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异星工厂中的高品质产率分析

2024-12-31 21:22:28

异星工厂的品质扩展包给游戏带来了新的生产规划挑战，比起像以前只能横向扩张工厂的规模，现在可以通过使用高品质的工厂和插件，大幅增加产量。为了最大化如传奇品质的高品质物品的生产，我们需要对高品质产率的计算和规划进行一些分析。

高品质物品生产蓝图

一般来说生产高品质的物品有两种方法，生产出目标物品然后慢慢回收提升质量，或者是直接从源头生产高品质的原料，然后直接生产高品质的物品。

这里先初步的设计了两个蓝图，一个是用于电星蓝图的高品质原料生产，通过读取当前物流网络的信号，自动的将多余物品拿去回收，不断的生产高品质的原料。对于原料级别的物品（如铁板、铜板），比起直接放入回收机拿到同样的物品，将其放入到组装机里生产更高级别的物品，然后再回收回来，可以获得更高的品质。

例如下图里的：
blueprint1
Fig. 1. 回收原料生产高品质物品的例子.

这个蓝图设计成完全全自动的模式，能自动进行负载均衡，回收多余的物品。

回收机+组装机3:

1
 
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回收机:

1
 
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另外一种模式是通过将低级的产物不断回收，不断循环直到生产出高品质的物品，如下所示。

blueprint2
Fig. 1. 回收产物生产高品质物品的例子.

回收机+组装机3:

1
 
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回收机+电磁工厂:

1
 
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

回收机+铸造厂:

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原理分析

如果是生产高品质的原料，比如说铜板，那么将铜板生产成铜线，就会考虑到底是使用产能插件好还是质量插件好。虽然质量插件提升了出现高品质物品的概率，但是产能插件大幅增加了产率，特别是高品质的高级产能插件，能提升相当大比例的产率，可能能生产出更多的高品质物品。

参考Figure 2，考虑以下的生产步骤：

flowchart LR    Input[(蓝箱输入)] --> Ass[组装机] --> |低品质| Rec[回收机]    Ass --> |高品质| Output[/红箱输出/]    Rec --> Ass

如果以上图表没有正确渲染，请刷新页面。

令每次的产物$X$都用百分比表示，其中1代表原料投入的数量，如果是0.1，则表示剩下了10%的物品。

$$ X = (x_{普通}, x_{罕见}, x_{稀有}, x_{史诗}, x_{传奇}) $$

而每次经过组装机或者回收机，则是将原料于一个转换矩阵$T$相乘，输出则是新的产物的数量。不过在生产出传奇物品之后，会将这部分收集起来，不参与矩阵乘法。

其中$T$可以表示为

$$
\begin{align*}
T &= \begin{pmatrix}
T_{普通到普通} & T_{普通到罕见} & T_{普通到稀有} & T_{普通到史诗} & T_{普通到传奇} \\
0 & T_{罕见到罕见} & T_{罕见到稀有} & T_{罕见到史诗} & T_{罕见到传奇} \\
0 & 0 & T_{稀有到稀有} & T_{稀有到史诗} & T_{稀有到传奇} \\
0 & 0 & 0 & T_{史诗到史诗} & T_{史诗到传奇} \\
0 & 0 & 0 & 0 & T_{传奇到传奇} \\
\end{pmatrix} \\
&= (1 + P)\begin{pmatrix}
Q_{普通到普通} & Q_{普通到罕见} & Q_{普通到稀有} & Q_{普通到史诗} & Q_{普通到传奇} \\
0 & Q_{罕见到罕见} & Q_{罕见到稀有} & Q_{罕见到史诗} & Q_{罕见到传奇} \\
0 & 0 & Q_{稀有到稀有} & Q_{稀有到史诗} & Q_{稀有到传奇} \\
0 & 0 & 0 & Q_{史诗到史诗} & Q_{史诗到传奇} \\
0 & 0 & 0 & 0 & Q_{传奇到传奇} \\
\end{pmatrix}
\end{align*}
$$
其中$P$是额外产率，比如说用了产能插件之后会增加，而对于回收机来说，应该取值$P=-0.75$。而矩阵中的$Q_{*}$是指在给定总共的质量加成$Q$的情况下，计算出的
每一个品级到另一个品级的转换率。这部分的计算可以参考官方wiki[1]。

定义每次产物的的起始状态为

$$X_{0} = (1, 0, 0, 0, 0)$$

则之后的每一次的产物状态可以表示为

$$X_{t} = X_{t-1}T_{回收机}T_{组装机}$$

但是需要注意的是，每次进入到下个循环之前，需要移除掉传奇物品，然后将其加到最终的产物中。

代码实现

当流程确定之后，就可以通过Scallop[2]来实现这个过程。Scallop是一个用Rust实现的，基于符号推理的编程语言，可以用来解决这类问题。

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type production_after_assembler(bound iter: i32, common: f32, uncommon: f32, rare: f32, epic: f32, legendary: f32)
type production_after_recycler(bound iter: i32, common: f32, uncommon: f32, rare: f32, epic: f32, legendary: f32)

type prod_rate(f32)
type qual_rate(f32)
type assembler_base_prod(f32)

type MachineType = ASSEMBLER | RECYCLER  // 0: ASSEMBLER, 1: RECYCLER
type n_slots(f32, MachineType)
type n_qual(f32, MachineType)
type n_prod(f32, MachineType)
type base_prod(f32, MachineType)
type P(f32, MachineType)
type Q(f32, MachineType)
type T_self(f32, MachineType)
type T_c2l(f32, MachineType)
type T_c2e(f32, MachineType)
type T_c2r(f32, MachineType)
type T_c2u(f32, MachineType)
type T_u2l(f32, MachineType)
type T_u2e(f32, MachineType)
type T_u2r(f32, MachineType)
type T_r2l(f32, MachineType)
type T_r2e(f32, MachineType)
type T_e2l(f32, MachineType)

rel n_slots(4, RECYCLER)
rel base_prod(-0.75, RECYCLER)  // only give 1/4 material back
rel base_prod(b_p, ASSEMBLER) = assembler_base_prod(b_p)
rel n_qual(4, RECYCLER)
rel n_prod(x, m) = n_qual(y, m) and x + y == n_s and n_slots(n_s, m)
// total production and quality rate for each machine
rel P(p_r * n_p + b_p, m) = n_prod(n_p, m) and base_prod(b_p, m) and prod_rate(p_r)
rel Q(q_r * n_q, m) = n_qual(n_q, m) and qual_rate(q_r)

// transform the quality to itself
rel T_self(1 - q, m) = Q(q, m)
// transform common to higher
rel T_c2l(q * 0.001, m) = Q(q, m)
rel T_c2e(x, m) = Q(q, m) and T_c2l(t, m) and q * 0.01 == x + t
rel T_c2r(x, m) = Q(q, m) and T_c2e(t1, m) and T_c2l(t2, m) and q * 0.1 == x + t1 + t2
rel T_c2u(x, m) = Q(q, m) and T_c2r(t1, m) and T_c2e(t2, m) and T_c2l(t3, m) and q == x + t1 + t2 + t3
// transform uncommon to higher
rel T_u2l(q * 0.01, m) = Q(q, m)
rel T_u2e(x, m) = Q(q, m) and T_u2l(t, m) and q * 0.1 == x + t
rel T_u2r(x, m) = Q(q, m) and T_u2e(t1, m) and T_u2l(t2, m) and q == x + t1 + t2
// transform rare to higher
rel T_r2l(q * 0.1, m) = Q(q, m)
rel T_r2e(x, m) = Q(q, m) and T_r2l(t, m) and q == x + t
// transform epic to higher
rel T_e2l(q, m) = Q(q, m)

rel production_after_recycler(0, 1.0, 0.0, 0.0, 0.0, 0.0) // initial state
rel production_after_assembler(
    iter, 
    (1 + p) * (c * t_self),
    (1 + p) * (c * t_c2u + u * t_self),
    (1 + p) * (c * t_c2r + u * t_u2r + r * t_self),
    (1 + p) * (c * t_c2e + u * t_u2e + r * t_r2e + e * t_self),
    (1 + p) * (c * t_c2l + u * t_u2l + r * t_r2l + e * t_e2l) + l
) = production_after_recycler(iter, c, u, r, e, l) and T_self(t_self, m) and T_c2u(t_c2u, m) 
    and T_c2r(t_c2r, m) and T_c2e(t_c2e, m) and T_c2l(t_c2l, m)
    and T_u2l(t_u2l, m) and T_u2e(t_u2e, m) and T_u2r(t_u2r, m)
    and T_r2l(t_r2l, m) and T_r2e(t_r2e, m) and T_e2l(t_e2l, m)
    and P(p, m) and m == ASSEMBLER and iter >= 0

rel production_after_recycler(
    iter + 1, 
    (1 + p) * (c * t_self),
    (1 + p) * (c * t_c2u + u * t_self),
    (1 + p) * (c * t_c2r + u * t_u2r + r * t_self),
    (1 + p) * (c * t_c2e + u * t_u2e + r * t_r2e + e * t_self),
    (1 + p) * (c * t_c2l + u * t_u2l + r * t_r2l + e * t_e2l) + l
) = production_after_assembler(iter, c, u, r, e, l) and T_self(t_self, m) and T_c2u(t_c2u, m)
    and T_c2r(t_c2r, m) and T_c2e(t_c2e, m) and T_c2l(t_c2l, m)
    and T_u2l(t_u2l, m) and T_u2e(t_u2e, m) and T_u2r(t_u2r, m)
    and T_r2l(t_r2l, m) and T_r2e(t_r2e, m) and T_e2l(t_e2l, m)
    and P(p, m) and m == RECYCLER and iter >= 0
    
rel legendary(l, n_s, n_q, p_r, q_r, b_p) = production_after_recycler(20, c, u, r, e, l) 
    and n_slots(n_s, ASSEMBLER) and n_qual(n_q, ASSEMBLER) and prod_rate(p_r) and qual_rate(q_r) and assembler_base_prod(b_p)

注意并没有直接在scallop代码中定义输入的数据，而是通过在和Python API中进行实现，方便一次编译，多次循环调用。

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from scallopy import ScallopContext
from scallopy.collection import ScallopCollection
import time
import pandas as pd

