<jupyter_start><jupyter_text>**Mount Drive**<jupyter_code>%cd "/content/drive/MyDrive/Colab Notebooks/CS106 - AI/pytorch-vsumm-reinforce"
from google.colab import drive
drive.mount('/content/drive')
!git add .<jupyter_output><empty_output><jupyter_text>**SUMME**  R_div + R_rep<jupyter_code>!python create_split.py -d datasets/eccv16_dataset_summe_google_pool5.h5 --save-dir datasets --save-name summe_splits  --num-splits 5
!python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits.json -m summe --gpu 0 --save-dir log/summe-split0 --split-id 0 --verbose --num-episode 10 
!python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits.json -m summe --gpu 0 --save-dir log/summe-split0 --split-id 0 --evaluate --resume log/summe-split0/model_epoch60.pth.tar --verbose --save-results
!python parse_log.py -p log/summe-split0/log_train.txt

!python parse_json.py -p log/summe-split0/rewards.json -i 19<jupyter_output><empty_output><jupyter_text>R_div<jupyter_code>!python create_split.py -d datasets/eccv16_dataset_summe_google_pool5.h5 --save-dir datasets --save-name summe_splits1  --num-splits 5
!python main_div.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits1.json -m summe --gpu 0 --save-dir log/summe-split1 --split-id 0 --verbose --num-episode 10 
!python parse_log.py -p log/summe-split1/log_train.txt
!python parse_json.py -p log/summe-split1/rewards.json -i 13<jupyter_output><empty_output><jupyter_text>R_rep<jupyter_code>!python create_split.py -d datasets/eccv16_dataset_summe_google_pool5.h5 --save-dir datasets --save-name summe_splits2  --num-splits 5
!python main_rep.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summe_splits2.json -m summe --gpu 0 --save-dir log/summe-split2 --split-id 0 --verbose --num-episode 10 
!python parse_log.py -p log/summe-split2/log_train.txt
!python parse_json.py -p log/summe-split2/rewards.json -i 5
%cd /content/drive/MyDrive/Colab Notebooks/CS106 - AI/SumMe/temp_video
!sh videos2frames.sh<jupyter_output><empty_output><jupyter_text>**TVSUM**  R_div + R_rep<jupyter_code>!python create_split.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 --save-dir datasets --save-name tvsum_splits  --num-splits 5
!python main.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 -s datasets/tvsum_splits.json -m tvsum --gpu 0 --save-dir log/tvsum-split0 --split-id 0 --verbose
!python main.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 -s datasets/tvsum_splits.json -m tvsum --gpu 0 --save-dir log/tvsum-split0 --split-id 0 --evaluate --resume log/tvsum-split0/model_epoch60.pth.tar --verbose --save-results
!python parse_log.py -p log/tvsum-split0/log_train.txt
!python parse_json.py -p log/tvsum-split0/rewards.json -i 24<jupyter_output><empty_output><jupyter_text>R_div<jupyter_code>!python create_split.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 --save-dir datasets --save-name tvsum_splits1  --num-splits 5
!python main_div.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 -s datasets/tvsum_splits1.json -m tvsum --gpu 0 --save-dir log/tvsum-split1 --split-id 0 --verbose
!python parse_log.py -p log/tvsum-split1/log_train.txt
!python parse_json.py -p log/tvsum-split1/rewards.json -i 10<jupyter_output><empty_output><jupyter_text>R_rep<jupyter_code>!python create_split.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 --save-dir datasets --save-name tvsum_splits2  --num-splits 5
!python main_rep.py -d datasets/eccv16_dataset_tvsum_google_pool5.h5 -s datasets/tvsum_splits2.json -m tvsum --gpu 0 --save-dir log/tvsum-split2 --split-id 0 --verbose
!python parse_log.py -p log/tvsum-split2/log_train.txt
!python parse_json.py -p log/tvsum-split2/rewards.json -i 21<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12728719172633254, 'fraction_numerical': 0.03606470432246089, 'mean_word_length': 4.723823975720789, 'pattern_counts': {'":': 0, '<': 29, '<?xml version=': 0, '>': 29, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '665344', 'license': 'MIT', 'n_tokens_mistral': 1569, 'n_tokens_neox': 1504, 'n_words': 271, 'path': 'Pytorch_Vsumm_Python2.ipynb', 'repo_name': 'TruongChiDien/DSN--Video-Summariztion', 'size': '3771'}