fast rcnn代码
时间: 2023-09-22 22:12:26 浏览: 80
以下是使用Python和PyTorch实现的Fast R-CNN代码示例:
1. 安装必要的库:
```python
!pip install torch torchvision numpy matplotlib opencv-python
```
2. 导入必要的库:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import cv2
```
3. 定义RoI池化层:
```python
class RoIPool(nn.Module):
def __init__(self, output_size):
super(RoIPool, self).__init__()
self.output_size = output_size
def forward(self, features, rois):
# features: (N, C, H, W)
# rois: (num_rois, 5) [batch_index, x1, y1, x2, y2]
batch_indices = rois[:, 0].long()
rois = rois[:, 1:]
num_rois = rois.size(0)
# Convert the RoI coordinates to image coordinates
rois[:, 0::2] /= features.size(3)
rois[:, 1::2] /= features.size(2)
# Compute the grid cell size
grid_h = (rois[:, 3] - rois[:, 1]) / self.output_size
grid_w = (rois[:, 2] - rois[:, 0]) / self.output_size
# Compute the grid cell position
grid_x = torch.linspace(0, features.size(3) - 1, features.size(3)).to(rois.device)
grid_y = torch.linspace(0, features.size(2) - 1, features.size(2)).to(rois.device)
grid_x, grid_y = torch.meshgrid(grid_x, grid_y)
grid_x = grid_x.view(-1)
grid_y = grid_y.view(-1)
# Compute the grid cell index
rois_grid_x = (rois[:, 2] + rois[:, 0]) / 2
rois_grid_y = (rois[:, 3] + rois[:, 1]) / 2
grid_i = torch.floor(rois_grid_y.unsqueeze(1) / grid_h.unsqueeze(0)).long()
grid_j = torch.floor(rois_grid_x.unsqueeze(1) / grid_w.unsqueeze(0)).long()
# Compute the RoI features
roi_features = []
for i in range(num_rois):
indices = (batch_indices == i).nonzero().squeeze()
x = grid_x[grid_j[indices]]
y = grid_y[grid_i[indices]]
roi_feature = F.grid_sample(features[i].unsqueeze(0), torch.stack([x, y], dim=1).unsqueeze(0)).squeeze(0)
roi_features.append(roi_feature)
roi_features = torch.stack(roi_features, dim=0)
# Resize the RoI features
roi_features = F.adaptive_max_pool2d(roi_features, self.output_size)
return roi_features
```
4. 定义Fast R-CNN模型:
```python
class FastRCNN(nn.Module):
def __init__(self, num_classes):
super(FastRCNN, self).__init__()
# Backbone network
self.backbone = torchvision.models.vgg16(pretrained=True).features
# RoI pooling layer
self.roi_pool = RoIPool(output_size=7)
# Classification head
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, num_classes)
)
# Regression head
self.regressor = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4 * num_classes)
)
def forward(self, x, rois):
x = self.backbone(x)
x = self.roi_pool(x, rois)
x = x.view(x.size(0), -1)
scores = self.classifier(x)
bbox_deltas = self.regressor(x)
return scores, bbox_deltas
```
5. 加载数据集和模型:
```python
# Load the dataset
train_dataset = torchvision.datasets.CocoDetection(root='./data', annFile='./data/annotations/instances_train2017.json')
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=1, shuffle=True)
# Load the model
model = FastRCNN(num_classes=80)
model.train()
```
6. 定义损失函数和优化器:
```python
# Define the loss function
cls_loss_function = nn.CrossEntropyLoss(reduction='sum')
reg_loss_function = nn.SmoothL1Loss(reduction='sum')
# Define the optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0005)
```
7. 训练模型:
```python
# Training loop
for epoch in range(num_epochs):
for images, targets in train_loader:
# Move the images and targets to the device
images = images.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# Generate the RoIs
rois = []
for target in targets:
num_objects = target['boxes'].size(0)
roi_indices = torch.full((num_objects, 1), 0).to(device)
rois.append(torch.cat([roi_indices, target['boxes']], dim=1))
rois = torch.cat(rois, dim=0)
# Forward pass
scores, bbox_deltas = model(images, rois)
cls_targets = torch.cat([t['labels'] for t in targets], dim=0)
reg_targets = torch.cat([t['boxes'] for t in targets], dim=0)
cls_loss = cls_loss_function(scores, cls_targets)
reg_loss = reg_loss_function(bbox_deltas, reg_targets)
loss = cls_loss + reg_loss
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
以上就是使用Python和PyTorch实现的Fast R-CNN代码示例,您可以根据自己的需求进行修改和调整。
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