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提交 e82f1008 编写于 作者: W wanghaoshuang
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Finish block expand op 

1. Add lod to output
2. Fix im2col arguments list
3. Refine code and doc
4. Fix output shape
上级 25a3d2d7
隐藏空白更改
内联 并排
Showing with 239 addition and 222 deletion
paddle/operators/block_expand_op.cc
浏览文件 @ e82f1008
......@@ -32,37 +32,27 @@ class BlockExpandOp : public framework::OperatorWithKernel {
auto in_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(in_dim.size(), 4,
"Input(X) format must be 4D tensor, eg., NCHW.");
PADDLE_ENFORCE_GE(in_dim[0], 1, "Input batchsize must >= 1.");
int block_height = ctx->Attrs().Get<int>("blockHeight");
int block_width = ctx->Attrs().Get<int>("blockWidth");
int stride_height = ctx->Attrs().Get<int>("strideHeight");
int stride_width = ctx->Attrs().Get<int>("strideWidth");
int padding_height = ctx->Attrs().Get<int>("paddingHeight");
int padding_width = ctx->Attrs().Get<int>("paddingWidth");
int block_height = ctx->Attrs().Get<int>("block_height");
int block_width = ctx->Attrs().Get<int>("block_width");
int stride_height = ctx->Attrs().Get<int>("stride_height");
int stride_width = ctx->Attrs().Get<int>("stride_width");
int padding_height = ctx->Attrs().Get<int>("padding_height");
int padding_width = ctx->Attrs().Get<int>("padding_width");
int N = in_dim[0];
int C = in_dim[1];
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
int output_height = 0;
int output_width = 0;
int output_height = get_output_size(img_height, block_height, stride_height,
padding_height);
int output_width =
get_output_size(img_width, block_width, stride_width, padding_width);
get_blockexpand_output_shape(img_height, img_width, block_height,
block_width, stride_height, stride_width,
padding_height, padding_width, output_height,
output_width);
// The result of im2col is [output_height, output_width,
// inputChannels, filterHeight, filterWidth], and it is easy to
// reshape into [seqLength, stepSize], where seqLength is equal
// output_height * output_width, stepSize is equal
// input_channels * blockHeight * blockWidth
ctx->SetOutputDim(
"Out", {N, output_height, output_width, C, block_height, block_width});
// ctx->ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", {batch_size * output_height * output_width,
img_channels * block_height * block_width});
// TODO(wanghaoshuang): cal lod in complie time
}
};
......@@ -79,28 +69,69 @@ class BlockExpandOpMaker : public framework::OpProtoAndCheckerMaker {
W: width
)DOC");
AddOutput("Out", "(LodTensor)The output data of block_expand op,");
AddAttr<int>("blockHeight", "(int)height of block.");
AddAttr<int>("blockWidth", "(int)width of block.");
AddAttr<int>("strideHeight", "(int)height of stride.");
AddAttr<int>("strideWidth", "(int)width of stride.");
AddAttr<int>("paddingHeight", "(int)height of padding.");
AddAttr<int>("paddingWidth", "(int)width of padding.");
AddAttr<int>("block_height", "(int)height of block.");
AddAttr<int>("block_width", "(int)width of block.");
AddAttr<int>("stride_height", "(int)height of stride.");
AddAttr<int>("stride_width", "(int)width of stride.");
AddAttr<int>("padding_height", "(int)height of padding.");
AddAttr<int>("padding_width", "(int)width of padding.");
AddComment(R"DOC(
Expand feature map to minibatch matrix.
- matirx height is: output_height * output_width
- matrix width is: blockHeight * blockWidth * channels
output_height =
1 + (2 * paddingHeight + img_height - blockHeight + strideHeight - 1) /
strideHeight;
output_width =
1 + (2 * paddingWidth + img_width - blockWidth + strideWidth - 1) /
strideWidth;
The expand method is the same with ExpandConvLayer, but saved the transposed
value. After expanding, The number of time steps are output_height * output_width
and the dimension of each time step is blockHeight * blockWidth * channels.
This layer can be used after convolution neural network, and before recurrent neural network.
- matrix width is: block_height * block_width * channels
output_height =
1 + (2 * padding_height + img_height - block_height + stride_height - 1) /
stride_height;
output_width =
1 + (2 * padding_width + img_width - block_width + stride_width - 1) /
stride_width;
After expanding, The number of time steps are output_height * output_width
and the dimension of each time step is block_height * block_width * channels.
This op can be used after convolution neural network, and before recurrent neural network.
Given:
x = [[[[ 6. 2. 1.]
[ 8. 3. 5.]
[ 0. 2. 6.]]
[[ 2. 4. 4.]
[ 6. 3. 0.]
[ 6. 4. 7.]]]
[[[ 6. 7. 1.]
[ 5. 7. 9.]
