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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mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/extract_process.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import get_dist_info
from mmselfsup.utils.collect import (dist_forward_collect,
nondist_forward_collect)
from .multi_pooling import MultiPooling
class ExtractProcess(object):
"""Global aver... | 3,770 | 36.71 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/multi_pooling.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
class MultiPooling(BaseModule):
"""Pooling layers for features from multiple depth.
Args:
pool_type (str): Pooling type for the feature map. Options are
'adaptive' and 'specified'. Def... | 1,756 | 34.14 | 70 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/knn_classifier.py | # Copyright (c) Facebook, Inc. and its affiliates.
# This file is borrowed from
# https://github.com/facebookresearch/dino/blob/main/eval_knn.py
import torch
import torch.nn as nn
@torch.no_grad()
def knn_classifier(train_features,
train_labels,
test_features,
... | 2,949 | 40.549296 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/sobel.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
class Sobel(BaseModule):
"""Sobel layer."""
def __init__(self):
super(Sobel, self).__init__()
grayscale = nn.Conv2d(3, 1, kernel_size=1, stride=1, padding=0)
grayscale... | 924 | 33.259259 | 74 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/transformer_blocks.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
import torch
import torch.nn as nn
from mmcls.models.backbones.vision_transformer import \
TransformerEncoderLayer as _TransformerEncoderLayer
from mmcls.models.utils import MultiheadAttention as _MultiheadAttention
from mmcv.cnn import bu... | 20,969 | 38.566038 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/gather_layer.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.distributed as dist
class GatherLayer(torch.autograd.Function):
"""Gather tensors from all process, supporting backward propagation."""
@staticmethod
def forward(ctx, input):
ctx.save_for_backward(input)
output = [
... | 669 | 26.916667 | 75 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/hog_layer.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class HOGLayerC(nn.Module):
"""Generate hog feature for each batch images. This module is used in
Maskfeat to generate hog feature. This code is borrowed from.
<https://github.c... | 3,958 | 36 | 89 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/utils/multi_prototypes.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
class MultiPrototypes(BaseModule):
"""Multi-prototypes for SwAV head.
Args:
output_dim (int): The output dim from SwAV neck.
num_prototypes (list[int]): The number of prototypes needed.
... | 851 | 30.555556 | 68 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/cae_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcls.models import VisionTransformer
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner.base_module import ModuleList
from torch import nn
from ..builder import BACKBONES
from ..utils import TransformerEncoderLayer... | 6,323 | 40.333333 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/simmim_swin.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union
import torch
import torch.nn as nn
from mmcls.models import SwinTransformer
from mmcv.cnn.utils.weight_init import trunc_normal_
from ..builder import BACKBONES
@BACKBONES.register_module()
class SimMIMSwinTransforme... | 5,446 | 36.054422 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/maskfeat_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Tuple, Union
import torch
from mmcls.models import VisionTransformer
from mmcv.cnn.utils.weight_init import trunc_normal_
from torch import nn
from ..builder import BACKBONES
@BACKBONES.register_module()
class MaskFeatViT(VisionTransformer... | 4,864 | 37.611111 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcls.models.backbones import ResNet as _ResNet
from mmcls.models.backbones.resnet import BasicBlock, Bottleneck
from ..builder import BACKBONES
@BACKBONES.register_module()
class ResNet(_ResNet):
"""ResNet backbone.
Please refer to the `paper <https://ar... | 6,699 | 38.411765 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/mae_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcls.models import VisionTransformer
from torch import nn
from ..builder import BACKBONES
from ..utils import build_2d_sincos_position_embedding
@BACKBONES.register_module()
class MAEViT(VisionTransformer):
"""Vision Transformer for MAE pre-train... | 6,229 | 36.305389 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcls.models.backbones.resnet import ResLayer
from mmcls.models.backbones.resnext import Bottleneck
from ..builder import BACKBONES
from .resnet import ResNet
@BACKBONES.register_module()
class ResNeXt(ResNet):
"""ResNeXt backbone.
