def forward(self, x): x = self.backbone.features(x) rpn_output = self.rpn(x) roi_boxes = self.convert_to_roi_boxes(rpn_output) roi_features = self.roi_pooling(x, roi_boxes) roi_features = roi_features.view(roi_features.size(0), -1) output = self.classifier(roi_features) return output
时间: 2024-03-28 19:38:54 浏览: 102
这是 Faster R-CNN 神经网络模型的前向传播方法。它首先通过输入 x 经过 backbone 网络的特征提取层,得到特征图,然后将特征图输入 RPN 网络,生成候选框及其对应的边界框回归参数。接着,使用这些候选框(roi_boxes)和特征图,通过 ROI Pooling 网络得到固定大小的特征向量。这些特征向量被展平成一个向量,然后输入分类器,得到最终的分类结果。
在 forward 方法中,还调用了一个 convert_to_roi_boxes 方法,用来将 RPN 网络输出的边界框回归参数转换为实际的候选框。这个方法中,先根据 RPN 网络输出的边界框回归参数计算出候选框的坐标,然后对候选框进行一些处理,如裁剪到图像边界内、去除面积过小的框等。最终,得到的候选框(roi_boxes)用于后续的 ROI Pooling 过程。
相关问题
mask R_CNN实现各部分代码
Mask R-CNN 是一种基于 Faster R-CNN 的目标检测和实例分割的算法,主要由以下几部分组成:
1. Backbone 网络:通常采用 ResNet 或者 ResNeXt 等深度卷积神经网络作为基础网络,用于特征提取。
2. RPN 网络:Region Proposal Network,用于生成候选区域。
3. ROI Align 操作:将不同大小的候选区域映射到固定大小的特征图上。
4. Mask Head 网络:用于实例分割,生成每个物体实例的掩模。
下面是 Mask R-CNN 的主要代码实现:
1. Backbone 网络:使用 ResNet50 作为基础网络,代码如下:
```python
import torch.nn as nn
import torchvision.models.resnet as resnet
class ResNet50Backbone(nn.Module):
def __init__(self):
super(ResNet50Backbone, self).__init__()
resnet50 = resnet.resnet50(pretrained=True)
self.conv1 = resnet50.conv1
self.bn1 = resnet50.bn1
self.relu = resnet50.relu
self.maxpool = resnet50.maxpool
self.layer1 = resnet50.layer1
self.layer2 = resnet50.layer2
self.layer3 = resnet50.layer3
self.layer4 = resnet50.layer4
self.out_channels = 2048
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
c1 = self.layer1(x)
c2 = self.layer2(c1)
c3 = self.layer3(c2)
c4 = self.layer4(c3)
return [c1, c2, c3, c4]
```
2. RPN 网络:使用 Pytorch 内置的 Conv2d 和 nn.ModuleList 实现,代码如下:
```python
import torch.nn.functional as F
class RPN(nn.Module):
def __init__(self, in_channels, num_anchors):
super(RPN, self).__init__()
self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.cls_logits = nn.Conv2d(in_channels, num_anchors, kernel_size=1, stride=1)
self.bbox_pred = nn.Conv2d(in_channels, num_anchors * 4, kernel_size=1, stride=1)
def forward(self, x):
x = F.relu(self.conv(x))
logits = self.cls_logits(x)
bbox_pred = self.bbox_pred(x)
return logits, bbox_pred
```
3. ROI Align 操作:使用 Pytorch 内置的 nn.AdaptiveMaxPool2d 实现,代码如下:
```python
import torch.nn.functional as F
class RoIAlign(nn.Module):
def __init__(self, output_size, spatial_scale):
super(RoIAlign, self).__init__()
self.output_size = output_size
self.spatial_scale = spatial_scale
def forward(self, features, rois):
x_min, y_min, x_max, y_max = rois.chunk(4, dim=1)
h = (y_max - y_min + 1) * self.spatial_scale
w = (x_max - x_min + 1) * self.spatial_scale
image_height, image_width = features.shape[-2:]
# Normalize coordinates to [0, h or w]
x_min = x_min / image_width * w
y_min = y_min / image_height * h
x_max = x_max / image_width * w
y_max = y_max / image_height * h
# Compute bin sizes
bin_size_h = h / self.output_size[0]
bin_size_w = w / self.output_size[1]
# Compute grid of bin indices
grid_y = torch.linspace(0, self.output_size[0] - 1, self.output_size[0]).to(rois.device)
grid_x = torch.linspace(0, self.output_size[1] - 1, self.output_size[1]).to(rois.device)
grid_y, grid_x = torch.meshgrid(grid_y, grid_x)
# Compute centers of each bin
grid_y = grid_y * bin_size_h + y_min
grid_x = grid_x * bin_size_w + x_min
# Reshape grid to (N, H*W, 2)
grid = torch.stack([grid_x, grid_y], dim=-1).reshape(-1, 2)
# Sample features using bilinear interpolation
features = F.grid_sample(features, grid.view(1, self.output_size[0] * self.output_size[1], 2), align_corners=False)
features = features.reshape(-1, *self.output_size, features.shape[-1])
return features
```
4. Mask Head 网络:使用 Pytorch 内置的 Conv2d 和 nn.ModuleList 实现,代码如下:
```python
class MaskHead(nn.Module):
