# 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. import warnings import paddle.fluid.core as core from ...fluid.framework import in_dygraph_mode, core from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.layers.tensor import Variable, fill_constant, zeros, concat # TODO: define the common functions to build a neural network from ...fluid.layers import dropout #DEFINE_ALIAS from ...fluid.layers import label_smooth #DEFINE_ALIAS from ...fluid import one_hot #DEFINE_ALIAS from ...fluid.layers import pad2d #DEFINE_ALIAS from ...fluid.layers import unfold #DEFINE_ALIAS from ...fluid.layers import assign #DEFINE_ALIAS from ...fluid.layers import squeeze #DEFINE_ALIAS from ...fluid.layers import unsqueeze #DEFINE_ALIAS from ...fluid.layers import elementwise_mul #DEFINE_ALIAS from ...tensor import clamp #DEFINE_ALIAS from ...tensor import sum #DEFINE_ALIAS from ...tensor import sqrt #DEFINE_ALIAS #from ...fluid.layers import fc #DEFINE_ALIAS from ...fluid.layers import pad_constant_like #DEFINE_ALIAS __all__ = [ 'dropout', # 'embedding', # 'fc', 'label_smooth', 'one_hot', 'pad', 'pad_constant_like', 'pad2d', 'unfold', # 'bilinear_tensor_product', 'assign', 'interpolate', 'cosine_similarity', ] def interpolate(input, size=None, scale_factor=None, mode='nearest', align_corners=False, align_mode=1, data_format='NCHW', name=None): """ :alias_main: paddle.nn.functional.interpolate :alias: paddle.nn.functional.interpolate,paddle.nn.functional.common.interpolate This op resizes a batch of images. The input must be a 3-D Tensor of the shape (num_batches, channels, in_w) or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape (num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels), and the resizing only applies on the three dimensions(depth, height and width). **Warning:** the parameter :attr:`actual_shape` will be deprecated in the future and only use :attr:`out_shape` instead. Supporting resample methods: 'linear' : Linear interpolation 'bilinear' : Bilinear interpolation 'trilinear' : Trilinear interpolation 'nearest' : Nearest neighbor interpolation 'bicubic' : Bicubic interpolation Linear interpolation is the method of using a line connecting two known quantities to determine the value of an unknown quantity between the two known quantities. Nearest neighbor interpolation is to perform nearest neighbor interpolation in both the 3rd dimension(in height direction) and the 4th dimension(in width direction) on input tensor. Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. 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. 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 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: input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8, its data format is specified by :attr:`data_format`. size (list|tuple|Variable|None): Output shape of image resize layer, the shape is (out_w, ) when input is a 3-D Tensor, the shape is (out_h, out_w) when input is a 4-D Tensor and is (out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If a list, each element can be an integer or a Tensor Variable of shape: [1]. If a Tensor Variable, its dimensions size should be a 1. scale_factor (float|Variable|None): The multiplier for the input height or width. At least one of :attr:`out_shape` or :attr:`scale_factor` must be set. And :attr:`out_shape` has a higher priority than :attr:`scale_factor`. Default: None. mode (str): The resample method. It supports 'linear', '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. Default: False align_mode(int) : An optional for linear/bilinear/trilinear interpolation. Refer to the formula in the example above, it can be \'0\' for src_idx = scale_factor*(dst_indx+0.5)-0.5 , can be \'1\' for src_idx = scale_factor*dst_index. data_format (str, optional): Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from:`NCW`, `NWC`, `"NCHW"`, `"NHWC"`, `"NCDHW"`, `"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` Returns: A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels), A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels), or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). Raises: TypeError: size should be a list or tuple or Variable. ValueError: The 'mode' of image_resize can only be 'linear', 'bilinear', 'trilinear', 'bicubic', or 'nearest' currently. ValueError: 'linear' only support 3-D tensor. ValueError: 'bilinear', 'bicubic' and 'nearest' only support 4-D tensor. ValueError: 'trilinear' only support 5-D tensor. ValueError: One of size and scale_factor must not be None. ValueError: size length should be 1 for input 3-D tensor. ValueError: size length should