pytorch计算图扩大,反传变慢问题debug

是这样的,我自己写了一个block,这个block的内容如下

# 为了更加集成,给定两个角度,生成compact的倾斜图片
class Compact_Homo(nn.Module):
    def __init__(self, device):
        super(Compact_Homo, self).__init__()
        # 假设内参数K为单位矩阵
        self.d = 5  # 表示物体到光心的距离
        self.device = device
    def forward(self, alpha, beta, size, d):
        # alpha: N, beta: N, size: N*C*W*H
        # pdb.set_trace()
        if d is not None:
            self.d = d
        B = alpha.shape[0]
        # 表示图像的尺寸
        if size is None:
            size = (B, 3, 1024, 1024)
        N, C, H, W = size
        N = B
        Rotx = torch.zeros(B, 3, 3).to(self.device).clone()
        ones = torch.ones(B,).to(self.device).clone()

        # pdb.set_trace()
        Rotx[:, 0, 0] =  ones
        Rotx[:,1, 1] = torch.cos(beta).squeeze(1)
        Rotx[:,1, 2] = -torch.sin(beta).squeeze(1)
        Rotx[:,2, 1] = torch.sin(beta).squeeze(1)
        Rotx[:,2, 2] = torch.cos(beta).squeeze(1)

        Roty = torch.zeros(B, 3, 3).to(self.device).clone()
        ones = torch.ones(B,).to(self.device).clone()
        Roty[:,1,1] = ones.clone()
        Roty[:,0,0] = torch.cos(alpha).squeeze(1)
        Roty[:,0,2] = torch.sin(alpha).squeeze(1)
        Roty[:,2,0] = -torch.sin(alpha).squeeze(1)
        Roty[:,2,2] = torch.cos(alpha).squeeze(1)
        
        # 以下过程构造homo
        R = torch.bmm(Rotx, Roty)
        R_1 = torch.inverse(R).clone()  # 版本不一样,需要的shape也不一样
        t = torch.zeros(B,3).to(self.device)
        # pdb.set_trace()
        t[:,2] = d.squeeze(1).clone() # 平移向量
        R_1[:,:,2] = t.clone()  # 将第三列赋值
        temp_homo = R_1.clone()
        homo = torch.inverse(R_1).clone()
        
        # -------------------
        # 以下过程构造单位圆,求解其center以及其scale
        C = torch.zeros(B, 3, 3).to(self.device).clone()
        C[:,0,0] = torch.tensor(1.)
        C[:,1,1] = torch.tensor(1.)
        C[:,2,2] = torch.tensor(-1.)
        C2 = torch.bmm(torch.inverse(torch.transpose(temp_homo,1,2)), C)
        C2_ = torch.bmm(C2, torch.inverse(temp_homo))

        C3 = torch.inverse(C2_)  # 对偶形式

        a = C3[:,0,0]
        b = C3[:,0,2]+C3[:,2,0]
        c = C3[:,2,2]

        right_x = (-b-torch.sqrt(b.mul(b)-4*a.mul(c)))/(2*a)
        left_x = (-b+torch.sqrt(b.mul(b)-4*a.mul(c)))/(2*a)
        right_x = -1./right_x
        left_x = -1./left_x

        width = right_x-left_x
        center_x = (right_x+left_x)/2


        a_ = C3[:,1,1]
        b_ = C3[:,1,2]+C3[:,2,1]
        c_ = C3[:,2,2]

        bottom_y = (-b_-torch.sqrt(b_.mul(b_)-4*a_.mul(c_)))/(2*a_)
        top_y = (-b_+torch.sqrt(b_.mul(b_)-4*a_.mul(c_)))/(2*a_)
        bottom_y = -1./bottom_y
        top_y = -1./top_y

        height = bottom_y-top_y
        center_y = (top_y+bottom_y)/2
        scale = torch.max(width, height)

        #---------------------
        # 根据求解得到的homo,中心点以及产生compact的grid
        # size = (1, 3, 1024, 1024)
        N, C, H, W = size
        N=B

        base_grid = torch.zeros(N, H, W, 2).to(self.device)
        linear_points = torch.linspace(-1, 1, W).to(self.device) if W > 1 else torch.Tensor([-1]).to(self.device)
        base_grid[:, :, :, 0] = torch.ger(torch.ones(H).to(self.device), linear_points).expand_as(base_grid[:, :, :, 0])
        linear_points = torch.linspace(-1, 1, H).to(self.device) if H > 1 else torch.Tensor([-1]).to(self.device)
        base_grid[:, :, :, 1] = torch.ger(linear_points, torch.ones(W).to(self.device)).expand_as(base_grid[:, :, :, 1])
        base_grid = base_grid.view(N, H * W, 2)

