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未验证 提交 8efd0876 编写于 作者: C chengduo 提交者： GitHub
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Merge pull request #5187 from chengduoZH/fix_pool_op 

fix pool op
上级 0049ce04 6bdf5c14
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内联 并排
Showing with 109 addition and 75 deletion
paddle/operators/pool_cudnn_op.cu
浏览文件 @ 8efd0876
......@@ -43,6 +43,7 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(input->dims()[i + 2]);
}
}
......@@ -97,9 +98,11 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i)
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(input->dims()[i + 2]);
}
}
const T *input_data = input->data<T>();
const T *output_data = output->data<T>();
......
paddle/operators/pool_op.cc
浏览文件 @ 8efd0876
......@@ -39,9 +39,11 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
if (ctx->Attrs().Get<bool>("globalPooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i)
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
}
}
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
"Input size and pooling size should be consistent.");
......@@ -84,15 +86,16 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
"(string), pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>(
"ksize",
"(vector ), the pooling window size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
AddAttr<std::vector<int>>("ksize",
"(vector ), the pooling window size(height, width) "
"of pooling operator."
"If globalPooling = true, ksize and paddings will "
"be ignored."); // TODO(Chengduo): Add checker.
// (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored.")
"If globalPooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>(
"strides",
......@@ -101,7 +104,8 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0,0}), paddings(height, width) of pooling operator.")
"(vector defalut:{0,0}), paddings(height, width) of pooling operator."
"If globalPooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......@@ -145,25 +149,28 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
"(string), pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>(
"ksize",
"(vector ), the pooling window size(depth, height, width) of pooling "
AddAttr<std::vector<int>>("ksize",
"(vector ), the pooling window size(depth, height, "
"width) of pooling "
"operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
"If globalPooling = true, ksize and paddings wille "
"be ignored."); // TODO(Chengduo): Add checker.
// (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored.")
"If globalPooling = true, ksize and paddings wille be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector, default:{1,1,1}), strides(depth, height, "
"width) of pooling operator.")
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>("paddings",
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0,0,0}), paddings(depth, height, "
"width) of pooling operator.")
"width) of pooling operator."
"If globalPooling = true, ksize and paddings wille be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......
paddle/operators/pool_op.h
浏览文件 @ 8efd0876
......@@ -63,6 +63,7 @@ class PoolKernel : public framework::OpKernel<T> {
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
......@@ -103,6 +104,7 @@ class PoolKernel : public framework::OpKernel<T> {
paddings, pool_process);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
}
}
};
......@@ -123,9 +125,11 @@ class PoolGradKernel : public framework::OpKernel<T> {
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i)
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
if (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace());
......@@ -164,6 +168,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
*out_grad, ksize, strides, paddings, pool_process);
}
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
}
}
}
......
paddle/operators/pool_with_index_op.cc
浏览文件 @ 8efd0876
......@@ -46,9 +46,11 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
if (ctx->Attrs().Get<bool>("globalPooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i)
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x_dims[i + 2]);
}
}
PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
"Input size and pooling size should be consistent.");
......@@ -87,31 +89,33 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"(Tensor) The input tensor of pooling operator. "
"(Tensor), the input tensor of pooling operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of image.");
AddOutput("Out",
"(Tensor) The output tensor of pooling operator."
"(Tensor), the output tensor of pooling operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of image.");
AddOutput("Mask",
"(Tensor) The Mask tensor of pooling operator."
"(Tensor), the Mask tensor of pooling operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is the number of channels, H and W "
"is the height and width of image."
"The value in it is the index in current feature map");
AddAttr<std::vector<int>>(
"ksize",
"(vector ), the pooling window size(height, width) of pooling operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
AddAttr<std::vector<int>>("ksize",
"(vector ), the pooling window size(height, "
"width) of pooling operator."
"If globalPooling = true, ksize and paddings "
"will be ignored."); // TODO(Chengduo): Add
// checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("globalPooling",
AddAttr<bool>(
"globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored.")
"If globalPooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>(
"strides",
......@@ -120,7 +124,8 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0,0}), paddings(height, width) of pooling operator.")
"(vector defalut:{0, 0}), paddings(height, width) of pooling operator."
"If globalPooling = true, paddings and will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......@@ -153,42 +158,46 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"(Tensor) The input tensor of pooling operator. "
"(Tensor), the input tensor of pooling operator. "
"The format of input tensor is NCDHW. Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and width of "
"image.");
AddOutput("Out",
"(Tensor) The output tensor of pooling operator."
"(Tensor), the output tensor of pooling operator."
"The format of output tensor is also NCDHW."
"Where N is batch size, C is "
"the number of channels, D, H and W is the depth, height and "
"width of image.");
AddOutput("Mask",
"(Tensor) The Mask tensor of pooling operator."
"(Tensor), the Mask tensor of pooling operator."
"The format of output tensor is also NCDHW."
"Where N is batch size, C is the number of channels, D, H and W "
"is the depth, height and width of image."
"The value in it is the index in current feature map");
AddAttr<std::vector<int>>(
"ksize",
"(vector ), the pooling window size(depth, height, width) of pooling "
AddAttr<std::vector<int>>("ksize",
"(vector), the pooling window size(depth, "
"height, width) of pooling "
"operator."
"If globalPooling = true, ksize is ignored and need not be "
"specified."); // TODO(Chengduo): Add checker. (Currently,
"If globalPooling = true, ksize and paddings "
"will be ignored."); // TODO(Chengduo): Add
// checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("globalPooling",
AddAttr<bool>(
"globalPooling",
"(bool default: false), whether to use the global pooling."