[ 2. 4. 8.]]
[[ 1. 2. 1.]
[ 1. 3. 5.]
[ 9. 0. 8.]]]]
x.dims = {2, 2, 3, 3}
And:
block_height = 2
block_width = 2
stride_height = 1
stride_width = 1
padding_height = 0
padding_width = 0
Then:
output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 2. 1. 3. 5. 4. 4. 3. 0.]
[ 8. 3. 0. 2. 6. 3. 6. 4.]
[ 3. 5. 2. 6. 3. 0. 4. 7.]
[ 6. 7. 5. 7. 1. 2. 1. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.lod = [[0, 4, 8]]
)DOC");
}
};
......
paddle/operators/block_expand_op.h
浏览文件 @ e82f1008
......@@ -23,20 +23,9 @@
namespace paddle {
namespace operators {
inline void get_blockexpand_output_shape(int img_height, int img_width,
int block_height, int block_width,
int stride_height, int stride_width,
int padding_height, int padding_width,
int& outputHeight, int& outputWidth) {
outputHeight =
1 +
(img_height + 2 * padding_height - block_height + stride_height - 1) /
stride_height;
outputWidth =
1 +
(img_width + 2 * padding_width - block_width + stride_width - 1) /
stride_width;
inline int get_output_size(int img_size, int block_size, int stride,
int padding) {
return (1 + (img_size + 2 * padding - block_size + stride - 1) / stride);
}
template <typename Place, typename T>
......@@ -45,40 +34,54 @@ class BlockExpandKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
using namespace framework;
const Tensor* in = ctx.Input<Tensor>("X");
Tensor* out = ctx.Output<Tensor>("Out");
LoDTensor* out = ctx.Output<LoDTensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto in_dim = in->dims();
int N = in_dim[0];
int C = in_dim[1];
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
int block_height = ctx.Attr<int>("blockHeight");
int block_width = ctx.Attr<int>("blockWidth");
int stride_height = ctx.Attr<int>("strideHeight");
int stride_width = ctx.Attr<int>("strideWidth");
int padding_height = ctx.Attr<int>("paddingHeight");
int padding_width = ctx.Attr<int>("paddingWidth");
int outputHeight = 0;
int outputWidth = 0;
get_blockexpand_output_shape(
img_height, img_width, block_height, block_width, stride_height,
stride_width, padding_height, padding_width, outputHeight, outputWidth);
std::vector<int> stride({stride_height, stride_width});
std::vector<int> padding({padding_height, padding_width});
for (int i = 0; i < N; i++) {
Tensor src = in->Slice(i, i + 1).Resize({C, img_height, img_width});
Tensor dst = out->Slice(i, i + 1).Resize(
{outputHeight, outputWidth, C, block_height, block_width});
int block_height = ctx.Attr<int>("block_height");
int block_width = ctx.Attr<int>("block_width");
int stride_height = ctx.Attr<int>("stride_height");
int stride_width = ctx.Attr<int>("stride_width");
int padding_height = ctx.Attr<int>("padding_height");
int padding_width = ctx.Attr<int>("padding_width");
int output_height = get_output_size(img_height, block_height, stride_height,
padding_height);
int output_width =
get_output_size(img_width, block_width, stride_width, padding_width);
const std::vector<int> dilations({1, 1});
const std::vector<int> strides(
{stride_height, stride_width, stride_height, stride_width});
const std::vector<int> paddings(
{padding_height, padding_width, padding_height, padding_width});
auto out_dims = out->dims();
out->Resize({batch_size, out->numel() / batch_size});
for (int i = 0; i < batch_size; i++) {
const Tensor src =
in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
Tensor dst = out->Slice(i, i + 1).Resize({output_height, output_width,
img_channels, block_height,
block_width});
math::Im2ColFunctor<math::ColFormat::kOCF, Place, T> f;
f(ctx.device_context(), src, stride, padding, &dst);
f(ctx.device_context(), src, dilations, strides, paddings, &dst);
}
out->Resize(out_dims);
// set lod information
// TODO(wanghaoshuang): Move this to InferShape
framework::LoD lod(1);
for (int i = 0, offset = 0; i < batch_size + 1; ++i) {
lod[0].push_back(offset);
offset += output_height * output_width;
}
out->set_lod(lod);
}
};
......@@ -88,7 +91,8 @@ class BlockExpandGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
using namespace framework;
auto* in = ctx.Input<Tensor>("X");
auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
Tensor* d_out =
const_cast<Tensor*>(ctx.Input<Tensor>(framework::GradVarName("Out")));
auto* d_x = ctx.Output<Tensor>(GradVarName("X"));
d_x->mutable_data<T>(ctx.GetPlace());