        # 对center和scale进行变换
        center_x = center_x.unsqueeze(1)
        center_y = center_y.unsqueeze(1)
        center = torch.cat((center_x,center_y), 1).unsqueeze(1).repeat(1,W*H,1)
        scale = scale.unsqueeze(1).repeat(1,H*W).unsqueeze(2).repeat(1,1,2)

        base_grid = base_grid*scale/2
        base_grid = base_grid+center
        
        # 将homo进行扩展,方便运算
        h = homo.unsqueeze(1).repeat(1, W*H, 1, 1)

        temp1 = (h[:, :, 0, 0] * base_grid[:, :, 0] + h[:, :, 0, 1] * base_grid[:, :, 1] + h[:, :, 0, 2])
        temp2 = (h[:, :, 2, 0] * base_grid[:, :, 0] + h[:, :, 2, 1] * base_grid[:, :, 1] + h[:, :, 2, 2])
        u1 = temp1 / temp2

        temp3 = (h[:, :, 1, 0] * base_grid[:, :, 0] + h[:, :, 1, 1] * base_grid[:, :, 1] + h[:, :, 1, 2])
        temp4 = (h[:, :, 2, 0] * base_grid[:, :, 0] + h[:, :, 2, 1] * base_grid[:, :, 1] + h[:, :, 2, 2])
        v1 = temp3 / temp4

        grid1 = u1.view(N, H, W, 1)
        grid2 = v1.view(N, H, W, 1)

        grid = torch.cat((grid1, grid2), 3)
        return grid

但是我在主程序中调用这个block的时候,计算loss,并且反传大概需要20多秒,但是前传很快。
一开始是怀疑是torch.inverse或者是torch.sqrt这些函数会拖慢反传速度,但是后来想了一下拟操作或者开方的导数并不复杂。
在pytorch forum上网上看了一个链接,他提出的问题是计算图进行了极大的扩展,而一开始我并没有往这方面想。通过逐步debug,我发现将center以及scale进行detach()之后,运算时长会极大的缩短,所以我想的是一定是不用反传所以很快,时长能从20秒降低到6秒。
继续debug
我发现将上述代码中的一段

        temp1 = (h[:, :, 0, 0] * base_grid[:, :, 0] + h[:, :, 0, 1] * base_grid[:, :, 1] + h[:, :, 0, 2])
        temp2 = (h[:, :, 2, 0] * base_grid[:, :, 0] + h[:, :, 2, 1] * base_grid[:, :, 1] + h[:, :, 2, 2])
        u1 = temp1 / temp2

        temp3 = (h[:, :, 1, 0] * base_grid[:, :, 0] + h[:, :, 1, 1] * base_grid[:, :, 1] + h[:, :, 1, 2])
        temp4 = (h[:, :, 2, 0] * base_grid[:, :, 0] + h[:, :, 2, 1] * base_grid[:, :, 1] + h[:, :, 2, 2])

其中的h换成homo中的一些元素,能保留前传的梯度,如果问题出现在torch.inverse或者torch.sqrt的话,理论上应该不会影响计算速度,但是我发现当我这么操作的时候,反传时间会极大的缩短。
于是我想之所以center和scale变量进行detach()的时候,计算时长也会极大缩短,原因可能是和repeat有关,因为h也是homo的repeat很多次(W*H),所以我果断将repeat给替换掉,

        h = homo
        # temp1 = (h[:, :, 0, 0] * base_grid[:, :, 0] + h[:, :, 0, 1] * base_grid[:, :, 1] + h[:, :, 0, 2])
        # temp2 = (h[:, :, 2, 0] * base_grid[:, :, 0] + h[:, :, 2, 1] * base_grid[:, :, 1] + h[:, :, 2, 2])
        temp1 = (h[:, 0, 0] * base_grid[:, :, 0] + h[:, 0, 1] * base_grid[:, :, 1] + h[:, 0, 2])
        temp2 = (h[:, 2, 0] * base_grid[:, :, 0] + h[:, 2, 1] * base_grid[:, :, 1] + h[:, 2, 2])
        u1 = temp1 / temp2

        # temp3 = (h[:, :, 1, 0] * base_grid[:, :, 0] + h[:, :, 1, 1] * base_grid[:, :, 1] + h[:, :, 1, 2])
        # temp4 = (h[:, :, 2, 0] * base_grid[:, :, 0] + h[:, :, 2, 1] * base_grid[:, :, 1] + h[:, :, 2, 2])
        temp3 = (h[:, 1, 0] * base_grid[:, :, 0] + h[:, 1, 1] * base_grid[:, :, 1] + h[:, 1, 2])
        temp4 = (h[:, 2, 0] * base_grid[:, :, 0] + h[:, 2, 1] * base_grid[:, :, 1] + h[:, 2, 2])
        v1 = temp3 / temp4
        # 对center和scale进行变换
        center_x = center_x.unsqueeze(1)
        center_y = center_y.unsqueeze(1)
        # center = torch.cat((center_x,center_y), 1).unsqueeze(1).repeat(1,W*H,1)
        # scale = scale.unsqueeze(1).repeat(1,H*W).unsqueeze(2).repeat(1,1,2)
        center = torch.cat((center_x,center_y), 1)
        scale = scale
        base_grid = base_grid*scale/2.
        base_grid = base_grid+center

所以时长一下子由下图

变成了

几乎不耗时
pytorch forum链接https://discuss.pytorch.org/t/why-loss-backward-is-so-slow-taking-about-20s/122956/3

posted on 2021-06-02 11:25  YongjieShi  阅读(251)  评论(0编辑  收藏  举报

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