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 |
|---|---|---|---|---|---|---|---|---|
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/model_utils.py | src/utils/model_utils.py | import torch
# noinspection PyProtectedMember
from torch.nn.modules.batchnorm import _BatchNorm
def get_paramnames_with_no_gradient(model):
return [name for name, param in model.named_parameters() if param.grad is None and param.requires_grad]
def get_output_shape_of_model(model, forward_fn, **forward_kwargs):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/checkpoint.py | src/utils/checkpoint.py | import functools
import math
import os
import re
@functools.total_ordering
class Checkpoint:
def __init__(self, epoch=None, update=None, sample=None):
self.epoch = epoch
self.update = update
self.sample = sample
def copy(self):
return Checkpoint(epoch=self.epoch, update=self.u... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/pytorch_cuda_timing.py | src/utils/pytorch_cuda_timing.py | import torch
import torch.distributed as dist
def cuda_start_event():
start_event = torch.cuda.Event(enable_timing=True)
start_event.record()
return start_event
def cuda_end_event(start_event):
if dist.is_available() and dist.is_initialized():
torch.cuda.synchronize()
dist.barrier()
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/log_once.py | src/utils/log_once.py | import logging
_MESSAGE_KEYS = set()
def log_once(log_fn_or_message, key, level=logging.INFO):
if key not in _MESSAGE_KEYS:
if isinstance(log_fn_or_message, str):
logging.log(level=level, msg=log_fn_or_message)
else:
log_fn_or_message()
_MESSAGE_KEYS.add(key)
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/save_image_utils.py | src/utils/save_image_utils.py | import einops
import numpy as np
import torch
from PIL import Image
from kappadata import get_denorm_transform, get_norm_transform
from kappadata.wrappers import XTransformWrapper
from matplotlib.pyplot import get_cmap
from torchvision.transforms.functional import to_pil_image
# region concat images
def concat_images... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/update_counter.py | src/utils/update_counter.py | from utils.checkpoint import Checkpoint
class UpdateCounter:
def __init__(
self,
start_checkpoint: Checkpoint,
end_checkpoint: Checkpoint,
updates_per_epoch: int,
effective_batch_size: int,
):
self.updates_per_epoch = updates_per_epoch
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/factory.py | src/utils/factory.py | import importlib
import inspect
import logging
from functools import partial
from itertools import product
def create(obj_or_kwargs, from_kwargs_fn, instantiate_if_ctor=True, **kwargs):
"""
avoid boilerplate code when allowing ctor arguments to be either an object or a dict with the object parameters
e.g.... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/memory_leak_util.py | src/utils/memory_leak_util.py | import gc
import torch
def get_tensors_in_memory():
# some warning was thrown when calling torch.is_tensor(_reduce_op) with a _reduce_op object
all_objs = gc.get_objects()
all_tensors = []
cuda_tensors = []
for obj in all_objs:
try:
if type(obj).__name__ != "_reduce_op" and to... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/__init__.py | src/utils/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/vit_util.py | src/utils/vit_util.py | import einops
from .param_checking import to_2tuple
def get_sequence_lengths(input_shape, patch_size):
assert len(input_shape) == len(patch_size)
ndim = len(patch_size)
assert all(input_shape[i] % patch_size[i] == 0 for i in range(ndim))
seqlens = [input_shape[i] // patch_size[i] for i in range(ndim)... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/select_with_path.py | src/utils/select_with_path.py | def select_with_path(obj, path):
if path is not None:
for p in path.split("."):
if isinstance(obj, dict):
obj = obj[p]
elif isinstance(obj, list):
obj = obj[int(p)]
else:
obj = getattr(obj, p)
return obj
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/infer_higher_is_better.py | src/utils/infer_higher_is_better.py | import logging
LOWER_IS_BETTER_KEYS = [
"loss",
"delta",
]
HIGHER_IS_BETTER_KEYS = [
"profiling/train_update_time",
"correlation",
"corerlation_time",
]
NEUTRAL_KEYS = [
"optim",
"profiling",
"mask_ratio",
"freezers",
"transform_scale",
"ctx",
"loss_weight",
"gradien... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/apply_transform_dataset.py | src/utils/apply_transform_dataset.py | from torch.utils.data import Dataset
class ApplyTransformDataset(Dataset):
"""
helper dataset to apply a transform in parallel fashion to some data
applying transforms via the pytorch DataLoader is much faster than applying it via joblib