......@@ -96,36 +100,40 @@ class BlockExpandGradKernel : public framework::OpKernel<T> {
x_v.device(ctx.GetEigenDevice<Place>()) = x_v.constant(0.0);
auto in_dim = in->dims();
int N = in_dim[0];
int C = in_dim[1];
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
int block_height = ctx.Attr<int>("blockHeight");
int block_width = ctx.Attr<int>("blockWidth");
int stride_height = ctx.Attr<int>("strideHeight");
int stride_width = ctx.Attr<int>("strideWidth");
int padding_height = ctx.Attr<int>("paddingHeight");
int padding_width = ctx.Attr<int>("paddingWidth");
int outputHeight = 0;
int outputWidth = 0;
get_blockexpand_output_shape(
img_height, img_width, block_height, block_width, stride_height,
stride_width, padding_height, padding_width, outputHeight, outputWidth);
std::vector<int> stride({stride_height, stride_width});
std::vector<int> padding({padding_height, padding_width});
// std::vector<int> stride({stride_height, stride_width});
for (int i = 0; i < N; i++) {
Tensor dst = d_x->Slice(i, i + 1).Resize({C, img_height, img_width});
Tensor src = d_out->Slice(i, i + 1).Resize(
{outputHeight, outputWidth, C, block_height, block_width});
int block_height = ctx.Attr<int>("block_height");
int block_width = ctx.Attr<int>("block_width");
int stride_height = ctx.Attr<int>("stride_height");
int stride_width = ctx.Attr<int>("stride_width");
int padding_height = ctx.Attr<int>("padding_height");
int padding_width = ctx.Attr<int>("padding_width");
int output_height = get_output_size(img_height, block_height, stride_height,
padding_height);
int output_width =
get_output_size(img_width, block_width, stride_width, padding_width);
const std::vector<int> dilations({1, 1});
const std::vector<int> strides(
{stride_height, stride_width, stride_height, stride_width});
const std::vector<int> paddings(
{padding_height, padding_width, padding_height, padding_width});
auto d_out_dims = d_out->dims();
d_out->Resize({batch_size, d_out->numel() / batch_size});
for (int i = 0; i < batch_size; i++) {
Tensor dst =
d_x->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
const Tensor src = d_out->Slice(i, i + 1).Resize(
{output_height, output_width, img_channels, block_height,
block_width});
math::Col2ImFunctor<math::ColFormat::kOCF, Place, T> f;
f(ctx.device_context(), dst, stride, padding, &src);
f(ctx.device_context(), src, dilations, strides, paddings, &dst);
}
d_out->Resize(d_out_dims);
}
};
......
python/paddle/v2/fluid/tests/test_block_expand_op.py
浏览文件 @ e82f1008
......@@ -4,20 +4,20 @@ from op_test import OpTest
def get_output_shape(attrs, x):
img_height = x.shape[1]
img_width = x.shape[2]
img_height = x.shape[2]
img_width = x.shape[3]
padding_height = attrs['paddingHeight']
padding_width = attrs['paddingWidth']
block_height = attrs['blockHeight']
block_width = attrs['blockWidth']
stride_height = attrs['strideHeight']
stride_width = attrs['strideWidth']
padding_height = attrs['padding_height']
padding_width = attrs['padding_width']
block_height = attrs['block_height']
block_width = attrs['block_width']
stride_height = attrs['stride_height']
stride_width = attrs['stride_width']
output_height = \
1 + \
(img_height + 2 * padding_height - block_height + stride_height - 1) / \
strideHeight
stride_height
output_width = \
1 + \
......@@ -42,10 +42,10 @@ def im2col(attrs, im, col):
filter_height = col.shape[3]
filter_width = col.shape[4]
stride_height = attrs['strideHeight']
stride_width = attrs['strideWidth']
padding_height = attrs['paddingHeight']
padding_width = attrs['paddingWidth']
stride_height = attrs['stride_height']
stride_width = attrs['stride_width']
padding_height = attrs['padding_height']
padding_width = attrs['padding_width']
for col_row_idx in range(0, output_height):
for col_col_idx in range(0, output_width):
......@@ -73,83 +73,51 @@ def im2col(attrs, im, col):
im_row_offset][im_col_offset]
def col2img(attrs, col, img):
"""
img: {CHW}
col:
{output_height, outputWidth, inputChannels, filterHeight, filterWidth}
"""
input_channels = im.shape[0]
input_height = im.shape[1]
input_width = im.shape[2]
output_height = col.shape[0]
output_width = col.shape[1]
filter_height = col.shape[3]
filter_width = col.shape[4]
def block_expand(inputs, attrs):
output_height, output_width = get_output_shape(attrs, inputs)
img_channels = inputs.shape[1]
batch_size = inputs.shape[0]
out = np.zeros([
batch_size, output_height, output_width, img_channels,
attrs['block_height'], attrs['block_width']
]).astype("float32")
stride_height = attrs['strideHeight']
stride_width = attrs['strideWidth']
padding_height = attrs['paddingHeight']
padding_width = attrs['paddingWidth']
for i in range(len(inputs)):
im2col(attrs, inputs[i], out[i])
for col_row_idx in range(0, output_height):
for col_col_idx in range(0, output_width):
for channel in range(0, input_channels):
for filter_row_idx in range(0, filter_height):
for filter_col_idx in range(0, filter_width):
im_row_offset = \
col_row_idx * stride_height + filter_row_idx - padding_height
im_col_offset = \
col_col_idx * stride_width + filter_col_idx - padding_width
if (im_row_offset >= 0 and
im_row_offset < input_height and
im_col_offset >= 0 and
im_col_offset < input_width):
im[channel][im_row_offset][im_col_offset] = \
col[col_row_idx][col_col_idx][channel][filter_row_idx][filter_col_idx]
def get_input_data(C, H, W):
x = np.random.uniform(0.1, 1, [C, H, W]).astype("float32")
for c in range(0, C):
for h in range(0, H):
for w in range(0, W):
#x[c][h][w] = c * H * W + h *W + w
x[c][h][w] = 0.2 + 0.01 * (c * H * W + h * W + w)
return x
out = out.reshape([
batch_size * output_height * output_width,
img_channels * attrs['block_height'] * attrs['block_width']
])
return out
class TestBlockExpandOp(OpTest):
def setUp(self):
C = 3
H = 4
W = 4
x = get_input_data(C, H, W)
attrs = {
'blockHeight': 2,
'blockWidth': 2,
'strideHeight': 1,
'strideWidth': 1,
'paddingHeight': 1,
'paddingWidth': 1,
def config(self):
self.batch_size = 1
self.img_channels = 3
self.img_height = 4
self.img_width = 4
self.attrs = {
'block_height': 2,
'block_width': 2,
'stride_height': 1,
'stride_width': 1,
'padding_height': 1,
'padding_width': 1,
}
output_height, output_width = get_output_shape(attrs, x)
out = np.random.uniform(0.1, 1,\
[output_height, output_width, x.shape[0], \
attrs['blockHeight'], attrs['blockWidth']]).astype("float32")
def setUp(self):
self.config()
self.op_type = "block_expand"
self.inputs = {'X': x.reshape(1, C, H, W)}
self.attrs = attrs
#x = np.random.uniform(0.1, 1,
x = np.random.randint(0, 10, [
self.batch_size, self.img_channels, self.img_height, self.img_width
]).astype("float32")
im2col(attrs, x, out)
self.outputs = {
'Out':out.reshape(1, output_height, output_width, x.shape[0], \
attrs['blockHeight'], attrs['blockWidth'])
}
out = block_expand(x, self.attrs)
self.inputs = {'X': x}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
......@@ -158,42 +126,52 @@ class TestBlockExpandOp(OpTest):
self.check_grad(['X'], 'Out')
class TestBlockExpandOp2(OpTest):
def setUp(self):
C = 3
H = 4
W = 5
x = get_input_data(C, H, W)
attrs = {
'blockHeight': 2,
'blockWidth': 1,
'strideHeight': 2,
'strideWidth': 1,
'paddingHeight': 2,
'paddingWidth': 1,
class TestBlockExpandOpCase2(TestBlockExpandOp):
def config(self):
self.batch_size = 2
self.img_channels = 3
self.img_height = 4
self.img_width = 5
self.attrs = {
'block_height': 2,
'block_width': 1,
'stride_height': 2,
'stride_width': 1,
'padding_height': 2,
'padding_width': 1,
}
output_height, output_width = get_output_shape(attrs, x)
out = np.random.uniform(0.1, 1,\
[output_height, output_width, x.shape[0], \
attrs['blockHeight'], attrs['blockWidth']]).astype("float32")
self.op_type = "block_expand"
self.inputs = {'X': x.reshape(1, C, H, W)}
self.attrs = attrs
im2col(attrs, x, out)
self.outputs = {
'Out':out.reshape(1, output_height, output_width, x.shape[0], \
attrs['blockHeight'], attrs['blockWidth'])
}
class TestBlockExpandOpCase3(TestBlockExpandOp):
def config(self):
self.batch_size = 3
self.img_channels = 1
self.img_height = 4
self.img_width = 5
self.attrs = {
'block_height': 2,
'block_width': 1,
'stride_height': 2,
'stride_width': 1,
'padding_height': 2,
'padding_width': 0,
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out')
class TestBlockExpandOpCase4(TestBlockExpandOp):
def config(self):
self.batch_size = 2
self.img_channels = 2
self.img_height = 3
self.img_width = 3
self.attrs = {
'block_height': 2,
'block_width': 2,
'stride_height': 1,
'stride_width': 1,
'padding_height': 0,
'padding_width': 0,
}
if __name__ == '__main__':
......
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