# 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. from __future__ import annotations import math import warnings from typing import TYPE_CHECKING, Literal import numpy import paddle from paddle import _C_ops, pir from paddle.base.layer_helper import LayerHelper from paddle.common_ops_import import Variable, default_main_program from paddle.framework import ( core, in_dynamic_mode, in_dynamic_or_pir_mode, in_pir_mode, ) from paddle.tensor.creation import full from paddle.utils import deprecated from paddle.utils.decorator_utils import ParamAliasDecorator from paddle.utils.layers_utils import NotSupportedTensorArgumentError from ...base.data_feeder import ( check_dtype, check_type, check_variable_and_dtype, ) from ...tensor import clip, concat, sqrt, sum from ...tensor.creation import zeros # TODO: define the common functions to build a neural network from ...tensor.manipulation import squeeze, unsqueeze if TYPE_CHECKING: from collections.abc import Sequence from typing_extensions import TypeAlias from paddle import Tensor from paddle._typing import ( DataLayout1DVariant, DataLayout2D, DataLayout3D, DataLayoutND, ShapeLike, Size2, Size4, ) from paddle.distributed.communication.group import Group _InterpolateMode: TypeAlias = Literal[ 'linear', 'area', 'nearest', 'bilinear', 'bicubic', 'trilinear' ] _DropoutMode: TypeAlias = Literal['upscale_in_train', 'downscale_in_infer'] _PaddingTensorMode: TypeAlias = Literal[ "zeros", "constant", "reflect", "replicate", "circular" ] _PaddingSizeMode: TypeAlias = Literal[ # noqa: PYI047 'valid', 'same', 'VALID', 'SAME' ] __all__ = [] def unfold( x: Tensor, kernel_sizes: Size2, strides: Size2 = 1, paddings: Size2 | Size4 = 0, dilations: Size2 = 1, name: str | None = None, ) -> Tensor: r""" Return a col buffer of sliding local blocks of input x, also known as im2col for batched 2D image tensors. For each block under the convolution filter, all element will be rearranged as a column. While the convolution filter sliding over the input feature map, a series of such columns will be formed. For each input :math:`x` with shape [N, C, H, W], the output shape [N, Cout, Lout] can be calculated as following. .. math:: dkernel[0] &= dilations[0] \times (kernel\_sizes[0] - 1) + 1 dkernel[1] &= dilations[1] \times (kernel\_sizes[1] - 1) + 1 hout &= \frac{H + paddings[0] + paddings[2] - dkernel[0]}{strides[0]} + 1 wout &= \frac{W + paddings[1] + paddings[3] - dkernel[1]}{strides[1]} + 1 Cout &= C \times kernel\_sizes[0] \times kernel\_sizes[1] Lout &= hout \times wout Parameters: x(Tensor): 4-D Tensor, input tensor of format [N, C, H, W], data type can be float32 or float64 kernel_sizes(int|list|tuple): The size of convolution kernel, should be [k_h, k_w] or an integer k treated as [k, k]. strides(int|list|tuple, optional): The strides, should be [stride_h, stride_w] or an integer stride treated as [stride, stride]. For default, strides will be [1, 1]. paddings(int|list|tuple, optional): The paddings of each dimension, should be [padding_top, padding_left, padding_bottom, padding_right] or [padding_h, padding_w] or an integer padding. If [padding_h, padding_w] was given, it will expanded to [padding_h, padding_w, padding_h, padding_w]. If an integer padding was given, [padding, padding, padding, padding] will be used. For default, paddings will be [0, 0, 0, 0] dilations(int|list|tuple, optional): the dilations of convolution kernel, should be [dilation_h, dilation_w], or an integer dilation treated as [dilation, dilation]. For default, it will be [1, 1]. 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: Tensor, The tensor corresponding to the sliding local blocks. The output shape is [N, Cout, Lout] as described above. Cout is the total number of values within each block, and Lout is the total number of such blocks. The data type of output is the same as the input :math:`x` Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> x = paddle.randn((100,3,224,224)) >>> y = F.unfold(x, [3, 3], 1, 1, 1) """ helper = LayerHelper("unfold", **locals()) check_variable_and_dtype( x, 'x', ['uint16', 'float16', 'float32', 'float64'], 'unfold' ) assert len(x.shape) == 4, "input should be the format of [N, C, H, W]" if isinstance(kernel_sizes, int): kernel_sizes = [kernel_sizes, kernel_sizes] else: if not ( isinstance(kernel_sizes, (list, tuple)) and (len(kernel_sizes) == 2) ): raise NotSupportedTensorArgumentError( "kernel_sizes should either be an integer or a list/tuple of two integers", "kernel_sizes", ) kernel_sizes = list(kernel_sizes) if isinstance(strides, int): strides = [strides, strides] else: if not (isinstance(strides, (list, tuple)) and (len(strides) == 2)): raise NotSupportedTensorArgumentError( "strides should either be an integer or a list/tuple of two integers", "strides", ) strides = list(strides) if isinstance(dilations, int): dilations = [dilations, dilations] else: if not (isinstance(dilations, (list, tuple)) and (len(dilations) == 2)): raise NotSupportedTensorArgumentError( "dilations should either be an integer or a list/tuple of two integers", "dilations", ) dilations = list(dilations) if isinstance(paddings, int): paddings = [paddings] * 4 elif isinstance(paddings, (list, tuple)): paddings = list(paddings) if len(paddings) == 2: paddings = paddings * 2 elif len(paddings) == 4: pass else: raise ValueError( "paddings should either be an integer or a list/tuple of 2 or 4 integers" ) else: raise NotSupportedTensorArgumentError( "Unexpected type of paddings, it should be either an integer or a list/tuple " "of 2 or 4 integers", "paddings", ) if in_dynamic_or_pir_mode(): return _C_ops.unfold(x, kernel_sizes, strides, paddings, dilations) out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="unfold", inputs={"X": x}, outputs={"Y": out}, attrs={ "kernel_sizes": kernel_sizes, "strides": strides, "paddings": paddings, "dilations": dilations, }, ) return out def interpolate( x: Tensor, size: ShapeLike | None = None, scale_factor: ShapeLike | float | None = None, mode: _InterpolateMode = 'nearest', align_corners: bool = False, align_mode: int = 0, data_format: ( DataLayout1DVariant | DataLayout2D | DataLayout3D | None ) = None, recompute_scale_factor: bool | None = None, name: str | None = None, ) -> Tensor: """ This API resizes a batch of images. The input must be a 3-D Tensor of the shape (num_batches, channels, in_w) or (num_batches, in_w, channels), or 4-D (num_batches, channels, in_h, in_w) or (num_batches, in_h, in_w, channels), 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), Where in_w is width of the input tensor, in_h is the height of the input tensor, in_d is the depth of the input tensor. and the resizing only applies on the three dimensions(depth, height and width). Supporting resample methods: - 'linear' : Linear interpolation - 'bilinear' : Bilinear interpolation - 'trilinear' : Trilinear interpolation - 'nearest' : Nearest neighbor interpolation - 'bicubic' : Bicubic interpolation - 'area': Area 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. 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. 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. Area interpolation is to perform area interpolation in both the 3rd dimension(in height direction) , the 4th dimension(in width direction) and the 5th dimension(in depth direction) on input tensor. Set to area will directly call `paddle.nn.functional.adaptive_avg_pool1d` or `paddle.nn.functional.adaptive_avg_pool2d` or `paddle.nn.functional.adaptive_avg_pool3d`. 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: 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}) # 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} For details of linear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Linear_interpolation. 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 trilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Trilinear_interpolation. For details of bicubic interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bicubic_interpolation Parameters: x (Tensor): 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`. If :attr:`data_format` is not provided, the data format will be presumed according to its dimension. See details in :attr:`data_format`. size (list|tuple|Tensor|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/tuple, each element can be an integer or a Tensor of shape: [1] or []. If a Tensor, its dimensions size should be a 1. scale_factor (float|Tensor|list|tuple|None): The multiplier for the input height or width. At least one of :attr:`size` or :attr:`scale_factor` must be set. And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if it is either a list or a tuple or a Tensor. If a list/tuple, each element can be an integer or a Tensor of shape: [1] or []. Default: None. mode (str): The resample method. It supports 'linear', 'area', 'nearest', '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.This only has an effect when 'linear', 'bilinear', 'bicubic' or 'trilinear'. 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_index+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 value is None. When :attr:`data_format` is not specified, it will be automatically inferred from the input dimension of :attr:`x`. When :attr:`x` is a 3-D Tensor, :attr:`data_format` will be set to `"NCW"`; When :attr:`x` is a 4-D Tensor, :attr:`data_format` will be set to `"NCHW"`; When :attr:`x` is a 5-D Tensor, :attr:`data_format` will be set to `"NCDHW"`. When it is `"NCHW"`, the data should be stored in the order of: `[batch_size, input_channels, input_height, input_width]`. When it is `"NCDHW"`, the data should be stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. recompute_scale_factor (bool, optional): Whether to recompute the scaling factor for interpolation calculation. When set to `True`, the `scale_factor` parameter must be provided, and the function will use it along with the input tensor shape to calculate the output tensor shape, then recalculate the scaling factor based on the output and input tensor shapes. This parameter is particularly useful when `scale_factor` is a floating-point value. When set to `False`, either `size` or `scale_factor` will be used directly for interpolation without recalculation. Default: None. 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, 4-D or 5-D Tensor, with the same data format of the input :attr:`x`. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32) >>> output_1 = F.interpolate(x=input_data, size=[12,12]) >>> print(output_1.shape) [2, 3, 12, 12] >>> # given scale >>> output_2 = F.interpolate(x=input_data, scale_factor=[2,1]) >>> print(output_2.shape) [2, 3, 12, 10] >>> # bilinear interp >>> output_3 = F.interpolate(x=input_data, scale_factor=[2,1], mode="bilinear") >>> print(output_2.shape) [2, 3, 12, 10] """ if data_format is None: dim_size = len(x.shape) if dim_size == 3: data_format = 'NCW' elif dim_size == 4: data_format = 'NCHW' elif dim_size == 5: data_format = 'NCDHW' else: raise ValueError( f"The dimension of the input tensor should only be 3-D, 4-D or 5-D, but the received dimension is {dim_size}." ) data_format = data_format.upper() resample = mode.upper() resample_type = mode.lower() resample_methods = [ 'LINEAR', 'BILINEAR', 'TRILINEAR', 'NEAREST', 'BICUBIC', 'AREA', ] if resample not in resample_methods: raise ValueError( "The 'resample' of image_resize can only be 'area', 'linear', 'bilinear', 'trilinear', " " 'bicubic' or 'nearest' currently." ) if resample in ['LINEAR'] and len(x.shape) != 3: raise ValueError("'linear' only support 3-D tensor.") if resample in ['NEAREST'] and len(x.shape) != 4 and len(x.shape) != 5: raise ValueError("'NEAREST' only support 4-D or 5-D tensor.") if resample in ['BILINEAR', 'BICUBIC'] and len(x.shape) != 4: raise ValueError("'bilinear' and 'bicubic' only support 4-D tensor.") if resample == 'TRILINEAR' and len(x.shape) != 5: raise ValueError("'trilinear'only support 5-D tensor.") if size is None and scale_factor is None: raise ValueError("One of size and scale_factor must not be None.") if isinstance(size, (tuple, list)) and (len(size) != x.ndim - 2): raise ValueError( 'The x and size should satisfy rank(x) - 2 == len(size).' ) if isinstance(size, (Variable, paddle.pir.Value)): size = size.cast("int32") # static mode only support int32 if size.ndim != 1: raise ValueError( f"If size is a tensor, it's rank must be 1, but received {size.ndim}." ) if size.shape[0] != x.ndim - 2: raise ValueError( 'The x and size should satisfy rank(x) - 2 == size.shape[0].' ) if not isinstance(align_corners, bool): raise TypeError("Attr align_corners should be a bool value") if align_mode != 0 and align_mode != 1: raise ValueError("align_mode can only be 0 or 1") if align_corners != 0 and resample == 'NEAREST': raise ValueError( "align_corners option can only be set with the interpolating modes: linear | bilinear | bicubic | trilinear" ) if resample == 'AREA': if isinstance(size, (list, tuple, Variable, paddle.pir.Value)): if len(size) == 0: raise ValueError("output size can not be empty") if size is None: raise ValueError("output size can not be None in AREA mode") if len(x.shape) == 3: return paddle.nn.functional.adaptive_avg_pool1d(x, size) elif len(x.shape) == 4: return paddle.nn.functional.adaptive_avg_pool2d(x, size) elif len(x.shape) == 5: return paddle.nn.functional.adaptive_avg_pool3d(x, size) helper = LayerHelper(f'{resample_type}_interp_v2', **locals()) if len(x.shape) == 3 and data_format not in ['NCW', 'NWC']: raise ValueError( "Got wrong value for param `data_format`: " + data_format + " received but only `NCW` or `NWC` supported for 3-D input." ) elif len(x.shape) == 4 and data_format not in ['NCHW', 'NHWC']: raise ValueError( "Got wrong value for param `data_format`: " + data_format + " received but only `NCHW` or `NHWC` supported for 4-D input." ) elif len(x.shape) == 5 and data_format not in ['NCDHW', 'NDHWC']: raise ValueError( "Got wrong value for param `data_format`: " + data_format + " received but only `NCDHW` or `NDHWC` supported for 5-D input." ) def _is_list_or_tuple_(data): return isinstance(data, (list, tuple)) if data_format == 'NCHW' or data_format == 'NCDHW' or data_format == 'NCW': data_layout = 'NCHW' if data_format == 'NHWC' or data_format == 'NDHWC' or data_format == 'NWC': data_layout = 'NHWC' if resample == 'NEAREST': align_corners = False inputs = {"X": x} attrs = { "out_d": -1, "out_h": -1, "out_w": -1, "interp_method": resample_type, "align_corners": align_corners, "align_mode": align_mode, "data_layout": data_layout, } out_shape = size scale = scale_factor if out_shape is not None and scale is not None: raise ValueError("Only one of size or scale_factor should be defined.") if out_shape is not None: if recompute_scale_factor: raise ValueError( "recompute_scale_factor is not meaningful with an explicit size." ) if ( isinstance(out_shape, (Variable, paddle.pir.Value)) and not in_dynamic_mode() ): out_shape.stop_gradient = True inputs['OutSize'] = out_shape else: if in_dynamic_mode(): if isinstance(out_shape, Variable): out_shape = list(out_shape.numpy(False)) else: out_shape = list(out_shape) for i, dim in enumerate(out_shape): if isinstance(dim, Variable): out_shape[i] = dim.item() if not (_is_list_or_tuple_(out_shape)): raise TypeError("size should be a list or tuple or Variable.") # Validate the shape contain_var = False for dim_idx, dim_size in enumerate(out_shape): if isinstance(dim_size, (Variable, paddle.pir.Value)): contain_var = True continue assert dim_size > 0, ( "Each dimension size given in out_shape must be greater than 0." ) if contain_var: new_size_tensor = [] size_list = [] for dim in out_shape: if isinstance(dim, (Variable, paddle.pir.Value)): dim.stop_gradient = True new_size_tensor.append(dim) size_list.append(-1) else: assert isinstance(dim, int) if in_pir_mode(): temp_out = paddle.tensor.fill_constant( [1], 'int32', dim, force_cpu=True ) else: temp_out = ( helper.create_variable_for_type_inference( 'int32' ) ) paddle.tensor.fill_constant( [1], 'int32', dim, force_cpu=True, out=temp_out ) new_size_tensor.append(temp_out) size_list.append(dim) inputs['SizeTensor'] = new_size_tensor if len(x.shape) == 3: if len(out_shape) != 1: raise ValueError( "size length should be 2 for input 3-D tensor" ) if contain_var: attrs['out_w'] = size_list[0] else: out_shape = list(map(int, out_shape)) attrs['out_w'] = out_shape[0] if len(x.shape) == 4: if len(out_shape) != 2: raise ValueError( "size length should be 2 for input 4-D tensor." ) if contain_var: attrs['out_h'] = size_list[0] attrs['out_w'] = size_list[1] else: out_shape = list(map(int, out_shape)) attrs['out_h'] = out_shape[0] attrs['out_w'] = out_shape[1] if len(x.shape) == 5: if len(out_shape) != 3: raise ValueError( "size length should be 3 for input 5-D tensor." ) if contain_var: attrs['out_d'] = size_list[0] attrs['out_h'] = size_list[1] attrs['out_w'] = size_list[2] else: out_shape = list(map(int, out_shape)) attrs['out_d'] = out_shape[0] attrs['out_h'] = out_shape[1] attrs['out_w'] = out_shape[2] elif scale is not None: # scale in python is float64, but in kernel is float32, so we need to recalculate the scale in float32 # Currently it is only used when x.size is 0. x_shape = x.shape if data_format == 'NCW': max_dim = x_shape[2] elif data_format == 'NWC': max_dim = x_shape[1] elif data_format == 'NCHW': max_dim = max(x.shape[2], x.shape[3]) elif data_format == 'NHWC': max_dim = max(x.shape[1], x.shape[2]) elif data_format == 'NCDHW': max_dim = max(x.shape[2], x.shape[3], x.shape[4]) elif data_format == 'NDHWC': max_dim = max(x.shape[1], x.shape[2], x.shape[3]) else: max_dim = 1 def _scale_to_float32(value): if len(str(value)) <= 10: return value # round down return numpy.float32(int(value * max_dim) / max_dim) if recompute_scale_factor: if in_dynamic_mode() and isinstance(scale, Variable): if scale.shape == []: scale = float(scale) else: scale = list(scale.numpy()) dim = len(x.shape) - 2 if isinstance(scale, (float, int, numpy.ndarray)): scale_list = [float(scale)] * dim elif isinstance(scale, (list, tuple)): if len(scale) != dim: raise ValueError( f"scale_shape length should be {dim} for " f"input {len(x.shape)}-D tensor." ) scale_list = list(map(float, scale)) else: raise TypeError( "Attr(scale)'s type should be float, int, list, tuple, or Tensor." ) out_shape = [] for i in range(dim): input_size = x.shape[i + 2] output_size = int( numpy.floor(float(input_size) * scale_list[i]) ) out_shape.append(output_size) if len(x.shape) == 3: attrs['out_w'] = out_shape[0] elif len(x.shape) == 4: attrs['out_h'] = out_shape[0] attrs['out_w'] = out_shape[1] elif len(x.shape) == 5: attrs['out_d'] = out_shape[0] attrs['out_h'] = out_shape[1] attrs['out_w'] = out_shape[2] scale = None else: dynamic_mode = False if in_dynamic_mode(): dynamic_mode = True if dynamic_mode and isinstance(scale, Variable): if scale.shape == []: scale = float(scale) else: scale = list(scale.numpy()) if isinstance(scale, (Variable, paddle.pir.Value)): scale.stop_gradient = True inputs["Scale"] = scale elif isinstance(scale, (float, int, numpy.ndarray)): if scale <= 0: raise ValueError("Attr(scale) should be greater than zero.") scale_list = [] for i in range(len(x.shape) - 2): scale_list.append(scale) if dynamic_mode and x.size == 0: attrs['scale'] = list(map(_scale_to_float32, scale_list)) else: attrs['scale'] = list(map(float, scale_list)) elif isinstance(scale, (list, tuple)): if len(scale) != len(x.shape) - 2: raise ValueError( f"scale_shape length should be {len(x.shape) - 2} for " f"input {len(x.shape)}-D tensor." ) for value in scale: if value <= 0: raise ValueError( "Attr(scale) should be greater than zero." ) if dynamic_mode and x.size == 0: attrs['scale'] = list(map(_scale_to_float32, scale)) else: attrs['scale'] = list(map(float, scale)) else: raise TypeError( "Attr(scale)'s type should be float, int, list, tuple, or Tensor." ) if in_dynamic_or_pir_mode(): attr_list = [] for k, v in attrs.items(): attr_list.append(k) attr_list.append(v) dy_attr = tuple(attr_list) if resample_type == "linear": out = _C_ops.linear_interp( x, inputs['OutSize'] if 'OutSize' in inputs else None, inputs['SizeTensor'] if 'SizeTensor' in inputs else None, inputs['Scale'] if 'Scale' in inputs else None, attrs['data_layout'], attrs['out_d'], attrs['out_h'], attrs['out_w'], attrs['scale'] if 'scale' in attrs else [], attrs['interp_method'], attrs['align_corners'], attrs['align_mode'], ) elif resample_type == "bilinear": out = _C_ops.bilinear_interp( x, inputs['OutSize'] if 'OutSize' in inputs else None, inputs['SizeTensor'] if 'SizeTensor' in inputs else None, inputs['Scale'] if 'Scale' in inputs else None, attrs['data_layout'], attrs['out_d'], attrs['out_h'], attrs['out_w'], attrs['scale'] if 'scale' in attrs else [], attrs['interp_method'], attrs['align_corners'], attrs['align_mode'], ) elif