repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
andybrandt/mcp-simple-pubmed | https://github.com/andybrandt/mcp-simple-pubmed/blob/de245d350c456df353363a50051ed5547dedafc0/mcp_simple_pubmed/pubmed_fetch.py | mcp_simple_pubmed/pubmed_fetch.py | """
Full text fetching functionality for PubMed articles.
This module focuses solely on retrieving full text content from PMC
using Bio.Entrez.
"""
import logging
from typing import Optional
import xml.etree.ElementTree as ET
from Bio import Entrez, Medline
logging.basicConfig(level=logging.INFO)
logger = logging.get... | python | MIT | de245d350c456df353363a50051ed5547dedafc0 | 2026-01-05T07:12:06.493732Z | false |
tmb5cg/Fifa-Autobidder | https://github.com/tmb5cg/Fifa-Autobidder/blob/a68e84e070c73e8595ea238791e49c9e025a7cd4/src/run.py | src/run.py | from cProfile import label
from posixpath import split
import tkinter as tk
from tkinter import ttk
from gui import GUI
if __name__ == "__main__":
root = tk.Tk()
root.title("TMB's FIFA Autobidder")
# Set theme
root.tk.call("source", "azure.tcl")
root.tk.call("set_theme", "dark")
app = GUI(ro... | python | MIT | a68e84e070c73e8595ea238791e49c9e025a7cd4 | 2026-01-05T07:11:03.703387Z | false |
tmb5cg/Fifa-Autobidder | https://github.com/tmb5cg/Fifa-Autobidder/blob/a68e84e070c73e8595ea238791e49c9e025a7cd4/src/gui.py | src/gui.py | from cProfile import label
from datetime import datetime
import importlib
from posixpath import split
import queue
import threading
import time
import tkinter as tk
from tkinter import ttk
import configparser
from turtle import color
from helpers import checkStartupFiles, create_driver, getFilters, log_event, setup_a... | python | MIT | a68e84e070c73e8595ea238791e49c9e025a7cd4 | 2026-01-05T07:11:03.703387Z | false |
tmb5cg/Fifa-Autobidder | https://github.com/tmb5cg/Fifa-Autobidder/blob/a68e84e070c73e8595ea238791e49c9e025a7cd4/src/autobidder.py | src/autobidder.py | import csv
from os import path
from platform import platform
import random
from csv import reader
from datetime import datetime
from datetime import date
from decimal import Decimal
from time import sleep
from turtle import position
from selenium import webdriver
from selenium.common.exceptions import (
NoSuchEle... | python | MIT | a68e84e070c73e8595ea238791e49c9e025a7cd4 | 2026-01-05T07:11:03.703387Z | true |
tmb5cg/Fifa-Autobidder | https://github.com/tmb5cg/Fifa-Autobidder/blob/a68e84e070c73e8595ea238791e49c9e025a7cd4/src/helpers.py | src/helpers.py | import configparser
from datetime import datetime
import json
import os
from os import path
import platform
import random
import re
from time import sleep
from selenium import webdriver
from selenium.common.exceptions import (
NoSuchElementException, TimeoutException, WebDriverException)
from selenium.webdriver imp... | python | MIT | a68e84e070c73e8595ea238791e49c9e025a7cd4 | 2026-01-05T07:11:03.703387Z | false |
MichaelStott/KivMob | https://github.com/MichaelStott/KivMob/blob/1e6591541a1164cf9080f4d0e64c166f54bd4c55/setup.py | setup.py | from setuptools import setup
setup(
name="kivmob",
version="2.0",
description="Provides AdMob support for Kivy.",
url="http://github.com/MichaelStott/KivMob",
author="Michael Stott",
license="MIT",
py_modules=["kivmob"],
install_requires=["kivy"],
zip_safe=False,
)
| python | MIT | 1e6591541a1164cf9080f4d0e64c166f54bd4c55 | 2026-01-05T07:11:54.574300Z | false |
MichaelStott/KivMob | https://github.com/MichaelStott/KivMob/blob/1e6591541a1164cf9080f4d0e64c166f54bd4c55/kivmob.py | kivmob.py | from kivy.core.window import Window
from kivy.logger import Logger
from kivy.metrics import dp
from kivy.utils import platform
if platform == "android":
try:
from jnius import autoclass, cast, PythonJavaClass, java_method
from android.runnable import run_on_ui_thread
activity = autoclass(... | python | MIT | 1e6591541a1164cf9080f4d0e64c166f54bd4c55 | 2026-01-05T07:11:54.574300Z | false |
MichaelStott/KivMob | https://github.com/MichaelStott/KivMob/blob/1e6591541a1164cf9080f4d0e64c166f54bd4c55/docs/conf.py | docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | python | MIT | 1e6591541a1164cf9080f4d0e64c166f54bd4c55 | 2026-01-05T07:11:54.574300Z | false |
MichaelStott/KivMob | https://github.com/MichaelStott/KivMob/blob/1e6591541a1164cf9080f4d0e64c166f54bd4c55/demo/main.py | demo/main.py | from kivmob import KivMob, TestIds, RewardedListenerInterface
import kivy.utils
from kivymd.app import MDApp
from kivy.lang import Builder
from kivy.config import Config
from kivy.utils import platform
from kivy.core.window import Window
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.screenmanager import S... | python | MIT | 1e6591541a1164cf9080f4d0e64c166f54bd4c55 | 2026-01-05T07:11:54.574300Z | false |
jacob-bd/notebooklm-mcp | https://github.com/jacob-bd/notebooklm-mcp/blob/1ca3bba360852de0534ca33e0ccf7258a0efd306/src/notebooklm_mcp/api_client.py | src/notebooklm_mcp/api_client.py | #!/usr/bin/env python3
"""NotebookLM MCP API client (notebooklm.google.com).
