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mask代码修改
This commit is contained in:
wrz1-zzzzz
commit
81ccb6681a
1
.gitignore
vendored
1
.gitignore
vendored
@ -366,3 +366,4 @@ FodyWeavers.xsd
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cmake-build-*
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.DS_Store
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DPL/dataset/*
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Binary file not shown.
Binary file not shown.
@ -1,12 +0,0 @@
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from symbol import pass_stmt
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class Model:
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def __init__(self):
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pass
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def load(self, weight_path: str):
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pass
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def predict(self, weight_path: str):
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pass
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@ -1,67 +0,0 @@
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import torch
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from PIL import Image
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from torch import nn
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import onnx
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import onnxruntime as ort # 用于ONNX推理
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from torchvision import datasets, models, transforms
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import os
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import matplotlib.pyplot as plt
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from train import device, class_names, imshow
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# 加载已训练的 ONNX 模型
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def load_onnx_model(model_path='model/best_model_11.14.19.30.onnx'):
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# 使用 ONNX Runtime 加载模型
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session = ort.InferenceSession(model_path)
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return session
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# 预测函数
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def visualize_model_predictions(onnx_session: str, img_path: str):
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img = Image.open(img_path)
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img = img.convert('RGB') # 转换为 RGB 模式
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data_transforms = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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img = data_transforms(img)
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img = img.unsqueeze(0)
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img = img.to(device)
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# 将输入转换为 ONNX 兼容的格式（numpy 数组）
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img = img.cpu().numpy()
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# 使用 ONNX Runtime 进行推理
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inputs = {onnx_session.get_inputs()[0].name: img}
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outputs = onnx_session.run(None, inputs)
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preds = outputs[0]
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# 获取预测类别
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_, predicted_class = torch.max(torch.tensor(preds), 1)
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# 可视化结果
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ax = plt.subplot(2, 2, 1)
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ax.axis('off')
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ax.set_title(f'Predicted: {class_names[predicted_class[0]]}')
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imshow(img[0])
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# 使用已训练的 ONNX 模型进行预测
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if __name__ == '__main__':
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# TODO:
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# 写一个模型类model = Model()
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# 1. 标准化模型加载接口 model.load(weight_path: str)
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# 2. 标准化预测接口调用方式为 output_image: torch.tensor = model.predict(input_image: torch.tensor, param: dict)
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# 加载 ONNX 模型
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model_path = 'model/best_model_11.14.19.30.onnx'
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onnx_session = load_onnx_model(model_path)
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# 图像路径
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img_path = 'd_2/train/dgd/transformed_1.jpg' # 更改为你的图像路径
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visualize_model_predictions(onnx_session, img_path)
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@ -1,188 +0,0 @@
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.optim import lr_scheduler
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import torch.backends.cudnn as cudnn
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import numpy as np
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import torchvision
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from torchvision import datasets, models, transforms
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import matplotlib.pyplot as plt
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import time
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import os
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from PIL import Image
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# 初始化 cudnn 优化
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cudnn.benchmark = True
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plt.ion() # interactive mode
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data_transforms = {
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'train': transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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'val': transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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]),
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}
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data_dir = 'd_2'
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image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
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data_transforms[x])
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for x in ['train', 'val']}
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dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
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shuffle=True, num_workers=4)
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for x in ['train', 'val']}
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dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
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class_names = image_datasets['train'].classes
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device = torch.device("cuda:0")
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#
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def imshow(inp, title=None):
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"""Display image for Tensor."""
