mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]): masks to be applied for each position in the convolution kernel. Default: None:作用在卷积操作中窗口内实际参与计算元素上的mask,可以简单理解为局部空间 attention 的作用。mask对应的offset_groups必须于...
mask(Tensor[batch_size,offset_groups * kernel_height * kernel_width,out_height,out_width]) - 应用于卷积核中每个位置的掩码。默认值:无 返回: 卷积的结果 返回类型: 张量[batch_sz, out_channels, out_h, out_w] 如果mask 不是None,则执行 Deformable ConvNets v2: More Deformable, Better Result...
mask (Tensor[batch_size, offset_groups * kernel_height * kernel_width, out_height, out_width]): masks to be applied for each position in the convolution kernel. Default: None:作用在卷积操作中窗口内实际参与计算元素上的mask,可以简单理解为局部空间attention的作用。mask对应的offset_groups必须于前面...
bias = self.bias weight = self.weight input = ops.torchvision.deform_conv2d(argument_1, weight, offset, mask, bias, 1, 1, 1, 1, 1, 1, 1, 1, True) ~~~ <--- HERE return input " Versions my Version: python3.8 : torch
paddle.static.nn.deform_conv2d函数定义 paddle.static.nn.deform_conv2d(x, offset, mask, num_filters, filter_size, stride=1, padding=0, dilation=1, groups=1, deformable_groups=1, im2col_step=1, weight_attr=None, bias_attr=None, name=None)预览...
mask = None x = deform_conv2d( x, offset=offset, weight=self.weight, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, mask=mask, ) return x if __name__ == "__main__": deformable_conv2d = DeformableConv2d(in_dim=3, out_dim=4, kernel_size=1, ...
🐛 Bug I try to test the speed of deformable conv2d. But always encountered memory error. To Reproduce $ ipython Python 3.8.5 (default, Jul 27 2020, 08:42:51) Type 'copyright', 'credits' or 'license' for more information IPython 7.17.0 --...
X W offset,另外的bias和mask其实一开始没填,还是报同样的错。 我参考看了一下conv2d,这里面其实也只有两个输入参数,但是ONNX上是输入参数也是2~3个。 auto node = helper_->MakeNode( "Conv", {input_info[0].name, kernel_info[0].name}, {output_info[0].name}); Zheng-Bicheng reviewed May ...