Internal API. See CLAUDE.md for full documentation.
"""
import json
import os
import re
import urllib.parse
from dataclasses import dataclass
from datetime import datetime, timezone
from typing import Any
import httpx
# Ownership constant... | python | MIT | 1ca3bba360852de0534ca33e0ccf7258a0efd306 | 2026-01-05T07:12:11.692365Z | true |
jacob-bd/notebooklm-mcp | https://github.com/jacob-bd/notebooklm-mcp/blob/1ca3bba360852de0534ca33e0ccf7258a0efd306/src/notebooklm_mcp/auth_cli.py | src/notebooklm_mcp/auth_cli.py | #!/usr/bin/env python3
"""CLI tool to authenticate with NotebookLM MCP.
This tool connects to Chrome via DevTools Protocol, navigates to NotebookLM,
and extracts authentication tokens. If the user is not logged in, it waits
for them to log in via the Chrome window.
Usage:
1. Start Chrome with remote debugging:
... | python | MIT | 1ca3bba360852de0534ca33e0ccf7258a0efd306 | 2026-01-05T07:12:11.692365Z | false |
jacob-bd/notebooklm-mcp | https://github.com/jacob-bd/notebooklm-mcp/blob/1ca3bba360852de0534ca33e0ccf7258a0efd306/src/notebooklm_mcp/__init__.py | src/notebooklm_mcp/__init__.py | """NotebookLM MCP Server.
This MCP provides access to NotebookLM (notebooklm.google.com)
using undocumented internal APIs. Tested with personal/free tier accounts.
May work with Google Workspace accounts but has not been tested.
WARNING: This uses undocumented internal APIs that may change at any time.
"""
__version... | python | MIT | 1ca3bba360852de0534ca33e0ccf7258a0efd306 | 2026-01-05T07:12:11.692365Z | false |
jacob-bd/notebooklm-mcp | https://github.com/jacob-bd/notebooklm-mcp/blob/1ca3bba360852de0534ca33e0ccf7258a0efd306/src/notebooklm_mcp/server.py | src/notebooklm_mcp/server.py | """NotebookLM MCP Server."""
from typing import Any
from fastmcp import FastMCP
from .api_client import NotebookLMClient, extract_cookies_from_chrome_export, parse_timestamp
# Initialize MCP server
mcp = FastMCP(
name="notebooklm",
instructions="""NotebookLM MCP - Access NotebookLM (notebooklm.google.com).
... | python | MIT | 1ca3bba360852de0534ca33e0ccf7258a0efd306 | 2026-01-05T07:12:11.692365Z | true |
jacob-bd/notebooklm-mcp | https://github.com/jacob-bd/notebooklm-mcp/blob/1ca3bba360852de0534ca33e0ccf7258a0efd306/src/notebooklm_mcp/auth.py | src/notebooklm_mcp/auth.py | """Authentication helper for NotebookLM MCP.
Uses Chrome DevTools MCP to extract auth tokens from an authenticated browser session.
If the user is not logged in, prompts them to log in via the Chrome window.