Please refer to the `pa... | 3,808 | 41.797753 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/vision_transformer.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
from functools import reduce
from operator import mul
import torch.nn as nn
from mmcls.models.backbones import VisionTransformer as _VisionTransformer
from mmcls.models.utils import to_2tuple
from mmcv.cnn.bricks.transformer import PatchEmbed
from torch.nn.mo... | 5,260 | 37.123188 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/backbones/mim_cls_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcls.models import VisionTransformer
from mmcv.cnn import build_norm_layer
from mmcv.runner.base_module import ModuleList
from ..builder import BACKBONES
from ..utils import TransformerEncoderLayer
@BACKBONES.register_module()
clas... | 5,282 | 37.007194 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/contrastive_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import HEADS
@HEADS.register_module()
class ContrastiveHead(BaseModule):
"""Head for contrastive learning.
The contrastive loss is implemented in this head and is used in SimC... | 1,282 | 28.159091 | 75 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/swav_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import BaseModule
from mmselfsup.utils import distributed_sinkhorn
from ..builder import HEADS
from ..utils import MultiPrototypes
@HEADS.register_module()
class SwA... | 4,523 | 39.035398 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/mae_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcls.models import LabelSmoothLoss
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner import BaseModule
from torch import nn
from ..builder import HEADS
@HEADS.register_module()
class MAEPretrainHead(BaseModule):
"""Pre-trainin... | 4,138 | 28.776978 | 77 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/cae_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
import torch
from mmcv.runner import BaseModule
from torch import nn
from ..builder import HEADS
from ..utils import Encoder
@HEADS.register_module()
class CAEHead(BaseModule):
"""Pretrain Head for CAE.
Compute the align loss and the... | 2,336 | 32.385714 | 151 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/maskfeat_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcls.models import LabelSmoothLoss
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner import BaseModule
from torch import nn
from ..builder import HEADS
@HEADS.register_module()
class MaskFeatPretrainHead(BaseModule):
"""Pre-tr... | 3,341 | 31.134615 | 75 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/cls_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule
from ..builder import HEADS
from ..utils import accuracy
@HEADS.register_module()
class ClsHead(BaseModule):
"""Simplest classifier head, with only one fc layer.
Args:
with_avg_pool (bool): Wheth... | 2,431 | 31.864865 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/simmim_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import BaseModule
from torch.nn import functional as F
from ..builder import HEADS
@HEADS.register_module()
class SimMIMHead(BaseModule):
"""Pretrain Head for SimMIM.
Args:
patch_size (int): Patch size of each token.
... | 1,114 | 29.972222 | 76 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/multi_cls_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_norm_layer
from mmcv.runner import BaseModule
from ..builder import HEADS
from ..utils import MultiPooling, accuracy
@HEADS.register_module()
class MultiClsHead(BaseModule):
"""Multiple classifier heads.
This he... | 3,687 | 35.88 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/mocov3_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmselfsup.utils import concat_all_gather
from ..builder import HEADS, build_neck
@HEADS.register_module()
class MoCoV3Head(BaseModule):
"""Head for MoCo v3 algorithms.
This head builds ... | 2,024 | 32.196721 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/models/heads/latent_pred_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule, get_dist_info
from ..builder import HEADS, build_neck
@HEADS.register_module()
class LatentPredictHead(BaseModule):
"""Head for latent feature prediction.
This head builds a predictor, whic... | 4,301 | 31.590909 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/base.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from mmcv.utils import build_from_cfg
from torch.utils.data import Dataset
from torchvision.transforms import Compose
from .builder import PIPELINES, build_datasource
class BaseDataset(Dataset, metaclass=ABCMeta)... | 1,698 | 34.395833 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/rotation_pred.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from .base import BaseDataset
from .builder import DATASETS
from .utils import to_numpy
def rotate(img):
"""Rotate input image with 0, 90, 180, and 270 degrees.
Args:
img (Tensor): input image of shape (C, H, W).
Returns:
list... | 1,716 | 29.660714 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/deepcluster.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from .base import BaseDataset
from .builder import DATASETS
from .utils import to_numpy
@DATASETS.register_module()
class DeepClusterDataset(BaseDataset):
"""Dataset for DC and ODC.
