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@ -1329,6 +1329,8 @@ def sequence_pool(input, pool_type):
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sqrt : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
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6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
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max : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
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last : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
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first : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
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Args:
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input(variable): The input variable which is a LoDTensor.
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@ -1348,6 +1350,8 @@ def sequence_pool(input, pool_type):
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sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
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sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
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max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
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last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
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first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
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"""
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helper = LayerHelper('sequence_pool', **locals())
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dtype = helper.input_dtype()
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@ -3263,35 +3267,35 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
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"""
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**Smooth L1 Loss Operator. **
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This operator computes the smooth l1 loss for X and Y.
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This operator computes the smooth L1 loss for X and Y.
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The operator takes the first dimension of X and Y as batch size.
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For each instance, it computes the smooth l1 loss element by element first
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For each instance, it computes the smooth L1 loss element by element first
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and then sums all the losses. So the shape of Out is [batch_size, 1].
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Args:
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x (Variable): A tensor with rank at least 2. The input value of smooth
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l1 loss op with shape [batch_size, dim1, ..., dimN].
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L1 loss op with shape [batch_size, dim1, ..., dimN].
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y (Variable): A tensor with rank at least 2. The target value of smooth
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l1 loss op with same shape as x.
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L1 loss op with same shape as x.
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inside_weight (Variable|None): A tensor with rank at least 2. This
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input is optional and should have same shape with x. If provided,
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the result of (x - y) will be multiplied by this tensor element by
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element.
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outside_weight (Variable|None): A tensor with rank at least 2. This
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input is optional and should have same shape with x. If provided,
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the out smooth l1 loss will be multiplied by this tensor element
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the out smooth L1 loss will be multiplied by this tensor element
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by element.
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sigma (float|None): Hyper parameter of smooth l1 loss op. A float scalar
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sigma (float|None): Hyper parameter of smooth L1 loss op. A float scalar
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with default value 1.0.
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Returns:
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Variable: A tensor with rank be 2. The output smooth l1 loss with
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Variable: A tensor with rank be 2. The output smooth L1 loss with
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shape [batch_size, 1].
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Examples:
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.. code-block:: python
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data = fluid.layers.data(name='data', shape=[128], dtype='float32')
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label = fluid.layers.data(name='label', shape=[100], dtype='int64')
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label = fluid.layers.data(name='label', shape=[100], dtype='float32')
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fc = fluid.layers.fc(input=data, size=100)
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out = fluid.layers.smooth_l1(x=fc, y=label)
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"""
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@ -3769,13 +3773,13 @@ def label_smooth(label,
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def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
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"""
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Region of interest pooling (also known as RoI pooling) is to perform
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Region of interest pooling (also known as RoI pooling) is to perform
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is to perform max pooling on inputs of nonuniform sizes to obtain
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fixed-size feature maps (e.g. 7*7).
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The operator has three steps:
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1. Dividing each region proposal into equal-sized sections with
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the pooled_width and pooled_height
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2. Finding the largest value in each section
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The operator has three steps:
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1. Dividing each region proposal into equal-sized sections with
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the pooled_width and pooled_height
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2. Finding the largest value in each section
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3. Copying these max values to the output buffer
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Args:
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@ -3783,8 +3787,8 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
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rois (Variable): ROIs (Regions of Interest) to pool over. It should
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be a 2-D one level LoTensor of shape [num_rois, 4].
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The layout is [x1, y1, x2, y2], where (x1, y1)
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is the top left coordinates, and (x2, y2) is the
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bottom right coordinates. The num_rois is the
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is the top left coordinates, and (x2, y2) is the
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bottom right coordinates. The num_rois is the
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total number of ROIs in this batch data.
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pooled_height (integer): The pooled output height. Default: 1
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pooled_width (integer): The pooled output width. Default: 1
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@ -3793,11 +3797,11 @@ def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
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to the scale used when pooling. Default: 1.0
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Returns:
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pool_out (Variable): The output is a 4-D tensor of the shape
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pool_out (Variable): The output is a 4-D tensor of the shape
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(num_rois, channels, pooled_h, pooled_w).
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Examples:
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pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
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pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
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"""
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helper = LayerHelper('roi_pool', **locals())
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dtype = helper.input_dtype()
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yuyang18
7 years ago