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

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

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

__all__ = [
    'Conv1d',
    'Conv2d',
    'Conv3d',
    'ConvTranspose1d',
    'ConvTranspose2d',
    'ConvTranspose3d',
]


[docs]class Conv1d(Module): """Applies a 1D convolution over an input signal composed of several input planes. Parameters ---------- out_channels : int Number of channels produced by the convolution kernel_size : int The kernel size stride : int The stride step dilation : int Specifying the dilation rate to use for dilated convolution. act : activation function The function that is applied to the layer activations padding : str or int The padding algorithm type: "SAME" or "VALID". data_format : str "channel_last" (NWC, default) or "channels_first" (NCW). W_init : initializer or str The initializer for the kernel weight matrix. b_init : initializer or None or str The initializer for 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 TensorLayerx >>> net = tlx.nn.Input([8, 100, 1], name='input') >>> conv1d = tlx.nn.Conv1d(out_channels =32, kernel_size=5, stride=2, b_init=None, in_channels=1, name='conv1d_1') >>> print(conv1d) >>> tensor = tlx.nn.Conv1d(out_channels =32, kernel_size=5, stride=2, act=tlx.ReLU, name='conv1d_2')(net) >>> print(tensor) """ def __init__( self, out_channels =32, kernel_size=5, stride=1, act=None, padding='SAME', data_format="channels_last", dilation=1, W_init='truncated_normal', b_init='constant', in_channels=None, name=None # 'conv1d' ): super().__init__(name, act=act) self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.data_format = data_format self.dilation = dilation self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "Conv1d %s: out_channels : %d kernel_size: %s stride: %d pad: %s act: %s" % ( self.name, out_channels , kernel_size, stride, padding, 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.dilation != 1: s += ', dilation={dilation}' if self.b_init 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] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] else: raise Exception("data_format should be either channels_last or channels_first") self.filter_shape = (self.kernel_size, self.in_channels, self.out_channels ) # TODO : check self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels , ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.conv1d = tlx.ops.Conv1D( stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation, 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): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.conv1d(inputs, self.W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs
[docs]class Conv2d(Module): """Applies a 2D convolution over an input signal composed of several input planes. Parameters ---------- out_channels : int Number of channels produced by the convolution kernel_size : tuple or int The kernel 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 : int, tuple or str The padding algorithm type: "SAME" or "VALID". If padding is int or tuple, padding added to all four sides of the input. Default: ‘SAME’ data_format : str "channels_last" (NHWC, default) or "channels_first" (NCHW). W_init : initializer or str The initializer for the the kernel weight matrix. b_init : 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 TensorLayerx >>> net = tlx.nn.Input([8, 400, 400, 3], name='input') >>> conv2d = tlx.nn.Conv2d(out_channels =32, kernel_size=(3, 3), stride=(2, 2), b_init=None, in_channels=3, name='conv2d_1') >>> print(conv2d) >>> tensor = tlx.nn.Conv2d(out_channels =32, kernel_size=(3, 3), stride=(2, 2), act=tlx.ReLU, name='conv2d_2')(net) >>> print(tensor) """ def __init__( self, out_channels =32, kernel_size=(3, 3), stride=(1, 1), act=None, padding='SAME', data_format='channels_last', dilation=(1, 1), W_init='truncated_normal', b_init='constant', in_channels=None, name=None, # 'conv2d', ): super(Conv2d, self).__init__(name, act=act) self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size) self._strides = self.stride = self.check_param(stride) self.padding = padding self.data_format = data_format self._dilation_rate = self.dilation = self.check_param(dilation) self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "Conv2d %s: out_channels : %d kernel_size: %s stride: %s pad: %s act: %s" % ( self.name, out_channels , str(kernel_size), str(stride), padding, 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.dilation != (1, ) * len(self.dilation): s += ', dilation={dilation}' if self.b_init 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._strides[0], self._strides[1], 1] self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] self._strides = [1, 1, self._strides[0], self._strides[1]] self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]] else: raise Exception("data_format should be either channels_last or channels_first") #TODO channels first filter shape [out_channel, in_channel, filter_h, filter_w] self.filter_shape = (self.kernel_size[0], self.kernel_size[1], self.in_channels, self.out_channels) self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels , ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.conv2d = tlx.ops.Conv2D( 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[0], self.kernel_size[1]) ) self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.conv2d(inputs, self.W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs
[docs]class Conv3d(Module): """Applies a 3D convolution over an input signal composed of several input planes. Parameters ---------- out_channels : int Number of channels produced by the convolution kernel_size : tuple or int The kernel size (depth, 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 : int, tuple or str The padding algorithm type: "SAME" or "VALID". data_format : str "channels_last" (NDHWC, default) or "channels_first" (NCDHW). W_init : initializer or str The initializer for the the kernel weight matrix. b_init : 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 TensorLayerx >>> net = tlx.nn.Input([8, 20, 20, 20, 3], name='input') >>> conv3d = tlx.nn.Conv3d(out_channels =32, kernel_size=(3, 3, 3), stride=(2, 2, 2), b_init=None, in_channels=3, name='conv3d_1') >>> print(conv3d) >>> tensor = tlx.nn.Conv3d(out_channels =32, kernel_size=(3, 3, 3), stride=(2, 2, 2), act=tlx.ReLU, name='conv3d_2')(net) >>> print(tensor) """ def __init__( self, out_channels =32, kernel_size=(3, 3, 3), stride=(1, 1, 1), act=None, padding='SAME', data_format='channels_last', dilation=(1, 1, 1), W_init='truncated_normal', b_init='constant', in_channels=None, name=None # 'conv3d', ): super().__init__(name, act=act) self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size, dim='3d') self._strides = self.stride = self.check_param(stride, dim='3d') self.padding = padding self.data_format = data_format self._dilation_rate = self.dilation = self.check_param(dilation, dim='3d') self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "Conv3d %s: out_channels : %d kernel_size: %s stride: %s pad: %s act: %s" % ( self.name, out_channels , str(kernel_size), str(stride), padding, 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.dilation != (1, ) * len(self.dilation): s += ', dilation={dilation}' if self.b_init 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._strides[0], self._strides[1], self._strides[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._strides[0], self._strides[1], self._strides[2]] self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1], self._dilation_rate[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.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels , ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True 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[0], self.kernel_size[1], self.kernel_size[2]) ) self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.conv3d(inputs, self.W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs
[docs]class ConvTranspose1d(Module): """Applies a 1D transposed convolution operator over an input image composed of several input planes. Parameters ---------- out_channels : int Number of channels produced by the convolution kernel_size : int The kernel size stride : int or list An int or list of `ints` that has length `1` or `3`. The number of entries by which the filter is moved right at each step. dilation : int or list Specifying the dilation rate to use for dilated convolution. act : activation function The function that is applied to the layer activations padding : str The padding algorithm type: "SAME" or "VALID". data_format : str "channel_last" (NWC, default) or "channels_first" (NCW). W_init : initializer or str The initializer for the kernel weight matrix. b_init : initializer or None or str The initializer for 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 TensorLayerx >>> net = tlx.nn.Input([8, 100, 1], name='input') >>> conv1d = tlx.nn.ConvTranspose1d(out_channels=32, kernel_size=5, stride=2, b_init=None, in_channels=1, name='Deonv1d_1') >>> print(conv1d) >>> tensor = tlx.nn.ConvTranspose1d(out_channels=32, kernel_size=5, stride=2, act=tlx.ReLU, name='ConvTranspose1d_2')(net) >>> print(tensor) """ def __init__( self, out_channels=32, kernel_size=15, stride=1, act=None, padding='SAME', data_format="channels_last", dilation=1, W_init='truncated_normal', b_init='constant', in_channels=None, name=None # 'conv1d_transpose' ): super(ConvTranspose1d, self).__init__(name, act=act) self.out_channels = out_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.data_format = data_format self.dilation = dilation self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "ConvTranspose1d %s: out_channels: %d kernel_size: %s stride: %d pad: %s act: %s" % ( self.name, out_channels, kernel_size, stride, padding, 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.dilation != 1: s += ', dilation={dilation}' if self.b_init 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] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] else: raise Exception("data_format should be either channels_last or channels_first") self.filter_shape = (self.kernel_size, self.out_channels, self.in_channels) # TODO : check self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels, ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.conv1d_transpose = tlx.ops.Conv1d_transpose( stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation, out_channel=self.out_channels, k_size=self.kernel_size, in_channels=self.in_channels, ) self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.conv1d_transpose(inputs, self.W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs
[docs]class ConvTranspose2d(Module): """Applies a 2D transposed convolution operator over an input image composed of several input planes. Parameters ---------- out_channels : int Number of channels produced by the convolution kernel_size : tuple or int The kernel 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" (NHWC, default) or "channels_first" (NCHW). W_init : initializer or str The initializer for the the kernel weight matrix. b_init : 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 TensorLayerx >>> net = tlx.nn.Input([8, 400, 400, 3], name='input') >>> conv2d_transpose = tlx.nn.ConvTranspose2d(out_channels=32, kernel_size=(3, 3), stride=(2, 2), b_init=None, in_channels=3, name='conv2d_transpose_1') >>> print(conv2d_transpose) >>> tensor = tlx.nn.ConvTranspose2d(out_channels=32, kernel_size=(3, 3), stride=(2, 2), act=tlx.ReLU, name='conv2d_transpose_2')(net) >>> print(tensor) """ def __init__( self, out_channels=32, kernel_size=(3, 3), stride=(1, 1), act=None, padding='SAME', data_format='channels_last', dilation=(1, 1), W_init='truncated_normal', b_init='constant', in_channels=None, name=None, # 'conv2d_transpose', ): super(ConvTranspose2d, self).__init__(name, act=act) self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size) self.stride = self.check_param(stride) self.padding = padding self.data_format = data_format self.dilation = self.check_param(dilation) self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "ConvTranspose2d %s: out_channels: %d kernel_size: %s stride: %s pad: %s act: %s" % ( self.name, out_channels, str(kernel_size), str(stride), padding, 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.dilation != (1, ) * len(self.dilation): s += ', dilation={dilation}' if self.b_init 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] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] else: raise Exception("data_format should be either channels_last or channels_first") #TODO channels first filter shape [out_channel, in_channel, filter_h, filter_w] self.filter_shape = (self.kernel_size[0], self.kernel_size[1], self.out_channels, self.in_channels) self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)#, transposed=True) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels, ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.conv2d_transpose = tlx.ops.Conv2d_transpose( strides=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation, out_channel=self.out_channels, k_size=(self.kernel_size[0], self.kernel_size[1]), in_channels=self.in_channels ) self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.conv2d_transpose(inputs, self.W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs
[docs]class ConvTranspose3d(Module): """Applies a 3D transposed convolution operator over an input image composed of several input planes. Parameters ---------- out_channels : int Number of channels produced by the convolution kernel_size : tuple or int The kernel size (depth, 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). W_init : initializer or str The initializer for the the kernel weight matrix. b_init : 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 TensorLayerx >>> net = tlx.nn.Input([8, 20, 20, 20, 3], name='input') >>> ConvTranspose3d = tlx.nn.ConvTranspose3d(out_channels=32, kernel_size=(3, 3, 3), stride=(2, 2, 2), b_init=None, in_channels=3, name='deconv3d_1') >>> print(deconv3d) >>> tensor = tlx.nn.ConvTranspose3d(out_channels=32, kernel_size=(3, 3, 3), stride=(2, 2, 2), act=tlx.ReLU, name='ConvTranspose3d_2')(net) >>> print(tensor) """ def __init__( self, out_channels=32, kernel_size=(3, 3, 3), stride=(1, 1, 1), act=None, padding='SAME', data_format='channels_last', dilation=(1, 1, 1), W_init='truncated_normal', b_init='constant', in_channels=None, name=None # 'deconv3d', ): super(ConvTranspose3d, self).__init__(name, act=act) self.out_channels = out_channels self.kernel_size = self.check_param(kernel_size, dim='3d') self.stride = self.check_param(stride, dim='3d') self.padding = padding self.data_format = data_format self.dilation = self.check_param(dilation, dim='3d') self.W_init = self.str_to_init(W_init) self.b_init = self.str_to_init(b_init) self.in_channels = in_channels if self.in_channels: self.build(None) self._built = True logging.info( "ConvTranspose3d %s: out_channels: %d kernel_size: %s stride: %s pad: %s act: %s" % ( self.name, out_channels, str(kernel_size), str(stride), padding, 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.dilation != (1, ) * len(self.dilation): s += ', dilation={dilation}' if self.b_init 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] elif self.data_format == 'channels_first': if self.in_channels is None: self.in_channels = inputs_shape[1] 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.out_channels, self.in_channels ) self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)#, transposed=True) self.b_init_flag = False if self.b_init: self.b = self._get_weights("biases", shape=(self.out_channels, ), init=self.b_init) self.bias_add = tlx.ops.BiasAdd(self.data_format) self.b_init_flag = True self.conv3d_transpose = tlx.ops.Conv3d_transpose( strides=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation, out_channel=self.out_channels, k_size=(self.kernel_size[0], self.kernel_size[1], self.kernel_size[2]), in_channels=self.in_channels ) self.act_init_flag = False if self.act: self.act_init_flag = True def forward(self, inputs): if self._forward_state == False: if self._built == False: self.build(tlx.get_tensor_shape(inputs)) self._built = True self._forward_state = True outputs = self.conv3d_transpose(inputs, self.W) if self.b_init_flag: outputs = self.bias_add(outputs, self.b) if self.act_init_flag: outputs = self.act(outputs) if not self._nodes_fixed and self._build_graph: self._add_node(inputs, outputs) self._nodes_fixed = True return outputs