(on ImageNet10-M3AE logging embeddings of a ViT-B takes 10:40 wi... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/formatting_util.py | src/utils/formatting_util.py | import numpy as np
import torch
_SI_PREFIXES = ["", "K", "M", "G", "T", "P", "E"]
def short_number_str(number, precision=1):
if number == 0:
return "{short_number:.{precision}f}".format(short_number=0., precision=precision)
if number < 0:
number = -number
sign = "-"
else:
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/version_check.py | src/utils/version_check.py | import logging
import sys
import packaging.version
expected_torch = "2.0.0"
expected_torchvision = "0.15.0"
expected_kappabenchmark = "0.0.10"
expected_kappaconfig = "1.0.29"
expected_kappadata = "1.3.78"
expected_kappamodules = "0.1.24"
expected_kappaprofiler = "1.0.11"
expected_kappaschedules = "0.0.18"
expected_ti... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/invariance_utils.py | src/utils/invariance_utils.py | import torch
from torch.nn.functional import softmax
# Method to calculate the invariance of the latent space representations
# Eq.6 in https://openreview.net/pdf?id=SCD0hn3kMHw
# features_no_aug.shape = (N,d)
# feature_dict_augs: each key represents an original sample -> number of keys = N
# each ... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/knn_predict.py | src/utils/knn_predict.py | import einops
import torch
import torch.nn.functional as F
@torch.no_grad()
def knn_predict(train_x, train_y, test_x, k=10, tau=0.07, batch_normalize=True, eps=1e-6):
# initialize onehot vector per class (used for counting votes in classification)
n_classes = train_y.max().item() + 1
if n_classes <= 1:
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/commands/copy_command.py | src/utils/commands/copy_command.py | from pathlib import Path
from .base.command_base import CommandBase
class CopyCommand(CommandBase):
def __init__(self, src: str, dst: str, **kwargs):
super().__init__(**kwargs)
self.src = Path(self._resolve_string(src)).expanduser()
self.dst = Path(self._resolve_string(dst)).expanduser()
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/commands/__init__.py | src/utils/commands/__init__.py | from utils.factory import instantiate
def command_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[f"utils.commands.{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/utils/commands/copy_yaml_command.py | src/utils/commands/copy_yaml_command.py | from pathlib import Path
import yaml
from .base.command_base import CommandBase
class CopyYamlCommand(CommandBase):
def __init__(self, src: str, dst: str, prepend: dict = None, **kwargs):
super().__init__(**kwargs)
self.src = Path(self._resolve_string(src)).expanduser()
self.dst = Path(s... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/commands/base/command_base.py | src/utils/commands/base/command_base.py | import logging
class CommandBase:
def __init__(self, stage_id, variables=None):
self.logger = logging.getLogger(type(self).__name__)
self.stage_id = stage_id
self.variables = variables or {}
self.variables["stage_id"] = self.stage_id
def __repr__(self):
return str(self... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/commands/base/__init__.py | src/utils/commands/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/utils/kappaconfig/schedule_template_postprocessor.py | src/utils/kappaconfig/schedule_template_postprocessor.py | import kappaconfig as kc
from utils.param_checking import to_2tuple
from .testrun_constants import TEST_RUN_EFFECTIVE_BATCH_SIZE, TEST_RUN_UPDATES_PER_EPOCH
# TODO workaround for missing feature of KappaConfig to enable list objects as template
class ScheduleTemplatePostProcessor(kc.Processor):
"""
resolves n... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/minmodel_postprocessor.py | src/utils/kappaconfig/minmodel_postprocessor.py | import kappaconfig as kc
class MinModelPostProcessor(kc.Processor):
def preorder_process(self, node, trace):
if len(trace) == 0:
return
if isinstance(node, dict):
if "initializers" in node:
i = 0
while i < len(node["initializers"]):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/mindata_preprocessor.py | src/utils/kappaconfig/mindata_preprocessor.py | import kappaconfig as kc
from kappaconfig.entities.wrappers import KCScalar
class MinDataPreProcessor(kc.Processor):
def preorder_process(self, node, trace):
if len(trace) == 0:
return
parent, parent_accessor = trace[-1]
if isinstance(parent_accessor, str):
# datase... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/util.py | src/utils/kappaconfig/util.py | import logging
import shutil
from pathlib import Path
import kappaconfig as kc
import yaml