"""
import json
import os
import time
from dataclasses import dataclass
from pathlib import Path
@dataclass
... | python | MIT | 1ca3bba360852de0534ca33e0ccf7258a0efd306 | 2026-01-05T07:12:11.692365Z | false |
eriklindernoren/PyTorch-Deep-Dream | https://github.com/eriklindernoren/PyTorch-Deep-Dream/blob/637de95ffca461d49ae49538d0d44f0e89ffdf0f/deep_dream.py | deep_dream.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from torchvision import models
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import argparse
import os
import tqdm
import scipy.ndimage as nd
from utils import deprocess, preprocess, clip
def dream(image, model, iteratio... | python | MIT | 637de95ffca461d49ae49538d0d44f0e89ffdf0f | 2026-01-05T07:12:12.077056Z | false |
eriklindernoren/PyTorch-Deep-Dream | https://github.com/eriklindernoren/PyTorch-Deep-Dream/blob/637de95ffca461d49ae49538d0d44f0e89ffdf0f/utils.py | utils.py | import numpy as np
import torch
from torchvision import transforms
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
preprocess = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
def deprocess(image_np):
image_np = image_np.squeeze().transpose(1, 2, 0)
... | python | MIT | 637de95ffca461d49ae49538d0d44f0e89ffdf0f | 2026-01-05T07:12:12.077056Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/eval_cfd.py | src/eval_cfd.py | import os
import platform
from argparse import ArgumentParser
from copy import deepcopy
from pathlib import Path
import wandb
import yaml
def parse_args():
parser = ArgumentParser()
parser.add_argument("--stage_id", type=str, required=True)
parser.add_argument("--checkpoint", type=str, default="best_mode... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/train_stage.py | src/train_stage.py | import logging
import os
from pathlib import Path
import kappaprofiler as kp
import yaml
from torch.distributed import broadcast_object_list
from wandb.util import generate_id
from callbacks.base.callback_base import CallbackBase
from configs.cli_args import CliArgs
from configs.static_config import StaticConfig
from... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/main_train.py | src/main_train.py | from utils.version_check import check_versions
check_versions(verbose=False)
import logging
import os
import kappaprofiler as kp
import torch
from configs.cli_args import parse_run_cli_args
from configs.static_config import StaticConfig
from distributed.config import barrier, get_rank, get_local_rank, get_world_siz... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/main_sbatch.py | src/main_sbatch.py | import os
import platform
import shlex
import sys
import uuid
from argparse import ArgumentParser
from datetime import datetime
from pathlib import Path
import yaml
import kappaconfig as kc
def get_parser():
parser = ArgumentParser()
# how many GPUs
gpus_group = parser.add_mutually_exclusive_group()
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/main_run_folder.py | src/main_run_folder.py | import argparse
import logging
import os
import random
import shutil
import subprocess
from pathlib import Path
from time import sleep
import yaml
from utils.logging_util import add_stdout_handler
from utils.version_check import check_versions
def parse_args():
parser = argparse.ArgumentParser()
parser.add_... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/default_initializer.py | src/initializers/default_initializer.py | from .base.initializer_base import InitializerBase
class DefaultInitializer(InitializerBase):
"""
implicitly applies the torch default initialization
useful e.g. when defining a list of initializers to sweep over
"""
def init_weights(self, model):
pass
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/functional.py | src/initializers/functional.py | import torch.nn as nn
ALL_BATCHNORMS = (
nn.BatchNorm1d,
nn.BatchNorm2d,
nn.BatchNorm3d,
nn.LazyBatchNorm1d,
nn.LazyBatchNorm2d,
nn.LazyBatchNorm3d,
nn.SyncBatchNorm,
)
_ALL_NORMS = (
*ALL_BATCHNORMS,
nn.LayerNorm,
nn.InstanceNorm1d,
nn.InstanceNorm2d,
nn.InstanceNorm3d... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/previous_run_initializer.py | src/initializers/previous_run_initializer.py | from .base.checkpoint_initializer import CheckpointInitializer
class PreviousRunInitializer(CheckpointInitializer):
"""
initializes a model from a checkpoint of a previous run (specified by the stage_id)
load_optim=False as this is usually used for frozen/pretrained models
"""
def __init__(
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/resume_initializer.py | src/initializers/resume_initializer.py | import os
import torch
from models.base.composite_model_base import CompositeModelBase
from models.base.single_model_base import SingleModelBase
from utils.checkpoint import Checkpoint
from .base.checkpoint_initializer import CheckpointInitializer
class ResumeInitializer(CheckpointInitializer):
"""
initiali... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/__init__.py | src/initializers/__init__.py | from utils.factory import instantiate
def initializer_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[f"initializers.{kind}"],
type_names=[kind.split(".")[-1]],
**kwargs
)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/pretrained_initializer.py | src/initializers/pretrained_initializer.py | from pathlib import Path
