Python tensorflow.python.ops.nn.conv2d_transpose() Examples
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Example #1
Source File: convolutional.py From lambda-packs with MIT License | 4 votes |
def call(self, inputs): inputs_shape = array_ops.shape(inputs) batch_size = inputs_shape[0] if self.data_format == 'channels_first': c_axis, h_axis, w_axis = 1, 2, 3 else: c_axis, h_axis, w_axis = 3, 1, 2 height, width = inputs_shape[h_axis], inputs_shape[w_axis] kernel_h, kernel_w = self.kernel_size stride_h, stride_w = self.strides # Infer the dynamic output shape: out_height = utils.deconv_output_length(height, kernel_h, self.padding, stride_h) out_width = utils.deconv_output_length(width, kernel_w, self.padding, stride_w) if self.data_format == 'channels_first': output_shape = (batch_size, self.filters, out_height, out_width) strides = (1, 1, stride_h, stride_w) else: output_shape = (batch_size, out_height, out_width, self.filters) strides = (1, stride_h, stride_w, 1) output_shape_tensor = array_ops.stack(output_shape) outputs = nn.conv2d_transpose( inputs, self.kernel, output_shape_tensor, strides, padding=self.padding.upper(), data_format=utils.convert_data_format(self.data_format, ndim=4)) # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters out_shape[h_axis] = utils.deconv_output_length(out_shape[h_axis], kernel_h, self.padding, stride_h) out_shape[w_axis] = utils.deconv_output_length(out_shape[w_axis], kernel_w, self.padding, stride_w) outputs.set_shape(out_shape) if self.bias: outputs = nn.bias_add( outputs, self.bias, data_format=utils.convert_data_format(self.data_format, ndim=4)) if self.activation is not None: return self.activation(outputs) return outputs
Example #2
Source File: backend.py From lambda-packs with MIT License | 4 votes |
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1), padding='valid', data_format=None): """2D deconvolution (i.e. transposed convolution). Arguments: x: Tensor or variable. kernel: kernel tensor. output_shape: 1D int tensor for the output shape. strides: strides tuple. padding: string, `"same"` or `"valid"`. data_format: `"channels_last"` or `"channels_first"`. Whether to use Theano or TensorFlow data format for inputs/kernels/ouputs. Returns: A tensor, result of transposed 2D convolution. Raises: ValueError: if `data_format` is neither `channels_last` or `channels_first`. """ if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: raise ValueError('Unknown data_format ' + str(data_format)) if isinstance(output_shape, (tuple, list)): output_shape = array_ops.stack(output_shape) x = _preprocess_conv2d_input(x, data_format) output_shape = _preprocess_deconv_output_shape(x, output_shape, data_format) padding = _preprocess_padding(padding) strides = (1,) + strides + (1,) x = nn.conv2d_transpose(x, kernel, output_shape, strides, padding=padding) x = _postprocess_conv2d_output(x, data_format) return x
Example #3
Source File: convolutional.py From auto-alt-text-lambda-api with MIT License | 4 votes |
def call(self, inputs): inputs_shape = array_ops.shape(inputs) batch_size = inputs_shape[0] if self.data_format == 'channels_first': c_axis, h_axis, w_axis = 1, 2, 3 else: c_axis, h_axis, w_axis = 3, 1, 2 height, width = inputs_shape[h_axis], inputs_shape[w_axis] 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) if self.data_format == 'channels_first': output_shape = (batch_size, self.filters, out_height, out_width) strides = (1, 1, stride_h, stride_w) else: output_shape = (batch_size, out_height, out_width, self.filters) strides = (1, stride_h, stride_w, 1) output_shape_tensor = array_ops.stack(output_shape) outputs = nn.conv2d_transpose( inputs, self.kernel, output_shape_tensor, strides, padding=self.padding.upper(), data_format=utils.convert_data_format(self.data_format, ndim=4)) # 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) outputs.set_shape(out_shape) if self.bias: outputs = nn.bias_add( outputs, self.bias, data_format=utils.convert_data_format(self.data_format, ndim=4)) if self.activation is not None: return self.activation(outputs) return outputs
Example #4
