Python mxnet.ndarray.swapaxes() Examples

The following are 17 code examples of mxnet.ndarray.swapaxes(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module mxnet.ndarray , or try the search function .
Example #1
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def preprocess_batch(batch):
    batch = F.swapaxes(batch, 0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch, 0, 1)
    return batch
Example #2
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def preprocess_batch(batch):
    batch = F.swapaxes(batch, 0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch, 0, 1)
    return batch
Example #3
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #4
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #5
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #6
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def preprocess_batch(batch):
    batch = F.swapaxes(batch, 0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch, 0, 1)
    return batch
Example #7
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #8
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #9
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #10
Source File: utils.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #11
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #12
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #13
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #14
Source File: tensor.py    From dgl with Apache License 2.0 5 votes vote down vote up 
def swapaxes(input, axis1, axis2):
    return nd.swapaxes(input, axis1, axis2)
Example #15
Source File: utils.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def preprocess_batch(batch):
    batch = F.swapaxes(batch, 0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch, 0, 1)
    return batch
Example #16
Source File: utils.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #17
Source File: utils.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch