 +228,31 @@ class InterpolateGradKernel : public framework::OpKernel<T> {
auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
const int n = input->dims()[0];
const int c = input->dims()[1];
const int in_h = input->dims()[2];
const int in_w = input->dims()[3];
std::string interp_method = ctx.Attr<std::string>("interp_method");
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
float scale = ctx.Attr<float>("scale");
if (scale > 0) {
out_h = static_cast<int>(in_h * scale);
out_w = static_cast<int>(in_w * scale);
}
auto out_size = ctx.Input<Tensor>("OutSize");
if (out_size != nullptr) {
auto out_size_data = out_size->data<int>();
out_h = out_size_data[0];
out_w = out_size_data[1];
}
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
const int n = input->dims()[0];
const int c = input->dims()[1];
const int in_h = input->dims()[2];
const int in_w = input->dims()[3];
input_grad->mutable_data<T>({n, c, in_h, in_w}, ctx.GetPlace());
auto& device_ctx =
ctx.template device_context<platform::CPUDeviceContext>();
......
python/paddle/fluid/layers/nn.py
浏览文件 @ ed971564
......@@ -7138,10 +7138,10 @@ def image_resize(input,
out_shape(list|tuple|Variable|None): Output shape of image resize
layer, the shape is (out_h, out_w).
Default: None
scale(float|None): The multiplier for the input height or width.
At least one of out_shape or scale must be set.
And out_shape has a higher priority than scale.
Default: None
scale(float|None): The multiplier for the input height or width. At
least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST'
......@@ -7179,6 +7179,7 @@ def image_resize(input,
or 'NEAREST' currently.
ValueError: One of out_shape and scale must not be None.
ValueError: out_shape length should be 2.
ValueError: scale should be greater than zero.
TypeError: align_corners shoule be a bool value
ValueError: align_mode can only be '0' or '1'
......@@ -7210,26 +7211,36 @@ def image_resize(input,
def _is_list_or_turple_(data):
return (isinstance(data, list) or isinstance(data, tuple))
out_h = 0
out_w = 0
inputs = {"X": input}
attrs = {
"out_h": 0,
"out_w": 0,
"interp_method": resample_type,
"align_corners": align_corners,
"align_mode": align_mode
}
if out_shape is not None:
if isinstance(out_shape, Variable):
warnings.warn("out_shape as Variable type is deprecated, \
it is recommended to use actual_shape instead of \
out_shape to specify output shape dynamically.")
inputs['OutSize'] = out_shape
elif not (_is_list_or_turple_(out_shape)):
raise TypeError("out_shape should be a list or tuple or Variable.")
elif len(out_shape) != 2:
else:
if not (_is_list_or_turple_(out_shape)):
raise TypeError(
"out_shape should be a list or tuple or Variable.")
if len(out_shape) != 2:
raise ValueError("out_shape length should be 2.")
out_shape = list(map(int, out_shape))
out_h = out_shape[0]
out_w = out_shape[1]
attrs['out_h'] = out_shape[0]
attrs['out_w'] = out_shape[1]
else:
out_h = int(input.shape[2] * scale)
out_w = int(input.shape[3] * scale)
if scale <= 0:
raise ValueError("scale should be greater than zero.")
attrs['scale'] = float(scale)
if isinstance(actual_shape, Variable):
inputs["OutSize"] = actual_shape
......@@ -7241,13 +7252,7 @@ def image_resize(input,
type='{}_interp'.format(resample_type),
inputs=inputs,
outputs={"Out": out},
attrs={
"out_h": out_h,
"out_w": out_w,
"interp_method": resample_type,
"align_corners": align_corners,
"align_mode": align_mode
})
attrs=attrs)
return out
......@@ -7315,11 +7320,14 @@ def resize_bilinear(input,
Args:
input(${x_type}): ${x_comment}.
out_shape(${out_size_type}): ${out_size_comment}.
out_shape(list|tuple|Variable|None): Output shape of resize bilinear
layer, the shape is (out_h, out_w).
