Week_06 장윤영

.github/workflows/basic_test.yml

@@ -0,0 +1,17 @@
+name: Basic Test
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  test_job:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Run a one-line script
+        run: echo "GitHub Actions are working!"

.github/workflows/update_submission_status.yml

@@ -0,0 +1,51 @@
+name: Update Submission Status
+
+on:
+  pull_request:
+    branches:
+      - '*'
+
+jobs:
+  update_status:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: '3.x'
+
+      - name: Install dependencies
+        run: pip install requests PyGithub
+
+      - name: Extract information from PR title
+        id: extract_info
+        run: |
+          echo "PR_TITLE=${{ github.event.pull_request.title }}" >> $GITHUB_ENV
+          echo "PR_CREATED_AT=${{ github.event.pull_request.created_at }}" >> $GITHUB_ENV
+          echo "PR Title: ${{ github.event.pull_request.title }}"
+          echo "PR Created At: ${{ github.event.pull_request.created_at }}"
+          if [[ "${{ github.event.pull_request.title }}" =~ ^Week_([0-9]+)\ (.*)$ ]]; then
+            echo "WEEK=${BASH_REMATCH[1]}" >> $GITHUB_ENV
+            echo "PARTICIPANT=${BASH_REMATCH[2]}" >> $GITHUB_ENV
+            echo "Extracted WEEK: ${BASH_REMATCH[1]}"
+            echo "Extracted PARTICIPANT: ${BASH_REMATCH[2]}"
+          else
+            echo "Invalid PR title format"
+            exit 1
+          fi
+
+      - name: Check GITHUB_TOKEN
+        run: echo "MY_GITHUB_TOKEN: ${{ secrets.MY_GITHUB_TOKEN }}"
+
+      - name: Run update script
+        run: python update_submission_status.py
+        env:
+          MY_GITHUB_TOKEN: ${{ secrets.MY_GITHUB_TOKEN }}
+          GITHUB_HEAD_REF: ${{ env.PR_TITLE }}
+          GITHUB_EVENT_PR_CREATED_AT: ${{ env.PR_CREATED_AT }}
+          WEEK: ${{ env.WEEK }}
+          PARTICIPANT: ${{ env.PARTICIPANT }}

README.md

@@ -9,7 +9,9 @@ CS231n은 스탠포드 대학교에서 제공하는 '딥 러닝을 활용한 컴
 - **방식**: 비대면 각 주마다 2강씩 학습, 해당 주차 일요일 23:59까지 블로그에 정리 포스팅하고 링크 제출하기  
 - **패널티**: 총 8만원 보증금, 포스팅 미실시 시 1만원씩 차감  
 
-해당 주차의 브랜치에 포스팅 글 링크를 Pull requests로 제출하시면 됩니다!
+해당 주차의 브랜치에 포스팅 글 링크를 Pull requests로 제출하시면 됩니다!  
+블로그 플랫폼은 네이버 블로그, 티스토리, 깃허브 블로그 등 자유롭게 선택하시면 됩니다.  
+또한, 포스팅 양식 또한 본인이 기록하기 쉬운 방식으로 자유롭게 기록하시면 됩니다.
 
 ## 💬 스터디의 효과
 다른 사람이 정리한 블로그 링크를 보면서 다양한 생각을 공유하고, 같은 강의를 들으며 궁금한 점이나 모르는 부분을 질문하여 학습 효과를 증가시킬 수 있습니다. 
@@ -29,22 +31,44 @@ CS231n은 스탠포드 대학교에서 제공하는 '딥 러닝을 활용한 컴
 
