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fix adaptive pool doc.test=develop

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revert-15774-anakin_subgraph_engine
dengkaipeng 6 years ago
parent 676995c86c
commit 19292ac6a1
2 changed files with 91 additions and 18 deletions
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75
paddle/fluid/operators/pool_op.cc
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@ -262,28 +262,52 @@ Example:
For exclusive = false: For exclusive = false:
$$ $$
hstart = i * strides[0] - paddings[0] hstart = i * strides[0] - paddings[0]
$$
$$
hend = hstart + ksize[0] hend = hstart + ksize[0]
$$
$$
wstart = j * strides[1] - paddings[1] wstart = j * strides[1] - paddings[1]
$$
$$
wend = wstart + ksize[1] wend = wstart + ksize[1]
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]} Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$ $$
For exclusive = true: For exclusive = true:
$$ $$
hstart = max(0, i * strides[0] - paddings[0]) hstart = max(0, i * strides[0] - paddings[0])
$$
$$
hend = min(H, hstart + ksize[0]) hend = min(H, hstart + ksize[0])
$$
$$
wstart = max(0, j * strides[1] - paddings[1]) wstart = max(0, j * strides[1] - paddings[1])
$$
$$
wend = min(W, wstart + ksize[1]) wend = min(W, wstart + ksize[1])
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)} Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$ $$
For adaptive = true: For adaptive = true:
$$ $$
hstart = floor(i * H_{in} / H_{out}) hstart = floor(i * H_{in} / H_{out})
hend = ceil((i + 1) * H_{in} / H_{out}) $$
wstart = floor(j * W_{in} / W_{out}) $$
wend = ceil((j + 1) * W_{in} / W_{out}) hend = ceil((i + 1) * H_{in} / H_{out})
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)} $$
$$ $$
wstart = floor(j * W_{in} / W_{out})
$$
$$
wend = ceil((j + 1) * W_{in} / W_{out})
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC"); )DOC");
} }
@ -403,35 +427,72 @@ Example:
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1] + strides[1] -1)}{strides[1]} + 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 W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2] + strides[2] -1)}{strides[2]} + 1
$$ $$
For exclusive = false: For exclusive = false:
$$ $$
dstart = i * strides[0] - paddings[0] dstart = i * strides[0] - paddings[0]
$$
$$
dend = dstart + ksize[0] dend = dstart + ksize[0]
$$
$$
hstart = j * strides[1] - paddings[1] hstart = j * strides[1] - paddings[1]
$$
$$
hend = hstart + ksize[1] hend = hstart + ksize[1]
$$
$$
wstart = k * strides[2] - paddings[2] wstart = k * strides[2] - paddings[2]
$$
$$
wend = wstart + ksize[2] wend = wstart + ksize[2]
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]} Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$ $$
For exclusive = true: For exclusive = true:
$$ $$
dstart = max(0, i * strides[0] - paddings[0]) dstart = max(0, i * strides[0] - paddings[0])
$$
$$
dend = min(D, dstart + ksize[0]) dend = min(D, dstart + ksize[0])
$$
$$
hstart = max(0, j * strides[1] - paddings[1]) hstart = max(0, j * strides[1] - paddings[1])
$$
$$
hend = min(H, hstart + ksize[1]) hend = min(H, hstart + ksize[1])
$$
$$
wstart = max(0, k * strides[2] - paddings[2]) wstart = max(0, k * strides[2] - paddings[2])
$$
$$
wend = min(W, wstart + ksize[2]) wend = min(W, wstart + ksize[2])
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)} Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$ $$
For adaptive = true: For adaptive = true:
$$ $$
dstart = floor(i * D_{in} / D_{out}) dstart = floor(i * D_{in} / D_{out})
$$
$$
dend = ceil((i + 1) * D_{in} / D_{out}) dend = ceil((i + 1) * D_{in} / D_{out})
$$
$$
hstart = floor(j * H_{in} / H_{out}) hstart = floor(j * H_{in} / H_{out})
$$
$$
hend = ceil((j + 1) * H_{in} / H_{out}) hend = ceil((j + 1) * H_{in} / H_{out})
$$
$$
wstart = floor(k * W_{in} / W_{out}) wstart = floor(k * W_{in} / W_{out})
$$
$$
wend = ceil((k + 1) * W_{in} / W_{out}) wend = ceil((k + 1) * W_{in} / W_{out})
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)} Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$ $$

34
python/paddle/fluid/layers/nn.py
Unescape View File

@ -2569,7 +2569,13 @@ def adaptive_pool2d(input,
require_index=False, require_index=False,
name=None): name=None):
""" """
${comment} **Adaptive Pool2d Operator**
The adaptive_pool2d operation calculates the output based on the input, pool_size,
pool_type parameters. Input(X) and output(Out) are in NCHW format, where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature. Parameters(pool_size) should contain two elements which
represent height and width, respectively. Also the H and W dimensions of output(Out)
is same as Parameter(pool_size).
Args: Args:
input (Variable): The input tensor of pooling operator. The format of input (Variable): The input tensor of pooling operator. The format of
@ -2579,8 +2585,8 @@ def adaptive_pool2d(input,
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (pool_size_Height, pool_size_Width). it must contain two integers, (pool_size_Height, pool_size_Width).
pool_type: ${pooling_type_comment} pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs. require_index (bool): If true, the index of max pooling point will be returned along
it cannot be set in average pooling type. with outputs. It cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically. layer will be named automatically.
@ -2661,18 +2667,24 @@ def adaptive_pool3d(input,
require_index=False, require_index=False,
name=None): name=None):
""" """
${comment} **Adaptive Pool3d Operator**
The adaptive_pool3d operation calculates the output based on the input, pool_size,
pool_type parameters. Input(X) and output(Out) are in NCDHW format, where N is batch
size, C is the number of channels, D is the depth of the feature, H is the height of
the feature, and W is the width of the feature. Parameters(pool_size) should contain
three elements which represent height and width, respectively. Also the D, H and W
dimensions of output(Out) is same as Parameter(pool_size).
Args: Args:
input (Variable): The input tensor of pooling operator. The format of input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is input tensor is NCDHW, where N is batch size, C is
the number of channels, H is the height of the the number of channels, D is the depth of the feature,
feature, and W is the width of the feature. H is the height of the feature, and W is the width of the feature.
pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
it must contain two integers, (Depth, Height, Width). it must contain three integers, (Depth, Height, Width).
pool_type: ${pooling_type_comment} pool_type: ${pooling_type_comment}
require_index (bool): If true, the index of max pooling point along with outputs. require_index (bool): If true, the index of max pooling point will be returned along
it cannot be set in average pooling type. with outputs. It cannot be set in average pooling type.
name (str|None): A name for this layer(optional). If set None, the name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically. layer will be named automatically.
@ -2709,7 +2721,7 @@ def adaptive_pool3d(input,
name='data', shape=[3, 32, 32], dtype='float32') name='data', shape=[3, 32, 32], dtype='float32')
pool_out, mask = fluid.layers.adaptive_pool3d( pool_out, mask = fluid.layers.adaptive_pool3d(
input=data, input=data,
pool_size=[3, 3], pool_size=[3, 3, 3],
pool_type='avg') pool_type='avg')
""" """
if pool_type not in ["max", "avg"]: if pool_type not in ["max", "avg"]:

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