Merge pull request #1306 from rstudio/tf-2.8-compat

TF 2.8 compat

Author: t-kalinowski

.github/workflows/R-CMD-check.yaml

@@ -38,16 +38,17 @@ jobs:
           # - {os: 'windows-latest', tf: 'release', r: 'release'}
           # - {os: 'macOS-latest'  , tf: 'release', r: 'release'}
 
+          - {os: 'ubuntu-20.04', tf: '2.7', r: 'release'}
           - {os: 'ubuntu-20.04', tf: '2.6', r: 'release'}
           - {os: 'ubuntu-20.04', tf: '2.5', r: 'release'}
           - {os: 'ubuntu-20.04', tf: '2.4', r: 'release'}
           - {os: 'ubuntu-20.04', tf: '2.3', r: 'release'}
-          - {os: 'ubuntu-20.04', tf: '2.2', r: 'release'}
+          # - {os: 'ubuntu-20.04', tf: '2.2', r: 'release'}
 
           # these are allowed to fail
           # - {os: 'ubuntu-20.04', tf: 'default', r: 'devel'}
           # - {os: 'ubuntu-20.04', tf: '2.7.0rc1', r: 'release'}
-          - {os: 'ubuntu-20.04', tf: 'nightly' , r: 'release'}
+          # - {os: 'ubuntu-20.04', tf: 'nightly' , r: 'release'}
 
     runs-on: ${{ matrix.os }}
     continue-on-error: ${{ matrix.tf == 'nightly' || contains(matrix.tf, 'rc') || matrix.r == 'devel' }}
@@ -57,12 +58,6 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - name: Configure RSPM
-        if: runner.os == 'Linux'
-        shell: bash
-        run: |
-          RSPM="https://packagemanager.rstudio.com/all/__linux__/$(lsb_release -cs)/latest"
-          echo "RSPM=$RSPM" >> $GITHUB_ENV
 
       - uses: actions/checkout@v2
 
@@ -71,7 +66,7 @@ jobs:
         with:
           r-version: ${{ matrix.r }}
           Ncpus: '2L'
-          crayon.enabled: 'TRUE'
+          use-public-rspm: true
 
       - uses: r-lib/actions/setup-pandoc@v2
 

NAMESPACE

@@ -39,6 +39,7 @@ S3method(expm1,keras.engine.keras_tensor.KerasTensor)
 S3method(export_savedmodel,keras.engine.training.Model)
 S3method(fit,keras.engine.training.Model)
 S3method(floor,keras.engine.keras_tensor.KerasTensor)
+S3method(format,keras.engine.training.Model)
 S3method(length,keras.engine.keras_tensor.KerasTensor)
 S3method(lgamma,keras.engine.keras_tensor.KerasTensor)
 S3method(log,keras.engine.keras_tensor.KerasTensor)
@@ -48,6 +49,7 @@ S3method(log2,keras.engine.keras_tensor.KerasTensor)
 S3method(plot,keras_training_history)
 S3method(predict,keras.engine.training.Model)
 S3method(print,keras.engine.keras_tensor.KerasTensor)
+S3method(print,keras.engine.training.Model)
 S3method(print,keras_training_history)
 S3method(print,kerastools.model.RModel)
 S3method(print,py_R6ClassGenerator)
@@ -405,6 +407,7 @@ export(layer_cudnn_gru)
 export(layer_cudnn_lstm)
 export(layer_dense)
 export(layer_dense_features)
+export(layer_depthwise_conv_1d)
 export(layer_depthwise_conv_2d)
 export(layer_discretization)
 export(layer_dot)

NEWS.md

@@ -24,6 +24,11 @@
     accept a named list for `y`, with names matching to the
     names of `outputs` when the model was constructed.
 
+- New layer `layer_depthwise_conv_1d()`.
+
+- Models gain default `format()` and `print()` S3 methods for compatibility
+  with the latest reticulate. Both are powered by `model$summary()`.
+
 - `keras_model_custom()` is soft deprecated. Please define custom models by
   subclassing `keras$Model` directly using `%py_class%` or `R6::R6Class()`.
 
@@ -35,9 +40,9 @@
 
 - Fixed issue where `create_layer_wrapper()` would not include arguments
   with a `NULL` default value in the returned wrapper.
-  
+
 - Fixed issue in `r_to_py.R6ClassGenerator` (and `%py_class%`) where
-  single-expression `initialize` functions defined without `{` would error. 
+  single-expression `initialize` functions defined without `{` would error.
 
