未验证 提交 176df91c 编写于 作者: Z zyfncg 提交者: GitHub

Add some op yaml (#41173)

* add real and imag yaml

* add roi_align and roi_pool yaml

* add qr yaml

* add psroi_pool yaml

* fix bug

* fix param bug of psroi_pool

* fix infrt problem

* fix merge bug
上级 7ed7c6c7
...@@ -165,7 +165,7 @@ cc_library(context_pool SRCS context_pool.cc DEPS phi_context phi_enforce place) ...@@ -165,7 +165,7 @@ cc_library(context_pool SRCS context_pool.cc DEPS phi_context phi_enforce place)
cc_library(kernel_dispatch SRCS kernel_dispatch.cc DEPS phi_tensor_raw phi_context kernel_factory context_pool) cc_library(kernel_dispatch SRCS kernel_dispatch.cc DEPS phi_tensor_raw phi_context kernel_factory context_pool)
cc_library(api_gen_utils SRCS api_gen_utils.cc DEPS phi_tensor_raw selected_rows sparse_csr_tensor sparse_coo_tensor) cc_library(api_gen_utils SRCS api_gen_utils.cc DEPS phi_tensor_raw selected_rows sparse_csr_tensor sparse_coo_tensor)
cc_library(phi_data_transform SRCS data_transform.cc DEPS phi_tensor_raw transfer_layout_kernel cast_kernel data_device_transform) cc_library(phi_data_transform SRCS data_transform.cc DEPS phi_tensor_raw transfer_layout_kernel cast_kernel data_device_transform)
cc_library(api_custom_impl SRCS api_custom_impl.cc DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils phi_data_transform backward_infermeta) cc_library(api_custom_impl SRCS api_custom_impl.cc DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils backward_infermeta phi_data_transform)
cc_library(sparse_api_custom_impl SRCS sparse_api_custom_impl.cc DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils phi_data_transform) cc_library(sparse_api_custom_impl SRCS sparse_api_custom_impl.cc DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils phi_data_transform)
cc_library(phi_function_api SRCS ${api_source_file} DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils phi_data_transform api_custom_impl) cc_library(phi_function_api SRCS ${api_source_file} DEPS phi_tensor_raw phi kernel_dispatch api_gen_utils phi_data_transform api_custom_impl)
......
...@@ -18,6 +18,7 @@ limitations under the License. */ ...@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/phi/api/lib/data_transform.h" #include "paddle/phi/api/lib/data_transform.h"
#include "paddle/phi/api/lib/kernel_dispatch.h" #include "paddle/phi/api/lib/kernel_dispatch.h"
#include "paddle/phi/api/lib/utils/storage.h" #include "paddle/phi/api/lib/utils/storage.h"
#include "paddle/phi/common/type_traits.h"
#include "paddle/phi/core/compat/convert_utils.h" #include "paddle/phi/core/compat/convert_utils.h"
#include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/core/meta_tensor.h" #include "paddle/phi/core/meta_tensor.h"
...@@ -716,6 +717,62 @@ std::vector<Tensor> concat_grad_impl(const std::vector<Tensor>& x, ...@@ -716,6 +717,62 @@ std::vector<Tensor> concat_grad_impl(const std::vector<Tensor>& x,
return x_grad; return x_grad;
} }
Tensor imag_grad_impl(const Tensor& out_grad) {
phi::KernelKey kernel_key{ParseBackend(out_grad),
out_grad.layout(),
phi::dtype::ToComplex(out_grad.dtype())};
auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
"imag_grad", kernel_key);
VLOG(6) << "imag_grad API kernel key: " << kernel_key;
VLOG(6) << "imag_grad API kernel: " << kernel;
auto* dev_ctx = GetDeviceContextByBackend(kernel_key.backend());
auto dense_out_grad = TensorToDenseTensor(out_grad);
Tensor out;
auto kernel_out = SetKernelOutput(kernel_key.backend(), &out);
phi::MetaTensor meta_out(kernel_out);
phi::RealAndImagGradInferMeta(*dense_out_grad, &meta_out);
using kernel_signature = void (*)(
