Merge pull request #50 from tensorflow/image-sample

Image sample

Author: ludwigschubert

lucid/optvis/param/__init__.py

@@ -17,3 +17,4 @@
 from lucid.optvis.param.lowres import lowres_tensor
 from lucid.optvis.param.color import to_valid_rgb
 from lucid.optvis.param.spatial import naive, fft_image, laplacian_pyramid
+from lucid.optvis.param.random import image_sample

lucid/optvis/param/random.py

@@ -0,0 +1,59 @@
+# Copyright 2018 The Lucid Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+import tensorflow as tf
+import numpy as np
+
+from lucid.optvis.param.color import to_valid_rgb
+
+
+def image_sample(shape, decorrelate=True, sd=None, decay_power=1):
+  raw_spatial = rand_fft_image(shape, sd=sd, decay_power=decay_power)
+  return to_valid_rgb(raw_spatial, decorrelate=decorrelate)
+
+def rand_fft_image(shape, sd=None, decay_power=1):
+  b, h, w, ch = shape
+  sd = 0.01 if sd is None else sd
+  
+  imgs = []
+  for _ in range(b):
+    freqs = _rfft2d_freqs(h, w)
+    fh, fw = freqs.shape
+    spectrum_var = sd*tf.random_normal([2, ch, fh, fw], dtype="float32")
+    spectrum = tf.complex(spectrum_var[0], spectrum_var[1])
+    spertum_scale = 1.0 / np.maximum(freqs, 1.0/max(h, w))**decay_power
+    # Scale the spectrum by the square-root of the number of pixels
+    # to get a unitary transformation. This allows to use similar
+    # leanring rates to pixel-wise optimisation.
+    spertum_scale *= np.sqrt(w*h)
+    scaled_spectrum = spectrum * spertum_scale
+    img = tf.spectral.irfft2d(scaled_spectrum)
+    # in case of odd input dimension we cut off the additional pixel
+    # we get from irfft2d length computation
+    img = img[:ch, :h, :w]
+    img = tf.transpose(img, [1, 2, 0])
+    imgs.append(img)
+  return tf.stack(imgs)/4.
+
+def _rfft2d_freqs(h, w):
+  """Compute 2d spectrum frequences."""
+  fy = np.fft.fftfreq(h)[:, None]
+  # when we have an odd input dimension we need to keep one additional
+  # frequency and later cut off 1 pixel
+  if w % 2 == 1:
+    fx = np.fft.fftfreq(w)[:w//2+2]
+  else:
+    fx = np.fft.fftfreq(w)[:w//2+1]
+  return np.sqrt(fx*fx + fy*fy)