未验证 提交 7e88026e 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #10725 from Haichao-Zhang/improve_seq_pool_doc

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