未验证 提交 e89bf25b 编写于 作者: H houj04 提交者: GitHub

update unittests for nearest_interp_v2_op_xpu: 'sync' from gpu. test=kunlun (#39768)

上级 574f3402
......@@ -65,6 +65,13 @@ inline std::vector<T> get_new_data_from_tensor(const Tensor* new_data_tensor) {
&cpu_starts_tensor);
new_data = cpu_starts_tensor.data<T>();
}
#endif
#ifdef PADDLE_WITH_XPU
if (platform::is_xpu_place(new_data_tensor->place())) {
paddle::framework::TensorCopySync(*new_data_tensor, platform::CPUPlace(),
&cpu_starts_tensor);
new_data = cpu_starts_tensor.data<T>();
}
#endif
vec_new_data = std::vector<T>(new_data, new_data + new_data_tensor->numel());
return vec_new_data;
......
......@@ -14,7 +14,7 @@
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/interpolate_op.h"
#include "paddle/fluid/operators/interpolate_v2_op.h"
#ifdef PADDLE_WITH_XPU
......@@ -41,18 +41,6 @@ inline std::vector<int> get_new_shape_xpu(
return vec_new_shape;
}
template <typename T>
inline std::vector<T> get_new_data_from_tensor_xpu(
const Tensor* new_data_tensor) {
std::vector<T> vec_new_data;
framework::Tensor cpu_starts_tensor;
paddle::framework::TensorCopySync(*new_data_tensor, platform::CPUPlace(),
&cpu_starts_tensor);
auto* new_data = cpu_starts_tensor.data<T>();
vec_new_data = std::vector<T>(new_data, new_data + new_data_tensor->numel());
return vec_new_data;
}
template <typename T>
class InterpolateV2XPUKernel : public framework::OpKernel<T> {
public:
......@@ -90,7 +78,7 @@ class InterpolateV2XPUKernel : public framework::OpKernel<T> {
auto scale_tensor = ctx.Input<Tensor>("Scale");
auto scale = ctx.Attr<std::vector<float>>("scale");
if (scale_tensor != nullptr) {
auto scale_data = get_new_data_from_tensor_xpu<float>(scale_tensor);
auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
if (scale_data.size() > 1) {
scale_h = scale_data[0];
scale_w = scale_data[1];
......@@ -202,7 +190,7 @@ class InterpolateV2GradXPUKernel : public framework::OpKernel<T> {
auto scale_tensor = ctx.Input<Tensor>("Scale");
auto scale = ctx.Attr<std::vector<float>>("scale");
if (scale_tensor != nullptr) {
auto scale_data = get_new_data_from_tensor_xpu<float>(scale_tensor);
auto scale_data = get_new_data_from_tensor<float>(scale_tensor);
if (scale_data.size() > 1) {
scale_h = scale_data[0];
scale_w = scale_data[1];
......
......@@ -81,7 +81,80 @@ def nearest_neighbor_interp_np(X,
if data_layout == "NHWC":
out = np.transpose(out, (0, 2, 3, 1)) # NCHW => NHWC
# out = np.expand_dims(out, 2)
return out.astype(X.dtype)
def nearest_neighbor_interp3d_np(X,
out_d,
out_h,
out_w,
scale_d=0,
scale_h=0,
scale_w=0,
out_size=None,
actual_shape=None,
align_corners=True,
data_layout='NCHW'):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if data_layout == "NHWC":
X = np.transpose(X, (0, 4, 1, 2, 3)) # NDHWC => NCDHW
if out_size is not None:
out_d = out_size[0]
out_h = out_size[1]
out_w = out_size[2]
if actual_shape is not None:
out_d = actual_shape[0]
out_h = actual_shape[1]
out_w = actual_shape[2]
n, c, in_d, in_h, in_w = X.shape
ratio_d = ratio_h = ratio_w = 0.0
if (out_d > 1):
if (align_corners):
ratio_d = (in_d - 1.0) / (out_d - 1.0)
else:
if scale_d > 0:
ratio_d = 1.0 / scale_d
else:
ratio_d = 1.0 * in_d / out_d
if (out_h > 1):
if (align_corners):
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
if scale_h > 0:
ratio_h = 1.0 / scale_h
else:
ratio_h = 1.0 * in_h / out_h
if (out_w > 1):
if (align_corners):
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
if scale_w > 0:
ratio_w = 1.0 / scale_w
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_d, out_h, out_w))
if align_corners:
for d in range(out_d):
in_d = int(ratio_d * d + 0.5)
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
else:
for d in range(out_d):
in_d = int(ratio_d * d)
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, d, i, j] = X[:, :, in_d, in_i, in_j]
if data_layout == "NDHWC":
out = np.transpose(out, (0, 2, 3, 4, 1)) # NCDHW => NDHWC
return out.astype(X.dtype)
......@@ -90,46 +163,86 @@ class TestNearestInterpOp(XPUOpTest):
self.use_xpu = True
self.out_size = None
self.actual_shape = None
self.data_layout = 'NCHW'
self.init_test_case()
self.op_type = "nearest_interp_v2"
self.shape_by_1Dtensor = False
self.scale_by_1Dtensor = False
self.attrs = {
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
input_np = np.random.random(self.input_shape).astype("float32")
self.inputs = {'X': input_np}
if self.scale_by_1Dtensor:
self.inputs['Scale'] = np.array([self.scale]).astype("float32")
elif self.scale:
if self.data_layout == "NCHW" and len(self.input_shape) == 4:
