repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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pmb-nll | pmb-nll-main/src/core/fastmurty/previous python implementation/murtysplitSimple.py | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
last mod 3/14/19
These functions reorder rows and columns before creating subproblems.
The goal is to set it up so the first subproblem fixes everything
but the first non-missing row.
One row and column is unfixed (w/ match or miss eliminated) every new problem.
"""
i... | 1,533 | 34.674419 | 78 | py |
pmb-nll | pmb-nll-main/src/core/fastmurty/previous python implementation/example_3frame.py | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
last mod 3/11/19
"""
import numpy as np
from time import time
#from daSparse import da, allocateWorkVarsforDA
#from sparsity import sparsify
from daDense import da, allocateWorkVarsforDA
from sspDense import SSP # used for evaluation
ntests = 10
max_ns = 1000
max_n... | 13,587 | 40.05136 | 95 | py |
pmb-nll | pmb-nll-main/src/core/fastmurty/previous python implementation/murtysplitLookaheadSparse.py | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
last mod 3/14/19
These functions reorder rows and columns before creating subproblems.
The goal is to set it up so the first subproblem fixes everything
but the first non-missing row.
One row and column is unfixed (w/ match or miss eliminated) every new problem.
"""
i... | 5,911 | 35.95 | 85 | py |
pmb-nll | pmb-nll-main/src/core/fastmurty/previous python implementation/example_2frame.py | # -*- coding: utf-8 -*-
"""
Runs single-input K-best associations algorithm on square random matrices.
This test is meant to be directly comparable to the test code included with
Miller+Stone+Cox's implementation of data association.
"""
import numpy as np
from time import time
from daSparse import da, allocateWorkVar... | 1,582 | 34.177778 | 76 | py |
pmb-nll | pmb-nll-main/src/core/fastmurty/previous python implementation/sspDense.py | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
The Jonker-Volgenant algorithm for finding the maximum assignment.
Michael Motro, University of Texas at Austin
last modified 10/23/2018
This is a direct adaptation of the Pascal code from
"A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment P... | 10,323 | 31.465409 | 88 | py |
pmb-nll | pmb-nll-main/src/core/datasets/metadata.py | from collections import ChainMap
# Detectron imports
from detectron2.data import MetadataCatalog
# Useful Dicts for OpenImages Conversion
OPEN_IMAGES_TO_COCO = {'Person': 'person',
'Bicycle': 'bicycle',
'Car': 'car',
'Motorcycle': 'motorcycle',
... | 5,503 | 39.77037 | 114 | py |
pmb-nll | pmb-nll-main/src/core/datasets/convert_openimages_to_coco.py | import argparse
import csv
import cv2
import json
import os
from tqdm import tqdm
# Project imports
import core.datasets.metadata as metadata
def main(args):
dataset_dir = args.dataset_dir
if args.output_dir is None:
output_dir = os.path.expanduser(
os.path.join(dataset_dir, 'COCO-Forma... | 10,246 | 45.789954 | 123 | py |
pmb-nll | pmb-nll-main/src/core/datasets/convert_openimages_odd_to_coco.py | import argparse
import csv
import cv2
import json
import os
from tqdm import tqdm
def main(args):
dataset_dir = args.dataset_dir
if args.output_dir is None:
output_dir = os.path.expanduser(
os.path.join(dataset_dir, 'COCO-Format'))
else:
output_dir = os.path.expanduser(args.o... | 8,433 | 46.117318 | 123 | py |
pmb-nll | pmb-nll-main/src/core/datasets/generate_coco_corrupted_dataset.py | import argparse
import contextlib
import cv2
import joblib
import numpy as np
import os
import random
from joblib import Parallel, delayed
from multiprocessing import Manager, cpu_count
from time import sleep
from tqdm import tqdm
# Project imports
from probabilistic_inference.inference_utils import corrupt
# Fix r... | 4,086 | 27.58042 | 120 | py |
pmb-nll | pmb-nll-main/src/core/datasets/convert_voc_to_coco.py | import argparse
import cv2
import json
import numpy as np
import os
from pascal_voc_tools import XmlParser
def create_coco_lists(ids_list, image_dir, annotations_dir, category_mapper):
"""
Creates lists in coco format to be written to JSON file.
