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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CSL | CSL-main/UEA.py | import os
import numpy as np
import torch
from torch import nn, optim
import random
from train import LearningShapeletsCL
from utils import z_normalize, eval_accuracy, TSC_multivariate_data_loader, get_weights_via_kmeans
from tqdm import tqdm
from sklearn.svm import SVC
from sklearn.cluster import KMeans
from sklea... | 9,491 | 38.385892 | 214 | py |
CSL | CSL-main/train.py | import os
import warnings
import numpy as np
import torch
import tsaug
from torch import nn
from tqdm import tqdm
from blocks import LearningShapeletsModel, LearningShapeletsModelMixDistances
class LearningShapeletsCL:
"""
Parameters
----------
shapelets_size_and_len : dict(int:int)
The ... | 15,906 | 34.988688 | 191 | py |
CSL | CSL-main/blocks.py | import numpy as np
import torch
from torch import nn
from torch.utils.checkpoint import checkpoint
from collections import OrderedDict
from utils import generate_binomial_mask
class MinEuclideanDistBlock(nn.Module):
def __init__(self, shapelets_size, num_shapelets, in_channels=1, to_cuda=True):
super(... | 15,212 | 40.793956 | 158 | py |
CSL | CSL-main/CSL_AD.py | import os
import numpy as np
import torch
from torch import nn, optim
import random
from train import LearningShapeletsCL
from utils import z_normalize, eval_accuracy, TSC_multivariate_data_loader, get_weights_via_kmeans
from tqdm import tqdm
from sklearn.svm import SVC
from sklearn.cluster import KMeans, SpectralC... | 10,907 | 37.54417 | 247 | py |
pyculib | pyculib-master/docs/source/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Pyculib documentation build configuration file, created by
# sphinx-quickstart on Fri Aug 7 17:43:03 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... | 9,687 | 31.186047 | 86 | py |
ttfnet | ttfnet-master/setup.py | import os
import platform
import subprocess
import time
from setuptools import Extension, dist, find_packages, setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
dist.Distribution().fetch_build_eggs(['Cython', 'numpy>=1.11.1'])
import numpy as np # noqa: E402
from Cython.Build import cythonize... | 6,484 | 30.177885 | 79 | py |
ttfnet | ttfnet-master/tools/test.py | import argparse
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from mmdet.apis import init_dist
from mmdet.core import coc... | 7,960 | 34.382222 | 77 | py |
ttfnet | ttfnet-master/tools/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,028 | 27.583333 | 77 | py |
ttfnet | ttfnet-master/tools/upgrade_model_version.py | import argparse
import re
from collections import OrderedDict
import torch
def convert(in_file, out_file):
"""Convert keys in checkpoints.
There can be some breaking changes during the development of mmdetection,
and this tool is used for upgrading checkpoints trained with old versions
to the latest... | 1,322 | 29.767442 | 77 | py |
ttfnet | ttfnet-master/tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import numpy as np
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, load_checkpoint
from pycocotools.coco import ... | 17,478 | 35.953488 | 79 | py |
ttfnet | ttfnet-master/tools/train.py | from __future__ import division
import argparse
import os
import torch
from mmcv import Config
from mmdet import __version__
from mmdet.apis import (get_root_logger, init_dist, set_random_seed,
train_detector)
from mmdet.datasets import build_dataset
from mmdet.models import build_detector
d... | 3,450 | 30.66055 | 77 | py |
ttfnet | ttfnet-master/tools/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,830 | 42.044944 | 78 | py |
ttfnet | ttfnet-master/tests/test_forward.py | """
pytest tests/test_forward.py
"""
import copy
from os.path import dirname, exists, join
import numpy as np
import torch
def _get_config_directory():
""" Find the predefined detector config directory """
try:
# Assume we are running in the source mmdetection repo
repo_dpath = dirname(dirnam... | 6,995 | 29.285714 | 79 | py |
ttfnet | ttfnet-master/tests/test_nms.py | """
CommandLine:
pytest tests/test_nms.py
"""
import numpy as np
import torch
from mmdet.ops.nms.nms_wrapper import nms
def test_nms_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_nms.py test_nms_device_and_dtypes_cpu
"""
iou_thr = 0.7
base_dets = np.array([[49.1, 32... | 2,297 | 31.366197 | 68 | py |
ttfnet | ttfnet-master/tests/test_config.py | from os.path import dirname, exists, join
def _get_config_directory():
""" Find the predefined detector config directory """
try:
# Assume we are running in the source mmdetection repo
repo_dpath = dirname(dirname(__file__))
except NameError:
# For IPython development when this __f... | 7,293 | 41.16185 | 79 | py |
ttfnet | ttfnet-master/demo/webcam_demo.py | import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector, show_result
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='chec... | 1,243 | 26.644444 | 79 | py |
ttfnet | ttfnet-master/configs/faster_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,328 | 29.451429 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_ind... | 7,572 | 30.036885 | 78 | py |
ttfnet | ttfnet-master/configs/retinanet_r101_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[2... | 3,802 | 28.253846 | 77 | py |
ttfnet | ttfnet-master/configs/fast_mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 4,901 | 30.831169 | 77 | py |
ttfnet | ttfnet-master/configs/faster_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck... | 5,385 | 29.429379 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 7,434 | 30.371308 | 78 | py |
ttfnet | ttfnet-master/configs/faster_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck... | 5,385 | 29.429379 | 78 | py |