const phi::DeviceContext&, const phi::DenseTensor&, phi::DenseTensor*);
auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
(*kernel_fn)(*dev_ctx, *dense_out_grad, kernel_out);
return out;
}
Tensor real_grad_impl(const Tensor& out_grad) {
phi::KernelKey kernel_key{ParseBackend(out_grad),
out_grad.layout(),
phi::dtype::ToComplex(out_grad.dtype())};
auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
"real_grad", kernel_key);
VLOG(6) << "real_grad API kernel key: " << kernel_key;
VLOG(6) << "real_grad API kernel: " << kernel;
auto* dev_ctx = GetDeviceContextByBackend(kernel_key.backend());
auto dense_out_grad = TensorToDenseTensor(out_grad);
Tensor out;
auto kernel_out = SetKernelOutput(kernel_key.backend(), &out);
phi::MetaTensor meta_out(kernel_out);
phi::RealAndImagGradInferMeta(*dense_out_grad, &meta_out);
using kernel_signature = void (*)(
const phi::DeviceContext&, const phi::DenseTensor&, phi::DenseTensor*);
auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
(*kernel_fn)(*dev_ctx, *dense_out_grad, kernel_out);
return out;
}
std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x, std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x,
const Tensor& out_grad, const Tensor& out_grad,
int axis) { int axis) {
......
...@@ -92,10 +92,16 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> batch_norm_impl( ...@@ -92,10 +92,16 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> batch_norm_impl(
bool trainable_statistics, bool trainable_statistics,
bool fuse_with_relu); bool fuse_with_relu);
/************************ backward api impl ***************************/
std::vector<Tensor> concat_grad_impl(const std::vector<Tensor>& x, std::vector<Tensor> concat_grad_impl(const std::vector<Tensor>& x,
const Tensor& out_grad, const Tensor& out_grad,
const Scalar& axis); const Scalar& axis);
Tensor imag_grad_impl(const Tensor& x);
Tensor real_grad_impl(const Tensor& x);
std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x, std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x,
const Tensor& out_grad, const Tensor& out_grad,
int axis); int axis);
......
...@@ -14,6 +14,7 @@ limitations under the License. */ ...@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/phi/infermeta/backward.h" #include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/common/type_traits.h"
#include "paddle/phi/kernels/funcs/axis_utils.h" #include "paddle/phi/kernels/funcs/axis_utils.h"
namespace phi { namespace phi {
...@@ -402,6 +403,12 @@ void PsroiPoolGradInferMeta(const MetaTensor& x, ...@@ -402,6 +403,12 @@ void PsroiPoolGradInferMeta(const MetaTensor& x,
dx->share_meta(x); dx->share_meta(x);
} }
void RealAndImagGradInferMeta(const MetaTensor& out_grad, MetaTensor* dx) {
dx->set_dims(out_grad.dims());
dx->set_dtype(dtype::ToComplex(out_grad.dtype()));
dx->set_layout(out_grad.layout());
}
void ScatterGradInferMeta(const MetaTensor& index, void ScatterGradInferMeta(const MetaTensor& index,
const MetaTensor& updates, const MetaTensor& updates,
const MetaTensor& out_grad, const MetaTensor& out_grad,
......
...@@ -174,6 +174,8 @@ void PoolGradInferMeta(const MetaTensor& x, ...@@ -174,6 +174,8 @@ void PoolGradInferMeta(const MetaTensor& x,
const std::string& padding_algorithm, const std::string& padding_algorithm,
MetaTensor* dx); MetaTensor* dx);
void RealAndImagGradInferMeta(const MetaTensor& out_grad, MetaTensor* dx);
void ScatterGradInferMeta(const MetaTensor& index, void ScatterGradInferMeta(const MetaTensor& index,
const MetaTensor& updates, const MetaTensor& updates,
const MetaTensor& out_grad, const MetaTensor& out_grad,
......
