git tip, delete all history (commit) information

 

.

# Warning: This will destroy your current history.

# Ensure that you understand the consequences before running these commands.

# Go to your repository directory

cd path/to/your/repository

# Create a new initial commit

git checkout --orphan newBranch

git add -A

git commit -m "Initial commit"

# Delete the main branch

git branch -D main

# Rename the current branch to main

git branch -m main

# Force push to overwrite the history on the remote repository

git push --force origin main

..

install dlib on ubuntu

 

1.

sudo apt update

sudo apt install cmake

2.

pip install cmake

3.

pip install dlib

Check amout of size with consider .gitignore files.

Put this cmd. 

git ls-files \

  | xargs du -ch \

  | grep total$

Thank you!

comfyui custom node developing v2

 add one more class 

save image to png 

.

# from .imagetransfer import ImageTransfer

# __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

from PIL import Image

import torch

from torchvision.transforms.functional import to_pil_image, to_tensor

import os

import folder_paths

from comfy.cli_args import args

from PIL.PngImagePlugin import PngInfo

import json

import numpy as np

class ImageTransfer:

@classmethod

def INPUT_TYPES(cls):

return {

"required": {

"image": ("IMAGE",),

},

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "transfer"

CATEGORY = "Utility"

def transfer(self, image):

# Ensure we're not tracking gradients for this operation

print(f"Type of the image tensor: {image.dtype}")

print(f"Dimensions of the image tensor: {image.shape}")

with torch.no_grad():

# Check if image tensor is in the expected format [1, H, W, C]

if image.dim() == 4 and image.shape[-1] in [1, 3]:

# Permute the tensor to match [C, H, W] format expected by to_pil_image

image_permuted = image.squeeze(0).permute(2, 0, 1)

# Convert the PyTorch tensor to a PIL Image

image_pil = to_pil_image(image_permuted)

# Resize the PIL Image

resized_image_pil = image_pil.resize((256, 256), Image.LANCZOS)

# Convert the PIL Image back to a PyTorch tensor

resized_image_tensor = to_tensor(resized_image_pil)

# Permute dimensions back to [1, H, W, C] and add the batch dimension

resized_image_tensor = resized_image_tensor.permute(1, 2, 0).unsqueeze(0)

print(f"Type of the resized image tensor: {resized_image_tensor.dtype}")

print(f"Dimensions of the resized image tensor: {resized_image_tensor.shape}")

return (resized_image_tensor,)

else:

raise ValueError("Image tensor format not recognized. Expected format: [1, H, W, C].")

class Save_png2svg:

def __init__(self):

self.output_dir = folder_paths.get_output_directory()

self.type = "output"

self.prefix_append = ""

self.compress_level = 4

@classmethod

def INPUT_TYPES(s):

return {"required":

{"images": ("IMAGE", ),

"filename_prefix": ("STRING", {"default": "ComfyUI"})},

"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},

}

RETURN_TYPES = ()

FUNCTION = "Save_png2svg"

OUTPUT_NODE = True

CATEGORY = "image"

def Save_png2svg(self, images, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):

filename_prefix += self.prefix_append

full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])

results = list()

for (batch_number, image) in enumerate(images):

i = 255. * image.cpu().numpy()

img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))

metadata = None

if not args.disable_metadata:

metadata = PngInfo()

if prompt is not None:

metadata.add_text("prompt", json.dumps(prompt))

if extra_pnginfo is not None:

for x in extra_pnginfo:

metadata.add_text(x, json.dumps(extra_pnginfo[x]))

filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))

file = f"{filename_with_batch_num}_{counter:05}_.png"

img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)

results.append({

"filename": file,

"subfolder": subfolder,

"type": self.type

})

counter += 1

return { "ui": { "images": results } }

NODE_CLASS_MAPPINGS = {

"ImageTransfer": ImageTransfer,

"Save_png2svg":Save_png2svg

}

NODE_DISPLAY_NAME_MAPPINGS = {

"ImageTransfer": "Image Transfer",

"Save_png2svg": "Save png2svg"