 ## 📅 커리큘럼
 
-
 |  Week  |     Start Date     |      End Date      |      Description        |     Course Materials     |
 |--------|---------------------|---------------------|---------------------------|--------------------------|
-| Week 1 | 2024.07.08(Mon) | 2024.07.14(Sun) | 07/09 Lecture 1: Introduction Computer vision overview, Course overview, Course logistics | [slides 1] [slides 2] |
-|        |                     |                     | 07/11 Lecture 2: Image Classification with Linear Classifiers The data-driven approach, K-nearest neighbor, Linear Classifiers, Algebraic / Visual / Geometric viewpoints, SVM and Softmax loss | [slides] |
-| Week 2 | 2024.07.15(Mon) | 2024.07.21(Sun) | 07/16 Lecture 3: Regularization and Optimization Regularization, Stochastic Gradient Descent, Momentum, AdaGrad, Adam, Learning rate schedules | [slides] |
-|        |                     |                     | 07/18 Lecture 4: Neural Networks and Backpropagation Multi-layer Perceptron, Backpropagation | [slides] |
-| Week 3 | 2024.07.22(Mon) | 2024.07.28(Sun) | 07/23 Lecture 5: Image Classification with CNNs History, Higher-level representations, image features, Convolution and pooling | [slides] |
-|        |                     |                     | 07/25 Lecture 6: CNN Architectures Batch Normalization, Transfer learning, AlexNet, VGG, GoogLeNet, ResNet | [slides 1] [slides 2] [review] |
-| Week 4 | 2024.07.29(Mon) | 2024.08.04(Sun) | 07/30 Lecture 7: Recurrent Neural Networks RNN, LSTM, GRU, Language modeling, Image captioning, Sequence-to-sequence | [slides] |
-|        |                     |                     | 08/01 Lecture 8: Attention and Transformers Self-Attention, Transformers | [slides] |
-| Week 5 | 2024.08.05(Mon) | 2024.08.11(Sun) | 08/06 Lecture 9: Object Detection and Image Segmentation Single-stage detectors, Two-stage detectors, Semantic/Instance/Panoptic segmentation | [slides] |
-|        |                     |                     | 08/08 Lecture 10: Video Understanding Video classification, 3D CNNs, Two-stream networks, Multimodal video understanding | [slides] |
-| Week 6 | 2024.08.12(Mon) | 2024.08.18(Sun) | 08/13 Lecture 11: Visualizing and Understanding Feature visualization and inversion, Adversarial examples, DeepDream and style transfer | [slides] |
-|        |                     |                     | 08/15 Lecture 12: Self-supervised Learning Pretext tasks, Contrastive learning, Multisensory supervision | [slides] |
-| Week 7 | 2024.08.19(Mon) | 2024.08.25(Sun) | 08/20 Lecture 13: Generative Models Generative Adversarial Network, Diffusion models, Autoregressive models | [slides] |
+| Week 1 | 2024.07.08(Mon) | 2024.07.14(Sun) | 07/09 Lecture 1: Introduction Computer vision overview, Course overview, Course logistics | [slides 1](https://cs231n.stanford.edu/slides/2024/lecture_1_part_1.pdf) [slides 2](https://cs231n.stanford.edu/slides/2024/lecture_1_part_2.pdf) |
+|        |                     |                     | 07/11 Lecture 2: Image Classification with Linear Classifiers The data-driven approach, K-nearest neighbor, Linear Classifiers, Algebraic / Visual / Geometric viewpoints, SVM and Softmax loss | [slides](https://cs231n.stanford.edu/slides/2024/lecture_2.pdf) |
+| Week 2 | 2024.07.15(Mon) | 2024.07.21(Sun) | 07/16 Lecture 3: Regularization and Optimization Regularization, Stochastic Gradient Descent, Momentum, AdaGrad, Adam, Learning rate schedules | [slides](https://cs231n.stanford.edu/slides/2024/lecture_3.pdf) |
+|        |                     |                     | 07/18 Lecture 4: Neural Networks and Backpropagation Multi-layer Perceptron, Backpropagation | [slides](https://cs231n.stanford.edu/slides/2024/lecture_4.pdf) |
