diff options
Diffstat (limited to 'tensorflow/python/layers/convolutional.py')
| -rw-r--r-- | tensorflow/python/layers/convolutional.py | 274 |
1 files changed, 255 insertions, 19 deletions
diff --git a/tensorflow/python/layers/convolutional.py b/tensorflow/python/layers/convolutional.py index b2fe9feb44..938161f426 100644 --- a/tensorflow/python/layers/convolutional.py +++ b/tensorflow/python/layers/convolutional.py @@ -972,7 +972,7 @@ def separable_conv2d(inputs, class Conv2DTranspose(Conv2D): - """Transposed convolution layer (sometimes called Deconvolution). + """Transposed 2D convolution layer (sometimes called 2D Deconvolution). The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction @@ -1086,19 +1086,9 @@ class Conv2DTranspose(Conv2D): kernel_h, kernel_w = self.kernel_size stride_h, stride_w = self.strides - def get_deconv_dim(dim_size, stride_size, kernel_size, padding): - if isinstance(dim_size, ops.Tensor): - dim_size = math_ops.multiply(dim_size, stride_size) - elif dim_size is not None: - dim_size *= stride_size - - if padding == 'valid' and dim_size is not None: - dim_size += max(kernel_size - stride_size, 0) - return dim_size - # Infer the dynamic output shape: - out_height = get_deconv_dim(height, stride_h, kernel_h, self.padding) - out_width = get_deconv_dim(width, stride_w, kernel_w, self.padding) + out_height = utils.get_deconv_dim(height, stride_h, kernel_h, self.padding) + out_width = utils.get_deconv_dim(width, stride_w, kernel_w, self.padding) if self.data_format == 'channels_first': output_shape = (batch_size, self.filters, out_height, out_width) @@ -1119,10 +1109,10 @@ class Conv2DTranspose(Conv2D): # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters - out_shape[h_axis] = get_deconv_dim( - out_shape[h_axis], stride_h, kernel_h, self.padding) - out_shape[w_axis] = get_deconv_dim( - out_shape[w_axis], stride_w, kernel_w, self.padding) + out_shape[h_axis] = utils.get_deconv_dim(out_shape[h_axis], stride_h, + kernel_h, self.padding) + out_shape[w_axis] = utils.get_deconv_dim(out_shape[w_axis], stride_w, + kernel_w, self.padding) outputs.set_shape(out_shape) if self.bias: @@ -1152,7 +1142,7 @@ def conv2d_transpose(inputs, trainable=True, name=None, reuse=None): - """Transposed convolution layer (sometimes called Deconvolution). + """Functional interface for transposed 2D convolution layer. The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction @@ -1177,6 +1167,250 @@ def conv2d_transpose(inputs, `channels_last` corresponds to inputs with shape `(batch, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)`. + activation: Activation function. Set it to `None` to maintain a + linear activation. + use_bias: Boolean, whether the layer uses a bias. + kernel_initializer: An initializer for the convolution kernel. + bias_initializer: An initializer for the bias vector. If `None`, then no + bias will be applied. + kernel_regularizer: Optional regularizer for the convolution kernel. + bias_regularizer: Optional regularizer for the bias vector. + activity_regularizer: Regularizer function for the output. + trainable: Boolean, if `True` also add variables to the graph collection + `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). + name: A string, the name of the layer. + reuse: Boolean, whether to reuse the weights of a previous layer + by the same name. + + Returns: + Output tensor. + """ + layer = Conv2DTranspose( + filters=filters, + kernel_size=kernel_size, + strides=strides, + padding=padding, + data_format=data_format, + activation=activation, + use_bias=use_bias, + kernel_initializer=kernel_initializer, + bias_initializer=bias_initializer, + kernel_regularizer=kernel_regularizer, + bias_regularizer=bias_regularizer, + activity_regularizer=activity_regularizer, + trainable=trainable, + name=name, + _reuse=reuse, + _scope=name) + return layer.apply(inputs) + + +class Conv3DTranspose(Conv3D): + """Transposed 3D convolution layer (sometimes