}

..

comfyui image resize simple custom node code

 refer to code for your scratch 

:

# from .imagetransfer import ImageTransfer

# __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

from PIL import Image

import torch

from torchvision.transforms.functional import to_pil_image, to_tensor

class ImageTransfer:

@classmethod

def INPUT_TYPES(cls):

return {

"required": {

"image": ("IMAGE",),

},

}

RETURN_TYPES = ("IMAGE",)

FUNCTION = "transfer"

CATEGORY = "Utility"

def transfer(self, image):

# Ensure we're not tracking gradients for this operation

print(f"Type of the image tensor: {image.dtype}")

print(f"Dimensions of the image tensor: {image.shape}")

with torch.no_grad():

# Check if image tensor is in the expected format [1, H, W, C]

if image.dim() == 4 and image.shape[-1] in [1, 3]:

# Permute the tensor to match [C, H, W] format expected by to_pil_image

image_permuted = image.squeeze(0).permute(2, 0, 1)

# Convert the PyTorch tensor to a PIL Image

image_pil = to_pil_image(image_permuted)

# Resize the PIL Image

resized_image_pil = image_pil.resize((256, 256), Image.LANCZOS)

# Convert the PIL Image back to a PyTorch tensor

resized_image_tensor = to_tensor(resized_image_pil)

# Permute dimensions back to [1, H, W, C] and add the batch dimension

resized_image_tensor = resized_image_tensor.permute(1, 2, 0).unsqueeze(0)

print(f"Type of the resized image tensor: {resized_image_tensor.dtype}")

print(f"Dimensions of the resized image tensor: {resized_image_tensor.shape}")

return (resized_image_tensor,)

else:

raise ValueError("Image tensor format not recognized. Expected format: [1, H, W, C].")

NODE_CLASS_MAPPINGS = {

"ImageTransfer": ImageTransfer

}

NODE_DISPLAY_NAME_MAPPINGS = {

"ImageTransfer": "Image Transfer"

}

Run multi comfyUI

 make different port number

Window

>.\python_embeded\python.exe -s ComfyUI\main.py --port 8189 --windows-standalone-build

Linux or Mac

> python ./main.py --port 8189 --windows-standalone-build

Thank you.

🙇🏻‍♂️

Search process by port number on ubuntu and kill it.

 

>sudo lsof -i :8188

python  231134 mare   45u  IPv4 2074469      0t0  TCP localhost:8188->localhost:42132 (CLOSE_WAIT)

python  231134 mare   46u  IPv4 2105633      0t0  TCP localhost:8188->localhost:57870 (CLOSE_WAIT)

python  231134 mare   47u  IPv4 2074473      0t0  TCP localhost:8188->localhost:42160 (CLOSE_WAIT)

python  231134 mare   48u  IPv4 2103693      0t0  TCP localhost:8188->localhost:57886 (CLOSE_WAIT)

> kill -9 231134

🙇🏻‍♂️

Retrieve my ssh keys and generate

 

Navigate to your SSH directory

cd ~/.ssh

List the RSA public key files:

ls -l *.pub

View the Contents of Your RSA Public Key

cat id_rsa.pub

To generate a new SSH RSA key pair, you can use the following command

ssh-keygen -t rsa -b 4096

Thank you. marearts.com

🙇🏻‍♂️

Create New swap file

 

Create a New Swap File

sudo fallocate -l 2G /swapfile2

sudo chmod 600 /swapfile2

sudo mkswap /swapfile2

sudo swapon /swapfile2

Make the swap file permanent

echo '/swapfile2 none swap sw 0 0' | sudo tee -a /etc/fstab

Check Current swap status

sudo swapon --show

Thank you 

🙇🏻‍♂️

How to stop docker under Linux

 I can stop docker after put two commend. 

> sudo systemctl stop docker

[sudo] password for mare: 

Warning: Stopping docker.service, but it can still be activated by:

  docker.socket

> sudo systemctl stop docker.socket

I hope it's helpful to you.

Thank you.

🙇🏻‍♂️

Dominant frequency extraction.

 

Let's say we have channel x Length signal data ex)EEG (electroencephalogram) or time series data.

We might wonder what dominant Hz is there.

The code analysis this question and return 5 top dominant frequency. 

.

import numpy as np

from collections import Counter

from scipy.signal import welch

def identify_dominant_frequencies(signal, fs, top_n=5):

freqs, psd = welch(signal, fs)

peak_indices = np.argsort(psd)[-top_n:]

dominant_freqs = freqs[peak_indices]

return dominant_freqs

..

dominant_freqs = identify_dominant_frequencies(signal, fs, top_n)

dominant_freqs_summary[channel].extend(dominant_freqs) # Append the frequencies

..

median_dominant_freqs = {channel: np.median(freqs) if freqs else None for channel, freqs in dominant_freqs_summary.items()}

..

def get_top_n_frequencies(freq_list, top_n=5, bin_width=1.0):

# Bin frequencies into discrete intervals

binned_freqs = np.round(np.array(freq_list) / bin_width) * bin_width

# Count the frequency of each binned frequency

freq_counter = Counter(binned_freqs)

# Find the top N most common binned frequencies

top_freqs = freq_counter.most_common(top_n)

# Extract just the frequencies from the top N tuples (freq, count)

top_freqs = [freq for freq, count in top_freqs]

return top_freqs

# Initialize a dictionary to store the top 5 frequencies for each channel

top_5_freqs_all_channels = {}

bin_width = 1.0

# Calculate the top 5 frequencies for each channel

for channel, freqs in dominant_freqs_summary.items():

top_5_freqs = get_top_n_frequencies(freqs, top_n=5, bin_width=bin_width)

top_5_freqs_all_channels[channel] = top_5_freqs

print(f"{channel}: Top 5 Frequencies = {top_5_freqs}")

..

GroupShuffleSplit, sklearn

 

There are same eeg_id in data, but we can split it based on same id to train, val using GroupShuffleSplit.

Refer to code:

.

import pandas as pd

from sklearn.model_selection import GroupShuffleSplit

# Load your dataset

train = pd.read_csv('./train.csv')

# Display the shape of the dataset

print("Dataset shape:", train.shape)

# Count unique eeg_id values

unique_eeg_id_count = train['eeg_id'].nunique()

print("Unique eeg_id count:", unique_eeg_id_count)

# Initialize the GroupShuffleSplit

gss = GroupShuffleSplit(n_splits=1, test_size=0.2, random_state=42)

# Split the dataset based on the 'eeg_id' to ensure group cohesion

for train_idx, val_idx in gss.split(train, groups=train['eeg_id']):

train_set = train.iloc[train_idx]

val_set = train.iloc[val_idx]

# Now, train_set and val_set are split according to unique eeg_ids,

# ensuring that all records of a single eeg_id are in the same subset

print("Training set shape:", train_set.shape)

print("Validation set shape:", val_set.shape)

..

Thank you.

🙇🏻‍♂️

interpolation 1d data list, ex) [1, 2, 3, 4] -> [1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ]

 

expand and interpolation n by m data to n x (m+l) 

.

    data = np.array( [[1, 2, 3, 4], [4, 3, 2, 1]] )

data_len = 7

x = np.linspace(0, 1, data.shape[-1])

x2 = np.linspace(0, 1, data_len)

f = interp1d(x, data)

data = f(x2)

..

import below lib.