# number of module slots of the "assembler" machine
possible_n_slots = [4, 5, 6, 8]
# all possible productivity module rates
possible_prod_rate = sorted(list({
    0.04, 0.05, 0.06, 0.07, 0.10, # tier 1
    0.06, 0.07, 0.09, 0.11, 0.15, # tier 2
    0.10, 0.13, 0.16, 0.19, 0.25, # tier 3
}))
# all possible quality module rates
possible_qual_rate = sorted(list({
    0.01, 0.013, 0.016, 0.019, 0.025, # tier 1
    0.02, 0.026, 0.032, 0.038, 0.05, # tier 2
    0.025, 0.032, 0.04, 0.047, 0.062, # tier 3
}))
# possible assembler base productivity 0 or 50%
possible_assembler_base_prod = [0.0, 0.5]
# number of quality modules will be 0..n_slots

# build the inputs
inputs = {
    "n_slots": [], "prod_rate": [], "qual_rate": [], "n_qual": [], "assembler_base_prod": []
}
for n_slots in possible_n_slots:
    for prod_rate in possible_prod_rate:
        for qual_rate in possible_qual_rate:
            for n_qual in range(n_slots + 1):
                for assembler_base_prod in possible_assembler_base_prod:
                    inputs["n_slots"].append([(n_slots, 0)])
                    inputs["prod_rate"].append([(prod_rate,)])
                    inputs["qual_rate"].append([(qual_rate,)])
                    inputs["n_qual"].append([(n_qual, 0)])
                    inputs["assembler_base_prod"].append([(assembler_base_prod,)])

print("Total number of inputs: ", len(inputs["n_slots"]))

ctx = ScallopContext()
ctx.import_file("model.scl")
t0 = time.time()
ctx.compile()
print("Time: ", (t1 := time.time()) - t0)

result = [
    ScallopCollection(ctx.provenance, coll) 
    for coll in ctx._internal.run_batch([["legendary"]] * len(inputs["n_slots"]), inputs, parallel=True)
]

output = [list(*each) for each in result]
print("Time: ", (t2 := time.time()) - t1)

# save to csv
output = [list(each[0]) for each in output]
df = pd.DataFrame(output)
df.to_csv("output.csv", index=False, header=["output", "n_slots", "n_qual", "prod_rate", "qual_rate", "assembler_base_prod"])

在代码中，预先定义了每一种组合（不同的产能插件和品质插件的组合，还有是否有50%自带产能），然后编译Scallop模型，把每一种可能性都放进去模拟，计算出传奇物品的总产量。

结果分析

通过以上计算的结果，画了一系列热力图，来展示不同的组合下，为了最大化传奇物品的产量，应该选择用多少产能插件和品质插件。

heatmap_slots_4_base_prod_0.0
Fig. 3. 最优的品质插件数量，4插槽组装机，无基础产能加成 (组装机3型)

heatmap_slots_5_base_prod_0.5
Fig. 4. 最优的品质插件数量，5插槽组装机，50%基础产能加成 (电磁工厂)

heatmap_slots_8_base_prod_0.0
Fig. 5. 最优的品质插件数量，8插槽组装机，无基础产能加成 (低温工厂)

结论

从上图可见，最大化传奇物品的产量，并不是直接堆质量插件即可，而是需要根据产能插件和品质插件提供的加成来灵活选择，主要是考虑产能插件的数值。具体的最优化选择可以以上面几张图作为参考。

  • [1] "Quality", Factorio Wiki, 2024. https://wiki.factorio.com/Quality.
  • [2] J. Huang et al., "Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning", in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2021, pp. 25134–25145. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2021/hash/d367eef13f90793bd8121e2f675f0dc2-Abstract.html

比之前更进一步的终极看番工具栈

2024-07-06 23:09:18

之前的文章中讨论了如何在Windows上配置最佳的视频播放器用于看番，然而这个方法具有很大的局限性。这篇文章将介绍如何搭建一个更加完善的系统，为了获得真正的快乐终极看番体验。

背景

之前的文章中介绍了如何在 Windows 系统中搭建一个拥有超分辨率和插帧的视频播放器，这个能保证很强的画面效果，但是这套看番的方法依然有很多局限性。

  • 基于本地: 由于这个系统是打开本地的文件进行播放，所以当需要远程访问的时候就会有很大的问题。而且由于本地磁盘容量的限制，也只能存储有限的番剧。
  • 缺少自动化: 由于这个系统是基于 MPC-HC 的，所以没有自动化的功能，比如自动下载番剧，自动更新番剧等等。
  • 缺少番剧管理: 整个播放体验是基于 Windows 的文件系统，并没有使用一些软件来提升观看的体验，也没有类似于海报墙的东西。
  • 落后的技术: 之前基于SVP的插帧已经比较落后，和现在SoTA的RIFE[1]插帧相比，画质和效果都有很大的差距。

解决方案

为了解决以上问题，我们需要一个更加完善的系统，这个系统将会搭载在一个上24小时开机的家庭服务器上，这个服务器可以是NAS，也可是软路由，或者是存算分离的Linux服务器。

考虑到不折腾，本人选择了自己DIY一个基于低功耗x86平台的NAS。考虑到折腾软路由可能会让网络没那么稳定，就没有打算在这套系统里使用软路由。家里现在是一台ASUS的硬路由，一台NAS，一台高性能的Linux服务器用于游戏服务器和爬虫脚本等等，然后是一台台式机用于日常游戏和学习。这样的设计是为了让每个设备都有自己的用途，不会因为一台设备的问题导致其他设备无法使用。这篇文章主要会讨论NAS上的部署。

硬件准备

这次是购买了一台蜗牛星际B型，带一个ITX主板+赛扬J6412。到手之后，换了一个靠谱的海韵Flex电源，又插了2根8G DDR4内存，确保之后的docker和vm不会有性能瓶颈。并且把机箱风扇换成了猫扇，确保散热效果。

硬盘方面，用了一个512G的老固态做缓存池，然后是4块8TB的机械硬盘做主存储。

hardware
Fig. 1. 硬件配置, 照片拍摄于刚刚买到基础的蜗牛星际机器时，很多硬件改装还没有开始

系统选择

NAS系统现在主要是四个，黑群晖，OMV[2]，TrueNAS[3] 和 Unraid[4]。

  • 黑群晖: 有很多自带软件，图形化界面对新手友好，但是感觉有点太花里胡哨了，系统的兼容性不是很好，也不方便升级，而且对存储系统的管理也很麻烦。
  • OMV: 一个基于debian的开源系统，相当于是一个debian系统+web管理界面。优势是可以很方便的进terminal进行操作，但是缺点是对文件系统用起来麻烦，而且对docker，vm的支持也不是很好。但是如果愿意折腾，OMV应该是比较强大的。还有一个是OMV的维护者感觉不是很靠谱，对这个系统的未来前景很担忧。
  • TrueNAS scale: 也是基于debian的开源系统，专门为NAS设计，有非常强大的存储系统管理，非常的专业，性能也很强。但是缺点是对docker的支持很糟糕，这个k3s用起来非常反人类。尽管这个系统可能在2024年下半年支持普通的docker，但是现在还没到时候。
  • Unraid: 一个商业系统，有很好的docker和vm支持，存储系统比较灵活，社区也很活跃。但是缺点是存储系统的性能比较差，而且还要收费。

综合考虑，自己不拿这个NAS作为热访问的存储，所以对性能需求没有那么高，只要能拖得动4K视频就行，所以最后选择了方便部署服务的Unraid。

Unraid

Unraid系统安装非常简单，买了一个U盘，然后下载Unraid的安装器，把系统安装在U盘上，然后就能用U盘引导启动了，不需要把系统安装在硬盘上。

Unraid的存储系统是基于JBOD(阵列)和Unraid(池)的。阵列是允许一系列不同大小的硬盘组合成一个大存储空间，并且用1~2块最大的硬盘用于校验，如果阵列中有少于校验盘数量的硬盘挂了，那么数据还能恢复。但是由于阵列的写入就是一块盘，而且还需要实时计算校验，所以性能比较差。而池是允许组成ZFS raid的，可以保证性能，也可以不用raid就单个盘。

Unraid本身是支持多级存储的，比如说可以用一个池来作为缓存池，然后把阵列当成主存储。这样的话，写入的时候会先写入缓存池，然后再定时把缓存池的数据写入主存储。通过这样的设计，IO性能就会提升很多。并且不用保持主存储的硬盘长时间开启，也可以节省电费。

所以，我就把512G的固态硬盘作为缓存池，然后把4块8TB的机械硬盘作为主存储(1块校验盘+3块数据盘=24TB)。这样的设计可以保证不会有IO瓶颈，也可以保证数据的安全。

除此之外，通过插件，unraid也可以在webui里把SMB/NFS之类的远程磁盘挂载到本地。比如说现在在这个Unraid NAS上挂载了一个老家的威廉通NAS。

unraid-disk
Fig. 2. Unraid的硬盘配置

这里推荐一些必装插件:

  • Dynamix Cache Directories: 可以把文件夹的metadata缓存到内存里
  • Mover Tuning: 可以调整mover从缓存池移动文件到主存储阵列的行为，比如说只移动3天前的文件，更加合理
  • Swapfile for unRAID 6.9: swap文件，不然内存容易爆。标题说是6.9，但是最新版本也可以用
  • Unassigned Devices: 用于挂载远程存储，比如说SMB/NFS，还能挂载USB设备等等
  • unbalanced: 用于在阵列中移动文件，从阵列中的一个磁盘里移动文件到另一个磁盘等等
  • User Scripts: 用于设定一些定时脚本, 比如说用rsync备份文件，或者用rclone同步文件等等

基础服务

虚拟网络

尽管有公网ipv4，但是为了避免把端口暴露在公网上，现在主要用虚拟网络(ZeroTier[5]和Tailscale[6])来进行内网穿透。现在在NAS上部署了Tailscale，通过route，可以让这两个虚拟网络的设备互通。

稍微画了一个不专业的网络拓扑图。

network
Fig. 3. 网络拓扑图(建议使用亮色模式观看)

文件服务

首先是aria2[7]和qBittorrent[8]用于NAS上的文件下载。Aria2建议使用这个docker[9]，这个docker里面包含了一些开箱即用的设置，比较方便。至于webui，可以使用AriaNg[10]。qBittorrent可以使用官方docker，然后用peerbanhelper服务[11]来ban掉吸血雷等客户端。

