experiments/STT-tensorflow
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Edited docstrings, added unit test, and fixed minor typos

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This commit is contained in:
“jaketae” 2020-03-11 08:29:05 +09:00
parent f673a86e1c
commit db1e40b9c5
12 changed files with 47 additions and 13 deletions
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4
tensorflow/python/keras/applications/densenet.py
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@ -393,7 +393,9 @@ 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 = """

16
tensorflow/python/keras/applications/imagenet_utils.py
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@ -66,7 +66,7 @@ PREPROCESS_INPUT_DOC = """
{ret}
Raises:
ValueError: In case of unknown `mode` or `data_format` argument.
{error}
"""
PREPROCESS_INPUT_MODE_DOC = """
@ -82,12 +82,21 @@ 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
@ -114,7 +123,8 @@ 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')

5
tensorflow/python/keras/applications/imagenet_utils_test.py
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@ -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')

4
tensorflow/python/keras/applications/inception_resnet_v2.py
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@ -412,5 +412,7 @@ 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__

4
tensorflow/python/keras/applications/inception_v3.py
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@ -440,5 +440,7 @@ 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__

4
tensorflow/python/keras/applications/mobilenet.py
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@ -473,5 +473,7 @@ 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__

4
tensorflow/python/keras/applications/mobilenet_v2.py
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@ -506,5 +506,7 @@ 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__

4
tensorflow/python/keras/applications/nasnet.py
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@ -819,5 +819,7 @@ 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__

4
tensorflow/python/keras/applications/resnet.py
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@ -553,7 +553,9 @@ 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 = """

4
tensorflow/python/keras/applications/resnet_v2.py
View File

@ -164,7 +164,9 @@ 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 = """

4
tensorflow/python/keras/applications/vgg16.py
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@ -262,5 +262,7 @@ 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__

3
tensorflow/python/keras/applications/vgg19.py
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@ -267,5 +267,6 @@ 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__