Source code for tensorlayerx.nn.layers.convolution.mask_conv

#! /usr/bin/python
# -*- coding: utf-8 -*-

import tensorlayerx as tlx
import numpy as np
from tensorlayerx import logging
from tensorlayerx.nn.core import Module

__all__ = ['MaskedConv3d']


[docs]class MaskedConv3d(Module): """ MaskedConv3D. Reference: [1] Nguyen D T , Quach M , Valenzise G , et al. Lossless Coding of Point Cloud Geometry using a Deep Generative Model[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, PP(99):1-1. Parameters ---------- mask_type : str The mask type('A', 'B') out_channels : int The number of filters. kernel_size : tuple or int The filter size (height, width). stride : tuple or int The sliding window stride of corresponding input dimensions. It must be in the same order as the ``shape`` parameter. dilation : tuple or int Specifying the dilation rate to use for dilated convolution. act : activation function The activation function of this layer. padding : str The padding algorithm type: "SAME" or "VALID". data_format : str "channels_last" (NDHWC, default) or "channels_first" (NCDHW). kernel_initializer : initializer or str The initializer for the the weight matrix. bias_initializer : initializer or None or str The initializer for the the bias vector. If None, skip biases. in_channels : int The number of in channels. name : None or str A unique layer name. Examples -------- With TensorLayer >>> net = tlx.nn.Input([8, 20, 20, 20, 3], name='input') >>> conv3d = tlx.nn.MaskedConv3d(mask_type='A', out_channels=32, kernel_size=(3, 3, 3), stride=(2, 2, 2), bias_initializer=None, in_channels=3, name='conv3d_1') >>> print(conv3d) >>> tensor = tlx.nn.MaskedConv3d(mask_type='B', out_channels=32, kernel_size=(3, 3, 3), stride=(2, 2, 2), act=tlx.ReLU, name='conv3d_2')(net) >>> print(tensor) """ def __init__( self, mask_type, out_channels, kernel_size=(3, 3, 3), stride=(1, 1, 1), dilation=(1, 1, 1), padding='SAME', act=None, in_channels=None, data_format='channels_last', kernel_initializer='he_normal', bias_initializer='zeros', name=None ): super(MaskedConv3d, self).__init__(name, act) assert mask_type in {'A', 'B'} self.mask_type = mask_type self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size, '3d') self.stride = self.check_param(stride, '3d') self.dilation = self.check_param(dilation, '3d') self.padding = padding self.kernel_initializer = self.str_to_init(kernel_initializer) self.bias_initializer = self.str_to_init(bias_initializer) self.in_channels = in_channels self.data_format = data_format if self.in_channels: self.build(None) self._built = True logging.info( "MaskedConv3D %s: out_channels: %d kernel_size: %s stride: %s mask_type: %s act: %s" % ( self.name, out_channels, str(kernel_size), str(stride), mask_type, self.act.__class__.__name__ if self.act is not None else 'No Activation' ) ) def __repr__(self): actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation' s = ( '{classname}(in_channels={in_channels}, out_channels={out_channels}, kernel_size={kernel_size}' ', stride={stride}, padding={padding}' ) if self.bias_initializer is None: s += ', bias=False' s += (', ' + actstr) if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): if self.data_format == 'channels_last': if self.in_channels is None: self.in_channels = inputs_shape[-1] self._strides = [1, self.stride[0], self.stride[1], self.stride[2], 1] self._dilation_rate = [1, self.dilation[0], self.dilation[1], self.dilation[2], 1] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] self._strides = [1, 1, self.stride[0], self.stride[1], self.stride[2]] self._dilation_rate = [1, 1, self.dilation[0], self.dilation[1], self.dilation[2]] else: raise Exception("data_format should be either channels_last or channels_first") self.filter_shape = ( self.kernel_size[0], self.kernel_size[1], self.kernel_size[2], self.in_channels, self.out_channels ) self.kernel = self._get_weights('kernel', shape=self.filter_shape, init=self.kernel_initializer) self.b_init = False if self.bias_initializer: self.bias = self._get_weights('bias', shape=(self.out_channels, ), init=self.bias_initializer) self.bias_add = tlx.ops.BiasAdd(data_format=self.data_format) self.b_init = True center = self.kernel_size[0] // 2 mask = np.ones(self.kernel.shape, dtype=np.float32) if tlx.BACKEND == 'tensorflow': mask[center, center, center + (self.mask_type == 'B'):, :, :] = 0. # centre depth layer, center row mask[center, center + 1:, :, :, :] = 0. # center depth layer, lower row mask[center + 1:, :, :, :, :] = 0. # behind layers,all row, columns else: mask[:, :, center + (self.mask_type == 'B'):, center, center] = 0. mask[:, :, :, center + 1:, center] = 0. mask[:, :, :, :, center + 1:] = 0 self.mask = tlx.ops.convert_to_tensor(mask, tlx.float32) self.conv3d = tlx.ops.Conv3D( strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate, out_channel=self.out_channels, k_size=self.kernel_size ) self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): #input´╝Ü[batch, in_depth, in_height, in_width, in_channels] if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True self.masked_kernel = tlx.ops.multiply(self.mask, self.kernel) x = self.conv3d(inputs, self.masked_kernel) if self.b_init: x = self.bias_add(x, self.bias) if self.act_init_flag: x = self.act(x) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, x) self._nodes_fixed = True return x