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fix by comments

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fix-profile-doc-typo
gongweibao 7 years ago
parent e11d44240f
commit 25a3d2d76f
3 changed files with 123 additions and 77 deletions
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3
paddle/operators/block_expand_op.cc
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@ -30,7 +30,8 @@ class BlockExpandOp : public framework::OperatorWithKernel {
"Output of BlockExpandOp op should not be null.");
auto in_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(in_dim.size(), 4, "Input format must be NCHW.");
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");

22
paddle/operators/block_expand_op.h
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@ -68,13 +68,16 @@ class BlockExpandKernel : public framework::OpKernel<T> {
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<T>(i, i + 1).Resize({C, img_height, img_width});
Tensor dst = out->Slice<T>(i, i + 1).Resize(
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});
math::Im2ColFunctor<math::ColFormat::kOCF, Place, T> f;
f(ctx.device_context(), src, dst, stride_height, stride_width,
padding_height, padding_width);
f(ctx.device_context(), src, stride, padding, &dst);
}
}
};
@ -112,13 +115,16 @@ class BlockExpandGradKernel : public framework::OpKernel<T> {
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<T>(i, i + 1).Resize({C, img_height, img_width});
Tensor src = d_out->Slice<T>(i, i + 1).Resize(
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});
math::Col2ImFunctor<math::ColFormat::kOCF, Place, T> f;
f(ctx.device_context(), dst, src, stride_height, stride_width,
padding_height, padding_width);
f(ctx.device_context(), dst, stride, padding, &src);
}
}
};

