# 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 paddle from ...fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype from ...fluid.layers import utils from ...fluid.dygraph import layers from ...fluid.layer_helper import LayerHelper from .. import functional as F __all__ = [ 'AdaptiveAvgPool2d', 'AdaptiveAvgPool3d', ] class AdaptiveAvgPool2d(layers.Layer): """ This operation applies 2D adaptive avg pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size. For avg adaptive pool2d: .. math:: hstart &= floor(i * H_{in} / H_{out}) hend &= ceil((i + 1) * H_{in} / H_{out}) wstart &= floor(j * W_{in} / W_{out}) wend &= ceil((j + 1) * W_{in} / W_{out}) Output(i ,j) &= \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)} Parameters: output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two element, (H, W). H and W can be either a int, or None which means the size will be the same as that of the input. data_format (str): The data format of the input and output data. An optional string from: "NCHW", "NHWC". The default is "NCHW". When it is "NCHW", the data is stored in the order of: [batch_size, input_channels, input_height, input_width]. name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Shape: x (Tensor): The input tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type can be float16, float32, float64, int32 or int64. output (Tensor): The output tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type is same as input x. Returns: A callable object of AdaptiveAvgPool2d. Examples: .. code-block:: python # adaptive avg pool2d # suppose input data in shape of [N, C, H, W], `output_size` is [m, n], # output shape is [N, C, m, n], adaptive pool divide H and W dimensions # of input data into m * n grids averagely and performs poolings in each # grid to get output. # adaptive avg pool performs calculations as follow: # # for i in range(m): # for j in range(n): # hstart = floor(i * H / m) # hend = ceil((i + 1) * H / m) # wstart = floor(i * W / n) # wend = ceil((i + 1) * W / n) # output[:, :, i, j] = avg(input[:, :, hstart: hend, wstart: wend]) # import paddle import numpy as np paddle.disable_static() input_data = np.random.rand(2, 3, 32, 32) x = paddle.to_tensor(input_data) # x.shape is [2, 3, 32, 32] adaptive_avg_pool = paddle.nn.AdaptiveAvgPool2d(output_size=3) pool_out = adaptive_avg_pool(x = x) # pool_out.shape is [2, 3, 3, 3] """ def __init__(self, output_size, data_format="NCHW", name=None): super(AdaptiveAvgPool2d, self).__init__() self._output_size = output_size self._data_format = data_format self._name = name def forward(self, x): return F.adaptive_avg_pool2d( x, output_size=self._output_size, data_format=self._data_format, name=self._name) class AdaptiveAvgPool3d(layers.Layer): """ This operation applies 3D adaptive avg pooling on input tensor. The h and w dimensions of the output tensor are determined by the parameter output_size. For avg adaptive pool3d: .. math:: dstart &= floor(i * D_{in} / D_{out}) dend &= ceil((i + 1) * D_{in} / D_{out}) hstart &= floor(j * H_{in} / H_{out}) hend &= ceil((j + 1) * H_{in} / H_{out}) wstart &= floor(k * W_{in} / W_{out}) wend &= ceil((k + 1) * W_{in} / W_{out}) Output(i ,j, k) &= \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)} Parameters: output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain three elements, (D, H, W). D, H and W can be either a int, or None which means the size will be the same as that of the input. data_format (str): The data format of the input and output data. An optional string from: "NCDHW", "NDHWC". The default is "NCDHW". When it is "NCDHW", the data is stored in the order of: [batch_size, input_channels, input_depth, input_height, input_width]. name(str, optional): For detailed information, please refer to :ref:`api_guide_Name`. Usually name is no need to set and None by default. Shape: x (Tensor): The input tensor of adaptive avg pool3d operator, which is a 5-D tensor. The data type can be float16, float32, float64, int32 or int64. output (Tensor): The output tensor of adaptive avg pool3d operator, which is a 5-D tensor. The data type is same as input x. Returns: A callable object of AdaptiveAvgPool3d. Examples: .. code-block:: python # adaptive avg pool3d # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n], # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions # of input data into l * m * n grids averagely and performs poolings in each # grid to get output. # adaptive avg pool performs calculations as follow: # # for i in range(l): # for j in range(m): # for k in range(n): # dstart = floor(i * D / l) # dend = ceil((i + 1) * D / l) # hstart = floor(j * H / m) # hend = ceil((j + 1) * H / m) # wstart = floor(k * W / n) # wend = ceil((k + 1) * W / n) # output[:, :, i, j, k] = # avg(input[:, :, dstart:dend, hstart: hend, wstart: wend]) import paddle import numpy as np paddle.disable_static() input_data = np.random.rand(2, 3, 8, 32, 32) x = paddle.to_tensor(input_data) # x.shape is [2, 3, 8, 32, 32] adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3d(output_size=3) pool_out = adaptive_avg_pool(x = x) # pool_out = [2, 3, 3, 3, 3] """ def __init__(self, output_size, data_format="NCDHW", name=None): super(AdaptiveAvgPool3d, self).__init__() self._output_size = output_size self._data_format = data_format self._name = name def forward(self, x): return F.adaptive_avg_pool3d( x, output_size=self._output_size, data_format=self._data_format, name=self._name)