Merge pull request #37438 from jaketae:refactor-imageutils

PiperOrigin-RevId: 313705788 Change-Id: Ifb7da82fb5547cc9241981ca37100cd64b64d7f0
2020-05-28 19:41:08 -07:00 · 2020-05-28 19:41:08 -07:00 · 4853a0bbf4
commit 4853a0bbf4
parent 0620d5912b aa37eea584
13 changed files with 58 additions and 22 deletions
--- a/tensorflow/python/keras/applications/densenet.py
+++ b/tensorflow/python/keras/applications/densenet.py
@ -368,7 +368,9 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

 DOC = """
--- a/tensorflow/python/keras/applications/imagenet_utils.py
+++ b/tensorflow/python/keras/applications/imagenet_utils.py
@ -66,11 +66,11 @@ PREPROCESS_INPUT_DOC = """
      {ret}

  Raises:
-      ValueError: In case of unknown `data_format` argument.
+      {error}
  """

 PREPROCESS_INPUT_MODE_DOC = """
-    mode: One of "caffe", "tf" or "torch".
+    mode: One of "caffe", "tf" or "torch". Defaults to "caffe".
      - caffe: will convert the images from RGB to BGR,
          then will zero-center each color channel with
          respect to the ImageNet dataset,
@ -82,12 +82,18 @@ PREPROCESS_INPUT_MODE_DOC = """
          ImageNet dataset.
  """

+PREPROCESS_INPUT_DEFAULT_ERROR_DOC = """
+    ValueError: In case of unknown `mode` or `data_format` argument."""
+
+PREPROCESS_INPUT_ERROR_DOC = """
+    ValueError: In case of unknown `data_format` argument."""
+
 PREPROCESS_INPUT_RET_DOC_TF = """
      The inputs pixel values are scaled between -1 and 1, sample-wise."""

 PREPROCESS_INPUT_RET_DOC_TORCH = """
      The input pixels values are scaled between 0 and 1 and each channel is
-      normalized with respect to the InageNet dataset."""
+      normalized with respect to the ImageNet dataset."""

 PREPROCESS_INPUT_RET_DOC_CAFFE = """
      The images are converted from RGB to BGR, then each color channel is
@ -97,9 +103,12 @@ PREPROCESS_INPUT_RET_DOC_CAFFE = """
@keras_export('keras.applications.imagenet_utils.preprocess_input')
 def preprocess_input(x, data_format=None, mode='caffe'):
  """Preprocesses a tensor or Numpy array encoding a batch of images."""
+  if mode not in {'caffe', 'tf', 'torch'}:
+    raise ValueError('Unknown mode ' + str(mode))
+
  if data_format is None:
    data_format = backend.image_data_format()
-  if data_format not in {'channels_first', 'channels_last'}:
+  elif data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('Unknown data_format ' + str(data_format))

  if isinstance(x, np.ndarray):
@ -111,7 +120,9 @@ def preprocess_input(x, data_format=None, mode='caffe'):


 preprocess_input.__doc__ = PREPROCESS_INPUT_DOC.format(
-    mode=PREPROCESS_INPUT_MODE_DOC, ret='')
+    mode=PREPROCESS_INPUT_MODE_DOC,
+    ret='',
+    error=PREPROCESS_INPUT_DEFAULT_ERROR_DOC)


@keras_export('keras.applications.imagenet_utils.decode_predictions')
@ -182,8 +193,7 @@ def _preprocess_numpy_input(x, data_format, mode):
    x /= 127.5
    x -= 1.
    return x
-
-  if mode == 'torch':
+  elif mode == 'torch':
    x /= 255.
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
@ -253,8 +263,7 @@ def _preprocess_symbolic_input(x, data_format, mode):
    x /= 127.5
    x -= 1.
    return x
-
-  if mode == 'torch':
+  elif mode == 'torch':
    x /= 255.
    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
@ -414,10 +423,10 @@ def validate_activation(classifier_activation, weights):
    return

  classifier_activation = activations.get(classifier_activation)
-  if classifier_activation not in [
+  if classifier_activation not in {
      activations.get('softmax'),
      activations.get(None)
-  ]:
+  }:
    raise ValueError('Only `None` and `softmax` activations are allowed '
                     'for the `classifier_activation` argument when using '
                     'pretrained weights, with `include_top=True`')
--- a/tensorflow/python/keras/applications/imagenet_utils_test.py
+++ b/tensorflow/python/keras/applications/imagenet_utils_test.py
@ -29,6 +29,11 @@ from tensorflow.python.platform import test
 class TestImageNetUtils(keras_parameterized.TestCase):

  def test_preprocess_input(self):
+    # Test invalid mode check
+    x = np.random.uniform(0, 255, (10, 10, 3))
+    with self.assertRaises(ValueError):
+      utils.preprocess_input(x, mode='some_unknown_mode')
+
    # Test image batch with float and int image input
    x = np.random.uniform(0, 255, (2, 10, 10, 3))
    xint = x.astype('int32')
--- a/tensorflow/python/keras/applications/inception_resnet_v2.py
+++ b/tensorflow/python/keras/applications/inception_resnet_v2.py
@ -389,5 +389,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/inception_v3.py
+++ b/tensorflow/python/keras/applications/inception_v3.py
@ -415,5 +415,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/mobilenet.py
+++ b/tensorflow/python/keras/applications/mobilenet.py
@ -451,5 +451,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/mobilenet_v2.py
+++ b/tensorflow/python/keras/applications/mobilenet_v2.py
@ -508,5 +508,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/nasnet.py
+++ b/tensorflow/python/keras/applications/nasnet.py
@ -794,5 +794,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/resnet.py
+++ b/tensorflow/python/keras/applications/resnet.py
@ -531,7 +531,9 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

 DOC = """
--- a/tensorflow/python/keras/applications/resnet_v2.py
+++ b/tensorflow/python/keras/applications/resnet_v2.py
@ -133,7 +133,9 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__

 DOC = """
--- a/tensorflow/python/keras/applications/vgg16.py
+++ b/tensorflow/python/keras/applications/vgg16.py
@ -237,5 +237,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/vgg19.py
+++ b/tensorflow/python/keras/applications/vgg19.py
@ -242,5 +242,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_CAFFE,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
--- a/tensorflow/python/keras/applications/xception.py
+++ b/tensorflow/python/keras/applications/xception.py
@ -325,5 +325,7 @@ def decode_predictions(preds, top=5):


 preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
-    mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF)
+    mode='',
+    ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
+    error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
 decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__