import torch
from models.ssl_heads.masked_decoder import MaskedDecoder
from .base.initializer_base import InitializerBase
class PretrainedInitializer(InitializerBase):
""" initialize with weights from an external, pretrained checkpoints (e.g. original facebook MAE checkpoints) """
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/base/initializer_base.py | src/initializers/base/initializer_base.py | import logging
from providers.path_provider import PathProvider
class InitializerBase:
def __init__(self, path_provider: PathProvider = None):
self.logger = logging.getLogger(type(self).__name__)
self.path_provider = path_provider
# check if children overwrite the correct method
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/base/checkpoint_initializer.py | src/initializers/base/checkpoint_initializer.py | import torch
from initializers.base.initializer_base import InitializerBase
from models.base.single_model_base import SingleModelBase
from utils.checkpoint import Checkpoint
from utils.factory import create
class CheckpointInitializer(InitializerBase):
def __init__(self, stage_id, model_name, checkpoint, load_op... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/initializers/base/__init__.py | src/initializers/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/optimizer_wrapper.py | src/optimizers/optimizer_wrapper.py | import logging
import torch
from kappaschedules import object_to_schedule
from torch.cuda.amp import GradScaler
from utils.bidict import Bidict
from utils.factory import create_collection, create
from utils.formatting_util import float_to_scientific_notation
from .lr_scalers import lr_scaler_from_kwargs
from .lr_scal... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/interleaved_optimizer.py | src/optimizers/interleaved_optimizer.py | import logging
from kappaschedules import object_to_schedule
class InterleavedOptimizer:
"""
selects an optimizer from a set of optimizers per update step according to a schedule
the schedule should return the index of the current optimizer
"""
def __init__(self, model, optim_ctors, schedule, up... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/__init__.py | src/optimizers/__init__.py | from copy import deepcopy
from functools import partial
from optimizers.interleaved_optimizer import InterleavedOptimizer
from optimizers.optimizer_wrapper import OptimizerWrapper
from utils.factory import get_ctor
def optim_ctor_from_kwargs(kind, **kwargs):
kwargs = deepcopy(kwargs)
if kind == "interleaved_... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/custom/lars.py | src/optimizers/custom/lars.py | # https://raw.githubusercontent.com/Lightning-AI/lightning-bolts/master/pl_bolts/optimizers/lars.py
import torch
from torch.optim.optimizer import Optimizer
class LARS(Optimizer):
def __init__(
self,
params,
lr,
momentum=0,
dampening=0,
weigh... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/custom/lion.py | src/optimizers/custom/lion.py | import torch
from torch.optim.optimizer import Optimizer
class Lion(Optimizer):
""" https://raw.githubusercontent.com/lucidrains/lion-pytorch/main/lion_pytorch/lion_pytorch.py """
def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
assert lr > 0.
assert all([0. <= beta <... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/custom/__init__.py | src/optimizers/custom/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/lr_scalers/linear_lr_scaler.py | src/optimizers/lr_scalers/linear_lr_scaler.py | class LinearLrScaler:
def __init__(self, divisor=256):
super().__init__()
self.divisor = divisor
def __str__(self):
return f"{type(self).__name__}(divisor={self.divisor})"
def scale_lr(self, base_lr, lr_scale_factor):
return base_lr * lr_scale_factor / self.divisor
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/lr_scalers/sqrt_lr_scaler.py | src/optimizers/lr_scalers/sqrt_lr_scaler.py | import math
class SqrtLrScaler:
def __init__(self, divisor=256):
super().__init__()
self.divisor = divisor
def __str__(self):
return f"{type(self).__name__}(divisor={self.divisor})"
def scale_lr(self, base_lr, lr_scale_factor):
return base_lr * math.sqrt(lr_scale_factor /... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/lr_scalers/__init__.py | src/optimizers/lr_scalers/__init__.py | from utils.factory import instantiate
def lr_scaler_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[f"optimizers.lr_scalers.{kind}"],
type_names=[kind],
**kwargs
)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/param_group_modifiers/vit_norm_lr_scale_modifier.py | src/optimizers/param_group_modifiers/vit_norm_lr_scale_modifier.py | from .base.param_group_modifier import ParamGroupModifier
class VitNormLrScaleModifier(ParamGroupModifier):
def __init__(self, scale, start_block_index=None):
self.scale = scale
self.start_block_index = start_block_index
def get_properties(self, model, name, param):
assert self.start_... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/param_group_modifiers/layerwise_lr_decay_modifier.py | src/optimizers/param_group_modifiers/layerwise_lr_decay_modifier.py | from .base.param_group_modifier import ParamGroupModifier
class LayerwiseLrDecayModifier(ParamGroupModifier):
def __init__(self, decay, skip_layers=None):
self.decay = decay
self.skip_layers = skip_layers
def get_properties(self, model, name, param):
# adapted from BEiT: https://githu... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/param_group_modifiers/exclude_from_wd_by_name_modifier.py | src/optimizers/param_group_modifiers/exclude_from_wd_by_name_modifier.py | from .base.param_group_modifier import ParamGroupModifier
class ExcludeFromWdByNameModifier(ParamGroupModifier):
def __init__(self, name):
super().__init__()
self.name = name
self.param_was_found = False
def get_properties(self, model, name, param):
if name == self.name:
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/param_group_modifiers/__init__.py | src/optimizers/param_group_modifiers/__init__.py | from utils.factory import instantiate
def param_group_modifier_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[f"optimizers.param_group_modifiers.{kind}"],
type_names=[kind],
**kwargs
)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/param_group_modifiers/base/param_group_modifier.py | src/optimizers/param_group_modifiers/base/param_group_modifier.py | class ParamGroupModifier:
def get_properties(self, model, name, param):
raise NotImplementedError
def __repr__(self):
return str(self)
def __str__(self):
raise NotImplementedError
@staticmethod
def was_applied_successfully():
return True
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/optimizers/param_group_modifiers/base/__init__.py | src/optimizers/param_group_modifiers/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/__init__.py | src/summarizers/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/summary_summarizers/best_metric_summary_summarizer.py | src/summarizers/summary_summarizers/best_metric_summary_summarizer.py | import fnmatch
import numpy as np
from utils.infer_higher_is_better import higher_is_better_from_metric_key
from .base.summary_summarizer_base import SummarySummarizerBase
class BestMetricSummarySummarizer(SummarySummarizerBase):
def __init__(self, pattern, **kwargs):
super().__init__(**kwargs)
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/summary_summarizers/__init__.py | src/summarizers/summary_summarizers/__init__.py | from utils.factory import instantiate
def summary_summarizer_from_kwargs(kind, **kwargs):
return instantiate(module_names=[f"summarizers.summary_summarizers.{kind}"], type_names=[kind], **kwargs)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/summary_summarizers/base/summary_summarizer_base.py | src/summarizers/summary_summarizers/base/summary_summarizer_base.py | import logging
from providers.summary_providers.base.summary_provider_base import SummaryProviderBase
from providers.summary_providers.noop_summary_provider import NoopSummaryProvider
class SummarySummarizerBase:
def __init__(self, summary_provider: SummaryProviderBase):
self.logger = logging.getLogger(t... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/summary_summarizers/base/__init__.py | src/summarizers/summary_summarizers/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/stage_summarizers/group_best_metric_summarizer.py | src/summarizers/stage_summarizers/group_best_metric_summarizer.py | from .base.stage_summarizer_base import StageSummarizerBase
from .best_metric_summarizer import BestMetricSummarizer
class GroupBestMetricSummarizer(StageSummarizerBase):
def __init__(self, source_group, target_group=None, **kwargs):
super().__init__(**kwargs)
self.source_group = source_group
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/stage_summarizers/best_metric_summarizer.py | src/summarizers/stage_summarizers/best_metric_summarizer.py | import numpy as np
from utils.infer_higher_is_better import higher_is_better_from_metric_key
from .base.stage_summarizer_base import StageSummarizerBase
class BestMetricSummarizer(StageSummarizerBase):
"""
looks at the best source_key metric and logs the target_key metric at that global_step
e.g. source_... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/stage_summarizers/__init__.py | src/summarizers/stage_summarizers/__init__.py | from utils.factory import instantiate
def stage_summarizer_from_kwargs(kind, **kwargs):
return instantiate(module_names=[f"summarizers.stage_summarizers.{kind}"], type_names=[kind], **kwargs)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/stage_summarizers/base/stage_summarizer_base.py | src/summarizers/stage_summarizers/base/stage_summarizer_base.py | import logging
import yaml
from providers.path_provider import PathProvider
from providers.summary_providers.base.summary_provider_base import SummaryProviderBase
from providers.summary_providers.noop_summary_provider import NoopSummaryProvider
from utils.checkpoint import Checkpoint
class StageSummarizerBase:
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/summarizers/stage_summarizers/base/__init__.py | src/summarizers/stage_summarizers/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/losses/elementwise_loss.py | src/losses/elementwise_loss.py | import torch
import torch.nn as nn
from losses import basic_loss_fn_from_kwargs
from utils.factory import create
from utils.loss_utils import apply_reduction
from utils.vit_util import patchify_as_1d
from .basic.mse_loss import MseLoss
class ElementwiseLoss(nn.Module):