The dataset initializes clustering labels and assigns it during t... | 1,623 | 35.088889 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/relative_loc.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torchvision.transforms.functional as TF
from mmcv.utils import build_from_cfg
from torchvision.transforms import Compose, RandomCrop
from .base import BaseDataset
from .builder import DATASETS, PIPELINES
from .utils import to_numpy
def image_to_patc... | 3,110 | 35.174419 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import gzip
import hashlib
import os
import os.path as osp
import shutil
import tarfile
import urllib.error
import urllib.request
import zipfile
import numpy as np
import torch
def to_numpy(pil_img):
np_img = np.array(pil_img, dtype=np.uint8)
if np_img.ndim < 3... | 6,307 | 28.476636 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/multi_view.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.utils import build_from_cfg
from torchvision.transforms import Compose
from .base import BaseDataset
from .builder import DATASETS, PIPELINES, build_datasource
from .utils import to_numpy
@DATASETS.register_module()
class MultiViewDataset(BaseDat... | 2,427 | 36.353846 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/single_view.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.utils import print_log
from .base import BaseDataset
from .builder import DATASETS
from .utils import to_numpy
@DATASETS.register_module()
class SingleViewDataset(BaseDataset):
"""The dataset outputs one view of an image, containing some othe... | 2,631 | 38.878788 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but... | 1,368 | 26.938776 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import platform
import random
import warnings
from functools import partial
import numpy as np
import torch
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg, digit_version
from torch.utils.data import... | 6,330 | 35.385057 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/data_sources/cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import numpy as np
import torch.distributed as dist
from mmcv.runner import get_dist_info
from ..builder import DATASOURCES
from ..utils import check_integrity, download_and_extract_archive
from .base import BaseDataSource
@DATASOUR... | 4,483 | 32.969697 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/data_sources/imagenet.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import numpy as np
from ..builder import DATASOURCES
from .base import BaseDataSource
def has_file_allowed_extension(filename, extensions):
"""Checks if a file is an allowed extension.
Args:
filename (string): path to a... | 3,380 | 32.147059 | 82 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/samplers/group_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import Sampler
class GroupSampler(Sampler):
def __init__(self, dataset, samples_per_gpu=1):
assert hasattr(dataset, 'flag')
self.dataset = datas... | 4,914 | 33.858156 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/samplers/distributed_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
from mmselfsup.utils import sync_random_seed
class DistributedSampler(_DistributedSampler):... | 6,637 | 34.881081 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/datasets/pipelines/transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import inspect
import math
import random
import warnings
from typing import Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torchvision.transforms.functional as F
from mmcv.utils import build_from_cfg
from PIL import Image, ImageFilter
from timm.d... | 21,246 | 34.709244 | 89 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/collect.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
from .gather import gather_tensors_batch
def nondist_forward_collect(func, data_loader, length):
"""Forward and collect network outputs.
This function performs forward propagation and collects outputs.
It can be ... | 3,043 | 33.988506 | 78 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/dist_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
from mmcv.runner import get_dist_info
def sync_random_seed(seed=None, device='cuda'):
"""Make sure different ranks share the same seed. All workers must call
this function, otherwise it will deadlo... | 1,587 | 34.288889 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/setup_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.syste... | 2,219 | 45.25 | 112 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/alias_multinomial.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
class AliasMethod(object):
"""The alias method for sampling.
From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
Args:
probs (Tensor): Sampling probabilities.
""" # ... | 2,195 | 27.894737 | 120 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/distributed_sinkhorn.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This file is modified from
# https://github.com/facebookresearch/swav/blob/main/main_swav.py
import torch
import torch.distributed as dist
@torch.no_grad()
def distributed_sinkhorn(out, sinkhorn_iterations, world_size, epsilon):
"""Apply the ... | 1,390 | 30.613636 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/gather.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
import torch.distributed as dist
def gather_tensors(input_array):
"""Gather tensor from all GPUs."""
world_size = dist.get_world_size()
# gather shapes first
myshape = input_array.shape
mycount = input_array.size
s... | 3,019 | 34.529412 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/clustering.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
# This file is modified from
# https://github.com/facebookresearch/deepcluster/blob/master/clustering.py
import time
try:
import faiss
except ImportError:
faiss = None
import numpy as np
import torch
from scipy.sparse import csr_matrix
__al... | 8,732 | 27.821782 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/extractor.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from torch.utils.data import Dataset
from mmselfsup.utils import dist_forward_collect, nondist_forward_collect
class Extractor(object):
"""Feature extractor.
Args:
dataset (Dataset | dict): A PyTorch dataset or dict that indicates... | 2,881 | 37.426667 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/mmselfsup/utils/batch_shuffle.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from .gather import concat_all_gather
@torch.no_grad()
def batch_shuffle_ddp(x):
"""Batch shuffle, for making use of BatchNorm.
