Merge pull request #10725 from Haichao-Zhang/improve_seq_pool_doc

improved the documentation for sequence_pool function
fix_gru_py
Tao Luo 7 years ago committed by GitHub
commit 7e88026e85
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@ -1329,6 +1329,8 @@ def sequence_pool(input, pool_type):
sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
last : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
first : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
Args:
input(variable): The input variable which is a LoDTensor.
@ -1348,6 +1350,8 @@ def sequence_pool(input, pool_type):
sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
"""
helper = LayerHelper('sequence_pool', **locals())
dtype = helper.input_dtype()
@ -3769,13 +3773,13 @@ def label_smooth(label,
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
Region of interest pooling (also known as RoI pooling) is to perform
Region of interest pooling (also known as RoI pooling) is to perform
is to perform max pooling on inputs of nonuniform sizes to obtain
fixed-size feature maps (e.g. 7*7).
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height
2. Finding the largest value in each section
The operator has three steps:
1. Dividing each region proposal into equal-sized sections with
the pooled_width and pooled_height
2. Finding the largest value in each section
3. Copying these max values to the output buffer
Args:
@ -3783,8 +3787,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
rois (Variable): ROIs (Regions of Interest) to pool over. It should
be a 2-D one level LoTensor of shape [num_rois, 4].
The layout is [x1, y1, x2, y2], where (x1, y1)
is the top left coordinates, and (x2, y2) is the
bottom right coordinates. The num_rois is the
is the top left coordinates, and (x2, y2) is the
bottom right coordinates. The num_rois is the
total number of ROIs in this batch data.
pooled_height (integer): The pooled output height. Default: 1
pooled_width (integer): The pooled output width. Default: 1
@ -3793,11 +3797,11 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
to the scale used when pooling. Default: 1.0
Returns:
pool_out (Variable): The output is a 4-D tensor of the shape
pool_out (Variable): The output is a 4-D tensor of the shape
(num_rois, channels, pooled_h, pooled_w).
Examples:
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
"""
helper = LayerHelper('roi_pool', **locals())
dtype = helper.input_dtype()

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