id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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3,669 | import logging
import os
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import time
import datetime
import json
from fvcore.common.timer import Timer
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer... | Create configs and perform basic setups. |
3,670 | import argparse
import glob
import multiprocessing as mp
import os
import time
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import VisualizationDemo
from centernet.config import add_cen... | null |
3,671 | import argparse
import glob
import multiprocessing as mp
import os
import time
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import VisualizationDemo
from centernet.config import add_cen... | null |
3,672 | from detectron2.data.datasets.register_coco import register_coco_instances
import os
categories = [
{'id': 0, 'name': 'car'},
{'id': 1, 'name': 'truck'},
{'id': 2, 'name': 'trailer'},
{'id': 3, 'name': 'bus'},
{'id': 4, 'name': 'construction_vehicle'},
{'id': 5, 'name': 'bicycle'},
{'id': 6,... | null |
3,673 | import os
from detectron2.data.datasets.register_coco import register_coco_instances
from detectron2.data.datasets.coco import load_coco_json
from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
from detectron2.data import DatasetCatalog, MetadataCatalog
def load_coco_json(json_file, image_root, dat... | add extra_annotation_keys |
3,674 | from detectron2.data.datasets.register_coco import register_coco_instances
import os
categories_v1 = [
{'id': 164, 'name': 'cutting/chopping board'} ,
{'id': 49, 'name': 'tie'} ,
{'id': 306, 'name': 'crosswalk sign'} ,
{'id': 145, 'name': 'gun'} ,
{'id': 14, 'name': 'street lights'} ,
{'id': 223, 'name': 'bar soap'} ,
... | null |
3,675 | import numpy as np
import math
from os.path import join
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.model_zoo as model_zoo
from detectron2.modeling.backbone.resnet import (
BasicStem, BottleneckBlock, DeformBottleneckBlock)
from de... | null |
3,676 | import numpy as np
import math
from os.path import join
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.model_zoo as model_zoo
from detectron2.modeling.backbone.resnet import (
BasicStem, BottleneckBlock, DeformBottleneckBlock)
from de... | null |
3,677 | import numpy as np
import math
from os.path import join
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
import torch.utils.model_zoo as model_zoo
from detectron2.modeling.backbone.resnet import (
BasicStem, BottleneckBlock, DeformBottleneckBlock)
from de... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,678 | import math
import fvcore.nn.weight_init as weight_init
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.fpn import FPN
from detectron2.modeling.backbone.build import BACKBO... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,679 | import math
import fvcore.nn.weight_init as weight_init
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone
from detectron2.modeling.backbone.fpn import FPN
from detectron2.modeling.backbone.build import BACKBO... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,680 | import math
from os.path import join
import numpy as np
from collections import OrderedDict
from typing import List
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.layers import ShapeSpec, Conv2d
from... | BiFPN config with sum. |
3,681 | import math
from os.path import join
import numpy as np
from collections import OrderedDict
from typing import List
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.layers import ShapeSpec, Conv2d
from... | Swish - Described in: https://arxiv.org/abs/1710.05941 |
3,682 | import math
from os.path import join
import numpy as np
from collections import OrderedDict
from typing import List
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.layers import ShapeSpec, Conv2d
from... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,683 | import math
from os.path import join
import numpy as np
from collections import OrderedDict
from typing import List
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.layers import ShapeSpec, Conv2d
from... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,684 | import numpy as np
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import (
CNNBlockBase,
Conv2d,
DeformConv,
ModulatedDeformConv,
ShapeSpec,
get_norm,
)
from detectron2.modeling.backbone import Backbone
from de... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,685 | import numpy as np
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import (
CNNBlockBase,
Conv2d,
DeformConv,
ModulatedDeformConv,
ShapeSpec,
get_norm,
)
from detectron2.modeling.backbone import Backbone
from de... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,686 | import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.modeling.backbone import FPN
from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2... | null |
3,687 | import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.modeling.backbone import FPN
from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2... | 3x3 convolution with padding |
3,688 | import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.modeling.backbone import FPN
from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2... | null |
3,689 | import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.modeling.backbone import FPN
from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,690 | import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.modeling.backbone import FPN