如果使用qb卡死，最好降低同时做种和下载的数量。

其次是文件同步，建议使用Syncthing[12]，这个软件可以在不同设备之间同步文件，而且是p2p的，不需要服务器。可以用这个来同步一些文件和文件夹。

顺便提一下，用Syncthing去同步git仓库，可能会导致git仓库损坏，所以不建议这样做。

用于文件分享的话，主要用Alist[13]，这个软件可以把本地的文件夹 (和网盘里的文件) 分享出去，然后可以通过webui下载文件。

用于NAS的文件管理，建议使用FileBrowser[14]，这个软件可以在webui里管理文件，比如说上传文件，删除文件等等。

filebrowser
Fig. 4. FileBrowser的webui

服务访问

为了能访问NAS上数不清的服务，可以使用Homepage[15]，这个软件可以把所有的服务整合到一个页面上，然后可以通过这个页面看到服务运行的情况，也可以访问所有的服务。

现在已经部署了一个Homepage，欢迎参观。https://portal.controlnet.space

另外也可以部署一个glances[16]，用于监控系统的运行情况。

homepage
Fig. 5. Homepage的webui

追番自动化

没有人喜欢需要手动去找动画资源，然后手动下载手动整理的，所以需要部署一些工具让它尽可能的自动化。

首先是考虑了以下的一个工具链:

  • 蜜柑计划[17]: 一个动画资源的聚合网站，可以通过RSS订阅动画资源
  • AutoBangumi[18]: 一个追番自动化的开源工具，通过RSS订阅动画资源，然后自动调用qb下载，并且自动按照规则整理文件
  • Jellyfin[19]: 一个开源的媒体服务器，可以把本地的动画资源刮削好，并且通过web或者别的客户端访问

蜜柑计划和 AutoBangumi 都是基于bangumi的元数据，其中蜜柑计划会完全采用和bangumi一样的动画标题来整理BT种子，另外蜜柑计划也会根据字幕组/压制组进行分类。这些种子都会作为RSS进行广播。在蜜柑计划中，可以注册账号，然后订阅自己感兴趣的番剧，然后这些番剧将会被聚合到一个RSS订阅中。

AutoBangumi 有两种使用方式: 订阅和收集。订阅是通过观测蜜柑计划的聚合RSS订阅，然后根据规则自动下载新出现的种子，这样可以保证一直保持新番更新，而不用手动去找。收集是通过输入RSS订阅，根据规则下载全部种子，用于下载已经完结的老番。

AutoBangumi 在下载种子之后，会自动的把文件重命名为{番剧名}/Season {季}/{番剧名} S{季}E{集}.{后缀名}，这样方便Jellyfin刮削。

例如,

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├── 怪人的沙拉碗
│   ├── Season 1
│   │   ├── 怪人的沙拉碗 S01E01.mp4
│   │   ├── 怪人的沙拉碗 S01E02.mp4
│   │   ├── 怪人的沙拉碗 S01E03.mp4
│   │   ├── 怪人的沙拉碗 S01E04.mp4
│   │   ├── 怪人的沙拉碗 S01E05.mp4
│   │   ├── 怪人的沙拉碗 S01E06.mp4
│   │   ├── 怪人的沙拉碗 S01E07.mp4
│   │   ├── 怪人的沙拉碗 S01E08.mp4
│   │   ├── 怪人的沙拉碗 S01E09.mp4
│   │   ├── 怪人的沙拉碗 S01E10.mp4
│   │   ├── 怪人的沙拉碗 S01E11.mp4
│   │   ├── 怪人的沙拉碗 S01E12.mp4

媒体服务器考虑了plex[20], emby[21]和Jellyfin。plex和emby的收费内容很多，体验很差。只有Jellyfin是免费开源的，而且生态也很强大，所以选择了Jellyfin。Jellyfin可以通过webui来管理媒体库，也可以通过客户端来观看。

Jellyfin有一个bangumi插件[22]，可以通过bangumi的API来刮削动画的元数据，因为下载的时候是根据bangumi的元数据下载的，所以通过这个方式可以保证绝大多数动画被刮削到Jellyfin里。但是bangumi的元数据相比于TMDB[23]和TVDB[24]来说还是有很多问题，比如说对不同季之间的合并做的很差。而且bangumi没有除了封面之外的图片，比如说背景图，logo等等。

所以最后用tinyMediaManager[25]手动进行元数据匹配来兜底。一般情况下，刮削好元数据之后，视频文件夹就会像以下这样，以后如果用别的播放器或者媒体服务器打开，也能确保一样的元数据。

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├── 怪人的沙拉碗
│   ├── Season 1
│   │   ├── folder.jpg
│   │   ├── metadata
│   │   │   ├── 怪人的沙拉碗 S01E01.jpg
│   │   │   ├── 怪人的沙拉碗 S01E02.jpg
│   │   │   ├── 怪人的沙拉碗 S01E03.jpg
│   │   │   ├── 怪人的沙拉碗 S01E04.jpg
│   │   │   ├── 怪人的沙拉碗 S01E05.jpg
│   │   │   ├── 怪人的沙拉碗 S01E06.jpg
│   │   │   ├── 怪人的沙拉碗 S01E07.jpg
│   │   │   ├── 怪人的沙拉碗 S01E08.jpg
│   │   │   ├── 怪人的沙拉碗 S01E09.jpg
│   │   │   └── 怪人的沙拉碗 S01E10.jpg
│   │   │   └── 怪人的沙拉碗 S01E11.jpg
│   │   │   └── 怪人的沙拉碗 S01E12.jpg
│   │   ├── season.nfo
│   │   ├── 怪人的沙拉碗 S01E01.mp4
│   │   ├── 怪人的沙拉碗 S01E01.nfo
│   │   ├── 怪人的沙拉碗 S01E02.mp4
│   │   ├── 怪人的沙拉碗 S01E02.nfo
│   │   ├── 怪人的沙拉碗 S01E03.mp4
│   │   ├── 怪人的沙拉碗 S01E03.nfo
│   │   ├── 怪人的沙拉碗 S01E04.mp4
│   │   ├── 怪人的沙拉碗 S01E04.nfo
│   │   ├── 怪人的沙拉碗 S01E05.mp4
│   │   ├── 怪人的沙拉碗 S01E05.nfo
│   │   ├── 怪人的沙拉碗 S01E06.mp4
│   │   ├── 怪人的沙拉碗 S01E06.nfo
│   │   ├── 怪人的沙拉碗 S01E07.mp4
│   │   ├── 怪人的沙拉碗 S01E07.nfo
│   │   ├── 怪人的沙拉碗 S01E08.mp4
│   │   ├── 怪人的沙拉碗 S01E08.nfo
│   │   ├── 怪人的沙拉碗 S01E09.mp4
│   │   ├── 怪人的沙拉碗 S01E09.nfo
│   │   ├── 怪人的沙拉碗 S01E10.mp4
│   │   ├── 怪人的沙拉碗 S01E10.nfo
│   │   ├── 怪人的沙拉碗 S01E11.mp4
│   │   ├── 怪人的沙拉碗 S01E11.nfo
│   │   ├── 怪人的沙拉碗 S01E12.mp4
│   │   └── 怪人的沙拉碗 S01E12.nfo
│   ├── backdrop.jpg
│   ├── clearlogo.png
│   ├── fanart.jpg
│   ├── folder.jpg
│   └── tvshow.nfo

jellyfin
Fig. 6. Jellyfin的番剧元数据界面

除了动画之外的影视剧

有些时候除了动画番剧之外，也想看下电影和美剧。这时候用 Servarr[26] 全套来做自动化就很方便了，反而日本动画因为格式不统一且高度以来字幕组，导致用这套系统效果很差，但是对西方的电影和美剧就很好。

这套系统包括了

  • 请求电影和美剧下载的前端 Jellyseerr[27]。
  • 自动搜索并下载电影的自动化后端 Radarr[28], 用于美剧的 Sonarr[29]。
  • 用于给自动化后端提供 indexer 的 Prowlarr[30]。
  • 用于给自动化后端作为下载工具的 Aria2 和 qBittorrent。

在 Jellyseerr 中，需要设置好 Radarr 和 Sonarr 的 API key，然后就可以通过 Jellyseerr 来搜索电影和美剧，然后自动下载。

jellyseerr-ui
Fig. 7. 在Jelyyseerr中可以订阅几乎任何的电影或电视剧

但是想要做到自动化，需要在 Jellyseerr, Radarr, Sonarr 和 Prowlarr 中设置好 indexer 和下载工具，这样才能保证自动化的流程。

用这套系统去自动化动画下载是很困难的，因为动画的资源很杂，而且不同字幕组之间的格式也很不同，元数据很混乱。动画自动化只建议使用AutoBangumi。

jellyseerr
Fig. 8. Jellyseerr中设置Radarr和Sonarr服务器

prowlarr1
prowlarr2
Fig. 9. Prowlarr中订阅indexer，并设定Radarr和Sonarr服务器

radarr1
radarr2
Fig. 10. Radarr中设置媒体库和下载工具 (Sonarr略)

这样，在 Jellyseerr 请求电影和美剧之后，就可以在 Radarr 和 Sonarr 中看到请求的电影和美剧，然后就可以自动下载了。然后在 Jellyfin 中就可以看到这些电影和美剧。

本地播放器

尽管可以在服务器上自动下载和整理番剧，但是依然需要通过本地的播放器软件来观看。为了使用先进的插帧和超分辨率技术，现在建议使用mpv[31]。mpv是一个开源的播放器，支持很多的插件，比如说插帧，超分辨率等等。

为了方便使用，可以使用MPV_lazy[32]，这是一个mpv的整合包，里面包括了很多的插件，基本上不需要自己再另外准备什么了。这个整合包里提供了一些vs脚本，可以用于调整插帧和超分的参数，也可以自己在上面改。比如说，

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# MAIN.vpy
import k7sfunc as k7f

clip = video_in
if container_fps < 60:
    clip = k7f.RIFE_NV(clip, lt_d2k=True, model=46, ext_proc=False, t_tta=False, fps_in=container_fps, fps_num=2, fps_den=1, sc_mode=1, gpu=0, gpu_t=2, st_eng=False, ws_size=8)

if clip.height < 1200:
    clip: Any = k7f.FMT_CTRL(clip, h_max=720, fmt_pix=0)
    clip: Any = k7f.ESRGAN_NV(clip, lt_hd=True, model=5005, gpu=0, gpu_t=2, st_eng=False, ws_size=0)

这样就可以在播放的时候同时用RIFE插帧和ESRGAN超分辨率了。

其次是需要在客户端上装一个后台服务来接管jellyfin播放，这样可以在jellyfin网页上点击播放之后，自动打开mpv。这里推荐油猴插件embyToLocalPlayer[33]。

