请问论文中的图5是怎么得到的矩形框？

[tectal]

请问论文中的图5是怎么得到的矩形框？

[jxingm]

请问论文中的图5是怎么得到的矩形框？

I also want to know this question.Have you solved it? Thanks.

[dawnsdaw]

Have you solved this problem? How to visualize it? thank you very much

[atnegam]

This is a brief version of what I've developed. replacing model.py ,you can visualize the anchor frame for network location. Note BATCH_ SIZE must be 1.

import datetime
from torch import nn
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from core import resnet
import numpy as np
from core.anchors import generate_default_anchor_maps, hard_nms
from config import CAT_NUM, PROPOSAL_NUM
import matplotlib.pyplot as plt
from torchvision import transforms
from PIL import Image

def transform_convert(img_tensor, transform):
    """
    reference "https://blog.csdn.net/qq_40206371/article/details/120596673"
    param img_tensor: tensor
    param transforms: torchvision.transforms
    """
    if 'Normalize' in str(transform):
        normal_transform = list(filter(lambda x: isinstance(
            x, transforms.Normalize), transform.transforms))
        mean = torch.tensor(
            normal_transform[0].mean, dtype=img_tensor.dtype, device=img_tensor.device)
        std = torch.tensor(
            normal_transform[0].std, dtype=img_tensor.dtype, device=img_tensor.device)
        img_tensor.mul_(std[:, None, None]).add_(mean[:, None, None])

    img_tensor = img_tensor.transpose(0, 2).transpose(
        0, 1)  # C x H x W  ---> H x W x C

    if 'ToTensor' in str(transform) or img_tensor.max() < 1:
        img_tensor = img_tensor.detach().cpu().numpy()*255

    if isinstance(img_tensor, torch.Tensor):
        img_tensor = img_tensor.numpy()

    if img_tensor.shape[2] == 3:
        img = Image.fromarray(img_tensor.astype('uint8')).convert('RGB')
    elif img_tensor.shape[2] == 1:
        img = Image.fromarray(img_tensor.astype('uint8')).squeeze()
    else:
        raise Exception("Invalid img shape, expected 1 or 3 in axis 2, but got {}!".format(
            img_tensor.shape[2]))

    return img

transform_jpg = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
    ])

class ProposalNet(nn.Module):
    def __init__(self):
        super(ProposalNet, self).__init__()
        self.down1 = nn.Conv2d(2048, 128, 3, 1, 1)
        self.down2 = nn.Conv2d(128, 128, 3, 2, 1)
        self.down3 = nn.Conv2d(128, 128, 3, 2, 1)
        self.ReLU = nn.ReLU()
        self.tidy1 = nn.Conv2d(128, 6, 1, 1, 0)
        self.tidy2 = nn.Conv2d(128, 6, 1, 1, 0)
        self.tidy3 = nn.Conv2d(128, 9, 1, 1, 0)

    def forward(self, x):
        batch_size = x.size(0)
        d1 = self.ReLU(self.down1(x))
        d2 = self.ReLU(self.down2(d1))
        d3 = self.ReLU(self.down3(d2))
        t1 = self.tidy1(d1).view(batch_size, -1)
        t2 = self.tidy2(d2).view(batch_size, -1)
        t3 = self.tidy3(d3).view(batch_size, -1)
        return torch.cat((t1,t2, t3), dim=1)   

class attention_net(nn.Module):
    def __init__(self, topN=4):
        super(attention_net, self).__init__()
        self.pretrained_model = resnet.resnet50(pretrained=True)
        self.pretrained_model.avgpool = nn.AdaptiveAvgPool2d(1)
        self.pretrained_model.fc = nn.Linear(512 * 4, 200)
        self.proposal_net = ProposalNet()
        self.topN = topN
        self.concat_net = nn.Linear(2048 * (CAT_NUM + 1), 200)
        self.partcls_net = nn.Linear(512 * 4, 200)
        _, edge_anchors, _ = generate_default_anchor_maps()
        self.pad_side = 224
        self.edge_anchors = (edge_anchors + 224).astype(np.int)

