chenghanc

#!/usr/bin/env python3
"""
CogVideoX-2b Gradio demo — LOW-VRAM build for 16GB GPUs (e.g., 4060 Ti 16G)

UI updated for Chinese support, added a quick reset button for low-VRAM defaults.
"""
from __future__ import annotations
import argparse
import os
from pathlib import Path

chenghanc

#!/usr/bin/env python3
"""
CogVideoX-2b Gradio demo — LOW-VRAM build for 16GB GPUs (e.g., 4060 Ti 16G)

Fix: added ndim existence checks to avoid AttributeError when output is None or unexpected.
"""
from __future__ import annotations
import argparse
import os
from pathlib import Path

chenghanc

from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
import argparse
import numpy as np
import matplotlib.pyplot as plt

def coco_eval(args):
    cocoGt = COCO(args.gt_json)
    cocoDt = cocoGt.loadRes(args.pred_json)
    cocoEval = COCOeval(cocoGt, cocoDt, args.eval_type)

chenghanc

# https://github.com/developer0hye/PyTorch-Darknet53

import torch
from torch import nn

def conv_batch(in_num, out_num, kernel_size=3, padding=1, stride=1):
    return nn.Sequential(
        nn.Conv2d(in_num, out_num, kernel_size=kernel_size, stride=stride, padding=padding, bias=False),
        nn.BatchNorm2d(out_num),
        nn.LeakyReLU())

chenghanc

# https://blog.csdn.net/weixin_43397208/article/details/131352685

import torch
import torch.nn as nn


class IdentityBlock(nn.Module):
 
    def __init__(self, in_channel, kl_size, filters):
        super(IdentityBlock, self).__init__()

chenghanc

# jpg files
a=1
for i in *.jpg; do
  new=$(printf "%04d.jpg" "$a")
  mv -i -- "$i" "$new"
  let a="$a+1"
done

# xml files
a=1

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layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  32
    1 conv     64  3 x 3 / 2   416 x 416 x  32   ->   208 x 208 x  64
    2 conv     32  1 x 1 / 1   208 x 208 x  64   ->   208 x 208 x  32
    3 conv     64  3 x 3 / 1   208 x 208 x  32   ->   208 x 208 x  64
    4 Shortcut Layer: 1
    5 conv    128  3 x 3 / 2   208 x 208 x  64   ->   104 x 104 x 128
    6 conv     64  1 x 1 / 1   104 x 104 x 128   ->   104 x 104 x  64
    7 conv    128  3 x 3 / 1   104 x 104 x  64   ->   104 x 104 x 128
    8 Shortcut Layer: 5

chenghanc

# In[98]:
import os
from os.path import splitext
from collections import Counter

def compare(path):
    # List containing all file names + their extension in path directory
    myDir = os.listdir(path)
    # List containing all file names without their extension
    l = [splitext(filename)[0] for filename in myDir]

chenghanc

# In[98]:

import pandas as pd
import os, glob
import sys
import shutil
import numpy as np
import cv2

# In[98]:

chenghanc

import os
import glob
import shutil


tp = 'TP'
fp = 'FP'
fn = 'FN'

model_name = 'yolo'