175
python/paddle/v2/fluid/tests/test_block_expand_op.py
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@ -4,27 +4,27 @@ from op_test import OpTest
def get_output_shape(attrs, x):
imgHeight = x.shape[1]
imgWidth = x.shape[2]
img_height = x.shape[1]
img_width = x.shape[2]
paddingHeight = attrs['paddingHeight']
paddingWidth = attrs['paddingWidth']
blockHeight = attrs['blockHeight']
blockWidth = attrs['blockWidth']
strideHeight = attrs['strideHeight']
strideWidth = attrs['strideWidth']
padding_height = attrs['paddingHeight']
padding_width = attrs['paddingWidth']
block_height = attrs['blockHeight']
block_width = attrs['blockWidth']
stride_height = attrs['strideHeight']
stride_width = attrs['strideWidth']
outputHeight = \
output_height = \
1 + \
(imgHeight + 2 * paddingHeight - blockHeight + strideHeight - 1) / \
(img_height + 2 * padding_height - block_height + stride_height - 1) / \
strideHeight
outputWidth = \
output_width = \
1 + \
(imgWidth + 2 * paddingWidth - blockWidth + strideWidth - 1) / \
strideWidth
(img_width + 2 * padding_width - block_width + stride_width - 1) / \
stride_width
return outputHeight, outputWidth
return output_height, output_width
def im2col(attrs, im, col):
@ -34,38 +34,39 @@ def im2col(attrs, im, col):
{outputHeight, outputWidth, inputChannels, filterHeight, filterWidth}
"""
input_channels = im.shape[0]
inputHeight = im.shape[1]
inputWidth = im.shape[2]
input_height = im.shape[1]
input_width = im.shape[2]
outputHeight = col.shape[0]
outputWidth = col.shape[1]
filterHeight = col.shape[3]
filterWidth = col.shape[4]
output_height = col.shape[0]
output_width = col.shape[1]
filter_height = col.shape[3]
filter_width = col.shape[4]
strideHeight = attrs['strideHeight']
strideWidth = attrs['strideWidth']
paddingHeight = attrs['paddingHeight']
paddingWidth = attrs['paddingWidth']
stride_height = attrs['strideHeight']
stride_width = attrs['strideWidth']
padding_height = attrs['paddingHeight']
padding_width = attrs['paddingWidth']
for col_row_idx in range(0, outputHeight):
for col_col_idx in range(0, outputWidth):
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, filterHeight):
for filter_col_idx in range(0, filterWidth):
im_row_offset = col_row_idx * strideHeight \
+ filter_row_idx - paddingHeight
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 * strideWidth \
+ filter_col_idx - paddingWidth
im_col_offset = col_col_idx * stride_width \
+ filter_col_idx - padding_width
if (im_row_offset < 0 or im_row_offset >= inputHeight or
if (im_row_offset < 0 or
im_row_offset >= input_height or
im_col_offset < 0 or
im_col_offset >= inputWidth):
im_col_offset >= input_width):
col[col_row_idx][col_col_idx][channel][\
filter_row_idx][filter_col_idx] = 0.0
else:
im_offset = (channel * inputHeight + im_row_offset \
) * inputWidth + im_col_offset
im_offset = (channel * input_height + im_row_offset \
) * input_width + im_col_offset
col[col_row_idx][col_col_idx][channel][\
filter_row_idx][filter_col_idx] = im[channel][ \
@ -76,55 +77,55 @@ def col2img(attrs, col, img):
"""
img: {CHW}
col:
{outputHeight, outputWidth, inputChannels, filterHeight, filterWidth}
{output_height, outputWidth, inputChannels, filterHeight, filterWidth}
"""
input_channels = im.shape[0]
inputHeight = im.shape[1]
inputWidth = im.shape[2]
input_height = im.shape[1]
input_width = im.shape[2]
outputHeight = col.shape[0]
outputWidth = col.shape[1]
filterHeight = col.shape[3]
filterWidth = col.shape[4]
output_height = col.shape[0]
output_width = col.shape[1]
filter_height = col.shape[3]
filter_width = col.shape[4]
strideHeight = attrs['strideHeight']
strideWidth = attrs['strideWidth']
paddingHeight = attrs['paddingHeight']
paddingWidth = attrs['paddingWidth']
stride_height = attrs['strideHeight']
stride_width = attrs['strideWidth']
padding_height = attrs['paddingHeight']
padding_width = attrs['paddingWidth']
for col_row_idx in range(0, outputHeight):
for col_col_idx in range(0, outputWidth):
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, filterHeight):
for filter_col_idx in range(0, filterWidth):
for filter_row_idx in range(0, filter_height):
for filter_col_idx in range(0, filter_width):
im_row_offset = \
col_row_idx * strideHeight + filter_row_idx - paddingHeight
col_row_idx * stride_height + filter_row_idx - padding_height
im_col_offset = \
col_col_idx * strideWidth + filter_col_idx - paddingWidth
col_col_idx * stride_width + filter_col_idx - padding_width
if (im_row_offset >= 0 and
im_row_offset < inputHeight and
im_row_offset < input_height and
im_col_offset >= 0 and
im_col_offset < inputWidth):
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]
class TestBlockExpandOp(OpTest):
def get_input_data(self, 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)
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
class TestBlockExpandOp(OpTest):
def setUp(self):
C = 3
H = 4
W = 4
x = self.get_input_data(C, H, W)
#print x
x = get_input_data(C, H, W)
attrs = {
'blockHeight': 2,
@ -135,9 +136,47 @@ class TestBlockExpandOp(OpTest):
'paddingWidth': 1,
}
outputHeight, outputWidth = get_output_shape(attrs, x)
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'])
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
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,
}
output_height, output_width = get_output_shape(attrs, x)
out = np.random.uniform(0.1, 1,\
[outputHeight, outputWidth, x.shape[0], \
[output_height, output_width, x.shape[0], \
attrs['blockHeight'], attrs['blockWidth']]).astype("float32")
self.op_type = "block_expand"
@ -146,7 +185,7 @@ class TestBlockExpandOp(OpTest):
im2col(attrs, x, out)
self.outputs = {
'Out':out.reshape(1, outputHeight, outputWidth, x.shape[0], \
'Out':out.reshape(1, output_height, output_width, x.shape[0], \
attrs['blockHeight'], attrs['blockWidth'])
}

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