be 2 for input 4-D tensor. ValueError: size length should be 3 for input 5-D tensor. ValueError: scale_factor should be greater than zero. TypeError: align_corners should be a bool value ValueError: align_mode can only be '0' or '1' ValueError: data_format can only be 'NCW', 'NWC', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'. Examples: .. code-block:: python #declarative mode import paddle import numpy as np input = fluid.data(name="input", shape=[None,3,6,10]) #1 output = paddle.nn.functional.interpolate(input=input, size=[12,12]) #2 #x = np.array([2]).astype("int32") #dim1 = fluid.data(name="dim1", shape=[1], dtype="int32") #fluid.layers.assign(input=x, output=dim1) #output = paddle.nn.functional.interpolate(input=input, size=[12,dim1]) #3 #x = np.array([3,12]).astype("int32") #shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32") #fluid.layers.assign(input=x, output=shape_tensor) #output = paddle.nn.functional.interpolate(input=input, size=shape_tensor) #4 #x = np.array([0.5]).astype("float32") #scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32") #fluid.layers.assign(x,scale_tensor) #output = paddle.nn.functional.interpolate(input=input, scale_factor=scale_tensor) place = fluid.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) input_data = np.random.rand(2,3,6,10).astype("float32") output_data = exe.run(fluid.default_main_program(), feed={"input":input_data}, fetch_list=[output], return_numpy=True) print(output_data[0].shape) #1 # (2, 3, 12, 12) #2 # (2, 3, 12, 2) #3 # (2, 3, 3, 12) #4 # (2, 3, 3, 5) #imperative mode import paddle.fluid.dygraph as dg with dg.guard(place) as g: input = dg.to_variable(input_data) output = paddle.nn.functional.interpolate(input=input, size=[12,12]) print(output.shape) # [2L, 3L, 12L, 12L] """ data_format = data_format.upper() resample = mode.upper() resample_type = mode.lower() resample_methods = [ 'LINEAR', 'BILINEAR', 'TRILINEAR', 'NEAREST', 'BICUBIC', ] if resample not in resample_methods: raise ValueError( "The 'resample' of image_resize can only be 'linaer', 'bilinear', 'trilinear', " " 'bicubic' or 'nearest' currently.") if resample in ['LINEAR'] and len(input.shape) != 3: raise ValueError("'linear' only support 3-D tensor.") if resample in ['BILINEAR', 'NEAREST', 'BICUBIC'] and len(input.shape) != 4: raise ValueError( "'bilinear', 'bicubic' and 'nearest' only support 4-D tensor.") if resample == 'TRILINEAR' and len(input.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 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") helper = LayerHelper('{}_interp'.format(resample_type), **locals()) dtype = helper.input_dtype() if len(input.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(input.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(input.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_turple_(data): return (isinstance(data, list) or isinstance(data, 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' inputs = {"X": input} 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: if isinstance(out_shape, Variable): out_shape.stop_gradient = True inputs['OutSize'] = out_shape else: if not (_is_list_or_turple_(out_shape)): raise TypeError( "out_shape 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): 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): dim.stop_gradient = True new_size_tensor.append(dim) size_list.append(-1) else: assert (isinstance(dim, int)) temp_out = helper.create_variable_for_type_inference( 'int32') 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(input.shape) == 3: if len(out_shape) != 1: raise ValueError( "out_shape 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(input.shape) == 4: if len(out_shape) != 2: raise ValueError("out_shape 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(input.shape) == 5: if len(out_shape) != 3: raise ValueError("out_shape 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] else: if isinstance(scale, Variable): scale.stop_gradient = True inputs["Scale"] = scale elif isinstance(scale, float) or isinstance(scale, int): if scale <= 0: raise ValueError("Attr(scale) should be greater than zero.") attrs['scale'] = float(scale) else: raise TypeError( "Attr(scale)'s type should be float, int or Variable.") out = helper.create_variable_for_type_inference(dtype) helper.append_op( type='{}_interp'.format(resample_type), inputs=inputs, outputs={"Out": out}, attrs=attrs) return out def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None): """ Pad tensor according to 'pad' and 'mode'. 