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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Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/models/hourglass_pose.py | """
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) Yichao Zhou (LCNN)
(c) YANG, Wei
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ["HourglassNet", "hg"]
class Bottleneck2D(nn.Module):
expansion = 2
def __init__(self, inplanes, pl... | 6,607 | 31.712871 | 87 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/models/HT.py | #encoding:utf-8
"""
Deep-Hough-Transform-Line-Priors (ECCV 2020) https://arxiv.org/abs/2007.09493
Yancong Lin, and Silvia Laura Pintea, and Jan C. van Gemert
e-mail: y.lin-1ATtudelftDOTnl
Vision Lab, Delft University of Technology
MIT license
"""
from torch.nn import functional as F
import math
import numpy as np... | 6,786 | 36.497238 | 134 | py |
Deep-Hough-Transform-Line-Priors | Deep-Hough-Transform-Line-Priors-master/ht-lcnn/lcnn/models/hourglass_ht.py | """
Hourglass network inserted in the pre-activated Resnet
Hourglass + HT-IHT block
Modified by Yancong LIN
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from lcnn.models import HT
class Bottleneck2D(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, stride=1, downsample=... | 6,503 | 31.683417 | 105 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/setup.py | #!/usr/bin/env python
import os
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return co... | 5,864 | 35.203704 | 125 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/test.py | import argparse
import os
import warnings
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
fro... | 8,655 | 38.167421 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/train.py | 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 mmcv.utils import get_git_hash
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector... | 6,889 | 35.648936 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/deployment/mmdet2torchserve.py | 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 ImportError:
package_model = None
def mmdet2t... | 3,605 | 32.388889 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/deployment/onnx2tensorrt.py | import argparse
import os
import os.path as osp
import numpy as np
import onnx
import onnxruntime as ort
import torch
from mmcv.ops import get_onnxruntime_op_path
from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt,
save_trt_engine)
from mmcv.visualization.image import ... | 5,713 | 30.744444 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/deployment/mmdet_handler.py | import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties = context.system_properties
self.map_loc... | 2,462 | 34.185714 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/deployment/pytorch2onnx.py | import argparse
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as rt
import torch
from mmcv import DictAction
from mmdet.core import (build_model_from_cfg, generate_inputs_and_wrap_model,
preprocess_example_input)
def pytorch2onnx(config_path,
... | 8,830 | 35.044898 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/model_converters/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... | 1,125 | 27.15 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/model_converters/regnet2mmdet.py | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... | 3,015 | 32.511111 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/model_converters/upgrade_model_version.py | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_co... | 6,800 | 31.385714 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/model_converters/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... | 3,530 | 41.542169 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/analysis_tools/benchmark.py | import argparse
import time
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint, wrap_fp16_model
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTenso... | 3,802 | 32.359649 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/analysis_tools/get_flops.py | import argparse
import torch
from mmcv import Config, DictAction
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a det... | 2,566 | 30.304878 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tools/analysis_tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import mmcv
import torch
from mmcv import DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from pycocotools.coco import... | 15,373 | 38.319693 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/mmcv_custom/checkpoint.py | # Copyright (c) Open-MMLab. All rights reserved.
import io
import os
import os.path as osp
import pkgutil
import time
import warnings
from collections import OrderedDict
from importlib import import_module
from tempfile import TemporaryDirectory
import torch
import torchvision
from torch.optim import Optimizer
from to... | 18,964 | 36.854291 | 110 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/mmcv_custom/runner/checkpoint.py | # Copyright (c) Open-MMLab. All rights reserved.
import os.path as osp
import time
from tempfile import TemporaryDirectory
import torch
from torch.optim import Optimizer
import mmcv
from mmcv.parallel import is_module_wrapper
from mmcv.runner.checkpoint import weights_to_cpu, get_state_dict
try:
import apex
exce... | 2,883 | 32.534884 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/mmcv_custom/runner/epoch_based_runner.py | # Copyright (c) Open-MMLab. All rights reserved.
