repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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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/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/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/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/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/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/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_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 |
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/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/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/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/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/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/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 |
mmyolo | mmyolo-main/tools/model_converters/convert_kd_ckpt_to_student.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from pathlib import Path
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Convert KD checkpoint to student-only checkpoint')
parser.add_argument('checkpoint', help='input checkpoint filename')
parser.add_... | 1,412 | 27.836735 | 71 | py |
mmyolo | mmyolo-main/tools/model_converters/yolov7_to_mmyolo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from collections import OrderedDict
import torch
convert_dict_tiny = {
# stem
'model.0': 'backbone.stem.0',
'model.1': 'backbone.stem.1',
# stage1 TinyDownSampleBlock
'model.2': 'backbone.stage1.0.short_conv',
... | 50,022 | 44.724863 | 78 | py |
mmyolo | mmyolo-main/tools/model_converters/yolov5_to_mmyolo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
convert_dict_p5 = {
'model.0': 'backbone.stem',
'model.1': 'backbone.stage1.0',
'model.2': 'backbone.stage1.1',
'model.3': 'backbone.stage2.0',
'model.4': 'backbone.stage2.1',
'mode... | 4,176 | 32.95935 | 75 | py |
mmyolo | mmyolo-main/tools/analysis_tools/benchmark.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import time
import torch
from mmengine import Config, DictAction
from mmengine.dist import get_world_size, init_dist
from mmengine.logging import MMLogger, print_log
from mmengine.registry import init_default_scope
from mmengine.runn... | 6,460 | 33.185185 | 79 | py |
mmyolo | mmyolo-main/tools/analysis_tools/optimize_anchors.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Optimize anchor settings on a specific dataset.
This script provides three methods to optimize YOLO anchors including k-means
anchor cluster, differential evolution and v5-k-means. You can use
``--algorithm k-means``, ``--algorithm differential_evolution`` and
``--alg... | 24,296 | 36.49537 | 79 | py |
mmyolo | mmyolo-main/tools/analysis_tools/get_flops.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from pathlib import Path
import torch
from mmdet.registry import MODELS
from mmengine.analysis import get_model_complexity_info
from mmengine.config import Config, DictAction
from mmengine.logging import MMLogger
from mmengine.model import... | 4,085 | 31.951613 | 78 | py |
mmyolo | mmyolo-main/tools/analysis_tools/vis_scheduler.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Hyper-parameter Scheduler Visualization.
This tool aims to help the user to check
the hyper-parameter scheduler of the optimizer(without training),
which support the "learning rate", "momentum", and "weight_decay".
Example:
```shell
python tools/analysis_tools/vis_sc... | 9,574 | 31.347973 | 115 | py |
mmyolo | mmyolo-main/projects/assigner_visualization/assigner_visualization.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import sys
import warnings
import mmcv
import numpy as np
import torch
from mmengine import ProgressBar
from mmengine.config import Config, DictAction
from mmengine.dataset import COLLATE_FUNCTIONS
from mmengine.runner.chec... | 6,558 | 35.848315 | 79 | py |
mmyolo | mmyolo-main/projects/assigner_visualization/visualization/assigner_visualizer.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import List, Union
import mmcv
import numpy as np
import torch
from mmdet.structures.bbox import HorizontalBoxes
from mmdet.visualization import DetLocalVisualizer
from mmdet.visualization.palette import _get_adaptive_scales, get_palette
from mmen... | 13,691 | 40.87156 | 154 | py |
mmyolo | mmyolo-main/projects/assigner_visualization/dense_heads/yolov5_head_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence, Union
import torch
from mmdet.models.utils import unpack_gt_instances
from mmengine.structures import InstanceData
from torch import Tensor
from mmyolo.models import YOLOv5Head
from mmyolo.registry import MODELS
@MODELS.register_module()
c... | 8,364 | 43.259259 | 79 | py |
mmyolo | mmyolo-main/projects/assigner_visualization/dense_heads/yolov8_head_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
import torch
from mmdet.utils import InstanceList
from torch import Tensor
from mmyolo.models import YOLOv8Head
from mmyolo.models.utils import gt_instances_preprocess
from mmyolo.registry import MODELS
@MODELS.register_module()
class YO... | 7,515 | 40.524862 | 79 | py |
mmyolo | mmyolo-main/projects/assigner_visualization/dense_heads/yolov7_head_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
import torch
from mmdet.utils import InstanceList
from torch import Tensor
from mmyolo.models import YOLOv7Head
from mmyolo.registry import MODELS
@MODELS.register_module()
class YOLOv7HeadAssigner(YOLOv7Head):
def assign_by_gt_and_... | 6,836 | 41.73125 | 79 | py |
mmyolo | mmyolo-main/projects/assigner_visualization/dense_heads/rtmdet_head_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Union
import torch
from mmdet.structures.bbox import distance2bbox
from mmdet.utils import InstanceList
from torch import Tensor
from mmyolo.models import RTMDetHead
from mmyolo.models.utils import gt_instances_preprocess
from mmyolo.registry im... | 7,517 | 41.715909 | 79 | py |
mmyolo | mmyolo-main/projects/easydeploy/backbone/common.py | import torch
import torch.nn as nn
from torch import Tensor
class DeployC2f(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__()
def forward(self, x: Tensor) -> Tensor:
x_main = self.main_conv(x)
x_main = [x_main, x_main[:, self.mid_channels:, ...]]
