# Copyright 2019 The TensorFlow Probability Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ConvolutionTranspose layers for building neural networks.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow.compat.v2 as tf from tensorflow_probability.python.distributions import distribution as distribution_lib from tensorflow_probability.python.experimental.nn import convolutional_layers as convolution_lib from tensorflow_probability.python.experimental.nn import layers as layers_lib from tensorflow_probability.python.experimental.nn import util as nn_util_lib from tensorflow_probability.python.experimental.nn import variational_base as vi_lib from tensorflow_probability.python.internal import prefer_static __all__ = [ 'ConvolutionTranspose', 'ConvolutionTransposeVariationalFlipout', 'ConvolutionTransposeVariationalReparameterization', ] # The following aliases ensure docstrings read more succinctly. tfd = distribution_lib kl_divergence_monte_carlo = vi_lib.kl_divergence_monte_carlo unpack_kernel_and_bias = vi_lib.unpack_kernel_and_bias class ConvolutionTranspose(layers_lib.KernelBiasLayer): """ConvolutionTranspose layer. This layer creates a ConvolutionTranspose kernel that is convolved (actually cross-correlated) with the layer input to produce a tensor of outputs. This layer has two learnable parameters, `kernel` and `bias`. - The `kernel` (aka `filters` argument of `tf.nn.conv_transpose`) is a `tf.Variable` with `rank + 2` `ndims` and shape given by `concat([filter_shape, [input_size, output_size]], axis=0)`. Argument `filter_shape` is either a length-`rank` vector or expanded as one, i.e., `filter_size * tf.ones(rank)` when `filter_shape` is an `int` (which we denote as `filter_size`). - The `bias` is a `tf.Variable` with `1` `ndims` and shape `[output_size]`. In summary, the shape of learnable parameters is governed by the following arguments: `filter_shape`, `input_size`, `output_size` and possibly `rank` (if `filter_shape` needs expansion). For more information on convolution layers, we recommend the following: - [Deconvolution Checkerboard][https://distill.pub/2016/deconv-checkerboard/] - [Convolution Animations][https://github.com/vdumoulin/conv_arithmetic] - [What are Deconvolutional Layers?][ https://datascience.stackexchange.com/questions/6107/what-are-deconvolutional-layers] #### Examples ```python import tensorflow as tf import tensorflow_probability as tfp tfb = tfp.bijectors tfd = tfp.distributions tfn = tfp.experimental.nn ConvolutionTranspose1D = functools.partial(tfn.ConvolutionTranspose, rank=1) ConvolutionTranspose2D = tfn.ConvolutionTranspose ConvolutionTranspose3D = functools.partial(tfn.ConvolutionTranspose, rank=3) ``` """ def __init__( self, input_size, output_size, # keras::Conv::filters # Conv specific. filter_shape, # keras::Conv::kernel_size rank=2, # keras::Conv::rank strides=1, # keras::Conv::strides padding='VALID', # keras::Conv::padding; 'CAUSAL' not implemented. # keras::Conv::data_format is not implemented dilations=1, # keras::Conv::dilation_rate output_padding=None, # keras::ConvTranspose::output_padding # Weights init_kernel_fn=None, # tfp.experimental.nn.initializers.glorot_uniform() init_bias_fn=None, # tf.initializers.zeros() make_kernel_bias_fn=nn_util_lib.make_kernel_bias, dtype=tf.float32, # Misc activation_fn=None, name=None): """Constructs layer. Note: `data_format` is not supported since all nn layers operate on the rightmost column. If your channel dimension is not rightmost, use `tf.transpose` before calling this layer. For example, if your channel dimension is second from the left, the following code will move it rightmost: ```python inputs = tf.transpose(inputs, tf.concat([ [0], tf.range(2, tf.rank(inputs)), [1]], axis=0)) ``` Args: input_size: ... In Keras, this argument is inferred from the rightmost input shape, i.e., `tf.shape(inputs)[-1]`. This argument specifies the size of the second from the rightmost dimension of both `inputs` and `kernel`. Default value: `None`. output_size: ... In Keras, this argument is called `filters`. This argument specifies the rightmost dimension size of both `kernel` and `bias`. filter_shape: ... In Keras, this argument is called `kernel_size`. This argument specifies the leftmost `rank` dimensions' sizes of `kernel`. rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution. This argument implies the number of `kernel` dimensions, i.e.`, `kernel.shape.rank == rank + 2`. In Keras, this argument has the same name and semantics. Default value: `2`. strides: An integer or tuple/list of n integers, specifying the stride length of the convolution. In Keras, this argument has the same name and semantics. Default value: `1`. padding: One of `"VALID"` or `"SAME"` (case-insensitive). In Keras, this argument has the same name and semantics (except we don't support `"CAUSAL"`). Default value: `'VALID'`. dilations: An integer or tuple/list of `rank` integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilations` value != 1 is incompatible with specifying any `strides` value != 1. In Keras, this argument is called `dilation_rate`. Default value: `1`. output_padding: An `int` or length-`rank` tuple/list representing the amount of padding along the input spatial dimensions (e.g., depth, height, width). A single `int` indicates the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to `None` (default), the output shape is inferred. In Keras, this argument has the same name and semantics. Default value: `None` (i.e., inferred). init_kernel_fn: ... Default value: `None` (i.e., `tfp.experimental.nn.initializers.glorot_uniform()`). init_bias_fn: ... Default value: `None` (i.e., `tf.initializers.zeros()`). make_kernel_bias_fn: ... Default value: `tfp.experimental.nn.util.make_kernel_bias`. dtype: ... Default value: `tf.float32`. activation_fn: ... Default value: `None`. name: ... Default value: `None` (i.e., `'ConvolutionTranspose'`). """ filter_shape = convolution_lib.prepare_tuple_argument( filter_shape, rank, 'filter_shape') kernel_shape = filter_shape + (output_size, input_size) # Note transpose. batch_ndims = 0 kernel, bias = make_kernel_bias_fn( kernel_shape, [output_size], init_kernel_fn, init_bias_fn, batch_ndims, batch_ndims, dtype) super(ConvolutionTranspose, self).__init__( kernel=kernel, bias=bias, apply_kernel_fn=_make_convolution_transpose_fn( rank, strides, padding, dilations, filter_shape, output_size, output_padding), dtype=dtype, activation_fn=activation_fn, name=name) class ConvolutionTransposeVariationalReparameterization( vi_lib.VariationalReparameterizationKernelBiasLayer): """ConvolutionTranspose layer class with reparameterization estimator. This layer implements the Bayesian variational inference analogue to a ConvolutionTranspose layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = matmul(inputs, kernel) + bias ``` It uses the reparameterization estimator [(Kingma and Welling, 2014)][1], which performs a Monte Carlo approximation of the distribution integrating over the `kernel` and `bias`. The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Upon being built, this layer adds losses (accessible via the `losses` property) representing the divergences of `kernel` and/or `bias` surrogate posteriors and their respective priors. When doing minibatch stochastic optimization, make sure to scale this loss such that it is applied just once per epoch (e.g. if `kl` is the sum of `losses` for each element of the batch, you should pass `kl / num_examples_per_epoch` to your optimizer). You can access the `kernel` and/or `bias` posterior and prior distributions after the layer is built via the `kernel_posterior`, `kernel_prior`, `bias_posterior` and `bias_prior` properties. #### Examples We illustrate a Bayesian autoencoder network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of images. Note that this examples is *not* a variational autoencoder, rather it is a Bayesian Autoencoder which also uses variational inference. ```python import functools import tensorflow.compat.v2 as tf import tensorflow_probability as tfp import tensorflow_datasets as tfds tfb = tfp.bijectors tfd = tfp.distributions tfn = tfp.experimental.nn # 1 Prepare Dataset [train_dataset, eval_dataset], datasets_info = tfds.load( name='mnist', split=['train', 'test'], with_info=True, as_supervised=True, shuffle_files=True) def _preprocess(image, label): # image = image < tf.random.uniform(tf.shape(image)) # Randomly binarize. image = tf.cast(image, tf.float32) / 255. # Scale to unit interval. lo = 0.001 image = (1. - 2. * lo) * image + lo # Rescale to *open* unit interval. return image, label batch_size = 32 train_size = datasets_info.splits['train'].num_examples train_dataset = tfn.util.tune_dataset( train_dataset, batch_shape=(batch_size,), shuffle_size=int(train_size / 