fix

Author: GreenWizard2015

Utils/CelebaImageProcessor.py

@@ -0,0 +1,9 @@
+from Utils.CImageProcessor import CImageProcessor
+
+def CelebaImageProcessor(image_size, to_grayscale):
+  return CImageProcessor(
+    image_size=image_size,
+    to_grayscale=to_grayscale,
+    format='RGB', # in CelebA images are in RGB format
+    range='0..255' # in CelebA images are in the 0..255 range
+  )

Utils/__init__.py

@@ -17,7 +17,7 @@ def ImageProcessor_from_config(config):
     config = dict(name='celeba', image_size=64, toGrayscale=True)
 
   if isinstance(config, dict) and ('celeba' == config['name'].lower()):
-    from Utils.celeba import CelebaImageProcessor
+    from Utils.CelebaImageProcessor import CelebaImageProcessor
     return CelebaImageProcessor(
       image_size=config['image_size'],
       to_grayscale=config.get('toGrayscale', True),

Utils/celeba.py

@@ -1,14 +1,6 @@
 import tensorflow_datasets as tfds
 from Utils.utils import masking_from_config
-from Utils.CImageProcessor import CImageProcessor
-
-def CelebaImageProcessor(image_size, to_grayscale):
-  return CImageProcessor(
-    image_size=image_size,
-    to_grayscale=to_grayscale,
-    format='RGB', # in CelebA images are in RGB format
-    range='0..255' # in CelebA images are in the 0..255 range
-  )
+from Utils.CelebaImageProcessor import CelebaImageProcessor
 
 class CCelebADataset:
   def __init__(self, batch_size=32, image_size=64, toGrayscale=True):

Utils/colors.py

@@ -13,8 +13,8 @@ def f(x):
 def _isRangeUInt8(x):
   tf.debugging.assert_integer(x)
   # x is in the 0..255 range
-  tf.debugging.assert_less_equal(tf.reduce_min(x), tf.cast(0, x.dtype))
-  tf.debugging.assert_greater_equal(tf.reduce_max(x), tf.cast(255, x.dtype))
+  tf.debugging.assert_greater_equal(tf.reduce_min(x), tf.cast(0, x.dtype))
+  tf.debugging.assert_less_equal(tf.reduce_max(x), tf.cast(255, x.dtype))
   return
 
 def _from01range(to_):
@@ -38,14 +38,14 @@ def _fromUInt8Range(to_):
   if ('-1..1' == to_):
     return CFakeObject(
       convert=lambda x: (tf.cast(x, tf.float32) / 127.5) - 1.0,
-      convertBack=lambda x: (x + 1.0) * 127.5,
+      convertBack=lambda x: tf.cast((x + 1.0) * 127.5, tf.uint8),
       check=_isRangeUInt8
     )
 
   if ('0..1' == to_):
     return CFakeObject(
       convert=lambda x: tf.cast(x, tf.float32) / 255.0,
-      convertBack=lambda x: x * 255.0,
+      convertBack=lambda x: tf.cast(x * 255.0, tf.uint8),
       check=_isRangeUInt8
     )
 

huggingface/app.py

@@ -29,7 +29,8 @@ def _processImage(modelName, inputImage, **kwargs):
     assert 3 == inputImage.shape[-1], f'Invalid image channels: {inputImage.shape[-1]}'
     # should be 64x64, because of preprocessing
     assert (64, 64) == inputImage.shape[:2], f'Invalid image shape: {inputImage.shape}'
-    input = image_processor.range.convert(inputImage[None])
+    # inputImage has 3 channels, but its grayscale
+    input = image_processor.range.convert(inputImage[None, ..., :1])
 
     model = models.get(modelName, None)
     assert model is not None, f'Invalid model name: {modelName}'