from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,691 | import math
from os.path import join
import numpy as np
import torch
from torch import nn
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F
import fvcore.nn.weight_init as weight_init
from detectron2.modeling.backbone import FPN
from detectron2.layers import ShapeSpec, ModulatedDeformConv, Conv2... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,692 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
def swish(x):
return x * x.sigmoid() | null |
3,693 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
def split_name(name):
for i, c in enumerat... | null |
3,694 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
The provided code snippet includes necessary d... | Assert that each stride is 2x times its preceding stride, i.e. "contiguous in log2". |
3,695 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
class BiFPN(Backbone):
"""
This module ... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,696 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
class BiFPN(Backbone):
"""
This module ... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,697 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
class BiFPN(Backbone):
"""
This module ... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,698 | import torch
import torch.nn.functional as F
from torch import nn
from detectron2.layers import Conv2d, ShapeSpec, get_norm
from detectron2.modeling.backbone import Backbone, build_resnet_backbone
from detectron2.modeling import BACKBONE_REGISTRY
from .dlafpn import dla34
class BiFPN(Backbone):
"""
This module ... | Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`. |
3,699 | import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `heatmap_focal_loss` function. Write a Python function `def heatmap_focal_loss( inputs, targets, pos_inds, labels, alpha: float = -1, beta: float = 4, gamma: floa... | Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs: (sum_l N*Hl*Wl, C) targets: (sum_l N*Hl*Wl, C) pos_inds: N labels: N Returns: Loss tensor with the reduction option applied. |
3,700 | import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `binary_heatmap_focal_loss` function. Write a Python function `def binary_heatmap_focal_loss( inputs, targets, pos_inds, alpha: float = -1, beta: float = 4, gamma: fl... | Args: inputs: (sum_l N*Hl*Wl,) targets: (sum_l N*Hl*Wl,) pos_inds: N Returns: Loss tensor with the reduction option applied. |
3,701 | import torch
from torch import nn
The provided code snippet includes necessary dependencies for implementing the `giou_loss` function. Write a Python function `def giou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor` to solve the followin... | Generalized Intersection over Union Loss (Hamid Rezatofighi et. al) https://arxiv.org/abs/1902.09630 Gradient-friendly IoU loss with an additional penalty that is non-zero when the boxes do not overlap and scales with the size of their smallest enclosing box. This loss is symmetric, so the boxes1 and boxes2 arguments a... |
3,702 | from detectron2.layers import batched_nms
The provided code snippet includes necessary dependencies for implementing the `ml_nms` function. Write a Python function `def ml_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores", label_field="labels")` to solve the following problem:
Performs non-max... | Performs non-maximum suppression on a boxlist, with scores specified in a boxlist field via score_field. Arguments: boxlist(BoxList) nms_thresh (float) max_proposals (int): if > 0, then only the top max_proposals are kept after non-maximum suppression score_field (str) |
3,703 | import cv2
import torch
from torch import nn
from detectron2.utils.comm import get_world_size
from detectron2.structures import pairwise_iou, Boxes
import torch.nn.functional as F
import numpy as np
from detectron2.structures import Boxes, ImageList, Instances
The provided code snippet includes necessary dependencies ... | This function is used to transpose image first training targets to level first ones :return: level first training targets |
3,704 | import cv2
import torch
from torch import nn
from detectron2.utils.comm import get_world_size
from detectron2.structures import pairwise_iou, Boxes
import torch.nn.functional as F
import numpy as np
from detectron2.structures import Boxes, ImageList, Instances
def get_world_size() -> int:
if not dist.is_available(... | null |
3,705 | import torch
import json
import numpy as np
from torch.nn import functional as F
def load_class_freq(
path='datasets/lvis/lvis_v1_train_cat_info.json',
freq_weight=0.5):
cat_info = json.load(open(path, 'r'))
cat_info = torch.tensor(
[c['image_count'] for c in sorted(cat_info, key=lambda x: x['... | null |
3,706 | import torch
import json
import numpy as np
from torch.nn import functional as F
def get_fed_loss_inds(
gt_classes, num_sample_cats=50, C=1203, \
weight=None, fed_cls_inds=-1):
appeared = torch.unique(gt_classes) # C'
prob = appeared.new_ones(C + 1).float()
prob[-1] = 0
if len(appeared) < num_s... | null |
3,707 | import cv2
import numpy as np
import torch
import torch.nn.functional as F
def _blend_image(image, color_map, a=0.7):
color_map = cv2.resize(color_map, (image.shape[1], image.shape[0]))
ret = np.clip(image * (1 - a) + color_map * a, 0, 255).astype(np.uint8)
return ret | null |
3,708 | import cv2
import numpy as np
import torch
import torch.nn.functional as F
def _get_color_image(heatmap):
heatmap = heatmap.reshape(
heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1)