"""
parser = XmlParser()
images_list = []
anno... | 7,734 | 37.869347 | 95 | py |
pmb-nll | pmb-nll-main/src/core/datasets/__init__.py | 0 | 0 | 0 | py | |
pmb-nll | pmb-nll-main/src/core/datasets/setup_datasets.py | import os
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.data.datasets import register_coco_instances
# Project imports
import core.datasets.metadata as metadata
def setup_all_datasets(dataset_dir, image_root_corruption_prefix=None):
"""
Registers all datasets as instances f... | 3,624 | 32.256881 | 147 | py |
pmb-nll | pmb-nll-main/src/core/evaluation_tools/scoring_rules.py | import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def sigmoid_compute_cls_scores(input_matches, valid_idxs):
"""
Computes proper scoring rule for multilabel classification results provided by retinanet.
Args:
input_matches (dict): dictionary containing input matc... | 9,442 | 37.542857 | 117 | py |
pmb-nll | pmb-nll-main/src/core/evaluation_tools/evaluation_utils.py | import json
import os
from collections import defaultdict
import numpy as np
import torch
import tqdm
# Project imports
from core.datasets import metadata
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.structures import Boxes, Instances, pairwise_iou
device = torch.device("cuda" if ... | 37,914 | 39.079281 | 126 | py |
pmb-nll | pmb-nll-main/src/core/evaluation_tools/__init__.py | 0 | 0 | 0 | py | |
pmb-nll | pmb-nll-main/src/core/visualization_tools/results_processing_tools.py | import glob
import itertools
import numpy as np
import os
import pickle
import torch
from collections import defaultdict
# Project imports
from core.setup import setup_config, setup_arg_parser
from probabilistic_inference.inference_utils import get_inference_output_dir
def get_clean_results_dict(config_names,
... | 30,031 | 53.703097 | 161 | py |
pmb-nll | pmb-nll-main/src/core/visualization_tools/probabilistic_visualizer.py | import matplotlib as mpl
import numpy as np
from detectron2.utils.colormap import random_color
from detectron2.utils.visualizer import _SMALL_OBJECT_AREA_THRESH, ColorMode, Visualizer
from scipy.stats import chi2, norm
class ProbabilisticVisualizer(Visualizer):
"""
Extends detectron2 Visualizer to draw corner... | 13,659 | 36.734807 | 98 | py |
pmb-nll | pmb-nll-main/src/core/visualization_tools/__init__.py | 0 | 0 | 0 | py | |
pmb-nll | pmb-nll-main/src/probabilistic_inference/image_corruptions.py | """
Code for image corruption based on: https://github.com/hendrycks/robustness/tree/master/ImageNet-C/imagenet_c
Code is modified by authors of this paper to support arbitrary image sizes.
"""
import ctypes
import cv2
import numpy as np
import skimage as sk
from io import BytesIO
from PIL import Image as PILImage
fro... | 16,125 | 30.55773 | 109 | py |
pmb-nll | pmb-nll-main/src/probabilistic_inference/probabilistic_retinanet_predictor.py | import numpy as np
import torch
import math
# Detectron Imports
from detectron2.layers import batched_nms, cat
from detectron2.structures import Boxes, Instances, pairwise_iou
# Project Imports
from probabilistic_inference import inference_utils
from probabilistic_inference.inference_core import ProbabilisticPredicto... | 23,910 | 44.894434 | 152 | py |
pmb-nll | pmb-nll-main/src/probabilistic_inference/probabilistic_rcnn_predictor.py | import numpy as np
import torch
# Detectron Imports
from detectron2.layers import batched_nms
from detectron2.structures import Boxes, Instances, pairwise_iou
# Project Imports
from probabilistic_inference import inference_utils
from probabilistic_inference.inference_core import ProbabilisticPredictor
from probabili... | 20,442 | 43.733042 | 150 | py |
pmb-nll | pmb-nll-main/src/probabilistic_inference/inference_utils.py | import os
import numpy as np
import torch
from detectron2.layers import batched_nms
# Detectron imports
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.structures import Boxes, BoxMode, Instances, pairwise_iou
from PIL import Image