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if isinstance(inp, np.ndarray):
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inp = inp.transpose((1, 2, 0))
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# inp = inp.numpy().transpose((1, 2, 0))
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mean = np.array([0.485, 0.456, 0.406])
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std = np.array([0.229, 0.224, 0.225])
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inp = std * inp + mean
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inp = np.clip(inp, 0, 1)
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plt.imshow(inp)
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if title is not None:
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plt.title(title)
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plt.pause(0.001) # pause a bit so that plots are updated
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def train_model(model, criterion, optimizer, scheduler, num_epochs=25, model_save_dir='model', save_onnx=False):
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since = time.time()
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# 创建保存模型的目录（如果不存在）
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os.makedirs(model_save_dir, exist_ok=True)
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# 设置模型保存路径
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best_model_params_path = os.path.join(model_save_dir, 'best_model_params_11.14.19.30.pt')
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best_model_onnx_path = os.path.join(model_save_dir, 'best_model_11.14.19.30.onnx')
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# 初始保存模型
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torch.save(model.state_dict(), best_model_params_path)
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best_acc = 0.0
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for epoch in range(num_epochs):
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print(f'Epoch {epoch}/{num_epochs - 1}')
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print('-' * 10)
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for phase in ['train', 'val']:
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if phase == 'train':
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model.train() # Set model to training mode
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else:
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model.eval() # Set model to evaluate mode
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running_loss = 0.0
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running_corrects = 0
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for inputs, labels in dataloaders[phase]:
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inputs = inputs.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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with torch.set_grad_enabled(phase == 'train'):
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outputs = model(inputs)
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_, preds = torch.max(outputs, 1)
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loss = criterion(outputs, labels)
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if phase == 'train':
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * inputs.size(0)
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running_corrects += torch.sum(preds == labels.data)
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if phase == 'train':
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scheduler.step()
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epoch_loss = running_loss / dataset_sizes[phase]
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epoch_acc = running_corrects.double() / dataset_sizes[phase]
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print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
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if phase == 'val' and epoch_acc > best_acc:
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best_acc = epoch_acc
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# 保存最佳模型
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torch.save(model.state_dict(), best_model_params_path)
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# 如果需要保存ONNX格式的模型，可以在这里执行
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if save_onnx:
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# 导出模型为 ONNX 格式
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model.eval()
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dummy_input = torch.randn(1, 3, 224, 224).to(device) # 用于推理的假输入（与训练时输入的大小一致）
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torch.onnx.export(model, dummy_input, best_model_onnx_path,
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export_params=True, opset_version=11,
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do_constant_folding=True, input_names=['input'], output_names=['output'])
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print(f"ONNX model saved at {best_model_onnx_path}")
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print()
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time_elapsed = time.time() - since
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print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
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print(f'Best val Acc: {best_acc:4f}')
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model.load_state_dict(torch.load(best_model_params_path, weights_only=True))
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return model
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# def visualize_model(model, num_images=6):
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# was_training = model.training
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# model.eval()
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# images_so_far = 0
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# fig = plt.figure()
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#
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# with torch.no_grad():
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# for i, (inputs, labels) in enumerate(dataloaders['train']):
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# inputs = inputs.to(device)
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# labels = labels.to(device)
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#
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# outputs = model(inputs)
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# _, preds = torch.max(outputs, 1)
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#
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# for j in range(inputs.size()[0]):
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# images_so_far += 1
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# ax = plt.subplot(num_images // 2, 2, images_so_far)
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# ax.axis('off')
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# ax.set_title(f'predicted: {class_names[preds[j]]}')
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# plt.imshow(inputs.cpu().data[j])
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#
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# if images_so_far == num_images:
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# model.train(mode=was_training)
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# return
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# model.train(mode=was_training)
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if __name__ == '__main__':
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# TODO:
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# 1. 能像yolo一样使用不同参数运行
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# 2. 能够比较不同模型的预测时间，根据配置可以切换模型。
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model_ft = models.resnet18(weights='IMAGENET1K_V1')
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num_ftrs = model_ft.fc.in_features
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model_ft.fc = nn.Linear(num_ftrs, 2)
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model_ft = model_ft.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
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exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)
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# 指定保存模型的目录
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model_save_dir = 'model' # 你可以修改为你希望保存的路径
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# 训练模型并保存 ONNX 格式的模型
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model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25,
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model_save_dir=model_save_dir, save_onnx=True)
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# visualize_model(model_ft)
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# TODO: 3. 新增dgd_class/export.py, 可以把训练得到的pt文件转换为onnx
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39
DPL/main.py
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39
DPL/main.py
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@ -0,0 +1,39 @@
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import argparse
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import os
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import sys
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import torch
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from model_cls.models import Model as ClsModel
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from pathlib import Path
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[0] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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def main():
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# 命令行参数解析
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parser = argparse.ArgumentParser(description="Use an ONNX model for inference.")