if heatmap.shape[0] == 1:
color_map = (heatmap * np.ones((1, 1, 1, 3), np.uint8) * 255).max(
axis=0).astyp... | images: N x 3 x H x W flattened_hms: LNHiWi x C shapes_per_level: L x 2 [(H_i, W_i)] locations: LNHiWi x 2 |
3,709 | import cv2
import numpy as np
import torch
import torch.nn.functional as F
COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
np.uint8).reshape(1300, 1, 1, 3)
def _get_color_image(heatmap):
heatmap = heatmap.reshape(
heatmap.shape[0], heatmap.shape[1], heatmap.shape[2], 1)
if heatmap.shape[0] == 1... | images: N x 3 x H x W class_target: LNHiWi x C cat_agn_heatmap: LNHiWi shapes_per_level: L x 2 [(H_i, W_i)] |
3,710 | import cv2
import numpy as np
import torch
import torch.nn.functional as F
COLORS = ((np.random.rand(1300, 3) * 0.4 + 0.6) * 255).astype(
np.uint8).reshape(1300, 1, 1, 3)
def _imagelist_to_tensor(images):
global cnt
cnt = 0
LVIS_CATEGORIES = [{'frequency': 'c', 'synset': 'aerosol.n.02', 'synonyms': ['aerosol_can', '... | null |
3,711 | import argparse
def gen_header(torch_versions):
return '<table class="docutils"><tbody><th width="80"> CUDA </th>' + "".join(
[
'<th valign="bottom" align="left" width="100">torch {}</th>'.format(t)
for t in torch_versions
]
) | null |
3,712 | import torch
from torch import nn
from torch.nn import functional as F
from omegaconf import OmegaConf
import torchvision
from torchvision.transforms import transforms as T
from torchvision.models.resnet import ResNet, Bottleneck
from fvcore.common.param_scheduler import MultiStepParamScheduler
from detectron2.solver i... | null |
3,713 | from fvcore.common.param_scheduler import MultiStepParamScheduler
from detectron2.config import LazyCall as L
from detectron2.solver import WarmupParamScheduler
The provided code snippet includes necessary dependencies for implementing the `default_X_scheduler` function. Write a Python function `def default_X_schedule... | Returns the config for a default multi-step LR scheduler such as "1x", "3x", commonly referred to in papers, where every 1x has the total length of 1440k training images (~12 COCO epochs). LR is decayed twice at the end of training following the strategy defined in "Rethinking ImageNet Pretraining", Sec 4. Args: num_X:... |
3,714 | import argparse
import glob
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
import warnings
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import Vis... | null |
3,715 | import argparse
import glob
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
import warnings
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import Vis... | null |
3,716 | import argparse
import glob
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
import warnings
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import Vis... | null |
3,717 | import numpy as np
import os
from pathlib import Path
import tqdm
from PIL import Image
def convert(input, output):
img = np.asarray(Image.open(input))
assert img.dtype == np.uint8
img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1
Image.fromarray(img).save(output) | null |
3,718 | import copy
import json
import os
from collections import defaultdict
COCO_SYNSET_CATEGORIES = [
{"synset": "person.n.01", "coco_cat_id": 1},
{"synset": "bicycle.n.01", "coco_cat_id": 2},
{"synset": "car.n.01", "coco_cat_id": 3},
{"synset": "motorcycle.n.01", "coco_cat_id": 4},
{"synset": "airplane.... | Filter LVIS instance segmentation annotations to remove all categories that are not included in COCO. The new json files can be used to evaluate COCO AP using `lvis-api`. The category ids in the output json are the incontiguous COCO dataset ids. Args: input_filename (str): path to the LVIS json file. output_filename (s... |
3,719 | import functools
import json
import multiprocessing as mp
import numpy as np
import os
import time
from fvcore.common.download import download
from panopticapi.utils import rgb2id
from PIL import Image
from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
def _process_panoptic_to_semantic(input_panoptic, ou... | Create semantic segmentation annotations from panoptic segmentation annotations, to be used by PanopticFPN. It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. It maps all stuff categories to contiguous ids starting from 1. Args: panoptic_json (str): path to the panoptic json file, in C... |
3,720 | import functools
import json
import multiprocessing as mp
import numpy as np
import os
import time
from fvcore.common.download import download
from panopticapi.utils import rgb2id
from PIL import Image
from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
def link_val100(dir_full, dir_100):
print("... | null |
3,721 | import argparse
import json
import numpy as np
import os
from collections import defaultdict
import cv2
import tqdm
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.structures import Boxes, BoxMode, Instances
from detectron2.utils.file_io import PathManager
from detectron2.utils.logger import... | null |
3,722 | import logging
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, instantiate
from detectron2.engine import (
AMPTrainer,
SimpleTrainer,
default_argument_parser,
default_setup,
default_writers,
hooks,
launch,
)
from detectron2.engine.defaults im... | Args: cfg: an object with the following attributes: model: instantiate to a module dataloader.{train,test}: instantiate to dataloaders dataloader.evaluator: instantiate to evaluator for test set optimizer: instantaite to an optimizer lr_multiplier: instantiate to a fvcore scheduler train: other misc config defined in `... |