Default: None
scale(float|None): The multiplier for the input height or width. At
least one of out_shape or scale must be set. And out_shape has
a higher priority than scale. Default: None.
least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None.
name(str|None): The output variable name.
actual_shape(Variable): An optional input to specify output shape
......@@ -7406,11 +7414,14 @@ def resize_nearest(input,
Args:
input(${x_type}): ${x_comment}.
out_shape(${out_size_type}): ${out_size_comment}.
out_shape(list|tuple|Variable|None): Output shape of resize nearest
layer, the shape is (out_h, out_w).
Default: None
scale(float|None): The multiplier for the input height or width. At
least one of out_shape or scale must be set. And out_shape has
a higher priority than scale. Default: None.
least one of :attr:`out_shape` or :attr:`scale` must be set.
And :attr:`out_shape` has a higher priority than :attr:`scale`.
Default: None.
name(str|None): The output variable name.
actual_shape(Variable): An optional input to specify output shape
......
python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py
浏览文件 @ ed971564
......@@ -91,17 +91,26 @@ class TestBilinearInterpOp(OpTest):
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,
self.out_size, self.actual_shape,
self.align_corners, self.align_mode)
if self.scale > 0:
out_h = int(self.input_shape[2] * self.scale)
out_w = int(self.input_shape[3] * self.scale)
else:
out_h = self.out_h
out_w = self.out_w
output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
self.actual_shape, self.align_corners,
self.align_mode)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'scale': self.scale,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'align_mode': self.align_mode
......@@ -119,6 +128,7 @@ class TestBilinearInterpOp(OpTest):
self.input_shape = [2, 3, 4, 4]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
self.align_mode = 1
......@@ -130,6 +140,7 @@ class TestBilinearInterpCase1(TestBilinearInterpOp):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.align_corners = True
self.align_mode = 1
......@@ -140,6 +151,7 @@ class TestBilinearInterpCase2(TestBilinearInterpOp):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.align_corners = True
self.align_mode = 1
......@@ -150,6 +162,7 @@ class TestBilinearInterpCase3(TestBilinearInterpOp):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.scale = 0.
self.align_corners = True
self.align_mode = 1
......@@ -160,6 +173,7 @@ class TestBilinearInterpCase4(TestBilinearInterpOp):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = True
self.align_mode = 1
......@@ -171,6 +185,7 @@ class TestBilinearInterpCase5(TestBilinearInterpOp):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = True
self.align_mode = 1
......@@ -182,6 +197,7 @@ class TestBilinearInterpCase6(TestBilinearInterpOp):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.scale = 0.
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = True
self.align_mode = 1
......@@ -193,6 +209,7 @@ class TestBilinearInterpActualShape(TestBilinearInterpOp):
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
self.align_mode = 1
......@@ -206,15 +223,25 @@ class TestBilinearInterpOpUint8(OpTest):
self.op_type = "bilinear_interp"
input_np = np.random.randint(
low=0, high=256, size=self.input_shape).astype("uint8")
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
self.out_size, self.actual_shape,
self.align_corners, self.align_mode)
if self.scale > 0:
out_h = int(self.input_shape[2] * self.scale)
out_w = int(self.input_shape[3] * self.scale)
else:
out_h = self.out_h
out_w = self.out_w
output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
self.actual_shape, self.align_corners,
self.align_mode)
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,
'scale': self.scale,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'align_mode': self.align_mode
......@@ -229,6 +256,7 @@ class TestBilinearInterpOpUint8(OpTest):
self.input_shape = [1, 3, 9, 6]
self.out_h = 10
self.out_w = 9
self.scale = 0.
self.align_corners = True
self.align_mode = 1
......@@ -239,6 +267,7 @@ class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8):
self.input_shape = [2, 3, 128, 64]
self.out_h = 120
self.out_w = 50
self.scale = 0.
self.align_corners = True
self.align_mode = 1
......@@ -249,6 +278,7 @@ class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8):
self.input_shape = [4, 1, 7, 8]
self.out_h = 5
self.out_w = 13
self.scale = 0.
self.out_size = np.array([6, 15]).astype("int32")
self.align_corners = True
self.align_mode = 1
......@@ -272,5 +302,38 @@ class TestBilinearInterpWithMethod3(TestBilinearInterpOp):
self.align_mode = 0
class TestBilinearInterpScale1(TestBilinearInterpOp):
def init_test_case(self):
self.interp_method = 'bilinear'
self.input_shape = [2, 3, 16, 32]
self.out_h = 60
self.out_w = 25
self.scale = 2.