总结

本文介绍了如何建立一个自动化的家庭服务器系统，用于自动化下载，管理，观看番剧和其他媒体内容。在这里分享了一些自己的经验，希望对大家有所帮助。

参考文献

  • [1] Z. Huang, T. Zhang, W. Heng, B. Shi, and S. Zhou, “Real-Time Intermediate Flow Estimation for Video Frame Interpolation,” in Proceedings of the European Conference on Computer Vision (ECCV), S. Avidan, G. Brostow, M. Cissé, G. M. Farinella, and T. Hassner, Eds., Cham: Springer Nature Switzerland, 2022, pp. 624–642. doi: 10.1007/978-3-031-19781-9_36.
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  • [10] "mayswind/AriaNg", GitHub, 2024. https://github.com/mayswind/AriaNg
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  • [15] "Home - homepage", Homepage, 2024. https://gethomepage.dev/latest/
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  • [22] "kookxiang/jellyfin-plugin-bangumi", GitHub, 2024. https://github.com/kookxiang/jellyfin-plugin-bangumi
  • [23] "The Movie Database (TMDB)", Themoviedb, 2024. https://www.themoviedb.org/
  • [24] "Welcome - TheTVDB.com", Thetvdb, 2024. https://www.thetvdb.com/
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探索数据潜力：预训练模型与Masked Autoencoder的表征学习之旅

2023-06-09 00:16:18

表征学习(Representation Learning)是一个深度学习中的概念，通过预训练一个特征提取器，把原始数据转换成有意义的低维特征，让下游任务基于这些特征进行训练，从而降低了对数据和计算能力的需求。本文将介绍表征学习的基本概念，以及以Masked Autoencoder[1]为主的最新进展。

深度学习的民主化问题

在过去的几年中，深度学习在各个领域取得了显著的成就。然而，深度学习的成功很大程度上依赖于大量的标注数据，而这些数据的获取成本往往很高。例如，对于计算机视觉领域，ImageNet数据集[2]的标注成本高达200万美元。因此，如何利用少量的标注数据来训练一个高性能的模型，是一个很有意义的研究方向。评价一个深度学习模型是否好用的时候，一般会以两个方面进行对比。一个是性能，比如说分类任务的准确度，生成任务中的PSNR，SSIM等。另一方面是运行的效率，包括训练和预测的时间，以及模型的大小。

但是，以GPT-3为例，其参数量高达1750亿，训练时间长达355个GPU年(V100)，数据集包含了4990亿个token，花了差不多460万美元[3]。这样的模型对于学术界和初创企业大部分人来说，最多只能通过API访问，不可能通过自己训练和微调来使用。这个就是深度学习的民主化问题，只有少数人能够享受到深度学习带来的好处。因此，如何在保证性能的同时，降低模型的大小和训练时间，是一个很有意义的研究方向。

提取特征

直觉上，通过提取有意义的特征可以降低数据的维度，使模型训练更加容易，包括减少了对数据的需求，也降低了模型的大小。这里列举几个在不同模态下常见的特征提取方法。

视觉

  • 在ImageNet[2]上的预训练模型，提取倒数第二层的输出作为特征
    • 通过torchvision库加载预训练模型
    • model_ft = models.resnet18(pretrained=True)
  • OpenFace[4]
    • 一个基于深度学习的人脸分析开源工具包
    • 使用预训练模型提取人脸特征
  • 光流(Optical Flow)
    • 通过计算相邻帧之间的位移，来提取视频中的运动信息
    • 例如，可以通过传统算法，比如卢卡斯-卡纳德法[5]来计算光流
    • 也可以通过深度学习的方法来计算光流，例如GMFlowNet[6]
    • 如下图所示

optical-flow
Fig. 1. 光流. Adapted from [7]

音频

  • 梅尔频谱图
    • 表示了音频信号在不同频率下的能量分布
    • 通过对音频信号进行傅里叶变换，然后对频谱进行滤波，最后再进行傅里叶逆变换，计算得到的结果
    • 把本来是1D的音频信号，转换成了2D的梅尔频谱图
  • 梅尔频率倒谱系数(MFCC)
    • 从梅尔频谱图中提取的特征

mel-mfcc
Fig. 2. 梅尔频谱图和MFCC. Adapted from [8]

  • DeepSpeech[9]
    • 一个用于语音识别的深度学习预训练模型，可以提取音频特征

文本

  • TF-IDF
    • 通过计算单词在文档中的出现频率，来计算文本特征
  • Word2Vec[10]
    • 通过预训练一个神经网络，把单词转换成低维的向量
    • 如图所示。

word2vec
Fig. 3. Word2Vec. Adapted from [10]

  • BERT[11]
    • 同样也是通过预训练一个神经网络，把单词转换成低维的向量
    • 例如，可以使用transformers库[12]加载预训练模型
    • model = BertModel.from_pretrained('bert-base-uncased')

表征学习

如果将刚才提到的全部特征类型分个类，那么大致上可以分成两类。

  • 一类是通过预训练模型提取的特征，例如ImageNet上的预训练模型，OpenFace，DeepSpeech，BERT等等
  • 另一类是通过传统方法提取的特征，例如光流，梅尔频谱图，TF-IDF等等

AI教父Yosha Bengio在第一次ICLR会议上提到

Learning representations of the data that make it easier to extract useful information when building classifiers or other predictors. [13]

那么表征学习的意义就是通过学习数据的表示形式，使得在构建分类器或其他预测器时更容易提取有用信息。换句话说，就是需要想办法获得一个更好的特征提取器。

steps-representation
Fig. 4. 智能系统的发展

如上图所示，智能系统的发展经历了四个阶段。

  • 第一个是基于规则的系统。
    • 开发者通过手动编写代码，来实现系统的功能，比如说分类或者预测等等。
  • 第二个是传统的机器学习。
    • 开发者需要通过手动提取特征，然后再基于这些特征训练传统的机器学习模型，从特征映射到标签。
  • 而第三个和第四个都属于表征学习。
    • 开发者通过训练模型，让模型的中间部分学习到数据的表示形式，然后再基于这些表示形式进行预测。
    • 而最新的第四个通过加深模型层数，可以让模型学习到更高层次的表示形式，从而提升模型的性能。

representation-arch
Fig. 5. 表征学习的架构

因为带有标签的数据集很贵很难获得，所以当我们训练模型的时候，一般不会从零开始直接使用有标签的数据集。相反，我们会先使用未标记的数据集，从零开始训练模型参数，将其训练到合理的水平。这一部分，我们称为预训练(pretraining)。

在预训练阶段，使用未标记的数据集从零开始训练模型参数，将其训练到合理的水平。一旦参数训练到了合适的水平，我们就可以使用标记数据集来对模型进行微调，以适应特定的下游任务。在这个阶段，我们不需要大量的标记数据，因为参数已经在第一个阶段训练到了较好的水平。

第一个阶段没有涉及任何具体任务，只是用一堆未标记的数据进行预训练。我们称之为“in a task-agnostic way”。第二个阶段是使用与任务相关的标记数据对模型进行微调。我们称之为“in a task-specific way”。

Masked Autoencoder

在了解了表征学习的概念之后，这里介绍一个领域内较为热门的前沿模型，Masked Autoencoder (MAE)[1]。

介绍

mae-intro
Fig. 6. Masked Autoencoder的介绍. Adapted from [1]

如图所示，MAE主要概念是遮盖输入图像的随机块并进行重建。它基于两个想法。

首先，研究人员提出了一个编码器-解码器架构，其中一个编码器只对图像的可见块子集进行操作。然后，一个简单的解码器可以从可见部分的潜在表示中重构原始图像。

其次，研究人员发现，如果他们遮盖了输入图像的大部分，比如约75%，实际上可以产生重要且有意义的自监督任务。通过结合这两个设计，我们可以高效地训练大型模型，将训练速度提高3倍或更多，同时提高准确性。

一些模型结构上的细节：

  • 使用ViT作为编码器，tokenize输入图像并且进行处理成token
  • 编码器的结构要大于解码器
  • 重建损失在隐藏的token之间进行计算，而不是在所有token之间进行计算

实验结果

在ImageNet上的实验结果如下图所示：

mae-imagenet
Fig. 7. MAE在ImageNet上的实验结果. Adapted from [1]

其中这里”scratch, original”代表从ViT原论文[14]拿到的结果。
“scratch, our impl”指的是一样的ViT结构，但是使用了作者自己的参数。主要是有更高的weight decay。
“baseline MAE”指的是从预训练的MAE模型开始，进行微调的结果。

这里看到相比于从零开始训练，使用MAE进行预训练，可以提升大约2%的准确率。

为了能更好的选择遮盖的比例，作者进行了一些实验，如下图所示：
mae-mask-ratio
Fig. 8. MAE在不同遮盖比例下的实验结果. Adapted from [1]

上面这张图是在finetune下，也就是同时训练编码器和分类器。而下面这张图是在linear-probing下，也就是只训练分类器。

可以看到，当遮盖比例过高和过低时，都会导致准确率下降。而在75%左右的遮盖比例下，可以达到最好的效果。

除了遮盖的比例之外，怎么选择遮盖的区域也是一个问题。作者进行了一些实验，如下图所示：
mae-mask-strategy
Fig. 9. MAE的不同遮盖策略可视化. Adapted from [1]
mae-mask-strategy-result
Fig. 10. MAE的不同遮盖策略的实验结果. Adapted from [1]

其中random就是完全随机遮盖，block是选择一个方框区域进行遮盖，grid是有规律的进行网格状覆盖。

从结果可以看出，random是最好的。

扩展到视频

既然MAE可以用在图像上，那么我们能不能将其扩展到视频上呢？答案是肯定的。我们可以将视频看作是一系列的图像，然后使用MAE进行预训练。

去年NeurIPS 2022有个spotlight工作就叫VideoMAE[15]，就是将MAE扩展到视频上。
videomae-intro
Fig. 11. VideoMAE的结构. Adapted from [15]

如上图所示，总体的结构其实没有什么大改变，依然是通过遮盖的方式进行预训练。改变的地方主要是使用了3D的cube而不是2D的patch作为token。

实验结果

作者在Something-Something-V2[16]上进行了实验，如下图所示：
videomae-result
Fig. 12. VideoMAE在Something-Something-V2上的实验结果. Adapted from [15]

可以看到效果还是不错的。作者在文章中提到，为了训练这个模型，他们使用了64个V100。

他们也讨论了最佳的遮盖方法，认为是tube masking是最好的，即在空间维度上进行遮盖，而时间维度上保持一致。

videomae-mask-strategy
Fig. 13. 不同遮盖策略可视化. Adapted from [17]
有趣的是，另一篇论文MAE-ST[17]，也是在NeurIPS 2022上发表的，也是将MAE扩展到视频上。而且方法和结构都非常类似，但是他们认为最佳的遮盖方法是随机遮盖。

videomae-mask-strategy-result
Fig. 14. 两篇论文关于不同遮盖策略的实验结果，左边来自于VideoMAE，右边是MAE-ST. Adapted from [15][17]

VideoMAE的作者认为，使用tube masking可以避免信息在时间维度上的泄露。这个泄露导致编码器看到了本来被遮盖的信息，导致性能受到影响。而MAE-ST的作者认为纯粹的随机遮盖可以防止模型对信息产生bias，从而提高了性能。

个人认为VideoMAE使用的数据集要小于MAE-ST所使用的，所以可能是数据集规模的区别导致了不同的结论。

videomae-mask-ratio
Fig. 15. 两篇论文在不同遮盖比例下的实验结果，左边来自于VideoMAE，右边是MAE-ST. Adapted from [15][17]

而关于遮盖的比例，两篇论文都认为90%是最好的。

总结

本文探讨了表征学习在深度学习中的重要性，并且介绍了MAE作为目前比较前沿的表征学习方法。MAE的核心思想是通过遮盖的方式，让模型学习到更多的信息，并且后来社区将其扩展到了视频领域。通过使用这些预训练的模型，普通的开发者也可以使用更少的数据和计算资源，将这些模型迁移到自己的任务上，从而获得更好的效果。

参考文献

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异星工厂原版原创蓝图分享

2023-05-06 02:22:28

前一阵子在异星工厂(Factorio)上玩了一段时间，这个游戏的玩法是建造工厂，从最初的手工采矿到自动化采矿，再到自动化生产，最后到自动化科研，最终建造火箭逃离星球。为了下次更好的游戏体验，最近特地的制作了一些用于原版的蓝图，方便下次游戏时直接使用。

这次设计的这些蓝图，大小都是以区块(32 x 23)为基础的，有的占用半个区块(32 x 16)，有的占用一整个区块(32 x 32)。在使用蓝图的过程中，摆放也是自动锁定到区块上，这样方便规划建设。

蓝图簿

1
 
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

超市模块

超市模块用于生产多种机器和建筑材料。

模块::超市

mall

早期超市 (红瓶科技)。

尺寸：

输入:

  • [上] 铜板
  • [左] 铁板
1
 
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

超市扩展::绿瓶

mall-green-science-extension

前中期扩展 (绿瓶科技) 用于超市。

放置于超市模块的底部。

尺寸: 32 x 32

输入:

  • [左] 铁齿轮
  • [左] 铁板
  • [左] 电路板
  • [左] 铜板
  • [左] 钢材
  • [右] 石块
  • [右] 组装机1
  • [右] 石头
1
 
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

生产模块

生产模块用于生产可以在总线上运输的中间产物。

模块::插件1工厂

module-1-factory

用于中期游戏(蓝瓶科技)的一级插件工厂。

产能: 54/min

尺寸: 32 x 16

输入:

  • [上] 电路板 270/min
  • [上] 集成电路 270/min

速度插件1工厂

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节能插件1工厂

1
 
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产能插件1工厂

1
 
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模块::插件1工厂2

module-1-factory-2

用于后期游戏(紫瓶科技)的一级插件工厂。

产能: 622.5/min

尺寸: 32 x 32

输入:

  • [上] 电路板 2037.5/min
  • [上] 集成电路 2037.5/min
  • [上] 电路板 1075/min
  • [上] 集成电路 1075/min

速度插件1工厂2

1
 
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节能插件1工厂2

1
 
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产能插件1工厂2

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模块::插件2工厂

mall

用于中期游戏(蓝瓶科技)的二级插件工厂。

产能: 27/min

尺寸: 32 x 16sd

输入:

  • [上] 处理器 135/min
  • [上] 集成电路 135/min
  • [上] 一级插件 108/min

速度插件2工厂

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节能插件2工厂

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产能插件2工厂

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模块::电路板工厂

electronic-circuit-factory

用于中期游戏(蓝瓶科技)的电路板工厂。

产能: 1080/min

尺寸: 32 x 32

输入:

  • [上] 铜板 1620/min
  • [上] 铁板 1080/min
1
 
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模块::集成电路工厂

advanced-circuit-factory

用于中期游戏(蓝瓶科技)的集成电路工厂。

产能: 180/min

尺寸: 32 x 32

输入:

  • [上] 塑料 360/min
  • [上] 铜板 360/min
  • [上] 电路板 360/min
1
 
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模块::处理器工厂

processing-unit-factory

用于中期游戏(蓝瓶科技)的处理器工厂。

产能: 72/min

尺寸: 32 x 16

输入:

  • [上] 硫酸 360/min
  • [上] 电路板 1440/min
  • [上] 集成电路 144/min
1
 
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模块::石油气工厂

petroleum-factory

用于中期游戏(蓝瓶科技)的石油气工厂。

产能: 9360/min

尺寸: 32 x 32

输入:

  • [上] 原油 9600/min
  • [上] 水 12720/min
1
 
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模块::硫酸工厂

sulfuric-acid-factory

用于中期游戏(蓝瓶科技)的硫酸工厂。

产能: 12000/min
尺寸: 32 x 16

输入:

  • [上] 石油气 18000/min
  • [上] 铁板 240/min
  • [上] 水 24000/min
1
 
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模块::塑料工厂

plastic-bar-factory

用于中期游戏(蓝瓶科技)的塑料工厂。

产能: 1800/min
尺寸: 32 x 16

输入:

  • [上] 煤 900/min
  • [上] 石油气 18000/min
1
 
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模块::电池工厂

battery-factory

用于中期游戏(蓝瓶科技)的电池工厂。

产能: 270/min
尺寸: 32 x 16

输入:

  • [上] 铁板 270/min
  • [上] 铜板 270/min
  • [上] 硫酸 5400/min
1
 
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模块::钢材工厂

steel-plate-factory

用于中期游戏(蓝瓶科技)的钢材工厂。

产能: 135/min
尺寸: 32 x 16

输入:

  • [上] 铁板 675/min
1
 
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模块::铁板工厂

用于中期游戏(蓝瓶科技)的铁板工厂。

产能: 675/min
尺寸: 32 x 16

输入:

  • [上] 铁矿 675/min
1
 
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模块::铜板工厂

用于中期游戏(蓝瓶科技)的铜板工厂。

产能: 675/min
尺寸: 32 x 16

输入:

  • [上] 铜矿 675/min
1
 
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模块::石砖工厂

用于中期游戏(蓝瓶科技)的石砖工厂。

产能: 675/min
尺寸: 32 x 16

输入:

  • [上] 石矿 1350/min
1
 
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科研模块

科研瓶工厂用于生产研究用的瓶子。科研中心模块则消耗瓶子用于研究。

模块::绿瓶工厂

green-science-factory

用于早期游戏(绿瓶科技)的绿瓶工厂

产能: 135/min
尺寸: 32 x 32

输入:

  • [上] 铁板 607.5/min
  • [上] 电路板 135/min
1
 
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模块::蓝瓶工厂

blue-science-factory

用于中期游戏(蓝瓶科技)的蓝瓶工厂。

产能: 56/min
尺寸: 32 x 32

输入:

  • [上] 钢材 56/min
  • [上] 铁板 224/min
  • [上] 集成电路 84/min
  • [上] 水 420/min
  • [上] 石油气 420/min
1
 
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模块::紫瓶工厂

purple-science-factory

用于中期游戏(蓝瓶科技)的紫瓶工厂。

产能: 90/min
尺寸: 32 x 32

输入:

  • [上] 铁板 225/min
  • [上] 石矿 450/min
  • [上] 钢材 750/min
  • [上] 石砖 300/min
  • [上] 集成电路 150/min
  • [上] 产能插件1 30/min
1
 
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模块::黄瓶工厂

yellow-science-factory

用于中期游戏(蓝瓶科技)的黄瓶工厂。

产能: 58/min
尺寸: 32 x 32

输入:

  • [上] 钢材 38.7/min
  • [上] 铁板 77.4/min
  • [上] 电路板 96.7/min
  • [上] 润滑油 290/min
  • [上] 电池 38.7/min
  • [上] 轻质框架 58/min
  • [上] 处理器 38.7/min
1
 
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其他模块

模块::机器人工厂

robot-factory

用于中期游戏(蓝瓶科技)的两种机器人工厂，会自动部署到机器人网络里。

产能:

  • 物流机器人 10/min
  • 建设机器人 10/min

尺寸: 32 x 32

输入:

  • [上] 钢材 40/min
  • [上] 铁板 80/min
  • [上] 电路板 120/min
  • [上] 润滑油 300/min
  • [上] 电池 40/min
  • [上] 集成电路 20/min

常量输入:

  • 物流机器人: 物流机器人的目标数量
  • 建设机器人: 建设机器人的目标数量
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物流模块

物流::总线3x4+8

bus-3x4+8

用于物流总线的3x4+8的总线结构，可以用于任意长度的总线。

尺寸: 32 x 32

1
 
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石墙结构

用于分割各种模块，便于管理和防御的石墙结构

walls

结构::石墙(内侧)

用于分割32x32模块结构的石墙，建造在单元格内侧。

尺寸: 32 x 32

1
 
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结构::石墙(外侧1)

用于分割32x32模块结构的石墙，建造在单元格外侧1格处。

尺寸: 32 x 32

1
 
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结构::石墙(外侧2)

用于分割32x32模块结构的石墙，建造在单元格外侧2格处。

尺寸: 32 x 32

1
 
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结构::半格石墙(内侧)

用于分割32x16模块结构的半格石墙，建造在单元格内侧。

尺寸: 32 x 16

1
 
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结构::半格石墙(外侧1)

用于分割32x16模块结构的半格石墙，建造在单元格外侧1格处。

尺寸: 32 x 16

1
 
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结构::半格石墙(外侧2)

用于分割32x16模块结构的半格石墙，建造在单元格外侧2格处。

尺寸: 32 x 16

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

学习AI绘画，从Diffusion和CLIP开始

2023-01-07 22:56:13

AI绘画在这几个月火了起来，它能从提供的文字和图片中生成新的绘画，质量很高，而且非常有趣。这个封面就是用AI生成的[1]。但是在使用AI绘画的过程中，搞不懂steps，sampler之类的意思。为了想要更好的使用AI绘画，也想要理解AI绘画中那些参数的含义，所以本着学习新技术的目的，写了这篇文章来学习一下AI绘画。

AI绘画

这几个月风靡的AI绘画，主要是指在统计学和计算机视觉领域，用深度学习模型从一些条件输入中生成新的图片。这些输入主要是文字或者图片。比如说封面图它就是用Anything-V3.0模型[1]从文字直接生成的。

封面图的输入参数

masterpiece,best quality,1 girl,loli, small breasts,animal ears, cute, Age:12, Height:145, hair ribbon, cute face,shy, long hair, white hair, dress , illustration, city
Negative prompt: nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,hands
Steps: 40, Sampler: Euler a, CFG scale: 13, Seed: 353263948, Size: 1216x512, Model hash: 6569e224, Clip skip: 2

对于文字生成图片而言，AI绘画系统有两个重要模块，第一个是理解文字输入，第二个是使用这个理解去生成新的图片。所幸，学术界在之前已经存在了能高质量完成这两步的技术基础。其中Denoising Diffusion Probabilistic Models (DDPM/Diffusion)[2]提供高质量的图片生成技术，而Contrastive LanguageImage Pre-training (CLIP)[3]提供了高水平的自然语言跨模态理解。

这篇文章我们将从DDPM和CLIP开始学习AI绘画。

DDPM

在DDPM出现之前，图片生成主要是通过变分自编码器(VAE)和生成对抗网络(GAN)来完成的。但是VAE生成的图片模糊，而GAN的训练很困难，最后生成的多样性比较有限。DDPM解决了这个难点，它能生成高质量的图片，而且也不需要对抗训练，训练起来也很简单。但是DDPM也有缺陷，它生成图片的速度比较慢，因为需要执行很多步的迭代。我们先从介绍DDPM开始。

generative-overview
Fig. 1. 生成式模型. Adapted from [4]

如上图所示，DDPM是从一个生成的噪声$z$中迭代多次生成图片的，而且相比于VAE和GAN存在一个低维的隐式表示$z$，DDPM的每次迭代生成的中间图片$x_t$都保持在相同的维度上。

DDPM之所以是Diffusion(扩散)，是因为它是通过扩散过程来生成噪声，然后再训练模型去预测这个噪声来去噪，从而达到生成图片的目的。让我们先从扩散的前向过程，也就是生成噪声开始。

前向扩散

forward-process
Fig. 2. 前向扩散过程. Adapted from [2]

首先需要定义用DDPM生成图片的过程。首先我们有一张真实图片$x_0\sim q(x)$从一个数据集中采样而来，我们希望能通过一系列手段去预测出$x_0$。

我们现在对$x_0$添加一点高斯噪声，一共添加$T$步，那么每步的结果可以表示为$x0$, $x1$, $x2$, …, $x_T$。那么每次从$x_{t-1}$添加噪声到$x_t$的过程可以表示为，

$$
\begin{equation}
x_t = \sqrt{\alpha_t}x_{t-1} + \sqrt{1 - \alpha_t}z_t
\end{equation}
$$

其中，$z_t \sim N(0, I)$是采样于标准正态分布的噪声。$\alpha_t$是一个一开始被决定好的常量，在原文中被称为步长，但是更像是一个权重，决定这一步中包含噪声的多少。这里可以看出$x_t$主要是取决于$x_{t-1}$和这个高斯分布$z_t$。所以，我们可以一步步递归计算到$x_0$，这里高斯分布被合并。

$$
\begin{equation}
x_t = \sqrt{\prod_{i=1}^t \alpha_i}x_0 + \sqrt{1 - \prod_{i=1}^t \alpha_i}z
\end{equation}
$$

为了表示简单，我们定义 $\bar{\alpha}_{t} = \prod_{i=1}^{t} \alpha_{i}$，则这个简化版的公式如下

$$
\begin{equation}
x_t = \sqrt{\bar{\alpha}_t}x_0 + \sqrt{1 - \bar{\alpha}_t}z
\end{equation}
$$

我们可以认为，因为每个噪声都符合标准正态分布，所以每步加一个小噪声可以当成一口气加个大噪声，极大的简化了前向扩散过程。

代码如下。

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def forward_process(x0, alpha_bar, t):
    z = torch.randn_like(x0)
    x_t = torch.sqrt(alpha_bar[t]) * x0 + torch.sqrt(1 - alpha_bar[t]) * z
    return x_t

反向扩散

既然我们已经知道了如何生成噪声，那么我们就可以通过预测这个噪声来去噪了。这个从$x_t$到$x_0$的过程就是反向扩散。

通过概率论的角度来看，这个前向扩散的过程可以记为条件概率分布的形式。其中从$x_0$加噪声到$x_t$的过程可以表示为$q(x_{t}|x_0)$。同理，我们也已知$q(x_{t-1}|x_0)$和$q(x_t|x_{t-1},x_0)$，根据贝叶斯公式，我们可以得到反向扩散的过程为，

$$
\begin{equation}
q(x_{t-1}|x_t,x_0)=\frac{q(x_t|x_{t-1},x_0)q(x_{t-1}|x_0)}{q(x_{t}|x_0)}
\end{equation}
$$

为了简单表示，我们定义$\beta_t = 1 - \alpha_t$

因为$q(x_t|x_{t-1}) \sim N(\sqrt{1-\beta_{t}}x_{t-1}, \beta_{t}I)$的方差是$\beta_{t}I$，所以我们把$q(x_{t-1}|x_t,x_0)$的方差记为$\tilde{\beta}_{t}I$，而均值则是$\tilde{\mu}_t$。我们的目标是求解$\tilde{\mu}_t$和$\tilde{\beta}_t$。

化简等式(4)得，

$$
\begin{eqnarray}
q(x_{t-1}|x_t,x_0)&\propto& \exp(-\frac{1}{2}((\frac{\alpha_t}{\beta_t}+\frac{1}{1-\bar{\alpha}_{t-1}})x^2_{t-1}-(\frac{2\sqrt{\alpha_t}}{\beta_t}x_t + \frac{2\sqrt{\bar{\alpha_{t-1}}}}{1-\bar{\alpha}_{t-1}}x_0)+C(x_t,x_0))) \\\
&=&\exp(-\frac{(x-\tilde{\mu}_t)^2}{2\tilde{\beta}_tI})
\end{eqnarray}
$$

省略常数项，求解以上等式，得到

$$
\begin{eqnarray}
\tilde{\mu}_t &=& \frac{\sqrt{\alpha_t}(1-\bar{\alpha}_{t-1})}{1-\bar{\alpha}_{t}}x_t + \frac{\sqrt{\bar{\alpha}_{t-1}}\beta_t}{1-\bar{\alpha}_t}x_0 \\\
\tilde{\beta}_t &=& \frac{1-\bar{\alpha}_{t-1}}{1-\bar{\alpha}_t}\cdot \beta_t
\end{eqnarray}
$$

这时候方差已知，而均值$\tilde{\mu}_t$只和$x_t$和$x_0$有关。而对于$x_0$来说，我们可以通过等式(3)估算得到，

$$
\begin{equation}
x_0 = \frac{1}{\sqrt{\bar{\alpha}_t}}(x_t - \sqrt{1-\bar{\alpha}_t}\tilde{z}_{t})
\end{equation}
$$

其中这里的$\tilde{z}_t$是一个未知的噪声，我们需要通过模型来预测。

这里的$x_0$只是一个估算的结果，不能作为最终结果输出。

通过等式(9)，我们可以消去等式(7)中的$x_0$，得到

$$
\begin{equation}
\tilde{\mu}_t = \frac{1}{\sqrt{\alpha_t}}(x_t - \frac{1-\alpha_t}{\sqrt{1-\bar{\alpha_t}}}\tilde{z}_{t})
\end{equation}
$$

然后我们就可以通过重参数化技巧来从$x_t$中采样$x_{t-1}$了。迭代这个过程，我们就可以得到$x_0$作为最终输出。

以下是这一步采样的代码实现。

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def sample_x0_from_xt(xt, alpha_bar, t, pred_eps):
    return 1 / torch.sqrt(alpha_bar[t]) * (xt - torch.sqrt(1 - alpha_bar[t]) * pred_eps[t])

def sample_pred_mean_from_x0_xt(xt, x0, t, alpha, alpha_bar):
    xt_term = torch.sqrt(alpha_bar[t]) * (1 - alpha_bar[t - 1]) / (1 - alpha_bar[t]) * xt
    x0_term = torch.sqrt(alpha_bar[t - 1]) * (1 - alpha[t]) / (1 - alpha_bar[t]) * x0
    return xt_term + x0_term

def backward_process_step(xt, t, alpha_bar, alpha, beta_tilde, model):
    pred_eps = model(xt, t)
    x0 = sample_x0_from_xt(xt, alpha_bar, t, pred_eps)
    pred_mean = sample_pred_mean_from_x0_xt(xt, x0, t, alpha, alpha_bar)
    return pred_mean + torch.sqrt(beta_tilde[t]) * torch.randn_like(xt)

训练过程

在上一节我们知道，我们需要一个模型去预测噪声$\tilde{z}_t$，所以这个模型的输入是$x_t$和$t$，输出是$\tilde{z}_t$。而我们在前向扩散的过程中就已经获取了这个ground truth的噪声$z_t$，所以我们可以通过这个ground truth的噪声和预测的噪声之间的差异来训练模型。

对于DDPM来说，这个损失函数是MSE。

$$
L = ||z_t - \tilde{z}_t||_2
$$

模型架构

尽管DDPM的主要思想是在如何进行扩散上，这个模型不是重点，但是我们还是需要大致了解一下的。

首先这个模型是基于UNet[5]的，UNet是一个经典的Encoder-Decoder图像分割模型，主要的特点是在上采样和下采样的过程中，都会通过一个跳跃连接来保留住低层次的特征信息。这个模型的架构如下图所示。

u-net
Fig. 3. U-Net. Adapted from [5]

相比于2015年的原版U-Net，DDPM中的每一步上采样或者下采样过程中，都有一个ResNet[6]的残差连接结构，然后再跟上一个Multi-Head Attention层[7]。

resnet-and-attention
Fig. 4. 残差连接和 MultiHead Attention. Adapted from [6][7]

除此之外，对于时间信息$t$会被编码成一个embedding。如果是有条件输入的话，我们也可以把输入的条件信息(作为embedding)和time embedding相加。然后在模型的一些层中，再通过相加的方式添加到feature map中。

建立了这个模型之后，我们就可以通过前向扩散和后向扩散的过程来训练模型了。

简单测试结果

通过实现以上的代码，用了一个小参数的简单模型和简单的数据集(CIFAR10)[8]。用了一个RTX8000训练了十几个小时，我们可以看到，模型的效果还是不错的。

ddpm-out
Fig. 5. DDPM的简单生成结果

CLIP

介绍完了DDPM，我们再来看一下CLIP[3]。相比于DDPM，CLIP并没有特别强的算法创新，但是它提供了一个很好的框架，用于建立自然语言和图像的关系。这个框架可以用于很多的任务，比如图像搜索，图像生成，图像分类等等。

CLIP是由OpenAI提出的多模态预训练算法，它的主要思想是通过一个超大的图像-文本数据集，来训练一个图像Encoder和一个文本Encoder。如果是相关的文本和图片，编码后的特征向量应该是相似的，如果是不相关的文本和图片，编码后的特征向量应该是不相似的。这个数据集有超过4亿个图片和文本的pair，完全是大力出奇迹。

模型架构

clip-arch
Fig. 6. CLIP的模型架构. Adapted from [3]

如上图所示，CLIP模型包含一个Text Encoder和一个Image Ecnoder。它们用于分别提取文本和图片的特征向量到同一个特征空间。在预训练的过程中，通过计算余弦相似度(cosine similarity)作为损失函数。原论文中也提供了伪代码来作为参考，如下所示。

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# image_encoder - ResNet or Vision Transformer
# text_encoder - CBOW or Text Transformer
# I[n, h, w, c] - minibatch of aligned images
# T[n, l] - minibatch of aligned texts
# W_i[d_i, d_e] - learned proj of image to embed
# W_t[d_t, d_e] - learned proj of text to embed
# t - learned temperature parameter

# extract feature representations of each modality
I_f = image_encoder(I) #[n, d_i]
T_f = text_encoder(T) #[n, d_t]

# joint multimodal embedding [n, d_e]
I_e = l2_normalize(np.dot(I_f, W_i), axis=1) 
T_e = l2_normalize(np.dot(T_f, W_t), axis=1) 

# scaled pairwise cosine similarities [n, n] 
logits = np.dot(I_e, T_e.T) * np.exp(t) 

# symmetric loss function 
labels = np.arange(n) 
loss_i = cross_entropy_loss(logits, labels, axis=0) 
loss_t = cross_entropy_loss(logits, labels, axis=1) 
loss = (loss_i + loss_t)/2

因为整体思路较为简单，所以不再赘述。

用于下游任务

CLIP的主要用途是将预训练好的模型用于下游任务。比如说作为zero-shot图像分类任务，如图6(2,3)所示。这个任务很好的展示了这个模型的一大优势，即通过超大的文本图像数据集建立了较为充分的知识，使得模型在没有针对性的训练的情况下，也能够很好的完成下游任务。

当然，模型也可以用于图像查询。在这个任务中，只要对需要查询的文本进行编码，然后和所有图片的编码计算余弦相似度，就可以通过找最大值得到最相关的图片。

另外就是用于文字生成图片的任务了，比如说AI绘画。我们可以将文本编码为特征向量，然后将这个特征向量作为输入，作为条件信息输入到刚才提到的DDPM中。

关于如何使用CLIP，请参考OpenAI的官方Github仓库(openai/CLIP)[9]。

总结

本文主要介绍了AI绘画的一些原理，包括了DDPM，CLIP。但是现在流行的模型Stable Diffusion (基于Latent Diffusion[10])还没有介绍。而且AI绘画中还有很多内容，比如说sampler(DPM[11], DDIM[12])，这也是以后需要继续学习的方向。

参考文献

  • [1] Linaqruf, “Linaqruf/anything-v3.0 · Hugging Face,” huggingface.co, 2022. [Online]. Available: https://huggingface.co/Linaqruf/anything-v3.0
  • [2] J. Ho, A. Jain, and P. Abbeel, "Denoising Diffusion Probabilistic Models," in Advances in Neural Information Processing Systems, 2020, vol. 33, pp. 6840–6851. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
  • [3] A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” in Proceedings of the 38th International Conference on Machine Learning, Jul. 2021, pp. 8748–8763. [Online]. Available: https://proceedings.mlr.press/v139/radford21a.html
  • [4] L. Weng, “What are Diffusion Models?,” lilianweng.github.io, Jul. 11, 2021. [Online]. Available: https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
  • [5] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham, 2015, pp. 234–241. doi: 10.1007/978-3-319-24574-4_28.
  • [6] K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” in Computer Vision – ECCV 2016, Cham, 2016, pp. 630–645. doi: 10.1007/978-3-319-46493-0_38.
  • [7] A. Vaswani et al., “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, Red Hook, NY, USA, Dec. 2017, pp. 6000–6010.
  • [8] A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” Master’s thesis, University of Tront, 2009.
  • [9] OpenAI, “CLIP,” GitHub, 2022. [Online]. Available: https://github.com/openai/CLIP
  • [10] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-Resolution Image Synthesis With Latent Diffusion Models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10684–10695. [Online]. Available: https://openaccess.thecvf.com/content/CVPR2022/html/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.html
  • [11] C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps,” in Advances in Neural Information Processing Systems, Oct. 2022. [Online]. Available: https://openreview.net/forum?id=2uAaGwlP_V
  • [12] J. Song, C. Meng, and S. Ermon, “Denoising Diffusion Implicit Models,” in International Conference on Learning Representations, Feb. 2022. [Online]. Available: https://openreview.net/forum?id=St1giarCHLP

怎么通信比较快？Python跨进程通信测试

2022-12-13 08:04:12

在一些场合下，我们需要同时运行多个Python程序，并且希望这些Python进程之间能互相通讯，发送一些值或者接收一些值。本文我们就来测试一下Python的跨进程通信不同方案的效率。

本文包含的内容有：HTTP, websocket, multiprocessing, gRPC, RabbitMQ等。

背景介绍

请考虑以下场景，要处理一个数据，我们需要有3步比较耗时的操作，而这个每一步的操作需要上一步的结果，如下图所示。

flowchart LR    Input --> Step1 --> Step2 --> Step3 --> Output

在这里，有两种使用多进程并行的思路，使用多个进程，每个进程接受一个数据，处理完全部三步之后返回结果，每个进程之间相互独立，如下图所示。

flowchart LR    subgraph Process3        direction LR        Input3 --> p3s1[Step1] --> p3s2[Step2] --> p3s3[Step3] --> Output3    end    subgraph Process2        direction LR        Input2 --> p2s1[Step1] --> p2s2[Step2] --> p2s3[Step3] --> Output2    end    subgraph Process1        direction LR        Input1 --> p1s1[Step1] --> p1s2[Step2] --> p1s3[Step3] --> Output1    end

这种方法操作简单，不需要进程间通信，也容易扩展到更多的进程数，在绝大多数情况下都推荐使用。然而这种模式需要将3个Step的上下文都载入内存中，如果这些Step是占用内存很高的深度学习模型，那么内存将会成为一个严重瓶颈。

为了解决这个问题，我们可以使用另一种模式将其并行。

flowchart LRInput --> Step1 .-> Step2 .-> Step3 .-> Outputsubgraph Process1    Step1endsubgraph Process2    Step2endsubgraph Process3    Step3end

其中这里的虚线表示进程间通信(IPC)。相比于第一种并行方式，这种并行方式操作复杂，需要进程间通信，但是可以有效的减少内存占用。

然而，这种并行相比于第一种方案，需要消耗额外的时间在IPC上，因此我们需要测试一下不同IPC方案的效率。

如果以上图表没有正确渲染，请刷新页面。

运行环境

以下实验都在以下环境运行:

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CPU: i7-10900X
RAM: 128GB

Python: 3.10.8
websockets: 10.4
fastapi: 0.88.0
grpcio: 1.56.2
pika: 1.3.2
aio-pika: 9.2.0
numpy: 1.23.4

需要通讯的内容为4种不同尺寸的numpy.ndarray[1]，数据类型为float64。分别为:

  • (1, 3, 224, 224), 模拟一张图片
  • (2, 1024, 16, 16), 模拟两张图像的低维特征
  • (2, 3, 16, 224, 224), 模拟两个16帧的视频片段
  • (128, 1024, 1024), 模拟128个序列特征

方案介绍

方案1: HTTP + JSON 序列化

这是最简单的方案，使用HTTP协议作为通信协议，将ndarray转换成Python的嵌套List，然后作为json发送。这种方案的优点是实现简单，不需要额外的依赖，缺点是从ndarray和List互相转换的开销大，而且json序列化的开销也很大。

这里HTTP通过fastapi[2]实现，fastapi是一个高性能的异步框架，可以很好的支持大量的并发请求。

方案2: HTTP + Base64 Bytes

这种方案和方案1类似，不过将ndarray通过numpy内置的方法转换成bytes，然后使用base64编码，这样可以避免ndarray和List之间的转换，但是HTTP传输大规模的base64编码的开销也很大。

方案3: Websocket + Bytes

这种方案和方案2类似，不过使用Websocket作为通信协议，这样可以避免HTTP的开销。因为Websocket可以发送ascii之外的字节，所以不需要base64编码。

方案4: HTTP + Shared Memory

这里采用了multiprocessing.shared_memory模块，使用SharedMemory对象将ndarray的地址共享给子进程。然后将SharedMemory对象名字作为HTTP的返回值，客户端再通过名字获取SharedMemory对象，这样可以避免ndarray和List之间的转换，也避免了base64编码的开销。

方案5: Websocket + Shared Memory

这种方案和方案4类似，不过使用Websocket作为通信协议，这样可以避免HTTP的开销。

方案6: Multiprocessing Listener / Client

这种方案使用multiprocessing模块的Listener和Client对象，使用multiprocessing的Pipe作为通信协议，这样可以避免HTTP的开销。

方案7: gRPC + Bytes

这种方案使用gRPC[3]作为通信协议，使用protobuf作为序列化协议，好处是方便客户端进行调用，但是gRPC有最大的消息长度限制(2GB)。

方案8: RabbitMQ + Bytes

这种方案使用RabbitMQ[4]作为通信协议，使用pika[5]作为Python的客户端和服务端。这种方案的好处是可以使用RabbitMQ的其他特性，比如消息队列，消息持久化等，但是有最大的消息长度限制(512MB)。

测试结果

ndarray Shape (1, 3, 224, 224) (2, 1024, 16, 16) (2, 3, 16, 224, 224) (128, 1024, 1024)
HTTP + JSON 290.00 ms 1090 ms 9230 ms 259.00 s
HTTP + Base64 Bytes 26.40 ms 51.5 ms 398 ms 12.30 s
Websocket + Bytes 4.27 ms 15.0 ms 171 ms 5.14 s
HTTP +
Shared Memory		 10.70 ms 18.1 ms 127 ms 3.13 s
Websocket +
Shared Memory		 4.34 ms 14.9 ms 127 ms 3.82 s
Multiprocessing
Listener		 7.00 ms 17.2 ms 162 ms 4.73 s
gRPC + Bytes 7.34 ms 28.6 ms 291 ms 7.92 s
RabbitMQ + Bytes 9.35 ms 25.7 ms 243 ms 超出消息长度

根据这个结果我们可以发现，方案4和方案5的性能是最好的，方案6的性能也很好，方案1和方案2的性能最差。

考虑网络传输协议，Websocket的性能是比HTTP好的。所以应该尽量使用Websocket作为网络传输协议。

考虑使用Base64还是Shared Memory，我们可以发现大数据的情况下，Shared Memory的性能是比较好的，但是它需要手动管理内存，可能会有一些问题。所以对于小数据，可以使用Base64，对于大数据，可以使用Shared Memory。

对于multiprocessing模块的Listener和Client，它的性能略弱于Shared Memory，但是它不需要手动管理共享内存，而且它不需要用fastapi之类的外部库，而且不需要转换成别的类型的数据，比较方便。但是正因为它没有使用fastapi，以至于它不是很方便的进行异步处理。

gRPC和RabbitMQ的性能比较差，只比HTTP好一点点，比不上Websocket，所以不推荐使用。而且它们有最大的消息大小限制，所以传输数据时不太方便。

在写以下比较重复的代码实现的时候，Github Copilot[6]起到了很大的帮助。

代码实现

方案1: HTTP + JSON 序列化

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# server.py
import numpy as np
from fastapi import FastAPI

app = FastAPI()
shape = (1, 3, 224, 224)

@app.get("/random_tolist")
async def random_tolist():
    return np.random.randn(*shape).tolist()
 
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# client.py
import requests
import numpy as np

result = requests.get("http://127.0.0.1:1234/random_tolist")
result = np.array(result.json())
print(result.shape)

方案2: HTTP + Base64 Bytes

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# server.py
import numpy as np
import base64
from fastapi import FastAPI
from fastapi.responses import PlainTextResponse

app = FastAPI()
shape = (1, 3, 224, 224)

@app.get("/random_tobytes", response_class=PlainTextResponse)
async def random_tobytes():
    return base64.b64encode(np.random.randn(*shape).tobytes())
 
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# client.py
import requests
import numpy as np
import base64

result = requests.get("http://127.0.0.1:1234/random_tobytes")
result = np.frombuffer(base64.b64decode(result.text)).reshape(*shape)
print(result.shape)

方案3: Websocket + Bytes

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# server.py
import numpy as np
from fastapi import FastAPI, WebSocket

app = FastAPI()
shape = (1, 3, 224, 224)

@app.websocket("/ws/random_tobytes")
async def websocket_random_tobytes(websocket: WebSocket):
    await websocket.accept()
    while True:
        await websocket.receive_text()
        print("Processing websocket")
        await websocket.send_text(np.random.randn(*shape).tobytes())

 
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# client.py
import numpy as np
from websocket import create_connection
ws = create_connection("ws://127.0.0.1:1234/ws/random_tobytes")

ws.send("")
result = np.frombuffer(ws.recv()).reshape(*shape)
print(result.shape)

方案4: HTTP + Shared Memory

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# server.py
import numpy as np
from fastapi import FastAPI
from fastapi.responses import PlainTextResponse
from multiprocessing import shared_memory

app = FastAPI()
shape = (1, 3, 224, 224)

@app.get("/random_sharedmemory", response_class=PlainTextResponse)
async def random_sharedmemory():
    arr = np.random.randn(*shape)
    shm = shared_memory.SharedMemory(create=True, size=arr.nbytes)
    out = np.ndarray(arr.shape, dtype=arr.dtype, buffer=shm.buf)
    out[:] = arr[:]
    shm.close()
    return shm.name
 
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# client.py
import numpy as np
import requests
from multiprocessing import shared_memory

shm_name = requests.get("http://127.0.0.1:1234/random_sharedmemory")
shm = shared_memory.SharedMemory(name=shm_name.text)
result = np.ndarray(shape, dtype=float, buffer=shm.buf)

shm.close()
shm.unlink()

print(result.shape)

方案5: Websocket + Shared Memory

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# server.py
import numpy as np
from fastapi import FastAPI, WebSocket
from multiprocessing import shared_memory

app = FastAPI()
shape = (1, 3, 224, 224)

@app.websocket("/ws/random_sharedmemory")
async def websocket_random_sharedmemory(websocket: WebSocket):
    await websocket.accept()
    while True:
        await websocket.receive_text()
        print("Processing websocket")
        arr = np.random.randn(*shape)
        shm = shared_memory.SharedMemory(create=True, size=arr.nbytes)
        out = np.ndarray(arr.shape, dtype=arr.dtype, buffer=shm.buf)
        out[:] = arr[:]
        shm.close()
        print(shm.name)
        await websocket.send_text(shm.name)

 
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# client.py
import numpy as np
from websocket import create_connection
from multiprocessing import shared_memory

ws = create_connection("ws://127.0.0.1:1234/ws/random_sharedmemory")
ws.send("")
shm_name = ws.recv()
shm = shared_memory.SharedMemory(name=shm_name)
result = np.ndarray(shape, dtype=float, buffer=shm.buf)

shm.close()
shm.unlink()

print(result.shape)

方案6: Multiprocessing Listener / Client

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# server.py
from multiprocessing.connection import Listener
import numpy as np

shape = (1, 3, 224, 224)

address = ('localhost', 1234)
listener = Listener(address)
conn = listener.accept()

while True:
    msg = conn.recv()
    print("Processing")
    conn.send(np.random.randn(*shape))

listener.close()
 
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# client.py
from multiprocessing.connection import Client
conn = Client(("localhost", 1234))
conn.send("")
result = conn.recv()
print(result.shape)

方案7: gRPC + Bytes

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// npy.proto
syntax = "proto3";
import "google/protobuf/empty.proto";

service Npy {
  rpc Get (google.protobuf.Empty) returns (ArrayData) {}
}

message ArrayData {
  bytes body = 1;
}
 
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# server.py
from concurrent import futures
import time

import grpc

import npy_pb2
import npy_pb2_grpc
import numpy as np

shape = (1, 3, 224, 224)

class Npy(npy_pb2_grpc.NpyServicer):
    def Get(self, request, context):
        arr = np.random.randn(*shape)
        return npy_pb2.ArrayData(body=np.ndarray.tobytes(arr))


def serve():
    port = "50051"
    server = grpc.server(futures.ThreadPoolExecutor(max_workers=8), 
        options=[
            ('grpc.max_send_message_length', 2 * 1024**3 - 1),
            ('grpc.max_receive_message_length', 2 * 1024**3 - 1),
    ])
    npy_pb2_grpc.add_NpyServicer_to_server(Npy(), server)
    server.add_insecure_port("[::]:" + port)
    server.start()
    print("Server started, listening on " + port)
    server.wait_for_termination()
    

if __name__ == '__main__':
    serve()
 
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# client.py
from __future__ import print_function

import grpc
import numpy as np

import npy_pb2
import npy_pb2_grpc

shape = (1, 3, 224, 224)
channel = grpc.insecure_channel('localhost:50051', 
    options=[
        ('grpc.max_send_message_length', 2 * 1024**3 - 1),
        ('grpc.max_receive_message_length', 2 * 1024**3 - 1),
    ]
)
stub = npy_pb2_grpc.NpyStub(channel)
response = stub.Get(npy_pb2.google_dot_protobuf_dot_empty__pb2.Empty())
arr = np.frombuffer(response.body, dtype=np.float64).reshape(shape)
print(arr.shape)

方案8: RabbitMQ + Bytes

部署RabbitMQ的Docker容器:

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docker run --name some-rabbit -p 5672:5672 rabbitmq:3
 
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# server.py
import pika
import numpy as np

connection = pika.BlockingConnection(
    pika.ConnectionParameters(host='localhost'))

channel = connection.channel()
channel.queue_declare(queue='rpc_queue')

shape = (1, 3, 224, 224)

def on_request(ch, method, props, body):
    
    arr = np.random.randn(*shape)
    data = np.ndarray.tobytes(arr)

    ch.basic_publish(exchange='',
                     routing_key=props.reply_to,
                     properties=pika.BasicProperties(correlation_id=props.correlation_id),
                     body=data)
    ch.basic_ack(delivery_tag=method.delivery_tag)

    
channel.basic_qos(prefetch_count=1)
channel.basic_consume(queue='rpc_queue', on_message_callback=on_request)

print(" [x] Awaiting RPC requests")
channel.start_consuming()
 
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# client.py
import pika
import uuid

connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()
result = channel.queue_declare(queue='', exclusive=True)
callback_queue = result.method.queue

response = None
corr_id = None

shape = (128, 1024, 1024)

def on_response(ch, method, props, body):
    global response
    if corr_id == props.correlation_id:
        response = body

channel.basic_consume(
    queue=callback_queue,
    on_message_callback=on_response, 
    auto_ack=True
)

def call():
    global response
    global corr_id
    response = None
    corr_id = str(uuid.uuid4())
    channel.basic_publish(
        exchange='',
        routing_key='rpc_queue',
        properties=pika.BasicProperties(
            reply_to=callback_queue,
            correlation_id=corr_id,
        ),
        body=""
    )
    connection.process_data_events(time_limit=None)
    return response

response = call()
arr = np.frombuffer(response, dtype=np.float64).reshape(shape)
print(arr.shape)

参考文献

  • [1] "NumPy", Numpy.org, 2022. https://numpy.org/.
  • [2] "FastAPI", FastAPI, 2022. https://fastapi.tiangolo.com/
  • [3] "gRPC" gRPC. https://grpc.io/
  • [4] “Messaging that just works — RabbitMQ,” Rabbitmq.com, 2019. https://www.rabbitmq.com/
  • [5] pika, “Pika,” GitHub, Jul. 28, 2023. https://github.com/pika/pika (accessed Jul. 28, 2023).
  • [6] "GitHub Copilot · Your AI pair programmer," GitHub, 2022. https://github.com/features/copilot