 - Deprecated functions are no longer included in the package documentation index.
 

R/install.R

@@ -60,7 +60,7 @@ install_keras <- function(method = c("auto", "virtualenv", "conda"),
   )
 }
 
-default_version <- numeric_version("2.7")
+default_version <- numeric_version("2.8")
 
 default_extra_packages <- function(tensorflow_version) {
   pkgs <- c("tensorflow-hub", "scipy", "requests", "pyyaml", "Pillow", "h5py", "pandas")

R/layers-convolutional.R

@@ -650,6 +650,123 @@ layer_separable_conv_2d <- function(object, filters, kernel_size, strides = c(1,
 
 }
 
+
+#' Depthwise 1D convolution
+#'
+#' @details
+#' Depthwise convolution is a type of convolution in which each input channel is
+#' convolved with a different kernel (called a depthwise kernel). You
+#' can understand depthwise convolution as the first step in a depthwise
+#' separable convolution.
+#'
+#' It is implemented via the following steps:
+#'
+#' - Split the input into individual channels.
+#' - Convolve each channel with an individual depthwise kernel with
+#'   `depth_multiplier` output channels.
+#' - Concatenate the convolved outputs along the channels axis.
+#'
+#' Unlike a regular 1D convolution, depthwise convolution does not mix
+#' information across different input channels.
+#'
+#' The `depth_multiplier` argument determines how many filter are applied to one
+#' input channel. As such, it controls the amount of output channels that are
+#' generated per input channel in the depthwise step.
+#'
+#' @param kernel_size An integer, specifying the height and width of the 1D
+#' convolution window. Can be a single integer to specify the same value for
+#' all spatial dimensions.
+#'
+#' @param strides An integer, specifying the strides of the convolution along the
+#' height and width. Can be a single integer to specify the same value for
+#' all spatial dimensions. Specifying any stride value != 1 is incompatible
+#' with specifying any `dilation_rate` value != 1.
+#'
+#' @param padding one of `'valid'` or `'same'` (case-insensitive). `"valid"` means no
+#' padding. `"same"` results in padding with zeros evenly to the left/right
+#' or up/down of the input such that output has the same height/width
+#' dimension as the input.
+#'
+#' @param depth_multiplier The number of depthwise convolution output channels for
+#' each input channel. The total number of depthwise convolution output
+#' channels will be equal to `filters_in * depth_multiplier`.
+#'
+#' @param 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_size, height, width, channels)` while
+#' `channels_first` corresponds to inputs with shape `(batch_size, channels,
+#' height, width)`. It defaults to the `image_data_format` value found in
+#' your Keras config file at `~/.keras/keras.json`. If you never set it, then
+#' it will be 'channels_last'.
+#'
+#' @param dilation_rate A single integer, specifying the dilation rate to use for
+#' dilated convolution. Currently, specifying any `dilation_rate` value != 1
+#' is incompatible with specifying any stride value != 1.
+#'
+#' @param activation Activation function to use. If you don't specify anything, no
+#' activation is applied (see `?activation_relu`).
+#'
+#' @param use_bias Boolean, whether the layer uses a bias vector.
+#'
+#' @param depthwise_initializer Initializer for the depthwise kernel matrix (see
+#' [`initializer_glorot_uniform`]). If NULL, the default initializer
+#' (`"glorot_uniform"`) will be used.
+#'
+#' @param bias_initializer Initializer for the bias vector (see
+#' `keras.initializers`). If NULL, the default initializer ('zeros') will be
+#' used.
+#'
+#' @param depthwise_regularizer Regularizer function applied to the depthwise kernel
+#' matrix (see  [`regularizer_l1()`]).
+#'
+#' @param bias_regularizer Regularizer function applied to the bias vector (see
+#'  [`regularizer_l1()`]).
+#'
+#' @param activity_regularizer Regularizer function applied to the output of the
+#' layer (its 'activation') (see  [`regularizer_l1()`]).
+#'
+#' @param depthwise_constraint Constraint function applied to the depthwise kernel
+#' matrix (see [`constraint_maxnorm()`]).
+#'
+#' @param bias_constraint Constraint function applied to the bias vector (see
+#' [`constraint_maxnorm()`]).
+#'
+#' @param ... standard layer arguments.
+#'
+#' @inheritParams layer_depthwise_conv_2d
+#' @family convolutional layers
+#'
+#' @seealso
+#'   +  <https://www.tensorflow.org/api_docs/python/tf/keras/layers/DepthwiseConv1D>
+#' @export
+layer_depthwise_conv_1d <-
+function(object,
+         kernel_size,
+         strides = 1L,
+         padding = "valid",
+         depth_multiplier = 1L,
+         data_format = NULL,
+         dilation_rate = 1L,
+         activation = NULL,
+         use_bias = TRUE,
+         depthwise_initializer = "glorot_uniform",
+         bias_initializer = "zeros",
+         depthwise_regularizer = NULL,
+         bias_regularizer = NULL,
+         activity_regularizer = NULL,
+         depthwise_constraint = NULL,
+         bias_constraint = NULL,
+         ...) {
+  args <- capture_args(match.call(),
+    list(kernel_size = as.integer,
+         strides = as.integer,
+         depth_multiplier = as.integer,
+         dilation_rate = as.integer),
+    ignore = "object"
+  )
+  create_layer(keras$layers$DepthwiseConv1D, object, args)
+}
+
 #' Depthwise separable 2D convolution.
 #'
 #' Depthwise Separable convolutions consists in performing just the first step

