FastRCNN的python代码

以下是Fast R-CNN的Python代码，包括模型的定义、训练和测试：

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np

class FastRCNN(nn.Module):
    def __init__(self, n_classes):
        super(FastRCNN, self).__init__()
        self.n_classes = n_classes
        self.roi_pool = nn.AdaptiveMaxPool2d((7, 7))
        self.fc1 = nn.Linear(7*7*512, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.cls_score = nn.Linear(4096, n_classes)
        self.bbox_pred = nn.Linear(4096, n_classes*4)

    def forward(self, conv_feats, rois):
        rois = torch.from_numpy(rois).cuda()
        roi_feats = self.roi_pool(conv_feats, rois)
        x = roi_feats.view(-1, 7*7*512)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        cls_scores = self.cls_score(x)
        bbox_preds = self.bbox_pred(x)
        return cls_scores, bbox_preds

def smooth_l1_loss(bbox_preds, bbox_targets, bbox_inside_weights, bbox_outside_weights, sigma=1.0):
    sigma_2 = sigma ** 2
    box_diff = bbox_preds - bbox_targets
    in_box_diff = bbox_inside_weights * box_diff
    abs_in_box_diff = torch.abs(in_box_diff)
    smoothL1_sign = (abs_in_box_diff < 1. / sigma_2).detach().float()
    in_loss_box = torch.pow(in_box_diff, 2) * (sigma_2 / 2.) * smoothL1_sign \
                  + (abs_in_box_diff - (0.5 / sigma_2)) * (1. - smoothL1_sign)
    out_loss_box = bbox_outside_weights * in_loss_box
    loss_box = out_loss_box
    return loss_box.mean()

def train(model, dataloader, optimizer, criterion_cls, criterion_bbox):
    model.train()
    running_loss = 0.0
    for i, data in enumerate(dataloader):
        img, gt_boxes, gt_labels = data
        img = img.cuda()
        gt_boxes = gt_boxes.cuda()
        gt_labels = gt_labels.cuda()
        optimizer.zero_grad()
        conv_feats = model.convnet(img)
        rois, roi_labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = \
            model.proposal_layer(conv_feats, gt_boxes, gt_labels)
        cls_scores, bbox_preds = model(conv_feats, rois)
        cls_loss = criterion_cls(cls_scores, roi_labels)
        bbox_loss = smooth_l1_loss(bbox_preds, bbox_targets, bbox_inside_weights, bbox_outside_weights)
        loss = cls_loss + bbox_loss
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    return running_loss / len(dataloader)

def test(model, dataloader):
    model.eval()
    correct = 0
    total = 0
    for i, data in enumerate(dataloader):
        img, gt_boxes, gt_labels = data
        img = img.cuda()
        gt_boxes = gt_boxes.cuda()
        gt_labels = gt_labels.cuda()
        conv_feats = model.convnet(img)
        rois, roi_labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = \
            model.proposal_layer(conv_feats, gt_boxes, gt_labels)
        cls_scores, bbox_preds = model(conv_feats, rois)
        _, predicted = torch.max(cls_scores.data, 1)
        total += roi_labels.size(0)
        correct += (predicted == roi_labels).sum().item()
    return correct / total

这里使用了PyTorch框架实现了Fast R-CNN模型，并提供了训练和测试函数。其中，`FastRCNN`类定义了模型的结构，`smooth_l1_loss`函数是bbox回归的损失函数，`train`函数和`test`函数分别用于训练和测试模型。