repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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aodisaggregation | aodisaggregation-main/utils/dict.py | import itertools
def product_dict(**kwargs):
keys = list(kwargs.keys())
vals = list(kwargs.values())
sorted_idx = sorted(range(len(keys)), key=keys.__getitem__)
sorted_keys = [keys[i] for i in sorted_idx]
sorted_vals = [vals[i] for i in sorted_idx]
for instance in itertools.product(*sorted_val... | 966 | 30.193548 | 73 | py |
aodisaggregation | aodisaggregation-main/utils/__init__.py | from .dict import *
| 20 | 9.5 | 19 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import warnings
import mmcv
import numpy as np
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from mmcls.apis import... | 7,640 | 36.455882 | 107 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/load_ckp.py | import torch
def main():
# PATH = 'resnet18_R182R18_common_network_20211112v2.pth'
# ckp_path = '/home/yangxingyi/NeuralFactor/Multi-task-Depth-Seg/result/NYUD/kd_resnet_50_to_resnet_18/multi_task_baseline/best_model.pth.tar'
ckp_path = ''
model_dict = torch.load(ckp_path)
# save_dict= dict(common_... | 1,245 | 37.9375 | 146 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import copy
import os
import os.path as osp
import time
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmcls import __version__
from mmcls.apis import set_random_seed, tr... | 6,600 | 35.269231 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/dist_train.py | import os
import subprocess
import time
import argparse
def assign_free_gpus(threshold_vram_usage=1500, max_gpus=1, wait=True, sleep_time=10):
"""
Assigns free gpus to the current process via the CUDA_AVAILABLE_DEVICES env variable
This function should be called after all imports,
in case you are settin... | 2,904 | 40.5 | 115 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/mmcls2torchserve.py | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except Imp... | 3,706 | 32.098214 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
import mmcv
import numpy as np
from mmcv import DictAction
from mmcv.parallel import MMDataParallel
from mmcls.apis import single_gpu_test
from mmcls.core.export import ONNXRuntimeClassifier, TensorRTClassifier
from mmcls.datasets import ... | 4,153 | 34.504274 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/onnx2tensorrt.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import numpy as np
def get_GiB(x: int):
"""return x GiB."""
return x * (1 << 30)
def onnx2tensorrt(onnx_file,
trt_file,
input_shape,
max_batch_size,
... | 4,419 | 29.909091 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/pytorch2onnx.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from functools import partial
import mmcv
import numpy as np
import onnxruntime as rt
import torch
from mmcv.onnx import register_extra_symbolics
from mmcv.runner import load_checkpoint
from mmcls.models import build_classifier
torch.manual_seed(3)
de... | 7,488 | 32.137168 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/mmcls_handler.py | # Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmcls.apis import inference_model, init_model
class MMclsHandler(BaseHandler):
def initialize(self, context):
properties = context.system_propertie... | 1,650 | 30.75 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/deployment/pytorch2torchscript.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from functools import partial
import mmcv
import numpy as np
import torch
from mmcv.runner import load_checkpoint
from torch import nn
from mmcls.models import build_classifier
torch.manual_seed(3)
def _demo_mm_inputs(i... | 4,363 | 30.171429 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/misc/print_config.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from mmcv import Config, DictAction
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
parser.add_argument(
'--options',
... | 1,896 | 32.875 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/mobilenetv2_to_mmcls.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def convert_conv1(model_key, model_weight, state_dict, converted_names):
if model_key.find('features.0.0') >= 0:
new_key = model_key.replace('features.0.0', 'backbone.conv1.conv')
else:
... | 4,732 | 33.801471 | 75 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/vgg_to_mmcls.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from collections import OrderedDict
import torch
def get_layer_maps(layer_num, with_bn):
layer_maps = {'conv': {}, 'bn': {}}
if with_bn:
if layer_num == 11:
layer_idxs = [0, 4, 8, 11, 15, 18, 22, 25]
elif la... | 4,084 | 33.618644 | 75 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/publish_model.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import datetime
import os
import subprocess
import torch
from mmcv import digit_version
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input ch... | 1,742 | 30.125 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/convert_models/shufflenetv2_to_mmcls.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict
import torch
def convert_conv1(model_key, model_weight, state_dict, converted_names):
if model_key.find('conv1.0') >= 0:
new_key = model_key.replace('conv1.0', 'backbone.conv1.conv')
else:
new_... | 4,137 | 35.298246 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/analysis_tools/analyze_results.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import warnings
import mmcv
from mmcv import DictAction
from mmcls.datasets import build_dataset
from mmcls.models import build_classifier
def parse_args():
parser = argparse.ArgumentParser(
description='MMCls evaluate... | 3,980 | 31.631148 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/analysis_tools/eval_metric.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmcls.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
... | 2,540 | 33.808219 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/analysis_tools/analysis_para.py | import argparse
import torch
from mmcv import Config
from prettytable import PrettyTable
from mmcls.models.builder import build_classifier
def count_parameters(model):
table = PrettyTable(["Modules", "Parameters"])
total_params = 0
for name, parameter in model.named_parameters():
if not parameter... | 953 | 22.268293 | 67 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/analysis_tools/get_flops.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from mmcv import Config
from mmcv.cnn.utils import get_model_complexity_info
from mmcls.models import build_classifier
def parse_args():
parser = argparse.ArgumentParser(description='Get model flops and params')
parser.add_argument('config', he... | 1,581 | 27.25 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/tools/analysis_tools/analyze_logs.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
def cal_train_time(log_dicts, args):
"""Compute the average time per training iteration."""
