Skip to content

Commit

Permalink
Merge pull request #8651 from wanghaoshuang/fix_pool
Browse files Browse the repository at this point in the history 
Add ceil_mode option for pool2d and pool3d
  • Loading branch information
@wanghaoshuang
wanghaoshuang committed Mar 6, 2018
2 parents 98ac929 + 0ffec51 commit f608bb2
Show file tree
Hide file tree
Showing 4 changed files with 152 additions and 29 deletions.
51 changes: 41 additions & 10 deletions paddle/fluid/operators/pool_op.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -17,8 +17,15 @@ limitations under the License. */
namespace paddle {
namespace operators {

int PoolOutputSize(int input_size, int filter_size, int padding, int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
bool ceil_mode) {
int output_size;
if (!ceil_mode) {
output_size = (input_size - filter_size + 2 * padding) / stride + 1;
} else {
output_size =
(input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
}
PADDLE_ENFORCE(output_size > 0,
"Due to the settings of padding(%d), filter_size(%d) and "
"stride(%d), the output size is less than 0, please check "
Expand All @@ -38,6 +45,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
bool ceil_mode = ctx->Attrs().Get<bool>("ceil_mode");

PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
"Pooling intput should be 4-D or 5-D tensor.");
Expand All @@ -59,8 +67,8 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {

std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
output_shape.push_back(
PoolOutputSize(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
output_shape.push_back(PoolOutputSize(in_x_dims[i + 2], ksize[i],
paddings[i], strides[i], ceil_mode));
}
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
ctx->ShareLoD("X", "Out");
Expand Down Expand Up @@ -167,6 +175,12 @@ Pool2dOpMaker::Pool2dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
.SetDefault(false);
AddAttr<bool>(
"ceil_mode",
"(bool, default false) Wether to use the ceil function to calculate "
"output height and width. False is the default. If it is set to False, "
"the floor function will be used.")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
Expand All @@ -187,16 +201,21 @@ Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Example:
Input:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
Where
$$
For ceil_mode = false:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
For ceil_mode = true:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
)DOC");
}
Expand Down Expand Up @@ -251,6 +270,12 @@ Pool3dOpMaker::Pool3dOpMaker(OpProto *proto, OpAttrChecker *op_checker)
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn")
.SetDefault(false);
AddAttr<bool>(
"ceil_mode",
"(bool, default false) Wether to use the ceil function to calculate "
"output height and width. False is the default. If it is set to False, "
"the floor function will be used.")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
Expand All @@ -267,21 +292,27 @@ The pooling3d operation calculates the output based on
the input, pooling_type, ksize, strides, and paddings parameters.
Input(X) and output(Out) are in NCDHW format, where N is batch
size, C is the number of channels, and D, H and W are the depth, height and
width of the feature, respectively. Parameters(ksize, strides, paddings)
are three elements. These three elements represent depth, height and
width of the feature, respectively. Parameters(ksize, strides, paddings)
are three elements. These three elements represent depth, height and
width, respectively. The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: $(N, C, D_{in}, H_{in}, W_{in})$
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
Where
For ceil_mode = false:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
For ceil_mode = true:
$$
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0] + strides[0] -1)}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$
)DOC");
}
Expand Down
4 changes: 3 additions & 1 deletion python/paddle/fluid/layers/nn.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1438,6 +1438,7 @@ def pool2d(input,
pool_padding=0,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
name=None):
"""
This function adds the operator for pooling in 2 dimensions, using the
Expand Down Expand Up @@ -1474,7 +1475,8 @@ def pool2d(input,
"global_pooling": global_pooling,
"strides": pool_stride,
"paddings": pool_padding,
"use_cudnn": use_cudnn
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode
})

return pool_out
Expand Down
59 changes: 51 additions & 8 deletions python/paddle/fluid/tests/unittests/test_pool2d_op.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -19,12 +19,21 @@
from op_test import OpTest


def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
def max_pool2D_forward_naive(x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
) / strides[0] + 1 if ceil_mode else (H - ksize[0] + 2 *
paddings[0]) / strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
) / strides[1] + 1 if ceil_mode else (W - ksize[1] + 2 *
paddings[1]) / strides[1] + 1
out = np.zeros((N, C, H_out, W_out))
for i in xrange(H_out):
for j in xrange(W_out):
Expand All @@ -38,12 +47,21 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
return out


def avg_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
def avg_pool2D_forward_naive(x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False):
N, C, H, W = x.shape
if global_pool == 1:
ksize = [H, W]
H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1
H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
) / strides[0] + 1 if ceil_mode else (H - ksize[0] + 2 *
paddings[0]) / strides[0] + 1
W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
) / strides[1] + 1 if ceil_mode else (W - ksize[1] + 2 *
paddings[1]) / strides[1] + 1
out = np.zeros((N, C, H_out, W_out))
for i in xrange(H_out):
for j in xrange(W_out):
Expand All @@ -65,12 +83,13 @@ def setUp(self):
self.init_global_pool()
self.init_op_type()
self.init_pool_type()
self.init_ceil_mode()
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).astype("float32")
self.paddings, self.global_pool,
self.ceil_mode).astype("float32")
self.inputs = {'X': input}

self.attrs = {
Expand All @@ -80,6 +99,7 @@ def setUp(self):
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}