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# 设置默认值，并允许用户通过命令行进行修改
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parser.add_argument('--input-dir', type=str, default='dataset/', help='Directory to input images')
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parser.add_argument('--save-dir', type=str, default='detect', help='Directory to save output images')
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parser.add_argument('--gpu', action='store_true', help='Use GPU for inference')
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args = parser.parse_args()
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# 设置设备
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device = torch.device('cuda' if args.gpu and torch.cuda.is_available() else 'cpu')
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if args.gpu and not torch.cuda.is_available():
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print("GPU not available, switching to CPU.")
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# 加载模型
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model = ClsModel(model_path=args.weights, device=device)
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if __name__ == '__main__':
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main()
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57
DPL/model_cls/models.py
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57
DPL/model_cls/models.py
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@ -0,0 +1,57 @@
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from typing import Optional
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from torchvision import transforms
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import numpy as np
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import onnxruntime as ort
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import torch
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# 模型类
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class Model:
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def __init__(self, model_path: str, device: torch.device, block_size: Optional[tuple] = None):
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"""
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初始化模型，加载 ONNX 模型，并设置设备（CPU 或 GPU）。
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"""
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self.device = device
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self.session = ort.InferenceSession(model_path)
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self.block_size = block_size
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self.data_transforms = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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def predict(self, img: np.ndarray) -> torch.Tensor:
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"""
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使用 ONNX 模型进行推理，返回预测结果。
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"""
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img = self.data_transforms(img)
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img = img.unsqueeze(0)
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# # 转换为 ONNX 输入格式
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img_numpy = img.cpu().numpy()
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inputs = {self.session.get_inputs()[0].name: img_numpy}
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outputs = self.session.run(None, inputs)
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pred = torch.tensor(outputs[0])
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_, predicted_class = torch.max(pred, 1)
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return predicted_class
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def load(self, model_path: str):
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"""
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重新加载模型。
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"""
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self.session = ort.InferenceSession(model_path, providers=['TensorrtExecutionProvider'])
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def preprocess_predict_img(self, img: np.ndarray, block_size: Optional[tuple] = None) -> np.ndarray:
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if block_size is None:
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block_size = self.block_size
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if block_size:
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img = img.reshape((self.block_size[0], -1, self.block_size[1], 3))
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img = img.transpose((1, 0, 2, 3))
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else:
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img = img[np.newaxis, ...]
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pred = self.predict(img)
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pred = pred.squeeze().numpy()
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return pred
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@ -1,6 +1,6 @@
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import argparse
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import torch
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import onnxruntime as ort
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from models import *
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from PIL import Image
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import os
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import matplotlib.pyplot as plt
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@ -9,34 +9,6 @@ from PIL import ImageDraw, ImageFont
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from torchvision import transforms
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import time # 导入time模块
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# 模型类
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class Model:
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def __init__(self, model_path: str, device: torch.device):
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"""
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初始化模型，加载 ONNX 模型，并设置设备（CPU 或 GPU）。
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"""
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self.device = device
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self.session = ort.InferenceSession(model_path)
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def predict(self, img_tensor: torch.Tensor) -> torch.Tensor:
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"""
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使用 ONNX 模型进行推理，返回预测结果。
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"""
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# 转换为 ONNX 输入格式
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img_numpy = img_tensor.cpu().numpy()
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# 获取输入名称和推理
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inputs = {self.session.get_inputs()[0].name: img_numpy}
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outputs = self.session.run(None, inputs)
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return torch.tensor(outputs[0])
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def load(self, model_path: str):
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"""
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重新加载模型。
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"""
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self.session = ort.InferenceSession(model_path)
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# 预测函数
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def visualize_model_predictions(model: Model, img_path: str, save_dir: str, class_names: list):
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@ -48,25 +20,12 @@ def visualize_model_predictions(model: Model, img_path: str, save_dir: str, clas
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img = Image.open(img_path)
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img = img.convert('RGB') # 转换为 RGB 模式
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# 图像预处理
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data_transforms = transforms.Compose([
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transforms.Resize(256),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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img_transformed = data_transforms(img)
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img_transformed = img_transformed.unsqueeze(0)
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img_transformed = img_transformed.to(model.device)
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# 使用模型进行预测
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preds = model.predict(img_transformed)
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# 获取预测类别
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_, predicted_class = torch.max(preds, 1)
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preds = model.predict(img)
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print(preds)
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# 在图像上添加预测结果文本
|
||||
predicted_label = class_names[predicted_class[0]]
|
||||
predicted_label = class_names[preds[0]]
|
||||
|
||||
# 在图片上绘制文本
|
||||
img_with_text = img.copy()
|
||||
@ -121,8 +80,9 @@ def main():
|
||||
parser = argparse.ArgumentParser(description="Use an ONNX model for inference.")