for i, log_dict in enumerate(log... | 6,433 | 33.967391 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/version.py | # Copyright (c) OpenMMLab. All rights reserved
__version__ = '0.15.0'
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed into
(1, 3, 0),... | 832 | 27.724138 | 72 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from packaging.version import parse
from .version import __version__
def digit_version(version_str: str, length: int = 4):
"""Convert a version string into a tuple of integers.
This method is usually used for comparing two versions... | 1,912 | 30.360656 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/inference.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
import numpy as np
import torch
from mmcv.parallel import collate, scatter
from mmcv.runner import load_checkpoint
from mmcls.datasets.pipelines import Compose
from mmcls.models import build_classifier
def init_model(config, checkpoint=None... | 3,971 | 35.777778 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/multitask_test.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
def multitask_single_gpu_test(model,
... | 7,703 | 36.398058 | 94 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pickle
import shutil
import tempfile
import time
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
def single_gpu_test(model,
... | 7,129 | 34.829146 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .inference import inference_model, init_model, show_result_pyplot
from .test import multi_gpu_test, single_gpu_test
from .multitask_test import multitask_multi_gpu_test, multitask_single_gpu_test
from .train import set_random_seed, train_model
__all__ = [
'set_r... | 506 | 41.25 | 90 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/apis/train.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
import numpy as np
import torch
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import DistSamplerSeedHook, build_optimizer, build_runner
from mmcls.core import DistOptimizerHook
from mmcls.datasets impo... | 5,723 | 33.481928 | 83 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .evaluation import * # noqa: F401, F403
from .fp16 import * # noqa: F401, F403
from .utils import * # noqa: F401, F403
| 175 | 34.2 | 47 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/multilabel_eval_metrics.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import torch
def average_performance(pred, target, thr=None, k=None):
"""Calculate CP, CR, CF1, OP, OR, OF1, where C stands for per-class
average, O stands for overall average, P stands for precision, R stands for
recall a... | 2,900 | 38.739726 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/eval_metrics.py | # Copyright (c) OpenMMLab. All rights reserved.
from numbers import Number
import numpy as np
import torch
def calculate_confusion_matrix(pred, target):
"""Calculate confusion matrix according to the prediction and target.
Args:
pred (torch.Tensor | np.array): The model prediction with shape (N, C).... | 10,811 | 42.421687 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/eval_hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from mmcv.runner import Hook
from torch.utils.data import DataLoader
class EvalHook(Hook):
"""Evaluation hook.
Args:
dataloader (DataLoader): A PyTorch dataloader.
interval (int): Evaluation interval (by epo... | 3,967 | 36.433962 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/multitask_eval_hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from mmcv.runner import Hook
from torch.utils.data import DataLoader
class MultiTaskEvalHook(Hook):
"""Evaluation hook.
Args:
dataloader (DataLoader): A PyTorch dataloader.
interval (int): Evaluation interv... | 4,076 | 36.75 | 86 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .eval_hooks import DistEvalHook, EvalHook
from .multitask_eval_hooks import MultiTaskEvalHook, DistMultiTaskEvalHook
from .eval_metrics import (calculate_confusion_matrix, f1_score, precision,
precision_recall_f1, recall, support)
from .mea... | 666 | 43.466667 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/evaluation/mean_ap.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
def average_precision(pred, target):
r"""Calculate the average precision for a single class.
