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python3.7,cuda10,pytorch1.1 #620

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MaLiXiaoGuo opened this issue Jul 26, 2019 · 5 comments
Open

python3.7,cuda10,pytorch1.1 #620

MaLiXiaoGuo opened this issue Jul 26, 2019 · 5 comments

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@MaLiXiaoGuo
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Traceback (most recent call last):
File "F:/faster-rcnn.pytorch-master/demo.py", line 27, in
from roi_data_layer.roidb import combined_roidb
File "F:\faster-rcnn.pytorch-master\lib\roi_data_layer\roidb.py", line 12, in
from datasets.factory import get_imdb
File "F:\faster-rcnn.pytorch-master\lib\datasets\factory.py", line 14, in
from datasets.pascal_voc import pascal_voc
File "F:\faster-rcnn.pytorch-master\lib\datasets\pascal_voc.py", line 23, in
from .imdb import imdb
File "F:\faster-rcnn.pytorch-master\lib\datasets\imdb.py", line 14, in
from model.utils.cython_bbox import bbox_overlaps
ModuleNotFoundError: No module named 'model.utils.cython_bbox'

@EMCP
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EMCP commented Jul 29, 2019

this is patched by replacing the pycocotools with the latest from github

example at

#466

@EMCP
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EMCP commented Aug 23, 2019

my tutorial to get this working is at https://mahr.io/tutorials/faster-r-cnn-pytorch-how-to

@harishkashyap
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The tutorial isn't working anymore. How I so wish, people building their modules on pytorch also update their libraries to work with latest versions in general.

@EMCP
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EMCP commented Sep 13, 2021

@harishkashyap this entire project which the tutorial was based on became out of date. Little reason to attempt to update what requires proper refactoring by the original author who clearly hasn't touched this repo in a while. I've personally moved into torchvision and pytorch 1.9 in order to attempt to do object detection on custom data

https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html

My full time job has not allowed me time to work beyond the basics

@harishkashyap
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Appreciate yoir quick response on this. I certainly understand given the way pytorch keeps updating with breaking changes. Tensorflow is worse. This repo would be incredibly useful if it works out of the box. Great work indeed!

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3 participants