def __init__(self, loss_function):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/losses/__init__.py | src/losses/__init__.py | from utils.factory import instantiate
def loss_fn_from_kwargs(kind, update_counter=None, **kwargs):
return instantiate(
module_names=[f"losses.{kind}", f"losses.basic.{kind}", "torch.nn"],
type_names=[kind.split(".")[-1]],
# pass update_counter to SchedulableLoss but not to e.g. torch.nn.M... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/losses/basic/l1_loss.py | src/losses/basic/l1_loss.py | import torch.nn as nn
import torch.nn.functional as F
class L1Loss(nn.Module):
@staticmethod
def forward(pred, target, reduction="mean"):
return F.l1_loss(pred, target, reduction=reduction)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/losses/basic/mse_loss.py | src/losses/basic/mse_loss.py | import torch.nn as nn
import torch.nn.functional as F
class MseLoss(nn.Module):
@staticmethod
def forward(pred, target, reduction="mean"):
return F.mse_loss(pred, target, reduction=reduction)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/losses/basic/__init__.py | src/losses/basic/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/freezers/full_freezer.py | src/freezers/full_freezer.py | from .base.freezer_base import FreezerBase
class FullFreezer(FreezerBase):
def __str__(self):
return type(self).__name__
def _update_state(self, model, requires_grad):
model.eval()
for param in model.parameters():
param.requires_grad = requires_grad
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/freezers/__init__.py | src/freezers/__init__.py | from utils.factory import instantiate
def freezer_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[f"freezers.{kind}"],
type_names=[kind.split(".")[-1]],
**kwargs
)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/freezers/base/freezer_base.py | src/freezers/base/freezer_base.py | import logging
from kappaschedules import object_to_schedule, PeriodicBoolSchedule
from utils.update_counter import UpdateCounter
class FreezerBase:
def __init__(self, update_counter: UpdateCounter = None, schedule=None):
self.logger = logging.getLogger(type(self).__name__)
self.update_counter =... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/freezers/base/__init__.py | src/freezers/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/dummy_dataset.py | src/datasets/dummy_dataset.py | import torch
from torchvision.transforms.functional import to_pil_image
from .base.dataset_base import DatasetBase
class DummyDataset(DatasetBase):
def __init__(
self,
x_shape,
size=None,
n_classes=10,
n_abspos=10,
is_multilabel=False,
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/lagrangian_dataset.py | src/datasets/lagrangian_dataset.py | import os
import json
import h5py
import wget
import zipfile
import torch
from torch_geometric.data import Data
from torch_geometric.nn.pool import radius_graph, radius
from torch_geometric.transforms import KNNGraph
from .base.dataset_base import DatasetBase
from kappadata.copying.image_folder import copy_imagefolde... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/__init__.py | src/datasets/__init__.py | import logging
from utils.factory import instantiate
def dataset_from_kwargs(
kind,
dataset_config_provider,
dataset_wrappers=None,
sample_wrappers=None,
**kwargs,
):
dataset = instantiate(
module_names=[f"datasets.{kind}", f"datasets.wrappers.{kind}"],
typ... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/cfd_dataset.py | src/datasets/cfd_dataset.py | import os
import einops
import numpy as np
import scipy
import torch
from kappadata.copying.image_folder import copy_imagefolder_from_global_to_local
from kappautils.param_checking import to_2tuple
from torch_geometric.nn.pool import radius, radius_graph
from distributed.config import barrier, is_data_rank0
from util... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | true |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/shapenet_car.py | src/datasets/shapenet_car.py | import einops
import scipy
import os
import shutil
import meshio
import numpy as np
import torch
from kappautils.param_checking import to_3tuple, to_2tuple
from torch_geometric.nn.pool import radius, radius_graph
from distributed.config import barrier, is_data_rank0
from .base.dataset_base import DatasetBase
class ... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/base/dataset_base.py | src/datasets/base/dataset_base.py | from kappadata.datasets import KDDataset
from providers.dataset_config_provider import DatasetConfigProvider
from providers.path_provider import PathProvider
from utils.collator_from_kwargs import collator_from_kwargs
from utils.factory import create_collection
from utils.param_checking import to_path
class DatasetB... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/base/__init__.py | src/datasets/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/rans_interpolated_collator.py | src/datasets/collators/rans_interpolated_collator.py | import einops
import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.utils.data import default_collate
class RansInterpolatedCollator(KDSingleCollator):
def collate(self, batch, dataset_mode, ctx=None):
# make sure that batch was not collated
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/cfd_interpolated_collator.py | src/datasets/collators/cfd_interpolated_collator.py | import einops
