See more details in :ref:`api_nn_vision_PixelShuffle` .
Parameters:
x(Tensor): 4-D tensor, the data type should be float32 or float64.
upscale_factor(int): factor to increase spatial resolution.
data_format (str): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
name (str, optional): The default value is None. Normally there is no need for user to set this property.
Returns:
Out(tensor): Reshaped tensor according to the new dimension.
Raises:
ValueError: If the square of upscale_factor cannot divide the channels of input.
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
x = np.random.randn(2, 9, 4, 4).astype(np.float32)
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: define specitial functions used in computer vision task
from...fluid.dygraphimportlayers
from..importfunctional
__all__=['PixelShuffle']
classPixelShuffle(layers.Layer):
"""
PixelShuffle Layer
This operator rearranges elements in a tensor of shape [N, C, H, W]
to a tensor of shape [N, C/upscale_factor**2, H*upscale_factor, W*upscale_factor],
or from shape [N, H, W, C] to [N, H*upscale_factor, W*upscale_factor, C/upscale_factor**2].
This is useful for implementing efficient sub-pixel convolution
with a stride of 1/upscale_factor.
Please refer to the paper: `Real-Time Single Image and Video Super-Resolution
Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/abs/1609.05158v2>`_ .
by Shi et. al (2016) for more details.
Parameters:
upscale_factor(int): factor to increase spatial resolution.
data_format (str): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width].
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Shape:
- x: 4-D tensor with shape: (N, C, H, W) or (N, H, W, C).
- out: 4-D tensor with shape: (N, C/upscale_factor**2, H*upscale_factor, W*upscale_factor) or (N, H*upscale_factor, W*upscale_factor, C/upscale_factor^2).
Examples:
.. code-block:: python
import paddle
import paddle.nn as nn
import numpy as np
paddle.disable_static()
x = np.random.randn(2, 9, 4, 4).astype(np.float32)
