记录mask-rcnn的复现所遇到的问题
介于水平有限，所以是主要参考现成的一篇博客跑的。不过还是遇到了一些问题，这里做下记录
参考链接
(57条消息) 手把手教你训练自己的Mask R-CNN图像实例分割模型（PyTorch官方教程）_一个菜鸟的奋斗的博客-CSDN博客_实例分割模型
第一个是文件夹的问题，你必须要把相关文件拷贝到当前目录，比如utils.py engine.py transforms.py等，不是傻傻地pip install
第二个是transforms.py有个函数 get_dimensions
我发现torchvision.transforms.functional模块当时的版本是没有这个函数的。
主要是实现
channels,height,width=get_dimensions(image)我有查看了下torchvision.transforms.functional有哪些方法，我发现有
get_image_size和get_image_num_channels方法
我想可能是包新版本这两个方法之后是合并了吧。（我猜的，也可能是拆开了，我太懒了= =）
所以我自己写了个函数
from torchvision.transforms.functional import get_image_size
from torchvision.transforms.functional import get_image_num_channels
def get_dimensions(image):
height, width = get_image_size(image)[1],get_image_size(image)[0]
channels = get_image_num_channels(image)
return channels,height,width解决了这个问题。
第三个是数据类型的问题
for images, targets in metric_logger.log_every(data_loader, print_freq, header):
images = list(image.to(device) for image in images)
#这里images里面是int8型这里的images原本是int型的，报错要改为float型，所以我改成了
for images, targets in metric_logger.log_every(data_loader, print_freq, header):
images = list(image.to(device) for image in images)
images = [ items.float() for items in images]
#images是个列表第四个是学习率设置太大，导致在train的时候loss特别大出现nan
只要把学习率改小就好了。
结果
Epoch: [9] Total time: 0:00:19 (0.3245 s / it)
hahahhaha
creating index...
index created!
Test: [ 0/50] eta: 0:00:13 model_time: 0.1376 (0.1376) evaluator_time: 0.0535 (0.0535) time: 0.2753 data: 0.0835 max mem: 3358
Test: [49/50] eta: 0:00:00 model_time: 0.1464 (0.1449) evaluator_time: 0.0807 (0.0910) time: 0.2470 data: 0.0035 max mem: 3358
outle
Test: Total time: 0:00:12 (0.2460 s / it)
Averaged stats: model_time: 0.1464 (0.1449) evaluator_time: 0.0807 (0.0910)
Accumulating evaluation results...
DONE (t=0.06s).
Accumulating evaluation results...
DONE (t=0.05s).
IoU metric: bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.044
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.178
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.013
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.048
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.056
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.178
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.317
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.025
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.338
IoU metric: segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.010
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.050
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.012
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.019
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.059
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.118
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.013
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.126第五个是会有数据集会出现.ipynb_checkpoints的隐藏文件，读取的时候会有问题
删掉这个文件就好了
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