基于R-CNN与FastR-CNN的目标检测模型实现

### 基于R - CNN与Fast R - CNN的目标检测模型实现 #### 1. 数据准备 在进行目标检测模型训练之前，需要准备好训练和验证数据集以及数据加载器。具体步骤如下： - 划分训练集和测试集： ```python n_train = 9*len(FPATHS)//10 train_ds = RCNNDataset(FPATHS[:n_train],ROIS[:n_train], CLSS[:n_train], DELTAS[:n_train], GTBBS[:n_train]) test_ds = RCNNDataset(FPATHS[n_train:], ROIS[n_train:], CLSS[n_train:], DELTAS[n_train:], GTBBS[n_train:]) ``` - 创建数据加载器： ```python from torch.utils.data import TensorDataset, DataLoader train_loader = DataLoader(train_ds, batch_size=2, collate_fn=train_ds.collate_fn, drop_last=True) test_loader = DataLoader(test_ds, batch_size=2, collate_fn=test_ds.collate_fn, drop_last=True) ``` #### 2. R - CNN网络架构 准备好数据后，开始构建R - CNN模型，该模型可以预测区域建议的类别和对应的偏移量，以在图像中的对象周围绘制紧密的边界框。具体策略如下： 1. 定义VGG骨干网络： ```python import torchvision.models as models import torch.nn as nn vgg_backbone = models.vgg16(pretrained=True) vgg_backbone.classifier = nn.Sequential() for param in vgg_backbone.parameters(): param.requires_grad = False vgg_backbone.eval().to(device) ``` 2. 定义RCNN网络模块： ```python class RCNN(nn.Module): def __init__(self): super().__init__() feature_dim = 25088 self.backbone = vgg_backbone self.cls_score = nn.Linear(feature_dim, len(label2target)) self.bbox = nn.Sequential( nn.Linear(feature_dim, 512), nn.ReLU(), nn.Linear(512, 4), nn.Tanh(), ) self.cel = nn.CrossEntropyLoss() self.sl1 = nn.L1Loss() def forward(self, input): feat = self.backbone(input) cls_score = self.cls_score(feat) bbox = self.bbox(feat) return cls_score, bbox def calc_loss(self, probs, _deltas, labels, deltas): detection_loss = self.cel(probs, labels) ixs, = torch.where(labels != 0) _deltas = _deltas[ixs] deltas = deltas[ixs] self.lmb = 10.0 if len(ixs) > 0: regression_loss = self.sl1(_deltas, deltas) return detection_loss + self.lmb * regression_loss, detection_loss.detach(), regression_loss.detach() else: regression_loss = 0 return detection_loss + self.lmb * regression_loss, detection_loss.detach(), regression_loss ``` 3. 定义训练和验证函数： ```python import torch.optim as optim def train_batch(inputs, model, optimizer, criterion): input, clss, deltas = inputs model.train() optimizer.zero_grad() _clss, _deltas = model(input) loss, loc_loss, regr_loss = criterion(_clss, _deltas, clss, deltas) accs = clss == decode(_clss) loss.backward() optimizer.step() return loss.detach(), loc_loss, regr_loss, accs.cpu().numpy() @torch.no_grad() def validate_batch(inputs, model, criterion): input, clss, deltas = inputs with torch.no_grad(): model.eval() _clss,_deltas = model(input) loss,loc_loss,regr_loss = criterion(_clss, _deltas, clss, deltas) _, _clss = _clss.max(-1) accs = clss == _clss return _clss,_deltas,loss.detach(), loc_loss, regr_loss, accs.cpu().numpy() ``` 4. 创建模型对象，定义损失函数、优化器和训练轮数： ```python rcnn = RCNN().to(device) criterion = rcnn.calc_loss optimizer = optim.SGD(rcnn.parameters(), lr=1e-3) n_epochs = 5 log = Report(n_epochs) ``` 5. 训练模型： ```python for epoch in range(n_epochs): _n = len(train_loader) for ix, inputs in enumerate(train_loader): loss, loc_loss,regr_loss,accs = train_batch(inputs, rcnn, optimizer, criterion) pos = (epoch + (ix+1)/_n) log.record(pos, trn_loss=loss.item(), trn_loc_loss=loc_loss, trn_regr_loss=regr_loss, trn_acc=accs.mean(), end='\r') _n = len(test_loader) for ix,inputs in enumerate(test_loader): _clss, _deltas, loss, loc_loss, regr_loss, \ accs = validate_batch(inputs, rcnn, criterion) pos = (epoch + (ix+1)/_n) log.record(pos, val_loss=loss.item(), val_loc_loss=loc_loss, val_regr_loss=regr_loss, val_acc=accs.mean(), end='\r') # Plotting training and validation metrics log.plot_epochs('trn_loss,val_loss'.split(',')) ``` #### 3. 在新图像上进行预测 训练好模型后，使用它在新图像上进行预测，具体步骤如下： 1. 定义预测函数： ```python import numpy as np import cv2 import torch def test_predictions(filename, show_output=True): img = np.array(cv2.imread(filename, 1)[...,::-1]) candidates = extract_candidates(img) candidates = [(x,y,x+w,y+h) for x,y,w,h in candidates] input = [] for candidate in candidates: x,y,X,Y = candidate crop = cv2.resize(img[y:Y,x:X], (224,224)) input.append(preprocess_image(crop/255.)[None]) input = torch.cat( ```