...@@ -95,7 +95,8 @@ class TestPSROIPoolOp(OpTest): ...@@ -95,7 +95,8 @@ class TestPSROIPoolOp(OpTest):
self.pooled_width).astype('float64') self.pooled_width).astype('float64')
self.inputs = { self.inputs = {
'X': self.x, 'X': self.x,
'ROIs': (self.rois_with_batch_id[:, 1:5], self.rois_lod) 'ROIs': (self.rois_with_batch_id[:, 1:5], self.rois_lod),
'RoisNum': self.boxes_num
} }
self.attrs = { self.attrs = {
'output_channels': self.output_channels, 'output_channels': self.output_channels,
...@@ -145,13 +146,14 @@ class TestPSROIPoolOp(OpTest): ...@@ -145,13 +146,14 @@ class TestPSROIPoolOp(OpTest):
def setUp(self): def setUp(self):
self.op_type = 'psroi_pool' self.op_type = 'psroi_pool'
self.python_api = lambda x, boxes, boxes_num, pooled_height, pooled_width, output_channels, spatial_scale: paddle.vision.ops.psroi_pool(x, boxes, boxes_num, (pooled_height, pooled_width), spatial_scale)
self.set_data() self.set_data()
def test_check_output(self): def test_check_output(self):
self.check_output() self.check_output(check_eager=True)
def test_check_grad(self): def test_check_grad(self):
self.check_grad(['X'], 'Out') self.check_grad(['X'], 'Out', check_eager=True)
class TestPSROIPoolDynamicFunctionAPI(unittest.TestCase): class TestPSROIPoolDynamicFunctionAPI(unittest.TestCase):
......
...@@ -39,6 +39,7 @@ class TestRealOp(OpTest): ...@@ -39,6 +39,7 @@ class TestRealOp(OpTest):
paddle.enable_static() paddle.enable_static()
# op test attrs # op test attrs
self.op_type = "real" self.op_type = "real"
self.python_api = paddle.real
self.dtype = np.float64 self.dtype = np.float64
self.init_input_output() self.init_input_output()
# backward attrs # backward attrs
...@@ -58,14 +59,15 @@ class TestRealOp(OpTest): ...@@ -58,14 +59,15 @@ class TestRealOp(OpTest):
self.grad_out.shape) self.grad_out.shape)
def test_check_output(self): def test_check_output(self):
self.check_output() self.check_output(check_eager=True)
def test_check_grad(self): def test_check_grad(self):
self.check_grad( self.check_grad(
['X'], ['X'],
'Out', 'Out',
user_defined_grads=[self.grad_x], user_defined_grads=[self.grad_x],
user_defined_grad_outputs=[self.grad_out]) user_defined_grad_outputs=[self.grad_out],
check_eager=True)
class TestImagOp(TestRealOp): class TestImagOp(TestRealOp):
...@@ -74,6 +76,7 @@ class TestImagOp(TestRealOp): ...@@ -74,6 +76,7 @@ class TestImagOp(TestRealOp):
paddle.enable_static() paddle.enable_static()
# op test attrs # op test attrs
self.op_type = "imag" self.op_type = "imag"
self.python_api = paddle.imag
self.dtype = np.float64 self.dtype = np.float64
self.init_input_output() self.init_input_output()
# backward attrs # backward attrs
......
