id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
34,393 | from typing import List
import torch
from torch import nn as nn
from annotator.mmpkg.mmcv.runner import force_fp32
from .furthest_point_sample import (furthest_point_sample,
furthest_point_sample_with_dist)
The provided code snippet includes necessary dependencies for implementing t... | Calculating square distance between a and b. Args: point_feat_a (Tensor): (B, N, C) Feature vector of each point. point_feat_b (Tensor): (B, M, C) Feature vector of each point. norm (Bool, optional): Whether to normalize the distance. Default: True. Returns: Tensor: (B, N, M) Distance between each pair points. |
34,394 | from typing import List
import torch
from torch import nn as nn
from annotator.mmpkg.mmcv.runner import force_fp32
from .furthest_point_sample import (furthest_point_sample,
furthest_point_sample_with_dist)
class DFPSSampler(nn.Module):
"""Using Euclidean distances of points for ... | Get the type and mode of points sampler. Args: sampler_type (str): The type of points sampler. The valid value are "D-FPS", "F-FPS", or "FS". Returns: class: Points sampler type. |
34,396 | import torch
from torch.autograd import Function
from torch.nn import functional as F
from annotator.mmpkg.mmcv.utils import to_2tuple
from ..utils import ext_loader
class UpFirDn2d(Function):
def forward(ctx, input, kernel, up, down, pad):
up_x, up_y = up
down_x, down_y = down
pad_x0, pad_x... | UpFRIDn for 2d features. UpFIRDn is short for upsample, apply FIR filter and downsample. More details can be found in: https://www.mathworks.com/help/signal/ref/upfirdn.html Args: input (Tensor): Tensor with shape of (n, c, h, w). kernel (Tensor): Filter kernel. up (int | tuple[int], optional): Upsampling factor. If gi... |
34,397 | import os
import numpy as np
import torch
from annotator.mmpkg.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated'])
class NMSop(torch.autograd.Function):
def forward(ctx, bboxes, scores, iou_threshold, o... | Dispatch to either CPU or GPU NMS implementations. The input can be either torch tensor or numpy array. GPU NMS will be used if the input is gpu tensor, otherwise CPU NMS will be used. The returned type will always be the same as inputs. Arguments: boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). scores (torc... |
34,398 | import os
import numpy as np
import torch
from annotator.mmpkg.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated'])
class SoftNMSop(torch.autograd.Function):
def forward(ctx, boxes, scores, iou_threshold... | Dispatch to only CPU Soft NMS implementations. The input can be either a torch tensor or numpy array. The returned type will always be the same as inputs. Arguments: boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). scores (torch.Tensor or np.ndarray): scores in shape (N, ). iou_threshold (float): IoU threshol... |
34,399 | import os
import numpy as np
import torch
from annotator.mmpkg.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader
The provided code snippet includes necessary dependencies for implementing the `batched_nms` function. Write a Python function `def batched_nms(boxes, scores, idxs, nms_cfg, class_agno... | Performs non-maximum suppression in a batched fashion. Modified from https://github.com/pytorch/vision/blob /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39. In order to perform NMS independently per class, we add an offset to all the boxes. The offset is dependent only on the class idx, and is la... |
34,400 | import os
import numpy as np
import torch
from annotator.mmpkg.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated'])
The provided code snippet includes necessary dependencies for implementing the `nms_match`... | Matched dets into different groups by NMS. NMS match is Similar to NMS but when a bbox is suppressed, nms match will record the indice of suppressed bbox and form a group with the indice of kept bbox. In each group, indice is sorted as score order. Arguments: dets (torch.Tensor | np.ndarray): Det boxes with scores, sha... |
34,401 | import os
import numpy as np
import torch
from annotator.mmpkg.mmcv.utils import deprecated_api_warning
from ..utils import ext_loader
ext_module = ext_loader.load_ext(
'_ext', ['nms', 'softnms', 'nms_match', 'nms_rotated'])
