dylanuys

import json
import wandb
import pandas as pd
from datetime import datetime, timezone
from collections import defaultdict
from typing import List, Dict, Tuple, Optional, Any


def query_wandb_runs(project_name, entity_name=None, start_date=None, end_date=None, filters=None):
    """

dylanuys

#!/usr/bin/env python3
"""
Simple Model Performance Tester using ContentManager for media sampling
This version uses the ContentManager class to sample media files, similar to the GAS validator.
"""

import os
import sys
import json
import time

dylanuys

#!/usr/bin/env python3
"""Simplified local inference server for model testing."""

print("🚀 Starting simple inference server script...")

import os
import sys
import time
from pathlib import Path
from typing import Dict, Any

dylanuys

#!/usr/bin/env python3
import argparse
import numpy as np
import onnxruntime as ort
from PIL import Image
import torch
import time
from pathlib import Path
import cv2

dylanuys

import wandb
import os
import numpy as np


def download_wandb_file(run, file_path, output_dir="downloaded_files"):
    """
    Download a file from a W&B run using the file path.
    
    Args:

dylanuys

import json
import wandb
import pandas as pd
from datetime import datetime, timezone
from collections import defaultdict
from typing import List, Dict, Tuple, Optional, Any

dylanuys

import json
import wandb
import pandas as pd
from datetime import datetime, timezone
from collections import defaultdict
from typing import List, Dict, Tuple, Optional, Any


def query_wandb_runs(project_name, entity_name=None, start_date=None, end_date=None, filters=None):
    """

dylanuys

import torch

class ModelWrapper(torch.nn.Module):
    def __init__(self, model):
        super(ModelWrapper, self).__init__()
        self.model = model
        
    def forward(self, x):
        data_dict = {'image': x}  # model expects data dict as input
        output = self.model(data_dict, inference=True)

dylanuys

import requests
import os
from pathlib import Path
import logging

def send_video_to_validator(
    video_path: str,
    api_url: str,
    auth_token: str,
    timeout: int = 40

dylanuys

# The MIT License (MIT)
# Copyright © 2023 Yuma Rao
# developer: dubm
# Copyright © 2023 Bitmind

# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the “Software”), to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so, subject to the following conditions: