vandanavk commented on a change in pull request #13955: adding the gluon implementation of deformable convolution URL: https://github.com/apache/incubator-mxnet/pull/13955#discussion_r254101008
########## File path: python/mxnet/gluon/contrib/nn/conv_layers.py ########## @@ -0,0 +1,223 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# coding: utf-8 +# pylint: disable= arguments-differ +"""Custom neural network layers in model_zoo.""" +__all__ = ['Deformable_Convolution'] + +from .... import symbol +from ...block import HybridBlock +from ....base import numeric_types +from ...nn import Activation + +class Deformable_Convolution(HybridBlock): + """2-D Deformable Convolution v_1 + + Normal Convolution uses sampling points in a regular grid, while the sampling points of Deformable Convolution[1] + can be offset. The offset is learned with a separately 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 (1,1) + Specifies the dimensions of the convolution window. + strides : int or tuple/list of 2 ints, default (1,1) + Specifies the strides of the convolution. + padding : int or tuple/list of 2 ints, default (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 (1,1) + Specifies the dilation rate to use for dilated convolution. + groups : int, default 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 1 + Number of deformable group partitions. + layout : str, (default 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 True) + Whether the layer for generating the output features uses a bias vector. + in_channels : int, default 0 + The number of input channels to this layer. If not specified, + initialization will be deferred to the first time `forward` is called + and `in_channels` will be inferred from the shape of input data. + activation : str (default 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 None) + Initializer for the `weight` weights matrix for the convolution layer + for generating the output features + bias_initializer : str or `Initializer` (default Zero) + Initializer for the bias vector for the convolution layer + for generating the output features + offset_weight_initializer : str or `Initializer` (default Zero, according to the paper[1]) + Initializer for the `weight` weights matrix for the convolution layer + for generating the offset + offset_bias_initializer : str or `Initializer` (default Zero) + Initializer for the bias vector for the convolution layer + for generating the offset + offset_use_bias: bool (default 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 + + Reference: + .. [1] Dai, Jifeng, et al. "Deformable convolutional networks." CoRR, abs/1703.06211 1.2 (2017): 3. + """ + + 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(Deformable_Convolution, 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 is not None: Review comment: `if adj` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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