Scene transformer

Scene Transformer是一种基于Transformer的神经网络模型，用于对场景图像进行处理和分析。它是一种端到端的模型，可以直接从原始图像中提取特征，并在此基础上进行场景理解和推理。Scene Transformer的主要思想是将图像分解为一组对象，然后对这些对象进行编码和关联，以获得对场景的全局理解。与传统的卷积神经网络不同，Scene Transformer可以处理不同大小和数量的对象，并且可以在不同的任务之间共享特征。

下面是Scene Transformer的一些关键特点和应用：

1. Scene Transformer可以用于多种场景理解任务，如目标检测、语义分割、实例分割等。

2. Scene Transformer可以处理不同大小和数量的对象，并且可以在不同的任务之间共享特征。

3. Scene Transformer可以直接从原始图像中提取特征，而无需使用手工设计的特征。

4. Scene Transformer可以通过学习对象之间的关系来进行场景理解和推理。

5. Scene Transformer已经在多个视觉任务中取得了优异的表现，如COCO目标检测、Cityscapes语义分割等。

下面是一个使用Scene Transformer进行目标检测的示例代码：

import torch
import torchvision
from torchvision.models.detection import FasterRCNN
from torchvision.models.detection.rpn import AnchorGenerator

# 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
anchor_generator = 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 is expected 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 = FasterRCNN(backbone,
                   num_classes=2,
                   rpn_anchor_generator=anchor_generator,
                   box_roi_pool=roi_pooler)

# now we have a model and we can train it