python 머신러닝에 관해서 질문합니다

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현재 토이 프로젝트 겸 개인 프로젝트를 파이썬을 사용하여 머신러닝을 구현하는 프로젝트를 진행 중입니다. 프로그램을 구현하면서, 문제가 생겨 경로랑 파일교체와 구글링하며 교수님께도 방문하여 답을 구해 에러를 찾아 문제를 해결해왔습니다. 제가 현재 구글의 colab을 사용하여 머신러닝을 작업중입니다.구글 colab으로 작업하고 있는 머신러닝 프로그램 # -*- coding: utf-8 -*- &#34;&#34;&#34;mmdetection_train_custom_coco_data_segmentation.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/github/Tony607/mmdetection_instance_segmentation_demo/blob/master/mmdetection_train_custom_coco_data_segmentation.ipynb # [How to create custom COCO data set for instance segmentation](https://www.dlology.com/blog/how-to-create-custom-coco-data-set-for-instance-segmentation/) | DLology blog ## Install Open MMLab Detection Toolbox &#34;&#34;&#34; # Commented out IPython magic to ensure Python compatibility. import os from os.path import exists, join, basename, splitext # TODO: change URL to your fork of my repository if necessary. git_repo_url &#61; 'https://github.com/Tony607/mmdetection_instance_segmentation_demo' # %cd /content project_name &#61; os.path.abspath(splitext(basename(git_repo_url))[0]) mmdetection_dir &#61; os.path.join(project_name, &#34;mmdetection&#34;) if not exists(project_name): # clone &#34;depth 1&#34; will only get the latest copy of the relevant files. !git clone -q --recurse-submodules --depth 1 $git_repo_url print(&#34;Update mmdetection repo&#34;) !cd {mmdetection_dir} && git checkout master && git pull # dependencies !pip install -q mmcv terminaltables # build !cd {mmdetection_dir} && python setup.py install !pip install -r {os.path.join(mmdetection_dir, &#34;requirements.txt&#34;)} # Commented out IPython magic to ensure Python compatibility. # You may need to restart your runtime prior to this, to let your installation take effect. import os from os.path import exists, join, basename, splitext # TODO: change URL to your fork of my repository if necessary. git_repo_url &#61; 'https://github.com/Tony607/mmdetection_instance_segmentation_demo' # %cd /content project_name &#61; os.path.abspath(splitext(basename(git_repo_url))[0]) mmdetection_dir &#61; os.path.join(project_name, &#34;mmdetection&#34;) &#34;&#34;&#34;## Your settings&#34;&#34;&#34; import os import sys sys.path.append(mmdetection_dir) import time import matplotlib import matplotlib.pylab as plt plt.rcParams[&#34;axes.grid&#34;] &#61; False # You can add more model configs like below. MODELS_CONFIG &#61; { 'mask_rcnn_r50_fpn_1x': { 'config_file': 'configs/mask_rcnn_r50_fpn_1x.py' }, 'cascade_mask_rcnn_r50_fpn_1x': { 'config_file': 'configs/cascade_mask_rcnn_r50_fpn_1x.py', } } # Pick the model you want to use # Select a model in &#96;MODELS_CONFIG&#96;. selected_model &#61; 'mask_rcnn_r50_fpn_1x' # 'cascade_mask_rcnn_r50_fpn_1x' # Total training epochs. total_epochs &#61; 20 # Name of the config file. config_file &#61; MODELS_CONFIG[selected_model]['config_file'] &#34;&#34;&#34;## Modify config file&#34;&#34;&#34; import os config_fname &#61; os.path.join(project_name, 'mmdetection', config_file) assert os.path.isfile(config_fname), '&#96;{}&#96; not exist'.format(config_fname) config_fname import json annotation_path &#61; os.path.join(project_name, &#34;data&#34;, &#34;trainval.json&#34;) json_file &#61; open(annotation_path) coco &#61; json.load(json_file) print(coco[&#34;categories&#34;]) classes_names &#61; [category[&#34;name&#34;] for category in coco[&#34;categories&#34;]] print(classes_names) import re fname &#61; config_fname with open(fname) as f: s &#61; f.read() work_dir &#61; re.findall(r&#34;work_dir &#61; \'(.*?)\'&#34;, s)[0] # Update &#96;num_classes&#96; including &#96;background&#96; class. s &#61; re.sub('num_classes&#61;.*?,', 'num_classes&#61;{},'.format(len(classes_names) &#43; 1), s) s &#61; re.sub('total_epochs &#61; \d&#43;', 'total_epochs &#61; {} #'.format(total_epochs), s) if &#34;CocoDataset&#34; in s: s &#61; re.sub(&#34;data_root &#61; 'data/coco/'&#34;, &#34;data_root &#61; 'data/'&#34;, s) s &#61; re.sub(&#34;annotations/instances_train2017.json&#34;, &#34;trainval.json&#34;, s) s &#61; re.sub(&#34;annotations/instances_val2017.json&#34;, &#34;trainval.json&#34;, s) s &#61; re.sub(&#34;annotations/instances_val2017.json&#34;, &#34;trainval.json&#34;, s) s &#61; re.sub(&#34;train2017&#34;, &#34;images&#34;, s) s &#61; re.sub(&#34;val2017&#34;, &#34;images&#34;, s) else: s &#61; re.sub('img_prefix&#61;.