AP summarizes a precision-recall curve as the weighted mean of maximum
precisions obtained for any r'>r, where r is the recall:
... | 2,414 | 31.2 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/hooks.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch
import torch.nn as nn
from mmcv.runner import OptimizerHook
from mmcv.utils.parrots_wrapper import _BatchNorm
from ..utils import allreduce_grads
from .utils import cast_tensor_type
class Fp16OptimizerHook(OptimizerHook):
"""FP16 optimizer... | 4,548 | 33.992308 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections import abc
import numpy as np
import torch
def cast_tensor_type(inputs, src_type, dst_type):
if isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs,... | 712 | 27.52 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .decorators import auto_fp16, force_fp32
from .hooks import Fp16OptimizerHook, wrap_fp16_model
__all__ = ['auto_fp16', 'force_fp32', 'Fp16OptimizerHook', 'wrap_fp16_model']
| 227 | 37 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/fp16/decorators.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
from inspect import getfullargspec
import torch
from .utils import cast_tensor_type
def auto_fp16(apply_to=None, out_fp32=False):
"""Decorator to enable fp16 training automatically.
This decorator is useful when you write custom modules and w... | 6,259 | 37.641975 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/export/test.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import numpy as np
import onnxruntime as ort
import torch
from mmcls.models.classifiers import BaseClassifier
class ONNXRuntimeClassifier(BaseClassifier):
"""Wrapper for classifier's inference with ONNXRuntime."""
def __init__(self, onnx_file,... | 3,439 | 34.463918 | 71 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/export/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .test import ONNXRuntimeClassifier, TensorRTClassifier
__all__ = ['ONNXRuntimeClassifier', 'TensorRTClassifier']
| 167 | 32.6 | 59 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/visualization/image.py | import matplotlib.pyplot as plt
import mmcv
import numpy as np
# A small value
EPS = 1e-2
def color_val_matplotlib(color):
"""Convert various input in BGR order to normalized RGB matplotlib color
tuples,
Args:
color (:obj:`mmcv.Color`/str/tuple/int/ndarray): Color inputs
Returns:
tu... | 4,341 | 32.145038 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/visualization/__init__.py | from .image import color_val_matplotlib, imshow_infos
__all__ = ['imshow_infos', 'color_val_matplotlib']
| 106 | 25.75 | 53 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/kd_hook.py | import torch
from mmcv.parallel import is_module_wrapper
from mmcv.runner import (HOOKS, OPTIMIZER_BUILDERS, OPTIMIZERS,
DefaultOptimizerConstructor, Hook, OptimizerHook)
from mmcv.utils import build_from_cfg
@OPTIMIZER_BUILDERS.register_module()
class KDOptimizerBuilder(DefaultOptimizerConst... | 1,003 | 37.615385 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/dist_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
import torch.distributed as dist
from mmcv.runner import OptimizerHook
from torch._utils import (_flatten_dense_tensors, _take_tensors,
_unflatten_dense_tensors)
def _allreduce_coalesced(tensors, world_size,... | 1,904 | 31.844828 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/misc.py | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
def multi_apply(func, *args, **kwargs):
pfunc = partial(func, **kwargs) if kwargs else func
map_results = map(pfunc, *args)
return tuple(map(list, zip(*map_results)))
| 259 | 27.888889 | 55 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import DistOptimizerHook, allreduce_grads
from .misc import multi_apply
from .kd_hook import KDOptimizerBuilder
from .visualize import TensorboardVisLoggerHook
__all__ = ['allreduce_grads', 'DistOptimizerHook',
'multi_apply', 'KDOptimizerBuild... | 364 | 35.5 | 58 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/core/utils/visualize.py | import os.path as osp
from mmcv.utils import TORCH_VERSION, digit_version
from mmcv.runner.dist_utils import master_only
from mmcv.runner.hooks import HOOKS
from mmcv.runner.hooks.logger.base import LoggerHook
from collections import OrderedDict
import numpy as np
@HOOKS.register_module()
class TensorboardVisLogge... | 3,792 | 34.12037 | 110 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .builder import (BACKBONES, CLASSIFIERS, HEADS, LOSSES, NECKS,
build_backbone, build_classifier, build_head, build_loss,
build_neck)
from .classifiers import * # noqa: F401,F403
... | 599 | 39 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import MODELS as MMCV_MODELS
from mmcv.cnn.bricks.registry import ATTENTION as MMCV_ATTENTION
from mmcv.utils import Registry
MODELS = Registry('models', parent=MMCV_MODELS)
BACKBONES = MODELS
NECKS = MODELS
HEADS = MODELS
LOSSES = MODELS
CLASSIFIERS = MOD... | 750 | 18.25641 | 64 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/necks/gap.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import NECKS
@NECKS.register_module()
class GlobalAveragePooling(nn.Module):
"""Global Average Pooling neck.