...@@ -14,6 +14,7 @@ ...@@ -14,6 +14,7 @@
from __future__ import print_function from __future__ import print_function
import paddle
import unittest import unittest
import numpy as np import numpy as np
import math import math
...@@ -32,6 +33,7 @@ class TestROIPoolOp(OpTest): ...@@ -32,6 +33,7 @@ class TestROIPoolOp(OpTest):
self.inputs = { self.inputs = {
'X': self.x, 'X': self.x,
'ROIs': (self.rois[:, 1:5], self.rois_lod), 'ROIs': (self.rois[:, 1:5], self.rois_lod),
'RoisNum': self.boxes_num
} }
self.attrs = { self.attrs = {
...@@ -130,16 +132,20 @@ class TestROIPoolOp(OpTest): ...@@ -130,16 +132,20 @@ class TestROIPoolOp(OpTest):
rois.append(roi) rois.append(roi)
self.rois_num = len(rois) self.rois_num = len(rois)
self.rois = np.array(rois).astype("float64") self.rois = np.array(rois).astype("float64")
self.boxes_num = np.array(
[bno + 1 for bno in range(self.batch_size)]).astype('int32')
def setUp(self): def setUp(self):
self.op_type = "roi_pool" self.op_type = "roi_pool"
self.python_api = lambda x, boxes, boxes_num, pooled_height, pooled_width, spatial_scale: paddle.vision.ops.roi_pool(x, boxes, boxes_num, (pooled_height, pooled_width), spatial_scale)
self.python_out_sig = ["Out"]
self.set_data() self.set_data()
def test_check_output(self): def test_check_output(self):
self.check_output() self.check_output(check_eager=True)
def test_check_grad(self): def test_check_grad(self):
self.check_grad(['X'], 'Out') self.check_grad(['X'], 'Out', check_eager=True)
class BadInputTestRoiPool(unittest.TestCase): class BadInputTestRoiPool(unittest.TestCase):
......
...@@ -18,12 +18,13 @@ from ..framework import core ...@@ -18,12 +18,13 @@ from ..framework import core
from ..fluid.layer_helper import LayerHelper from ..fluid.layer_helper import LayerHelper
from ..fluid.data_feeder import check_variable_and_dtype from ..fluid.data_feeder import check_variable_and_dtype
# TODO: define functions to get tensor attributes # TODO: define functions to get tensor attributes
from ..fluid.layers import rank # noqa: F401 from ..fluid.layers import rank # noqa: F401
from ..fluid.layers import shape # noqa: F401 from ..fluid.layers import shape # noqa: F401
import paddle import paddle
from paddle import _C_ops from paddle import _C_ops
from paddle.static import Variable from paddle.static import Variable
from ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode
__all__ = [] __all__ = []
...@@ -185,7 +186,9 @@ def real(x, name=None): ...@@ -185,7 +186,9 @@ def real(x, name=None):
# [[1., 2., 3.], # [[1., 2., 3.],
# [4., 5., 6.]]) # [4., 5., 6.]])
""" """
if paddle.in_dynamic_mode(): if in_dygraph_mode():
return _C_ops.final_state_real(x)
if _in_legacy_dygraph():
return _C_ops.real(x) return _C_ops.real(x)
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'real') check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'real')
...@@ -229,7 +232,9 @@ def imag(x, name=None): ...@@ -229,7 +232,9 @@ def imag(x, name=None):
# [[6., 5., 4.], # [[6., 5., 4.],
# [3., 2., 1.]]) # [3., 2., 1.]])
""" """
if paddle.in_dynamic_mode(): if in_dygraph_mode():
return _C_ops.final_state_imag(x)
if _in_legacy_dygraph():
return _C_ops.imag(x) return _C_ops.imag(x)
check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'imag') check_variable_and_dtype(x, 'x', ['complex64', 'complex128'], 'imag')
......