import os.path as osp
import platform
import shutil
import torch
from torch.optim import Optimizer
import mmcv
from mmcv.runner import RUNNERS, EpochBasedRunner
from .checkpoint import save_checkpoint
try:
import apex
except:
print('apex is not installed')
@... | 3,956 | 36.685714 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/.dev_scripts/benchmark_filter.py | import argparse
import os
import os.path as osp
import mmcv
def parse_args():
parser = argparse.ArgumentParser(description='Filter configs to train')
parser.add_argument(
'--basic-arch',
action='store_true',
help='to train models in basic arch')
parser.add_argument(
'--dat... | 6,553 | 40.220126 | 92 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/.dev_scripts/gather_models.py | import argparse
import glob
import json
import os.path as osp
import shutil
import subprocess
import mmcv
import torch
def process_checkpoint(in_file, out_file):
checkpoint = torch.load(in_file, map_location='cpu')
# remove optimizer for smaller file size
if 'optimizer' in checkpoint:
del checkpo... | 5,750 | 34.282209 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/.dev_scripts/batch_test.py | """
some instructions
1. Fill the models that needs to be checked in the modelzoo_dict
2. Arange the structure of the directory as follows, the script will find the
corresponding config itself:
model_dir/model_family/checkpoints
e.g.: models/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
... | 7,820 | 35.71831 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_runtime/async_benchmark.py | import asyncio
import os
import shutil
import urllib
import mmcv
import torch
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.utils.contextmanagers import concurrent
from mmdet.utils.profiling import profile_time
async def main():
"""Benchm... | 3,166 | 30.04902 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_runtime/test_async.py | """Tests for async interface."""
import asyncio
import os
import sys
import asynctest
import mmcv
import torch
from mmdet.apis import async_inference_detector, init_detector
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import concurrent
class AsyncTestCase(asynctest.TestCase):
use_defau... | 2,560 | 29.855422 | 75 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_runtime/test_config.py | from os.path import dirname, exists, join, relpath
from unittest.mock import Mock
import pytest
import torch
from mmcv.runner import build_optimizer
from mmdet.core import BitmapMasks, PolygonMasks
from mmdet.datasets.builder import DATASETS
from mmdet.datasets.utils import NumClassCheckHook
def _get_config_directo... | 16,823 | 39.152745 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_runtime/test_eval_hook.py | import os.path as osp
import tempfile
import unittest.mock as mock
from collections import OrderedDict
from unittest.mock import MagicMock, patch
import pytest
import torch
import torch.nn as nn
from mmcv.runner import EpochBasedRunner, build_optimizer
from mmcv.utils import get_logger
from torch.utils.data import Dat... | 9,028 | 33.200758 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_runtime/test_fp16.py | import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import auto_fp16, force_fp32
from mmcv.runner.fp16_utils import cast_tensor_type
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type... | 9,714 | 31.275748 | 75 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_forward.py | """pytest tests/test_forward.py."""
import copy
from os.path import dirname, exists, join
import numpy as np
import pytest
import torch
def _get_config_directory():
"""Find the predefined detector config directory."""
try:
# Assume we are running in the source mmdetection repo
repo_dpath = di... | 19,144 | 30.436782 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_necks.py | import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.necks import FPN, ChannelMapper
def test_fpn():
"""Tests fpn."""
s = 64
in_channels = [8, 16, 32, 64]
feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8]
out_channels = 8
# `num_outs` is n... | 7,781 | 31.560669 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_hourglass.py | import pytest
import torch
from mmdet.models.backbones.hourglass import HourglassNet
def test_hourglass_backbone():
with pytest.raises(AssertionError):
# HourglassNet's num_stacks should larger than 0
HourglassNet(num_stacks=0)
with pytest.raises(AssertionError):
# len(stage_channels... | 1,301 | 27.933333 | 65 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_res2net.py | import pytest
import torch
from mmdet.models.backbones import Res2Net
from mmdet.models.backbones.res2net import Bottle2neck
from .utils import is_block
def test_res2net_bottle2neck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
Bottle2neck(64, 64, base_width=26, s... | 1,961 | 30.142857 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_resnet.py | import pytest
import torch
from mmcv import assert_params_all_zeros
from mmcv.ops import DeformConv2dPack
from torch.nn.modules import AvgPool2d, GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones import ResNet, ResNetV1d
from mmdet.models.backbones.resnet import BasicBlock, Bottle... | 23,477 | 34.252252 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/utils.py | from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.res2net import Bottle2neck
