如果设置了 divisor_override,把分母改为 divisor_override。 img_tensor=torch.ones((1,1,4,4))avgpool_layer=nn.AvgPool2d((2,2),stride=(2,2))img_pool=avgpool_layer(img_tensor)print("raw_img:\n{}\npooling_img:\n{}".format(img_tensor,img_pool)) 输出如下: raw_img:tensor([[[1.,1...
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) torch.nn.AvgPool2d(kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_overrid...
• divisor_override :除法因子 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=...
divisor_override:除法因子(自定义分母) 平均池化代码: # === avgpoolflag = 1#flag = 0if flag:avgpoollayer = nn.AvgPool2d((2, 2), stride=(2, 2)) # input:(i, o, size) weights:(o, i , h, w)img_pool = avgpoollayer(img_tensor) 我们来看一下平均池化的效果: 输出尺寸变化: 最...
torch.nn.functional.avg_pool2d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True, divisor_override=None) → Tensor Applies 2D average-pooling operation in kH×kWkH \times kWkH×kW regions by step size sH×sWsH \times sWsH×sW steps. The number of outpu...
y = pool(inputs, 0.0, jax.lax.add, kernel_size, strides, padding) if count_include_pad: y = y / np.prod(kernel_size) if divisor_override is not None: y = y / jnp.array(divisor_override, y.dtype) elif count_include_pad: y = y / jnp.array(np.prod(kernel_size), y.dtype)...
Failure - onnx_lowering: onnx.AveragePool "AdaptiveAvgPool1dGeneralDynamicNoBatches_basic", "AvgPool2dDivisorOverrideModule_basic", Failure - onnx_lowering: onnx.SoftmaxCrossEntropyLoss "CrossEntropyLossModule_basic", "CrossEntropyLossNoReductionModule_basic", ...
divisor_override: Optional[int]): def backward(grad_output): grad_self = torch.avg_pool2d_backward(grad_output, self, kernel_size, stride, padding, ceil_mode, count_include_pad, divisor_override) return grad_self, None, None, None, None, None, None return torch.avg_pool2d(self, ...
MaxPool1d MaxPool2d MaxPool3d MaxUnpool1d MaxUnpool2d MaxUnpool3d AvgPool1d AvgPool2d AvgPool3d FractionalMaxPool2d LPPool1d LPPool2d AdaptiveMaxPool1d AdaptiveMaxPool2d AdaptiveMaxPool3d AdaptiveAvgPool1d AdaptiveAvgPool2d AdaptiveAvgPool3d ...
[1.,2,3,4,5,6,7]]])) tensor([[[ 2., 4., 6.]]]) 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 ...