import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.utils.data import default_collate
from torch.nn.utils.rnn import pad_sequence
class CfdInterpolatedCollator(KDSingleCollator):
def collate(self, batch, dataset_mode, ctx=None):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/lagrangian_simformer_collator.py | src/datasets/collators/lagrangian_simformer_collator.py | import einops
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import default_collate
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.utils.data import default_collate
from torch_geometric.data import Data
from torch_geometric.transfo... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/rans_baseline_collator.py | src/datasets/collators/rans_baseline_collator.py | import einops
import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.utils.data import default_collate
class RansBaselineCollator(KDSingleCollator):
def collate(self, batch, dataset_mode, ctx=None):
# make sure that batch was not collated
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/rans_simformer_nognn_collator.py | src/datasets/collators/rans_simformer_nognn_collator.py | import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import default_collate
class RansSimformerNognnCollator(KDSingleCollator):
def collate(self, batch, dataset_mode, ctx=None):
# make sur... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/cfd_simformer_collator.py | src/datasets/collators/cfd_simformer_collator.py | import einops
import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.nn.utils.rnn import pad_sequence
from torch.utils.data import default_collate
class CfdSimformerCollator(KDSingleCollator):
def __init__(self, num_supernodes=None, **kwargs):
s... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/__init__.py | src/datasets/collators/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/cfd_baseline_collator.py | src/datasets/collators/cfd_baseline_collator.py | import einops
import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.utils.data import default_collate
class CfdBaselineCollator(KDSingleCollator):
def collate(self, batch, dataset_mode, ctx=None):
# make sure that batch was not collated
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/datasets/collators/rans_gino_encdec_sdf_collator.py | src/datasets/collators/rans_gino_encdec_sdf_collator.py | import einops
import torch
from kappadata.collators import KDSingleCollator
from kappadata.wrappers import ModeWrapper
from torch.utils.data import default_collate
class RansGinoEncdecSdfCollator(KDSingleCollator):
def collate(self, batch, dataset_mode, ctx=None):
# make sure that batch was not collated
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/metrics/__init__.py | src/metrics/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/cfd_interpolated_trainer.py | src/trainers/cfd_interpolated_trainer.py | from functools import cached_property
import einops
from kappadata.wrappers import ModeWrapper
from torch import nn
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.cfd_interpolated_collator import CfdInterpolatedCollator
from losses import loss_fn_from_kwargs... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/rans_gino_encdec_sdf_trainer.py | src/trainers/rans_gino_encdec_sdf_trainer.py | from functools import cached_property
import torch
from kappadata.wrappers import ModeWrapper
from torch import nn
from torch_scatter import segment_csr
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.rans_gino_encdec_sdf_collator import RansGinoEncdecSdfColl... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/rans_simformer_nognn_sdf_trainer.py | src/trainers/rans_simformer_nognn_sdf_trainer.py | from functools import cached_property
import torch
from kappadata.wrappers import ModeWrapper
from torch import nn
from torch_scatter import segment_csr
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.rans_simformer_nognn_collator import RansSimformerNognnCol... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/gns_trainer.py | src/trainers/gns_trainer.py | import kappamodules.utils.tensor_cache as tc
import os
from functools import cached_property
import torch
import einops
from torch import nn
from kappadata.wrappers import ModeWrapper
from losses import loss_fn_from_kwargs
from utils.factory import create
from .base.sgd_trainer import SgdTrainer
from datasets.collator... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/cfd_baseline_trainer.py | src/trainers/cfd_baseline_trainer.py | from torch_scatter import segment_csr
import torch
from functools import cached_property
import einops
from kappadata.wrappers import ModeWrapper
from torch import nn
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.cfd_baseline_collator import CfdBaselineCol... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/lagrangian_simformer_trainer.py | src/trainers/lagrangian_simformer_trainer.py | import kappamodules.utils.tensor_cache as tc
import os
from functools import cached_property
import torch
import einops
from torch import nn
from kappadata.wrappers import ModeWrapper
from losses import loss_fn_from_kwargs
from utils.factory import create
from .base.sgd_trainer import SgdTrainer
