add
Build-Deploy-Actions
Details
Build-Deploy-Actions
Details
This commit is contained in:
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name: Build
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run-name: ${{ github.actor }} is upgrade release 🚀
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on: [push]
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env:
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REPOSITORY: ${{ github.repository }}
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COMMIT_ID: ${{ github.sha }}
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jobs:
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Build-Deploy-Actions:
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runs-on: ubuntu-latest
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steps:
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- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
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- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
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- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
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- name: Check out repository code
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uses: actions/checkout@v3
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-
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name: Setup Git LFS
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run: |
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git lfs install
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git lfs fetch
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git lfs checkout
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- name: List files in the repository
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run: |
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ls ${{ github.workspace }}
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-
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name: Docker Image Info
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id: image-info
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run: |
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echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
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echo "::set-output name=image_tag::${COMMIT_ID:0:10}"
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-
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name: Login to Docker Hub
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uses: docker/login-action@v2
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with:
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registry: artifacts.iflytek.com
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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-
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name: Build and push
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run: |
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docker version
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docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
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docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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- run: echo "🍏 This job's status is ${{ job.status }}."
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FROM python:3.7.4-slim
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WORKDIR /app
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COPY requirements.txt /app
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RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/
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RUN apt-get update && \
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apt-get upgrade -y && \
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apt-get install -y git
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RUN apt-get install -y tk
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RUN python -m pip install git+https://github.com/cocodataset/panopticapi.git
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RUN pip3 install --trusted-host pypi.python.org -r requirements.txt
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COPY . /app
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CMD ["python", "app.py"]
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#全景分割
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from PIL import Image
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import io
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import matplotlib.pyplot as plt
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import torch
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import torchvision.transforms as T
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import numpy
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import gradio as gr
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import itertools
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import seaborn as sns
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from panopticapi.utils import rgb2id
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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# These are the COCO classes
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CLASSES = [
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'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
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'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
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'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
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'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
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'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
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'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
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'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
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'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
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'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
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'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
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'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
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'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
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'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
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'toothbrush'
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]
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# Detectron2 uses a different numbering scheme, we build a conversion table
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coco2d2 = {}
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count = 0
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for i, c in enumerate(CLASSES):
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if c != "N/A":
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coco2d2[i] = count
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count+=1
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# Draw the bounding boxes on image.
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def fig2img(fig):
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buf = io.BytesIO()
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fig.savefig(buf)
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buf.seek(0)
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img = Image.open(buf)
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return img
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# Draw the bounding boxes.
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def visualize_prediction(result):
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palette = itertools.cycle(sns.color_palette())
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# The segmentation is stored in a special-format png
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panoptic_seg = Image.open(io.BytesIO(result['png_string']))
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panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8).copy()
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# We retrieve the ids corresponding to each mask
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panoptic_seg_id = rgb2id(panoptic_seg)
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# Finally we color each mask individually
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panoptic_seg[:, :, :] = 0
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for id in range(panoptic_seg_id.max() + 1):
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panoptic_seg[panoptic_seg_id == id] = numpy.asarray(next(palette)) * 255
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plt.figure(figsize=(15,15))
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plt.imshow(panoptic_seg)
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plt.axis('off')
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return fig2img(plt.gcf())
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model, postprocessor = torch.hub.load('facebookresearch/detr',
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'detr_resnet101_panoptic',
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pretrained=True,
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return_postprocessor=True,
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num_classes=250)
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transform = T.Compose([
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T.Resize(800),
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T.ToTensor(),
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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model.eval();
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def detect_objects(image_input):
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model.eval();
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# if image comes from upload
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if image_input:
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image = image_input
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# mean-std normalize the input image (batch-size: 1)
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img = transform(image).unsqueeze(0)
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out = model(img)
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# the post-processor expects as input the target size of the predictions (which we set here to the image size)
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result = postprocessor(out, torch.as_tensor(img.shape[-2:]).unsqueeze(0))[0]
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#Visualize prediction
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viz_img = visualize_prediction(result)
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return viz_img
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>全景分割</font></div>
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""")
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with gr.Row():
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with gr.Column():
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image = gr.Image(label="图片", type="pil")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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box2 = gr.Image(label="图片")
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button.click(fn=detect_objects, inputs=[image], outputs=box2)
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examples = gr.Examples(examples=[['1.jpg'], ['2.jpg']], inputs=[image], label="例子")
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if __name__ == '__main__':
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demo.queue().launch(server_name = "0.0.0.0")
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gradio
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huggingface
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torch
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transformers
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seaborn
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matplotlib
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pillow
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requests
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torchvision
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numpy
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scipy
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