The provided code snippet includes necessary dependencies for implementing the `nms_rotate... | Performs non-maximum suppression (NMS) on the rotated boxes according to their intersection-over-union (IoU). Rotated NMS iteratively removes lower scoring rotated boxes which have an IoU greater than iou_threshold with another (higher scoring) rotated box. Args: boxes (Tensor): Rotated boxes in shape (N, 5). They are ... |
34,406 | import math
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.function import Function, once_differentiable
from annotator.mmpkg.mmcv import deprecated_api_warning
from annotator.mmpkg.mmcv.cnn import constant_init, xavier_init
from annotator.mmpkg.mmcv.cnn.bricks.re... | CPU version of multi-scale deformable attention. Args: value (Tensor): The value has shape (bs, num_keys, mum_heads, embed_dims//num_heads) value_spatial_shapes (Tensor): Spatial shape of each feature map, has shape (num_levels, 2), last dimension 2 represent (h, w) sampling_locations (Tensor): The location of sampling... |
34,409 | from os import path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
from torch.onnx.operators import shape_as_tensor
def bilinear_grid_sample(im, grid, align_corners=False):
"""Given an input and a flow-field grid, computes the output using input
... | A wrapper around :func:`grid_sample` to support 3D point_coords tensors Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to lie inside ``[0, 1] x [0, 1]`` square. Args: input (Tensor): Feature map, shape (N, C, H, W). points (Tensor): Image based absolute point coordinates (normalized), range [0, ... |
34,416 | import os
import os.path as osp
import subprocess
import tempfile
from annotator.mmpkg.mmcv.utils import requires_executable
def convert_video(in_file,
out_file,
print_cmd=False,
pre_options='',
**kwargs):
"""Convert a video with ffmpeg.
Th... | Resize a video. Args: in_file (str): Input video filename. out_file (str): Output video filename. size (tuple): Expected size (w, h), eg, (320, 240) or (320, -1). ratio (tuple or float): Expected resize ratio, (2, 0.5) means (w*2, h*0.5). keep_ar (bool): Whether to keep original aspect ratio. log_level (str): Logging l... |
34,417 | import os
import os.path as osp
import subprocess
import tempfile
from annotator.mmpkg.mmcv.utils import requires_executable
def convert_video(in_file,
out_file,
print_cmd=False,
pre_options='',
**kwargs):
"""Convert a video with ffmpeg.
Th... | Cut a clip from a video. Args: in_file (str): Input video filename. out_file (str): Output video filename. start (None or float): Start time (in seconds). end (None or float): End time (in seconds). vcodec (None or str): Output video codec, None for unchanged. acodec (None or str): Output audio codec, None for unchange... |
34,418 | import os
import os.path as osp
import subprocess
import tempfile
from annotator.mmpkg.mmcv.utils import requires_executable
def convert_video(in_file,
out_file,
print_cmd=False,
pre_options='',
**kwargs):
"""Convert a video with ffmpeg.
Th... | Concatenate multiple videos into a single one. Args: video_list (list): A list of video filenames out_file (str): Output video filename vcodec (None or str): Output video codec, None for unchanged acodec (None or str): Output audio codec, None for unchanged log_level (str): Logging level of ffmpeg. print_cmd (bool): Wh... |
34,419 | import os.path as osp
from collections import OrderedDict
import cv2
from cv2 import (CAP_PROP_FOURCC, CAP_PROP_FPS, CAP_PROP_FRAME_COUNT,
CAP_PROP_FRAME_HEIGHT, CAP_PROP_FRAME_WIDTH,
CAP_PROP_POS_FRAMES, VideoWriter_fourcc)
from annotator.mmpkg.mmcv.utils import (check_file_exist, mkd... | Read the frame images from a directory and join them as a video. Args: frame_dir (str): The directory containing video frames. video_file (str): Output filename. fps (float): FPS of the output video. fourcc (str): Fourcc of the output video, this should be compatible with the output file type. filename_tmpl (str): File... |
34,420 | import warnings
import cv2
import numpy as np
from annotator.mmpkg.mmcv.arraymisc import dequantize, quantize
from annotator.mmpkg.mmcv.image import imread, imwrite
from annotator.mmpkg.mmcv.utils import is_str
def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
"""Recover from quantized flow.