Note that we use `view` to remove extra channel after pooling. We do not
use `squeeze` as it will... | 1,492 | 31.456522 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/necks/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .gap import GlobalAveragePooling
__all__ = ['GlobalAveragePooling']
| 122 | 23.6 | 47 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/base.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import mmcv
import torch
import torch.distributed as dist
from mmcv.runner import BaseModule
from mmcls.core.visualization import imshow_infos
# TODO import `auto_fp16` from mmc... | 7,775 | 35 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/image.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import numpy as np
import warnings
from re import S
import torch.nn as nn
import torch.nn.functional as F
from ..builder import CLASSIFIERS, build_backbone, build_head, build_neck
from ..utils.augment import Augments
from .base import BaseClassifier
@CLASSI... | 5,829 | 37.355263 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/kf.py | import copy
import numpy as np
import torch
import torch.nn.functional as F
import warnings
from shutil import ExecError
from torch import nn
from mmcls.models.losses.kd_loss import (InfoMax_loss, InfoMin_loss)
from ..builder import (CLASSIFIERS, build_backbone, build_head, build_loss,
build_nec... | 12,546 | 38.332288 | 113 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/kd.py | import copy
import warnings
from shutil import ExecError
import torch
import torch.nn.functional as F
from torch import nn
from ..builder import (CLASSIFIERS, build_backbone, build_head, build_loss,
build_neck)
from ..utils.augment import Augments
from .base import BaseClassifier
@CLASSIFIERS... | 7,148 | 37.643243 | 82 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/classifiers/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseClassifier
from .image import ImageClassifier
from .kd import KDImageClassifier
from .kf import KFImageClassifier
| 187 | 19.888889 | 47 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/embed.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner.base_module import BaseModule
from .helpers import to_2tuple
class PatchEmbed(BaseModule):
"""Image to Patch Embedding.
We use a conv layer to implement... | 9,624 | 36.893701 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/se_layer.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .make_divisible import make_divisible
class SELayer(BaseModule):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) ch... | 2,989 | 38.866667 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/make_divisible.py | # Copyright (c) OpenMMLab. All rights reserved.
def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
"""Make divisible function.
This function rounds the channel number down to the nearest value that can
be divisible by the divisor.
Args:
value (int): The original channel number.... | 1,046 | 39.269231 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/inverted_residual.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
# class InvertedResidual(nn.Module):
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels ... | 3,688 | 31.078261 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .attention import ShiftWindowMSA
from .augment.augments import Augments
from .channel_shuffle import channel_shuffle
from .embed import HybridEmbed, PatchEmbed, PatchMerging
from .helpers import is_tracing, to_2tuple, to_3tuple, to_4tuple, to_ntuple
from .inverted_re... | 659 | 40.25 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/attention.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.transformer import build_dropout
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner.base_module import BaseModule
from ..builder import ATTENTION
from .helpers impo... | 11,410 | 38.213058 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/helpers.py | # Copyright (c) OpenMMLab. All rights reserved.
import collections.abc
import warnings
from distutils.version import LooseVersion
from itertools import repeat
import torch
def is_tracing() -> bool:
if LooseVersion(torch.__version__) >= LooseVersion('1.6.0'):
on_trace = torch.jit.is_tracing()
# In... | 1,127 | 25.232558 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/channel_shuffle.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
def channel_shuffle(x, groups):
"""Channel Shuffle operation.
This function enables cross-group information flow for multiple groups
convolution layers.