x_main.exte... | 444 | 25.176471 | 67 | py |
mmyolo | mmyolo-main/projects/easydeploy/backbone/focus.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
class DeployFocus(nn.Module):
def __init__(self, orin_Focus: nn.Module):
super().__init__()
self.__dict__.update(orin_Focus.__dict__)
def forward(self, ... | 2,834 | 34.4375 | 79 | py |
mmyolo | mmyolo-main/projects/easydeploy/tools/build_engine.py | import argparse
from pathlib import Path
from typing import List, Optional, Tuple, Union
try:
import tensorrt as trt
except Exception:
trt = None
import warnings
import numpy as np
import torch
warnings.filterwarnings(action='ignore', category=DeprecationWarning)
class EngineBuilder:
def __init__(
... | 5,007 | 35.554745 | 79 | py |
mmyolo | mmyolo-main/projects/easydeploy/tools/export.py | import argparse
import os
import warnings
from io import BytesIO
import onnx
import torch
from mmdet.apis import init_detector
from mmengine.config import ConfigDict
from mmengine.utils.path import mkdir_or_exist
from mmyolo.utils import register_all_modules
from projects.easydeploy.model import DeployModel
warnings... | 4,623 | 31.335664 | 79 | py |
mmyolo | mmyolo-main/projects/easydeploy/tools/image-demo.py | # Copyright (c) OpenMMLab. All rights reserved.
from projects.easydeploy.model import ORTWrapper, TRTWrapper # isort:skip
import os
import random
from argparse import ArgumentParser
import cv2
import mmcv
import numpy as np
import torch
from mmcv.transforms import Compose
from mmdet.utils import get_test_pipeline_cfg... | 4,968 | 31.477124 | 78 | py |
mmyolo | mmyolo-main/projects/easydeploy/bbox_code/bbox_coder.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
from torch import Tensor
def yolov5_bbox_decoder(priors: Tensor, bbox_preds: Tensor,
stride: Tensor) -> Tensor:
bbox_preds = bbox_preds.sigmoid()
x_center = (priors[..., 0] + priors[..., 2]) * 0.... | 1,608 | 33.234043 | 70 | py |
mmyolo | mmyolo-main/projects/easydeploy/model/backendwrapper.py | import warnings
from collections import namedtuple
from functools import partial
from pathlib import Path
from typing import List, Optional, Union
import numpy as np
import onnxruntime
try:
import tensorrt as trt
except Exception:
trt = None
import torch
warnings.filterwarnings(action='ignore', category=Depr... | 6,885 | 32.921182 | 78 | py |
mmyolo | mmyolo-main/projects/easydeploy/model/model.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import List, Optional
import torch
import torch.nn as nn
from mmdet.models.backbones.csp_darknet import Focus
from mmengine.config import ConfigDict
from torch import Tensor
from mmyolo.models import RepVGGBlock
from mmyolo.mode... | 5,871 | 37.887417 | 79 | py |
mmyolo | mmyolo-main/projects/easydeploy/nms/ort_nms.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import Tensor
_XYWH2XYXY = torch.tensor([[1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0],
[-0.5, 0.0, 0.5, 0.0], [0.0, -0.5, 0.0, 0.5]],
dtype=torch.float32)
def select_nms_index(scores: Tensor,
... | 4,445 | 35.146341 | 79 | py |
mmyolo | mmyolo-main/projects/easydeploy/nms/trt_nms.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from torch import Tensor
_XYWH2XYXY = torch.tensor([[1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0],
[-0.5, 0.0, 0.5, 0.0], [0.0, -0.5, 0.0, 0.5]],
dtype=torch.float32)
class TRTEfficientNMSop(torch.autograd.... | 8,045 | 34.444934 | 78 | py |
mmyolo | mmyolo-main/.dev_scripts/gather_models.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os
import os.path as osp
import shutil
import subprocess
import time
from collections import OrderedDict
import torch
import yaml
from mmengine.config import Config
from mmengine.fileio import dump
from mmengine.utils import mkdir_or_ex... | 11,017 | 34.314103 | 79 | py |
