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@ -202,6 +202,7 @@ __all__ = [
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'continuous_value_model',
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'where',
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'sign',
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'deformable_conv',
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]
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kIgnoreIndex = -100
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@ -11745,3 +11746,175 @@ def sign(x):
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helper.append_op(type='sign', inputs={'X': [x]}, outputs={'Out': [out]})
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return out
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def deformable_conv(input,
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offset,
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mask,
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num_filters,
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filter_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=None,
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deformable_groups=None,
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im2col_step=None,
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param_attr=None,
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bias_attr=None,
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name=None):
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"""
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**Deformable Convolution Layer**
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Compute 2-D deformable convolution on 4-D input.
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Given input image x, output feature map y, the deformable convolution operation can be expressed as follow:
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.. math::
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y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k}
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Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location, respectively.
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Refer to `Deformable ConvNets v2: More Deformable, Better Results
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<https://arxiv.org/abs/1811.11168v2>`_ .
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Example:
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- Input:
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Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
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Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
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Offset shape: :math:`(N, 2 * deformable\_groups * H_f * H_w, H_{in}, W_{in})`
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Mask shape: :math:`(N, deformable\_groups * H_f * H_w, H_{in}, W_{in})`
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- Output:
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Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
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Where
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.. math::
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H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
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W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
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Args:
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input (Variable): The input image with [N, C, H, W] format.
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offset (Variable): The input coord offset of deformable convolution layer.
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Mask (Variable): The input mask of deformable covolution layer.
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num_filters(int): The number of filter. It is as same as the output
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image channel.
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filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
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it must contain two integers, (filter_size_H, filter_size_W).
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Otherwise, the filter will be a square.
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stride (int|tuple): The stride size. If stride is a tuple, it must
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contain two integers, (stride_H, stride_W). Otherwise, the
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stride_H = stride_W = stride. Default: stride = 1.
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padding (int|tuple): The padding size. If padding is a tuple, it must
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contain two integers, (padding_H, padding_W). Otherwise, the
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padding_H = padding_W = padding. Default: padding = 0.
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dilation (int|tuple): The dilation size. If dilation is a tuple, it must
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contain two integers, (dilation_H, dilation_W). Otherwise, the
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dilation_H = dilation_W = dilation. Default: dilation = 1.
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groups (int): The groups number of the deformable conv layer. According to
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grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
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the first half of the filters is only connected to the first half
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of the input channels, while the second half of the filters is only
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connected to the second half of the input channels. Default: groups=1.
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deformable_groups (int): The number of deformable group partitions.
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Default: deformable_groups = 1.
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im2col_step (int): Maximum number of images per im2col computation;
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The total batch size should be divisable by this value or smaller
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than this value; if you face out of memory problem, you can try
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to use a smaller value here.
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Default: im2col_step = 64.
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param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
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of deformable conv. If it is set to None or one attribute of ParamAttr,
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deformable conv will create ParamAttr as param_attr.
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If the Initializer of the param_attr is not set, the parameter is
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initialized with :math:`Normal(0.0, std)`, and the
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:math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
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bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of
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deformable conv layer. If it is set to False, no bias will be added
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to the output units. If it is set to None or one attribute of ParamAttr, conv2d
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will create ParamAttr as bias_attr. If the Initializer of the bias_attr
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is not set, the bias is initialized zero. Default: None.
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name (str|None): A name for this layer(optional). If set None, the layer
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will be named automatically. Default: None
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Returns:
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Variable: The tensor variable storing the deformable convolution \
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result.
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Raises:
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ValueError: If the shapes of input, filter_size, stride, padding and
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groups mismatch.
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Examples:
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.. code-block:: python
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data = fluid.layers.data(name='data', shape=[3, 32, 32], dtype='float32')
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offset = fluid.layers.data(name='offset', shape=[18, 32, 32], dtype='float32')
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mask = fluid.layers.data(name='mask', shape=[9, 32, 32], dtype='float32')
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out = fluid.layers.deformable_conv(input=data, offset=offset, mask=mask,
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num_filters=2, filter_size=3, padding=1)
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"""
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num_channels = input.shape[1]
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assert param_attr is not False, "param_attr should not be False here."
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helper = LayerHelper('deformable_conv', **locals())
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dtype = helper.input_dtype()
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if not isinstance(input, Variable):
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raise TypeError("Input of deformable_conv must be Variable")
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if not isinstance(offset, Variable):
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raise TypeError("Input Offset of deformable_conv must be Variable")
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if not isinstance(mask, Variable):
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raise TypeError("Input Mask of deformable_conv must be Variable")
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if groups is None:
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num_filter_channels = num_channels
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else:
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if num_channels % groups != 0:
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raise ValueError("num_channels must be divisible by groups.")
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num_filter_channels = num_channels // groups
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filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
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stride = utils.convert_to_list(stride, 2, 'stride')
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padding = utils.convert_to_list(padding, 2, 'padding')
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dilation = utils.convert_to_list(dilation, 2, 'dilation')
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input_shape = input.shape
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filter_shape = [num_filters, int(num_filter_channels)] + filter_size
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def _get_default_param_initializer():
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filter_elem_num = filter_size[0] * filter_size[1] * num_channels
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std = (2.0 / filter_elem_num)**0.5
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return Normal(0.0, std, 0)
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filter_param = helper.create_parameter(
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attr=helper.param_attr,
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shape=filter_shape,
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dtype=dtype,
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default_initializer=_get_default_param_initializer())
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pre_bias = helper.create_variable_for_type_inference(dtype)
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helper.append_op(
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type='deformable_conv',
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inputs={
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'Input': input,
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'Filter': filter_param,
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'Offset': offset,
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'Mask': mask,
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},
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outputs={"Output": pre_bias},
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attrs={
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'strides': stride,
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'paddings': padding,
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'dilations': dilation,
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'groups': groups,
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'deformable_groups': deformable_groups,
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'im2col_step': im2col_step,
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})
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output = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
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return output
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Bai Yifan
6 years ago
committed by
GitHub