from utils.factory import create_collection
from utils.processors import processor_from_kwargs
from .mindata_postprocessor import MinDataPostProcessor
from .mindata_preprocessor import MinDataPreProcessor
from .minduration_post... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/remove_large_collections_postprocessor.py | src/utils/kappaconfig/remove_large_collections_postprocessor.py | import kappaconfig as kc
class RemoveLargeCollectionsProcessor(kc.Processor):
"""
remove large list/dicts for prettier storing of the resolved yaml
"""
def preorder_process(self, node, trace):
if len(trace) == 0:
return
parent, parent_accessor = trace[-1]
if isinst... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/minduration_postprocessor.py | src/utils/kappaconfig/minduration_postprocessor.py | import kappaconfig as kc
from .testrun_constants import TEST_RUN_EPOCHS, TEST_RUN_UPDATES, TEST_RUN_SAMPLES, TEST_RUN_EFFECTIVE_BATCH_SIZE
class MinDurationPostProcessor(kc.Processor):
""" limit training duration to a minimum by maniuplating the configuration yaml """
def preorder_process(self, node, trace)... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/none_postprocessor.py | src/utils/kappaconfig/none_postprocessor.py | import kappaconfig as kc
class NonePostProcessor(kc.Processor):
def preorder_process(self, node, trace):
if len(trace) == 0:
return
if isinstance(node, str) and node.lower() == "none":
parent, accessor = trace[-1]
parent[accessor] = None
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/precision_preprocessor.py | src/utils/kappaconfig/precision_preprocessor.py | import kappaconfig as kc
from utils.amp_utils import FLOAT32_ALIASES, FLOAT16_ALIASES, BFLOAT16_ALIASES
class PrecisionPreProcessor(kc.Processor):
def preorder_process(self, node, trace):
if len(trace) == 0:
return
parent, parent_accessor = trace[-1]
if isinstance(parent_acces... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/testrun_constants.py | src/utils/kappaconfig/testrun_constants.py | TEST_RUN_EFFECTIVE_BATCH_SIZE = 8
TEST_RUN_EPOCHS = 2
TEST_RUN_UPDATES_PER_EPOCH = 3
TEST_RUN_UPDATES = TEST_RUN_EPOCHS * TEST_RUN_UPDATES_PER_EPOCH
TEST_RUN_SAMPLES = TEST_RUN_UPDATES * TEST_RUN_EFFECTIVE_BATCH_SIZE
| python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/__init__.py | src/utils/kappaconfig/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/mindata_postprocessor.py | src/utils/kappaconfig/mindata_postprocessor.py | import kappaconfig as kc
from utils.param_checking import to_2tuple
from .testrun_constants import TEST_RUN_EFFECTIVE_BATCH_SIZE, TEST_RUN_UPDATES_PER_EPOCH
class MinDataPostProcessor(kc.Processor):
"""
hyperparams for specific properties in the dictionary and replace it such that the training duration is
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/kappaconfig/minmodel_preprocessor.py | src/utils/kappaconfig/minmodel_preprocessor.py | import kappaconfig as kc
from kappaconfig.entities.wrappers import KCScalar
class MinModelPreProcessor(kc.Processor):
def preorder_process(self, node, trace):
if len(trace) == 0:
return
parent, parent_accessor = trace[-1]
if isinstance(parent_accessor, str):
if "mod... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/utils/processors/__init__.py | src/utils/processors/__init__.py | from utils.factory import instantiate
def processor_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[f"utils.processors.{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/callbacks/__init__.py | src/callbacks/__init__.py | from utils.factory import instantiate
def callback_from_kwargs(kind, **kwargs):
return instantiate(
module_names=[
f"callbacks.{kind}",
f"callbacks.checkpoint_callbacks.{kind}",
f"callbacks.default_callbacks.{kind}",
f"callbacks.monitor_callbacks.{kind}",
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_lagrangian_large_t_rollout_mesh_loss_callback.py | src/callbacks/offline_callbacks/offline_lagrangian_large_t_rollout_mesh_loss_callback.py | from torch_geometric.utils import scatter
from kappadata.wrappers import ModeWrapper
from functools import partial
import einops
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import dict_to_string
class OfflineLagrangianLargeTRolloutMeshLossCallback(PeriodicCa... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_rollout_mesh_callback.py | src/callbacks/offline_callbacks/offline_rollout_mesh_callback.py | import matplotlib.pyplot as plt
import os
from torchvision.transforms.functional import to_tensor, to_pil_image
from PIL import Image
import io
from datasets.collators.cfd_simformer_collator import CfdSimformerCollator
import scipy
from functools import partial
import einops
import torch