resample_type == "trilinear": out = _C_ops.trilinear_interp( x, inputs['OutSize'] if 'OutSize' in inputs else None, inputs['SizeTensor'] if 'SizeTensor' in inputs else None, inputs['Scale'] if 'Scale' in inputs else None, attrs['data_layout'], attrs['out_d'], attrs['out_h'], attrs['out_w'], attrs['scale'] if 'scale' in attrs else [], attrs['interp_method'], attrs['align_corners'], attrs['align_mode'], ) elif resample_type == "nearest": out = _C_ops.nearest_interp( x, inputs['OutSize'] if 'OutSize' in inputs else None, inputs['SizeTensor'] if 'SizeTensor' in inputs else None, inputs['Scale'] if 'Scale' in inputs else None, attrs['data_layout'], attrs['out_d'], attrs['out_h'], attrs['out_w'], attrs['scale'] if 'scale' in attrs else [], attrs['interp_method'], attrs['align_corners'], attrs['align_mode'], ) elif resample_type == "bicubic": out = _C_ops.bicubic_interp( x, inputs['OutSize'] if 'OutSize' in inputs else None, inputs['SizeTensor'] if 'SizeTensor' in inputs else None, inputs['Scale'] if 'Scale' in inputs else None, attrs['data_layout'], attrs['out_d'], attrs['out_h'], attrs['out_w'], attrs['scale'] if 'scale' in attrs else [], attrs['interp_method'], attrs['align_corners'], attrs['align_mode'], ) return out dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) helper.append_op( type=f'{resample_type}_interp_v2', inputs=inputs, outputs={"Out": out}, attrs=attrs, ) return out def upsample( x: Tensor, size: ShapeLike | None = None, scale_factor: ShapeLike | None = None, mode: _InterpolateMode = 'nearest', align_corners: bool = False, align_mode: int = 0, data_format: ( DataLayout1DVariant | DataLayout2D | DataLayout3D | None ) = None, name: str | None = None, ) -> Tensor: """ This API resizes a batch of images. The input must be a 3-D Tensor of the shape (num_batches, channels, in_w) or (num_batches, in_w, channels), or 4-D (num_batches, channels, in_h, in_w) or (num_batches, in_h, in_w, channels), 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), Where in_w is width of the input tensor, in_h is the height of the input tensor, in_d is the depth of the input tensor. and the resizing only applies on the three dimensions(depth, height and width). Supporting resample methods: - 'linear' : Linear interpolation - 'bilinear' : Bilinear interpolation - 'trilinear' : Trilinear interpolation - 'nearest' : Nearest neighbor interpolation - 'bicubic' : Bicubic interpolation - 'area': Area 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. Area interpolation is to perform area interpolation in both the 3rd dimension(in height direction) , the 4th dimension(in width direction) and the 5th dimension(in depth direction) on input tensor. Set to area will directly call `paddle.nn.functional.adaptive_avg_pool1d` or `paddle.nn.functional.adaptive_avg_pool2d` or `paddle.nn.functional.adaptive_avg_pool3d`. 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} For details of linear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Linear_interpolation. 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: x (Tensor): 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`. If :attr:`data_format` is not provided, the data format will be presumed according to its dimension. See details in :attr:`data_format`. size (list|tuple|Tensor|None, optional): 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/tuple, each element can be an integer or a Tensor of shape: [1] or []. If a Tensor , its dimensions size should be a 1. scale_factor (float|Tensor|list|tuple|None, optional): The multiplier for the input height or width. At least one of :attr:`size` or :attr:`scale_factor` must be set. And :attr:`size` has a higher priority than :attr:`scale_factor`.Has to match input size if it is either a list or a tuple or a Tensor. If a list/tuple, each element can be an integer or a Tensor of shape: [1] or []. Default: None. mode (str, optional): The resample method. It supports 'linear', 'nearest', 'bilinear', 'area', 'bicubic' and 'trilinear' currently. Default: 'nearest' align_corners(bool, optional) : 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, optional) : 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_index+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 value is None. When :attr:`data_format` is not specified, it will be automatically inferred from the input dimension of :attr:`x`. When :attr:`x` is a 3-D Tensor, :attr:`data_format` will be set to `"NCW"`; When :attr:`x` is a 4-D Tensor, :attr:`data_format` will be set to `"NCHW"`; When :attr:`x` is a 5-D Tensor, :attr:`data_format` will be set to `"NCDHW"`. When it is `"NCHW"`, the data should be stored in the order of: `[batch_size, input_channels, input_height, input_width]`. When it is `"NCDHW"`, the data should be 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, 4-D or 5-D Tensor, with the same data format of the input :attr:`x`. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32) >>> upsample_out = paddle.nn.Upsample(size=[12,12]) >>> output = upsample_out(x=input_data) >>> print(output.shape) [2, 3, 12, 12] """ return interpolate( x, size, scale_factor, mode, align_corners, align_mode, data_format ) def bilinear( x1: Tensor, x2: Tensor, weight: Tensor, bias: Tensor | None = None, name: str | None = None, ) -> Tensor: """ This layer performs bilinear on two inputs. See :ref:`api_paddle_nn_Bilinear` for details and output shape. Parameters: x1 (Tensor): the first input tensor, it's data type should be float32, float64. x2 (Tensor): the second input tensor, it's data type should be float32, float64. weight (Tensor): The learnable weights of this layer, shape is [out_features, in1_features, in2_features]. bias (Tensor, optional): The learnable bias(Bias) of this layer, shape is [1, out_features]. If it is set to None, no bias will be added to the output units. The default value is None. 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`. Default: None. Returns: Tensor: A 2-D Tensor of shape [batch_size, out_features]. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> x1 = paddle.randn((5, 5)).astype(paddle.float32) >>> x2 = paddle.randn((5, 4)).astype(paddle.float32) >>> w = paddle.randn((1000, 5, 4)).astype(paddle.float32) >>> b = paddle.randn((1, 1000)).astype(paddle.float32) >>> result = F.bilinear(x1, x2, w, b) >>> print(result.shape) [5, 1000] """ if in_dynamic_or_pir_mode(): return _C_ops.bilinear(x1, x2, weight, bias) else: check_variable_and_dtype(x1, 'x1', ['float32', 'float64'], 'bilinear') check_variable_and_dtype(x2, 'x2', ['float32', 'float64'], 'bilinear') inputs = {"X": x1, "Y": x2, "Weight": weight} if bias is not None: inputs["Bias"] = bias helper = LayerHelper("bilinear", **locals()) out = helper.create_variable_for_type_inference(dtype=x1.dtype) helper.append_op( type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out} ) return out def dropout( x: Tensor, p: float = 0.5, axis: int | Sequence[int] | None = None, training: bool = True, mode: _DropoutMode = "upscale_in_train", name: str | None = None, ) -> Tensor: r""" Dropout is a regularization technique for reducing overfitting by preventing neuron co-adaption during training. The dropout operator randomly sets the outputs of some units to zero, while upscale others according to the given dropout probability. Args: x (Tensor): The input tensor. The data type is float16, float32 or float64. p (float|int, optional): Probability of setting units to zero. Default: 0.5. axis (int|list|tuple, optional): The axis along which the dropout is performed. Default: None. training (bool, optional): A flag indicating whether it is in train phrase or not. Default: True. mode(str, optional): ['upscale_in_train'(default) | 'downscale_in_infer']. 1. upscale_in_train (default), upscale the output at training time - train: :math:`out = input \times \frac{mask}{(1.0 - dropout\_prob)}` - inference: :math:`out = input` 2. downscale_in_infer, downscale the output at inference - train: :math:`out = input \times mask` - inference: :math:`out = input \times (1.0 - dropout\_prob)` name (str, optional): Name for the operation, Default: None. For more information, please refer to :ref:`api_guide_Name`. Returns: A Tensor representing the dropout, has same shape and data type as `x` . Examples: We use ``p=0.5`` in the following description for simplicity. 1. When ``axis=None`` , this is commonly used dropout, which dropout each element of x randomly. .. code-block:: text Let's see a simple case when x is a 2d tensor with shape 2*3: [[1 2 3] [4 5 6]] we generate mask with the same shape as x, which is 2*3. The value of mask is sampled from a Bernoulli distribution randomly. For example, we may get such mask: [[0 1 0] [1 0 1]] So the output is obtained from elementwise multiply of x and mask: [[0 2 0] [4 0 6]] Using default setting, i.e. ``mode='upscale_in_train'`` , if in training phase, the final upscale output is: [[0 4 0 ] [8 0 12]] if in test phase, the output is the same as input: [[1 2 3] [4 5 6]] we can also set ``mode='downscale_in_infer'`` , then if in training phase, the final output is: [[0 2 0] [4 0 6]] if in test phase, the scale output is: [[0.5 1. 1.5] [2. 2.5 3. ]] 2. When ``axis!=None`` , this is useful for dropping whole channels from an image or sequence. .. code-block:: text Let's see the simple case when x is a 2d tensor with shape 2*3 again: [[1 2 3] [4 5 6]] (1) If ``axis=0`` , this means the dropout is only performed in axis `0` . we generate mask with the shape 2*1. Only in axis `0` the value is randomly selected. For example, we may get such mask: [[1] [0]] The output is obtained from elementwise multiply of x and mask. Doing that the mask will be broadcast from 2*1 to 2*3: [[1 1 1] [0 0 0]] and the result after elementwise multiply is: [[1 2 3] [0 0 0]] then we can do upscale or downscale according to the setting of other arguments. (2) If ``axis=1`` , this means the dropout is only performed in axis `1` . we generate mask with the shape 1*3. Only in axis `1` the value is randomly selected. For example, we may get such mask: [[1 0 1]] Doing elementwise multiply the mask will be broadcast from 1*3 to 2*3: [[1 0 1] [1 0 1]] and the result after elementwise multiply is: [[1 0 3] [4 0 6]] (3) What about ``axis=[0, 1]`` ? This means the dropout is performed in all axes of x, which is the same case as default setting ``axis=None`` . (4) You may note that logically `axis=None` means the dropout is performed in none axis of x, We generate mask with the shape 1*1. Whole input is randomly selected or dropped. For example, we may get such mask: [[0]] Doing elementwise multiply the mask will be broadcast from 1*1 to 2*3: [[0 0 0] [0 0 0]] and the result after elementwise multiply is: [[0 0 0] [0 0 0]] Actually this is not what we want because all elements may set to zero~ When x is a 4d tensor with shape `NCHW`, where `N` is batch size, `C` is the number of channels, H and W are the height and width of the feature, we can set ``axis=[0,1]`` and the dropout will be performed in channel `N` and `C`, `H` and `W` is tied, i.e. paddle.nn.dropout(x, p, axis=[0,1]) . Please refer to ``paddle.nn.functional.dropout2d`` for more details. Similarly, when x is a 5d tensor with shape `NCDHW`, where `D` is the depth of the feature, we can set ``axis=[0,1]`` to perform dropout3d. Please refer to ``paddle.nn.functional.dropout3d`` for more details. .