*** Only support DistributedDataParallel (DDP) model. ***
"""
# gather from all gpus
batch_size_this = x.sh... | 1,381 | 24.592593 | 61 | py |
mmselfsup-0.x | mmselfsup-0.x/configs/benchmarks/mmdetection/_base_/models/mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(ty... | 4,054 | 32.512397 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/configs/benchmarks/mmdetection/_base_/models/mask_rcnn_r50_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
... | 4,024 | 31.459677 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/configs/benchmarks/mmdetection/_base_/models/faster_rcnn_r50_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
... | 3,657 | 31.371681 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/configs/benchmarks/mmsegmentation/_base_/models/fcn_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=(1, 2, 1, 1),
norm_cfg=norm_cfg,
... | 1,317 | 27.652174 | 79 | py |
mmselfsup-0.x | mmselfsup-0.x/docs/en/conf.py | # Copyright (c) OpenMMLab. All rights reserved.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup -----------------------... | 4,244 | 32.425197 | 134 | py |
mmselfsup-0.x | mmselfsup-0.x/docs/zh_cn/conf.py | # Copyright (c) OpenMMLab. All rights reserved.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup -----------------------... | 4,100 | 31.039063 | 90 | py |
unlimiformer | unlimiformer-main/src/inference-example.py | from unlimiformer import Unlimiformer
from random_training_unlimiformer import RandomTrainingUnlimiformer
from usage import UnlimiformerArguments, training_addin
from transformers import BartForConditionalGeneration, AutoTokenizer
from datasets import load_dataset
import torch
device = torch.device('cuda' if torch.cu... | 2,317 | 38.965517 | 115 | py |
unlimiformer | unlimiformer-main/src/random_training_unlimiformer.py | import contextlib
import numpy as np
import torch
from torch import nn
from enum import Enum, auto
from unlimiformer import Unlimiformer, ModelType, UnlimiformerBART, UnlimiformerT5, UnlimiformerLED
from transformers import BartModel, BartForConditionalGeneration, \
T5Model, T5ForConditionalGeneration, \
LEDMod... | 11,565 | 50.633929 | 139 | py |
unlimiformer | unlimiformer-main/src/unlimiformer.py | import logging
import numpy as np
import torch
from torch import nn
from enum import Enum, auto
from transformers import BartModel, BartForConditionalGeneration, \
T5Model, T5ForConditionalGeneration, \
LEDModel, LEDForConditionalGeneration, \
AutoModelForSeq2SeqLM
from typing import TypeVar, Generic
from... | 50,326 | 53.52546 | 178 | py |
unlimiformer | unlimiformer-main/src/run.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | 52,495 | 43.450466 | 171 | py |
unlimiformer | unlimiformer-main/src/index_building.py | import faiss
import faiss.contrib.torch_utils
import time
import logging
import torch
import numpy as np
code_size = 64
class DatastoreBatch():
def __init__(self, dim, batch_size, flat_index=False, gpu_index=False, verbose=False) -> None:
self.indices = []
self.batch_size = batch_size
for... | 5,479 | 39 | 116 | py |
unlimiformer | unlimiformer-main/src/utils/override_training_args.py |
import os
import sys
import torch.cuda
from transformers.utils import logging
sys.path.insert(0, os.getcwd())
from dataclasses import dataclass, field
from transformers.trainer_utils import IntervalStrategy
from transformers import Seq2SeqTrainingArguments
logger = logging.get_logger('swed_logger')
@dataclass
cl... | 5,685 | 52.140187 | 138 | py |
unlimiformer | unlimiformer-main/src/utils/custom_seq2seq_trainer.py | import json
import math
import os
import time
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from datasets import Dataset
from torch import nn
from transformers.debug_utils import DebugOption
from transformers.deepspeed import is_deepspeed_zero3_enabled
from... | 14,785 | 43.942249 | 158 | py |
hessian-eff-dim | hessian-eff-dim-master/setup.py | from setuptools import setup
import os
import sys
_here = os.path.abspath(os.path.dirname(__file__))
if sys.version_info[0] < 3:
with open(os.path.join(_here, 'README.rst')) as f:
long_description = f.read()
else:
with open(os.path.join(_here, 'README.rst'), encoding='utf-8') as f:
long_descri... | 1,026 | 25.333333 | 72 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/utils.py | import torch
import time
import numpy as np
import hess
from torch import nn
import torch.nn.functional as F
from torch.autograd import Variable
from gpytorch.utils.lanczos import lanczos_tridiag, lanczos_tridiag_to_diag
def unflatten_like(vector, likeTensorList):
# Takes a flat torch.tensor and unflattens it to ... | 9,414 | 36.066929 | 94 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/net_utils.py | import torch
import time
import numpy as np
#import hess
from .nets import SubLayerLinear, GetSubnet, MaskedLinear
from torch import nn
def freeze_model_weights(model):
print("=> Freezing model weights")
