take-cheeze

// 既定値を上書きするには、このファイル内にキー バインドを挿入しますauto[]
[
    {
        "key": "ctrl+x g",
        "command": "magit.status"
    },
    {
        "key": "ctrl+x 0",
        "command": "workbench.action.closeGroup",
        "when": "editorFocus"

take-cheeze

// 既定値を上書きするには、このファイル内にキー バインドを挿入しますauto[]
[
    {
        "key": "ctrl+x g",
        "command": "magit.status"
    },
    {
        "key": "ctrl+x 0",
        "command": "workbench.action.closeGroup",
        "when": "editorFocus"

take-cheeze

{
    // See https://go.microsoft.com/fwlink/?LinkId=733558
    // for the documentation about the tasks.json format
    "version": "2.0.0",
    "tasks": [
        {
            "label": "git grep",
            "type": "shell",
            "command": "git --no-pager grep ${input:gitGrepExpr}",
            "presentation": {

take-cheeze

買ったもの

https://weactstudio.aliexpress.com/ で買った

• type-cなRP2040
  • https://www.aliexpress.com/item/1005003708090298.html
• epaper module
  • https://www.aliexpress.com/item/1005004644515880.html

コード

https://github.com/WeActStudio/WeActStudio.EpaperModule/tree/master/Example でESP32のサンプルはあるけどRP2040はない

take-cheeze

import torch
from torchvision.models.detection import maskrcnn_resnet50_fpn, MaskRCNN_ResNet50_FPN_Weights

weights = MaskRCNN_ResNet50_FPN_Weights.DEFAULT
transforms = weights.transforms()

model = maskrcnn_resnet50_fpn(weights=weights, progress=False)
# model = model.eval()
model = model.train()

take-cheeze

import torch
import torch.onnx.symbolic_helper as sym_hel
from typing import Any


def _maybe_get_scalar(value: torch._C.Value) -> Any:
    value_t = _maybe_get_const(value, "t")
    if isinstance(value_t, torch.Tensor) and value_t.shape == ():
        return value_t
    node_v = sym_hel._node_get(value, "value")

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modified   onnx/checker.cc
@@ -795,13 +795,14 @@ void check_model(const ModelProto& model, CheckerContext& ctx) {
 }
 
 void check_model(const std::string& model_path) {
-  ModelProto model;
+  google::protobuf::Arena arena;
+  ModelProto* model = google::protobuf::Arena::CreateMessage<ModelProto>(&arena);
   std::fstream model_stream(model_path, std::ios::in | std::ios::binary);
   if (!model_stream.good()) {

take-cheeze

name: Update book list

on:
  push:
    branches: [ master ]
  schedule:
    - cron: "0 * * * *"

jobs:
  build:

take-cheeze

document.querySelectorAll('.icon-download').forEach((v, idx) => {
  setTimeout(() => {
    let e = document.createElement('a');
    e.href = v.parentNode.href;
    e.target = '_blank';
    document.body.appendChild(e);
    e.click();
  }, (Math.random() * 1000 + 500) * idx);
});

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document.querySelectorAll('#contents_list a').forEach((v) => {
  fetch(v).
  then((response) => response.text()).
  then((txt) => {
    const parser = new DOMParser();
    const src = parser.parseFromString(txt, 'text/html');
    const mp4_url = src.querySelector('video source').src;
    const elem = document.createElement('a');
    elem.download = '';
    elem.href = mp4_url;