interpolate(in_cpu, size=(osize, ), recompute_scale_factor=False, mode="nearest-exact") out_mps = F.interpolate(in_mps, size=(osize, ), recompute_scale_factor=False, mode="nearest-exact") out_cpu - out_mps.cpu() # tensor([[[0., 0., 0., 0., 0., 1., 0., 0., 0.,...
dw_conv(F.adaptive_max_pool2d(x, (h // self.down_scale, w // self.down_scale))) x_v = torch.var(x, dim=(-2, -1), keepdim=True) x_l = x * F.interpolate(self.gelu(self.linear_1(x_s * self.alpha + x_v * self.belt)), size=(h, w), mode='nearest') y_d = ...
The size of the Parametric, Lookup, Arrays and Integral windows can be changed by dragging the bottom left corner. If the new width and/or height is greater than needed to contain the table, the window size is constrained to just fit the table. Press the Ctrl key if you wish to overrid...
kernel_size=7): # 继承父类初始化方法 super(spatial_attention, self).__init__() # 为了保持卷积前后的特征图shape相同,卷积时需要padding padding = kernel_size // 2 # 7*7卷积融合通道信息 [b,2,h,w]==>[b,1,h,w] self.conv = nn.Conv2d(in_channels=2, out_channels...
["type"] == "upsample": # 根据 stride 扩大特征图谱的宽和高 # 目前, 新版本的 PyTorch 已经逐渐启用 Upsample, 而推荐使用更加一般化的 nn.functional.interpolate upsample = nn.Upsample(scale_factor=int(module_def["stride"]), mode="nearest") modules.add_module("upsample_%d" % i, upsample) ...
Figure.figsize属性:图像的尺寸,(w,h),以英寸为单位 Figure.dpi属性:图像分辨率 Figure.facecolor属性:背景色 Figure.edgecolor属性:edge color Figure.linewidth:edge linewidth Figure.frameon:如果为False,则不绘制图像 Figure.subplotpars:一个SubplotParams实例 ...
interpolate( quantized, size=int(quantized.shape[-1] * 2), mode="nearest" ) x, m_p, logs_p, y_mask = self.enc_p( quantized, y_lengths, text, text_lengths, ge ) z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=ge) z_p = self.flow(z, y_mas...
nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, dtype=dtype, device=device) def forward(self, x): x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") x = self.conv(x) return x class VAEDecoder(torch.nn.Module): def __init__(self,...
[2]) up3 = F.interpolate(output3, size=[output2.shape[2], output2.shape[3]], mode="nearest") output2 = output2 + up3 output2 = self.merge2(output2) up2 = F.interpolate(output2, size=[output1.shape[2], output1.shape[3]], mode="nearest") output1 = output1 + up2 output...
#include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkResampleImageFilter.h" #include "itkCenteredEuler3DTransform.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkRescaleIntensityImageFilt...