一、在GPT中输入指令
根据以下Python模块代码，自动生成对应的Graphviz流程图代码，并保持图表简洁清晰，仅展示主流程：
<模块代码>
1. 以YOLOv9中ADown下采样为例：
根据以下Python模块代码，自动生成对应的Graphviz流程图代码，并保持图表简洁清晰，仅展示主流程：
class ADown(nn.Module):
def __init__(self, c1, c2): # ch_in, ch_out, shortcut, kernels, groups, expand
super().__init__()
self.c = c2 // 2
self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)
self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)
def forward(self, x):
x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
x1,x2 = x.chunk(2, 1)
x1 = self.cv1(x1)
x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)
x2 = self.cv2(x2)
return torch.cat((x1, x2), 1)2. 系统生成的输出示例：
digraph ADown {
rankdir=TB;
node [fontname="Arial", shape=box, style=rounded];
input [label="Input", shape=oval, width=1.2];
avg_pool [label="AvgPool2D\n(k=2, s=1, p=0)", fontsize=10];
chunk [label="Chunk (dim=1)", fontsize=10];
x1_cv1 [label="Conv\n(k=3, s=2, p=1)", fontsize=10];
x2_max_pool [label="MaxPool2D\n(k=3, s=2, p=1)", fontsize=10];
x2_cv2 [label="Conv\n(k=1, s=1, p=0)", fontsize=10];
concat [label="Concat\n(dim=1)", shape=diamond, fontsize=10];
output [label="Output", shape=oval, width=1.2];
input -> avg_pool;
avg_pool -> chunk;
chunk -> x1_cv1 [label="x1"];
chunk -> x2_max_pool [label="x2"];
x1_cv1 -> concat;
x2_max_pool -> x2_cv2;
x2_cv2 -> concat;
concat -> output;
}
二：复制dot代码到GraphvizOnline
GraphvizOnline链接：Graphviz Online
1. GraphvizOnline页面展示
2. 将dot代码复制到GraphvizOnline左面代码框中
3. 右面流程图框自动生成
三、流程图中添加流程说明
1. 以YOLOv9中ADown下采样为例：
根据以下Python模块代码，自动生成对应的Graphviz流程图代码，流程图中添加详细的流程说明，并保持图表简洁清晰：
class ADown(nn.Module):
def __init__(self, c1, c2):
super().__init__()
self.c = c2 // 2
self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)
self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)
def forward(self, x):
x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)
x1, x2 = x.chunk(2, 1)
x1 = self.cv1(x1)
x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)
x2 = self.cv2(x2)
return torch.cat((x1, x2), 1)
系统生成的输出示例：
digraph ADown {
rankdir=LR;
node [shape=box, fontname="Arial"];
subgraph cluster_legend {
label="流程说明";
fontsize=12;
style=dotted;
legend1 [label="1. 输入数据通过平均池化处理 (AvgPool2D)。", shape=note];
legend2 [label="2. 输入数据分割成两部分 (x1 和 x2)。", shape=note];
legend3 [label="3. x1 通过一个 3x3 的卷积操作 (Conv)。", shape=note];
legend4 [label="4. x2 首先经过最大池化 (MaxPool2D)，然后通过 1x1 的卷积操作 (Conv)。", shape=note];
legend5 [label="5. 两个分支的结果在通道维度上进行拼接，得到输出。", shape=note];
}
input [label="Input", shape=oval];
avg_pool [label="AvgPool2D (k=2, s=1, p=0)"];
chunk [label="Chunk (2, dim=1)"];
x1_cv1 [label="Conv (k=3, s=2, p=1)"];
x2_max_pool [label="MaxPool2D (k=3, s=2, p=1)"];
x2_cv2 [label="Conv (k=1, s=1, p=0)"];
concat [label="Concat (dim=1)", shape=diamond];
output [label="Output", shape=oval];
input -> avg_pool;
avg_pool -> chunk;
chunk -> x1_cv1 [label="x1"];
chunk -> x2_max_pool [label="x2"];
x1_cv1 -> concat;
x2_max_pool -> x2_cv2;
x2_cv2 -> concat;
concat -> output;
}
