output_cpu = torch.nn.AvgPool2d(3, stride=2)(input_tensor) # Performing the average pooling on GPU output_gpu = torch.nn.AvgPool2d(3, stride=2)(input_tensor.cuda()) output_cpu, output_gpu
class torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) class torch.nn.AdaptiveMaxPool2d(output_size, return_indices=False) # 输入输出的HW不变 ③ class torch.nn.BatchNorm2d(num_features, eps=1e-05, momentum=0.1, affine=True) 在每一个小...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - torch.nn.AvgPool2d works with long on cpu but not gpu · pytorch/pytorch@b9618c9
nn.AvgPool2d(kernel_size,stride=None,padding=0,ceil_mode=False,count_include_pad=True,divisor_override=None) eg. # pool of square window of size=3, stride=2m = nn.AvgPool2d(3, stride=2)# pool of non-square windowm = nn.AvgPool2d((3, 2), stride=(2, 1))input = torch.randn(...
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CLASStorch.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. ...
torch.nn 是 torch 的神经网络计算部分,其中有许多基础的功能。本文主要记录一下torch.nn的Pooling layers。 Pooling layers部分主要看nn.MaxPool2d和nn.AvgPool2d两部分。 输入参数 torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) ...
nn.AvgPool2d:二维平均池化。 nn.AvgPool3d:三维平均池化。 nn.FractionalMaxPool2d:二维分数最大池化。普通最大池化通常输入尺寸是输出的整数倍。而分数最大池化则可以不必是整数。分数最大池化使用了一些随机采样策略,有一定的正则效果,可以用它来代替普通最大池化和Dropout层。
plt.imshow(pool2_out_im[1].data, cmap=plt.cm.gray) plt.axis('off') plt.show() # 对卷积之后的张量进行平均g池化,也就是锐化的操作 avgpool2d = nn.AvgPool2d(2, stride=2) pool2_out = avgpool2d(imgconv2dout) pool2_out_im = pool2_out.squeeze() ...
classtorch.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. ...