Args:
... | Read an optical flow map. Args: flow_or_path (ndarray or str): A flow map or filepath. quantize (bool): whether to read quantized pair, if set to True, remaining args will be passed to :func:`dequantize_flow`. concat_axis (int): The axis that dx and dy are concatenated, can be either 0 or 1. Ignored if quantize is Fals... |
34,421 | import warnings
import cv2
import numpy as np
from annotator.mmpkg.mmcv.arraymisc import dequantize, quantize
from annotator.mmpkg.mmcv.image import imread, imwrite
from annotator.mmpkg.mmcv.utils import is_str
def quantize_flow(flow, max_val=0.02, norm=True):
"""Quantize flow to [0, 255].
After this step, the ... | Write optical flow to file. If the flow is not quantized, it will be saved as a .flo file losslessly, otherwise a jpeg image which is lossy but of much smaller size. (dx and dy will be concatenated horizontally into a single image if quantize is True.) Args: flow (ndarray): (h, w, 2) array of optical flow. filename (st... |
34,422 | import warnings
import cv2
import numpy as np
from annotator.mmpkg.mmcv.arraymisc import dequantize, quantize
from annotator.mmpkg.mmcv.image import imread, imwrite
from annotator.mmpkg.mmcv.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `flow_warp` function. Write a... | Use flow to warp img. Args: img (ndarray, float or uint8): Image to be warped. flow (ndarray, float): Optical Flow. filling_value (int): The missing pixels will be set with filling_value. interpolate_mode (str): bilinear -> Bilinear Interpolation; nearest -> Nearest Neighbor. Returns: ndarray: Warped image with the sam... |
34,423 | import warnings
import cv2
import numpy as np
from annotator.mmpkg.mmcv.arraymisc import dequantize, quantize
from annotator.mmpkg.mmcv.image import imread, imwrite
from annotator.mmpkg.mmcv.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `flow_from_bytes` function. W... | Read dense optical flow from bytes. .. note:: This load optical flow function works for FlyingChairs, FlyingThings3D, Sintel, FlyingChairsOcc datasets, but cannot load the data from ChairsSDHom. Args: content (bytes): Optical flow bytes got from files or other streams. Returns: ndarray: Loaded optical flow with the sha... |
34,424 | import warnings
import cv2
import numpy as np
from annotator.mmpkg.mmcv.arraymisc import dequantize, quantize
from annotator.mmpkg.mmcv.image import imread, imwrite
from annotator.mmpkg.mmcv.utils import is_str
The provided code snippet includes necessary dependencies for implementing the `sparse_flow_from_bytes` func... | Read the optical flow in KITTI datasets from bytes. This function is modified from RAFT load the `KITTI datasets <https://github.com/princeton-vl/RAFT/blob/224320502d66c356d88e6c712f38129e60661e80/core/utils/frame_utils.py#L102>`_. Args: content (bytes): Optical flow bytes got from files or other streams. Returns: Tupl... |
34,425 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.mmpkg.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
try:
# If PyTorch version >= 1.6.0, to... | Decorator to enable fp16 training automatically. This decorator is useful when you write custom modules and want to support mixed precision training. If inputs arguments are fp32 tensors, they will be converted to fp16 automatically. Arguments other than fp32 tensors are ignored. If you are using PyTorch >= 1.6, torch.... |
34,426 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.mmpkg.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
try:
# If PyTorch version >= 1.6.0, to... | Decorator to convert input arguments to fp32 in force. This decorator is useful when you write custom modules and want to support mixed precision training. If there are some inputs that must be processed in fp32 mode, then this decorator can handle it. If inputs arguments are fp16 tensors, they will be converted to fp3... |
34,427 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.mmpkg.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
def allreduce_grads(params, coalesce=True... | null |
34,428 | import functools
import warnings
from collections import abc
from inspect import getfullargspec
import numpy as np
import torch
import torch.nn as nn
from annotator.mmpkg.mmcv.utils import TORCH_VERSION, digit_version
from .dist_utils import allreduce_grads as _allreduce_grads
def patch_norm_fp32(module):