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. Parameters: x (Tensor): The input tensor with data type float32/double/int32/int64_t. pad (Tensor | List[int32]): The padding size with data type int32. [len(padding)/2] dimensions of input will be padded. 1. If input dimension is 3, then the pad has the form (pad_left, pad_right). 2. If the input dimension is 4, then the pad has the form (pad_left, pad_right, pad_top, pad_bottom). 3. If the input dimension is 5, then the pad has the form (pad_left, pad_right, pad_top, pad_bottom, pad_front, pad_back). mode (str): Four modes: 'constant' (default), 'reflect', 'replicate', 'circular'. When in 'constant' mode, this op uses a constant value to pad the input tensor. When in 'reflect' mode, uses reflection of the input boundaries to pad the input tensor. When in 'replicate' mode, uses input boundaries to pad the input tensor. When in 'circular' mode, uses circular input to pad the input tensor. Default is 'constant' value (float32): The value to fill the padded areas in 'constant' mode . Default is 0.0 data_format (str): An string from: "NCL", "NLC", NHWC", "NCHW", "NCDHW", "NDHWC". Specify the data format of the input data. Default is "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: a Tensor padded according to pad and mode and data type is same as input. Return Type: Tensor Examples: .. code-block:: text x = [[[[[1., 2., 3.], [4., 5., 6.]]]]] Case 0: 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.]]]]] Case 1: 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.]]]]] Case 2: 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.]]]]] Case 3: 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.]]]]] Code Examples: .. code-block:: python import numpy as np import paddle import paddle.nn.functional as F paddle.disable_static() # example 1 x_shape = (1, 1, 3) x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape) + 1 tensor_x = paddle.to_tensor(x) y = F.pad(tensor_x, pad=[2, 3], value=1, mode='constant') print(y.numpy()) # [[[1. 1. 1. 2. 3. 1. 1. 1.]]] # example 2 x_shape = (1, 1, 2, 3) x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape) + 1 tensor_x = paddle.to_tensor(x) y = F.pad(tensor_x, pad=[1, 2, 1, 1], value=1, mode='circular') print(y.numpy()) # [[[[6. 4. 5. 6. 4. 5.] # [3. 1. 2. 3. 1. 2.] # [6. 4. 5. 6. 4. 5.] # [3. 1. 2. 3. 1. 2.]]]] """ assert mode in ['reflect', 'replicate', 'constant', 'circular'], \ "mode should be one of constant, reflect, replicate, circular, but got {}.".format(mode) 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], " \ "but got {}".format(data_format) x_dim = len(x.shape) original_data_format = data_format unsqueezed_dim = [] if isinstance(pad, Variable): 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, axes=unsqueezed_dim) elif x_dim == 4: pad = concat([pad, zeros((2, ), dtype="int32")], axis=0) unsqueezed_dim = [2] x = unsqueeze(x, axes=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, axes=unsqueezed_dim) elif x_dim == 4: pad = concat([pad, zeros((2, ), dtype="int32")], axis=0) unsqueezed_dim = [1] x = unsqueeze(x, axes=unsqueezed_dim) else: 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, axes=unsqueezed_dim) elif x_dim == 4: pad = pad + [0, 0] unsqueezed_dim = [2] x = unsqueeze(x, axes=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, axes=unsqueezed_dim) elif x_dim == 4: pad = pad + [0, 0] unsqueezed_dim = [1] x = unsqueeze(x, axes=unsqueezed_dim) if in_dygraph_mode(): if isinstance(pad, Variable): pad = pad.numpy() out = core.ops.pad3d(x, "paddings", pad, "mode", mode, "value", value, "data_format", data_format, "name", name) 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, axes=unsqueezed_dim) return out def cosine_similarity(x1, x2, dim=1, eps=1e-8): """ Compute cosine similarity between x1 and x2 along dim. Parameters: x1 (Tensor): First input. float32/double. x2 (Tensor): Second input. float32/double. dim (int): Dimension of vectors to compute cosine similarity. Default is 1. eps(float): Small value to avoid division by zero. Default is 1e-8. Returns: a Tensor representing cosine similarity between x1 and x2 along dim. Return Type: Tensor 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 ]] dim = 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 import numpy as np paddle.disable_static() np.random.seed(0) x1 = np.random.rand(2,3) x2 = np.random.rand(2,3) x1 = paddle.to_tensor(x1) x2 = paddle.to_tensor(x2) result = paddle.nn.functional.cosine_similarity(x1, x2, dim=0) print(result.numpy()) # [0.99806249 0.9817672 0.94987036] """ w12 = sum(elementwise_mul(x1, x2), axis=dim) w1 = sum(elementwise_mul(x1, x1), axis=dim) w2 = sum(elementwise_mul(x2, x2), axis=dim) n12 = sqrt(clamp(w1 * w2, min=eps * eps)) cos_sim = w12 / n12 return cos_sim