mmyolo | mmyolo-main/tests/test_engine/test_hooks/test_yolox_mode_switch_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.config import Config
from mmengine.runner import Runner
from torch.utils.data import Dataset
from mmyolo.engine.hooks import YOLOXModeSwitchHook
from mmyolo.utils import register_all... | 1,810 | 25.632353 | 75 | py |
mmyolo | mmyolo-main/tests/test_engine/test_hooks/test_yolov5_param_scheduler_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.config import Config
from mmengine.optim import build_optim_wrapper
from mmengine.runner import Runner
from torch import nn
from torch.utils.data import Dataset
from mmyolo.engine.ho... | 3,619 | 27.96 | 77 | py |
mmyolo | mmyolo-main/tests/test_engine/test_optimizers/test_yolov5_optim_constructor.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from unittest import TestCase
import torch
import torch.nn as nn
from mmengine.optim import build_optim_wrapper
from mmyolo.engine import YOLOv5OptimizerConstructor
from mmyolo.utils import register_all_modules
register_all_modules()
class ExampleModel(n... | 2,934 | 34.792683 | 79 | py |
mmyolo | mmyolo-main/tests/test_engine/test_optimizers/test_yolov7_optim_wrapper_constructor.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from unittest import TestCase
import torch
import torch.nn as nn
from mmengine.optim import build_optim_wrapper
from mmyolo.engine import YOLOv7OptimWrapperConstructor
from mmyolo.utils import register_all_modules
register_all_modules()
class ExampleMode... | 2,958 | 35.085366 | 79 | py |
mmyolo | mmyolo-main/tests/test_deploy/test_object_detection.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
from tempfile import NamedTemporaryFile, TemporaryDirectory
import numpy as np
import pytest
import torch
from mmengine import Config
try:
import importlib
importlib.import_module('mmdeploy')
except ImportError:
pytest.skip('mmdeploy is not install... | 3,052 | 30.474227 | 71 | py |
mmyolo | mmyolo-main/tests/test_deploy/test_mmyolo_models.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import random
import numpy as np
import pytest
import torch
from mmengine import Config
try:
import importlib
importlib.import_module('mmdeploy')
except ImportError:
pytest.skip('mmdeploy is not installed.', allow_module_level=True)
from mmdeploy.... | 5,546 | 32.415663 | 77 | py |
mmyolo | mmyolo-main/tests/test_models/test_layers/test_ema.py | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
import math
from unittest import TestCase
import torch
import torch.nn as nn
from mmengine.testing import assert_allclose
from mmyolo.models.layers import ExpMomentumEMA
class TestEMA(TestCase):
def test_exp_momentum_ema(self):
model = nn... | 3,634 | 37.263158 | 79 | py |
mmyolo | mmyolo-main/tests/test_models/test_layers/test_yolo_bricks.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.layers import SPPFBottleneck
from mmyolo.utils import register_all_modules
register_all_modules()
class TestSPPFBottleneck(TestCase):
def test_forward(self):
input_tensor = torch.randn((1, 3,... | 1,111 | 30.771429 | 69 | py |
mmyolo | mmyolo-main/tests/test_models/test_backbone/test_csp_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmyolo.models import PPYOLOECSPResNet
from mmyolo.utils import register_all_modules
from .utils import check_norm_state, is_norm
register_all_modules()
cla... | 4,162 | 35.517544 | 78 | py |
mmyolo | mmyolo-main/tests/test_models/test_backbone/test_efficient_rep.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmyolo.models.backbones import YOLOv6CSPBep, YOLOv6EfficientRep
from mmyolo.utils import register_all_modules
from .utils import check_norm_state, is_norm
re... | 7,688 | 36.876847 | 79 | py |
mmyolo | mmyolo-main/tests/test_models/test_backbone/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.backbones.res2net import Bottle2neck
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
