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, expandsuper().__init__()self.c = c2 // 2self.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 // 2self.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;
}