git remote add origin http://172.16.59.16:3000/jianjiang/DeepDanbooru.git

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitea/workflows/build.yaml

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+name: Build
+run-name: ${{ github.actor }} is upgrade release 🚀
+on: [push]
+env:
+  REPOSITORY: ${{ github.repository }}
+  COMMIT_ID: ${{ github.sha }}
+jobs:
+  Build-Deploy-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v3
+      -
+        name: Setup Git LFS
+        run: |
+          git lfs install
+          git lfs fetch
+          git lfs checkout                    
+      - name: List files in the repository
+        run: |
+                                        ls ${{ github.workspace }}
+      -
+        name: Docker Image Info
+        id: image-info
+        run: |
+          echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
+          echo "::set-output name=image_tag::${COMMIT_ID:0:10}"                    
+      -
+        name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          registry: artifacts.iflytek.com
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      -
+        name: Build and push
+        run: |
+          docker version
+          docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
+          docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
+          docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}                    
+      - run: echo "🍏 This job's status is ${{ job.status }}."

.gitignore

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+images

.style.yapf

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+[style]
+based_on_style = pep8
+blank_line_before_nested_class_or_def = false
+spaces_before_comment = 2
+split_before_logical_operator = true

Dockerfile

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+#FROM python:3.8.13
+FROM artifacts.iflytek.com/docker-private/atp/base_image_for_ailab:0.0.1
+
+WORKDIR /app
+
+COPY requirements.txt /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple 
+RUN pip install -r requirements.txt
+
+COPY . /app
+ENV HF_TOKEN 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
+
+CMD ["python", "app.py"]

README.md

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+---
+title: DeepDanbooru
+emoji: 🏃
+colorFrom: gray
+colorTo: purple
+sdk: gradio
+sdk_version: 3.6
+app_file: app.py
+pinned: false
+---
+
+# Configuration
+
+`title`: _string_
+Display title for the Space
+
+`emoji`: _string_
+Space emoji (emoji-only character allowed)
+
+`colorFrom`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`colorTo`: _string_
+Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
+
+`sdk`: _string_
+Can be either `gradio`, `streamlit`, or `static`
+
+`sdk_version` : _string_
+Only applicable for `streamlit` SDK.  
+See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
+
+`app_file`: _string_
+Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
+Path is relative to the root of the repository.
+
+`pinned`: _boolean_
+Whether the Space stays on top of your list.

