#! /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.
filter_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, filter_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, filter_size=(3, 3, 3), stride=(2, 2, 2), act=tlx.ReLU, name='conv3d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
mask_type,
out_channels,
filter_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.filter_size = self.check_param(filter_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 filter_size: %s stride: %s mask_type: %s act: %s" % (
self.name, out_channels, str(filter_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={filter_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.filter_size[0], self.filter_size[1], self.filter_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_flag = 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_flag = True
center = self.filter_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.filter_size
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs): #input：[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
masked_kernel = tlx.ops.multiply(self.mask, self.kernel)
x = self.conv3d(inputs, masked_kernel)
if self.b_init_flag:
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