Args:
x (Tensor): The input tensor.
groups (int): The number of groups... | 889 | 28.666667 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/identity.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from .builder import AUGMENT
@AUGMENT.register_module(name='Identity')
class Identity(object):
"""Change gt_label to one_hot encoding and keep img as the same.
Args:
num_classes (int): The number of classes.
prob... | 857 | 26.677419 | 70 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/cutmix.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import numpy as np
import torch
import torch.nn.functional as F
from .builder import AUGMENT
class BaseCutMixLayer(object, metaclass=ABCMeta):
"""Base class for CutMixLayer.
Args:
alpha (float): Parameters for B... | 5,453 | 37.680851 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/mixup.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import numpy as np
import torch
import torch.nn.functional as F
from .builder import AUGMENT
class BaseMixupLayer(object, metaclass=ABCMeta):
"""Base class for MixupLayer.
Args:
alpha (float): Parameters for Bet... | 1,674 | 27.87931 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .augments import Augments
from .cutmix import BatchCutMixLayer
from .identity import Identity
from .mixup import BatchMixupLayer
__all__ = ['Augments', 'BatchCutMixLayer', 'Identity', 'BatchMixupLayer']
| 257 | 31.25 | 73 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
AUGMENT = Registry('augment')
def build_augment(cfg, default_args=None):
return build_from_cfg(cfg, AUGMENT, default_args)
| 226 | 24.222222 | 53 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/utils/augment/augments.py | # Copyright (c) OpenMMLab. All rights reserved.
import random
import numpy as np
from .builder import build_augment
class Augments(object):
"""Data augments.
We implement some data augmentation methods, such as mixup, cutmix.
Args:
augments_cfg (list[`mmcv.ConfigDict`] | obj:`mmcv.ConfigDict`)... | 2,799 | 36.837838 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/label_smooth_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn as nn
from ..builder import LOSSES
from .cross_entropy_loss import CrossEntropyLoss
from .utils import convert_to_one_hot
@LOSSES.register_module()
class LabelSmoothLoss(nn.Module):
r"""Intializer for the label smoothed... | 6,591 | 38.238095 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/asymmetric_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weight_reduce_loss
def asymmetric_loss(pred,
target,
weight=None,
gamma_pos=1.0,
gamma_neg=4.0,
... | 3,887 | 33.40708 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import torch
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... | 3,827 | 30.377049 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/accuracy.py | # Copyright (c) OpenMMLab. All rights reserved.
from numbers import Number
import numpy as np
import torch
import torch.nn as nn
def accuracy_numpy(pred, target, topk=1, thrs=0.):
if isinstance(thrs, Number):
thrs = (thrs, )
res_single = True
elif isinstance(thrs, tuple):
res_single =... | 4,342 | 32.152672 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/focal_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def sigmoid_focal_loss(pred,
target,
weight=None,
gamma=2.0,
... | 4,089 | 34.565217 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/cross_entropy_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
reduction='mean',
avg_factor=Non... | 6,753 | 34.547368 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .asymmetric_loss import AsymmetricLoss, asymmetric_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy)
from .focal_loss import FocalLoss, sigmoid_focal_... | 825 | 47.588235 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/losses/kd_loss.py | import re
from numpy import inf
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@LOSSES.register_module()
class Logits(nn.Module):
'''
Do Deep Nets Really Need to be Deep?
http://papers.nips.cc/paper/5484-do-deep-nets-really-need-to-be-deep.pdf
'''
... | 1,876 | 26.602941 | 96 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/mobilenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import make_divisible
from ..builder import BACKBONES
from .base_backbon... | 9,588 | 35.184906 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer,
constant_init)
from mmcv.utils.parrots_wrapper import _BatchNorm
from ..builder import BACKBONES
from .base_backbone impo... | 26,579 | 33.474708 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/tsn.py | from re import S
import torch.nn as nn
import torch
from ..builder import BACKBONES, build_backbone
from .base_backbone import BaseBackbone
import torch.nn.functional as F
@BACKBONES.register_module()
class TSN_backbone(BaseBackbone):
def __init__(self, backbone, in_channels, out_channels):
super().__init... | 860 | 25.90625 | 76 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/disentangle.py | import torch
import torch.nn as nn
from ..builder import BACKBONES
class Flatten3D(nn.Module):
def forward(self, x):
x = x.view(x.size()[0], -1)
return x
@BACKBONES.register_module()
class SimpleConv64(nn.Module):
def __init__(self,
latent_dim=10,
num_chann... | 2,028 | 27.577465 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/base_backbone.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseBackbone(BaseModule, metaclass=ABCMeta):
"""Base backbone.
