importtorchimporttorch.nn.functionalasF#3D图像输入img=torch.randn(1,1,160,192,160)#[batch_size, channel, D, H, W]#3D仿射变换矩阵theta=torch.tensor([[1,0,0,10],[0,1,0,0],[0,0,1,0]],dtype=torch.float)grid=F.affine_grid(theta.unsqueeze(0),img.size())output=F.grid_sample(im...
torch.nn.functional.grid_sample能够处理的输入为4D或者5D,分别对应特征为(batch,channel,(depth),height,width)和坐标grid(batch,(depth),height,wdith,2(3)),这里我们depth为ray上采样的N_sample个点,因此可以做如下reshape操作,同时我们可以将view外的点(即坐标最终在-1~1外的点)给mask掉。 # [1,1,h,...
# 添加第三条日志:这个batch下的128张图像 img = vutils.make_grid(x, nrow=12) logger.add_image("train image sample", img, global_step=global_iter_num) # 添加第三条日志:网络中的参数分布直方图 forname, paraminMyConvNet.n...
问pytorch的grid_sample返回不正确的值ENGorm 连接 mysql 使用的是 github.com/go-sql-driver/mysql ...
'addmv', 'addmv_', 'addr', 'affine_grid_generator', 'all', 'allclose', 'alpha_dropout', 'alpha_dropout_', 'amax', 'amin', 'aminmax', 'angle', 'any', 'arange', 'arccos', 'arccos_', 'arccosh', 'arccosh_', 'arcsin', 'arcsin_', 'arcsinh', 'arcsinh_', 'arctan', '...
grid_sample 函数将范围 [-1, 1] 映射到旧张量和新张量的范围(如果大小不同,则会隐式地进行重新缩放)。这个范围映射意味着为了镜像数据,我们只需要将变换矩阵的相关元素乘以 -1。 通过随机偏移进行移动 将结节候选物体移动一下不会产生很大的影响,因为卷积是独立于平移的,尽管这会使我们的模型对不完全居中的结节...
img = vutils.make_grid(x, nrow=12) logger.add_image("train image sample", img, global_step=global_iter_num) # 添加第三条日志:网络中的参数分布直方图 for name, param in MyConvNet.named_parameters(): logger.add_histogram(name, param.data.numpy(), global_step=global_iter_num) ...
sample according to the smallest mask num_rays = num_rays or mask.sum(dim=(1, 2)).min().int().item() if num_rays is not None: if mask is not None: assert mask.shape == xy_grid.shape[:3] weights = mask.reshape(batch_size, -1) else: # it is probably more efficient to us...
It is said pytorch devs are going to change the default behavior of grid_sample() in pytorch v1.4.0: from align_corners=True to align_corners=False Here is the link: https://pytorch.org/docs/stable/nn.functional.html#grid-sample What do ...
•空间变换神经网络通过F.grid_sample和F.affine_grid。 •nn.SeLU和nn.AlphaDropout被引入,论文:《自标准化神经网络》。 •nn.GLU(门控线性单元)被引入,论文:《卷积序列到序列学习》。 •权重归一化现在通过torch.utils.weight_norm来实现。