# Project imports
from probabilistic_inference.image_... | 27,584 | 36.995868 | 120 | py |
pmb-nll | pmb-nll-main/src/probabilistic_inference/__init__.py | 0 | 0 | 0 | py | |
pmb-nll | pmb-nll-main/src/probabilistic_inference/inference_core.py | import cv2
import os
from abc import ABC, abstractmethod
# Detectron Imports
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.modeling import build_model
from core.visualization_tools.probabilistic_visualizer import ProbabilisticVisualizer
# Project Imports
from probabilistic_inference import ... | 9,017 | 35.959016 | 166 | py |
pmb-nll | pmb-nll-main/src/probabilistic_inference/probabilistic_detr_predictor.py | import numpy as np
import torch
import torch.nn.functional as F
# DETR imports
from detr.util.box_ops import box_cxcywh_to_xyxy
# Detectron Imports
from detectron2.structures import Boxes
# Project Imports
from probabilistic_inference import inference_utils
from probabilistic_inference.inference_core import Probabi... | 9,040 | 40.095455 | 119 | py |
pmb-nll | pmb-nll-main/src/probabilistic_modeling/losses.py | from collections import defaultdict
from math import comb
from math import factorial
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from core.fastmurty.mhtdaClink import (allocateWorkvarsforDA,
deallocateWorkvarsforDA, mhtda, sparse)
from co... | 21,481 | 37.846293 | 152 | py |
pmb-nll | pmb-nll-main/src/probabilistic_modeling/probabilistic_retinanet.py | import logging
import math
from typing import List, Tuple
import numpy as np
import torch
from core.visualization_tools.probabilistic_visualizer import ProbabilisticVisualizer
from detectron2.data.detection_utils import convert_image_to_rgb
# Detectron Imports
from detectron2.layers import ShapeSpec, batched_nms, cat... | 58,037 | 39.164706 | 127 | py |
pmb-nll | pmb-nll-main/src/probabilistic_modeling/modeling_utils.py | import copy
import math
import torch
from sklearn.mixture._gaussian_mixture import _compute_precision_cholesky
from torch import nn
from torch.distributions import Distribution
from torch.distributions.categorical import Categorical
from torch.distributions.independent import Independent
from torch.distributions.lapla... | 24,254 | 36.488408 | 242 | py |
pmb-nll | pmb-nll-main/src/probabilistic_modeling/__init__.py | 0 | 0 | 0 | py | |
pmb-nll | pmb-nll-main/src/probabilistic_modeling/probabilistic_generalized_rcnn.py | import logging
from typing import Dict, List, Optional, Tuple, Union
# Detectron imports
import fvcore.nn.weight_init as weight_init
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.data.detection_utils import convert_image_to_rgb
from detectron2.layers import Conv2d, Linear, ... | 66,644 | 40.523364 | 159 | py |
pmb-nll | pmb-nll-main/src/probabilistic_modeling/probabilistic_detr.py | import numpy as np
import torch
import torch.nn.functional as F
# Detectron imports
from detectron2.modeling import META_ARCH_REGISTRY, detector_postprocess
from detectron2.utils.events import get_event_storage
# Detr imports
from models.detr import DETR, MLP, SetCriterion
from torch import distributions, nn
from torch... | 31,909 | 38.541512 | 135 | py |
pmb-nll | pmb-nll-main/src/detr/main.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import datetime
import json
import random
import time
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import datasets
import util.misc as utils
from datasets impo... | 11,532 | 45.317269 | 116 | py |
pmb-nll | pmb-nll-main/src/detr/engine.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Train and eval functions used in main.py
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
... | 6,626 | 42.598684 | 103 | py |
pmb-nll | pmb-nll-main/src/detr/hubconf.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer imp... | 6,265 | 36.076923 | 117 | py |
pmb-nll | pmb-nll-main/src/detr/run_with_submitit.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
A script to run multinode training with submitit.