called 3D Deconvolution). + + Arguments: + filters: Integer, the dimensionality of the output space (i.e. the number + of filters in the convolution). + kernel_size: An integer or tuple/list of 3 integers, specifying the + depth, height and width of the 3D convolution window. + Can be a single integer to specify the same value for all spatial + dimensions. + strides: An integer or tuple/list of 3 integers, specifying the strides + of the convolution along the depth, height and width. + Can be a single integer to specify the same value for all spatial + dimensions. + padding: One of `"valid"` or `"same"` (case-insensitive). + data_format: A string, one of `channels_last` (default) or `channels_first`. + The ordering of the dimensions in the inputs. + `channels_last` corresponds to inputs with shape + `(batch, depth, height, width, channels)` while `channels_first` + corresponds to inputs with shape + `(batch, channels, depth, height, width)`. + activation: Activation function. Set it to `None` to maintain a + linear activation. + use_bias: Boolean, whether the layer uses a bias. + kernel_initializer: An initializer for the convolution kernel. + bias_initializer: An initializer for the bias vector. If `None`, then no + bias will be applied. + kernel_regularizer: Optional regularizer for the convolution kernel. + bias_regularizer: Optional regularizer for the bias vector. + activity_regularizer: Regularizer function for the output. + trainable: Boolean, if `True` also add variables to the graph collection + `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). + name: A string, the name of the layer. + """ + + def __init__(self, + filters, + kernel_size, + strides=(1, 1, 1), + padding='valid', + data_format='channels_last', + activation=None, + use_bias=True, + kernel_initializer=None, + bias_initializer=init_ops.zeros_initializer(), + kernel_regularizer=None, + bias_regularizer=None, + activity_regularizer=None, + trainable=True, + name=None, + **kwargs): + super(Conv3DTranspose, self).__init__( + filters=filters, + kernel_size=kernel_size, + strides=strides, + padding=padding, + data_format=data_format, + activation=activation, + use_bias=use_bias, + kernel_initializer=kernel_initializer, + bias_initializer=bias_initializer, + kernel_regularizer=kernel_regularizer, + bias_regularizer=bias_regularizer, + activity_regularizer=activity_regularizer, + trainable=trainable, + name=name, + **kwargs) + + def build(self, input_shape): + if len(input_shape) != 5: + raise ValueError('Inputs should have rank 5, received input shape:', + str(input_shape)) + if self.data_format == 'channels_first': + channel_axis = 1 + else: + channel_axis = -1 + if input_shape[channel_axis] is None: + raise ValueError('The channel dimension of the inputs ' + 'should be defined, found None: ' + str(input_shape)) + input_dim = input_shape[channel_axis] + kernel_shape = self.kernel_size + (self.filters, input_dim) + + self.kernel = self.add_variable( + 'kernel', + shape=kernel_shape, + initializer=self.kernel_initializer, + regularizer=self.kernel_regularizer, + trainable=True, + dtype=self.dtype) + if self.use_bias: + self.bias = self.add_variable( + 'bias', + shape=(self.filters,), + initializer=self.bias_initializer, + regularizer=self.bias_regularizer, + trainable=True, + dtype=self.dtype) + else: + self.bias = None + + def call(self, inputs): + inputs_shape = array_ops.shape(inputs) + batch_size = inputs_shape[0] + if self.data_format == 'channels_first': + c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4 + else: + c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3 + + depth = inputs_shape[d_axis] + height = inputs_shape[h_axis] + width = inputs_shape[w_axis] + + kernel_d, kernel_h, kernel_w = self.kernel_size + stride_d, stride_h, stride_w = self.strides + + # Infer the dynamic output shape: + out_depth = utils.get_deconv_dim(depth, stride_d, kernel_d, self.padding) + out_height = utils.get_deconv_dim(height, stride_h, kernel_h, self.padding) + out_width = utils.get_deconv_dim(width, stride_w, kernel_w, self.padding) + + if self.data_format == 'channels_first': + output_shape = (batch_size, self.filters, out_depth, out_height, + out_width) + strides = (1, 1, stride_d, stride_h, stride_w) + else: + output_shape = (batch_size, out_depth, out_height, out_width, + self.filters) + strides = (1, stride_d, stride_h, stride_w, 1) + + output_shape_tensor = array_ops.stack(output_shape) + outputs = nn.conv3d_transpose( + inputs, + self.kernel, + output_shape_tensor, + strides, + data_format=utils.convert_data_format(self.data_format, ndim=5), + padding=self.padding.upper()) + + # Infer the static output shape: + out_shape = inputs.get_shape().as_list() + out_shape[c_axis] = self.filters + out_shape[d_axis] = utils.get_deconv_dim(out_shape[d_axis], stride_d, + kernel_d, self.padding) + out_shape[h_axis] = utils.get_deconv_dim(out_shape[h_axis], stride_h, + kernel_h, self.padding) + out_shape[w_axis] = utils.get_deconv_dim(out_shape[w_axis], stride_w, + kernel_w, self.padding) + outputs.set_shape(out_shape) + + if self.bias: + outputs_shape = outputs.shape.as_list() + if self.data_format == 'channels_first': + outputs_4d = array_ops.reshape(outputs, [ + outputs_shape[0], outputs_shape[1], + outputs_shape[2] * outputs_shape[3], outputs_shape[4] + ]) + else: + outputs_4d = array_ops.reshape(outputs, [ + outputs_shape[0], outputs_shape[1] * outputs_shape[2], + outputs_shape[3], outputs_shape[4] + ]) + outputs_4d = nn.bias_add( + outputs_4d, + self.bias, + data_format=utils.convert_data_format(self.data_format, ndim=4)) + outputs = array_ops.reshape(outputs_4d, outputs_shape) + + if self.activation is not None: + return self.activation(outputs) + return outputs + + +def conv3d_transpose(inputs, + filters, + kernel_size, + strides=(1, 1, 1), + padding='valid', + data_format='channels_last', + activation=None, + use_bias=True, + kernel_initializer=None, + bias_initializer=init_ops.zeros_initializer(), + kernel_regularizer=None, + bias_regularizer=None, + activity_regularizer=None, + trainable=True, + name=None, + reuse=None): + """Functional interface for transposed 3D convolution layer. + + Arguments: + inputs: Input tensor. + filters: Integer, the dimensionality of the output space (i.e. the number + of filters in the convolution). + kernel_size: A tuple or list of 3 positive integers specifying the spatial + dimensions of of the filters. Can be a single integer to specify the same + value for all spatial dimensions. + strides: A tuple or list of 3 positive integers specifying the strides + of the convolution. Can be a single integer to specify the same value for + all spatial dimensions. + padding: one of `"valid"` or `"same"` (case-insensitive). + data_format: A string, one of `channels_last` (default) or `channels_first`. + The ordering of the dimensions in the inputs. + `channels_last` corresponds to inputs with shape + `(batch, height, width, channels)` while `channels_first` corresponds to + inputs with shape `(batch, channels, height, width)`. activation: Activation function. Set it to None to maintain a linear activation. use_bias: Boolean, whether the layer uses a bias. @@ -1195,7 +1429,7 @@ def conv2d_transpose(inputs, Returns: Output tensor. """ - layer = Conv2DTranspose( + layer = Conv3DTranspose( filters=filters, kernel_size=kernel_size, strides=strides, @@ -1222,8 +1456,10 @@ Convolution2D = Conv2D Convolution3D = Conv3D SeparableConvolution2D = SeparableConv2D Convolution2DTranspose = Deconvolution2D = Deconv2D = Conv2DTranspose +Convolution3DTranspose = Deconvolution3D = Deconv3D = Conv3DTranspose convolution1d = conv1d convolution2d = conv2d convolution3d = conv3d separable_convolution2d = separable_conv2d convolution2d_transpose = deconvolution2d = deconv2d = conv2d_transpose +convolution3d_transpose = deconvolution3d = deconv3d = conv3d_transpose |