    def forward(self, x):
        resnet_out, rpn_feature, feature = self.pretrained_model(x)
        x_pad = F.pad(x, (self.pad_side, self.pad_side, self.pad_side, self.pad_side), mode='constant', value=0)
        batch = x.size(0)
        # we will reshape rpn to shape: batch * nb_anchor
        rpn_score = self.proposal_net(rpn_feature.detach())
        all_cdds = [
            np.concatenate((x.reshape(-1, 1), self.edge_anchors.copy(), np.arange(0, len(x)).reshape(-1, 1)), axis=1)
            for x in rpn_score.data.cpu().numpy()]
        top_n_cdds = [hard_nms(x, topn=self.topN, iou_thresh=0.25) for x in all_cdds]
        top_n_cdds = np.array(top_n_cdds)
        top_n_index = top_n_cdds[:, :, -1].astype(np.int)
        top_n_index = torch.from_numpy(top_n_index).cuda()
        top_n_prob = torch.gather(rpn_score, dim=1, index=top_n_index)
        part_imgs = torch.zeros([batch, self.topN, 3, 224, 224]).cuda()

        jpg = torch.squeeze(x, 0)
        see_jpg = transform_convert(jpg, transform_jpg)
        plt.imshow(see_jpg)
        current_axis = plt.gca() # 
        colors = 'white'  #The color of the box
        for i in range(batch):
            for j in range(self.topN):
                [y0, x0, y1, x1] = top_n_cdds[i][j, 1:5].astype(np.int)
                if j<3: #Draw only three boxes
                    current_axis.add_patch(plt.Rectangle((x0-224, y0-224), x1-x0, y1-y0, color=colors, fill=False, linewidth=2)) #draw now

                part_imgs[i:i + 1, j] = F.interpolate(x_pad[i:i + 1, :, y0:y1, x0:x1], size=(224, 224), mode='bilinear',
                                                      align_corners=True)

        plt.savefig("../path"+str(datetime.datetime.now())+".jpg") #Save in path
        current_axis.clear()

        part_imgs = part_imgs.view(batch * self.topN, 3, 224, 224)
        _, _, part_features = self.pretrained_model(part_imgs.detach())
        part_feature = part_features.view(batch, self.topN, -1)
        part_feature = part_feature[:, :CAT_NUM, ...].contiguous()
        part_feature = part_feature.view(batch, -1)
        # concat_logits have the shape: B*200
        concat_out = torch.cat([part_feature, feature], dim=1)
        concat_logits = self.concat_net(concat_out)
        raw_logits = resnet_out
        # part_logits have the shape: B*N*200
        part_logits = self.partcls_net(part_features).view(batch, self.topN, -1)
        return [raw_logits, concat_logits, part_logits, top_n_index, top_n_prob]

def soft_loss(part_logits, raw_logits):
    soft_part = F.log_softmax(part_logits, 1)
    soft_raw = F.softmax(raw_logits, 1)
    return -soft_part * soft_raw

def list_loss(logits, targets):
    temp = F.log_softmax(logits, -1)
    loss = [-temp[i][targets[i].item()] for i in range(logits.size(0))]
    return torch.stack(loss)

def ranking_loss(score, targets, proposal_num=PROPOSAL_NUM):
    loss = Variable(torch.zeros(1).cuda())
    batch_size = score.size(0)
    for i in range(proposal_num):                         
        targets_p = (targets > targets[:, i].unsqueeze(1)).type(torch.cuda.FloatTensor)
        pivot = score[:, i].unsqueeze(1)
        loss_p = (1 - pivot + score) * targets_p
        loss_p = torch.sum(F.relu(loss_p))
        loss += loss_p
    return loss / batch_size