for n, m in model.named_modules():
if hasattr(m, "weight") and m.weight is not None:
p... | 2,720 | 31.011765 | 72 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/loss_surfaces/dataloader_loss_surface.py | import math
import torch
import numpy as np
from .. import utils
from .loss_surfaces import get_plane
def dataloader_loss_surface(basis, model,
dataloader,
loss=torch.nn.MSELoss(),
rng=0.1, n_pts=25, **kwargs):
start_pars = model.state_dict()
## get ... | 1,191 | 31.216216 | 75 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/loss_surfaces/loss_surfaces.py | import math
import torch
import numpy as np
from .. import utils
def get_loss_surface(basis, model,
train_x, train_y,
loss,
rng=0.1, n_pts=25,
use_cuda=False, **kwargs):
"""
note that loss should be a lambda function that just take... | 1,846 | 27.859375 | 76 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/linear_subnet_layers.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import math
from .conv_type import GetSubnet
class SubLayerLinear(nn.Linear):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.scores = nn.Parameter(torch.Tensor(self.we... | 1,291 | 29.046512 | 87 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/moon_net.py | import torch
import math
from torch import nn
class MoonNet(nn.Module):
"""docstring for SimpleNet."""
def __init__(self, x, y, hidden_size=10, n_hidden=2,
activation=torch.nn.ReLU(), bias=False):
super(MoonNet, self).__init__()
self.x = x #inputs in G space
self.y = y #... | 1,203 | 31.540541 | 73 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/simple_net.py | import torch
import math
from torch import nn
class SimpleNet(nn.Module):
"""docstring for SimpleNet."""
def __init__(self, in_dim, out_dim, hidden_size=10, n_hidden=2,
activation=torch.nn.ReLU(), bias=False):
super(SimpleNet, self).__init__()
## initialize the network ##
... | 772 | 29.92 | 73 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/masked_layer.py | import math
import torch
import torch.nn as nn
from torch.nn import Module, init, Linear, Conv2d
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from torch.nn.modules.utils import _pair
class MaskedLinear(Linear):
#__constants__ = ['bias', 'in_features', 'out_features']
def __init__(s... | 3,241 | 45.314286 | 110 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/preresnet.py | """
PreResNet model definition
ported from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/preresnet.py
"""
import torch.nn as nn
import torchvision.transforms as transforms
import math
from .masked_layer import MaskedConv2d, MaskedLinear
__all__ = ["PreResNet110", "PreResNet56", "... | 9,014 | 29.150502 | 106 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/conv_type.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import math
DenseConv = nn.Conv2d
class GetSubnet(autograd.Function):
@staticmethod
def forward(ctx, scores, k):
# Get the subnetwork by sorting the scores and using the top k%
out = scores.c... | 4,184 | 25.826923 | 82 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/resnet.py | ## ResNet18 for CIFAR
## Based on: https://github.com/kuangliu/pytorch-cifar/blob/master/models/preact_resnet.py
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
__all__ = ["ResNet18"]
class PreActBlock(nn.Module):
'''Pre-activation version of the BasicBlock.'''
expan... | 3,536 | 35.84375 | 92 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/linear_subnets.py | import torch
from torch import nn
from .linear_subnet_layers import SubLayerLinear
from .masked_layer import MaskedLinear
class SubNetLinear(nn.Module):
"""
Small MLP
"""
def __init__(self, in_dim, out_dim, k=16,
n_layers=5,
activation=nn.ReLU(), bias=True):
sup... | 1,712 | 27.55 | 60 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/vgg.py | """
VGG model definition
ported from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import math
import torch.nn as nn
import torchvision.transforms as transforms
from .masked_layer import MaskedLinear, MaskedConv2d
__all__ = ["VGG16", "VGG16BN", "VGG19", "VGG19BN"]
def make_laye... | 3,610 | 22.448052 | 110 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/cifar_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def ConvBNrelu(in_channels,out_channels,stride=1):
return nn.Sequential(
nn.Conv2d(in_channels,out_channels,3,padding=1,stride=stride),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
class cifar_net(nn.Module):
"""
Ve... | 1,252 | 26.844444 | 70 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/wide_resnet.py | """
WideResNet model definition
ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
from .masked_layer import MaskedConv2d... | 3,816 | 31.07563 | 100 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/transformer.py | import math
import torch
from torch import nn
from .simple_net import SimpleNet
class Transformer(nn.Module):
"""docstring for Transformer."""