7), preprocess_fn=_preprocess) train_iter = iter(train_dataset) eval_iter = iter(eval_dataset) x, _ = next(train_iter) # Ignore labels. evidence_shape = x.shape[1:] # 2 Specify Model bottleneck_size = 2 BayesConv2D = functools.partial( tfn.ConvolutionVariationalReparameterization, rank=2, padding='same', filter_shape=5, # Use `he_uniform` because we'll use the `relu` family. init_kernel_fn=tf.initializers.he_uniform()) BayesDeconv2D = functools.partial( tfn.ConvolutionTransposeVariationalReparameterization, rank=2, padding='same', filter_shape=5, # Use `he_uniform` because we'll use the `relu` family. init_kernel_fn=tf.initializers.he_uniform()) scale = tfp.util.TransformedVariable(1., tfb.Softplus()) bnn = tfn.Sequential([ BayesConv2D(evidence_shape[-1], 32, filter_shape=5, strides=2, activation_fn=tf.nn.leaky_relu), # [b, 14, 14, 32] tfn.util.flatten_rightmost(ndims=3), # [b, 14 * 14 * 32] tfn.AffineVariationalReparameterization( 14 * 14 * 32, bottleneck_size), # [b, 2] lambda x: x[..., tf.newaxis, tf.newaxis, :], # [b, 1, 1, 2] BayesDeconv2D(2, 64, filter_shape=7, strides=1, padding='valid', activation_fn=tf.nn.leaky_relu), # [b, 7, 7, 64] BayesDeconv2D(64, 32, filter_shape=4, strides=4, activation_fn=tf.nn.leaky_relu), # [2, 28, 28, 32] BayesConv2D(32, 1, filter_shape=2, strides=1), # [2, 28, 28, 1] tfn.Lambda( eval_fn=lambda loc: ( tfd.Independent(tfb.Sigmoid()(tfd.Normal(loc, scale)), reinterpreted_batch_ndims=3)), also_track=scale), # [b, 28, 28, 1] ], name='bayesian_autoencoder') print(bnn.summary()) # 3 Train. def loss_fn(): x, _ = next(train_iter) # Ignore the label. nll = -tf.reduce_mean(bnn(x).log_prob(x), axis=-1) kl = bnn.extra_loss / tf.cast(train_size, tf.float32) loss = nll + kl return loss, (nll, kl) opt = tf.optimizers.Adam() fit_op = tfn.util.make_fit_op(loss_fn, opt, bnn.trainable_variables) for _ in range(200): loss, (nll, kl), g = fit_op() ``` This example uses reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. #### References [1]: Diederik Kingma and Max Welling. Auto-Encoding Variational Bayes. In _International Conference on Learning Representations_, 2014. https://arxiv.org/abs/1312.6114 """ def __init__( self, input_size, output_size, # keras::Conv::filters # Conv specific. filter_shape, # keras::Conv::kernel_size rank=2, # keras::Conv::rank strides=1, # keras::Conv::strides padding='VALID', # keras::Conv::padding; 'CAUSAL' not implemented. # keras::Conv::data_format is not implemented dilations=1, # keras::Conv::dilation_rate output_padding=None, # keras::ConvTranspose::output_padding # Weights init_kernel_fn=None, # tfp.experimental.nn.initializers.glorot_uniform() init_bias_fn=None, # tf.initializers.zeros() make_posterior_fn=nn_util_lib.make_kernel_bias_posterior_mvn_diag, make_prior_fn=nn_util_lib.make_kernel_bias_prior_spike_and_slab, posterior_value_fn=tfd.Distribution.sample, unpack_weights_fn=unpack_kernel_and_bias, dtype=tf.float32, # Penalty. penalty_weight=None, posterior_penalty_fn=kl_divergence_monte_carlo, # Misc activation_fn=None, seed=None, name=None): """Constructs layer. Note: `data_format` is not supported since all nn layers operate on the rightmost column. If your channel dimension is not rightmost, use `tf.transpose` before calling this layer. For example, if your channel dimension is second from the left, the following code will move it rightmost: ```python inputs = tf.transpose(inputs, tf.concat([ [0], tf.range(2, tf.rank(inputs)), [1]], axis=0)) ``` Args: input_size: ... In Keras, this argument is inferred from the rightmost input shape, i.e., `tf.shape(inputs)[-1]`. This argument specifies the size of the second from the rightmost dimension of both `inputs` and `kernel`. Default value: `None`. output_size: ... In Keras, this argument is called `filters`. This argument specifies the rightmost dimension size of both `kernel` and `bias`. filter_shape: ... In Keras, this argument is called `kernel_size`. This argument specifies the leftmost `rank` dimensions' sizes of `kernel`. rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution. This argument implies the number of `kernel` dimensions, i.e.