from kappadata.wrappers impor... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_rollout_speed_callback.py | src/callbacks/offline_callbacks/offline_rollout_speed_callback.py | import kappaprofiler as kp
import einops
from functools import partial
import torch
from kappadata.wrappers import ModeWrapper
from callbacks.base.periodic_callback import PeriodicCallback
from datasets.collators.cfd_simformer_collator import CfdSimformerCollator
class OfflineRolloutSpeedCallback(PeriodicCallback):... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_loss_callback.py | src/callbacks/offline_callbacks/offline_loss_callback.py | import torch
from functools import partial
from callbacks.base.periodic_callback import PeriodicCallback
from utils.object_from_kwargs import objects_from_kwargs
class OfflineLossCallback(PeriodicCallback):
def __init__(
self,
dataset_key,
output_patterns_to_log=None,
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_rollout_mesh_gif_callback.py | src/callbacks/offline_callbacks/offline_rollout_mesh_gif_callback.py | import matplotlib.pyplot as plt
import os
from torchvision.transforms.functional import to_tensor, to_pil_image
from PIL import Image
import io
from datasets.collators.cfd_simformer_collator import CfdSimformerCollator
import scipy
from functools import partial
import einops
import torch
from kappadata.wrappers impor... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_correlation_time_interpolated_callback.py | src/callbacks/offline_callbacks/offline_correlation_time_interpolated_callback.py | import einops
from functools import partial
import torch
from kappadata.wrappers import ModeWrapper
from callbacks.base.periodic_callback import PeriodicCallback
from datasets.collators.cfd_simformer_collator import CfdSimformerCollator
class OfflineCorrelationTimeInterpolatedCallback(PeriodicCallback):
def __i... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_lagrangian_large_t_rollout_speed_callback.py | src/callbacks/offline_callbacks/offline_lagrangian_large_t_rollout_speed_callback.py | from torch_geometric.utils import scatter
import kappaprofiler as kp
from kappadata.wrappers import ModeWrapper
from functools import partial
import einops
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import dict_to_string
class OfflineLagrangianLargeTRollout... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_cfd_rollout_callback.py | src/callbacks/offline_callbacks/offline_cfd_rollout_callback.py | import einops
from functools import partial
import torch
from kappadata.wrappers import ModeWrapper
from callbacks.base.periodic_callback import PeriodicCallback
from datasets.collators.cfd_simformer_collator import CfdSimformerCollator
class OfflineCfdRolloutCallback(PeriodicCallback):
def __init__(
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_cfd_rollout_mesh_gif_callback.py | src/callbacks/offline_callbacks/offline_cfd_rollout_mesh_gif_callback.py | import io
from functools import partial
import einops
import torch
from PIL import Image
from kappadata.wrappers import ModeWrapper
from kappautils.images.png import png_writer_viridis
from kappautils.images.points_to_image import coords_to_image
from callbacks.base.periodic_callback import PeriodicCallback
from util... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_pred_callback.py | src/callbacks/offline_callbacks/offline_pred_callback.py | import torch
from functools import partial
from callbacks.base.periodic_callback import PeriodicCallback
from utils.object_from_kwargs import objects_from_kwargs
class OfflinePredCallback(PeriodicCallback):
def __init__(self, dataset_key, forward_kwargs=None, **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/callbacks/offline_callbacks/offline_correlation_time_callback.py | src/callbacks/offline_callbacks/offline_correlation_time_callback.py | import einops
from functools import partial
import torch
from kappadata.wrappers import ModeWrapper
from callbacks.base.periodic_callback import PeriodicCallback
from datasets.collators.cfd_simformer_collator import CfdSimformerCollator
class OfflineCorrelationTimeCallback(PeriodicCallback):
def __init__(
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/__init__.py | src/callbacks/offline_callbacks/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_rollout2d_callback.py | src/callbacks/offline_callbacks/offline_rollout2d_callback.py | from functools import partial
import einops
import torch
from kappadata.wrappers import ModeWrapper
from kappautils.images.png import png_writer_viridis
from torchvision.datasets.folder import default_loader