from datasets.collator... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/rans_baseline_trainer.py | src/trainers/rans_baseline_trainer.py | from functools import cached_property
import torch
from kappadata.wrappers import ModeWrapper
from torch import nn
from torch_scatter import segment_csr
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.rans_baseline_collator import RansBaselineCollator
from lo... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/rans_interpolated_trainer.py | src/trainers/rans_interpolated_trainer.py | from functools import cached_property
import torch
from kappadata.wrappers import ModeWrapper
from torch import nn
from torch_scatter import segment_csr
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.rans_interpolated_collator import RansInterpolatedCollator... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/cfd_simformer_trainer.py | src/trainers/cfd_simformer_trainer.py | from functools import cached_property
import kappamodules.utils.tensor_cache as tc
import torch
from kappadata.wrappers import ModeWrapper
from torch import nn
from torch_geometric.nn.pool import radius_graph
from torch_scatter import segment_csr
from callbacks.online_callbacks.update_output_callback import UpdateOut... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/lagrangian_large_t_simformer_trainer.py | src/trainers/lagrangian_large_t_simformer_trainer.py | import kappamodules.utils.tensor_cache as tc
import os
from functools import cached_property
import torch
import einops
from torch import nn
from kappadata.wrappers import ModeWrapper
from losses import loss_fn_from_kwargs
from utils.factory import create
from .base.sgd_trainer import SgdTrainer
from datasets.collator... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/__init__.py | src/trainers/__init__.py | from utils.factory import instantiate
def trainer_from_kwargs(kind, **kwargs):
if "eval" in kind:
return instantiate(module_names=[f"trainers.eval.{kind}"], type_names=[kind], **kwargs)
return instantiate(module_names=[f"trainers.{kind}"], type_names=[kind.split(".")[-1]], **kwargs)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/rans_simformer_nognn_trainer.py | src/trainers/rans_simformer_nognn_trainer.py | from functools import cached_property
import torch
from kappadata.wrappers import ModeWrapper
from torch import nn
from torch_scatter import segment_csr
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.rans_simformer_nognn_collator import RansSimformerNognnCol... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/cfd_hybrid_trainer.py | src/trainers/cfd_hybrid_trainer.py | import torch
from functools import cached_property
import einops
from kappadata.wrappers import ModeWrapper
from torch import nn
from callbacks.online_callbacks.update_output_callback import UpdateOutputCallback
from datasets.collators.cfd_hybrid_collator import CfdHybridCollator
from losses import loss_fn_from_kwarg... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/base/sgd_trainer.py | src/trainers/base/sgd_trainer.py | import logging
from functools import partial
import kappaprofiler as kp
import torch
import torch.nn as nn
from kappadata.wrappers import KDMultiViewWrapper, XRepeatWrapper
from torch.cuda.amp import GradScaler
from torch.distributed import all_gather_object
from torch.nn.parallel import DistributedDataParallel
from ... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | true |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/base/functional.py | src/trainers/base/functional.py | import logging
import einops
import torch
from torch.utils.data import DataLoader
from distributed.config import get_world_size
from utils.functional import get_powers_of_two, is_power_of_two
def calculate_effective_batch_size_per_device(effective_batch_size, world_size=None):
world_size = world_size or get_wor... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/base/__init__.py | src/trainers/base/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/early_stoppers/metric_early_stopper.py | src/trainers/early_stoppers/metric_early_stopper.py | from callbacks.base.callback_base import CallbackBase
from utils.infer_higher_is_better import higher_is_better_from_metric_key
from .base.early_stopper_base import EarlyStopperBase
class MetricEarlyStopper(EarlyStopperBase):
def __init__(self, metric_key, tolerance, **kwargs):
super().__init__(**kwargs)
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/early_stoppers/__init__.py | src/trainers/early_stoppers/__init__.py | from utils.factory import instantiate
def early_stopper_from_kwargs(kind, **kwargs):
return instantiate(module_names=[f"trainers.early_stoppers.{kind}"], type_names=[kind], **kwargs)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/trainers/early_stoppers/loss_divergence_early_stopper.py | src/trainers/early_stoppers/loss_divergence_early_stopper.py | from collections import deque
import numpy as np
from callbacks.base.callback_base import CallbackBase
from .base.early_stopper_base import EarlyStopperBase
class LossDivergenceEarlyStopper(EarlyStopperBase):
"""
stop training if loss diverges
organize losses into [...] + [reference_window] + [tolerance... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
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