Thank you!!

from scipy.interpolate import interp1d

.

this is output:

array([[1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ],

                 [4. , 3.5, 3. , 2.5, 2. , 1.5, 1. ]])

pytorch lightning, save pth with ckpt for top k

 

it's custom checkpoint function

.

class CustomModelCheckpoint(ModelCheckpoint):

def __init__(self, save_top_k_pth=0, *args, **kwargs):

super(CustomModelCheckpoint, self).__init__(*args, **kwargs)

self.save_top_k_pth = save_top_k_pth

# Keep track of saved .pth files to manage the top K

self.saved_pth_files = []

def on_save_checkpoint(self, trainer, pl_module, checkpoint):

# Construct checkpoint path manually (simplified example)

epoch = trainer.current_epoch

metric_score = "{:.2f}".format(trainer.callback_metrics['val_loss'].item())

filename = f"model-epoch={epoch}-val_loss={metric_score}.pth"

dirpath = self.dirpath if self.dirpath else trainer.default_root_dir

pth_path = os.path.join(dirpath, filename)

torch.save(pl_module.state_dict(), pth_path)

self.saved_pth_files.append(pth_path)

# Manage the top K saved .pth files

while len(self.saved_pth_files) > self.save_top_k_pth:

oldest_pth = self.saved_pth_files.pop(0)

if os.path.exists(oldest_pth):

os.remove(oldest_pth)

# Ensure to call the superclass method

return super().on_save_checkpoint(trainer, pl_module, checkpoint)

..

call it on training process

.

logger = loggers.TensorBoardLogger(save_dir="lightning_logs", name=config.model_version)

# Define the checkpoint callback

checkpoint_callback = CustomModelCheckpoint(

monitor='val_loss',

dirpath=f"{logger.save_dir}/{logger.name}/version_{logger.version}",

filename='model-{epoch:02d}-{val_loss:.2f}',

save_top_k=2, # Top 2 checkpoints

save_top_k_pth=2, # Also save top 2 .pth files

mode='min'

)

trainer = Trainer(max_epochs=config.num_epochs, accelerator='gpu',

devices=1, callbacks=[checkpoint_callback],

logger=logger, log_every_n_steps=10)

..

saved top k files (ckpt, pth) file showing up on folder.

Thank you.

🙇🏻‍♂️

git find large big file which committed.

 Find large file in GitHub repository

.

git rev-list --objects --all | \

git cat-file --batch-check='%(objecttype) %(objectname) %(objectsize) %(rest)' | \

awk '$3 > 100*1024*1024' | sort -k3nr

..

• git rev-list --objects --all lists all objects in the repository.
• git cat-file --batch-check='...' checks the type, size, and other details of these objects.
• awk '$3 > 100*1024*1024' filters objects larger than 100 MB (note: 1024*1024 bytes = 1MB).
• sort -k3nr sorts these objects by size in descending order.

🙇🏻‍♂️

iOS swift dictionary example code

 .

// Existing dictionary of ages

var ages: [String: Int] = ["John": 30, "Emma": 25]

// Adding a new dictionary with String keys and String values

var occupations: [String: String] = ["John": "Engineer", "Emma": "Doctor"]

// Adding a new key-value pair to the occupations dictionary

occupations["Mike"] = "Teacher"

// Updating a value for a key in the occupations dictionary

occupations["Emma"] = "Senior Doctor" // Emma got a promotion!

// Accessing a value for a given key in the occupations dictionary

if let occupation = occupations["John"] {

    print("John's occupation is \(occupation).")

} else {

    print("John's occupation is not available.")

}

// Merging the ages and occupations dictionaries

// Assuming you want to create a summary for each person

for (name, age) in ages {

    if let occupation = occupations[name] {

        print("\(name) is \(age) years old and works as a \(occupation).")

    } else {

        print("\(name) is \(age) years old.")

    }

}

// Removing a key-value pair from the occupations dictionary

occupations["Mike"] = nil // Mike's occupation is removed

// Iterating over all key-value pairs in the occupations dictionary

for (name, occupation) in occupations {

    print("\(name) works as a \(occupation).")

}

// Checking the count of elements in both dictionaries

print("There are \(ages.count) people in the ages dictionary.")

print("There are \(occupations.count) occupations listed.")

..

refer to code, hope to get some useful idea.

Thank you.

🙇🏻‍♂️