`, `kernel.shape.rank == rank + 2`. In Keras, this argument has the same name and semantics. Default value: `2`. strides: An integer or tuple/list of n integers, specifying the stride length of the convolution. In Keras, this argument has the same name and semantics. Default value: `1`. padding: One of `"VALID"` or `"SAME"` (case-insensitive). In Keras, this argument has the same name and semantics (except we don't support `"CAUSAL"`). Default value: `'VALID'`. dilations: An integer or tuple/list of `rank` integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilations` value != 1 is incompatible with specifying any `strides` value != 1. In Keras, this argument is called `dilation_rate`. Default value: `1`. output_padding: An `int` or length-`rank` tuple/list representing the amount of padding along the input spatial dimensions (e.g., depth, height, width). A single `int` indicates the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to `None` (default), the output shape is inferred. In Keras, this argument has the same name and semantics. Default value: `None` (i.e., inferred). init_kernel_fn: ... Default value: `None` (i.e., `tfp.experimental.nn.initializers.glorot_uniform()`). init_bias_fn: ... Default value: `None` (i.e., `tf.initializers.zeros()`). make_posterior_fn: ... Default value: `tfp.experimental.nn.util.make_kernel_bias_posterior_mvn_diag`. make_prior_fn: ... Default value: `tfp.experimental.nn.util.make_kernel_bias_prior_spike_and_slab`. posterior_value_fn: ... Default valye: `tfd.Distribution.sample` unpack_weights_fn: Default value: `unpack_kernel_and_bias` dtype: ... Default value: `tf.float32`. penalty_weight: ... Default value: `None` (i.e., weight is `1`). posterior_penalty_fn: ... Default value: `kl_divergence_monte_carlo`. activation_fn: ... Default value: `None`. seed: ... Default value: `None` (i.e., no seed). name: ... Default value: `None` (i.e., `'ConvolutionTransposeVariationalReparameterization'`). """ filter_shape = convolution_lib.prepare_tuple_argument( filter_shape, rank, 'filter_shape') kernel_shape = filter_shape + (output_size, input_size) # Note transpose. batch_ndims = 0 super(ConvolutionTransposeVariationalReparameterization, self).__init__( posterior=make_posterior_fn( kernel_shape, [output_size], init_kernel_fn, init_bias_fn, batch_ndims, batch_ndims, dtype), prior=make_prior_fn( kernel_shape, [output_size], init_kernel_fn, init_bias_fn, batch_ndims, batch_ndims, dtype), apply_kernel_fn=_make_convolution_transpose_fn( rank, strides, padding, dilations, filter_shape, output_size, output_padding), posterior_value_fn=posterior_value_fn, unpack_weights_fn=unpack_weights_fn, dtype=dtype, penalty_weight=penalty_weight, posterior_penalty_fn=posterior_penalty_fn, activation_fn=activation_fn, seed=seed, name=name) class ConvolutionTransposeVariationalFlipout( vi_lib.VariationalFlipoutKernelBiasLayer): """ConvolutionTranspose layer class with Flipout estimator. This layer implements the Bayesian variational inference analogue to a ConvolutionTranspose layer by assuming the `kernel` and/or the `bias` are drawn from distributions. By default, the layer implements a stochastic forward pass via sampling from the kernel and bias posteriors, ```none kernel, bias ~ posterior outputs = tf.nn.conv_transpose(inputs, kernel) + bias ``` It uses the Flipout estimator [(Wen et al., 2018)][1], which performs a Monte Carlo approximation of the distribution integrating over the `kernel` and `bias`. Flipout uses roughly twice as many floating point operations as the reparameterization estimator but has the advantage of significantly lower variance. The arguments permit separate specification of the surrogate posterior (`q(W|x)`), prior (`p(W)`), and divergence for both the `kernel` and `bias` distributions. Upon being built, this layer adds losses (accessible via the `losses` property) representing the divergences of `kernel` and/or `bias` surrogate posteriors and their respective priors. When doing minibatch stochastic optimization, make sure to scale this loss such that it is applied just once per epoch (e.g. if `kl` is the sum of `losses` for each element of the batch, you should pass `kl / num_examples_per_epoch` to your optimizer). ```python inputs = tf.transpose(inputs, tf.concat([ [0], tf.range(2, tf.rank(inputs)), [1]], axis=0)) ``` #### Examples We illustrate a Bayesian autoencoder network with [variational inference]( https://en.wikipedia.org/wiki/Variational_Bayesian_methods), assuming a dataset of images. Note that this examples is *not* a variational autoencoder, rather it is a Bayesian Autoencoder which also uses variational inference. ```python # Using the