from mmdet.models.backbones.resnext import Bottleneck as BottleneckX
from mmdet.models.layers import SimplifiedBasicBlock
from torch.nn.modules import G... | 1,026 | 31.09375 | 77 | py |
mmyolo | mmyolo-main/tests/test_models/test_backbone/test_yolov7_backbone.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmyolo.models.backbones import YOLOv7Backbone
from mmyolo.utils import register_all_modules
from .utils import check_norm_state
register_all_modules()
clas... | 5,705 | 35.812903 | 77 | py |
mmyolo | mmyolo-main/tests/test_models/test_backbone/test_csp_darknet.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from parameterized import parameterized
from torch.nn.modules.batchnorm import _BatchNorm
from mmyolo.models.backbones import (YOLOv5CSPDarknet, YOLOv8CSPDarknet,
YOLOXCSPDarkn... | 4,481 | 36.35 | 78 | py |
mmyolo | mmyolo-main/tests/test_models/test_data_preprocessor/test_data_preprocessor.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmdet.structures import DetDataSample
from mmengine import MessageHub
from mmyolo.models import PPYOLOEBatchRandomResize, PPYOLOEDetDataPreprocessor
from mmyolo.models.data_preprocessors import (YOLOv5DetDataPreprocessor,
... | 5,829 | 36.133758 | 79 | py |
mmyolo | mmyolo-main/tests/test_models/test_utils/test_misc.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmengine.structures import InstanceData
from torch import Tensor
from mmyolo.models.utils import gt_instances_preprocess
from mmyolo.utils import register_all_modules
register_all_modules()
class TestGtInstancesPrepro... | 1,330 | 35.972222 | 77 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_ppyoloe_csppan.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models import PPYOLOECSPPAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestPPYOLOECSPPAFPN(TestCase):
def test_forward(self):
s = 64
in_channels = [8, 16, 32... | 1,741 | 31.259259 | 71 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_cspnext_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.necks import CSPNeXtPAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestCSPNeXtPAFPN(TestCase):
def test_forward(self):
s = 64
in_channels = [8, 16, 32... | 1,159 | 29.526316 | 79 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_yolov8_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models import YOLOv8PAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestYOLOv8PAFPN(TestCase):
def test_YOLOv8PAFPN_forward(self):
s = 64
in_channels = [8, 16... | 879 | 29.344828 | 78 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_yolox_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.necks import YOLOXPAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestYOLOXPAFPN(TestCase):
def test_forward(self):
s = 64
in_channels = [8, 16, 32]
... | 858 | 28.62069 | 77 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_yolov7_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmcv.cnn import ConvModule
from mmyolo.models.necks import YOLOv7PAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestYOLOv7PAFPN(TestCase):
def test_forward(self):
# test P5... | 2,718 | 32.9875 | 78 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_yolov6_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.necks import YOLOv6CSPRepPAFPN, YOLOv6RepPAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestYOLOv6PAFPN(TestCase):
def test_YOLOv6RepPAFP_forward(self):
s = 6... | 1,593 | 32.914894 | 71 | py |
mmyolo | mmyolo-main/tests/test_models/test_necks/test_yolov5_pafpn.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.necks import YOLOv5PAFPN
from mmyolo.utils import register_all_modules
register_all_modules()
class TestYOLOv5PAFPN(TestCase):
def test_forward(self):
s = 64
in_channels = [8, 16, 32]
... | 873 | 29.137931 | 78 | py |
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