*?\],', &#34;img_prefix&#61;data_root &#43; 'images',&#34;.format(total_epochs), s) with open(fname, 'w') as f: f.write(s) !cat {config_fname} &#34;&#34;&#34;## Train the model&#34;&#34;&#34; # Commented out IPython magic to ensure Python compatibility. # %cd {project_name} !python mmdetection/tools/train.py {config_fname} &#34;&#34;&#34;### Verify the checkpoint file exists.&#34;&#34;&#34; # Commented out IPython magic to ensure Python compatibility. # %cd {project_name} checkpoint_file &#61; os.path.join(work_dir, &#34;latest.pth&#34;) assert os.path.isfile( checkpoint_file), '&#96;{}&#96; not exist'.format(checkpoint_file) checkpoint_file &#61; os.path.abspath(checkpoint_file) checkpoint_file &#34;&#34;&#34;## Test predict Turn down the &#96;score_thr&#96; if you think the model is missing any bbox. Turn up the &#96;score_thr&#96; if you see too much overlapping bboxes with low scores. &#34;&#34;&#34; import time import matplotlib import matplotlib.pylab as plt plt.rcParams[&#34;axes.grid&#34;] &#61; False import mmcv from mmcv.runner import load_checkpoint import mmcv.visualization.image as mmcv_image # fix for colab def imshow(img, win_name&#61;'', wait_time&#61;0): plt.figure( figsize&#61;(50, 50)); plt.imshow(img) mmcv_image.imshow &#61; imshow from mmdet.models import build_detector from mmdet.apis import inference_detector, show_result, init_detector score_thr &#61; 0.5 # build the model from a config file and a checkpoint file model &#61; init_detector(config_fname, checkpoint_file) # test a single image and show the results img &#61; 'data/images/15.jpg' result &#61; inference_detector(model, img) show_result(img, result, classes_names, score_thr&#61;score_thr, out_file&#61;&#34;result.jpg&#34;) from IPython.display import Image Image(filename&#61;'result.jpg') &#34;&#34;&#34;## Benchmark inference speed&#34;&#34;&#34; import time import numpy as np times &#61; [] for i in range(20): start_time &#61; time.time() result &#61; inference_detector(model, img) delta &#61; time.time() - start_time times.append(delta) mean_delta &#61; np.array(times).mean() fps &#61; 1 / mean_delta print(&#34;Average(sec):{:.2f},fps:{:.2f}&#34;.format(mean_delta, fps)) &#34;&#34;&#34;## Download the config file&#34;&#34;&#34; from google.colab import files files.download(config_fname) &#34;&#34;&#34;## Download checkpoint file. ### Option1 : upload the checkpoint file to your Google Drive Then download it from your Google Drive to local file system. During this step, you will be prompted to enter the token. &#34;&#34;&#34; from google.colab import drive drive.mount('/content/drive') !cp {checkpoint_file} /content/drive &#34;&#34;&#34;### Option2 : Download the checkpoint file directly to your local file system This method may not be stable when downloading large files like the model checkpoint file. Try **option 1** instead if not working. &#34;&#34;&#34; from google.colab import files files.download(checkpoint_file) 하지만 이부분에 있어서 %cd {project_name} !python mmdetection/tools/train.py {config_fname}미처 해결하지 못한 문제가 발생하였습니다.Traceback (most recent call last): File &#34;mmdetection/tools/train.py&#34;, line 161, in <module> main() File &#34;mmdetection/tools/train.py&#34;, line 134, in main cfg.model, train_cfg&#61;cfg.train_cfg, test_cfg&#61;cfg.test_cfg) File &#34;/usr/local/lib/python3.6/dist-packages/mmdet-2.0.0&#43;c298a0a-py3.6-linux-x86_64.egg/mmdet/models/builder.py&#34;, line 48, in build_detector return build(cfg, DETECTORS, dict(train_cfg&#61;train_cfg, test_cfg&#61;test_cfg)) File &#34;/usr/local/lib/python3.6/dist-packages/mmdet-2.0.0&#43;c298a0a-py3.6-linux-x86_64.egg/mmdet/models/builder.py&#34;, line 20, in build return build_from_cfg(cfg, registry, default_args) File &#34;/usr/local/lib/python3.6/dist-packages/mmcv/utils/registry.py&#34;, line 168, in build_from_cfg return obj_cls(**args) TypeError: __init__() got an unexpected keyword argument 'bbox_roi_extractor' 인터넷강의를 들으면서 처음으로 해보는 토이프로젝트인데 사이즈가 점점 커져서, 도움을 구하고자 요청합니다. 어떤부분이 잘못되어서, 어떤 방식으로 고치면 되는지 알려주시며 감사하겠습니다

페코옹 · Answer

mm.. train.py파일에 문제가 있는 걸로 보이네요.

python 머신러닝에 관해서 질문합니다

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