experiments/STT-tensorflow
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Minor refactoring of conditionals

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This commit is contained in:
“jaketae” 2020-03-09 18:49:32 +09:00
parent 28195763a0
commit aa070f5be6
1 changed files with 10 additions and 9 deletions
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19
tensorflow/python/keras/applications/imagenet_utils.py
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@ -66,11 +66,11 @@ PREPROCESS_INPUT_DOC = """
{ret} {ret}
Raises: Raises:
ValueError: In case of unknown `data_format` argument. ValueError: In case of unknown `mode` or `data_format` argument.
""" """
PREPROCESS_INPUT_MODE_DOC = """ 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, - caffe: will convert the images from RGB to BGR,
then will zero-center each color channel with then will zero-center each color channel with
respect to the ImageNet dataset, respect to the ImageNet dataset,
@ -97,9 +97,12 @@ PREPROCESS_INPUT_RET_DOC_CAFFE = """
@keras_export('keras.applications.imagenet_utils.preprocess_input') @keras_export('keras.applications.imagenet_utils.preprocess_input')
def preprocess_input(x, data_format=None, mode='caffe'): def preprocess_input(x, data_format=None, mode='caffe'):
"""Preprocesses a tensor or Numpy array encoding a batch of images.""" """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: if data_format is None:
data_format = backend.image_data_format() 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)) raise ValueError('Unknown data_format ' + str(data_format))
if isinstance(x, np.ndarray): if isinstance(x, np.ndarray):
@ -182,8 +185,7 @@ def _preprocess_numpy_input(x, data_format, mode):
x /= 127.5 x /= 127.5
x -= 1. x -= 1.
return x return x
elif mode == 'torch':
if mode == 'torch':
x /= 255. x /= 255.
mean = [0.485, 0.456, 0.406] mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225] std = [0.229, 0.224, 0.225]
@ -253,8 +255,7 @@ def _preprocess_symbolic_input(x, data_format, mode):
x /= 127.5 x /= 127.5
x -= 1. x -= 1.
return x return x
elif mode == 'torch':
if mode == 'torch':
x /= 255. x /= 255.
mean = [0.485, 0.456, 0.406] mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225] std = [0.229, 0.224, 0.225]
@ -414,10 +415,10 @@ def validate_activation(classifier_activation, weights):
return return
classifier_activation = activations.get(classifier_activation) classifier_activation = activations.get(classifier_activation)
if classifier_activation not in [ if classifier_activation not in {
activations.get('softmax'), activations.get('softmax'),
activations.get(None) activations.get(None)
]: }:
raise ValueError('Only `None` and `softmax` activations are allowed ' raise ValueError('Only `None` and `softmax` activations are allowed '
'for the `classifier_activation` argument when using ' 'for the `classifier_activation` argument when using '
'pretrained weights, with `include_top=True`') 'pretrained weights, with `include_top=True`')