...@@ -802,6 +802,15 @@ ...@@ -802,6 +802,15 @@
func : huber_loss func : huber_loss
# backward : huber_loss_grad # backward : huber_loss_grad
- api : imag
args : (Tensor x)
output : Tensor
infer_meta :
func : RealAndImagInferMeta
kernel :
func : imag
backward : imag_grad
# increment # increment
- api : increment - api : increment
args : (Tensor x, float value) args : (Tensor x, float value)
...@@ -1336,6 +1345,16 @@ ...@@ -1336,6 +1345,16 @@
func : prelu func : prelu
backward : prelu_grad backward : prelu_grad
- api : psroi_pool
args : (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, int output_channels, float spatial_scale)
output : Tensor
infer_meta :
func : PsroiPoolInferMeta
kernel :
func : psroi_pool
optional : boxes_num
backward : psroi_pool_grad
# put_along_axis # put_along_axis
- api : put_along_axis - api : put_along_axis
args : (Tensor x, Tensor index, Tensor value, int axis, str reduce) args : (Tensor x, Tensor index, Tensor value, int axis, str reduce)
...@@ -1348,6 +1367,15 @@ ...@@ -1348,6 +1367,15 @@
data_type : x data_type : x
backward : put_along_axis_grad backward : put_along_axis_grad
- api : qr
args : (Tensor x, str mode)
output : Tensor(q), Tensor(r)
infer_meta :
func : QrInferMeta
kernel :
func : qr
# backward : qr_grad
- api : randint - api : randint
args : (int low, int high, IntArray shape, DataType dtype=DataType::INT64, Place place={}) args : (int low, int high, IntArray shape, DataType dtype=DataType::INT64, Place place={})
output : Tensor(out) output : Tensor(out)
...@@ -1372,6 +1400,15 @@ ...@@ -1372,6 +1400,15 @@
data_type : dtype data_type : dtype
backend : place backend : place
- api : real
args : (Tensor x)
output : Tensor
infer_meta :
func : RealAndImagInferMeta
kernel :
func : real
backward : real_grad
- api : reciprocal - api : reciprocal
args : (Tensor x) args : (Tensor x)
output : Tensor output : Tensor
...@@ -1423,6 +1460,17 @@ ...@@ -1423,6 +1460,17 @@
optional : boxes_num optional : boxes_num
backward : roi_align_grad backward : roi_align_grad
- api : roi_pool
args : (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, float spatial_scale)
output : Tensor(out), Tensor(arg_max)
infer_meta :
func : RoiPoolInferMeta
kernel :
func : roi_pool
optional : boxes_num
intermediate : arg_max
backward : roi_pool_grad
- api : roll - api : roll
args : (Tensor x, IntArray shifts, int64_t[] axis) args : (Tensor x, IntArray shifts, int64_t[] axis)
output : Tensor(out) output : Tensor(out)
......
...@@ -537,6 +537,12 @@ ...@@ -537,6 +537,12 @@
kernel : kernel :
func : hard_sigmoid_grad func : hard_sigmoid_grad
- backward_api : imag_grad
forward : imag (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : imag_grad_impl(out_grad)
- backward_api : index_sample_grad - backward_api : index_sample_grad
forward : index_sample (Tensor x, Tensor index) -> Tensor(out) forward : index_sample (Tensor x, Tensor index) -> Tensor(out)
args : (Tensor x, Tensor index, Tensor out_grad) args : (Tensor x, Tensor index, Tensor out_grad)
...@@ -961,15 +967,15 @@ ...@@ -961,15 +967,15 @@
func : prelu_grad func : prelu_grad
- backward_api : psroi_pool_grad - backward_api : psroi_pool_grad
forward : psroi_pool (Tensor x, Tensor rois, Tensor rois_num, int pooled_weight, int pooled_width, int output_channels, float spatial_scale ) -> Tensor(out) forward : psroi_pool (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, int output_channels, float spatial_scale) -> Tensor(out)
args : (Tensor x, Tensor rois, Tensor rois_num, Tensor out_grad, int pooled_weight, int pooled_width, int output_channels, float spatial_scale) args : (Tensor x, Tensor boxes, Tensor boxes_num, Tensor out_grad, int pooled_height, int pooled_width, int output_channels, float spatial_scale)
output : Tensor(x_grad) output : Tensor(x_grad)
infer_meta : infer_meta :
func : UnchangedInferMeta func : GeneralUnaryGradInferMeta
param : [x] param : [x]
kernel : kernel :
