point-e
Build-Deploy-Actions Details

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jianjiang 2023-04-25 21:20:00 +08:00
commit 11731090f4
7 changed files with 195 additions and 0 deletions

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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 }}."

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Subproject commit a9b1bf5920416aaeaec965c25dd9e8f98c864f16

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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 . /app
RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
RUN pip install -r requirements.txt
RUN cd point-e && python setup.py install && cd ../CLIP && python setup.py install
CMD ["python", "app.py"]

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import gradio as gr
import plotly.graph_objects as go
import torch
from tqdm.auto import tqdm
from point_e.diffusion.configs import DIFFUSION_CONFIGS, diffusion_from_config
from point_e.diffusion.sampler import PointCloudSampler
from point_e.models.download import load_checkpoint
from point_e.models.configs import MODEL_CONFIGS, model_from_config
from point_e.util.plotting import plot_point_cloud
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('creating base model...')
base_name = 'base40M-textvec'
base_model = model_from_config(MODEL_CONFIGS[base_name], device)
base_model.eval()
base_diffusion = diffusion_from_config(DIFFUSION_CONFIGS[base_name])
print('creating upsample model...')
upsampler_model = model_from_config(MODEL_CONFIGS['upsample'], device)
upsampler_model.eval()
upsampler_diffusion = diffusion_from_config(DIFFUSION_CONFIGS['upsample'])
print('downloading base checkpoint...')
base_model.load_state_dict(load_checkpoint(base_name, device))
print('downloading upsampler checkpoint...')
upsampler_model.load_state_dict(load_checkpoint('upsample', device))
sampler = PointCloudSampler(
device=device,
models=[base_model, upsampler_model],
diffusions=[base_diffusion, upsampler_diffusion],
num_points=[1024, 4096 - 1024],
aux_channels=['R', 'G', 'B'],
guidance_scale=[3.0, 0.0],
model_kwargs_key_filter=('texts', ''), # Do not condition the upsampler at all
)
def inference(prompt):
samples = None
for x in sampler.sample_batch_progressive(batch_size=1, model_kwargs=dict(texts=[prompt])):
samples = x
pc = sampler.output_to_point_clouds(samples)[0]
pc = sampler.output_to_point_clouds(samples)[0]
colors=(238, 75, 43)
fig = go.Figure(
data=[
go.Scatter3d(
x=pc.coords[:,0], y=pc.coords[:,1], z=pc.coords[:,2],
mode='markers',
marker=dict(
size=2,
color=['rgb({},{},{})'.format(r,g,b) for r,g,b in zip(pc.channels["R"], pc.channels["G"], pc.channels["B"])],
)
)
],
layout=dict(
scene=dict(
xaxis=dict(visible=False),
yaxis=dict(visible=False),
zaxis=dict(visible=False)
)
),
)
return fig
demo = gr.Interface(
fn=inference,
inputs="text",
outputs=gr.Plot(),
examples=[
["a red motorcycle"],
["a RED pumpkin"],
["a yellow rubber duck"]
],
)
demo.queue(max_size=30)
demo.launch(server_name = "0.0.0.0")

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point-e Submodule

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Subproject commit fc8a607c08a3ea804cc82bf1ef8628f88a3a5d2f

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requirements.txt Normal file
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#git+https://github.com/openai/point-e
plotly
scipy==1.10.1
filelock
Pillow
torch
fire
humanize
requests
tqdm
matplotlib
scikit-image
numpy
torch
torchvision
ftfy
regex
tqdm