R/model.R

@@ -1424,34 +1424,67 @@ pop_layer <- function(object) {
 
 #' Print a summary of a Keras model
 #'
-#' @param object Keras model instance
+#' @param object,x Keras model instance
 #' @param line_length Total length of printed lines
 #' @param positions Relative or absolute positions of log elements in each line.
 #'   If not provided, defaults to `c(0.33, 0.55, 0.67, 1.0)`.
-#' @param ... Unused
+#' @param expand_nested Whether to expand the nested models. If not provided,
+#'   defaults to `FALSE`.
+#' @param show_trainable Whether to show if a layer is trainable. If not
+#'   provided, defaults to `FALSE`.
+#' @param ... for `summary()` and `print()`, passed on to `format()`. For
+#'   `format()`, passed on to `model$summary()`.
 #'
 #' @family model functions
 #'
+#' @return `format()` returns a length 1 character vector. `print()` returns the
+#'   model object invisibly. `summary()` returns the output of `format()`
+#'   invisibly after printing it.
+#'
 #' @export
-summary.keras.engine.training.Model <- function(object, line_length = getOption("width"), positions = NULL, ...) {
-  if (py_is_null_xptr(object))
-    cat("<pointer: 0x0>\n")
-  else {
-    if (keras_version() >= "2.0.6")
-      object$summary(line_length = getOption("width"), print_fn = function(object) cat(object, "\n", sep = ""))
-    else
-      cat(py_str(object, line_length = line_length, positions = positions), "\n")
-  }
+summary.keras.engine.training.Model <- function(object, ...) {
+  writeLines(f <- format.keras.engine.training.Model(object, ...))
+  invisible(f)
+}
+
+#' @rdname summary.keras.engine.training.Model
+#' @export
+format.keras.engine.training.Model <-
+  function(x,
+           line_length = getOption("width"),
+           positions = NULL,
+           expand_nested = FALSE,
+           show_trainable = FALSE,
+           ...) {
+    if (py_is_null_xptr(x))
+      return("<pointer: 0x0>")
+
+    args <- capture_args(match.call(), ignore = "x")
+
+    # ensure `line_length` in args, even if not passed by user
+    args$line_length <- as_nullable_integer(line_length)
+
+    if (x$built)
+      trimws(py_capture_output(do.call(x$summary, args),
+                               type = "stdout"))
+     else
+      "Model: <no summary available, model was not built>"
+}
+
+#
+#' @rdname summary.keras.engine.training.Model
+#' @export
+print.keras.engine.training.Model <- function(x, ...) {
+  writeLines(format.keras.engine.training.Model(x, ...))
+  invisible(x)
 }
 
 #' @importFrom reticulate py_str
 #' @export
-py_str.keras.engine.training.Model <- function(object,  line_length = getOption("width"), positions = NULL, ...) {
-  if (!object$built) {
-    cat("Model\n<no summary available, model was not built>\n")
-  } else {
-    paste0("Model\n", py_capture_output(object$summary(line_length = line_length, positions = positions), type = "stdout"))
-  }
+py_str.keras.engine.training.Model <- function(object, line_length = getOption("width"), positions = NULL, ...) {
+  # still invoked by utils::str()
+  # warning("`py_str()` generic is deprecated")
+  format.keras.engine.training.Model(object, line_length = line_length, positions = positions, ...)
 }