3,723 | import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, detection_utils
from dete... | null |
3,724 | import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, detection_utils
from dete... | null |
3,725 | import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, detection_utils
from dete... | null |
3,726 | import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, detection_utils
from dete... | null |
3,727 | import argparse
import os
from typing import Dict, List, Tuple
import torch
from torch import Tensor, nn
import detectron2.data.transforms as T
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, detection_utils
from dete... | null |
3,728 | import logging
import os
import time
import weakref
from collections import OrderedDict
from typing import Any, Dict, List
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, build_det... | null |
3,729 | import logging
import os
import time
import weakref
from collections import OrderedDict
from typing import Any, Dict, List
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader, build_det... | Create configs and perform basic setups. |
3,730 | import argparse
import os
from itertools import chain
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader
from detectron2.data import detection_utils as utils
from detectron2.data.build import filter_images_with_few_keypo... | null |
3,731 | import argparse
import os
from itertools import chain
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader
from detectron2.data import detection_utils as utils
from detectron2.data.build import filter_images_with_few_keypo... | null |
3,732 | import argparse
import os
from itertools import chain
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_train_loader
from detectron2.data import detection_utils as utils
from detectron2.data.build import filter_images_with_few_keypo... | null |
3,733 | import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.data import build_detection_test_loader
from detectron2.en... | null |
3,734 | import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.data import build_detection_test_loader
from detectron2.en... | null |
3,735 | import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.data import build_detection_test_loader
from detectron2.en... | null |
3,736 | import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.data import build_detection_test_loader
from detectron2.en... | null |
3,737 | import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.data import build_detection_test_loader
from detectron2.en... | null |
3,738 | import logging
import os
from collections import OrderedDict
import torch
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultTrainer, default_argument_parser, ... | Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset. For your own dataset, you can simply create an evaluator manually in your script and do not have to worry about the hacky if-else logic here. |
3,739 | import logging
import os
from collections import OrderedDict
import torch
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.engine import DefaultTrainer, default_argument_parser, ... | Create configs and perform basic setups. |
3,740 | import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import DistributedDataParallel
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, get_cfg, instantiate
from detectron2.data import (
Data... | null |
3,741 | import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import DistributedDataParallel
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, get_cfg, instantiate
from detectron2.data import (
Data... | null |
3,742 | import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import DistributedDataParallel
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, get_cfg, instantiate
from detectron2.data import (
Data... | null |
3,743 | import itertools
import logging
import psutil
import torch
import tqdm
from fvcore.common.timer import Timer
from torch.nn.parallel import DistributedDataParallel
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import LazyConfig, get_cfg, instantiate
from detectron2.data import (
Data... | null |
3,744 | import logging
import os
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
Metad... | null |
3,745 | import logging
import os
from collections import OrderedDict
import torch
from torch.nn.parallel import DistributedDataParallel
import detectron2.utils.comm as comm
from detectron2.checkpoint import DetectionCheckpointer, PeriodicCheckpointer
from detectron2.config import get_cfg
from detectron2.data import (
Metad... | Create configs and perform basic setups. |
3,746 | from PIL import Image
import os
import json
from tqdm import tqdm
from argparse import ArgumentParser
from paddleocr import PaddleOCR
def save_json(json_list,save_path):
with open(save_path, 'w') as file:
json.dump(json_list, file, indent=4) | null |
3,747 | from PIL import Image
import os
import json
from tqdm import tqdm
from argparse import ArgumentParser
from paddleocr import PaddleOCR
def get_image_files(folder_path):
image_files = []
for root, dirs, files in os.walk(folder_path):
for file in files:
if file.endswith('.jpg') or file... | null |
3,748 | from PIL import Image
import os
import json
from tqdm import tqdm
from argparse import ArgumentParser
from paddleocr import PaddleOCR
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./images")
parser.add_argument("--output_path", type=str, default="./outp... | null |
3,749 | import json
import openai
from argparse import ArgumentParser
