def preprocess_input(x, **kwargs):
kwargs = {
k: v
for k, v in kwargs.items()
if k in ['backend', 'layers', 'models', 'utils']
}
return _preprocess_input(x, mode='torch', **kwargs)
def preprocess_input(x):
"""
"mode" option description in preprocess_input
mode: One of "caffe", "tf" or "torch".
- caffe: will convert the images from RGB to BGR,
then will zero-center each color channel with
respect to the ImageNet dataset,
without scaling.
- tf: will scale pixels between -1 and 1,
sample-wise.
- torch: will scale pixels between 0 and 1 and then
will normalize each channel with respect to the
ImageNet dataset.
"""
x = _preprocess_input(x, mode='tf', backend=K)
#x /= 255.
#mean = [0.485, 0.456, 0.406]
#std = [0.229, 0.224, 0.225]
#x[..., 0] -= mean[0]
#x[..., 1] -= mean[1]
#x[..., 2] -= mean[2]
#if std is not None:
#x[..., 0] /= std[0]
#x[..., 1] /= std[1]
#x[..., 2] /= std[2]
return x
def preprocess_input(x, **kwargs):
kwargs = {
k: v
for k, v in kwargs.items()
if k in ["backend", "layers", "models", "utils"]
}
return _preprocess_input(x, mode="torch", **kwargs)
def preprocess_input(x, **kwargs):
"""Preprocesses a numpy array encoding a batch of images.
# Arguments
x: a 4D numpy array consists of RGB values within [0, 255].
# Returns
Preprocessed array.
"""
return _preprocess_input(x, mode='tf', backend=K, **kwargs)
def preprocess_input(x):
"""
"mode" option description in preprocess_input
mode: One of "caffe", "tf" or "torch".
- caffe: will convert the images from RGB to BGR,
then will zero-center each color channel with
respect to the ImageNet dataset,
without scaling.
- tf: will scale pixels between -1 and 1,
sample-wise.
- torch: will scale pixels between 0 and 1 and then
will normalize each channel with respect to the
ImageNet dataset.
"""
x = _preprocess_input(x, mode='tf', backend=K)
return x
def preprocess_input(x, **kwargs):
return _preprocess_input(x, mode='torch', **kwargs)