"""Recurs... | Wrap the FP32 model to FP16. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the backend, otherwise, original mmcv implementation will be adopted. For PyTorch >= 1.6, this function will 1. Set fp16 flag inside the model to True. Otherwise: 1. Convert FP32 model to FP16. 2. Remain some necessary layers to be ... |
34,430 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.mmpkg.mmcv as mmcv
The provided code snippet includes necessary dependencies for implementing the `get_host_info` function. Write a Python function ... | Get hostname and username. Return empty string if exception raised, e.g. ``getpass.getuser()`` will lead to error in docker container |
34,431 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.mmpkg.mmcv as mmcv
def get_time_str():
return time.strftime('%Y%m%d_%H%M%S', time.localtime()) | null |
34,432 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.mmpkg.mmcv as mmcv
The provided code snippet includes necessary dependencies for implementing the `obj_from_dict` function. Write a Python function ... | Initialize an object from dict. The dict must contain the key "type", which indicates the object type, it can be either a string or type, such as "list" or ``list``. Remaining fields are treated as the arguments for constructing the object. Args: info (dict): Object types and arguments. parent (:class:`module`): Module... |
34,433 | import os
import random
import sys
import time
import warnings
from getpass import getuser
from socket import gethostname
import numpy as np
import torch
import annotator.mmpkg.mmcv as mmcv
The provided code snippet includes necessary dependencies for implementing the `set_random_seed` function. Write a Python functio... | Set random seed. Args: seed (int): Seed to be used. deterministic (bool): Whether to set the deterministic option for CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` to True and `torch.backends.cudnn.benchmark` to False. Default: False. rank_shift (bool): Whether to add rank number to the random seed to h... |
34,434 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | load checkpoint by local file path. Args: filename (str): local checkpoint file path map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint. |
34,435 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | load checkpoint through the file path prefixed with pavi. In distributed setting, this function download ckpt at all ranks to different temporary directories. Args: filename (str): checkpoint file path with pavi prefix map_location (str, optional): Same as :func:`torch.load`. Default: None Returns: dict or OrderedDict:... |
34,436 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | load checkpoint through the file path prefixed with s3. In distributed setting, this function download ckpt at all ranks to different temporary directories. Args: filename (str): checkpoint file path with s3 prefix map_location (str, optional): Same as :func:`torch.load`. backend (str, optional): The storage backend ty... |
34,437 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | load checkpoint through the file path prefixed with modelzoo or torchvision. Args: filename (str): checkpoint file path with modelzoo or torchvision prefix map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint. |
34,438 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | load checkpoint through the file path prefixed with open-mmlab or openmmlab. Args: filename (str): checkpoint file path with open-mmlab or openmmlab prefix map_location (str, optional): Same as :func:`torch.load`. Default: None Returns: dict or OrderedDict: The loaded checkpoint. |
34,439 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | load checkpoint through the file path prefixed with mmcls. Args: filename (str): checkpoint file path with mmcls prefix map_location (str, optional): Same as :func:`torch.load`. Returns: dict or OrderedDict: The loaded checkpoint. |
34,440 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | Load partial pretrained model with specific prefix. Args: prefix (str): The prefix of sub-module. filename (str): Accept local filepath, URL, ``torchvision://xxx``, ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for details. map_location (str | None): Same as :func:`torch.load`. Default: None. Returns: dic... |
34,441 | import io
import os
import os.path as osp
import pkgutil
import re
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from torch.utils import model_zoo
import annota... | Save checkpoint to file. The checkpoint will have 3 fields: ``meta``, ``state_dict`` and ``optimizer``. By default ``meta`` will contain version and time info. Args: model (Module): Module whose params are to be saved. filename (str): Checkpoint filename. optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. m... |
34,443 | import copy
import inspect
import torch
from ...utils import Registry, build_from_cfg
def build_optimizer_constructor(cfg):
def build_optimizer(model, cfg):
optimizer_cfg = copy.deepcopy(cfg)
constructor_type = optimizer_cfg.pop('constructor',
'DefaultOptimizerConstruct... | null |
34,444 | import numbers
from math import cos, pi
import annotator.mmpkg.mmcv as mmcv
from .hook import HOOKS, Hook
The provided code snippet includes necessary dependencies for implementing the `get_position_from_periods` function. Write a Python function `def get_position_from_periods(iteration, cumulative_periods)` to solve ... | Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_periods = [100, 200, 300, 400], if iteration == 50, return 0; if iteration == 210, return 2; if iteration == 300, return 3. Args: iteration (int): Current iteration. cumulative_perio... |
34,445 | import numbers
from math import cos, pi
import annotator.mmpkg.mmcv as mmcv
from .hook import HOOKS, Hook
The provided code snippet includes necessary dependencies for implementing the `annealing_cos` function. Write a Python function `def annealing_cos(start, end, factor, weight=1)` to solve the following problem:
Ca... | Calculate annealing cos learning rate. Cosine anneal from `weight * start + (1 - weight) * end` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the cosine annealing. end (float): The ending learing rate of the cosine annealing. factor (float): The coefficient of `pi` when... |
34,446 | import numbers
from math import cos, pi
import annotator.mmpkg.mmcv as mmcv
from .hook import HOOKS, Hook
The provided code snippet includes necessary dependencies for implementing the `annealing_linear` function. Write a Python function `def annealing_linear(start, end, factor)` to solve the following problem:
Calcul... | Calculate annealing linear learning rate. Linear anneal from `start` to `end` as percentage goes from 0.0 to 1.0. Args: start (float): The starting learning rate of the linear annealing. end (float): The ending learing rate of the linear annealing. factor (float): The coefficient of `pi` when calculating the current pe... |
34,447 | import numbers
from math import cos, pi
import annotator.mmpkg.mmcv as mmcv
from .hook import HOOKS, Hook
def format_param(name, optim, param):
if isinstance(param, numbers.Number):
return [param] * len(optim.param_groups)
elif isinstance(param, (list, tuple)): # multi param groups
if len(para... | null |
34,452 | import copy
from ..utils import Registry
def build_runner_constructor(cfg):
return RUNNER_BUILDERS.build(cfg)
def build_runner(cfg, default_args=None):
runner_cfg = copy.deepcopy(cfg)
constructor_type = runner_cfg.pop('constructor',
'DefaultRunnerConstructor')
runn... | null |
34,454 | import importlib
import os
import pkgutil
import warnings
from collections import namedtuple
import torch
if torch.__version__ != 'parrots':
else:
from parrots import extension
from parrots.base import ParrotsException
has_return_value_ops = [
'nms',
'softnms',
'nms_match',
'... | null |
34,460 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def is_str(x):
def is_filepath(x):
return is_str(x) or isinstance(x, Path) | null |
34,463 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
import os
os.environ['IGNORE_CMD_ARGS_ERRORS'] = 'True'
def mkdir_or_exist(dir_name, mode=0o777):
if dir_name == '':
return
dir_name = osp.expanduser(dir_name)
os.makedirs(dir_name, mode=mode, exist_ok=True) | null |
34,464 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
import os
os.environ['IGNORE_CMD_ARGS_ERRORS'] = 'True'
def symlink(src, dst, overwrite=True, **kwargs):
if os.path.lexists(dst) and overwrite:
os.remove(dst)
os.symlink(src, dst, **kwargs) | null |
34,465 | import os
import os.path as osp
from pathlib import Path
from .misc import is_str
import os
os.environ['IGNORE_CMD_ARGS_ERRORS'] = 'True'
The provided code snippet includes necessary dependencies for implementing the `scandir` function. Write a Python function `def scandir(dir_path, suffix=None, recursive=False, ... | Scan a directory to find the interested files. Args: dir_path (str | obj:`Path`): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. case_sensitive (bool, optional) ... |
