add

.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 }}."

Base-RCNN-FPN.yml

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+MODEL:
+  MASK_ON: True
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  BACKBONE:
+    NAME: "build_vit_fpn_backbone"
+  VIT:
+    OUT_FEATURES: ["layer3", "layer5", "layer7", "layer11"]
+    DROP_PATH: 0.1
+    IMG_SIZE: [224,224]
+    POS_TYPE: "abs"
+  FPN:
+    IN_FEATURES: ["layer3", "layer5", "layer7", "layer11"]
+  ANCHOR_GENERATOR:
+    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
+    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
+  RPN:
+    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
+    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
+    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
+    # Detectron1 uses 2000 proposals per-batch,
+    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
+    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
+    POST_NMS_TOPK_TRAIN: 1000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "StandardROIHeads"
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+    NUM_CLASSES: 5
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+  ROI_MASK_HEAD:
+    NAME: "MaskRCNNConvUpsampleHead"
+    NUM_CONV: 4
+    POOLER_RESOLUTION: 14
+DATASETS:
+  TRAIN: ("publaynet_train",)
+  TEST: ("publaynet_val",)
+SOLVER:
+  LR_SCHEDULER_NAME: "WarmupCosineLR"
+  AMP:
+    ENABLED: True
+  OPTIMIZER: "ADAMW"
+  BACKBONE_MULTIPLIER: 1.0
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "full_model"
+    CLIP_VALUE: 1.0
+    NORM_TYPE: 2.0
+  WARMUP_FACTOR: 0.01
+  BASE_LR: 0.0004
+  WEIGHT_DECAY: 0.05
+  IMS_PER_BATCH: 32
+INPUT:
+  CROP:
+    ENABLED: True
+    TYPE: "absolute_range"
+    SIZE: (384, 600)
+  MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+  FORMAT: "RGB"
+DATALOADER:
+  FILTER_EMPTY_ANNOTATIONS: False
+VERSION: 2
+AUG:
+  DETR: True
+SEED: 42

Dockerfile

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+# FROM python:3.9
+# COPY . /app
+# WORKDIR /app
+# RUN pip install --trusted-host pypi.python.org -r requirements.txt
+# CMD python app.py
+
+# Use an official Python runtime as a parent image
+FROM python:3.7.4-slim
+RUN sed -i 's#http://deb.debian.org#https://mirrors.ustc.edu.cn#g' /etc/apt/sources.list && sed -i 's|security.debian.org/debian-security|mirrors.ustc.edu.cn/debian-security|g' /etc/apt/sources.list
+
+WORKDIR /app
+COPY requirements.txt /app
+RUN   pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/ 
+
+RUN  apt update &&  apt install -y  libgl1-mesa-glx   libglib2.0-0 build-essential
+RUN pip3 install --trusted-host pypi.python.org -r requirements.txt 
+RUN python -m pip install detectron2 -f \
+  https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
+# Set the working directory to /app
+
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+
+# Run main.py when the container launches
+CMD ["python", "app.py"]

app.py

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+import os
+
+import sys
+sys.path.append("unilm")
+
+import cv2
+
+from unilm.dit.object_detection.ditod import add_vit_config
+
+import torch
+
+from detectron2.config import CfgNode as CN
+from detectron2.config import get_cfg
+from detectron2.utils.visualizer import ColorMode, Visualizer
+from detectron2.data import MetadataCatalog
+from detectron2.engine import DefaultPredictor
+
+import gradio as gr
+from gradio.themes.utils import sizes
+
+
+theme = gr.themes.Default(radius_size=sizes.radius_none).set(
+    block_label_text_color = '#4D63FF',
+    block_title_text_color = '#4D63FF',
+    button_primary_text_color = '#4D63FF',
+    button_primary_background_fill='#FFFFFF',
+    button_primary_border_color='#4D63FF',
+    button_primary_background_fill_hover='#EDEFFF',
+)
+
+
+# Step 1: instantiate config
+cfg = get_cfg()
+add_vit_config(cfg)
+cfg.merge_from_file("cascade_dit_base.yml")
+
+# Step 2: add model weights URL to config
+cfg.MODEL.WEIGHTS = "https://layoutlm.blob.core.windows.net/dit/dit-fts/publaynet_dit-b_cascade.pth"
+
+# Step 3: set device
+cfg.MODEL.DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Step 4: define model
+predictor = DefaultPredictor(cfg)
+
+
+def analyze_image(img):
+    md = MetadataCatalog.get(cfg.DATASETS.TEST[0])
+    if cfg.DATASETS.TEST[0]=='icdar2019_test':
+        md.set(thing_classes=["table"])
+    else:
+        md.set(thing_classes=["text","title","list","table","figure"])
+    
+    output = predictor(img)["instances"]
+    v = Visualizer(img[:, :, ::-1],
+                    md,
+                    scale=1.0,
+                    instance_mode=ColorMode.SEGMENTATION)
+    result = v.draw_instance_predictions(output.to("cpu"))
+    result_image = result.get_image()[:, :, ::-1]
+    
+    return result_image
+    
+
+with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
+    gr.Markdown("""
+    <div align='center' ><font size='60'>版面分析</font></div>
+    """)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="图片", type="numpy")
+            with gr.Row():
+                button = gr.Button("提交", variant="primary")
+        box2 = gr.Image(label="图片", type="numpy")
+
+    button.click(fn=analyze_image, inputs=[image], outputs=box2)
+    examples = gr.Examples(examples=[['publaynet_example.jpeg']],  inputs=[image], label="例子")
+
+
+demo.launch(server_name = "0.0.0.0")

cascade_dit_base.yml

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+_BASE_: "Base-RCNN-FPN.yml"
+MODEL:
+  PIXEL_MEAN: [ 127.5, 127.5, 127.5 ]
+  PIXEL_STD: [ 127.5, 127.5, 127.5 ]
+  WEIGHTS: "https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth"
+  VIT:
+    NAME: "dit_base_patch16"
+  ROI_HEADS:
+    NAME: CascadeROIHeads
+  ROI_BOX_HEAD:
+    CLS_AGNOSTIC_BBOX_REG: True
+  RPN:
+    POST_NMS_TOPK_TRAIN: 2000
+SOLVER:
+  WARMUP_ITERS: 1000
+  IMS_PER_BATCH: 16
+  MAX_ITER: 60000
+  CHECKPOINT_PERIOD: 2000
+TEST:
+  EVAL_PERIOD: 2000

requirements.txt

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+gradio
+opencv-python
+shapely

unilm

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+Subproject commit b5b99a36ab8ea83ef64d1efaa307133184243c57