def __init__(self, in_channels):
super(MaskHead, self).__init__()
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
self.conv5 = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
self.conv6 = nn.Conv2d(in_channels // 2, 1, kernel_size=1, stride=1)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = F.relu(self.conv3(x))
x = F.relu(self.conv4(x))
x = F.relu(self.conv5(x))
x = torch.sigmoid(self.conv6(x))
return x
```
给出class-agnostic模块作用于faster_RCNN目标检测的pytorch训练示例代码,并给出注释
以下是class-agnostic模块作用于faster_RCNN目标检测的PyTorch训练示例代码,注释已经加入到代码中:
``` python
import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor
# load a pre-trained model for classification and return only the features
backbone = torchvision.models.mobilenet_v2(pretrained=True).features
# FasterRCNN needs to know the number of output channels in a backbone.
# For mobilenet_v2, it's 1280, so we need to add it here
backbone.out_channels = 1280
# let's make the RPN generate 5 x 3 anchors per spatial location, with 5 different sizes and 3 different aspect ratios.
# We have a Tuple[Tuple[int]] because each feature map could potentially have different sizes and aspect ratios
# (e.g., if your backbone produces a few feature maps of different sizes).
anchor_generator = torchvision.models.detection.rpn.AnchorGenerator(sizes=((32, 64, 128, 256, 512),),
aspect_ratios=((0.5, 1.0, 2.0),))
# let's define what are the feature maps that we will use to perform the region of interest cropping,
# as well as the size of the crop after rescaling.
# if your backbone returns a Tensor, featmap_names needs to be ['0']. More generally, the backbone should return an
# OrderedDict[Tensor], and in featmap_names you can choose which feature maps to use.
roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
output_size=7,
sampling_ratio=2)
# put the pieces together inside a FasterRCNN model
model = torchvision.models.detection.FasterRCNN(backbone,
num_classes=2,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler)
# define a class-agnostic module
class ClassAgnosticModule(torch.nn.Module):
def __init__(self, in_channels, num_classes):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, num_classes, kernel_size=1, stride=1, padding=0)
def forward(self, x):
# pass through the 1x1 convolution layer
x = self.conv(x)
# flatten the tensor
x = x.flatten(start_dim=2)
# apply softmax to get the class probabilities
x = torch.nn.functional.softmax(x, dim=1)
# reshape the tensor to match the output shape of the FasterRCNN model
num_boxes = x.shape[1]
x = x.reshape(-1, num_boxes, num_classes)
return x
# replace the FastRCNNPredictor with the ClassAgnosticModule
in_channels = model.roi_heads.box_predictor.cls_score.in_features
num_classes = 2
model.roi_heads.box_predictor = ClassAgnosticModule(in_channels, num_classes)
# define the loss function
def loss_fn(preds, targets):
return torch.nn.functional.cross_entropy(preds.squeeze(), targets)
# define the optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# define the data loader
data_loader = torch.utils.data.DataLoader(dataset, batch_size=4, shuffle=True)
# train the model
for epoch in range(num_epochs):
for images, targets in data_loader:
# move the images and targets to the device
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# forward pass
preds = model(images, targets)
# compute the loss
loss_dict = preds['losses']
losses = sum(loss_dict.values())
# backward pass
optimizer.zero_grad()
losses.backward()
optimizer.step()
```
该示例代码中,我们首先加载了一个预训练的分类模型,并删去了分类层。然后,我们定义了一个class-agnostic模块,并将FastRCNNPredictor替换为该模块。模型的其余部分与标准的FasterRCNN模型相同。最后,我们定义了一个损失函数和一个优化器,并使用数据加载器训练模型。
需要注意的是,该示例代码中的dataset和num_epochs变量没有给出,需要根据具体情况进行设置。
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