34,467 | import warnings
import torch
from annotator.mmpkg.mmcv.utils import digit_version
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',
'list_cast', 'tuple_cast', 'is_seq_of', 'is_list_of', 'is_tuple_of',
'slice_list', 'concat_lis... | null |
34,469 | import os.path as osp
import subprocess
import sys
from collections import defaultdict
import cv2
import torch
import annotator.mmpkg.mmcv as mmcv
from .parrots_wrapper import get_build_config
try:
import torch
except ImportError:
__all__ = [
'Config', 'ConfigDict', 'DictAction', 'is_str', 'iter_cast',... | Collect the information of the running environments. Returns: dict: The environment information. The following fields are contained. - sys.platform: The variable of ``sys.platform``. - Python: Python version. - CUDA available: Bool, indicating if CUDA is available. - GPU devices: Device type of each GPU. - CUDA_HOME (o... |
34,483 | from time import time
class Timer:
"""A flexible Timer class.
:Example:
>>> import time
>>> import annotator.mmpkg.mmcv as mmcv
>>> with mmcv.Timer():
>>> # simulate a code block that will run for 1s
>>> time.sleep(1)
1.000
>>> with mmcv.Timer(print_tmpl='it takes {:.1f} seco... | Add check points in a single line. This method is suitable for running a task on a list of items. A timer will be registered when the method is called for the first time. :Example: >>> import time >>> import annotator.mmpkg.mmcv as mmcv >>> for i in range(1, 6): >>> # simulate a code block >>> time.sleep(i) >>> mmcv.ch... |
34,494 | import torch
import torch.nn as nn
import torch.nn.functional as F
def norm_normalize(norm_out):
min_kappa = 0.01
norm_x, norm_y, norm_z, kappa = torch.split(norm_out, 1, dim=1)
norm = torch.sqrt(norm_x ** 2.0 + norm_y ** 2.0 + norm_z ** 2.0) + 1e-10
kappa = F.elu(kappa) + 1.0 + min_kappa
final_out... | null |
34,495 | import torch
import torch.nn as nn
import torch.nn.functional as F
def sample_points(init_normal, gt_norm_mask, sampling_ratio, beta):
device = init_normal.device
B, _, H, W = init_normal.shape
N = int(sampling_ratio * H * W)
beta = beta
# uncertainty map
uncertainty_map = -1 * init_normal[:, ... | null |
34,496 | import argparse
import numpy as np
from caffe2.python import core, workspace, model_helper
from caffe2.proto import caffe2_pb2
from data import create_loader, resolve_data_config, Dataset
from utils import AverageMeter
import time
def accuracy_np(output, target):
max_indices = np.argsort(output, axis=1)[:, ::-1]
... | null |
34,497 | import os
The provided code snippet includes necessary dependencies for implementing the `accuracy` function. Write a Python function `def accuracy(output, target, topk=(1,))` to solve the following problem:
Computes the precision@k for the specified values of k
Here is the function:
def accuracy(output, target, top... | Computes the precision@k for the specified values of k |
34,498 | import os
def get_outdir(path, *paths, inc=False):
outdir = os.path.join(path, *paths)
if not os.path.exists(outdir):
os.makedirs(outdir)
elif inc:
count = 1
outdir_inc = outdir + '-' + str(count)
while os.path.exists(outdir_inc):
count = count + 1
ou... | null |
34,499 | import argparse
import numpy as np
import onnxruntime
from data import create_loader, resolve_data_config, Dataset
from utils import AverageMeter
import time
def accuracy_np(output, target):
max_indices = np.argsort(output, axis=1)[:, ::-1]
top5 = 100 * np.equal(max_indices[:, :5], target[:, np.newaxis]).sum(a... | null |
34,500 | import argparse
import warnings
import onnx
from onnx import optimizer
def traverse_graph(graph, prefix=''):
content = []
indent = prefix + ' '
graphs = []
num_nodes = 0
for node in graph.node:
pn, gs = onnx.helper.printable_node(node, indent, subgraphs=True)
assert isinstance(gs, ... | null |
34,501 | from .config import set_layer_config
from .helpers import load_checkpoint
from .gen_efficientnet import *
from .mobilenetv3 import *
def load_checkpoint(model, checkpoint_path):
if checkpoint_path and os.path.isfile(checkpoint_path):
print("=> Loading checkpoint '{}'".format(checkpoint_path))
check... | null |
34,502 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates ... | MNASNet B1, depth multiplier of 0.5. |
34,503 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates ... | MNASNet B1, depth multiplier of 0.75. |
