一. torch.cat（）函数解析
1. 函数说明
1.1 官网：torch.cat()，函数定义及参数说明如下图所示：
1.2 函数功能
函数将两个张量（tensor）按指定维度拼接在一起，注意：除拼接维数dim数值可不同外其余维数数值需相同，方能对齐，如下面例子所示。torch.cat()函数不会新增维度，而torch.stack()函数会新增一个维度，相同的是两个都是对张量进行拼接
2. 代码举例
2.1 输入两个二维张量（dim=0）：dim=0对行进行拼接
a = torch.randn(2,3)b = torch.randn(3,3)c = torch.cat((a,b),dim=0)a,b,c输出结果如下：(tensor([[-0.90, -0.37, 1.96], [-2.65, -0.60, 0.05]]), tensor([[ 1.30, 0.24, 0.27], [-1.99, -1.09, 1.67], [-1.62, 1.54, -0.14]]), tensor([[-0.90, -0.37, 1.96], [-2.65, -0.60, 0.05], [ 1.30, 0.24, 0.27], [-1.99, -1.09, 1.67], [-1.62, 1.54, -0.14]]))2.2 输入两个二维张量（dim=1）: dim=1对列进行拼接
a = torch.randn(2,3)b = torch.randn(2,4)c = torch.cat((a,b),dim=1)a,b,c输出结果如下：(tensor([[-0.55, -0.84, -1.60], [ 0.39, -0.96, 1.02]]), tensor([[-0.83, -0.09, 0.05, 0.17], [ 0.28, -0.74, -0.27, -0.85]]), tensor([[-0.55, -0.84, -1.60, -0.83, -0.09, 0.05, 0.17], [ 0.39, -0.96, 1.02, 0.28, -0.74, -0.27, -0.85]]))2.3 输入两个三维张量：dim=0 对通道进行拼接
a = torch.randn(2,3,4)b = torch.randn(1,3,4)c = torch.cat((a,b),dim=0)a,b,c输出结果如下：(tensor([[[ 0.51, -0.72, -0.02, 0.76], [ 0.72, 1.01, 0.39, -0.13], [ 0.37, -0.63, -2.69, 0.74]], [[ 0.72, -0.31, -0.27, 0.10], [ 1.66, -0.06, 1.91, -0.66], [ 0.34, -0.23, -0.18, -1.22]]]), tensor([[[ 0.94, 0.77, -0.41, -1.20], [-0.23, -1.03, -0.25, 1.67], [-1.00, -0.68, -0.35, -0.50]]]), tensor([[[ 0.51, -0.72, -0.02, 0.76], [ 0.72, 1.01, 0.39, -0.13], [ 0.37, -0.63, -2.69, 0.74]], [[ 0.72, -0.31, -0.27, 0.10], [ 1.66, -0.06, 1.91, -0.66], [ 0.34, -0.23, -0.18, -1.22]], [[ 0.94, 0.77, -0.41, -1.20], [-0.23, -1.03, -0.25, 1.67], [-1.00, -0.68, -0.35, -0.50]]]))2.4 输入两个三维张量：dim=1对行进行拼接
a = torch.randn(2,3,4)b = torch.randn(2,4,4)c = torch.cat((a,b),dim=1)a,b,c输出结果如下：(tensor([[[-0.86, 0.00, -1.26, 1.20], [-0.46, -1.08, -0.82, 2.03], [-0.89, 0.43, 1.92, 0.49]], [[ 0.24, -0.02, 0.32, 0.97], [ 0.33, -1.34, 0.76, -1.55], [ 0.38, 1.45, 0.27, -0.64]]]), tensor([[[ 0.82, 0.85, -0.30, -0.58], [-0.09, 0.40, 0.02, 0.75], [-0.70, 0.67, -0.88, -0.50], [-0.62, -1.65, -1.10, -1.39]], [[-0.85, -1.61, -0.35, -0.56], [ 0.00, 1.40, 0.41, 0.39], [-0.01, 0.04, 0.80, 0.41], [-1.21, -0.64, 1.14, 1.64]]]), tensor([[[-0.86, 0.00, -1.26, 1.20], [-0.46, -1.08, -0.82, 2.03], [-0.89, 0.43, 1.92, 0.49], [ 0.82, 0.85, -0.30, -0.58], [-0.09, 0.40, 0.02, 0.75], [-0.70, 0.67, -0.88, -0.50], [-0.62, -1.65, -1.10, -1.39]], [[ 0.24, -0.02, 0.32, 0.97], [ 0.33, -1.34, 0.76, -1.55], [ 0.38, 1.45, 0.27, -0.64], [-0.85, -1.61, -0.35, -0.56], [ 0.00, 1.40, 0.41, 0.39], [-0.01, 0.04, 0.80, 0.41], [-1.21, -0.64, 1.14, 1.64]]]))2.5 输入两个三维张量：dim=2对列进行拼接
a = torch.randn(2,3,4)b = torch.randn(2,3,5)c = torch.cat((a,b),dim=2)a,b,c输出结果如下：(tensor([[[ 0.13, -0.02, 0.13, -0.25], [ 1.42, -0.22, -0.87, 0.27], [-0.07, 1.04, -0.06, 0.91]], [[ 0.88, -1.46, 0.04, 0.35], [ 1.36, 0.64, 0.75, 0.39], [ 0.36, 1.13, 0.83, 0.56]]]), tensor([[[-0.47, -2.30, -0.49, -1.02, 1.74], [ 0.71, 0.89, 0.80, -0.05, -1.35], [-0.40, 0.26, -0.78, -1.50, -0.92]], [[-0.77, -0.01, 1.23, 0.70, -0.66], [ 0.28, -0.18, -0.91, 2.23, 1.14], [-1.93, -0.17, 0.15, 0.40, 0.32]]]), tensor([[[ 0.13, -0.02, 0.13, -0.25, -0.47, -2.30, -0.49, -1.02, 1.74], [ 1.42, -0.22, -0.87, 0.27, 0.71, 0.89, 0.80, -0.05, -1.35], [-0.07, 1.04, -0.06, 0.91, -0.40, 0.26, -0.78, -1.50, -0.92]], [[ 0.88, -1.46, 0.04, 0.35, -0.77, -0.01, 1.23, 0.70, -0.66], [ 1.36, 0.64, 0.75, 0.39, 0.28, -0.18, -0.91, 2.23, 1.14], [ 0.36, 1.13, 0.83, 0.56, -1.93, -0.17, 0.15, 0.40, 0.32]]]))来源地址：https://blog.csdn.net/flyingluohaipeng/article/details/125038212