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import dict_to_string... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_rollout_mesh_loss_callback.py | src/callbacks/offline_callbacks/offline_rollout_mesh_loss_callback.py | from torch_geometric.utils import scatter
from kappadata.wrappers import ModeWrapper
from functools import partial
import einops
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import dict_to_string
class OfflineRolloutMeshLossCallback(PeriodicCallback):
def... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_lagrangian_rollout_mesh_loss_callback.py | src/callbacks/offline_callbacks/offline_lagrangian_rollout_mesh_loss_callback.py | from torch_geometric.utils import scatter
from kappadata.wrappers import ModeWrapper
from functools import partial
import einops
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import dict_to_string
class OfflineLagrangianRolloutMeshLossCallback(PeriodicCallback... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/offline_callbacks/offline_cfd_rollout_mesh_loss_callback.py | src/callbacks/offline_callbacks/offline_cfd_rollout_mesh_loss_callback.py | from torch_geometric.utils import scatter
from kappadata.wrappers import ModeWrapper
from functools import partial
import einops
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import dict_to_string
class OfflineCfdRolloutMeshLossCallback(PeriodicCallback):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/checkpoint_callbacks/recover_from_latest_checkpoint_callback.py | src/callbacks/checkpoint_callbacks/recover_from_latest_checkpoint_callback.py | from collections import defaultdict
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.config import is_rank0
from utils.select_with_path import select_with_path
from initializers.resume_initializer import ResumeInitializer
from callbacks.checkpoint_callbacks.checkpoint_callba... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/checkpoint_callbacks/ema_callback.py | src/callbacks/checkpoint_callbacks/ema_callback.py | from collections import defaultdict
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.config import is_rank0
from utils.select_with_path import select_with_path
class EmaCallback(PeriodicCallback):
def __init__(self, target_factors, model_paths=None, **kwargs):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/checkpoint_callbacks/checkpoint_callback.py | src/callbacks/checkpoint_callbacks/checkpoint_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import short_number_str
from utils.model_utils import get_trainable_param_count, get_frozen_param_count
class CheckpointCallback(PeriodicCallback):
def __init__(
self,
save_weights=True,
sa... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/checkpoint_callbacks/__init__.py | src/callbacks/checkpoint_callbacks/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/checkpoint_callbacks/best_checkpoint_callback.py | src/callbacks/checkpoint_callbacks/best_checkpoint_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
from utils.infer_higher_is_better import higher_is_better_from_metric_key
class BestCheckpointCallback(PeriodicCallback):
def __init__(
self,
metric_key,
save_frozen_weights=True,
save_optim=False,
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/online_callbacks/num_supernodes_callback.py | src/callbacks/online_callbacks/num_supernodes_callback.py | from collections import defaultdict
import numpy as np
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.gather import all_reduce_mean_grad
class NumSupernodesCallback(PeriodicCallback):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.is_fi... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/online_callbacks/update_output_callback.py | src/callbacks/online_callbacks/update_output_callback.py | from collections import defaultdict
import numpy as np
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.gather import all_reduce_mean_grad, all_gather_nograd
class UpdateOutputCallback(PeriodicCallback):
def __init__(
self,
keys=None,
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/online_callbacks/__init__.py | src/callbacks/online_callbacks/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/base/callback_base.py | src/callbacks/base/callback_base.py | import logging
from collections import defaultdict
import kappaprofiler as kp
import torch
from distributed.gather import all_gather_nograd
from providers.config_providers.base.config_provider_base import ConfigProviderBase
from providers.config_providers.noop_config_provider import NoopConfigProvider
from providers.... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/base/periodic_callback.py | src/callbacks/base/periodic_callback.py | import math
import kappaprofiler as kp
import numpy as np
import torch