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import argparse
+import functools
+import os
+import pathlib
+import tarfile
+
+import deepdanbooru as dd
+import gradio as gr
+import huggingface_hub
+import numpy as np
+import PIL.Image
+import tensorflow as tf
+
+TITLE = 'KichangKim/DeepDanbooru'
+DESCRIPTION = 'This is an unofficial demo for https://github.com/KichangKim/DeepDanbooru.'
+ARTICLE = '<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.deepdanbooru" alt="visitor badge"/></center>'
+
+HF_TOKEN = os.environ['HF_TOKEN']
+MODEL_REPO = 'hysts/DeepDanbooru'
+MODEL_FILENAME = 'model-resnet_custom_v3.h5'
+LABEL_FILENAME = 'tags.txt'
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--score-slider-step', type=float, default=0.05)
+    parser.add_argument('--score-threshold', type=float, default=0.5)
+    parser.add_argument('--share', action='store_true')
+    return parser.parse_args()
+
+
+def load_sample_image_paths() -> list[pathlib.Path]:
+    image_dir = pathlib.Path('images')
+    if not image_dir.exists():
+        dataset_repo = 'hysts/sample-images-TADNE'
+        path = huggingface_hub.hf_hub_download(dataset_repo,
+                                               'images.tar.gz',
+                                               repo_type='dataset',
+                                               use_auth_token=HF_TOKEN)
+        with tarfile.open(path) as f:
+            f.extractall()
+    return sorted(image_dir.glob('*'))
+
+
+def load_model() -> tf.keras.Model:
+#    path = huggingface_hub.hf_hub_download(MODEL_REPO,
+#                                           MODEL_FILENAME,
+#                                           use_auth_token=HF_TOKEN)
+    model = tf.keras.models.load_model("./model-resnet_custom_v3.h5")
+    return model
+
+
+def load_labels() -> list[str]:
+#    path = huggingface_hub.hf_hub_download(MODEL_REPO,
+#                                           LABEL_FILENAME,
+#                                           use_auth_token=HF_TOKEN)
+
+    path = "./tags.txt"
+    with open(path) as f:
+        labels = [line.strip() for line in f.readlines()]
+    return labels
+
+
+def predict(image: PIL.Image.Image, score_threshold: float,
+            model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
+    _, height, width, _ = model.input_shape
+    image = np.asarray(image)
+    image = tf.image.resize(image,
+                            size=(height, width),
+                            method=tf.image.ResizeMethod.AREA,
+                            preserve_aspect_ratio=True)
+    image = image.numpy()
+    image = dd.image.transform_and_pad_image(image, width, height)
+    image = image / 255.
+    probs = model.predict(image[None, ...])[0]
+    probs = probs.astype(float)
+    res = dict()
+    for prob, label in zip(probs.tolist(), labels):
+        if prob < score_threshold:
+            continue
+        res[label] = prob
+    return res
+
+
+def main():
+    args = parse_args()
+
+    #image_paths = load_sample_image_paths()
+    #examples = [[path.as_posix(), args.score_threshold]
+    #            for path in image_paths]
+
+    model = load_model()
+    labels = load_labels()
+
+    func = functools.partial(predict, model=model, labels=labels)
+
+    gr.Interface(
+        func,
+        [
+            gr.Image(type='pil', label='Input'),
+            gr.Slider(0,
+                      1,
+                      step=args.score_slider_step,
+                      value=args.score_threshold,
+                      label='Score Threshold'),
+        ],
+        gr.Label(label='Output'),
+        #examples=examples,
+        #title=TITLE,
+        #description=DESCRIPTION,
+        #article=ARTICLE,
+        allow_flagging='never',
+    ).launch(
+        enable_queue=True,
+        server_name = "0.0.0.0",
+    )
+
+
+if __name__ == '__main__':
+    main()

categories.json

@@ -0,0 +1,14 @@
+[
+    {
+        "name": "General",
+        "start_index": 0
+    },
+    {
+        "name": "Character",
+        "start_index": 6891
+    },
+    {
+        "name": "System",
+        "start_index": 9173
+    }
+]

project.json

@@ -0,0 +1,32 @@
+{
+    "image_width": 512,
+    "image_height": 512,
+    "database_path": "F:/Dataset/danbooru-family/danbooru-training-20211112.sqlite",
+    "minimum_tag_count": 20,
+    "model": "resnet_custom_v3",
+    "minibatch_size": 4,
+    "epoch_count": 32,
+    "export_model_per_epoch": 4,
+    "checkpoint_frequency_mb": 1000,
+    "console_logging_frequency_mb": 50,
+    "optimizer": "sgd",
+    "learning_rates": [
+        {
+            "used_epoch": 0,
+            "learning_rate": 5.0
+        }
+    ],
+    "rotation_range": [
+        0.0,
+        360.0
+    ],
+    "scale_range": [
+        0.9,
+        1.1
+    ],
+    "shift_range": [
+        -0.1,
+        0.1
+    ],
+    "mixed_precision": false
+}

requirements.txt

@@ -0,0 +1,3 @@
+pillow>=9.0.0
+tensorflow>=2.7.0
+git+https://github.com/KichangKim/DeepDanbooru@v3-20200915-sgd-e30#egg=deepdanbooru

tags_log.json

@@ -0,0 +1,5 @@
+{
+    "date": "2021/11/12 22:30:46",
+    "limit": 10000,
+    "minimum_post_count": 500
+}