def __init__(self, x, y, net=SimpleNet, **kwargs):
super(Transformer, self).__init__()
self.x = x
self.y = y
self.net = net(x, y, **kwa... | 855 | 24.176471 | 71 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/simple_lstm.py | import torch
import math
from torch import nn
class LSTM(nn.Module):
def __init__(self, train_x, train_y, n_hidden=2, hidden_size=10,
input_dim=1, output_dim=1,
batch_size=1):
super(LSTM, self).__init__()
self.input_dim = 1
self.hidden_dim = hidden_size
... | 686 | 26.48 | 77 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/convnet.py | ## 5-Layer CNN for CIFAR
## Based on https://myrtle.ai/learn/how-to-train-your-resnet-4-architecture/
# based on https://gitlab.com/harvard-machine-learning/double-descent/blob/master/models/mcnn.py
from torchvision import transforms
import torch.nn as nn
def block(input, output):
# Layer i
list = [nn.Conv2d... | 4,293 | 32.286822 | 96 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/nets/masked_net.py | import torch
import math
from torch import nn
from .masked_layer import MaskedLinear
class MaskedNet(nn.Module):
"""docstring for SimpleNet."""
def __init__(self, x, y, hidden_size=10, n_hidden=2,
activation=torch.nn.ReLU(), bias=False, pct_keep=0.6):
super(MaskedNet, self).__init__()
... | 1,350 | 32.775 | 75 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/plotting/decision_boundary.py | import math
import torch
import numpy as np
import matplotlib.pyplot as plt
def plot_decision_boundary(train_x, train_y, classifier, use_cuda=False,
buffer=0.5, h=0.1):
x_min, x_max = train_x[:, 0].min() - buffer, train_x[:, 0].max() + buffer
y_min, y_max = train_x[:, 1].min() - buff... | 852 | 39.619048 | 100 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/runners/cifar10_runner.py | import math
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import argparse
import sys
from hess.nets.cifar_net import cifar_net
def parse():
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', help="number of epochs",... | 2,768 | 32.768293 | 82 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/data/data.py | import numpy as np
import torch
import torchvision
import os
from .fake import FakeData
__all__ = ['loaders']
c10_classes = np.array([[0, 1, 2, 8, 9], [3, 4, 5, 6, 7]], dtype=np.int32)
def svhn_loaders(
path,
batch_size,
num_workers,
transform_train,
transform_test,
use_validation,
val_s... | 5,916 | 29.34359 | 87 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/data/fake.py | import torch
from torchvision.datasets.vision import VisionDataset
from torchvision import transforms
class FakeData(VisionDataset):
"""A fake dataset that returns randomly generated images and returns them as PIL images
Args:
size (int, optional): Size of the dataset. Default: 1000 images
ima... | 2,405 | 39.1 | 91 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/eigs/fisher_vec_prod.py | """
compute hessian vector products as well as eigenvalues of the hessian
# copied from https://github.com/tomgoldstein/loss-landscape/blob/master/hess_vec_prod.py
# code re-written to use gpu by default and then to use gpytorch
"""
import torch
import time
# import numpy as np
# from torch import nn
# fr... | 6,396 | 33.208556 | 112 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/eigs/hess_vec_prod.py | """
compute hessian vector products as well as eigenvalues of the hessian
# copied from https://github.com/tomgoldstein/loss-landscape/blob/master/hess_vec_prod.py
# code re-written to use gpu by default and then to use gpytorch
"""
import torch
import time
import numpy as np
from torch import nn
from torc... | 6,027 | 35.533333 | 108 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/eigs/obsfisher_vec_prod.py | """
compute hessian vector products as well as eigenvalues of the hessian
# copied from https://github.com/tomgoldstein/loss-landscape/blob/master/hess_vec_prod.py
# code re-written to use gpu by default and then to use gpytorch
"""
import torch
import time
# import numpy as np
# from torch import nn
# fr... | 5,670 | 36.556291 | 136 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/eigs/hessian_eigenpairs.py | import torch
import time
import numpy as np
from torch import nn
from torch.autograd import Variable
from gpytorch.utils.lanczos import lanczos_tridiag, lanczos_tridiag_to_diag
from hess.utils import flatten, unflatten_like, gradtensor_to_tensor