def save_json(json_list,save_path):
with open(save_path, 'w') as file:
json.dump(json_list, file, indent=4) | null |
3,750 | import json
import openai
from argparse import ArgumentParser
def _get_args():
parser = ArgumentParser()
parser.add_argument("--ann_path", type=str, default="./outputs/ann_all.json")
parser.add_argument("--output_path", type=str, default="./outputs/detailed_caption.json")
args = parser.parse_args()
... | null |
3,751 | import json
import openai
from argparse import ArgumentParser
def get_question(ann):
sentence1 = "I want you to act as an intelligent image captioner. You should generate a descriptive, coherent and logical description of the image based on the given descriptions from different people for the same image. The posit... | null |
3,752 | import json
import os
from pycocotools import mask as mask_utils
from PIL import Image
import cv2
from operator import itemgetter
import numpy as np
import random
from lavis.models import load_model_and_preprocess
import torch
from tqdm import tqdm
from argparse import ArgumentParser
def get_json_files(folder_path):
... | null |
3,753 | import json
import os
from pycocotools import mask as mask_utils
from PIL import Image
import cv2
from operator import itemgetter
import numpy as np
import random
from lavis.models import load_model_and_preprocess
import torch
from tqdm import tqdm
from argparse import ArgumentParser
def generate_random_color():
r ... | null |
3,754 | import json
import os
from pycocotools import mask as mask_utils
from PIL import Image
import cv2
from operator import itemgetter
import numpy as np
import random
from lavis.models import load_model_and_preprocess
import torch
from tqdm import tqdm
from argparse import ArgumentParser
def crop_image(img, mask, bbox):
... | null |
3,755 | import json
import os
from pycocotools import mask as mask_utils
from PIL import Image
import cv2
from operator import itemgetter
import numpy as np
import random
from lavis.models import load_model_and_preprocess
import torch
from tqdm import tqdm
from argparse import ArgumentParser
def get_image_files(folder_path): ... | null |
3,756 | import json
import os
from pycocotools import mask as mask_utils
from PIL import Image
import cv2
from operator import itemgetter
import numpy as np
import random
from lavis.models import load_model_and_preprocess
import torch
from tqdm import tqdm
from argparse import ArgumentParser
def save_json(json_list,save_path)... | null |
3,757 | import json
import os
from pycocotools import mask as mask_utils
from PIL import Image
import cv2
from operator import itemgetter
import numpy as np
import random
from lavis.models import load_model_and_preprocess
import torch
from tqdm import tqdm
from argparse import ArgumentParser
def _get_args():
parser = Argu... | null |
3,758 | import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
from argparse import ArgumentParser
import os
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import torchvision.utils as vutils
import json
def save_json(json_list,save_path):
with open(save_path, 'w') as ... | null |
3,759 | import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
from argparse import ArgumentParser
import os
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import torchvision.utils as vutils
import json
def get_image_files(folder_path):
image_files = []
for ro... | null |
3,760 | import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
from argparse import ArgumentParser
import os
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import torchvision.utils as vutils
import json
def _get_args():
parser = ArgumentParser()
parser.add_argumen... | null |
3,761 | import torch
from PIL import Image
from lavis.models import load_model_and_preprocess
from argparse import ArgumentParser
import os
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import torchvision.utils as vutils
import json
def collate_fn(batch):
image = [item['image'].squeeze(0) for item... | null |
3,762 | import argparse
import multiprocessing as mp
import os
import time
import cv2
from tqdm import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from grit.config ... | null |
3,763 | import argparse
import multiprocessing as mp
import os
import time
import cv2
from tqdm import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from grit.config ... | null |
3,764 | import argparse
import multiprocessing as mp
import os
import time
import cv2
from tqdm import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from grit.config ... | null |
3,765 | import argparse
import multiprocessing as mp
import os
import time
import cv2
from tqdm import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from grit.config ... | null |
3,766 | import argparse
import multiprocessing as mp
import os
import time
import cv2
from tqdm import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from grit.config ... | null |
3,767 | import argparse
import multiprocessing as mp
import os
import time
import cv2
from tqdm import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from centernet.config import add_centernet_config
from grit.config ... | null |
3,768 | from argparse import ArgumentParser
from pathlib import Path
import copy
import gradio as gr
import os
import re
import secrets
import tempfile
from PIL import Image
from monkey_model.modeling_monkey import MonkeyLMHeadModel
from monkey_model.tokenization_qwen import QWenTokenizer
from monkey_model.configuration_monkey... | null |
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