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.to_tensor([[1,2,3], [4,5,6]]).astype(paddle.float32) >>> y_train = paddle.nn.functional.dropout(x, 0.5) >>> y_test = paddle.nn.functional.dropout(x, 0.5, training=False) >>> y_0 = paddle.nn.functional.dropout(x, axis=0) >>> y_1 = paddle.nn.functional.dropout(x, axis=1) >>> y_01 = paddle.nn.functional.dropout(x, axis=[0,1]) >>> print(x) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) >>> print(y_train) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[2., 4., 0.], [8., 0., 0.]]) >>> print(y_test) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[1., 2., 3.], [4., 5., 6.]]) >>> print(y_0) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[2., 4., 6.], [8. , 10., 12.]]) >>> print(y_1) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[2. , 4. , 6. ], [8. , 10., 12.]]) >>> print(y_01) Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[0., 0., 6.], [0., 0., 0.]]) """ if not isinstance(p, (float, int, Variable, pir.Value)): raise TypeError("p argument should be a number or Variable") if isinstance(p, (int, float)): # fast return for p == 0 if p == 0: return x elif p < 0 or p > 1: raise ValueError("p argument should between 0 and 1") if mode not in ('downscale_in_infer', 'upscale_in_train'): raise ValueError( "mode argument should be 'downscale_in_infer' or 'upscale_in_train'" ) if axis and not isinstance(axis, (int, list, tuple)): raise TypeError("datatype of axis argument should be int or list") if axis is None: # commonly used dropout seed = None mode = ( 'downgrade_in_infer' if mode == 'downscale_in_infer' else mode ) # semantic transfer if in_dynamic_or_pir_mode(): if paddle.static.default_main_program().random_seed != 0: seed = paddle.static.default_main_program().random_seed out = _C_ops.dropout( x, None, p, not training, mode, seed if seed is not None else 0, seed is not None, ) return out else: helper = LayerHelper('dropout', **locals()) check_variable_and_dtype( x, 'x', ['float16', 'uint16', 'float32', 'float64'], 'dropout' ) out = helper.create_variable_for_type_inference(dtype=x.dtype) mask = helper.create_variable_for_type_inference( dtype=core.VarDesc.VarType.UINT8, stop_gradient=True ) def get_attrs(prog, dropout_prob, is_test, seed): if (seed is None or seed == 0) and prog.random_seed != 0: seed = prog.random_seed if isinstance( dropout_prob, Variable ) and not dropout_prob.shape != [1]: raise TypeError( f"Required p.shape == [1] if type(p) is Variable, but received p.shape = {p.shape}" ) attrs = { 'dropout_prob': dropout_prob, 'is_test': is_test, 'fix_seed': seed is not None, 'seed': seed if seed is not None else 0, 'dropout_implementation': mode, } return attrs attrs = get_attrs(helper.main_program, p, not training, seed) helper.append_op( type='dropout', inputs={'X': [x]}, outputs={'Out': [out], 'Mask': [mask]}, attrs=attrs, ) return out else: # sometimes called dropout_nd #TODO: optimize with c++ if not in_dynamic_mode(): check_variable_and_dtype( x, 'x', ['float16', 'uint16', 'float32', 'float64'], 'dropout' ) dtype = x.dtype keep_prob = 1 - p if training: if in_dynamic_mode() and p == 1.0: return paddle.scale(x, scale=0.0) elif in_pir_mode() and isinstance(p, (float, int)) and p == 1.0: return paddle.scale(x, scale=0.0) scale_input = ( paddle.scale(x, scale=1 / keep_prob) if mode == 'upscale_in_train' else x ) # get mask shape input_shape = x.shape if not in_dynamic_mode(): input_shape_tensor = paddle.shape(x) drop_axes = [axis] if isinstance(axis, int) else list(axis) if min(drop_axes) < 0 or max(drop_axes) > len(input_shape) - 1: raise ValueError( f"axis value should be greater than or equal to 0 and less than dimensions of x:{len(input_shape)}, but get axis value:{max(drop_axes)} " ) if len(drop_axes) > len(input_shape): raise ValueError( f"length of axis should not be greater than dimensions of x:{len(input_shape)}, but get length of axis: {len(drop_axes)}" ) mask_shape = [1] * len(input_shape) if not in_dynamic_mode(): for i in drop_axes: mask_shape[i] = input_shape_tensor[i] else: for i in drop_axes: mask_shape[i] = input_shape[i] # get mask random_tensor = paddle.uniform( mask_shape, dtype='float32', min=0.0, max=1.0 ) p = full(shape=[1], fill_value=p, dtype='float32') keep_mask = paddle.greater_equal(random_tensor, p) scale_input = paddle.cast(scale_input, dtype) keep_mask = paddle.cast(keep_mask, dtype) ret = paddle.multiply(scale_input, keep_mask, name=name) return ret else: # test ret = ( paddle.scale(x, scale=keep_prob) if mode == 'downscale_in_infer' else x ) return ret def dropout1d( input: paddle.Tensor, p: float = 0.5, training: bool = True, inplace: bool = False, ) -> paddle.Tensor: """ Randomly zero out entire 1D channels (feature maps) during training. Args: input: Input tensor of shape [C, L] (2D) or [N, C, L] (3D) p: Probability of a channel being zeroed. Default: 0.5 training: If False, returns input unchanged. Default: True inplace: If True, modifies input tensor in-place. Default: False WARNING: Currently not implemented (will behave as False). TODO: Implement in-place operation in future versions. Default: False Returns: Tensor with the same shape as input, where entire channels are zeroed with probability p Examples: .. code-block:: python >>> import paddle # Case 1: 3D input (batched) >>> x = paddle.randn([2, 3, 10]) # [N, C, L] >>> y_train = paddle.nn.functional.dropout1d(x, p=0.2) # Training mode >>> y_test = paddle.nn.functional.dropout1d(x, p=0.2, training=False) # Test mode >>> print("Original first channel:", x[0, 0, :]) >>> print("Train output (may be zeroed):", y_train[0, 0, :]) >>> print("Test output (always unchanged):", y_test[0, 0, :]) # Case 2: 2D input (single sample) >>> x = paddle.randn([3, 8]) # [C, L] >>> y = paddle.nn.functional.dropout1d(x, p=0.5) >>> print("Input shape:", x.shape) >>> print("Output shape:", y.shape) >>> print("Zeroed channels count:", paddle.sum(y == 0).item()) """ if p < 0 or p > 1: raise ValueError(f"dropout probability must be in [0, 1], got {p}") ndim = input.ndim if ndim not in [2, 3]: raise RuntimeError(f"dropout1d expects 2D or 3D input, got {ndim}D") if inplace: warnings.warn( "inplace=True is currently not supported in dropout1d and will be ignored. " "This parameter is reserved for future implementation." ) # TODO: Implement actual in-place operation when supported by dropout need_squeeze = ndim == 2 if need_squeeze: input = input.unsqueeze(0) # [C, L] -> [1, C, L] # Apply dropout along channel dimension result = dropout(input, p=p, axis=1, training=training) if need_squeeze: result = result.squeeze(0) # [1, C, L] -> [C, L] return result def dropout2d( x: Tensor, p: float = 0.5, training: bool = True, data_format: DataLayout2D = 'NCHW', name: str | None = None, ) -> Tensor: """ Randomly zero out entire channels (in the batched input 4d tensor with the shape `NCHW` , a channel is a 2D feature map with the shape `HW` ). Each channel will be zeroed out independently on every forward call with probability `p` using samples from a Bernoulli distribution. See :ref:`api_paddle_nn_functional_dropout` for more details. Args: x (Tensor): The input is 4-D Tensor with shape [N, C, H, W] or [N, H, W, C]. The data type is float16, float32 or float64. p (float, optional): Probability of setting units to zero. Default: 0.5. training (bool, optional): A flag indicating whether it is in train phrase or not. Default: True. 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 `NCHW` or `NHWC` . When it is `NCHW` , the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. Default: `NCHW` . name (str, optional): Name for the operation, Default: None. For more information, please refer to :ref:`api_guide_Name`. Returns: A Tensor representing the dropout2d, has same shape and data type as `x` . Examples: .. code-block:: python >>> import paddle >>> paddle.seed(1) >>> x = paddle.randn(shape=(2, 3, 4, 5)).astype(paddle.float32) >>> y_train = paddle.nn.functional.dropout2d(x) #train >>> y_test = paddle.nn.functional.dropout2d(x, training=False) #test >>> for i in range(2): ... for j in range(3): ... print(x[i,j,:,:]) ... print(y_train[i,j,:,:]) # may all 0 ... print(y_test[i,j,:,:]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.30557564, 0.11855337, 0.41220093, -0.09968963, 1.50014710], [ 1.24004936, -0.92485696, 0.08612321, 1.15149164, -0.09276631], [ 1.22873247, -1.46587241, -1.30802727, 0.19496460, 1.73776841], [ 0.40092674, 0.67630458, 0.72265440, 1.31720388, -1.41899264]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.61115128, 0.23710674, 0.82440186, -0.19937925, 3.00029421], [ 2.48009872, -1.84971392, 0.17224643, 2.30298328, -0.18553263], [ 2.45746493, -2.93174481, -2.61605453, 0.38992921, 3.47553682], [ 0.80185348, 1.35260916, 1.44530880, 2.63440776, -2.83798528]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.30557564, 0.11855337, 0.41220093, -0.09968963, 1.50014710], [ 1.24004936, -0.92485696, 0.08612321, 1.15149164, -0.09276631], [ 1.22873247, -1.46587241, -1.30802727, 0.19496460, 1.73776841], [ 0.40092674, 0.67630458, 0.72265440, 1.31720388, -1.41899264]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.88350385, -1.14767575, 0.51043051, -0.10051888, -0.61305630], [-0.12084112, 0.48506257, -1.13189507, 0.62806708, -0.80003673], [ 0.51513153, -0.08890446, 0.22753835, 0.11557858, 0.78117645], [ 1.47505593, 0.84618902, -0.38528305, -1.05887091, 0.16592593]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 1.76700771, -2.29535151, 1.02086103, -0.20103776, -1.22611260], [-0.24168225, 0.97012514, -2.26379013, 1.25613415, -1.60007346], [ 1.03026307, -0.17780893, 0.45507669, 0.23115715, 1.56235290], [ 2.95011187, 1.69237804, -0.77056611, -2.11774182, 0.33185187]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.88350385, -1.14767575, 0.51043051, -0.10051888, -0.61305630], [-0.12084112, 0.48506257, -1.13189507, 0.62806708, -0.80003673], [ 0.51513153, -0.08890446, 0.22753835, 0.11557858, 0.78117645], [ 1.47505593, 0.84618902, -0.38528305, -1.05887091, 0.16592593]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1.46668839, -0.38117948, 1.18678427, 0.38740095, 0.29117522], [-0.13538910, -0.14527084, -0.04912176, -0.26063353, 0.23640174], [ 0.45643106, 0.60587281, -1.03242552, -0.45319262, -1.57911122], [-0.08732958, -0.75898546, 0.14563090, -1.73751652, -0.89109969]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0., -0., 0. , 0. , 0. ], [-0., -0., -0., -0., 0. ], [0. , 0. , -0., -0., -0.], [-0., -0., 0. , -0., -0.]