34,504 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def mnasnet_100(pretrained=False, **kwargs):
""" MNASNet B1, depth multiplier of 1.0. """
... | MNASNet B1, depth multiplier of 1.0. |
34,505 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates ... | MNASNet B1, depth multiplier of 1.4 |
34,506 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates ... | MNASNet A1 (w/ SE), depth multiplier of 0.5 |
34,507 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates ... | MNASNet A1 (w/ SE), depth multiplier of 0.75. |
34,508 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def semnasnet_100(pretrained=False, **kwargs):
""" MNASNet A1 (w/ SE), depth multiplier of 1.... | MNASNet A1 (w/ SE), depth multiplier of 1.0. |
34,509 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates ... | MNASNet A1 (w/ SE), depth multiplier of 1.4. |
34,510 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creat... | MNASNet Small, depth multiplier of 1.0. |
34,511 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_he... | MobileNet V2 w/ 1.0 channel multiplier |
34,512 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_he... | MobileNet V2 w/ 1.4 channel multiplier |
34,513 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_he... | MobileNet V2 w/ 1.1 channel, 1.2 depth multipliers |
34,514 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_mobilenet_v2(
variant, channel_multiplier=1.0, depth_multiplier=1.0, fix_stem_he... | MobileNet V2 w/ 1.2 channel, 1.4 depth multipliers |
34,515 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
""" FBNet-C
... | FBNet-C |
34,516 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
"""Creates th... | Single-Path NAS Pixel1 |
34,517 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B0 |
34,518 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B1 |
34,519 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B2 |
34,520 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B3 |
34,521 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B4 |
34,522 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B5 |
34,523 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B6 |
34,524 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B7 |
34,525 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B8 |
34,526 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-L2. |
34,527 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Edge Small. |
34,528 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Edge-Medium. |
34,529 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Edge-Large. |
34,530 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, ex... | EfficientNet-CondConv-B0 w/ 8 Experts |
34,531 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, ex... | EfficientNet-CondConv-B0 w/ 8 Experts |
34,532 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_condconv(
variant, channel_multiplier=1.0, depth_multiplier=1.0, ex... | EfficientNet-CondConv-B1 w/ 8 Experts |
34,533 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite0 |
34,534 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite1 |
34,535 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite2 |
34,536 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite3 |
34,537 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=Fals... | EfficientNet-Lite4 |
34,538 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B0 AutoAug. Tensorflow compatible variant |
34,539 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B1 AutoAug. Tensorflow compatible variant |
34,540 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B2 AutoAug. Tensorflow compatible variant |
34,541 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B3 AutoAug. Tensorflow compatible variant |
34,542 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B4 AutoAug. Tensorflow compatible variant |
34,543 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B5 RandAug. Tensorflow compatible variant |
34,544 | import torch.nn as nn
import torch.nn.functional as F
from .config import layer_config_kwargs, is_scriptable
from .conv2d_layers import select_conv2d
from .helpers import load_pretrained
from .efficientnet_builder import *
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **... | EfficientNet-B6 AutoAug. Tensorflow compatible variant |
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