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visual.py
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visual.py
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import torch
from torch import nn
import torch.nn.functional as F
from torchviz import make_dot
from graphviz import Digraph
# 定义递归函数，用于遍历网络的子模块并添加节点到图形对象
def add_nodes(graph, module, prefix=""):
for name, child in module.named_children():
node_name = f"{prefix}.{name}" if prefix else name
node_label = str(child)
if isinstance(child, nn.Sequential):
add_nodes(graph, child, prefix=node_name)
else:
graph.node(node_name, label=node_label, shape="box")
# 定义您的神经网络类
class REBNCONV(nn.Module):
def __init__(self, in_ch=3, out_ch=3, dirate=1):
super(REBNCONV, self).__init__()
self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1*dirate, dilation=1*dirate)
self.bn_s1 = nn.BatchNorm2d(out_ch)
self.relu_s1 = nn.ReLU(inplace=True)
def forward(self, x):
hx = x
xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
return xout
# 双线性上采样
def _upsample_like(src, tar):
src = F.interpolate(src, size=tar.shape[2:], mode='bilinear')
return src
### RSU-7 ###
class RSU7(nn.Module): # UNet07DRES(nn.Module):
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
super(RSU7,self).__init__()
self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)
self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)
self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)
self.rebnconv6d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv5d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv4d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv3d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv2d = REBNCONV(mid_ch*2, mid_ch, dirate=1)
self.rebnconv1d = REBNCONV(mid_ch*2, out_ch, dirate=1)
def forward(self, x):
hx = x
hxin = self.rebnconvin(hx)
hx1 = self.rebnconv1(hxin)
hx = self.pool1(hx1)
hx2 = self.rebnconv2(hx)
hx = self.pool2(hx2)
hx3 = self.rebnconv3(hx)
hx = self.pool3(hx3)
hx4 = self.rebnconv4(hx)
hx = self.pool4(hx4)
hx5 = self.rebnconv5(hx)
hx = self.pool5(hx5)
hx6 = self.rebnconv6(hx)
hx7 = self.rebnconv7(hx6)
hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
hx6dup = _upsample_like(hx6d, hx5)
hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
hx5dup = _upsample_like(hx5d, hx4)
hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
hx4dup = _upsample_like(hx4d, hx3)
hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
hx3dup = _upsample_like(hx3d, hx2)
hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
hx2dup = _upsample_like(hx2d, hx1)
hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))
return hx1d + hxin
# 创建网络实例
model = RSU7()
# 创建示例输入
input_tensor = torch.randn(1, 3, 320, 320) # 示例输入张量
# 生成神经网络图
output = model(input_tensor)
dot = make_dot(output, params=dict(model.named_parameters()))
# 创建Graphviz图形对象
graph = Digraph(format='png')
# 解析torchviz生成的dot源码，并添加节点到Graphviz图形对象
dot_source = dot.source
lines = dot_source.split("\n")
for i, line in enumerate(lines):
if line.startswith(" "):
items = line.split("[")
node_name = items[0].strip().replace("\"", "")
node_label = items[1].split("=")[1].replace("label=", "").replace("\"", "").replace("\\", "")
graph.node(str(i), label=node_label, shape="box")
if i > 0:
graph.edge(str(i - 1), str(i))
# 遍历网络的子模块并添加节点到图形对象
add_nodes(graph, model)
# 保存为PNG图像文件
graph.render("network_graph")