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1.46668839, -0.38117948, 1.18678427, 0.38740095, 0.29117522], [-0.13538910, -0.14527084, -0.04912176, -0.26063353, 0.23640174], [ 0.45643106, 0.60587281, -1.03242552, -0.45319262, -1.57911122], [-0.08732958, -0.75898546, 0.14563090, -1.73751652, -0.89109969]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.32110816, -0.76044011, 0.34456784, -0.39410326, 0.37896338], [ 0.52747023, 0.72711533, 0.29204839, 0.72493637, 0.31128070], [ 0.58046782, -1.78499067, -1.67504823, -0.38590902, -0.26243693], [ 0.96669912, 0.43670532, -0.38109761, 0.78405094, -2.17882323]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0., -0., 0. , -0., 0. ], [0. , 0. , 0. , 0. , 0. ], [0. , -0., -0., -0., -0.], [0. , 0. , -0., 0. , -0.]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.32110816, -0.76044011, 0.34456784, -0.39410326, 0.37896338], [ 0.52747023, 0.72711533, 0.29204839, 0.72493637, 0.31128070], [ 0.58046782, -1.78499067, -1.67504823, -0.38590902, -0.26243693], [ 0.96669912, 0.43670532, -0.38109761, 0.78405094, -2.17882323]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.17168395, 0.45112833, 0.63307828, 2.38763475, -1.27247131], [ 0.56171960, -1.09584677, 0.38300961, -0.57512099, 0.31011426], [-0.95336407, -1.04852903, -0.21312937, -0.53549880, -0.00074209], [ 2.22819090, 1.12403083, -0.04198794, -1.51167727, -0.42699185]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[0. , 0. , 0. , 0. , -0.], [0. , -0., 0. , -0., 0. ], [-0., -0., -0., -0., -0.], [0. , 0. , -0., -0., -0.]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.17168395, 0.45112833, 0.63307828, 2.38763475, -1.27247131], [ 0.56171960, -1.09584677, 0.38300961, -0.57512099, 0.31011426], [-0.95336407, -1.04852903, -0.21312937, -0.53549880, -0.00074209], [ 2.22819090, 1.12403083, -0.04198794, -1.51167727, -0.42699185]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.62503546, -0.20989063, -0.22046235, -0.38679042, -1.02590704], [ 1.04561794, 1.08428383, -0.52219963, -1.56003857, 0.89213932], [-0.16578521, 0.14524542, -0.45563069, 0.48180851, 1.35843253], [ 1.07669640, -0.84535235, -1.18651557, 0.79144061, -0.45565742]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[0. , -0., -0., -0., -0.], [0. , 0. , -0., -0., 0. ], [-0., 0. , -0., 0. , 0. ], [0. , -0., -0., 0. , -0.]]) Tensor(shape=[4, 5], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.62503546, -0.20989063, -0.22046235, -0.38679042, -1.02590704], [ 1.04561794, 1.08428383, -0.52219963, -1.56003857, 0.89213932], [-0.16578521, 0.14524542, -0.45563069, 0.48180851, 1.35843253], [ 1.07669640, -0.84535235, -1.18651557, 0.79144061, -0.45565742]]) """ input_shape = x.shape if len(input_shape) != 4: raise ValueError( f"dimensions of x should be 4, but received {len(input_shape)} != 4" ) if data_format not in ["NCHW", "NHWC"]: raise ValueError( "Attr(data_format) should be 'NCHW' or 'NHWC'. Received " f"Attr(data_format): {data_format}." ) return dropout( x, p=p, axis=[0, 1] if data_format == 'NCHW' else [0, 3], training=training, mode="upscale_in_train", name=name, ) def dropout3d( x: Tensor, p: float = 0.5, training: bool = True, data_format: DataLayout3D = 'NCDHW', name: str | None = None, ) -> Tensor: """ Randomly zero out entire channels (in the batched input 5d tensor with the shape `NCDHW` , a channel is a 3D feature map with the shape `DHW` ). Each channel will be zeroed out independently on every forward call with probability `p` using samples from a Bernoulli distribution. See :ref:`api_paddle_nn_functional_dropout` for more details. Args: x (Tensor): The input is 5-D Tensor with shape [N, C, D, H, W] or [N, D, H, W, C]. The data type is float32 or float64. p (float, optional): Probability of setting units to zero. Default: 0.5. training (bool, optional): A flag indicating whether it is in train phrase or not. Default: True. 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 ``NCDHW`` or ``NDHWC``. When it is ``NCDHW`` , the data is stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width]. Default: ``NCDHW`` . name (str, optional): Name for the operation, Default: None. For more information, please refer to :ref:`api_guide_Name`. Returns: A Tensor representing the dropout3d, has same shape and data type with `x` . Examples: .. code-block:: python >>> import paddle >>> x = paddle.randn(shape=(2, 3, 4, 5, 6)).astype(paddle.float32) >>> y_train = paddle.nn.functional.dropout3d(x) #train >>> y_test = paddle.nn.functional.dropout3d(x, training=False) #test >>> print(x[0,0,:,:,:]) >>> print(y_train[0,0,:,:,:]) # may all 0 >>> print(y_test[0,0,:,:,:]) """ input_shape = x.shape if len(input_shape) != 5: raise ValueError( f"dimensions of x should be 5, but received {len(input_shape)} != 5" ) if data_format not in ["NCDHW", "NDHWC"]: raise ValueError( "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received " f"Attr(data_format): {data_format}." ) return dropout( x, p=p, axis=[0, 1] if data_format == 'NCDHW' else [0, 4], training=training, mode="upscale_in_train", name=name, ) def _feature_alpha_dropout_impl( x: Tensor, feature_dropout: bool, p: float, training: bool = True, name: str | None = None, ) -> Tensor: if not isinstance(p, (float, int)): raise TypeError("p argument should be a float or int") if p < 0 or p > 1: raise ValueError("p argument should between 0 and 1") if not in_dynamic_mode(): check_variable_and_dtype( x, 'x', ['float16', 'uint16', 'float32', 'float64'], 'alpha_dropout' ) if training: if p == 1: return paddle.scale(x, scale=0.0) # get transformation params alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 alpha_p = -alpha * scale a = ((1 - p) * (1 + p * alpha_p**2)) ** -0.5 b = -a * alpha_p * p dtype = x.dtype if not feature_dropout: input_shape = x.shape else: if x.ndim < 2: raise ValueError( 'Feature alpha dropout needs at least 2D input.' ) input_shape = list(x.shape[:2]) + [1] * len(x.shape[2:]) # get mask random_tensor = paddle.uniform( input_shape, dtype='float32', min=0.0, max=1.0 ) p = full(shape=input_shape, fill_value=p, dtype='float32') keep_mask = paddle.greater_equal(random_tensor, p) keep_mask = paddle.cast(keep_mask, dtype) drop_mask = paddle.subtract( full(shape=input_shape, fill_value=1.0, dtype=dtype), keep_mask ) # apply mask b = full(shape=input_shape, fill_value=b, dtype=dtype) y = paddle.add( paddle.multiply(x, keep_mask), paddle.scale(drop_mask, scale=alpha_p), ) res = paddle.add(paddle.scale(y, scale=a), b, name=name) return res else: # test return x def alpha_dropout( x: Tensor, p: float = 0.5, training: bool = True, name: str | None = None, ) -> Tensor: """ Alpha Dropout is a type of Dropout that maintains the self-normalizing property. For an input with zero mean and unit standard deviation, the output of Alpha Dropout maintains the original mean and standard deviation of the input. Alpha Dropout fits well to SELU activate function by randomly setting activations to the negative saturation value. Args: x (Tensor): The input tensor. The data type is bfloat16, float16, float32 or float64. p (float | int, optional): Probability of setting units to zero. Default 0.5. training (bool, optional): A flag indicating whether it is in train phrase or not. Default True. name (str | None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor representing the dropout, has same shape and data type as `x`. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(1) >>> x = paddle.to_tensor([[-1, 1], [-1, 1]]).astype(paddle.float32) >>> y_train = paddle.nn.functional.alpha_dropout(x, 0.5) >>> y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False) >>> print(y_train) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.77919382, 1.66559887], [-0.10721093, -0.77919382]]) >>> print(y_test) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1., 1.], [-1., 1.]]) """ return _feature_alpha_dropout_impl( x, feature_dropout=False, p=p, training=training, name=name ) def feature_alpha_dropout( x: Tensor, p: float = 0.5, training: bool = True, name: str | None = None, ) -> Tensor: """ A channel is a feature map, Feature Alpha Dropout randomly masks out entire channels. Alpha Dropout is a type of Dropout that maintains the self-normalizing property. For an input with zero mean and unit standard deviation, the output of Alpha Dropout maintains the original mean and standard deviation of the input. Alpha Dropout fits well to SELU activate function by randomly setting activations to the negative saturation value. Args: x (Tensor): The input tensor. The data type is bfloat16, float16, float32 or float64. p (float | int, optional): Probability of setting units to zero. Default 0.5. training (bool, optional): A flag indicating whether it is in train phrase or not. Default True. name (str | None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor representing the dropout, has same shape and data type as `x`. Examples: .. code-block:: python >>> import paddle >>> paddle.seed(1) >>> x = paddle.to_tensor([[-1, 1], [-1, 1]]).astype(paddle.float32) >>> y_train = paddle.nn.functional.feature_alpha_dropout(x, 0.5) >>> y_test = paddle.nn.functional.feature_alpha_dropout(x, 0.5, training=False) >>> print(y_train) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-0.77919382, 1.66559887], [-0.10721093, -0.77919382]]) >>> print(y_test) Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1., 1.], [-1., 1.]]) """ return _feature_alpha_dropout_impl( x, feature_dropout=True, p=p, training=training, name=name ) @ParamAliasDecorator({"x": ["input"]}) def pad( x: Tensor, pad: ShapeLike, mode: _PaddingTensorMode = 'constant', value: float = 0.0, data_format: DataLayoutND | None = None, pad_from_left_axis: bool = True, name: str | None = None, ) -> Tensor: """ Pad tensor according to ``'pad'`` and ``'mode'``. Note: 1. Denote ``'x'``'s dimension as N (same in the following). If mode is ``'constant'``, the length of ``'pad'`` can be any even number less than or equal to 2*N. 2. When mode is ``'constant'``, and ``'pad'`` is a list or tuple, and the length of ``'pad'`` is not equal to 2*(N - 2): 2.1. If the length of ``'pad'`` is 2*N, the order of padding can be customized by ``'pad_from_left_axis'``. if ``'pad_from_left_axis'`` is True, then the padding order will be started from the first dimension of ``'x'`` and moving backward according to ``'pad'``; else if ``'pad_from_left_axis'`` is False, then the padding order will be started from the last dimension of ``'x'`` and moving forward according to ``'pad'``. 