"""
import argparse
import os
import uuid
from pathlib import Path
import main as detection
import submitit
def parse_args():
detection_parser = detection.get_args_parser()
parser = ar... | 3,476 | 30.044643 | 102 | py |
pmb-nll | pmb-nll-main/src/detr/test_all.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import io
import unittest
import torch
from torch import nn, Tensor
from typing import List
from models.matcher import HungarianMatcher
from models.position_encoding import PositionEmbeddingSine, PositionEmbeddingLearned
from models.backbone impor... | 8,804 | 40.928571 | 119 | py |
pmb-nll | pmb-nll-main/src/detr/models/detr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR model and criterion classes.
"""
import torch
import torch.nn.functional as F
from torch import nn
from util import box_ops
from util.misc import (NestedTensor, nested_tensor_from_tensor_list,
accuracy, get_world_siz... | 17,088 | 46.469444 | 113 | py |
pmb-nll | pmb-nll-main/src/detr/models/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
from scipy.optimize import linear_sum_assignment
from torch import nn
from util.box_ops import box_cxcywh_to_xyxy, generalized_box_iou
class HungarianMatc... | 4,250 | 47.862069 | 119 | py |
pmb-nll | pmb-nll-main/src/detr/models/segmentation.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This file provides the definition of the convolutional heads used to predict masks, as well as the losses
"""
import io
from collections import defaultdict
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.fun... | 15,573 | 41.785714 | 120 | py |
pmb-nll | pmb-nll-main/src/detr/models/position_encoding.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
from util.misc import NestedTensor
class PositionEmbeddingSine(nn.Module):
"""
This is a more standard version of the position embeddi... | 3,336 | 36.077778 | 103 | py |
pmb-nll | pmb-nll-main/src/detr/models/backbone.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Backbone modules.
"""
from collections import OrderedDict
import torch
import torch.nn.functional as F
import torchvision
from torch import nn
from torchvision.models._utils import IntermediateLayerGetter
from typing import Dict, List
from uti... | 4,437 | 35.983333 | 113 | py |
pmb-nll | pmb-nll-main/src/detr/models/transformer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR Transformer class.
Copy-paste from torch.nn.Transformer with modifications:
* positional encodings are passed in MHattention
* extra LN at the end of encoder is removed
* decoder returns a stack of activations from all decoding... | 12,162 | 39.815436 | 98 | py |
pmb-nll | pmb-nll-main/src/detr/models/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from .detr import build
def build_model(args):
return build(args)
| 143 | 19.571429 | 70 | py |
pmb-nll | pmb-nll-main/src/detr/d2/converter.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Helper script to convert models trained with the main version of DETR to be used with the Detectron2 version.
"""
import json
import argparse
import numpy as np
import torch
def parse_args():
parser = argparse.ArgumentParser("D2 model con... | 2,590 | 36.014286 | 114 | py |
pmb-nll | pmb-nll-main/src/detr/d2/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
DETR Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
import os
import sys
import itertools
# fmt: off
sys.path.insert(1, os.path.join(sys.path[0], '..'))
# fmt: on
import time
from typing i... | 4,999 | 33.246575 | 115 | py |
pmb-nll | pmb-nll-main/src/detr/d2/detr/detr.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import math
from typing import List
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from scipy.optimize import linear_sum_assignment
from torch import nn
from detectron2.layers import... | 11,143 | 41.534351 | 118 | py |
pmb-nll | pmb-nll-main/src/detr/d2/detr/dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.transforms import TransformGen
__all__ = ["DetrDatasetMapper"]
def ... | 4,570 | 36.162602 | 111 | py |
pmb-nll | pmb-nll-main/src/detr/d2/detr/config.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from detectron2.config import CfgNode as CN
def add_detr_config(cfg):
"""
Add config for DETR.
"""
cfg.MODEL.DETR = CN()
cfg.MODEL.DETR.NUM_CLASSES = 80
# For Segmentation
cfg.MODEL.DETR.FROZEN_... | 888 | 24.4 | 70 | py |
pmb-nll | pmb-nll-main/src/detr/d2/detr/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from .config import add_detr_config
from .detr import Detr
from .dataset_mapper import DetrDatasetMapper
| 176 | 34.4 | 70 | py |
pmb-nll | pmb-nll-main/src/detr/util/plot_utils.py | """
Plotting utilities to visualize training logs.
