Add option to control anti-aliasing in the resize layer (#555)

Author: jonatanklosko

lib/axon.ex

@@ -390,7 +390,7 @@ defmodule Axon do
 
   You may specify the parameter shape as either a static shape or
   as function of the inputs to the given layer. If you specify the
-  parameter shape as a function, it will be given the 
+  parameter shape as a function, it will be given the
 
   ## Options
 
@@ -2122,18 +2122,22 @@ defmodule Axon do
 
     * `:method` - resize method. Defaults to `:nearest`.
 
+    * `:antialias` - whether an anti-aliasing filter should be used
+      when downsampling. Defaults to `true`.
+
     * `:channels` - channel configuration. One of `:first` or
       `:last`. Defaults to `:last`.
 
   """
   @doc type: :shape
   def resize(%Axon{} = x, resize_shape, opts \\ []) do
-    opts = Keyword.validate!(opts, [:name, method: :nearest, channels: :last])
+    opts = Keyword.validate!(opts, [:name, method: :nearest, antialias: true, channels: :last])
     channels = opts[:channels]
 
     layer(:resize, [x],
       name: opts[:name],
       method: opts[:method],
+      antialias: opts[:antialias],
       channels: channels,
       size: resize_shape,
       op_name: :resize

lib/axon/layers.ex

@@ -1915,8 +1915,21 @@ defmodule Axon.Layers do
   must be at least rank 3, with fixed `batch` and `channel` dimensions.
   Resizing will upsample or downsample using the given resize method.
 
-  Supported resize methods are `:nearest, :linear, :bilinear, :trilinear,
-  :cubic, :bicubic, :tricubic`.
+  ## Options
+
+    * `:size` - a tuple specifying the resized spatial dimensions.
+      Required.
+
+    * `:method` - the resizing method to use, either of `:nearest`,
+      `:bilinear`, `:bicubic`, `:lanczos3`, `:lanczos5`. Defaults to
+      `:nearest`.
+
+    * `:antialias` - whether an anti-aliasing filter should be used
+      when downsampling. This has no effect with upsampling. Defaults
+      to `true`.
+
+    * `:channels` - channels location, either `:first` or `:last`.
+      Defaults to `:last`.
 
   ## Examples
 
@@ -1951,6 +1964,7 @@ defmodule Axon.Layers do
         :size,
         method: :nearest,
         channels: :last,
+        antialias: true,
         mode: :inference
       ])
 
@@ -1962,22 +1976,36 @@ defmodule Axon.Layers do
         {axis, put_elem(out_shape, axis, out_size)}
       end)
 
+    antialias = opts[:antialias]
+
     resized_input =
       case opts[:method] do
         :nearest ->
           resize_nearest(input, out_shape, spatial_axes)
 
         :bilinear ->
-          resize_with_kernel(input, out_shape, spatial_axes, &fill_linear_kernel/1)
+          resize_with_kernel(input, out_shape, spatial_axes, antialias, &fill_linear_kernel/1)
 
         :bicubic ->
-          resize_with_kernel(input, out_shape, spatial_axes, &fill_cubic_kernel/1)
+          resize_with_kernel(input, out_shape, spatial_axes, antialias, &fill_cubic_kernel/1)
 
         :lanczos3 ->
-          resize_with_kernel(input, out_shape, spatial_axes, &fill_lanczos_kernel(3, &1))
+          resize_with_kernel(
+            input,
+            out_shape,
+            spatial_axes,
+            antialias,
+            &fill_lanczos_kernel(3, &1)
+          )
 
         :lanczos5 ->
-          resize_with_kernel(input, out_shape, spatial_axes, &fill_lanczos_kernel(5, &1))
+          resize_with_kernel(
+            input,
+            out_shape,
+            spatial_axes,
+            antialias,
+            &fill_lanczos_kernel(5, &1)
+          )
 
         method ->
           raise ArgumentError,
@@ -2038,12 +2066,13 @@ defmodule Axon.Layers do
 
   @f32_eps :math.pow(2, -23)
 
-  deftransformp resize_with_kernel(input, out_shape, spatial_axes, kernel_fun) do
+  deftransformp resize_with_kernel(input, out_shape, spatial_axes, antialias, kernel_fun) do
     for axis <- spatial_axes, reduce: input do
       input ->
         resize_axis_with_kernel(input,
           axis: axis,
           output_size: elem(out_shape, axis),
+          antialias: antialias,
           kernel_fun: kernel_fun
         )
     end
@@ -2052,12 +2081,19 @@ defmodule Axon.Layers do
   defnp resize_axis_with_kernel(input, opts) do
     axis = opts[:axis]
     output_size = opts[:output_size]
+    antialias = opts[:antialias]
     kernel_fun = opts[:kernel_fun]
 
     input_size = Nx.axis_size(input, axis)
 
     inv_scale = input_size / output_size
-    kernel_scale = max(1, inv_scale)
+
+    kernel_scale =
+      if antialias do
+        max(1, inv_scale)
+      else
+        1
+      end
 
     sample_f = (Nx.iota({1, output_size}) + 0.5) * inv_scale - 0.5
     x = Nx.abs(sample_f - Nx.iota({input_size, 1})) / kernel_scale

test/axon/layers_test.exs

@@ -1009,6 +1009,46 @@ defmodule Axon.LayersTest do
         atol: 1.0e-4
       )
     end
+
+    test "without anti-aliasing" do
+      # Upscaling
+
+      image = Nx.iota({1, 4, 4, 3}, type: :f32)
+
+      assert_all_close(
+        Axon.Layers.resize(image, size: {3, 3}, method: :bicubic, antialias: false),
+        Nx.tensor([
+          [
+            [
+              [[1.5427, 2.5427, 3.5427], [5.7341, 6.7341, 7.7341], [9.9256, 10.9256, 11.9256]],
+              [[18.3085, 19.3085, 20.3085], [22.5, 23.5, 24.5], [26.6915, 27.6915, 28.6915]],
+              [
+                [35.0744, 36.0744, 37.0744],
+                [39.2659, 40.2659, 41.2659],
+                [43.4573, 44.4573, 45.4573]
+              ]
+            ]
+          ]
+        ]),
+        atol: 1.0e-4
+      )
+
+      # Downscaling (no effect)
+
+      image = Nx.iota({1, 2, 2, 3}, type: :f32)
+
+      assert_all_close(
+        Axon.Layers.resize(image, size: {3, 3}, method: :bicubic, antialias: false),
+        Nx.tensor([
+          [
+            [[-0.5921, 0.4079, 1.4079], [1.1053, 2.1053, 3.1053], [2.8026, 3.8026, 4.8026]],
+            [[2.8026, 3.8026, 4.8026], [4.5, 5.5, 6.5], [6.1974, 7.1974, 8.1974]],
+            [[6.1974, 7.1974, 8.1974], [7.8947, 8.8947, 9.8947], [9.5921, 10.5921, 11.5921]]
+          ]
+        ]),
+        atol: 1.0e-4
+      )
+    end
   end
 
   describe "lstm_cell" do