1b87a98261
## 主要更新 - ✨ 更新所有依赖到最新稳定版本 - 📝 添加详细的项目文档和模型推荐 - 🔧 配置 VSCode Cloud Studio 预览功能 - 🐛 修复 PyTorch API 弃用警告 ## 依赖更新 - diffusers: 0.27.2 → 0.35.2 - gradio: 4.21.0 → 5.46.0 - peft: 0.7.1 → 0.18.0 - Pillow: 9.5.0 → 11.3.0 - fastapi: 0.108.0 → 0.116.2 ## 新增文件 - CLAUDE.md - 项目架构和开发指南 - UPGRADE_NOTES.md - 详细的升级说明 - .vscode/preview.yml - 预览配置 - .vscode/LAUNCH_GUIDE.md - 启动指南 - .gitignore - 更新的忽略规则 ## 代码修复 - 修复 iopaint/model/ldm.py 中的 torch.cuda.amp.autocast() 弃用警告 ## 文档更新 - README.md - 添加模型推荐和使用指南 - 完整的项目源码（iopaint/） - Web 前端源码（web_app/） 🤖 Generated with Claude Code
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
def conv3x3(in_planes, out_planes, stride=1):
|
|
"""3x3 convolution with padding"""
|
|
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
|
|
|
|
|
|
class BasicBlock(nn.Module):
|
|
|
|
def __init__(self, in_chan, out_chan, stride=1):
|
|
super(BasicBlock, self).__init__()
|
|
self.conv1 = conv3x3(in_chan, out_chan, stride)
|
|
self.bn1 = nn.BatchNorm2d(out_chan)
|
|
self.conv2 = conv3x3(out_chan, out_chan)
|
|
self.bn2 = nn.BatchNorm2d(out_chan)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.downsample = None
|
|
if in_chan != out_chan or stride != 1:
|
|
self.downsample = nn.Sequential(
|
|
nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
|
|
nn.BatchNorm2d(out_chan),
|
|
)
|
|
|
|
def forward(self, x):
|
|
residual = self.conv1(x)
|
|
residual = F.relu(self.bn1(residual))
|
|
residual = self.conv2(residual)
|
|
residual = self.bn2(residual)
|
|
|
|
shortcut = x
|
|
if self.downsample is not None:
|
|
shortcut = self.downsample(x)
|
|
|
|
out = shortcut + residual
|
|
out = self.relu(out)
|
|
return out
|
|
|
|
|
|
def create_layer_basic(in_chan, out_chan, bnum, stride=1):
|
|
layers = [BasicBlock(in_chan, out_chan, stride=stride)]
|
|
for i in range(bnum - 1):
|
|
layers.append(BasicBlock(out_chan, out_chan, stride=1))
|
|
return nn.Sequential(*layers)
|
|
|
|
|
|
class ResNet18(nn.Module):
|
|
|
|
def __init__(self):
|
|
super(ResNet18, self).__init__()
|
|
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
|
|
self.bn1 = nn.BatchNorm2d(64)
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
|
|
self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
|
|
self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
|
|
self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = F.relu(self.bn1(x))
|
|
x = self.maxpool(x)
|
|
|
|
x = self.layer1(x)
|
|
feat8 = self.layer2(x) # 1/8
|
|
feat16 = self.layer3(feat8) # 1/16
|
|
feat32 = self.layer4(feat16) # 1/32
|
|
return feat8, feat16, feat32
|