self.align_corners = True
self.align_mode = 1
class TestBilinearInterpScale2(TestBilinearInterpOp):
def init_test_case(self):
self.interp_method = 'bilinear'
self.input_shape = [2, 3, 16, 32]
self.out_h = 60
self.out_w = 25
self.scale = 1.
self.align_corners = True
self.align_mode = 1
class TestBilinearInterpScale3(TestBilinearInterpOp):
def init_test_case(self):
self.interp_method = 'bilinear'
self.input_shape = [2, 3, 16, 32]
self.out_h = 60
self.out_w = 25
self.scale = 1.5
self.align_corners = True
self.align_mode = 1
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_nearest_interp_op.py
浏览文件 @ ed971564
......@@ -73,7 +73,14 @@ class TestNearestInterpOp(OpTest):
self.op_type = "nearest_interp"
input_np = np.random.random(self.input_shape).astype("float32")
output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
if self.scale > 0:
out_h = int(self.input_shape[2] * self.scale)
out_w = int(self.input_shape[3] * self.scale)
else:
out_h = self.out_h
out_w = self.out_w
output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
self.out_size, self.actual_shape,
self.align_corners)
self.inputs = {'X': input_np}
......@@ -84,6 +91,7 @@ class TestNearestInterpOp(OpTest):
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'scale': self.scale,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
......@@ -100,6 +108,7 @@ class TestNearestInterpOp(OpTest):
self.input_shape = [2, 3, 4, 4]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
......@@ -110,6 +119,7 @@ class TestNearestNeighborInterpCase1(TestNearestInterpOp):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.align_corners = True
......@@ -119,6 +129,7 @@ class TestNearestNeighborInterpCase2(TestNearestInterpOp):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.align_corners = True
......@@ -128,6 +139,7 @@ class TestNearestNeighborInterpCase3(TestNearestInterpOp):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.scale = 0.
self.align_corners = True
......@@ -137,6 +149,7 @@ class TestNearestNeighborInterpCase4(TestNearestInterpOp):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = True
......@@ -147,6 +160,7 @@ class TestNearestNeighborInterpCase5(TestNearestInterpOp):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.scale = 0.
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = True
......@@ -157,6 +171,7 @@ class TestNearestNeighborInterpCase6(TestNearestInterpOp):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.scale = 0.
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = True
......@@ -167,6 +182,7 @@ class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 0.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
......@@ -179,7 +195,15 @@ class TestNearestInterpOpUint8(OpTest):
self.op_type = "nearest_interp"
input_np = np.random.randint(
low=0, high=256, size=self.input_shape).astype("uint8")
output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
if self.scale > 0:
out_h = int(self.input_shape[2] * self.scale)
out_w = int(self.input_shape[3] * self.scale)
else:
out_h = self.out_h
out_w = self.out_w
output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
self.out_size, self.actual_shape,
self.align_corners)
self.inputs = {'X': input_np}
......@@ -188,6 +212,7 @@ class TestNearestInterpOpUint8(OpTest):
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'scale': self.scale,
'interp_method': self.interp_method,
'align_corners': self.align_corners
}
......@@ -201,6 +226,7 @@ class TestNearestInterpOpUint8(OpTest):
self.input_shape = [1, 3, 9, 6]
self.out_h = 10
self.out_w = 9
self.scale = 0.
self.align_corners = True
......@@ -210,6 +236,7 @@ class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
self.input_shape = [2, 3, 128, 64]
self.out_h = 120
self.out_w = 50
self.scale = 0.
self.align_corners = True
......@@ -219,6 +246,7 @@ class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
self.input_shape = [4, 1, 7, 8]
self.out_h = 5
self.out_w = 13
self.scale = 0.
self.out_size = np.array([6, 15]).astype("int32")
self.align_corners = True
......@@ -228,5 +256,38 @@ class TestNearestInterpWithoutCorners(TestNearestInterpOp):
self.align_corners = False
class TestNearestNeighborInterpScale1(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 2.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpScale2(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 1.5
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpScale3(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 32, 16]
self.out_h = 64
self.out_w = 32
self.scale = 1.
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
if __name__ == "__main__":
unittest.main()
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或