|
||||
|
||||
# 设置默认值，并允许用户通过命令行进行修改
|
||||
parser.add_argument('--weights', type=str, default='model/model_1.onnx', help='Path to ONNX model file')
|
||||
parser.add_argument('--img-path', type=str, default='d_2/val/dgd',
|
||||
parser.add_argument('--weights', type=str, default=r'..\onnxs\dgd_class_11.14.onnx',
|
||||
help='Path to ONNX model file')
|
||||
parser.add_argument('--img-path', type=str, default='../dataset/val/dgd',
|
||||
help='Path to image or folder for inference')
|
||||
parser.add_argument('--save-dir', type=str, default='detect', help='Directory to save output images')
|
||||
parser.add_argument('--gpu', action='store_true', help='Use GPU for inference')
|
||||
196
DPL/model_cls/preprocess.py
Normal file
196
DPL/model_cls/preprocess.py
Normal file
@ -0,0 +1,196 @@
|
||||
import argparse
|
||||
import os
|
||||
import random
|
||||
import shutil
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import json
|
||||
from shapely.geometry import Polygon, box
|
||||
from shapely.affinity import translate
|
||||
|
||||
|
||||
def create_dataset_from_folder(image_folder, label_folder, block_size, output_dir):
|
||||
"""
|
||||
批量处理文件夹中的图片和对应标签，生成分类模型的数据集。
|
||||
|
||||
Args:
|
||||
image_folder (str): 图片文件夹路径。
|
||||
label_folder (str): 标签文件夹路径，对应Labelme生成的JSON文件。
|
||||
block_size (tuple): 分块的尺寸 (width, height)。
|
||||
output_dir (str): 输出数据集的根目录。
|
||||
"""
|
||||
# 创建输出文件夹
|
||||
has_label_dir = os.path.join(output_dir, "has_label")
|
||||
no_label_dir = os.path.join(output_dir, "no_label")
|
||||
os.makedirs(has_label_dir, exist_ok=True)
|
||||
os.makedirs(no_label_dir, exist_ok=True)
|
||||
|
||||
# 遍历图片文件夹
|
||||
for filename in os.listdir(image_folder):
|
||||
if filename.lower().endswith(('.jpg', '.jpeg', '.png', '.bmp', '.tif')):
|
||||
image_path = os.path.join(image_folder, filename)
|
||||
label_path = os.path.join(label_folder, os.path.splitext(filename)[0] + ".json")
|
||||
|
||||
# 检查标签文件是否存在
|
||||
if not os.path.exists(label_path):
|
||||
print(f"Label file not found for image: {filename}")
|
||||
continue
|
||||
|
||||
print(f"Processing {filename}...")
|
||||
process_single_image(image_path, label_path, block_size, has_label_dir, no_label_dir)
|
||||
|
||||
|
||||
def process_single_image(image_path, label_path, block_size, has_label_dir, no_label_dir):
|
||||
"""
|
||||
处理单张图片并分块保存到对应的文件夹。
|
||||
|
||||
Args:
|
||||
image_path (str): 图片路径。
|
||||
label_path (str): 标签路径。
|
||||
block_size (tuple): 分块的尺寸 (width, height)。
|
||||
has_label_dir (str): 包含标注的分块保存目录。
|
||||
no_label_dir (str): 无标注的分块保存目录。
|
||||
"""
|
||||
# 加载图片
|
||||
image = cv2.imread(image_path)
|
||||
img_height, img_width, _ = image.shape
|
||||
|
||||
# 加载Labelme JSON文件
|
||||
with open(label_path, 'r', encoding='utf-8') as f:
|
||||
label_data = json.load(f)
|
||||
|
||||
# 提取多边形标注
|
||||
polygons = []
|
||||
for shape in label_data['shapes']:
|
||||
if shape['shape_type'] == 'polygon':
|
||||
points = shape['points']
|
||||
polygons.append(Polygon(points))
|
||||
|
||||
if roi:
|
||||
x_min, y_min, x_max, y_max = roi
|
||||
x_min, y_min = max(0, x_min), max(0, y_min)
|
||||
x_max, y_max = min(img_width, x_max), min(img_height, y_max)
|
||||
image = image[y_min:y_max, x_min:x_max]
|
||||
img_height, img_width = y_max - y_min, x_max - x_min
|
||||
# 偏移标注的多边形
|
||||