func : psroi_pool_grad func : psroi_pool_grad
optional : rois_num optional : boxes_num
# output is optional # output is optional
- backward_api : put_along_axis_grad - backward_api : put_along_axis_grad
...@@ -982,6 +988,12 @@ ...@@ -982,6 +988,12 @@
kernel : kernel :
func : put_along_axis_grad func : put_along_axis_grad
- backward_api : real_grad
forward : real (Tensor x) -> Tensor(out)
args : (Tensor out_grad)
output : Tensor(x_grad)
invoke : real_grad_impl(out_grad)
- backward_api : reciprocal_grad - backward_api : reciprocal_grad
forward : reciprocal (Tensor x) -> Tensor(out) forward : reciprocal (Tensor x) -> Tensor(out)
args : (Tensor out, Tensor out_grad) args : (Tensor out, Tensor out_grad)
...@@ -1048,6 +1060,17 @@ ...@@ -1048,6 +1060,17 @@
func : roi_align_grad func : roi_align_grad
optional : boxes_num optional : boxes_num
- backward_api : roi_pool_grad
forward : roi_pool (Tensor x, Tensor boxes, Tensor boxes_num, int pooled_height, int pooled_width, float spatial_scale) -> Tensor(out), Tensor(arg_max)
args : (Tensor x, Tensor boxes, Tensor boxes_num, Tensor arg_max, Tensor out_grad, int pooled_height, int pooled_width, float spatial_scale)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param : [x]
kernel :
func : roi_pool_grad
optional : boxes_num
- backward_api : roll_grad - backward_api : roll_grad
forward : roll(Tensor x, IntArray shifts, int64_t[] axis) -> Tensor(out) forward : roll(Tensor x, IntArray shifts, int64_t[] axis) -> Tensor(out)
args : (Tensor x, Tensor out_grad, IntArray shifts, int64_t[] axis) args : (Tensor x, Tensor out_grad, IntArray shifts, int64_t[] axis)
......
...@@ -959,7 +959,11 @@ def psroi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None): ...@@ -959,7 +959,11 @@ def psroi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None):
assert len(x.shape) == 4, \ assert len(x.shape) == 4, \
"Input features with shape should be (N, C, H, W)" "Input features with shape should be (N, C, H, W)"
output_channels = int(x.shape[1] / (pooled_height * pooled_width)) output_channels = int(x.shape[1] / (pooled_height * pooled_width))
if _non_static_mode(): if in_dygraph_mode():
return _C_ops.final_state_psroi_pool(x, boxes, boxes_num, pooled_height,
pooled_width, output_channels,
spatial_scale)
if _in_legacy_dygraph():
return _C_ops.psroi_pool(x, boxes, boxes_num, "output_channels", return _C_ops.psroi_pool(x, boxes, boxes_num, "output_channels",
output_channels, "spatial_scale", output_channels, "spatial_scale",
spatial_scale, "pooled_height", pooled_height, spatial_scale, "pooled_height", pooled_height,
...@@ -1069,7 +1073,11 @@ def roi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None): ...@@ -1069,7 +1073,11 @@ def roi_pool(x, boxes, boxes_num, output_size, spatial_scale=1.0, name=None):
output_size = (output_size, output_size) output_size = (output_size, output_size)
pooled_height, pooled_width = output_size pooled_height, pooled_width = output_size
if _non_static_mode(): if in_dygraph_mode():
assert boxes_num is not None, "boxes_num should not be None in dygraph mode."
return _C_ops.final_state_roi_pool(x, boxes, boxes_num, pooled_height,
pooled_width, spatial_scale)
if _in_legacy_dygraph():
assert boxes_num is not None, "boxes_num should not be None in dygraph mode." assert boxes_num is not None, "boxes_num should not be None in dygraph mode."
pool_out, argmaxes = _C_ops.roi_pool( pool_out, argmaxes = _C_ops.roi_pool(
x, boxes, boxes_num, "pooled_height", pooled_height, "pooled_width", x, boxes, boxes_num, "pooled_height", pooled_height, "pooled_width",
......
{ {
"phi_apis":["conj", "nll_loss", "flatten", "expand_as", "dropout", "roi_align"], "phi_apis":["conj", "dropout", "expand_as", "flatten", "nll_loss", "psroi_pool", "roi_align", "roi_pool"],
"phi_kernels":["equal_all"] "phi_kernels":["equal_all"]
} }
Markdown is supported
0% .
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