"""
import torch
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from pathlib import Path, PurePath
def plot_logs(logs, fields=('class_error', 'loss_bbox_unscaled', 'mAP'), ewm_col=0, log_name='log.txt'):
'''
Func... | 4,514 | 40.805556 | 120 | py |
pmb-nll | pmb-nll-main/src/detr/util/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from typing import Optional, List... | 15,304 | 31.702991 | 116 | py |
pmb-nll | pmb-nll-main/src/detr/util/box_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
(x_c + 0.5 * w), (y_... | 2,561 | 27.786517 | 110 | py |
pmb-nll | pmb-nll-main/src/detr/util/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
| 71 | 35 | 70 | py |
pmb-nll | pmb-nll-main/src/detr/datasets/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch.utils.data
import torchvision
from .coco import build as build_coco
def get_coco_api_from_dataset(dataset):
for _ in range(10):
# if isinstance(dataset, torchvision.datasets.CocoDetection):
# break
if ... | 897 | 33.538462 | 70 | py |
pmb-nll | pmb-nll-main/src/detr/datasets/coco_eval.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO evaluator that works in distributed mode.
Mostly copy-paste from https://github.com/pytorch/vision/blob/edfd5a7/references/detection/coco_eval.py
The difference is that there is less copy-pasting from pycocotools
in the end of the file, as... | 8,735 | 32.860465 | 103 | py |
pmb-nll | pmb-nll-main/src/detr/datasets/coco_panoptic.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from panopticapi.utils import rgb2id
from util.box_ops import masks_to_boxes
from .coco import make_coco_transforms
class CocoPanoptic:
def __init__(... | 3,723 | 36.24 | 111 | py |
pmb-nll | pmb-nll-main/src/detr/datasets/coco.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
from pathlib import Path
import torch
import torch.utils.data
import torchvision
f... | 5,253 | 32.044025 | 118 | py |
pmb-nll | pmb-nll-main/src/detr/datasets/panoptic_eval.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
import os
import util.misc as utils
try:
from panopticapi.evaluation import pq_compute
except ImportError:
pass
class PanopticEvaluator(object):
def __init__(self, ann_file, ann_folder, output_dir="panoptic_eval"):
... | 1,493 | 32.2 | 116 | py |
pmb-nll | pmb-nll-main/src/detr/datasets/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Transforms and data augmentation for both image + bbox.
"""
import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc impor... | 8,524 | 29.776173 | 104 | py |
pmb-nll | pmb-nll-main/src/offline_evaluation/compute_probabilistic_metrics.py | import json
import os
import pickle
from collections import defaultdict
import numpy as np
import torch
import torch.distributions as distributions
import tqdm
# Project imports
from core.evaluation_tools import evaluation_utils, scoring_rules
from core.evaluation_tools.evaluation_utils import (
calculate_iou,
... | 44,542 | 40.785178 | 149 | py |
pmb-nll | pmb-nll-main/src/offline_evaluation/compute_average_precision.py | import os
import numpy as np
# Project imports
from core.setup import setup_arg_parser, setup_config
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
from probabilistic_inference.inference_utils import get_inference_output_dir
# Coco evaluator tools
from pycocotoo... | 3,798 | 31.194915 | 87 | py |
pmb-nll | pmb-nll-main/src/offline_evaluation/compute_ood_probabilistic_metrics.py | import itertools
import os
import torch
import ujson as json
import pickle
from prettytable import PrettyTable
# Detectron imports
from detectron2.engine import launch
# Project imports
from core.evaluation_tools import scoring_rules
from core.evaluation_tools.evaluation_utils import eval_predictions_preprocess
from... | 7,146 | 38.486188 | 116 | py |
pmb-nll | pmb-nll-main/src/offline_evaluation/compute_calibration_errors.py | import calibration as cal
import os
import pickle
import torch
from prettytable import PrettyTable
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.evaluation_tools import evaluation_utils
from core.evaluation_tools.evaluation_utils impo... | 14,295 | 45.718954 | 116 | py |
pmb-nll | pmb-nll-main/src/offline_evaluation/__init__.py | 0 | 0 | 0 | py | |
pmb-nll | pmb-nll-main/src/offline_evaluation/average_metrics_over_iou_thresholds.py | import numpy as np
import os
import pickle
from prettytable import PrettyTable
# Detectron imports
from detectron2.engine import launch
# Project imports
from core.setup import setup_config, setup_arg_parser
from offline_evaluation import compute_probabilistic_metrics, compute_calibration_errors
from probabilistic_i... | 8,797 | 41.095694 | 124 | py |
pmb-nll | pmb-nll-main/visualization/visualize_errors.py | import cv2
import numpy as np
import os
import ujson as json
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.setup import setup_config, setup_arg_parser
from core.evaluation_tools import evaluation_utils
from core.visualization_tools.pro... | 13,513 | 36.643454 | 120 | py |
pmb-nll | pmb-nll-main/visualization/visualize_predictions.py | import cv2
import numpy as np
import os
import ujson as json
from scipy.stats import entropy
from matplotlib import cm
# Detectron imports
from detectron2.data import MetadataCatalog
from detectron2.engine import launch
# Project imports
from core.setup import setup_config, setup_arg_parser
from core.evaluation_too... | 5,668 | 34.879747 | 131 | py |
FDS | FDS-main/main.py | """
This is the base code to learn the learning rate, momentum and weight decay
non-greedily with forward mode differentiation, over long horizons (e.g. CIFAR10)
"""
import os
import time
import shutil
import torch
import torch.optim as optim
import pickle
from utils.logger import *
from utils.helpers import *
from u... | 37,988 | 54.866176 | 243 | py |
FDS | FDS-main/theorem4_checker_simple.py | """
This is to check that Theorem 4.1 holds in the case where all the cross term of the covariance matrix are zero, i.e.
each hypergradient is independant of all other hypergradients. We also use a constant variance=sigma^2 for all steps
"""
import numpy as np
import random
from utils.helpers import *
class ProofChe... | 5,039 | 45.666667 | 163 | py |
FDS | FDS-main/theorem4_checker_advanced.py | """
This is to check that Theorem 4.1 holds in the case where each step has its own variance,
and where all steps are correlated with one another
"""
import numpy as np
import random
from sklearn.datasets import make_spd_matrix
from utils.helpers import *
class ProofChecker(object):
def __init__(self, args):
... | 7,505 | 52.234043 | 176 | py |
FDS | FDS-main/figure2_hypergradients_fluctuation.py | """
Here we measure hypergradients for several runs when perturbing
the training data and weight initialization. This must be done on toy
datasets where reverse-mode differentiation is tractable. This corresponds
to figure 2 in the paper.
"""
import torch.optim as optim
import pickle
import os
import warnings
impo... | 9,738 | 49.201031 | 178 | py |
FDS | FDS-main/models/wresnet.py | """
Base architecture taken from https://github.com/xternalz/WideResNet-pytorch
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.meta_factory import ReparamModule
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate):
super(BasicBlock, ... | 5,745 | 38.627586 | 116 | py |
FDS | FDS-main/models/lenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from models.meta_factory import ReparamModule
from models.helpers import *
class Flatten(nn.Module):
"""
NN module version of torch.nn.functional.flatten
"""
def __init__(self):
super().__init__()
def forward(self, input):
... | 2,829 | 25.203704 | 75 | py |
FDS | FDS-main/models/meta_factory.py | """
This is a slim version of the code from https://github.com/SsnL/dataset-distillation
"""
import torch
import torchvision
import logging
import torch.nn as nn
import torch.nn.functional as F
import functools
import math
import types
from contextlib import contextmanager
from torch.optim import lr_scheduler
from si... | 8,482 | 35.722944 | 127 | py |
FDS | FDS-main/models/helpers.py | import torch.nn as nn
from torch.nn import init
def initialize(net, init_type, init_param, init_norm_weights=1):
""" various initialization schemes """
def init_func(m):
classname = m.__class__.__name__
if classname.startswith('Conv') or classname == 'Linear':
if getattr(m, 'bias'... | 1,798 | 43.975 | 106 | py |
FDS | FDS-main/models/selector.py | from models.lenet import *
from models.wresnet import *
def select_model(meta,
dataset,
architecture,
init_type='xavier',
init_param=1,
device='cpu'):
"""
Meta models require device to be provided during init.