polygons = [translate(poly.intersection(box(x_min, y_min, x_max, y_max)), -x_min, -y_min) for poly in polygons]
|
||||
|
||||
# 分割图片并保存到对应的文件夹
|
||||
block_width, block_height = block_size
|
||||
block_id = 0
|
||||
base_name = os.path.splitext(os.path.basename(image_path))[0]
|
||||
for y in range(0, img_height, block_height):
|
||||
for x in range(0, img_width, block_width):
|
||||
# 当前分块的边界框
|
||||
block_polygon = box(x, y, x + block_width, y + block_height)
|
||||
|
||||
# 判断是否与任何标注的多边形相交
|
||||
contains_label = any(poly.intersects(block_polygon) for poly in polygons)
|
||||
|
||||
# 裁剪当前块
|
||||
block = image[y:y + block_height, x:x + block_width]
|
||||
|
||||
# 保存到对应文件夹
|
||||
folder = has_label_dir if contains_label else no_label_dir
|
||||
block_filename = os.path.join(folder, f"{base_name}_block_{block_id}.jpg")
|
||||
cv2.imwrite(block_filename, block)
|
||||
block_id += 1
|
||||
|
||||
print(f"Saved {block_filename} to {'has_label' if contains_label else 'no_label'} folder.")
|
||||
|
||||
|
||||
def split_dataset(input_dir, output_dir, train_ratio=0.7, val_ratio=0.2, test_ratio=0.1):
|
||||
"""
|
||||
将分类后的数据集划分为 train、val 和 test 数据集，保持来源目录结构。
|
||||
|
||||
Args:
|
||||
input_dir (str): 分类结果根目录，包含多个子文件夹（标签文件夹）。
|
||||
output_dir (str): 输出数据集目录，将生成 train、val 和 test 文件夹。
|
||||
train_ratio (float): train 集比例或固定数量。
|
||||
val_ratio (float): val 集比例或固定数量。
|
||||
test_ratio (float): test 集比例或固定数量。
|
||||
"""
|
||||
# 定义输出子目录
|
||||
train_dir = os.path.join(output_dir, "train")
|
||||
val_dir = os.path.join(output_dir, "val")
|
||||
test_dir = os.path.join(output_dir, "test")
|
||||
|
||||
# 遍历所有子文件夹（标签文件夹）
|
||||
for category in os.listdir(input_dir):
|
||||
category_dir = os.path.join(input_dir, category)
|
||||
if not os.path.isdir(category_dir): # 忽略非文件夹
|
||||
continue
|
||||
|
||||
# 为当前类别在 train/val/test 创建相同的子文件夹结构
|
||||
os.makedirs(os.path.join(train_dir, category), exist_ok=True)
|
||||
os.makedirs(os.path.join(val_dir, category), exist_ok=True)
|
||||
os.makedirs(os.path.join(test_dir, category), exist_ok=True)
|
||||
|
||||
# 获取当前类别下的所有文件
|
||||
files = os.listdir(category_dir)
|
||||
random.shuffle(files)
|
||||
|
||||
# 计算分割点
|
||||
total_files = len(files)
|
||||
if train_ratio < 1:
|
||||
train_count = int(total_files * train_ratio)
|
||||
val_count = int(total_files * val_ratio)
|
||||
test_count = total_files - train_count - val_count
|
||||
else:
|
||||
train_count = int(train_ratio)
|
||||
val_count = int(val_ratio)
|
||||
test_count = int(test_ratio)
|
||||
|
||||
# 确保不超过文件总数
|
||||
train_count = min(train_count, total_files)
|
||||
val_count = min(val_count, total_files - train_count)
|
||||
test_count = min(test_count, total_files - train_count - val_count)
|
||||
|
||||
print(f"Category {category}: {train_count} train, {val_count} val, {test_count} test files")
|
||||
|
||||
# 划分数据集
|
||||
train_files = files[:train_count]
|
||||
val_files = files[train_count:train_count + val_count]
|
||||
test_files = files[train_count + val_count:]
|
||||
|
||||
# 复制文件到对应的子目录
|
||||
for file in train_files:
|
||||
shutil.copy(os.path.join(category_dir, file), os.path.join(train_dir, category, file))
|
||||
for file in val_files:
|
||||
shutil.copy(os.path.join(category_dir, file), os.path.join(val_dir, category, file))
|
||||
for file in test_files:
|
||||
shutil.copy(os.path.join(category_dir, file), os.path.join(test_dir, category, file))
|
||||
|
||||
print(f"Category {category} processed. Train: {len(train_files)}, Val: {len(val_files)}, Test: {len(test_files)}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser(description="Preprocess images to required shapes")
|
||||
|
||||
# 设置默认值，并允许用户通过命令行进行修改
|
||||
parser.add_argument('--img-dir', type=str, default=r'..\dataset\dgd\test_img', help='Directory to input images')
|
||||
parser.add_argument('--label-dir', type=str, default=r'..\dataset\dgd\test_img', help='Directory to input labels')
|
||||
|
||||
parser.add_argument('--output-dir', type=str, default=r'..\dataset\dgd\runs', help='Directory to save output images')
|
||||
parser.add_argument('--roi', type=int, nargs=4, default=None, help='ROI region (x_min y_min x_max y_max)')
|
||||
parser.add_argument('--train-ratio', type=float, default=0.7, help='Train set ratio or count')
|
||||
parser.add_argument('--val-ratio', type=float, default=0.2, help='Validation set ratio or count')
|
||||
parser.add_argument('--test-ratio', type=float, default=0.1, help='Test set ratio or count')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
# 输入文件夹路径
|
||||
image_folder = args.img_dir
|
||||
label_folder = args.label_dir
|
||||
# 输出路径
|
||||
output_dir = args.output_dir
|
||||
|
||||
# 分块大小
|
||||
block_size = (170, 170) # 替换为希望的分块尺寸 (宽, 高)
|
||||
roi = tuple(args.roi) if args.roi else None
|
||||
all_output = os.path.join(output_dir, 'all')
|
||||
|
||||
# 批量生成数据集
|
||||
create_dataset_from_folder(image_folder, label_folder, block_size, all_output)
|
||||
split_dataset(all_output, output_dir, args.train_ratio, args.val_ratio, args.test_ratio)
|
||||
@ -1,6 +1,8 @@
|
||||
import os
|
||||
import argparse
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
@ -10,7 +12,7 @@ import matplotlib.pyplot as plt
|
||||
|
||||
# 设置 cudnn 优化
|
||||
torch.backends.cudnn.benchmark = True
|
||||
plt.ion()
|
||||
|
||||
|
||||
def get_next_model_name(save_dir, base_name, ext):
|
||||
"""
|
||||
@ -131,37 +133,58 @@ def train_model(model, criterion, optimizer, scheduler, dataloaders, dataset_siz
|
||||
return model
|
||||
|
||||
|
||||
# 参数解析部分
|
||||
parser = argparse.ArgumentParser(description="Train a ResNet18 model on custom dataset.")
|
||||
parser.add_argument('--data-dir', type=str, default='d_2', help='Path to the dataset directory.')
|
||||
parser.add_argument('--model-save-dir', type=str, default='model', help='Directory to save the trained model.')
|
||||
parser.add_argument('--batch-size', type=int, default=8, help='Batch size for training.')
|
||||
parser.add_argument('--epochs', type=int, default=5, help='Number of training epochs.')
|
||||
parser.add_argument('--lr', type=float, default=0.0005, help='Learning rate.')
|
||||
parser.add_argument('--momentum', type=float, default=0.95, help='Momentum for SGD.')
|
||||
parser.add_argument('--step-size', type=int, default=5, help='Step size for LR scheduler.')
|
||||
parser.add_argument('--gamma', type=float, default=0.2, help='Gamma for LR scheduler.')
|
||||
parser.add_argument('--num-classes', type=int, default=2, help='Number of output classes.')
|
||||
parser.add_argument('--model-base-name', type=str, default='best_model', help='Base name for saved models.')
|
||||
|
||||
def main(args):
|
||||
"""Main function to train the model based on command-line arguments."""
