build
python3 setup.py sdist bdist_wheel
travis encrypt
travis encrypt my_password --add deploy.password
bump2version
from google.colab import drive | |
drive.mount('/content/drive') |
version: '2.3' | |
services: | |
nvsmi: | |
image: ubuntu:16.04 # tensorflow:tensorflow-gpu | |
runtime: nvidia | |
environment: | |
- NVIDIA_VISIBLE_DEVICES=all | |
command: nvidia-smi | |
# Mount a volume to /tf/notebooks to work on your own notebooks |
build
python3 setup.py sdist bdist_wheel
travis encrypt
travis encrypt my_password --add deploy.password
bump2version
-- Remove the history from | |
rm -rf .git | |
-- recreate the repos from the current content only | |
git init | |
git add . | |
git commit -m "Initial commit" | |
-- push to the github remote repos ensuring you overwrite history | |
git remote add origin [email protected]:<YOUR ACCOUNT>/<YOUR REPOS>.git |
import os | |
def get_files(folder): | |
for item in os.listdir(folder): | |
full_item = os.path.join(folder, item) | |
if os.path.isfile(full_item): | |
yield full_item | |
elif os.path.isdir(full_item): | |
yield from get_files(full_item) |
{"format": "graph-model", "generatedBy": "1.15.0", "convertedBy": "TensorFlow.js Converter v1.5.2", "userDefinedMetadata": {"signature": {"inputs": {"image_tensor:0": {"name": "image_tensor:0", "dtype": "DT_UINT8", "tensorShape": {"dim": [{"size": "-1"}, {"size": "-1"}, {"size": "-1"}, {"size": "3"}]}}}, "outputs": {"detection_multiclass_scores:0": {"name": "detection_multiclass_scores:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "100"}, {"size": "4"}]}}, "num_detections:0": {"name": "num_detections:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}]}}, "raw_detection_scores:0": {"name": "raw_detection_scores:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "-1"}, {"size": "4"}]}}, "detection_boxes:0": {"name": "detection_boxes:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"size": "100"}, {"size": "4"}]}}, "detection_scores:0": {"name": "detection_scores:0", "dtype": "DT_FLOAT", "tensorShape": {"dim": [{"size": "-1"}, {"siz |
ffmpeg -i <input> -c:v mpeg4 -force_key_frames "expr:gte(t,n_forced*1)" -c:a pcm_s16le <output>.mov |
<annotation> | |
<folder>VOC2009</folder> | |
<filename>2007_000027.jpg</filename> | |
<source> | |
<database>The VOC2007 Database</database> | |
<annotation>PASCAL VOC2007</annotation> | |
<image>flickr</image> | |
</source> | |
<size> | |
<width>486</width> |
import glob | |
import os | |
import re | |
import logging | |
import traceback | |
filelist=glob.glob("/path/to/*.jpg") | |
for file_obj in filelist: | |
try: |
<html> | |
<head> | |
<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" /> | |
<script defer src="https://pyscript.net/alpha/pyscript.min.js"></script> | |
<py-env> | |
- numpy | |
- matplotlib | |
</py-env> | |
</head> |