2.2. Otherwise, the padding will be started from the last dimension. 3. When mode is any of ``'reflect'``, ``'replicate'``, ``'circular'``, or ``'pad'`` is a tensor, or the length of ``'pad'`` is 2*(N - 2), and the dimension of ``'x'`` only supports 3-D, 4-D and 5-D. In these cases, input ``'x'`` will be padded on [D, H, W] axes according to ``'data_format'``. It will pad from the last dimension to the first dimension of [D, H, W] axes. Specifically, if N = 3, then the pad has the form (pad_left, pad_right); if N = 4, then the pad has the form (pad_left, pad_right, pad_top, pad_bottom); if N = 5, then the pad has the form (pad_left, pad_right, pad_top, pad_bottom, pad_front, pad_back). 4. If mode is ``'reflect'``, pad[0] and pad[1] must be no greater than width-1. The height and depth dimension has the same condition. .. note:: Alias Support: The parameter name ``input`` can be used as an alias for ``x``. For example, ``input=tensor_x`` is equivalent to ``x=tensor_x``. Args: x (Tensor): The input tensor with data type float32, float64, int32, int64, complex64 or complex128. input: An alias for ``x`` , with identical behavior. pad (Tensor|list[int]|tuple[int]): The padding size with data type int. Refer to Note for details. mode (str, optional): Four modes: ``'constant'`` (default), ``'reflect'``, ``'replicate'``, ``'circular'``. Default is ``'constant'``. - 'constant' mode, uses a constant value to pad the input tensor. - 'reflect' mode, uses reflection of the input boundaries to pad the input tensor. - 'replicate' mode, uses input boundaries to pad the input tensor. - 'circular' mode, uses circular input to pad the input tensor. value (float, optional): The value to fill the padded areas in 'constant' mode . Default is :math:`0.0`. data_format (str, optional): An string from: ``'NCL'``, ``'NLC'``, ``'NHWC'``, ``'NCHW'``, ``'NCDHW'``, ``'NDHWC'``. Specify the data format of the input data when: 1. mode is any of ``'reflect'``, ``'replicate'`` or ``'circular'``; or 2. the input ``'pad'`` is a tensor; or 3. the length of ``'pad'`` is ``2*(x.ndim - 2)``. The default value is None, which means it will be automatically inferred from the input dimension of ``'x'``. When ``'x'`` is a 3-D Tensor, data_format will be set to ``'NCL'``; When ``'x'`` is a 4-D Tensor, data_format will be set to ``'NCHW'``; When ``'x'`` is a 5-D Tensor, data_format will be set to ``'NCDHW'``. pad_from_left_axis (bool, optional): The parameter is only valid when mode is ``'constant'`` and the input ``'pad'`` is length of ``'pad'`` is ``2*x.ndim``, the order of padding can be customized. If True, the padding will be started from the first axis of ``'x'``; if False, it will be started from the last axis of ``'x'``. Default: True. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: ``'None'``. Returns: Tensor, a Tensor padded according to pad and mode and data type is same as input. Example: .. code-block:: text x = [[[[[1., 2., 3.], [4., 5., 6.]]]]] Case 0: pad = [0, 0, 0, 0, 0, 0, 1, 1, 0, 0], mode = 'constant' value = 0 pad_from_left_axis = True Out = [[[[[0., 0., 0.], [1., 2., 3.], [4., 5., 6.], [0., 0., 0.]]]]] Out.shape = [1, 1, 1, 4, 3] Case 1: pad = [0, 0, 0, 0, 0, 0, 1, 1, 0, 0], mode = 'constant' value = 0 pad_from_left_axis = False Out = [[[[[0., 0., 0.], [0., 0., 0.]]], [[[1., 2., 3.], [4., 5., 6.]]], [[[0., 0., 0.], [0., 0., 0.]]]]] Out.shape = [1, 3, 1, 2, 3] Case 3: pad = [1, 0, 0, 1], mode = 'constant' value = 0 Out = [[[[[0., 1., 2., 3.], [0., 4., 5., 6.], [0., 0., 0., 0.]]]]] Out.shape = [1, 1, 1, 3, 4] Case 4: pad = [2, 2, 1, 1, 0, 0], mode = 'constant' value = 0 Out = [[[[[0. 0. 0. 0. 0. 0. 0.] [0. 0. 1. 2. 3. 0. 0.] [0. 0. 4. 5. 6. 0. 0.] [0. 0. 0. 0. 0. 0. 0.]]]]] Out.shape = [1, 1, 1, 4, 7] Case 5: pad = [2, 2, 1, 1, 0, 0], mode = 'reflect' Out = [[[[[6. 5. 4. 5. 6. 5. 4.] [3. 2. 1. 2. 3. 2. 1.] [6. 5. 4. 5. 6. 5. 4.] [3. 2. 1. 2. 3. 2. 1.]]]]] Out.shape = [1, 1, 1, 4, 7] Case 6: pad = [2, 2, 1, 1, 0, 0], mode = 'replicate' Out = [[[[[1. 1. 1. 2. 3. 3. 3.] [1. 1. 1. 2. 3. 3. 3.] [4. 4. 4. 5. 6. 6. 6.] [4. 4. 4. 5. 6. 6. 6.]]]]] Out.shape = [1, 1, 1, 4, 7] Case 7: pad = [2, 2, 1, 1, 0, 0], mode = 'circular' Out = [[[[[5. 6. 4. 5. 6. 4. 5.] [2. 3. 1. 2. 3. 1. 2.] [5. 6. 4. 5. 6. 4. 5.] [2. 3. 1. 2. 3. 1. 2.]]]]] Out.shape = [1, 1, 1, 4, 7] Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> # example 1 >>> x_shape = (1, 1, 3) >>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1 >>> y = F.pad(x, [0, 0, 0, 0, 2, 3], value=1, mode='constant', data_format="NCL") >>> print(y) Tensor(shape=[1, 1, 8], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1., 1., 1., 2., 3., 1., 1., 1.]]]) >>> # example 2 >>> x_shape = (1, 1, 3) >>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1 >>> y = F.pad(x, [2, 3], value=1, mode='constant', data_format="NCL") >>> print(y) Tensor(shape=[1, 1, 8], dtype=float32, place=Place(cpu), stop_gradient=True, [[[1., 1., 1., 2., 3., 1., 1., 1.]]]) >>> # example 3 >>> x_shape = (1, 1, 2, 3) >>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1 >>> y = F.pad(x, [1, 2, 1, 1], value=1, mode='circular') >>> print(y) Tensor(shape=[1, 1, 4, 6], dtype=float32, place=Place(cpu), stop_gradient=True, [[[[6., 4., 5., 6., 4., 5.], [3., 1., 2., 3., 1., 2.], [6., 4., 5., 6., 4., 5.], [3., 1., 2., 3., 1., 2.]]]]) >>> # example 4 >>> x_shape = (1, 1, 3) >>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1 >>> y = F.pad(x, [1, 0, 0, 1, 0, 0], value=0, mode='constant', pad_from_left_axis=True) >>> print(y) Tensor(shape=[2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0., 0., 0.], [0., 0., 0.]], [[1., 2., 3.], [0., 0., 0.]]]) >>> # example 5 >>> x_shape = (1, 1, 3) >>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1 >>> y = F.pad(x, [1, 0, 0, 1, 0, 0], value=0, mode='constant', pad_from_left_axis=False) >>> print(y) Tensor(shape=[1, 2, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0., 1., 2., 3.], [0., 0., 0., 0.]]]) >>> # example 6 >>> x_shape = (1, 1, 3) >>> x = paddle.arange(paddle.prod(paddle.to_tensor(x_shape)), dtype="float32").reshape(x_shape) + 1 >>> y = F.pad(x, [1, 0, 0, 1], value=0, mode='constant') >>> print(y) Tensor(shape=[1, 2, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[[0., 1., 2., 3.], [0., 0., 0., 0.]]]) """ assert mode in [ 'reflect', 'replicate', 'constant', 'circular', ], ( f"mode should be one of constant, reflect, replicate, circular, but got {mode}." ) x_dim = len(x.shape) if in_dynamic_mode(): if isinstance(pad, (Variable, paddle.Tensor)) and pad.size == 0: return x.clone() if ( mode == "constant" and isinstance(pad, (list, tuple)) and len(pad) != (x_dim - 2) * 2 ): paddings = pad pad_value = value padding_len = len(paddings) # pad the length of paddings to 2*x_dim if padding_len < 2 * x_dim: pad_len_for_paddings = 2 * x_dim - padding_len paddings = paddings + ([0] if isinstance(pad, list) else (0,)) * ( pad_len_for_paddings ) # since the kernel pad from left axis, if we want to pad from right axis, we need to reverse the paddings if not (len(pad) == x_dim * 2 and pad_from_left_axis): paddings = [ paddings[i - 1] if i % 2 == 1 else paddings[i + 1] for i in range(2 * x_dim - 1, -1, -1) ] if in_dynamic_mode(): out = _C_ops.pad(x, paddings, float(pad_value)) return out if in_pir_mode(): if isinstance(pad_value, paddle.pir.Value): return _C_ops.pad(x, paddings, pad_value) else: return _C_ops.pad(x, paddings, float(pad_value)) check_variable_and_dtype( x, 'x', [ 'float16', 'float32', 'float64', 'int32', 'int64', 'complex64', 'complex128', 'uint16', ], "pad", ) check_type(pad_value, 'pad_value', (float, int, Variable), 'pad') if isinstance(pad_value, int): pad_value = float(pad_value) helper = LayerHelper('pad', **locals()) dtype = helper.input_dtype(input_param_name='x') out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='pad', inputs={'X': x}, outputs={'Out': out}, attrs={'paddings': paddings, 'pad_value': pad_value}, ) return out assert x_dim in [ 3, 4, 5, ], f"input tensor dimension must be in [3, 4, 5] but got {x_dim}" if data_format is None: if x_dim == 3: data_format = "NCL" elif x_dim == 4: data_format = "NCHW" elif x_dim == 5: data_format = "NCDHW" data_format = data_format.upper() assert data_format in ["NCL", "NCHW", "NCDHW", "NLC", "NHWC", "NDHWC"], ( "data_format should be in one of [NCL, NCHW, NCDHW, NLC, NHWC, NDHWC], " f"but got {data_format}" ) supported_format_map = { 3: ["NCL", "NLC"], 4: ["NCHW", "NHWC"], 5: ["NCDHW", "NDHWC"], } assert data_format in supported_format_map[x_dim], ( f"input tensor dimension is {x_dim}, it's data format should be in {supported_format_map[x_dim]} but got {data_format}" ) unsqueezed_dim = [] if isinstance(pad, (Variable, pir.Value)): if data_format in ["NCL", "NCHW", "NCDHW"]: data_format = "NCDHW" if x_dim == 3: pad = concat([zeros((4,), dtype="int32"), pad], axis=0) unsqueezed_dim = [3, 4] x = unsqueeze(x, axis=unsqueezed_dim) elif x_dim == 4: pad = concat([pad, zeros((2,), dtype="int32")], axis=0) unsqueezed_dim = [2] x = unsqueeze(x, axis=unsqueezed_dim) elif data_format in ["NLC", "NHWC", "NDHWC"]: data_format = "NDHWC" if x_dim == 3: pad = concat([zeros((4,), dtype="int32"), pad], axis=0) unsqueezed_dim = [2, 3] x = unsqueeze(x, axis=unsqueezed_dim) elif x_dim == 4: pad = concat([pad, zeros((2,), dtype="int32")], axis=0) unsqueezed_dim = [1] x = unsqueeze(x, axis=unsqueezed_dim) else: pad = list(pad) if data_format in ["NCL", "NCHW", "NCDHW"]: data_format = "NCDHW" if x_dim == 3: pad = [0, 0, 0, 0, *pad] unsqueezed_dim = [3, 4] x = unsqueeze(x, axis=unsqueezed_dim) elif x_dim == 4: pad = [*pad, 0, 0] unsqueezed_dim = [2] x = unsqueeze(x, axis=unsqueezed_dim) elif data_format in ["NLC", "NHWC", "NDHWC"]: data_format = "NDHWC" if x_dim == 3: pad = [0, 0, 0, 0, *pad] unsqueezed_dim = [2, 3] x = unsqueeze(x, axis=unsqueezed_dim) elif x_dim == 4: pad = [*pad, 0, 0] unsqueezed_dim = [1] x = unsqueeze(x, axis=unsqueezed_dim) if in_dynamic_or_pir_mode(): if isinstance(pad, Variable): pad = pad.tolist() out = _C_ops.pad3d(x, pad, mode, value, data_format) else: attrs = {'mode': mode, 'value': value, 'data_format': data_format} inputs = {'X': [x]} if isinstance(pad, Variable): inputs['Paddings'] = [pad] attrs['paddings'] = [] else: attrs['paddings'] = pad helper = LayerHelper('pad3d', **locals()) dtype = helper.input_dtype(input_param_name='input') out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='pad3d', inputs=inputs, outputs={"Out": out}, attrs=attrs ) if len(unsqueezed_dim) != 0: out = squeeze(out, axis=unsqueezed_dim) return out @deprecated( since="3.0.0", update_to="paddle.nn.ZeroPad2D", level=1, reason="Please use class ZeroPad2D", ) def zeropad2d( x: Tensor, padding: ShapeLike, data_format: DataLayout2D = "NCHW", name: str | None = None, ) -> Tensor: """ Pads the input tensor boundaries with zero according to 'pad'. Args: x(Tensor): The input tensor with data type float16/float32/float64/int32/int64. padding(int | Tensor | List[int] | Tuple[int]): The padding size with data type int. The input dimension should be 4 and pad has the form (pad_left, pad_right, pad_top, pad_bottom). data_format(str, optional): An string from: "NHWC", "NCHW". Specify the data format of the input data. Default: "NCHW". 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: Tensor, padded with 0 according to pad and data type is same as input. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> x_shape = paddle.to_tensor([1, 1, 2, 3]) >>> x = paddle.arange(paddle.prod(x_shape), dtype="float32").reshape(x_shape) + 1 >>> y = F.zeropad2d(x, [1, 2, 1, 1]) >>> print(y) Tensor(shape=[1, 1, 4, 6], dtype=float32, place=Place(cpu), stop_gradient=True, [[[[0., 0., 0., 0., 0., 0.], [0., 1., 2., 3., 0., 0.], [0., 4., 5., 6., 0., 0.], [0., 0., 0., 0., 0., 0.]]]]) """ return pad( x, pad=padding, mode='constant', value=0, data_format=data_format, name=name, ) def cosine_similarity( x1: Tensor, x2: Tensor, axis: int = 1, eps: float = 1e-8 ) -> Tensor: """ Compute cosine similarity between x1 and x2 along axis. Parameters: x1 (Tensor): First input. float32/double. x2 (Tensor): Second input. float32/double. axis (int, optional): Dimension of vectors to compute cosine similarity. Default is 1. eps(float, optional): Small value to avoid division by zero. Default is 1e-8. Returns: Tensor, a Tensor representing cosine similarity between x1 and x2 along axis. Examples: .. code-block:: text Case 0: x1 = [[0.8024077 0.9927354 0.27238318 0.8344984 ] [0.48949873 0.5797396 0.65444374 0.66510963] [0.1031398 0.9614342 0.08365563 0.6796464 ] [0.10760343 0.7461209 0.7726148 0.5801006 ]] x2 = [[0.62913156 0.1536727 0.9847992 0.04591406] [0.9098952 0.15715368 0.8671125 0.3156102 ] [0.4427798 0.54136837 0.5276275 0.32394758] [0.3769419 0.8535014 0.48041078 0.9256797 ]] axis = 1 eps = 1e-8 Out: [0.5275037 0.8368967 0.75037485 0.9245899] Code Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> paddle.seed(1) >>> x1 = paddle.randn(shape=[2, 3]) >>> x2 = paddle.randn(shape=[2, 3]) >>> result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0) >>> print(result) Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, [ 0.97689527, 0.99996042, -0.55138415]) """ if x1.shape[axis] == 0 or x2.shape[axis] == 0: return sum(paddle.multiply(x1, x2), axis=axis) bs = paddle.broadcast_shape([x1.shape[axis]], [x2.shape[axis]]) w12 = sum(paddle.multiply(x1, x2), axis=axis) w1 = sum(paddle.multiply(x1, x1), axis=axis) w2 = sum(paddle.multiply(x2, x2), axis=axis) m1, m2 = bs[0] / x1.shape[axis], bs[0] / x2.shape[axis] if m1 != 1: w1 = w1 * m1 if m2 != 1: w2 = w2 * m2 n12 = sqrt(clip(w1 * w2, min=eps * eps)) cos_sim = w12 / n12 return cos_sim def linear( x: Tensor, weight: Tensor, bias: Tensor | None = None, name: str | None = None, ) -> Tensor: r""" Fully-connected linear transformation operator. For each input :math:`X` , the equation is: .. math:: Out = XW + b where :math:`W` is the weight and :math:`b` is the bias. If the weight is a 2-D tensor of shape :math:`[in\_features, out\_features]` , input should be a multi-dimensional tensor of shape :math:`[batch\_size, *, in\_features]` , where :math:`*` means any number of additional dimensions. The linear operator multiplies input tensor with weight and produces an output tensor of shape :math:`[batch\_size, *, out\_features]` , If :math:`bias` is not None, the bias should be a 1-D tensor of shape :math:`[out\_features]` and will be added to the output. Parameters: x (Tensor): Input tensor. The data type should be bfloat16, float16, float32 or float64. weight (Tensor): Weight tensor. The data type should be float16, float32 or float64. bias (Tensor, optional): Bias tensor. The data type should be float16, float32 or float64. If it is set to None, no bias will be added to the output units. name (str, optional): Normally there is no need for user to set this parameter. For detailed information, please refer to :ref:`api_guide_Name` . Returns: Tensor, the shape is :math:`[batch\_size, *, out\_features]` and the data type is the same with input :math:`x` . Examples: .. code-block:: python >>> import paddle >>> paddle.seed(2023) >>> x = paddle.randn((3, 2), dtype="float32") >>> print(x) Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 0.06132207, 1.11349595], [ 0.41906244, -0.24858207], [-1.85169315, -1.50370061]]) >>> weight = paddle.full(shape=[2, 4], fill_value=0.5, dtype="float32", name="weight") >>> print(weight) Tensor(shape=[2, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.50000000, 0.50000000, 0.50000000, 0.50000000], [0.50000000, 0.50000000, 0.50000000, 0.50000000]]) >>> bias = paddle.ones(shape=[4], dtype="float32", name="bias") >>> print(bias) Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, [1., 1., 1., 1.]) >>> y = paddle.nn.functional.linear(x, weight, bias) >>> print(y) Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, [[ 1.58740902, 1.58740902, 1.58740902, 1.58740902], [ 1.08524013, 1.08524013, 1.08524013, 1.08524013], [-0.67769694, -0.67769694, -0.67769694, -0.67769694]]) """ if in_dynamic_mode(): # TODO(jiabin): using addmm for fast forward route return _C_ops.linear(x, weight, bias) elif in_pir_mode(): out = _C_ops.matmul(x, weight, False, False) if bias is not None: return _C_ops.add(out, bias) else: return out else: helper = LayerHelper('linear', **locals()) dtype = x.dtype check_variable_and_dtype( x, 'x', ["uint16", 'float16', 'float32', 'float64'], 'linear' ) check_dtype( dtype, 'dtype', ["uint16", 'float16', 'float32', 'float64'], 'linear', ) inputs = {'X': [x], 'Y': [weight]} attrs = {'trans_x': False, 'trans_y': False} tmp = helper.create_variable_for_type_inference(dtype) helper.append_op( type='matmul_v2', inputs=inputs, outputs={'Out': tmp}, attrs=attrs, ) if bias is not None: res = helper.create_variable_for_type_inference(dtype) helper.append_op( type='elementwise_add', inputs={'X': [tmp], 'Y': [bias]}, outputs={'Out': [res]}, attrs={'axis': -1}, ) else: res = tmp return res def label_smooth( label: Tensor, prior_dist: Tensor | None = None, epsilon: float = 0.1, name: str | None = None, ) -> Tensor: r""" Label smoothing is a mechanism to regularize the classifier layer and is called label-smoothing regularization (LSR).Label smoothing is proposed to encourage the model to be less confident, since optimizing the log-likelihood of the correct label directly may cause overfitting and reduce the ability of the model to adapt. Label smoothing replaces the ground-truth label :math:`y` with the weighted sum of itself and some fixed distribution :math:`\mu`. For class :math:`k`, i.e. .. math:: \\tilde{y_k} = (1 - \epsilon) * y_k + \epsilon * \mu_k, where :math:`1 - \epsilon` and :math:`\epsilon` are the weights respectively, and :math:`\\tilde{y}_k` is the smoothed label. Usually uniform distribution is used for :math:`\mu`. See more details about label smoothing in https://arxiv.org/abs/1512.00567. Parameters: label(Tensor): The input variable containing the label data. The label data should use one-hot representation. It's a multidimensional tensor with a shape of :math:`[N_1, ..., Depth]`, where Depth is class number. The dtype can be "float16" "float32" and "float64". prior_dist(Tensor, optional): The prior distribution to be used to smooth labels. If not provided, an uniform distribution is used. It's a multidimensional tensor with a shape of :math:`[1, class\_num]` . The default value is None. epsilon(float, optional): The weight used to mix up the original ground-truth distribution and the fixed distribution. The default value is 0.1. 