from hess.utils import eval_hess_vec_prod
def hessian_eigenpairs(net, cri... | 1,524 | 32.152174 | 77 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/eigs/run_hess_eigs.py | """
script to compute maximum and minimum eigenvalues of the hessian
"""
import argparse
import torch
# import torch.nn.functional as F
import numpy as np
# import os
# import tqdm
from swag import models, data
from hess_vec_prod import min_max_hessian_eigs
from fisher_vec_prod import min_max_fisher_eigs
from o... | 3,984 | 25.744966 | 89 | py |
hessian-eff-dim | hessian-eff-dim-master/hess/losses/trace_loss.py | import torch
import torch.nn.functional as F
import copy
from ..utils import flatten, unflatten_like
def fisher_trace(inputs, targets, diag_pars, model, base_loss = F.cross_entropy, beta = 0.01, samples = 1, nugget=1e-5):
model_state_dict = copy.deepcopy(model.state_dict())
param_vec = flatten(model.parameter... | 1,150 | 33.878788 | 120 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/test_subnets_cifar.py | import math
import torch
import hess
import hess.utils as utils
import hess.nets
import numpy as np
import pickle
import argparse
import os, sys
import time
import tabulate
import swag.utils as training_utils
import swag
from hess import data
import hess.nets as models
from parser import parser
columns = ["ep", "lr",... | 5,889 | 32.089888 | 103 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/test_subnets_spirals.py | import math
import torch
import hess
import hess.utils as utils
import hess.nets
import numpy as np
import pickle
def twospirals(n_points, noise=.5, random_state=920):
"""
Returns the two spirals dataset.
"""
n = np.sqrt(np.random.rand(n_points,1)) * 600 * (2*np.pi)/360
d1x = -1.5*np.cos(n)*n + np... | 3,461 | 30.761468 | 82 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/gen-bounds/compute_sigma_norm.py | import math
import torch
import torchvision
import hess
from hess.nets import ConvNetDepth
import torchvision
from torchvision import transforms
from norms import perturb_model, compute_accuracy, sharpness_sigma
def main():
use_cuda = torch.cuda.is_available()
## load in a loader just for sizing ##
tra... | 2,225 | 30.352113 | 85 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/gen-bounds/model_checker.py | import math
import torch
import torchvision
import hess
from hess.nets import ConvNetDepth
import torchvision
from torchvision import transforms
## model sizes ##
depths = torch.arange(9)
widths = torch.arange(4, 65, 4)
num_classes = 100
use_cuda = torch.cuda.is_available()
for d_ind, dpth in enumerate(depths):
... | 848 | 26.387097 | 79 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/gen-bounds/norms.py | import torch
import math
import copy
import warnings
import torch.nn as nn
import numpy as np
from hess import utils
# This function calculates path-norm introduced in Neyshabur et al. 2015
def lp_path_norm(model, device, p=2, input_size=[3, 32, 32]):
tmp_model = copy.deepcopy(model)
tmp_model.eval()
for ... | 2,782 | 31.360465 | 91 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/gen-bounds/compute_sigma_norm_resnets.py | import math
import torch
import torchvision
import hess
from hess.nets import PreActBlock, PreActResNet
import torchvision
from torchvision import transforms
from norms import sharpness_sigma
def main():
use_cuda = torch.cuda.is_available()
print("use cuda = ", use_cuda)
## load in a loader just for si... | 2,052 | 29.641791 | 85 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/gen-bounds/compute_pac_bayes.py | import math
import torch
import torchvision
import hess
from hess.nets import PreActBlock, PreActResNet
import torchvision
from torchvision import transforms
from norms import lp_path_norm
from hess import utils
def main():
use_cuda = torch.cuda.is_available()
## load in a loader just for sizing ##
tra... | 1,734 | 26.983871 | 85 | py |
hessian-eff-dim | hessian-eff-dim-master/experiments/gen-bounds/compute_path_norm_resenets.py | import math
import torch
import torchvision
import hess
from hess.nets import PreActResNet, PreActBlock
import torchvision
from torchvision import transforms
from norms import lp_path_norm
def main():
## load in a loader just for sizing ##
transform = transforms.Compose(
[
transforms.Resiz... | 1,799 | 29.508475 | 93 | py |
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