adaptive_avg_pool2d¶
- paddle.nn.functional. adaptive_avg_pool2d ( x, output_size, data_format='NCHW', name=None ) [source]
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This API implements adaptive average pooling 2d operation. See more details in api_nn_pooling_AdaptiveAvgPool2d .
- Parameters
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x (Tensor) – The input tensor of adaptive avg pool2d operator, which is a 4-D tensor. The data type can be float32 or float64.
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 Name. Usually name is no need to set and None by default.
- Returns
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The output tensor of avg adaptive pool2d result. The data type is same as input tensor.
- Return type
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Tensor
- Raises
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ValueError – If data_format is not “NCHW” or “NHWC”.
Examples
# 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 input_data = np.random.rand(2, 3, 32, 32) x = paddle.to_tensor(input_data) # x.shape is [2, 3, 32, 32] out = paddle.nn.functional.adaptive_avg_pool2d( x = x, output_size=[3, 3]) # out.shape is [2, 3, 3, 3]