# 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 the common classes to build a neural network from ...fluid.dygraph import BilinearTensorProduct #DEFINE_ALIAS from ...fluid.dygraph import Pool2D #DEFINE_ALIAS from ...fluid.dygraph import Embedding #DEFINE_ALIAS from ...fluid.dygraph import Linear #DEFINE_ALIAS from ...fluid.dygraph import Flatten #DEFINE_ALIAS from ...fluid.dygraph import layers from .. import functional as F __all__ = [ 'BilinearTensorProduct', 'Pool2D', 'Embedding', 'Linear', 'UpSample', 'Pad2D' ] class UpSample(layers.Layer): """ This op resizes a batch of images. The input must be a 3-D Tensor of the shape (num_batches, channels, in_w) or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape (num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels), and the resizing only applies on the three dimensions(depth, height and width). **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Supporting resample methods: 'linear' : Linear interpolation 'bilinear' : Bilinear interpolation 'trilinear' : Trilinear interpolation 'nearest' : Nearest neighbor interpolation 'bicubic' : Bicubic interpolation Linear interpolation is the method of using a line connecting two known quantities to determine the value of an unknown quantity between the two known quantities. Nearest neighbor interpolation is to perform nearest neighbor interpolation in both the 3rd dimension(in height direction) and the 4th dimension(in width direction) on input tensor. Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. Bicubic interpolation is an extension of cubic interpolation for interpolating data points on a two-dimensional regular grid. The interpolated surface is smoother than corresponding surfaces obtained by bilinear interpolation or nearest-neighbor interpolation. Trilinear interpolation is an extension of linear interpolation for interpolating functions of three variables (e.g. D-direction, H-direction and W-direction in this op) on a rectilinear 3D grid. The linear interpolation is performed on three directions. Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them. Example: .. code-block:: text For scale_factor: if align_corners = True && out_size > 1 : scale_factor = (in_size-1.0)/(out_size-1.0) else: scale_factor = float(in_size/out_size) Linear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,W_in) output: (N,C,W_out) where: W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,W_in) output: (N,C,W_out) where: W_out = W_{in} * scale_{factor} Nearest neighbor interpolation: if: align_corners = False input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = floor (H_{in} * scale_{factor}) W_out = floor (W_{in} * scale_{factor}) else: align_corners = True input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = round(H_{in} * scale_{factor}) W_out = round(W_{in} * scale_{factor}) Bilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Bicubic interpolation: if: align_corners = False input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,H_in,W_in) output: (N,C,H_out,W_out) where: H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} Trilinear interpolation: if: align_corners = False , align_mode = 0 input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = (D_{in}+0.5) * scale_{factor} - 0.5 H_out = (H_{in}+0.5) * scale_{factor} - 0.5 W_out = (W_{in}+0.5) * scale_{factor} - 0.5 else: input : (N,C,D_in,H_in,W_in) output: (N,C,D_out,H_out,W_out) where: D_out = D_{in} * scale_{factor} H_out = H_{in} * scale_{factor} W_out = W_{in} * scale_{factor} https://en.wikipedia.org/wiki/Linear_interpolation. For details of linear interpolation, please refer to Wikipedia: For details of nearest neighbor interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation. For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation. For details of bicubic interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bicubic_interpolation For details of trilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Trilinear_interpolation. Parameters: input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. size (list|tuple|Variable|None): Output shape of image resize layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w) when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1]. If a Tensor Variable, its dimensions size should be a 1. scale_factor (float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale_factor` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale_factor`. Default: None. mode (str): The resample method. It supports 'linear', 'nearst', 'bilinear', 'bicubic' and 'trilinear' currently. Default: 'nearest' align_corners(bool) : An optional bool, If True, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels. Default: False align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the formula in the example above, it can be \'0\' for src_idx = scale_factor*(dst_indx+0.5)-0.5 , can be \'1\' for src_idx = scale_factor*dst_index. data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`, `"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels), A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels), or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). Raises: TypeError: size should be a list or tuple or Variable. ValueError: The 'mode' of image_resize can only be 'linear', 'bilinear', 'trilinear', 'bicubic', or 'nearest' currently. ValueError: 'linear' only support 3-D tensor. ValueError: 'bilinear', 'bicubic' and 'nearest' only support 4-D tensor. ValueError: 'trilinear' only support 5-D tensor. ValueError: One of size and scale_factor must not be None. ValueError: size length should be 1 for input 3-D tensor. ValueError: size length should be 2 for input 4-D tensor. ValueError: size length should be 3 for input 5-D tensor. ValueError: scale_factor should be greater than zero. TypeError: align_corners should be a bool value ValueError: align_mode can only be '0' or '1' ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'. Examples: .. code-block:: python import paddle import numpy as np import paddle.fluid.dygraph as dg upsample_op = paddle.nn.UpSample(size=[12,12]) input_data = np.random.rand(2,3,6,10).astype("float32") place = paddle.fluid.CPUPlace() with dg.guard(place) as g: input = dg.to_variable(input_data) output = upsample_op(input=input) print(output.shape) # [2L, 3L, 12L, 12L] """ def __init__(self, size=None, scale_factor=None, mode='nearest', align_corners=False, align_mode=1, data_format='NCHW'): super(UpSample, self).__init__() self.size = size self.scale_factor = scale_factor self.mode = mode.lower() self.align_corners = align_corners self.align_mode = align_mode self.data_format = data_format def forward(self, input): out = F.interpolate( input, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners, align_mode=self.align_mode, data_format=self.data_format) return out class Pad2D(layers.Layer): """ :alias_main: paddle.nn.Pad2D :alias: paddle.nn.Pad2D,paddle.nn.layer.Pad2D,paddle.nn.layer.common.Pad2D This interface is used to construct a callable object of the ``Pad2D`` class. The Pad2D layer pads the input tensor boundaries according to 'paddings' and 'mode'. If mode is 'reflect', paddings[0] and paddings[1] must be no greater than height-1. And the width dimension has the same condition. Parameters: paddings (int | List[int32]): The padding size. If padding is a int, uses the same padding in all boundaries, if padding is a List, it must contain four integers, (padding_top, padding_bottom, padding_left, padding_right). Default is [0, 0, 0, 0]. mode (str): Three modes: 'constant' (default), 'reflect', 'edge' . When in 'constant' mode, this op uses a constant value to pad the input tensor. When in 'reflect' mode, uses reflection of the input boundaries to pad the input tensor. When in 'edge' mode, uses input boundaries to pad the input tensor. Default is 'constant' pad_value (float32): The value to fill the padded areas in 'constant' mode . Default is 0.0 data_format (str): An string from: "NHWC", "NCHW". Specify the data format of the input data. Default is "NCHW" Returns: None Examples: .. code-block:: text Input = [[[[1., 2., 3.], [4., 5., 6.]]]] Case 0: paddings = [0, 1, 2, 3], mode = 'constant' pad_value = 0 Out = [[[[0., 0., 1., 2., 3., 0., 0., 0.], [0., 0., 4., 5., 6., 0., 0., 0.], [0., 0., 0., 0., 0., 0., 0., 0.]]]] Case 1: paddings = [0, 1, 2, 1], mode = 'reflect' Out = [[[[3., 2., 1., 2., 3., 2.], [6., 5., 4., 5., 6., 5.], [3., 2., 1., 2., 3., 2.]]]] Case 2: paddings = [0, 1, 2, 1], mode = 'edge' Out = [[[[1., 1., 1., 2., 3., 3.], [4., 4., 4., 5., 6., 6.], [4., 4., 4., 5., 6., 6.]]]] Code Examples: .. code-block:: python import paddle.fluid as fluid import paddle.nn as nn import numpy as np data = np.ones((2, 2, 2, 2)).astype('float32') my_pad = nn.Pad2D(paddings=[1, 1, 1, 1]) with fluid.dygraph.guard(): data = fluid.dygraph.to_variable(data) result = my_pad(data) """ def __init__(self, paddings=0, mode='constant', pad_value=0.0, data_format="NCHW"): super(Pad2D, self).__init__() self._mode = mode self._pad_value = pad_value self._data_format = data_format self._paddings = [paddings] * 4 if isinstance(paddings, int) else paddings def forward(self, input): return F.pad2d( input, paddings=self._paddings, mode=self._mode, pad_value=self._pad_value, data_format=self._data_format)