Source code for mxnet.gluon.contrib.cnn.conv_layers

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# coding: utf-8
# pylint: disable= arguments-differ
"""Custom convolutional neural network layers in model_zoo."""

__all__ = ['DeformableConvolution']

from .... import symbol
from ...block import HybridBlock
from ....base import numeric_types
from ...nn import Activation

[docs]class DeformableConvolution(HybridBlock): """2-D Deformable Convolution v_1 (Dai, 2017). Normal Convolution uses sampling points in a regular grid, while the sampling points of Deformablem Convolution can be offset. The offset is learned with a separate convolution layer during the training. Both the convolution layer for generating the output features and the offsets are included in this gluon layer. Parameters ---------- channels : int, The dimensionality of the output space i.e. the number of output channels in the convolution. kernel_size : int or tuple/list of 2 ints, (Default value = (1,1)) Specifies the dimensions of the convolution window. strides : int or tuple/list of 2 ints, (Default value = (1,1)) Specifies the strides of the convolution. padding : int or tuple/list of 2 ints, (Default value = (0,0)) If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation : int or tuple/list of 2 ints, (Default value = (1,1)) Specifies the dilation rate to use for dilated convolution. groups : int, (Default value = 1) Controls the connections between inputs and outputs. At groups=1, all inputs are convolved to all outputs. At groups=2, the operation becomes equivalent to having two convolution layers side by side, each seeing half the input channels, and producing half the output channels, and both subsequently concatenated. num_deformable_group : int, (Default value = 1) Number of deformable group partitions. layout : str, (Default value = NCHW) Dimension ordering of data and weight. Can be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW', 'NDHWC', etc. 'N', 'C', 'H', 'W', 'D' stands for batch, channel, height, width and depth dimensions respectively. Convolution is performed over 'D', 'H', and 'W' dimensions. use_bias : bool, (Default value = True) Whether the layer for generating the output features uses a bias vector. in_channels : int, (Default value = 0) The number of input channels to this layer. If not specified, initialization will be deferred to the first time `forward` is called and input channels will be inferred from the shape of input data. activation : str, (Default value = None) Activation function to use. See :func:`~mxnet.ndarray.Activation`. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`). weight_initializer : str or `Initializer`, (Default value = None) Initializer for the `weight` weights matrix for the convolution layer for generating the output features. bias_initializer : str or `Initializer`, (Default value = zeros) Initializer for the bias vector for the convolution layer for generating the output features. offset_weight_initializer : str or `Initializer`, (Default value = zeros) Initializer for the `weight` weights matrix for the convolution layer for generating the offset. offset_bias_initializer : str or `Initializer`, (Default value = zeros), Initializer for the bias vector for the convolution layer for generating the offset. offset_use_bias: bool, (Default value = True) Whether the layer for generating the offset uses a bias vector. Inputs: - **data**: 4D input tensor with shape `(batch_size, in_channels, height, width)` when `layout` is `NCHW`. For other layouts shape is permuted accordingly. Outputs: - **out**: 4D output tensor with shape `(batch_size, channels, out_height, out_width)` when `layout` is `NCHW`. out_height and out_width are calculated as:: out_height = floor((height+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0])+1 out_width = floor((width+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1])+1 """ def __init__(self, channels, kernel_size=(1, 1), strides=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, num_deformable_group=1, layout='NCHW', use_bias=True, in_channels=0, activation=None, weight_initializer=None, bias_initializer='zeros', offset_weight_initializer='zeros', offset_bias_initializer='zeros', offset_use_bias=True, op_name='DeformableConvolution', adj=None, prefix=None, params=None): super(DeformableConvolution, self).__init__(prefix=prefix, params=params) with self.name_scope(): self._channels = channels self._in_channels = in_channels assert layout in ('NCHW', 'NHWC'), "Only supports 'NCHW' and 'NHWC' layout for now" if isinstance(kernel_size, numeric_types): kernel_size = (kernel_size,) * 2 if isinstance(strides, numeric_types): strides = (strides,) * len(kernel_size) if isinstance(padding, numeric_types): padding = (padding,) * len(kernel_size) if isinstance(dilation, numeric_types): dilation = (dilation,) * len(kernel_size) self._op_name = op_name offset_channels = 2 * kernel_size[0] * kernel_size[1] * num_deformable_group self._kwargs_offset = { 'kernel': kernel_size, 'stride': strides, 'dilate': dilation, 'pad': padding, 'num_filter': offset_channels, 'num_group': groups, 'no_bias': not offset_use_bias, 'layout': layout} self._kwargs_deformable_conv = { 'kernel': kernel_size, 'stride': strides, 'dilate': dilation, 'pad': padding, 'num_filter': channels, 'num_group': groups, 'num_deformable_group': num_deformable_group, 'no_bias': not use_bias, 'layout': layout} if adj: self._kwargs_offset['adj'] = adj self._kwargs_deformable_conv['adj'] = adj dshape = [0] * (len(kernel_size) + 2) dshape[layout.find('N')] = 1 dshape[layout.find('C')] = in_channels op = getattr(symbol, 'Convolution') offset = op(symbol.var('data', shape=dshape), **self._kwargs_offset) offsetshapes = offset.infer_shape_partial()[0] self.offset_weight = self.params.get('offset_weight', shape=offsetshapes[1], init=offset_weight_initializer, allow_deferred_init=True) if offset_use_bias: self.offset_bias = self.params.get('offset_bias', shape=offsetshapes[2], init=offset_bias_initializer, allow_deferred_init=True) else: self.offset_bias = None deformable_conv_weight_shape = [0] * (len(kernel_size) + 2) deformable_conv_weight_shape[0] = channels deformable_conv_weight_shape[2] = kernel_size[0] deformable_conv_weight_shape[3] = kernel_size[1] self.deformable_conv_weight = self.params.get('deformable_conv_weight', shape=deformable_conv_weight_shape, init=weight_initializer, allow_deferred_init=True) if use_bias: self.deformable_conv_bias = self.params.get('deformable_conv_bias', shape=(channels,), init=bias_initializer, allow_deferred_init=True) else: self.deformable_conv_bias = None if activation: self.act = Activation(activation, prefix=activation + '_') else: self.act = None
[docs] def hybrid_forward(self, F, x, offset_weight, deformable_conv_weight, offset_bias=None, deformable_conv_bias=None): if offset_bias is None: offset = F.Convolution(x, offset_weight, cudnn_off=True, **self._kwargs_offset) else: offset = F.Convolution(x, offset_weight, offset_bias, cudnn_off=True, **self._kwargs_offset) if deformable_conv_bias is None: act = F.contrib.DeformableConvolution(data=x, offset=offset, weight=deformable_conv_weight, name='fwd', **self._kwargs_deformable_conv) else: act = F.contrib.DeformableConvolution(data=x, offset=offset, weight=deformable_conv_weight, bias=deformable_conv_bias, name='fwd', **self._kwargs_deformable_conv) if self.act: act = self.act(act) return act
def _alias(self): return 'deformable_conv' def __repr__(self): s = '{name}({mapping}, kernel_size={kernel}, stride={stride}' len_kernel_size = len(self._kwargs_deformable_conv['kernel']) if self._kwargs_deformable_conv['pad'] != (0,) * len_kernel_size: s += ', padding={pad}' if self._kwargs_deformable_conv['dilate'] != (1,) * len_kernel_size: s += ', dilation={dilate}' if hasattr(self, 'out_pad') and self.out_pad != (0,) * len_kernel_size: s += ', output_padding={out_pad}'.format(out_pad=self.out_pad) if self._kwargs_deformable_conv['num_group'] != 1: s += ', groups={num_group}' if self.deformable_conv_bias is None: s += ', bias=False' if self.act: s += ', {}'.format(self.act) s += ')' shape = self.deformable_conv_weight.shape return s.format(name=self.__class__.__name__, mapping='{0} -> {1}'.format(shape[1] if shape[1] else None, shape[0]), **self._kwargs_deformable_conv)