"If globalPooling = true, ksize is ignored.")
"If globalPooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector, default:{1,1,1}), strides(depth, "
"height, width) of pooling operator.")
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>("paddings",
AddAttr<std::vector<int>>(
"paddings",
"(vector defalut:{0,0,0}), paddings(depth, "
"height, width) of pooling operator.")
"height, width) of pooling operator."
"If globalPooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......
paddle/operators/pool_with_index_op.h
浏览文件 @ 8efd0876
......@@ -37,6 +37,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
}
}
......@@ -54,6 +55,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
pool3d_forward(context.device_context(), *in_x, *out, *mask, ksize,
strides, paddings);
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
}
}
};
......@@ -72,6 +74,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);
}
}
......@@ -95,6 +98,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
pool3d_backward(context.device_context(), *in_x_grad, *out_grad,
*mask, ksize, strides, paddings);
} break;
default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
}
}
}
......
python/paddle/v2/framework/tests/test_pool2d_op.py
浏览文件 @ 8efd0876
......@@ -49,9 +49,12 @@ class TestPool2d_Op(OpTest):
self.init_test_case()
self.init_op_type()
self.init_pool_type()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype("float32")
output = self.pool2D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool)
self.paddings,
self.global_pool).astype("float32")
self.inputs = {'X': input}
self.attrs = {
......
python/paddle/v2/framework/tests/test_pool3d_op.py
浏览文件 @ 8efd0876
......@@ -54,10 +54,13 @@ def avg_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class TestPool3d_Op(OpTest):
def setUp(self):
self.initTestCase()
self.init_test_case()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype("float32")
output = self.pool3D_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool)
self.paddings,
self.global_pool).astype("float32")
self.inputs = {'X': input}
self.attrs = {
......@@ -77,7 +80,7 @@ class TestPool3d_Op(OpTest):
if self.pool_type != "max":
self.check_grad(set(['X']), 'Out', max_relative_error=0.07)
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "pool3d"
self.pool_type = "avg"
......@@ -89,7 +92,7 @@ class TestPool3d_Op(OpTest):
class TestCase1(TestPool3d_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "pool3d"
self.pool_type = "avg"
......@@ -101,7 +104,7 @@ class TestCase1(TestPool3d_Op):
class TestCase2(TestPool3d_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "pool3d"
self.pool_type = "avg"
......@@ -113,7 +116,7 @@ class TestCase2(TestPool3d_Op):
class TestCase3(TestPool3d_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "pool3d"
self.pool_type = "max"
......@@ -125,7 +128,7 @@ class TestCase3(TestPool3d_Op):
class TestCase4(TestPool3d_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "pool3d"
self.pool_type = "max"
......@@ -137,7 +140,7 @@ class TestCase4(TestPool3d_Op):
class TestCase5(TestPool3d_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "pool3d"
self.pool_type = "max"
......
python/paddle/v2/framework/tests/test_pool_max_op.py
浏览文件 @ 8efd0876
......@@ -3,11 +3,7 @@ import numpy as np
from op_test import OpTest
def max_pool3D_forward_naive(x,
ksize,
strides,
paddings=[0, 0, 0],
global_pool=0):
def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
N, C, D, H, W = x.shape
if global_pool == 1:
......@@ -44,7 +40,7 @@ def max_pool3D_forward_naive(x,
return out, mask
def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
N, C, H, W = x.shape
if global_pool == 1:
......@@ -77,10 +73,14 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
class TestMaxPoolWithIndex_Op(OpTest):
def setUp(self):
self.initTestCase()
self.init_test_case()
if self.global_pool:
self.paddings = [0 for _ in range(len(self.paddings))]
input = np.random.random(self.shape).astype("float32")
output, mask = self.pool_forward_naive(input, self.ksize, self.strides,
self.paddings, self.global_pool)
output = output.astype("float32")
mask = mask.astype("float32")
self.attrs = {
'strides': self.strides,
......@@ -98,7 +98,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
# def test_check_grad(self):
# self.check_grad(set(['X']), ['Out'], max_relative_error=0.07)
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.index = "max_pool3d_with_index"
self.op_type = "%s" % self.index
......@@ -110,7 +110,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
class TestCase1(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
......@@ -121,7 +121,7 @@ class TestCase1(TestMaxPoolWithIndex_Op):
class TestCase2(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
......@@ -132,7 +132,7 @@ class TestCase2(TestMaxPoolWithIndex_Op):
class TestCase3(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
......@@ -143,7 +143,7 @@ class TestCase3(TestMaxPoolWithIndex_Op):
class TestCase4(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
......@@ -154,7 +154,7 @@ class TestCase4(TestMaxPoolWithIndex_Op):
class TestCase5(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool3d_with_index"
self.pool_forward_naive = max_pool3D_forward_naive
......@@ -165,7 +165,7 @@ class TestCase5(TestMaxPoolWithIndex_Op):
class TestCase6(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
......@@ -176,7 +176,7 @@ class TestCase6(TestMaxPoolWithIndex_Op):
class TestCase7(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = False
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
......@@ -187,7 +187,7 @@ class TestCase7(TestMaxPoolWithIndex_Op):
class TestCase8(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
......@@ -198,7 +198,7 @@ class TestCase8(TestMaxPoolWithIndex_Op):
class TestCase9(TestMaxPoolWithIndex_Op):
def initTestCase(self):
def init_test_case(self):
self.global_pool = True
self.op_type = "max_pool2d_with_index"
self.pool_forward_naive = max_pool2D_forward_naive
......
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