following substitution, see: tfn = tfp.experimental.nn help(tfn.ConvolutionTransposeVariationalReparameterization) BayesConv2D = functools.partial( tfn.ConvolutionVariationalFlipout, rank=2, padding='same', filter_shape=5, # Use `he_uniform` because we'll use the `relu` family. init_kernel_fn=tf.initializers.he_uniform()) BayesDeconv2D = functools.partial( tfn.ConvolutionTransposeVariationalFlipout, rank=2, padding='same', filter_shape=5, # Use `he_uniform` because we'll use the `relu` family. init_kernel_fn=tf.initializers.he_uniform()) ``` This example uses reparameterization gradients to minimize the Kullback-Leibler divergence up to a constant, also known as the negative Evidence Lower Bound. It consists of the sum of two terms: the expected negative log-likelihood, which we approximate via Monte Carlo; and the KL divergence, which is added via regularizer terms which are arguments to the layer. #### References [1]: Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse. Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches. In _International Conference on Learning Representations_, 2018. https://arxiv.org/abs/1803.04386 """ def __init__( self, input_size, output_size, # keras::Conv::filters # ConvTranspose specific. filter_shape, # keras::Conv::kernel_size rank=2, # keras::Conv::rank strides=1, # keras::Conv::strides padding='VALID', # keras::Conv::padding; 'CAUSAL' not implemented. # keras::Conv::data_format is not implemented dilations=1, # keras::Conv::dilation_rate output_padding=None, # keras::ConvTranspose::output_padding # Weights init_kernel_fn=None, # tfp.experimental.nn.initializers.glorot_uniform() init_bias_fn=None, # tf.initializers.zeros() make_posterior_fn=nn_util_lib.make_kernel_bias_posterior_mvn_diag, make_prior_fn=nn_util_lib.make_kernel_bias_prior_spike_and_slab, posterior_value_fn=tfd.Distribution.sample, unpack_weights_fn=unpack_kernel_and_bias, dtype=tf.float32, # Penalty. penalty_weight=None, posterior_penalty_fn=kl_divergence_monte_carlo, # Misc activation_fn=None, seed=None, name=None): """Constructs layer. Note: `data_format` is not supported since all nn layers operate on the rightmost column. If your channel dimension is not rightmost, use `tf.transpose` before calling this layer. For example, if your channel dimension is second from the left, the following code will move it rightmost: ```python inputs = tf.transpose(inputs, tf.concat([ [0], tf.range(2, tf.rank(inputs)), [1]], axis=0)) ``` Args: input_size: ... In Keras, this argument is inferred from the rightmost input shape, i.e., `tf.shape(inputs)[-1]`. This argument specifies the size of the second from the rightmost dimension of both `inputs` and `kernel`. Default value: `None`. output_size: ... In Keras, this argument is called `filters`. This argument specifies the rightmost dimension size of both `kernel` and `bias`. filter_shape: ... In Keras, this argument is called `kernel_size`. This argument specifies the leftmost `rank` dimensions' sizes of `kernel`. rank: An integer, the rank of the convolution, e.g. "2" for 2D convolution. This argument implies the number of `kernel` dimensions, i.e.`, `kernel.shape.rank == rank + 2`. In Keras, this argument has the same name and semantics. Default value: `2`. strides: An integer or tuple/list of n integers, specifying the stride length of the convolution. In Keras, this argument has the same name and semantics. Default value: `1`. padding: One of `"VALID"` or `"SAME"` (case-insensitive). In Keras, this argument has the same name and semantics (except we don't support `"CAUSAL"`). Default value: `'VALID'`. dilations: An integer or tuple/list of `rank` integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any `dilations` value != 1 is incompatible with specifying any `strides` value != 1. In Keras, this argument is called `dilation_rate`. Default value: `1`. output_padding: An `int` or length-`rank` tuple/list representing the amount of padding along the input spatial dimensions (e.g., depth, height, width). A single `int` indicates the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to `None` (default), the output shape is inferred. In Keras, this argument has the same name and semantics. Default value: `None` (i.e., inferred). init_kernel_fn: ... Default value: `None` (i.e., `tfp.experimental.nn.initializers.glorot_uniform()`). init_bias_fn: ... Default value: `None` (i.e., `tf.initializers.zeros()`). make_posterior_fn: ... Default value: `tfp.experimental.nn.util.make_kernel_bias_posterior_mvn_diag`. make_prior_fn: ... Default value: `tfp.experimental.nn.util.make_kernel_bias_prior_spike_and_slab`. posterior_value_fn: ... Default valye: `tfd.Distribution.sample` unpack_weights_fn: Default value: `unpack_kernel_and_bias` dtype: ... Default value: `tf.float32`. penalty_weight: ... Default value: `None` (i.e., weight is `1`). posterior_penalty_fn: ... Default value: `kl_divergence_monte_carlo`. activation_fn: ... Default value: `None`. seed: ... Default value: `None` (i.e., no seed). name: ... Default value: `None` (i.e., `'ConvolutionTransposeVariationalFlipout'`). """ filter_shape = convolution_lib.prepare_tuple_argument( filter_shape, rank, 'filter_shape') kernel_shape = filter_shape + (output_size, input_size) # Note transpose. batch_ndims = 0 super(ConvolutionTransposeVariationalFlipout, self).__init__( posterior=make_posterior_fn( kernel_shape, [output_size], init_kernel_fn, init_bias_fn, batch_ndims, batch_ndims, dtype), prior=make_prior_fn( kernel_shape, [output_size], init_kernel_fn, init_bias_fn, batch_ndims, batch_ndims, dtype), apply_kernel_fn=_make_convolution_transpose_fn( rank, strides, padding, dilations, filter_shape, output_size, output_padding), posterior_value_fn=posterior_value_fn, unpack_weights_fn=unpack_weights_fn, dtype=dtype, penalty_weight=penalty_weight, posterior_penalty_fn=posterior_penalty_fn, activation_fn=activation_fn, seed=seed, name=name) def _make_convolution_transpose_fn(rank, strides, padding, dilations, filter_shape, output_size, output_padding): """Helper to create tf convolution op.""" [ rank, strides, padding, dilations, data_format, ] = convolution_lib.prepare_conv_args(rank, strides, padding, dilations) def op(x, kernel): output_shape, strides_ = _get_output_shape( rank, strides, padding, dilations, prefer_static.shape(x), output_size, filter_shape, output_padding) return tf.nn.conv_transpose( x, kernel, output_shape=output_shape, strides=strides_, padding=padding, data_format=data_format, dilations=dilations) return lambda x, kernel: convolution_lib.batchify_op(op, rank + 1, x, kernel) def _get_output_shape(rank, strides, padding, dilations, input_shape, output_size, filter_shape, output_padding): """Compute the `output_shape` and `strides` arg used by `conv_transpose`.""" if output_padding is None: output_padding = (None,) * rank else: output_padding = convolution_lib.prepare_tuple_argument( output_padding, rank, 'output_padding') for stride, out_pad in zip(strides, output_padding): if out_pad >= stride: raise ValueError('Stride {} must be greater than output ' 'padding {}.'.format(strides, output_padding)) assert len(filter_shape) == rank assert len(strides) == rank assert len(output_padding) == rank event_shape = [] for i in range(-rank, 0): event_shape.append(_deconv_output_length( input_shape[i - 1], filter_shape[i], padding=padding, output_padding=output_padding[i], stride=strides[i], dilation=dilations[i])) event_shape.append(output_size) batch_shape = input_shape[:-rank-1] output_shape = prefer_static.concat([batch_shape, event_shape], axis=0) strides = (1,) + strides + (1,) return output_shape, strides def _deconv_output_length(input_size, filter_size, padding, output_padding, stride, dilation): """Determines output length of a transposed convolution given input length. Args: input_size: `int`. filter_size: `int`. padding: one of `"SAME"`, `"VALID"`, `"FULL"`. output_padding: `int`, amount of padding along the output dimension. Can be set to `None` in which case the output length is inferred. stride: `int`. dilation: `int`. Returns: output_length: The output length (`int`). """ assert padding in {'SAME', 'VALID', 'FULL'} if input_size is None: return None # Get the dilated kernel size filter_size = filter_size + (filter_size - 1) * (dilation - 1) # Infer length if output padding is None, else compute the exact length if output_padding is None: if padding == 'VALID': return input_size * stride + max(filter_size - stride, 0) elif padding == 'FULL': return input_size * stride - (stride + filter_size - 2) elif padding == 'SAME': return input_size * stride if padding == 'SAME': pad = filter_size // 2 elif padding == 'VALID': pad = 0 elif padding == 'FULL': pad = filter_size - 1 return (input_size - 1) * stride + filter_size - 2 * pad + output_padding