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
from mmdet.models.backbones.resnext import Bottleneck as BottleneckX
from mmdet.models.utils import Simplified... | 978 | 29.59375 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_renext.py | import pytest
import torch
from mmdet.models.backbones import ResNeXt
from mmdet.models.backbones.resnext import Bottleneck as BottleneckX
from .utils import is_block
def test_renext_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckX(64, 64, grou... | 3,513 | 32.150943 | 73 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_trident_resnet.py | import pytest
import torch
from mmdet.models.backbones import TridentResNet
from mmdet.models.backbones.trident_resnet import TridentBottleneck
def test_trident_resnet_bottleneck():
trident_dilations = (1, 2, 3)
test_branch_idx = 1
concat_output = True
trident_build_config = (trident_dilations, test_... | 6,353 | 34.104972 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_resnest.py | import pytest
import torch
from mmdet.models.backbones import ResNeSt
from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckS(64, 64, radix=2, reduction_factor=4, st... | 1,420 | 31.295455 | 76 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_backbones/test_regnet.py | import pytest
import torch
from mmdet.models.backbones import RegNet
regnet_test_data = [
('regnetx_400mf',
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
bot_mul=1.0), [32, 64, 160, 384]),
('regnetx_800mf',
dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16,
bot_mul=1.0),... | 2,168 | 35.762712 | 73 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_utils/test_position_encoding.py | import pytest
import torch
from mmdet.models.utils import (LearnedPositionalEncoding,
SinePositionalEncoding)
def test_sine_positional_encoding(num_feats=16, batch_size=2):
# test invalid type of scale
with pytest.raises(AssertionError):
module = SinePositionalEncoding... | 1,373 | 34.230769 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_utils/test_transformer.py | from unittest.mock import patch
import pytest
import torch
from mmdet.models.utils import (FFN, MultiheadAttention, Transformer,
TransformerDecoder, TransformerDecoderLayer,
TransformerEncoder, TransformerEncoderLayer)
def _ffn_forward(self, x, residua... | 19,873 | 36.927481 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_transformer_head.py | import torch
from mmdet.models.dense_heads import TransformerHead
def test_transformer_head_loss():
"""Tests transformer head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3),
'batch_input_s... | 3,034 | 35.130952 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_anchor_head.py | import mmcv
import torch
from mmdet.models.dense_heads import AnchorHead
def test_anchor_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg = mmcv.Con... | 2,500 | 34.728571 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_ga_anchor_head.py | import mmcv
import torch
from mmdet.models.dense_heads import GuidedAnchorHead
def test_ga_anchor_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg =... | 3,362 | 35.956044 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_vfnet_head.py | import mmcv
import torch
from mmdet.models.dense_heads import VFNetHead
def test_vfnet_head_loss():
"""Tests vfnet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.C... | 2,513 | 38.904762 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_pisa_head.py | import mmcv
import torch
from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead
from mmdet.models.roi_heads import PISARoIHead
def test_pisa_retinanet_head_loss():
"""Tests pisa retinanet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
... | 8,757 | 34.746939 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_corner_head.py | import torch
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from mmdet.models.dense_heads import CornerHead
def test_corner_head_loss():
"""Tests corner head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
... | 6,708 | 39.173653 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_fsaf_head.py | import mmcv
import torch
from mmdet.models.dense_heads import FSAFHead
def test_fsaf_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg = dict(
... | 3,049 | 36.195122 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_ld_head.py | import mmcv
import torch
from mmdet.models.dense_heads import GFLHead, LDHead
def test_ld_head_loss():
"""Tests vfnet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmc... | 4,557 | 36.669421 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_paa_head.py | import mmcv
import numpy as np
import torch
from mmdet.models.dense_heads import PAAHead, paa_head
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
class mock_skm(object):
def GaussianMixture(self, ... | 4,201 | 33.162602 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_fcos_head.py | import mmcv
import torch
from mmdet.models.dense_heads import FCOSHead
def test_fcos_head_loss():
"""Tests fcos head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.Conf... | 2,358 | 35.859375 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_yolact_head.py | import mmcv
import torch
from mmdet.models.dense_heads import YOLACTHead, YOLACTProtonet, YOLACTSegmHead
def test_yolact_head_loss():
"""Tests yolact head losses when truth is empty and non-empty."""