from kappadata.samplers import InterleavedSamplerConfig
from kappadata.wrappers import ModeWrapper
from torch.utils.data import SequentialSampler, DistributedSampler
from tqdm import tqdm
from distributed.config import is_distributed, is_managed, ... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/base/__init__.py | src/callbacks/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/callbacks/base/writers/log_writer.py | src/callbacks/base/writers/log_writer.py | import logging
from collections import defaultdict
from contextlib import contextmanager
import torch
import wandb
import yaml
from distributed.config import is_rank0
from providers.path_provider import PathProvider
from utils.update_counter import UpdateCounter
class LogWriter:
def __init__(self, path_provider... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/base/writers/__init__.py | src/callbacks/base/writers/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/base/writers/checkpoint_writer.py | src/callbacks/base/writers/checkpoint_writer.py | import logging
import torch
import yaml
from torch.nn.parallel import DistributedDataParallel
from distributed.config import is_rank0
from models.base.composite_model_base import CompositeModelBase
from models.base.single_model_base import SingleModelBase
from providers.path_provider import PathProvider
from utils.ch... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/retroactive_callbacks/__init__.py | src/callbacks/retroactive_callbacks/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/visualization/__init__.py | src/callbacks/visualization/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/gradient_monitor_callback.py | src/callbacks/monitor_callbacks/gradient_monitor_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
class GradientMonitorCallback(PeriodicCallback):
def before_every_optim_step(self, model, **kwargs):
for name, param in model.named_parameters():
if not param.requires_grad:
continue
grad_norm = param.gra... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/kill_on_loss_spike_callback.py | src/callbacks/monitor_callbacks/kill_on_loss_spike_callback.py | from collections import defaultdict
import torch
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.config import is_rank0, get_rank
from utils.select_with_path import select_with_path
from initializers.resume_initializer import ResumeInitializer
from callbacks.checkpoint_callbacks.checkpo... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/activation_monitor_callback.py | src/callbacks/monitor_callbacks/activation_monitor_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
from utils.factory import create_collection
from models.extractors import extractor_from_kwargs
from models.extractors.generic_extractor import GenericExtractor
class ActivationMonitorCallback(PeriodicCallback):
def __init__(self, model_paths, **kwargs)... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/weight_monitor_callback.py | src/callbacks/monitor_callbacks/weight_monitor_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
class WeightMonitorCallback(PeriodicCallback):
def _track_after_update_step(self, model, **kwargs):
for name, param in model.named_parameters():
if not param.requires_grad:
continue
param_norm = param.nor... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/gradient_spike_monitor_callback.py | src/callbacks/monitor_callbacks/gradient_spike_monitor_callback.py | from collections import defaultdict, deque
import torch
from callbacks.base.callback_base import CallbackBase
class GradientSpikeMonitorCallback(CallbackBase):
def __init__(self, verbose=False, **kwargs):
super().__init__(**kwargs)
self.verbose = verbose
def _before_training(self, model, **k... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/skip_loss_spikes_callback.py | src/callbacks/monitor_callbacks/skip_loss_spikes_callback.py | import torch
from callbacks.base.periodic_callback import PeriodicCallback
from collections import deque
class SkipLossSpikesCallback(PeriodicCallback):
def __init__(self, max_skipped_updates_in_a_row=100, queue_size=50, tolerance_factor=0.2, **kwargs):
super().__init__(**kwargs)
self.max_skipped_u... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/skip_nan_loss_callback.py | src/callbacks/monitor_callbacks/skip_nan_loss_callback.py | import torch
from callbacks.base.periodic_callback import PeriodicCallback
from collections import deque
class SkipNanLossCallback(PeriodicCallback):
def __init__(self, max_skipped_updates_in_a_row=50, **kwargs):
super().__init__(**kwargs)
self.max_skipped_updates_in_a_row = max_skipped_updates_in_... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/nan_monitor_callback.py | src/callbacks/monitor_callbacks/nan_monitor_callback.py | import torch
from callbacks.base.callback_base import CallbackBase
class NanMonitorCallback(CallbackBase):
def __init__(self, verbose=False, **kwargs):
super().__init__(**kwargs)
self.verbose = verbose
def _before_training(self, model, **kwargs):
for name, module in model.named_modul... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/__init__.py | src/callbacks/monitor_callbacks/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/monitor_callbacks/debug_monitor_callback.py | src/callbacks/monitor_callbacks/debug_monitor_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
class DebugMonitorCallback(PeriodicCallback):
def __init__(self, verbose=False, **kwargs):
super().__init__(**kwargs)
self.verbose = verbose
def before_every_update(self, model, **kwargs):
for name, param in model.named_par... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/copy_previous_config_callback.py | src/callbacks/default_callbacks/copy_previous_config_callback.py | from callbacks.base.callback_base import CallbackBase
from initializers.previous_run_initializer import PreviousRunInitializer
from models.base.composite_model_base import CompositeModelBase
from utils.model_utils import get_named_models
class CopyPreviousConfigCallback(CallbackBase):
@staticmethod
def _shoul... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/copy_previous_summary_callback.py | src/callbacks/default_callbacks/copy_previous_summary_callback.py | from callbacks.base.callback_base import CallbackBase
from initializers.previous_run_initializer import PreviousRunInitializer
from models.base.composite_model_base import CompositeModelBase
from utils.model_utils import get_named_models
class CopyPreviousSummaryCallback(CallbackBase):
@staticmethod
def _shou... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/eta_callback.py | src/callbacks/default_callbacks/eta_callback.py | import logging
from datetime import datetime, timedelta
import numpy as np
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.config import is_rank0
from utils.formatting_util import short_number_str, seconds_to_duration_str
class EtaCallback(PeriodicCallback):
class LoggerWasCalledH... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/online_loss_callback.py | src/callbacks/default_callbacks/online_loss_callback.py | from collections import defaultdict
import numpy as np
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.gather import all_reduce_mean_grad
class OnlineLossCallback(PeriodicCallback):
def __init__(self, verbose=False, **kwargs):
super().__init__(**kwargs)
self.verbos... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/dataset_stats_callback.py | src/callbacks/default_callbacks/dataset_stats_callback.py | import torch
from kappadata.utils.class_counts import get_class_counts
from kappadata.wrappers import ModeWrapper, LabelSmoothingWrapper
from callbacks.base.callback_base import CallbackBase
class DatasetStatsCallback(CallbackBase):
def _before_training(self, **_):
for dataset_key, dataset in self.data_c... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/train_time_callback.py | src/callbacks/default_callbacks/train_time_callback.py | import numpy as np
from callbacks.base.periodic_callback import PeriodicCallback
from distributed.gather import all_gather_nograd
from utils.formatting_util import list_to_string
class TrainTimeCallback(PeriodicCallback):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.train_data_ti... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/copy_previous_entries_callback.py | src/callbacks/default_callbacks/copy_previous_entries_callback.py | import yaml
from callbacks.base.callback_base import CallbackBase
from initializers.previous_run_initializer import PreviousRunInitializer
from models.base.composite_model_base import CompositeModelBase
from utils.checkpoint import Checkpoint
from utils.model_utils import get_named_models
class CopyPreviousEntriesCa... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/lr_callback.py | src/callbacks/default_callbacks/lr_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
from models.base.composite_model_base import CompositeModelBase
from optimizers.interleaved_optimizer import InterleavedOptimizer
from utils.model_utils import get_named_models
class LrCallback(PeriodicCallback):
def should_log_after_update(self, check... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/progress_callback.py | src/callbacks/default_callbacks/progress_callback.py | from datetime import datetime
from callbacks.base.periodic_callback import PeriodicCallback
from utils.formatting_util import seconds_to_duration_str