+| Week 3 | 2024.07.22(Mon) | 2024.07.28(Sun) | 07/23 Lecture 5: Image Classification with CNNs History, Higher-level representations, image features, Convolution and pooling | [slides](https://cs231n.stanford.edu/slides/2024/lecture_5.pdf) |
+|        |                     |                     | 07/25 Lecture 6: CNN Architectures Batch Normalization, Transfer learning, AlexNet, VGG, GoogLeNet, ResNet | [slides 1](https://cs231n.stanford.edu/slides/2024/lecture_6_part_1.pdf) [slides 2](https://cs231n.stanford.edu/slides/2024/lecture_6_part_2.pdf) [review](https://cs231n.stanford.edu/slides/2024/lecture_6_review.pdf) |
+| Week 4 | 2024.07.29(Mon) | 2024.08.04(Sun) | 07/30 Lecture 7: Recurrent Neural Networks RNN, LSTM, GRU, Language modeling, Image captioning, Sequence-to-sequence | [slides](https://cs231n.stanford.edu/slides/2024/lecture_7.pdf) |
+|        |                     |                     | 08/01 Lecture 8: Attention and Transformers Self-Attention, Transformers | [slides](https://cs231n.stanford.edu/slides/2024/lecture_8.pdf) |
+| Week 5 | 2024.08.05(Mon) | 2024.08.11(Sun) | 08/06 Lecture 9: Object Detection and Image Segmentation Single-stage detectors, Two-stage detectors, Semantic/Instance/Panoptic segmentation | [slides](https://cs231n.stanford.edu/slides/2024/lecture_9.pdf) |
+|        |                     |                     | 08/08 Lecture 10: Video Understanding Video classification, 3D CNNs, Two-stream networks, Multimodal video understanding | [slides](https://cs231n.stanford.edu/slides/2024/lecture_10.pdf) |
+| Week 6 | 2024.08.12(Mon) | 2024.08.18(Sun) | 08/13 Lecture 11: Visualizing and Understanding Feature visualization and inversion, Adversarial examples, DeepDream and style transfer | [slides](https://cs231n.stanford.edu/slides/2024/lecture_11.pdf) |
+|        |                     |                     | 08/15 Lecture 12: Self-supervised Learning Pretext tasks, Contrastive learning, Multisensory supervision | [slides](https://cs231n.stanford.edu/slides/2024/lecture_12.pdf) |
+| Week 7 | 2024.08.19(Mon) | 2024.08.25(Sun) | 08/20 Lecture 13: Generative Models Generative Adversarial Network, Diffusion models, Autoregressive models | [slides](https://cs231n.stanford.edu/slides/2024/lecture_13.pdf) |
 |        |                     |                     | 08/22 Lecture 14: OpenAI Sora Guest Lecture by William (Bill) Peebles and Tim Brooks | |
-| Week 8 | 2024.08.26(Mon) | 2024.09.01(Sun) | 08/27 Lecture 15: Robot Learning Deep Reinforcement Learning, Model Learning, Robotic Manipulation | [slides] |
+| Week 8 | 2024.08.26(Mon) | 2024.09.01(Sun) | 08/27 Lecture 15: Robot Learning Deep Reinforcement Learning, Model Learning, Robotic Manipulation | [slides](https://cs231n.stanford.edu/slides/2024/lecture_14.pdf) |
 |        |                     |                     | 08/29 Lecture 16: Human-Centered Artificial Intelligence | |
+
+
+---
+
+## 👥 참여 인원
+김동환, 우동협, 장윤영, 정명훈, 진태완, 최종렬, 한서연
+
+---
+
+## 📋 과제 제출
+
+|  Week  |  김동환  |  우동협  |  장윤영  |  정명훈  |  진태완  |  최종렬  |  한서연  |
+|--------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
+| Week 1 |          |          |          |          |          |          |          |
+| Week 2 |          |          |          |          |          |          |          |
+| Week 3 |          |          |          |          |          |          |          |
+| Week 4 |          |          |          |          |          |          |          |
+| Week 5 |          |          |          |          |          |          |          |
+| Week 6 |          |          |          |          |          |          |          |
+| Week 7 |          |          |          |          |          |          |          |
+| Week 8 |          |          |          |          |          |          |          |
+
+미제출 시 환급금이 1만원씩 차감됩니다.