Source File: backend.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
def conv2d_transpose(x, kernel, output_shape, strides=(1, 1), padding='valid', data_format=None): """2D deconvolution (i.e. transposed convolution). Arguments: x: Tensor or variable. kernel: kernel tensor. output_shape: 1D int tensor for the output shape. strides: strides tuple. padding: string, `"same"` or `"valid"`. data_format: `"channels_last"` or `"channels_first"`. Whether to use Theano or TensorFlow data format for inputs/kernels/outputs. Returns: A tensor, result of transposed 2D convolution. Raises: ValueError: if `data_format` is neither `channels_last` or `channels_first`. """ if data_format is None: data_format = image_data_format() if data_format not in {'channels_first', 'channels_last'}: raise ValueError('Unknown data_format ' + str(data_format)) if isinstance(output_shape, (tuple, list)): output_shape = array_ops.stack(output_shape) x = _preprocess_conv2d_input(x, data_format) output_shape = _preprocess_deconv_output_shape(x, output_shape, data_format) padding = _preprocess_padding(padding) strides = (1,) + strides + (1,) x = nn.conv2d_transpose(x, kernel, output_shape, strides, padding=padding) x = _postprocess_conv2d_output(x, data_format) return x
Example #5
Source File: convolutional.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 4 votes |
def call(self, inputs): inputs_shape = array_ops.shape(inputs) batch_size = inputs_shape[0] if self.data_format == 'channels_first': c_axis, h_axis, w_axis = 1, 2, 3 else: c_axis, h_axis, w_axis = 3, 1, 2 height, width = inputs_shape[h_axis], inputs_shape[w_axis] kernel_h, kernel_w = self.kernel_size stride_h, stride_w = self.strides # Infer the dynamic output shape: out_height = utils.deconv_output_length(height, kernel_h, self.padding, stride_h) out_width = utils.deconv_output_length(width, kernel_w, self.padding, stride_w) if self.data_format == 'channels_first': output_shape = (batch_size, self.filters, out_height, out_width) strides = (1, 1, stride_h, stride_w) else: output_shape = (batch_size, out_height, out_width, self.filters) strides = (1, stride_h, stride_w, 1) output_shape_tensor = array_ops.stack(output_shape) outputs = nn.conv2d_transpose( inputs, self.kernel, output_shape_tensor, strides, padding=self.padding.upper(), data_format=utils.convert_data_format(self.data_format, ndim=4)) if context.in_graph_mode(): # Infer the static output shape: out_shape = inputs.get_shape().as_list() out_shape[c_axis] = self.filters out_shape[h_axis] = utils.deconv_output_length(out_shape[h_axis], kernel_h, self.padding, stride_h) out_shape[w_axis] = utils.deconv_output_length(out_shape[w_axis], kernel_w, self.padding, stride_w) outputs.set_shape(out_shape) if self.use_bias: outputs = nn.bias_add( outputs, self.bias, data_format=utils.convert_data_format(self.data_format, ndim=4)) if self.activation is not None: return self.activation(outputs) return outputs
Example #6
Source File: convolutional.py From keras-lambda with MIT License | 4 votes |
def call(self, inputs): inputs_shape = array_ops.shape(inputs) batch_size = inputs_shape[0] if self.data_format == 'channels_first': c_axis, h_axis, w_axis = 1, 2, 3 else: c_axis, h_axis, w_axis = 3, 1, 2 height, width = inputs_shape[h_axis], inputs_shape[w_axis] 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) if self.data_format == 'channels_first': output_shape = (batch_size, self.filters, out_height, out_width) strides = (1, 1, stride_h, stride_w) else: output_shape = (batch_size, out_height, out_width, self.filters) strides = (1, stride_h, stride_w, 1) output_shape_tensor = array_ops.stack(output_shape) outputs = nn.conv2d_transpose( inputs, self.kernel, output_shape_tensor, strides, padding=self.padding.upper(), data_format=utils.convert_data_format(self.data_format, ndim=4)) # 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) outputs.set_shape(out_shape) if self.bias: outputs = nn.bias_add( outputs, self.bias, data_format=utils.convert_data_format(self.data_format, ndim=4)) if self.activation is not None: return self.activation(outputs) return outputs