class ProgressCallback(PeriodicCallback):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._start_time = None
se... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/__init__.py | src/callbacks/default_callbacks/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/freezer_callback.py | src/callbacks/default_callbacks/freezer_callback.py | from callbacks.base.periodic_callback import PeriodicCallback
from models.base.composite_model_base import CompositeModelBase
from utils.model_utils import get_named_models
class FreezerCallback(PeriodicCallback):
def should_log_after_update(self, checkpoint):
if checkpoint.update == 1:
return... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/callbacks/default_callbacks/param_count_callback.py | src/callbacks/default_callbacks/param_count_callback.py | import numpy as np
from callbacks.base.callback_base import CallbackBase
from models.base.composite_model_base import CompositeModelBase
from utils.model_utils import get_trainable_param_count, get_frozen_param_count
from utils.naming_util import join_names, snake_type_name
class ParamCountCallback(CallbackBase):
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/dataset_config_provider.py | src/providers/dataset_config_provider.py | import platform
from pathlib import Path
class DatasetConfigProvider:
def __init__(
self,
global_dataset_paths,
local_dataset_path=None,
data_source_modes=None,
):
self.global_dataset_paths = global_dataset_paths
self.local_dataset_path = local_d... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/__init__.py | src/providers/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/path_provider.py | src/providers/path_provider.py | from pathlib import Path
class PathProvider:
def __init__(
self,
output_path: Path,
model_path: Path,
stage_name: str,
stage_id: str,
temp_path: Path = None,
):
self.output_path = output_path
self.model_path = model_path
... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/config_providers/wandb_config_provider.py | src/providers/config_providers/wandb_config_provider.py | import wandb
from .base.config_provider_base import ConfigProviderBase
from .primitive_config_provider import PrimitiveConfigProvider
from ..path_provider import PathProvider
class WandbConfigProvider(ConfigProviderBase):
def __init__(self, path_provider: PathProvider):
super().__init__()
self.pr... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/config_providers/primitive_config_provider.py | src/providers/config_providers/primitive_config_provider.py | import yaml
from .base.config_provider_base import ConfigProviderBase
from ..path_provider import PathProvider
class PrimitiveConfigProvider(ConfigProviderBase):
def __init__(self, path_provider: PathProvider):
super().__init__()
self.path_provider = path_provider
self.config = {}
de... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/config_providers/__init__.py | src/providers/config_providers/__init__.py | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false | |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/config_providers/noop_config_provider.py | src/providers/config_providers/noop_config_provider.py | from .base.config_provider_base import ConfigProviderBase
class NoopConfigProvider(ConfigProviderBase):
def update(self, *args, **kwargs):
pass
def __setitem__(self, key, value):
pass
def __contains__(self, key):
return False
def get_config_of_previous_stage(self, stage_name... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/config_providers/base/config_provider_base.py | src/providers/config_providers/base/config_provider_base.py | class ConfigProviderBase:
def update(self, *args, **kwargs):
raise NotImplementedError
def __setitem__(self, key, value):
raise NotImplementedError
def __contains__(self, key):
raise NotImplementedError
def get_config_of_previous_stage(self, stage_name, stage_id):
rais... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
ml-jku/UPT | https://github.com/ml-jku/UPT/blob/f148ef187973ef4958e8a5324c6692dd2582ad97/src/providers/config_providers/base/__init__.py | src/providers/config_providers/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/providers/summary_providers/wandb_summary_provider.py | src/providers/summary_providers/wandb_summary_provider.py | import wandb
from .base.summary_provider_base import SummaryProviderBase
from .primitive_summary_provider import PrimitiveSummaryProvider
from ..path_provider import PathProvider
class WandbSummaryProvider(SummaryProviderBase):
def __init__(self, path_provider: PathProvider):
super().__init__()
s... | python | MIT | f148ef187973ef4958e8a5324c6692dd2582ad97 | 2026-01-05T07:12:15.158856Z | false |
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