ttfnet | ttfnet-master/configs/mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,747 | 29.412698 | 78 | py |
ttfnet | ttfnet-master/configs/mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
# pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
... | 5,767 | 28.88601 | 78 | py |
ttfnet | ttfnet-master/configs/faster_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
... | 5,400 | 29.005556 | 78 | py |
ttfnet | ttfnet-master/configs/retinanet_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=... | 3,856 | 28.219697 | 77 | py |
ttfnet | ttfnet-master/configs/fast_mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 4,904 | 30.850649 | 77 | py |
ttfnet | ttfnet-master/configs/rpn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
... | 3,924 | 28.734848 | 78 | py |
ttfnet | ttfnet-master/configs/faster_rcnn_ohem_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,326 | 29.44 | 78 | py |
ttfnet | ttfnet-master/configs/mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 5,774 | 29.394737 | 78 | py |
ttfnet | ttfnet-master/configs/ssd512_coco.py | # model settings
input_size = 512
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,959 | 28.333333 | 79 | py |
ttfnet | ttfnet-master/configs/faster_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[... | 5,331 | 29.468571 | 78 | py |
ttfnet | ttfnet-master/configs/mask_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=d... | 5,801 | 29.376963 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,377 | 30.395745 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_mask_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 8,033 | 30.382813 | 78 | py |
ttfnet | ttfnet-master/configs/rpn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
... | 3,924 | 28.734848 | 78 | py |
ttfnet | ttfnet-master/configs/fast_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 4,344 | 31.185185 | 78 | py |
ttfnet | ttfnet-master/configs/rpn_r50_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512,... | 3,867 | 28.753846 | 78 | py |
ttfnet | ttfnet-master/configs/retinanet_r50_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256... | 3,799 | 28.230769 | 77 | py |
ttfnet | ttfnet-master/configs/retinanet_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=... | 3,856 | 28.219697 | 77 | py |
ttfnet | ttfnet-master/configs/fast_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 4,341 | 31.162963 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,976 | 30.405512 | 78 | py |
ttfnet | ttfnet-master/configs/rpn_r50_caffe_c4_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://resnet50_caffe',
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=dict(type='BN', requ... | 3,893 | 28.953846 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 7,434 | 30.371308 | 78 | py |
ttfnet | ttfnet-master/configs/fast_mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
f... | 4,653 | 29.418301 | 75 | py |
ttfnet | ttfnet-master/configs/cascade_mask_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='... | 8,033 | 30.382813 | 78 | py |
ttfnet | ttfnet-master/configs/ssd300_coco.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,942 | 28.207407 | 79 | py |
ttfnet | ttfnet-master/configs/mask_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=d... | 5,801 | 29.376963 | 78 | py |
ttfnet | ttfnet-master/configs/fast_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
f... | 4,525 | 30.65035 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_ind... | 7,929 | 29.976563 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,979 | 30.417323 | 78 | py |
ttfnet | ttfnet-master/configs/cascade_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,380 | 30.408511 | 78 | py |
ttfnet | ttfnet-master/configs/rpn_r101_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,870 | 28.776923 | 78 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r34_2x.py | # model settings
model = dict(
type='TTFNet',
pretrained='modelzoo://resnet34',
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_eval=False,
style='pytorch'),
neck=None,
bbox_head=dict(
... | 3,277 | 28.00885 | 77 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r34_aug_10x.py | # model settings
model = dict(
type='TTFNet',
pretrained='modelzoo://resnet34',
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_eval=False,
style='pytorch'),
neck=None,
bbox_head=dict(
... | 3,725 | 28.109375 | 77 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r18_scratch_aug_10x.py | # model settings
model = dict(
type='TTFNet',
pretrained=None,
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_eval=False,
zero_init_residual=False,
style='pytorch'),
neck=None,
bbo... | 3,751 | 28.085271 | 77 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r18_1x.py | # model settings
model = dict(
type='TTFNet',
pretrained='modelzoo://resnet18',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_eval=False,
style='pytorch'),
neck=None,
bbox_head=dict(
... | 3,276 | 28 | 77 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r18_aug_10x.py | # model settings
model = dict(
type='TTFNet',
pretrained='modelzoo://resnet18',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_eval=False,
style='pytorch'),
neck=None,
bbox_head=dict(
... | 3,725 | 28.109375 | 77 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r18_2x.py | # model settings
model = dict(
type='TTFNet',
pretrained='modelzoo://resnet18',
backbone=dict(
type='ResNet',
depth=18,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_eval=False,
style='pytorch'),
neck=None,
bbox_head=dict(
... | 3,277 | 28.00885 | 77 | py |
ttfnet | ttfnet-master/configs/ttfnet/ttfnet_r34_scratch_aug_10x.py | # model settings