update_submission_status.py

@@ -0,0 +1,86 @@
+import os
+import re
+from datetime import datetime
+from github import Github
+
+# 환경 변수에서 GitHub 토큰 가져오기
+GITHUB_TOKEN = os.getenv('MY_GITHUB_TOKEN')
+REPO_OWNER = 'konkuk-kuggle'
+REPO_NAME = '10th_Study_CS231n'
+
+# GitHub 토큰 값 출력 (디버깅용, 이후 삭제)
+print(f"GITHUB_TOKEN: {GITHUB_TOKEN}")
+
+# 참여 인원 목록
+participants = ["김동환", "우동협", "장윤영", "정명훈", "진태완", "최종렬", "한서연"]
+
+# 주차별 시작 날짜와 종료 날짜
+weeks = {
+    1: ('2024-07-08', '2024-07-14'),
+    2: ('2024-07-15', '2024-07-21'),
+    3: ('2024-07-22', '2024-07-28'),
+    4: ('2024-07-29', '2024-08-04'),
+    5: ('2024-08-05', '2024-08-11'),
+    6: ('2024-08-12', '2024-08-18'),
+    7: ('2024-08-19', '2024-08-25'),
+    8: ('2024-08-26', '2024-09-01'),
+}
+
+# GitHub API 클라이언트 생성
+g = Github(GITHUB_TOKEN)
+repo = g.get_repo(f"{REPO_OWNER}/{REPO_NAME}")
+
+# PR 제목에서 주차와 참여자 이름 추출하는 함수
+def extract_info_from_title(title):
+    match = re.match(r"Week_([0-9]+)\ (.*)", title)
+    if match:
+        week = int(match.group(1))
+        name = match.group(2).strip()
+        return week, name
+    return None, None
+
+# 제출 상태 업데이트 함수
+def update_submission_status(week, name):
+    print(f"Updating submission status for Week {week}, {name}")
+    with open('README.md', 'r') as file:
+        lines = file.readlines()
+
+    # 제출 상태를 업데이트할 텍스트 생성
+    new_lines = []
+    for line in lines:
+        if f"| Week {week} |" in line:
+            for participant in participants:
+                if participant == name:
+                    line = line.replace(f"|          |", f"|    ✅    |", 1)
+        new_lines.append(line)
+
+    with open('README.md', 'w') as file:
+        file.writelines(new_lines)
+
+    # README.md 파일 커밋 및 푸시
+    contents = repo.get_contents("README.md")
+    repo.update_file(contents.path, f"Update submission status for Week {week}, {name}", ''.join(new_lines), contents.sha)
+
+# PR 이벤트 처리 함수
+def handle_pr_event():
+    pr_title = os.getenv('GITHUB_HEAD_REF', '')
+    pr_created_at = datetime.strptime(os.getenv('GITHUB_EVENT_PR_CREATED_AT'), "%Y-%m-%dT%H:%M:%SZ")
+    print(f"PR Title: {pr_title}, Created At: {pr_created_at}")
+
+    week, name = extract_info_from_title(pr_title)
+    if week and name:
+        start_date, end_date = weeks[week]
+        start_datetime = datetime.strptime(start_date, "%Y-%m-%d")
+        end_datetime = datetime.strptime(end_date + ' 23:59:59', "%Y-%m-%d %H:%M:%S")
+
+        # PR 생성 시간이 해당 주차의 시작 날짜와 종료 날짜 사이인지 확인
+        if start_datetime <= pr_created_at <= end_datetime:
+            update_submission_status(week, name)
+            print(f"Updated submission status for {name} in Week {week}")
+        else:
+            print(f"PR was created outside the valid date range for Week {week}")
+    else:
+        print("PR title format is incorrect")
+
+if __name__ == "__main__":
+    handle_pr_event()