srch

MaxPool2d. Applies a 2D max pooling over an input signal composed of several input planes. If padding is non-zero, then the input is implicitly padded with negative infinity on both sides for padding number of points. dilation controls the spacing between the kernel points.

Applies a 2D max pooling over an input signal composed of several input planes. ... If padding is non-zero, then the input is implicitly padded with negative ...

AdaptiveAvgPool2d. Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. output_size – the target output size of the image of the form H x W. Can be a tuple (H, W) or a single H for a ...

MaxPool2d — PyTorch 1.10.0 documentation MaxPool2d class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) [source] Applies a 2D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size

Assuming your image is a numpy.array upon loading (please see comments for explanation of each step): import numpy as np import torch ...

MaxUnpool2d takes in as input the output of MaxPool2d including the indices of the maximal values and computes a partial inverse in which all non-maximal values ...

FractionalMaxPool2d — PyTorch 1.10.1 documentation FractionalMaxPool2d class torch.nn.FractionalMaxPool2d(kernel_size, output_size=None, output_ratio=None, return_indices=False, _random_samples=None) [source] Applies a 2D fractional max pooling over an input signal composed of several input planes.

The following are 30 code examples for showing how to use torch.nn.functional.max_pool2d().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

AdaptiveAvgPool2d — PyTorch 1.10.0 documentation AdaptiveAvgPool2d class torch.nn.AdaptiveAvgPool2d(output_size) [source] Applies a 2D adaptive average pooling over an input signal composed of several input planes. The output is of size H x W, for any input size. The number of output features is equal to the number of input planes. Parameters

torch.nn · Containers · Convolution Layers · Pooling layers · Padding Layers · Non-linear Activations (weighted sum, nonlinearity) · Non-linear Activations (other).

torch.nn.functional.avg_pool2d. torch.nn.functional. avg_pool2d (input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, ...

AdaptiveMaxPool2d. class torch.nn. AdaptiveMaxPool2d (output_size, return_indices=False)[source]. Applies a 2D adaptive max pooling over an input signal ...

torch.nn.functional.max_pool2d(*args, **kwargs) Applies a 2D max pooling over an input signal composed of several input planes. See MaxPool2d for details. torch.nn.functional.max_pool2d.

torch.nn.functional. Convolution functions. conv1d. Applies a 1D convolution over an input signal composed of several input planes.

Applies a 2D average pooling over an input signal composed of several input planes. If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input.

ReLU(inplace=True) # maxpool different from pytorch-resnet, to match tf-faster-rcnn self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ...

torch.nn.functional.adaptive_max_pool2d. Applies a 2D adaptive max pooling over an input signal composed of several input planes. See AdaptiveMaxPool2d for details and output shape. return_indices – whether to return pooling indices. Default: False.

AvgPool2d — PyTorch 1.10.0 documentation AvgPool2d class torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) [source] Applies a 2D average pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size

torch.quantized_max_pool2d — PyTorch 1.10.0 documentation torch.quantized_max_pool2d torch.quantized_max_pool2d(input, kernel_size, stride=[], padding=0, dilation=1, ceil_mode=False) → Tensor Applies a 2D max pooling over an input quantized tensor composed of several input planes. Parameters input ( Tensor) – quantized tensor

from typing import List, Optional. from torch import Tensor. from .module import Module. from .utils import _single, _pair, _triple.