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: Tensor: The tensor containing the smoothed labels. Examples: .. code-block:: python >>> import paddle >>> paddle.disable_static() >>> x = paddle.to_tensor([[[0, 1, 0], >>> [ 1, 0, 1]]], dtype="float32", stop_gradient=False) >>> output = paddle.nn.functional.label_smooth(x) >>> print(output) Tensor(shape=[1, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=False, [[[0.03333334, 0.93333334, 0.03333334], [0.93333334, 0.03333334, 0.93333334]]]) """ if epsilon > 1.0 or epsilon < 0.0: raise ValueError("The value of epsilon must be between 0 and 1.") if in_dynamic_or_pir_mode(): return _C_ops.label_smooth(label, prior_dist, float(epsilon)) check_variable_and_dtype( label, 'label', ['uint16', 'float16', 'float32', 'float64'], 'label_smooth', ) helper = LayerHelper("label_smooth", **locals()) label.stop_gradient = True smooth_label = helper.create_variable_for_type_inference(label.dtype) helper.append_op( type="label_smooth", inputs=( {"X": label, "PriorDist": prior_dist} if prior_dist else {"X": label} ), outputs={"Out": smooth_label}, attrs={"epsilon": float(epsilon)}, ) return smooth_label def class_center_sample( label: Tensor, num_classes: int, num_samples: int, group: Group | bool | None = None, ) -> tuple[Tensor, Tensor]: """ Class center sample method is proposed from the paper PartialFC that only sample a subset of the class centers. The process of sampling subset class centers is straightforward: 1. First select the positive class centers; 2. Then randomly sample negative class centers. Specifically, given a label tensor, shape [batch_size], select all the positive class centers and randomly sample negative class centers, then remap the input label tensor using the sampled class centers. For more information, Partial FC: Training 10 Million Identities on a Single Machine arxiv: https://arxiv.org/abs/2010.05222 Note: If the number of the positive class centers is greater than the input num_samples, it keeps all the positive class centers and the shape of sampled_class_center will be [num_positive_class_centers]. The API supports CPU, single GPU and multi GPU. For data parallel mode, set ``group=False``. For model parallel mode, set ``group=None`` or the group instance return by paddle.distributed.new_group. Args: label (Tensor): 1-D tensor with shape [N], each label in [0, num_classes) num_classes (int): A positive integer to specify the number of classes at local rank. Note that num_classes of each GPU can be different. num_samples (int): A positive integer to specify the number of class center to sample. group (Group, optional): The group instance return by paddle.distributed.new_group or ``None`` for global default group or ``False`` for data parallel (do not communication cross ranks). Default is ``None``. Returns: Tuple of two ``Tensor`` : (remapped_label, sampled_class_center), remapped label using sampled class center, sampled class center from [0, num_classes). Examples: .. code-block:: python :name: code-example1 >>> # CPU or single GPU >>> import paddle >>> num_classes = 20 >>> batch_size = 10 >>> num_samples = 6 >>> paddle.seed(2023) >>> label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64') >>> remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples) >>> print(label) Tensor(shape=[10], dtype=int64, place=Place(cpu), stop_gradient=True, [17, 10, 5 , 18, 8 , 8 , 19, 14, 10, 14]) >>> print(remapped_label) Tensor(shape=[10], dtype=int64, place=Place(cpu), stop_gradient=True, [4, 2, 0, 5, 1, 1, 6, 3, 2, 3]) >>> print(sampled_class_index) Tensor(shape=[7], dtype=int64, place=Place(cpu), stop_gradient=True, [5 , 8 , 10, 14, 17, 18, 19]) .. code-block:: python :name: code-example2 >>> # doctest: +REQUIRES(env:DISTRIBUTED) >>> # Multi GPU, test_class_center_sample.py >>> import paddle >>> import paddle.distributed as dist >>> strategy = dist.fleet.DistributedStrategy() >>> dist.fleet.init(is_collective=True, strategy=strategy) >>> batch_size = 10 >>> num_samples = 6 >>> rank_id = dist.get_rank() >>> # num_classes of each GPU can be different, e.g num_classes_list = [10, 8] >>> num_classes_list = [10, 10] >>> num_classes = paddle.sum(paddle.to_tensor(num_classes_list)) >>> label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64') # type: ignore >>> label_list = [] # type: ignore >>> dist.all_gather(label_list, label) >>> label = paddle.concat(label_list, axis=0) >>> remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples) >>> print(label) >>> print(remapped_label) >>> print(sampled_class_index) >>> #python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py >>> # rank 0 output: Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True, [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ]) Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True, [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ]) Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True, [0, 2, 4, 8, 9, 3]) >>> # rank 1 output: Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True, [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ]) Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True, [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ]) Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True, [0, 1, 2, 3, 5, 7, 8]) """ if not (group is False or group is None or hasattr(group, 'is_member')): raise ValueError( f'Expected group is False, None or instance of paddle.distributed.collective.Group \ (got group: {group})' ) return if hasattr(group, 'is_member') and not group.is_member(): return ring_id = 0 rank = 0 nranks = 1 if group is not False: if core.is_compiled_with_dist(): parallel_env = paddle.distributed.ParallelEnv() global_rank = parallel_env.rank rank = ( global_rank if group is None else group.get_group_rank(global_rank) ) nranks = parallel_env.world_size if group is None else group.nranks if num_samples > num_classes: raise ValueError( f'Expected num_samples less than or equal to {num_classes}, got num_samples {num_samples}' ) label_size = 1 for dim in list(label.shape): label_size *= dim if label_size != -1 and label_size < 1: raise ValueError( f'Expected label_size > 0 \ (got label_size: {label_size})' ) label_dims = len(list(label.shape)) if label_dims != 1: raise ValueError( f'Expected label_dims == 1 \ (got label_dims: {label_dims})' ) seed = None if (seed is None or seed == 0) and default_main_program().random_seed != 0: seed = default_main_program().random_seed if in_dynamic_or_pir_mode(): return _C_ops.class_center_sample( label, num_classes, num_samples, ring_id, rank, nranks, seed is not None, seed if seed is not None else 0, ) check_variable_and_dtype( label, 'label', ['int64', 'int32'], 'class_center_sample' ) op_type = 'class_center_sample' helper = LayerHelper(op_type, **locals()) remapped_label = helper.create_variable_for_type_inference( dtype=label.dtype ) sampled_class_center = helper.create_variable_for_type_inference( dtype=label.dtype ) helper.append_op( type=op_type, inputs={'Label': label}, outputs={ 'RemappedLabel': remapped_label, 'SampledLocalClassCenter': sampled_class_center, }, attrs={ 'num_classes': num_classes, 'num_samples': num_samples, 'ring_id': ring_id, 'nranks': nranks, 'rank': rank, 'fix_seed': seed is not None, 'seed': seed if seed is not None else 0, }, ) return remapped_label, sampled_class_center def fold( x: Tensor, output_sizes: Size2, kernel_sizes: Size2, strides: Size2 = 1, paddings: Size2 | Size4 = 0, dilations: Size2 = 1, name: str | None = None, ) -> Tensor: r""" Combines an array of sliding local blocks into a large containing tensor. also known as col2im when operated on batched 2D image tensor. Fold calculates each combined value in the resulting large tensor by summing all values from all containing blocks. For each input :math:`x` with shape [N, C_in , L], the output shape [N, C_out, H_out, W_out] can be calculated as following. .. math:: H_{out} &= output\_size[0] \\ W_{out} &= output\_size[1] \\ C_{out} &= \frac{C_{in}}{kernel\_sizes[0]\times kernel\_sizes[1]} \\ Parameters: x(Tensor): 3-D Tensor, input tensor of format [N, C, L], data type can be float32, float64, complex64 or complex128 output_sizes(int|list|tuple): The size of output size, should be [output_size_h, output_size_w] or an integer o treated as [o, o]. kernel_sizes(int|list|tuple): The size of convolution kernel, should be [k_h, k_w] or an integer k treated as [k, k]. strides(int|list|tuple, optional): The strides, should be [stride_h, stride_w] or an integer stride treated as [stride, stride]. For default, strides will be [1, 1]. paddings(int|list|tuple, optional): The paddings of each dimension, should be [padding_top, padding_left, padding_bottom, padding_right] or [padding_h, padding_w] or an integer padding. If [padding_h, padding_w] was given, it will expanded to [padding_h, padding_w, padding_h, padding_w]. If an integer padding was given, [padding, padding, padding, padding] will be used. For default, paddings will be [0, 0, 0, 0] dilations(int|list|tuple, optional): the dilations of convolution kernel, should be [dilation_h, dilation_w], or an integer dilation treated as [dilation, dilation]. For default, it will be [1, 1]. 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: The tensor formed by combining a group of sliding local blocks The output shape is [N, Cout, H, W] as described above. Examples: .. code-block:: python >>> import paddle >>> import paddle.nn.functional as F >>> x = paddle.randn([2,3*2*2,12]) >>> y = F.fold(x, output_sizes=[4, 5], kernel_sizes=2) >>> x = paddle.randn([2,3*2*2,12]) >>> y = F.fold(x, output_sizes=[4, 5], kernel_sizes=2) >>> print(y.shape) [2, 3, 4, 5] """ helper = LayerHelper("fold", **locals()) check_variable_and_dtype( x, 'x', ['float32', 'float64', 'complex64', 'complex128'], 'fold' ) assert len(x.shape) == 3, "input should be the format of [N, C, L]" assert math.prod(x.shape) >= 0, ( "The number of elements must greater or equal than zero." ) def _is_list_or_tuple_(data): return isinstance(data, (list, tuple)) if isinstance(output_sizes, int): output_sizes = [output_sizes, output_sizes] else: assert _is_list_or_tuple_(output_sizes) and (len(output_sizes) == 2), ( "output_sizes should either be an integer or a list/tuple of two integers" ) if isinstance(kernel_sizes, int): kernel_sizes = [kernel_sizes, kernel_sizes] else: assert _is_list_or_tuple_(kernel_sizes) and (len(kernel_sizes) == 2), ( "kernel_sizes should either be an integer or a list/tuple of two integers" ) if isinstance(strides, int): strides = [strides, strides] else: assert _is_list_or_tuple_(strides) and (len(strides) == 2), ( "strides should either be an integer or a list/tuple of two integers" ) if isinstance(dilations, int): dilations = [dilations, dilations] else: assert _is_list_or_tuple_(dilations) and (len(dilations) == 2), ( "dilations should either be an integer or a list/tuple of two integers" ) if isinstance(paddings, int): paddings = [paddings] * 4 elif isinstance(paddings, list): if len(paddings) == 2: paddings = paddings * 2 elif len(paddings) == 4: pass else: raise ValueError( "paddings should either be an integer or a list of 2 or 4 integers" ) else: raise ValueError( "Unexpected type of paddings, it should be either an integer or a list" "of 2 or 4 integers" ) if in_dynamic_or_pir_mode(): out = _C_ops.fold( x, output_sizes, kernel_sizes, strides, paddings, dilations ) else: out = helper.create_variable_for_type_inference(dtype=x.dtype) helper.append_op( type="fold", inputs={"X": x}, outputs={"Y": out}, attrs={ "output_sizes": output_sizes, "kernel_sizes": kernel_sizes, "strides": strides, "paddings": paddings, "dilations": dilations, }, ) return out