"""
if ... | 6,359 | 45.764706 | 150 | py |
FDS | FDS-main/utils/logger.py | import csv
import os
class Logger:
def __init__(self, filepath='./', filename='results.csv'):
if not os.path.exists(filepath): os.makedirs(filepath)
self.csv_file_path = os.path.join(filepath, filename)
def write(self, data_dict):
"""warning: this allows for wrong keys to be passed"""
... | 1,079 | 29 | 109 | py |
FDS | FDS-main/utils/datasets.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
import os
import math
import numpy as np
import matplotlib.pyp... | 10,895 | 41.232558 | 155 | py |
FDS | FDS-main/utils/helpers.py | import csv
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.utils.data import Dataset, DataLoader
import shutil
import datetime
import json
import os
import argparse
import gc
import numpy as np
import torchvision
import functools
import time
import warnings
#warning... | 11,853 | 30.442971 | 153 | py |
corrupted_data_classification | corrupted_data_classification-main/main.py | # -*- coding: utf-8 -*-
'''
The following libraries are used:
[1] NIFTy – Numerical Information Field Theory, https://gitlab.mpcdf.mpg.de/ift/nifty
[2] NumPy - Numerical Python, https://numpy.org/
[3] Tensorflow - Tensorflow, https://www.tensorflow.org/
[4] Keras - Keras, https://keras.io/
[5] Matplotlib - Matplotli... | 18,972 | 42.71659 | 219 | py |
corrupted_data_classification | corrupted_data_classification-main/operators/multinomial_energy.py | # This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# bu... | 1,779 | 36.083333 | 87 | py |
corrupted_data_classification | corrupted_data_classification-main/operators/tensorflow_operator.py | # This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# bu... | 5,271 | 37.481752 | 89 | py |
corrupted_data_classification | corrupted_data_classification-main/helper_functions/helper_functions.py | import pandas as pd
import numpy as np
import math
import torch.optim as optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
import io
import cv2
import numpy as np
import matplotlib.pyplot as plt
impo... | 3,772 | 30.705882 | 120 | py |
corrupted_data_classification | corrupted_data_classification-main/helper_functions/Mask.py | import matplotlib.pyplot as plt
import numpy as np
import io
import cv2
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import random
from skimage.transform import resize
from scipy.special import binom
import nifty6 as ift
def no_mask(position_space):
mask = np.ones(position_space.shap... | 10,324 | 35.22807 | 116 | py |
corrupted_data_classification | corrupted_data_classification-main/helper_functions/Conv.py | '''
The Code for creating a 'convolution' [Conv.py] is mainly based on the following
GitHub Repository "Convolution as Matrix Multiplication":
https://github.com/alisaaalehi/convolution_as_multiplication
Author: Salehi, Ali, [https://github.com/alisaaalehi]
Date of last commit by author: 08. Jun 201... | 14,422 | 38.952909 | 114 | py |
corrupted_data_classification | corrupted_data_classification-main/NNs/Fashion-MNIST/pretrained_supervised_ae10/autoencoder_fmnist.py | # -*- coding: utf-8 -*-
# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
# Commented out IPython magic to ensure Python compatibility.
# Colab and system related
import os
import sys
###
# Necessary to convert tensorflow-object (e.g. Neural Network) to Nifty-Operator
sys.path.append('c... | 4,227 | 40.048544 | 119 | py |
corrupted_data_classification | corrupted_data_classification-main/NNs/MNIST/pretrained_supervised_ae10/autoencoder.py | # -*- coding: utf-8 -*-
# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
# Commented out IPython magic to ensure Python compatibility.
# Colab and system related
import os
import sys
###
# Necessary to convert tensorflow-object (e.g. Neural Network) to Nifty-Operator
sys.path.append('c... | 4,195 | 39.346154 | 113 | py |
mmyolo | mmyolo-main/setup.py | #!/usr/bin/env python
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import platform
import shutil
import sys
import warnings
from setuptools import find_packages, setup
from torch.utils.cpp_extension import BuildExtension
def readme():
with open('README.md', encoding='utf-8') as... | 6,862 | 34.744792 | 125 | py |
mmyolo | mmyolo-main/tools/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmdet.engine.hooks.utils import trigger_visualization_hook
from mmengine.config import Config, ConfigDict, DictAction
from mmengine.evaluator import DumpResults
from mmengine.runner import Runner
from mmyolo.registry ... | 5,443 | 35.05298 | 79 | py |
mmyolo | mmyolo-main/tools/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.runner import Runner
from mmyolo.registry import RUNNERS
from mmyolo.utils import is_metainfo_lower
def p... | 3,969 | 33.224138 | 79 | py |
mmyolo | mmyolo-main/tools/misc/download_dataset.py | import argparse
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Download datasets for training')
parser.add_argument(... | 3,814 | 32.761062 | 113 | py |
mmyolo | mmyolo-main/tools/misc/print_config.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from mmdet.utils import replace_cfg_vals, update_data_root
from mmengine import Config, DictAction
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file... | 1,796 | 28.95 | 78 | py |
mmyolo | mmyolo-main/tools/misc/publish_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', h... | 1,744 | 29.086207 | 78 | py |
mmyolo | mmyolo-main/tools/misc/extract_subcoco.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Extracting subsets from coco2017 dataset.
This script is mainly used to debug and verify the correctness of the
program quickly.
The root folder format must be in the following format:
├── root
│ ├── annotations
│ ├── train2017
│ ├── val2017
│ ├── test2017
C... | 5,005 | 30.093168 | 79 | py |
mmyolo | mmyolo-main/tools/misc/coco_split.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
import random
from pathlib import Path
import numpy as np
from pycocotools.coco import COCO
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
'--json', type=str, required=True, help='COCO json label pa... | 3,963 | 31.227642 | 76 | py |
mmyolo | mmyolo-main/tools/model_converters/yolov6_to_mmyolo.py | import argparse
from collections import OrderedDict
import torch
def convert(src, dst):
import sys
sys.path.append('yolov6')
try:
ckpt = torch.load(src, map_location=torch.device('cpu'))
except ModuleNotFoundError:
raise RuntimeError(
'This script must be placed under the ... | 4,403 | 36.965517 | 73 | py |
mmyolo | mmyolo-main/tools/model_converters/yolox_to_mmyolo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
neck_dict = {
'backbone.lateral_conv0': 'neck.reduce_layers.2',
'backbone.C3_p4.conv': 'neck.top_down_layers.0.0.cv',
'backbone.C3_p4.m.0.': 'neck.top_down_layers.0.0.m.0.',
'backbone.reduc... | 4,218 | 37.009009 | 78 | py |
mmyolo | mmyolo-main/tools/model_converters/yolov8_to_mmyolo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
convert_dict_s = {
# backbone
'model.0': 'backbone.stem',
'model.1': 'backbone.stage1.0',
'model.2': 'backbone.stage1.1',
'model.3': 'backbone.stage2.0',
'model.4': 'backbone.stage2... | 2,937 | 31.644444 | 75 | py |
mmyolo | mmyolo-main/tools/model_converters/rtmdet_to_mmyolo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def convert(src, dst):
"""Convert keys in pretrained RTMDet models to MMYOLO style."""
blobs = torch.load(src)['state_dict']
state_dict = OrderedDict()
for key, weight in blobs.items():
... | 2,142 | 33.564516 | 75 | py |
mmyolo | mmyolo-main/tools/model_converters/ppyoloe_to_mmyolo.py | import argparse
import pickle
from collections import OrderedDict
import torch
def convert_bn(k: str):
name = k.replace('._mean',
'.running_mean').replace('._variance', '.running_var')
return name
def convert_repvgg(k: str):
if '.conv2.conv1.' in k:
name = k.replace('.conv2... | 7,738 | 40.832432 | 78 | py |
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