model = dict(
type='TTFNet',
pretrained=None,
backbone=dict(
type='ResNet',
depth=34,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_eval=False,
zero_init_residual=False,
style='pytorch'),
neck=None,
bbo... | 3,743 | 28.023256 | 77 | py |
ttfnet | ttfnet-master/configs/ghm/retinanet_ghm_r50_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256... | 3,805 | 28.053435 | 77 | py |
ttfnet | ttfnet-master/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=True, deformable_gr... | 5,492 | 29.859551 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=False, deformable_gro... | 5,901 | 29.739583 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=... | 7,534 | 30.659664 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=True, deformable_gr... | 5,485 | 29.820225 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
d... | 5,589 | 29.546448 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/faster_rcnn_dpool_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,468 | 29.21547 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/faster_rcnn_mdpool_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,478 | 29.270718 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=... | 8,133 | 30.649805 | 78 | py |
ttfnet | ttfnet-master/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=False, deformable_g... | 5,485 | 29.820225 | 78 | py |
ttfnet | ttfnet-master/configs/htc/htc_r101_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet101',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
st... | 8,666 | 30.402174 | 79 | py |
ttfnet | ttfnet-master/configs/htc/htc_x101_32x4d_fpn_20e_16gpu.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(... | 8,720 | 30.370504 | 79 | py |
ttfnet | ttfnet-master/configs/htc/htc_r50_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,663 | 30.391304 | 79 | py |
ttfnet | ttfnet-master/configs/htc/htc_x101_64x4d_fpn_20e_16gpu.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(... | 8,720 | 30.370504 | 79 | py |
ttfnet | ttfnet-master/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(... | 9,015 | 30.305556 | 79 | py |
ttfnet | ttfnet-master/configs/htc/htc_r50_fpn_1x.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,661 | 30.384058 | 79 | py |
ttfnet | ttfnet-master/configs/htc/htc_without_semantic_r50_fpn_1x.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,049 | 30.445313 | 78 | py |
ttfnet | ttfnet-master/configs/reppoints/bbox_r50_grid_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,342 | 28.344595 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_x101_dcn_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0,... | 4,457 | 28.72 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r101_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,289 | 28.383562 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0,... | 4,533 | 28.441558 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_minmax_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,209 | 28.647887 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_partial_minmax_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,225 | 28.760563 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/bbox_r50_grid_center_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,239 | 28.65035 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r101_dcn_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,362 | 29.089655 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r50_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,210 | 28.65493 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,438 | 28.791946 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r101_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,213 | 28.676056 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,209 | 28.647887 | 79 | py |
ttfnet | ttfnet-master/configs/reppoints/reppoints_moment_r50_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,286 | 28.363014 | 79 | py |
ttfnet | ttfnet-master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dic... | 5,379 | 29.224719 | 78 | py |
ttfnet | ttfnet-master/configs/fp16/retinanet_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict... | 3,850 | 27.954887 | 77 | py |
ttfnet | ttfnet-master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(... | 5,825 | 29.186528 | 78 | py |
ttfnet | ttfnet-master/configs/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py | # model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(... | 3,982 | 27.45 | 77 | py |
ttfnet | ttfnet-master/configs/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py | # model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://resnet101_caffe',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe'),
... | 3,995 | 27.748201 | 75 | py |
ttfnet | ttfnet-master/configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py | # model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://resnet50_caffe',
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=False),
style='caffe'),
ne... | 3,902 | 27.911111 | 75 | py |
ttfnet | ttfnet-master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
pretrained='open-mmlab://resnet101_caffe',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='ca... | 6,072 | 29.365 | 78 | py |
ttfnet | ttfnet-master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
... | 6,064 | 29.174129 | 78 | py |
ttfnet | ttfnet-master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
pretrained='open-mmlab://resnet50_caffe',
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=False),
style='caff... | 6,069 | 29.35 | 78 | py |
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