in_d = 1
in_h = self.input_shape[2]
in_w = self.input_shape[3]
else:
in_d = 1
in_h = self.input_shape[1]
in_w = self.input_shape[2]
if self.data_layout == "NCDHW" and len(self.input_shape) == 5:
in_d = self.input_shape[2]
in_h = self.input_shape[3]
in_w = self.input_shape[4]
else:
in_d = self.input_shape[1]
in_h = self.input_shape[2]
in_w = self.input_shape[3]
scale_d = 0
scale_h = 0
scale_w = 0
if self.scale:
if isinstance(self.scale, float) or isinstance(self.scale, int):
if self.scale > 0:
scale_h = scale_w = float(self.scale)
scale_d = scale_h = scale_w = float(self.scale)
if isinstance(self.scale, list) and len(self.scale) == 1:
scale_w = scale_h = self.scale[0]
scale_d = scale_w = scale_h = self.scale[0]
elif isinstance(self.scale, list) and len(self.scale) > 1:
if len(self.scale) == 5:
scale_w = self.scale[2]
scale_h = self.scale[1]
scale_d = self.scale[0]
else:
scale_w = self.scale[1]
scale_h = self.scale[0]
out_h = int(self.input_shape[2] * scale_h)
out_w = int(self.input_shape[3] * scale_w)
out_h = int(in_h * scale_h)
out_w = int(in_w * scale_w)
out_d = int(in_d * scale_d)
else:
if len(self.input_shape) == 5:
out_d = self.out_d
out_h = self.out_h
out_w = self.out_w
if self.shape_by_1Dtensor:
if len(self.input_shape) == 4:
output_np = nearest_neighbor_interp_np(
input_np, out_h, out_w, scale_h, scale_w, self.out_size,
self.actual_shape, self.align_corners, self.data_layout)
elif len(self.input_shape) == 5:
output_np = nearest_neighbor_interp3d_np(
input_np, out_d, out_h, out_w, scale_d, scale_h, scale_w,
self.out_size, self.actual_shape, self.align_corners,
self.data_layout)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
elif self.out_size is not None:
size_tensor = []
for index, ele in enumerate(self.out_size):
size_tensor.append(("x" + str(index), np.ones(
(1)).astype('int32') * ele))
self.inputs['SizeTensor'] = size_tensor
self.attrs['out_h'] = self.out_h
self.attrs['out_w'] = self.out_w
if self.actual_shape is not None:
self.inputs['OutSize'] = self.actual_shape
if len(self.input_shape) == 5:
self.attrs = {
'out_d': self.out_d,
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout
}
else:
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'data_layout': self.data_layout
}
if self.scale:
if isinstance(self.scale, float) or isinstance(self.scale, int):
if self.scale > 0:
......@@ -137,9 +250,6 @@ class TestNearestInterpOp(XPUOpTest):
if isinstance(self.scale, list) and len(self.scale) == 1:
self.scale = [self.scale[0], self.scale[0]]
self.attrs['scale'] = self.scale
output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, 0, 0,
self.out_size, self.actual_shape,
self.align_corners)
self.outputs = {'Out': output_np}
def test_check_output(self):
......@@ -154,22 +264,26 @@ class TestNearestInterpOp(XPUOpTest):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [2, 5, 4, 4]
self.out_h = 3
self.out_w = 3
self.input_shape = [2, 3, 4, 5]
self.out_h = 2
self.out_w = 2
self.scale = 0.
self.out_size = [3, 3]
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
"""
# case copied form gpu but disabled in xpu: not support 5-dim input_shape
class TestNearestNeighborInterpCase1(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [4, 1, 7, 8]
self.input_shape = [4, 1, 1, 7, 8]
self.out_d = 1
self.out_h = 1
self.out_w = 1
self.scale = 0.
self.align_corners = True
"""
class TestNearestNeighborInterpCase2(TestNearestInterpOp):
......@@ -246,6 +360,8 @@ class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
self.align_corners = True
"""
# case copied form gpu but disabled in xpu: not support NHWC data_layout
class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
......@@ -256,6 +372,7 @@ class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
self.out_size = np.array([3, 8]).astype("int32")
self.align_corners = True
self.data_layout = "NHWC"
"""
class TestNearestInterpWithoutCorners(TestNearestInterpOp):
......@@ -296,6 +413,21 @@ class TestNearestNeighborInterpScale3(TestNearestInterpOp):
self.align_corners = True
"""
# case copied form gpu but disabled in xpu: not support 5-dim input_shape
class TestNearestNeighbor3DInterp(TestNearestInterpOp):
def init_test_case(self):
self.interp_method = 'nearest'
self.input_shape = [3, 2, 4, 7, 5]
self.out_d = 8
self.out_h = 64
self.out_w = 32
self.scale = [4.0, 2.0, 3.0]
self.out_size = np.array([8, 66, 40]).astype("int32")
self.align_corners = True
"""
class TestNearestInterpOp_attr_tensor(XPUOpTest):
def setUp(self):
self.use_xpu = True
......
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