|
||||
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
|
||||
|
||||
dataloaders, dataset_sizes, class_names = get_data_loaders(args.data_dir, args.batch_size)
|
||||
|
||||
model = models.resnet18(weights='IMAGENET1K_V1')
|
||||
num_ftrs = model.fc.in_features
|
||||
model.fc = nn.Linear(num_ftrs, args.num_classes)
|
||||
if args.model_name == 'resnet18':
|
||||
model = models.resnet18(weights='IMAGENET1K_V1')
|
||||
num_ftrs = model.fc.in_features
|
||||
model.fc = nn.Linear(num_ftrs, args.num_classes)
|
||||
elif args.model_name == 'resnet50':
|
||||
model = models.resnet50(weights='IMAGENET1K_V1')
|
||||
num_ftrs = model.fc.in_features
|
||||
model.fc = nn.Linear(num_ftrs, args.num_classes)
|
||||
elif args.model_name == 'alexnet':
|
||||
model = models.alexnet(pretrained=True)
|
||||
num_ftrs = model.classifier[-1].in_features
|
||||
model.classifier[-1] = nn.Linear(num_ftrs, args.num_classes)
|
||||
elif args.model_name == 'vgg16':
|
||||
model = models.vgg16(pretrained=True)
|
||||
num_ftrs = model.classifier[-1].in_features
|
||||
model.classifier[-1] = nn.Linear(num_ftrs, args.num_classes)
|
||||
elif args.model_name == 'densenet':
|
||||
model = models.densenet121(pretrained=True)
|
||||
num_ftrs = model.classifier.in_features
|
||||
model.classifier = nn.Linear(num_ftrs, args.num_classes)
|
||||
elif args.model_name == 'inceptionv3':
|
||||
model = models.inception_v3(pretrained=True)
|
||||
num_ftrs = model.fc.in_features
|
||||
model.fc = nn.Linear(num_ftrs, args.num_classes)
|
||||
model = model.to(device)
|
||||
|
||||
# training start time and model info
|
||||
model_base_name = f"{args.model_name}_bs{args.batch_size}_ep{args.epochs}_{datetime.now().strftime('%y_%m_%d')}.pt"
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
|
||||
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.step_size, gamma=args.gamma)
|
||||
|
||||
train_model(model, criterion, optimizer, scheduler, dataloaders, dataset_sizes, device,
|
||||
args.epochs, args.model_save_dir, args.model_base_name)
|
||||
args.epochs, args.model_save_dir, model_base_name)
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 参数解析部分
|
||||
parser = argparse.ArgumentParser(description="Train a ResNet18 model on custom dataset.")
|
||||
parser.add_argument('--model_name', type=str, default='resnet18', help='Which model to train as base model')
|
||||
parser.add_argument('--data-dir', type=str, default='dataset', help='Path to the dataset directory.')
|
||||
parser.add_argument('--model-save-dir', type=str, default='model', help='Directory to save the trained model.')
|
||||
parser.add_argument('--batch-size', type=int, default=8, help='Batch size for training.')
|
||||
parser.add_argument('--epochs', type=int, default=5, help='Number of training epochs.')
|
||||
parser.add_argument('--lr', type=float, default=0.0005, help='Learning rate.')
|
||||
parser.add_argument('--momentum', type=float, default=0.95, help='Momentum for SGD.')
|
||||
parser.add_argument('--step-size', type=int, default=5, help='Step size for LR scheduler.')
|
||||
parser.add_argument('--gamma', type=float, default=0.2, help='Gamma for LR scheduler.')
|
||||
parser.add_argument('--num-classes', type=int, default=2, help='Number of output classes.')
|
||||
args = parser.parse_args()
|
||||
main(args)
|
||||
0
DPL/model_cls/utils.py
Normal file
0
DPL/model_cls/utils.py
Normal file
@ -2,9 +2,9 @@ from model import Model
|
||||
|
||||
# 初始化 Model 对象，实例化时自动生成并保存掩膜
|
||||
model = Model(
|
||||
image_folder="runs/detect/exp6",
|
||||
label_folder="runs/detect/labels",
|
||||
output_folder="datasets/mask"
|
||||
image_folder="",
|
||||
label_folder="",
|
||||
output_folder=""
|
||||
)
|
||||
|
||||
# 读取某张图片对应的掩膜
|
||||
|
||||
Loading…
Reference in New Issue
Block a user