Expand Down Expand Up @@ -116,6 +136,9 @@ def init_pool_type(self):
def init_global_pool(self):
self.global_pool = True

def init_ceil_mode(self):
self.ceil_mode = False


class TestCase1(TestPool2d_Op):
def init_test_case(self):
Expand Down Expand Up @@ -217,5 +240,25 @@ def init_op_type(self):
self.op_type = "pool2d"


class TestCeilModeCase1(TestCUDNNCase1):
def init_ceil_mode(self):
self.ceil_mode = True


class TestCeilModeCase2(TestCUDNNCase2):
def init_ceil_mode(self):
self.ceil_mode = True


class TestCeilModeCase3(TestCase1):
def init_ceil_mode(self):
self.ceil_mode = True


class TestCeilModeCase4(TestCase2):
def init_ceil_mode(self):
self.ceil_mode = True


if __name__ == '__main__':
unittest.main()
67 changes: 57 additions & 10 deletions python/paddle/fluid/tests/unittests/test_pool3d_op.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -19,13 +19,24 @@
from op_test import OpTest


def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
def max_pool3D_forward_naive(x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1
H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1
W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1
D_out = (D - ksize[0] + 2 * paddings[0] + strides[0] - 1
) / strides[0] + 1 if ceil_mode else (H - ksize[0] + 2 *
paddings[0]) / strides[0] + 1
H_out = (H - ksize[1] + 2 * paddings[1] + strides[1] - 1
) / strides[1] + 1 if ceil_mode else (W - ksize[1] + 2 *
paddings[1]) / strides[1] + 1
W_out = (W - ksize[2] + 2 * paddings[2] + strides[2] - 1
) / strides[2] + 1 if ceil_mode else (W - ksize[2] + 2 *
paddings[2]) / strides[2] + 1
out = np.zeros((N, C, D_out, H_out, W_out))
for k in xrange(D_out):
d_start = np.max((k * strides[0] - paddings[0], 0))
Expand All @@ -42,13 +53,24 @@ def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
return out


def avg_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
def avg_pool3D_forward_naive(x,
ksize,
strides,
paddings,
global_pool=0,
ceil_mode=False):
N, C, D, H, W = x.shape
if global_pool == 1:
ksize = [D, H, W]
D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1
H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1
W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1
D_out = (D - ksize[0] + 2 * paddings[0] + strides[0] - 1
) / strides[0] + 1 if ceil_mode else (H - ksize[0] + 2 *
paddings[0]) / strides[0] + 1
H_out = (H - ksize[1] + 2 * paddings[1] + strides[1] - 1
) / strides[1] + 1 if ceil_mode else (W - ksize[1] + 2 *
paddings[1]) / strides[1] + 1
W_out = (W - ksize[2] + 2 * paddings[2] + strides[2] - 1
) / strides[2] + 1 if ceil_mode else (W - ksize[2] + 2 *
paddings[2]) / strides[2] + 1
out = np.zeros((N, C, D_out, H_out, W_out))
for k in xrange(D_out):
d_start = np.max((k * strides[0] - paddings[0], 0))
Expand All @@ -73,13 +95,14 @@ def setUp(self):
self.init_global_pool()
self.init_op_type()
self.init_pool_type()
self.init_ceil_mode()

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).astype("float32")
self.paddings, self.global_pool,
self.ceil_mode).astype("float32")
self.inputs = {'X': input}

self.attrs = {
Expand All @@ -89,6 +112,7 @@ def setUp(self):
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'use_cudnn': self.use_cudnn,
'ceil_mode': self.ceil_mode,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
}

Expand Down Expand Up @@ -125,6 +149,9 @@ def init_pool_type(self):
def init_global_pool(self):
self.global_pool = True

def init_ceil_mode(self):
self.ceil_mode = False


class TestCase1(TestPool3d_Op):
def init_test_case(self):
Expand Down Expand Up @@ -227,5 +254,25 @@ def init_op_type(self):
self.op_type = "pool3d"


class TestCeilModeCase1(TestCUDNNCase1):
def init_ceil_mode(self):
self.ceil_mode = True


class TestCeilModeCase2(TestCUDNNCase2):
def init_ceil_mode(self):
self.ceil_mode = True


class TestCeilModeCase3(TestCase1):
def init_ceil_mode(self):
self.ceil_mode = True


class TestCeilModeCase4(TestCase2):
def init_ceil_mode(self):
self.ceil_mode = True


if __name__ == '__main__':
unittest.main()

0 comments on commit f608bb2

Please sign in to comment.