This class defines the basic functions of a backbone. Any backbone that
inherits this class should at least defi... | 954 | 27.088235 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/resnet_cifar.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from .resnet import ResNet
@BACKBONES.register_module()
class ResNet_CIFAR(ResNet):
"""ResNet backbone for CIFAR.
Compared to standard ResNet, it uses... | 3,707 | 44.219512 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .mobilenet_v2 import MobileNetV2
from .mobilenet_v2_cifar import MobileNetV2_CIFAR
from .resnet import ResNet, ResNetV1d
from .resnet_cifar import ResNet_CIFAR
from .shufflenet_v2 import ShuffleNetV2
from .tsn import TSN_backbone
from .wideresnet import WideResNet_CI... | 384 | 37.5 | 59 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/mobilenet_v2_cifar.py | from mmcls.models.backbones.mobilenet_v2 import MobileNetV2
from mmcls.models.builder import BACKBONES
from mmcv.cnn import ConvModule
from mmcls.models.utils import make_divisible
@BACKBONES.register_module()
class MobileNetV2_CIFAR(MobileNetV2):
arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 1],
... | 2,067 | 35.280702 | 66 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/shufflenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, constant_init, normal_init
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from mmcls.models.utils import channel_shuffle
from ..b... | 10,408 | 33.92953 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/backbones/wideresnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer)
from .resnet import ResNet, WideBasicBlock
from ..builder import BACKBONES
@BACKBONES.register_module()
class WideResNet_CIFAR(ResNet):
"""Wide ResNet-50-2 model from
`"Wide Residual Networks"... | 2,163 | 33.349206 | 75 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/base_head.py | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseHead(BaseModule, metaclass=ABCMeta):
"""Base head."""
def __init__(self, init_cfg=None):
super(BaseHead, self).__init__(init_cfg)
@abstractmethod
def forward_... | 369 | 22.125 | 51 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/cls_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from mmcls.models.losses import Accuracy
from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead
@HEADS.register_module()
class ClsHead(BaseHead):
"""classification head.
... | 2,636 | 31.9625 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/multi_label_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn.functional as F
from ..builder import HEADS, build_loss
from ..utils import is_tracing
from .base_head import BaseHead
@HEADS.register_module()
class MultiLabelClsHead(BaseHead):
"""Classification head for multilabel task.
Args:
... | 1,887 | 28.046154 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .cls_head import ClsHead
from .linear_head import LinearBCEClsHead, LinearClsHead
from .multi_label_head import MultiLabelClsHead
from .multitask_linear_head import MultiTaskLinearClsHead
| 242 | 33.714286 | 57 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/multitask_linear_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class MultiTaskLinearClsHead(ClsHead):
"""Linear classifier head.
Args:
num_classes (int): Number of categories exc... | 2,253 | 30.746479 | 74 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/models/heads/linear_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import HEADS
from .cls_head import ClsHead
@HEADS.register_module()
class LinearClsHead(ClsHead):
"""Linear classifier head.
Args:
num_classes (int): Number of categories excluding th... | 3,723 | 30.559322 | 79 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/base_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
from abc import ABCMeta, abstractmethod
import mmcv
import numpy as np
from torch.utils.data import Dataset
from mmcls.core.evaluation import precision_recall_f1, support
from mmcls.models.losses import accuracy
from .pipelines import Compose
class BaseDat... | 7,191 | 33.576923 | 78 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import gzip
import hashlib
import os
import os.path
import shutil
import tarfile
import urllib.error
import urllib.request
import zipfile
__all__ = ['rm_suffix', 'check_integrity', 'download_and_extract_archive']
def rm_suffix(s, suffix=None):
if suffix is None:
... | 4,549 | 28.545455 | 77 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
import math
from collections import defaultdict
import numpy as np
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A wrapper of con... | 6,092 | 34.219653 | 167 | py |
KnowledgeFactor | KnowledgeFactor-main/cls/mmcls/datasets/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
from .base_dataset import BaseDataset
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cifar import CIFAR10, CIFAR100, CIFAR10_MultiTask, CIFAR10_2Task, CIFAR10_Select
from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
... | 503 | 41 | 86 | py |
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