基础:mxnet.gluon.block.HybridBlockPixel-shuffle 层用于 3 维上采样。Pixel-shuffling(或 3D 中的 voxel-shuffling)是沿 channel 维度获取值组并将它们重新分组为沿 D、H 和W 维度的体素块的操作,从而有效地相乘这些尺寸由一个常数因子决定。例如,形状 的特征图通过形成小的体素 块并将它们排列在 网格中而被...
# 需要导入模块: from torch import nn [as 别名]# 或者: from torch.nn importPixelShuffle[as 别名]def__init__(self, input_size, output_size, scale_factor, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm='batch'):super(PSBlock, self).__init__() self.conv ...
或者如果pixelshuffle/depth_to_space层跟随卷积层，你可以对卷积的权重通道进行 Shuffle 。具体来说，如...
PixelBlockCollection 已重置的 PixelBlockCollection 对象。 shuffle () 返回值 数据类型 说明 PixelBlockCollection 包含按乱序排列的像素块的 PixelBlockCollection 对象。 代码示例 PixelBlockCollection 示例 1(Python 窗口) 遍历像素块以计算土地覆被栅格中的总城区面积。 import arcpy # Specify the input raster ...
// pixelshuffle int scale_factor = get_node_attr_i(node, "blocksize", 1); std::string mode = get_node_attr_s(node, "mode"); fprintf(pp, " 0=%d", scale_factor); if (mode == "CRD") { int scale_factor = get_node_attr_i(node, "blocksize", 1); fprintf(pp, " 0=%d",...
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To address this issue, we propose a pedestrian detection model named Shuffle Expansion YOLOv4 (SE-YOLOv4) composed of a path aggregation network based on PixelShuffle (Shuffle-PANet) and an efficient pyramid atrous convolutional block attention module (EPA-CBAM), to improve the detection ...
Understanding the Residual Dense Block architecture Implementing Pixel Shuffle Showcasing an array of pre- and post-processing methodologies Training our own Super-Resolution model Drawing inferences from model results Assessing the results against standard metrics ...
An address generator for a pixel shuffler used in a relective liquid crystal display (RLCD) digital video system, and a pixel shuffler incorporating such an address generator. The address generator includes a small, dual port SRAM 160×8, a combinatorial converter having a pair of input...
# 需要导入模块: from torch.nn import functional [as 别名]# 或者: from torch.nn.functional importpixel_shuffle[as 别名]defforward(self, x):x = self.upsample_conv(x) x = F.pixel_shuffle(x, self.scale_factor)returnx 开发者ID:open-mmlab,项目名称:mmcv,代码行数:6,代码来源:upsample.py ...