s = 550
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s... | 5,199 | 36.956204 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_dense_heads/test_sabl_retina_head.py | import mmcv
import torch
from mmdet.models.dense_heads import SABLRetinaHead
def test_sabl_retina_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg =... | 3,032 | 38.907895 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_roi_heads/test_roi_extractor.py | import pytest
import torch
from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor
def test_groie():
# test with pre/post
cfg = dict(
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(... | 3,209 | 27.157895 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_roi_heads/utils.py | import torch
from mmdet.core import build_assigner, build_sampler
def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels):
"""Create sample results that can be passed to BBoxHead.get_targets."""
num_imgs = 1
feat = torch.rand(1, 1, 3, 3)
assign_config = dict(
type='MaxIoUAssigner',
... | 1,201 | 30.631579 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_roi_heads/test_mask_head.py | import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import FCNMaskHead, MaskIoUHead
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask target is empty."""
self = FCNMaskHead(
num_convs=1,
roi_feat_size=6,
in_channels=8,
... | 2,440 | 33.871429 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_roi_heads/test_sabl_bbox_head.py | import mmcv
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import SABLHead
from .utils import _dummy_bbox_sampling
def test_sabl_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = SABLHead(
num_classes=4,
cls_in_channels... | 2,931 | 37.077922 | 75 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_models/test_roi_heads/test_bbox_head.py | import mmcv
import pytest
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import BBoxHead
from .utils import _dummy_bbox_sampling
def test_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = BBoxHead(in_channels=8, roi_feat_size=3)
#... | 8,253 | 31.242188 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_onnx/utils.py | import warnings
from os import path as osp
import numpy as np
import onnx
import onnxruntime as ort
import torch
import torch.nn as nn
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
except (ImportError, ModuleNotFoundError):
warning... | 3,360 | 28.482456 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_onnx/test_neck.py | import os.path as osp
import mmcv
import pytest
import torch
from mmdet import digit_version
from mmdet.models.necks import FPN, YOLOV3Neck
from .utils import ort_validate
if digit_version(torch.__version__) <= digit_version('1.5.0'):
pytest.skip(
'ort backend does not support version below 1.5.0',
... | 4,797 | 27.223529 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_onnx/test_head.py | import os.path as osp
from functools import partial
import mmcv
import numpy as np
import pytest
import torch
from mmdet import digit_version
from mmdet.models.dense_heads import RetinaHead, YOLOV3Head
from .utils import (WrapFunction, convert_result_list, ort_validate,
verify_model)
data_path = ... | 6,848 | 29.171806 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_data/test_datasets/test_common.py | import copy
import logging
import os
import os.path as osp
import tempfile
from unittest.mock import MagicMock, patch
import mmcv
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.runner import EpochBasedRunner
from torch.utils.data import DataLoader
from mmdet.core.evaluation import DistE... | 11,621 | 31.463687 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_data/test_pipelines/test_sampler.py | import torch
from mmdet.core.bbox.assigners import MaxIoUAssigner
from mmdet.core.bbox.samplers import (OHEMSampler, RandomSampler,
ScoreHLRSampler)
def test_random_sampler():
assigner = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
ignore_iof_thr... | 9,675 | 28.410334 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_data/test_pipelines/test_transform/test_transform.py | import copy
import os.path as osp
import mmcv
import numpy as np
import pytest
import torch
from mmcv.utils import build_from_cfg
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from mmdet.datasets.builder import PIPELINES
def test_resize():
# test assertion if img_scale is a list
with pytest.... | 27,933 | 36.096946 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py | import os.path as osp
import mmcv
import torch
from mmcv.parallel import collate
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
from mmdet.models import build_detector
def model_aug_test_template(cfg_file):
# get config
cfg = mmcv.Config.fromfile(cfg_file)
# init mode... | 4,079 | 30.627907 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_utils/test_anchor.py | """
CommandLine:
pytest tests/test_utils/test_anchor.py
xdoctest tests/test_utils/test_anchor.py zero
"""
import torch
def test_standard_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
anchor_generator_cfg = dict(
type='AnchorGenerator',
scales=[8],
ra... | 17,744 | 42.175182 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_utils/test_visualization.py | # Copyright (c) Open-MMLab. All rights reserved.
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import visualization as vis
def test_color():
assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
assert vis.color_val_matpl... | 4,431 | 33.625 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_utils/test_coder.py | import pytest
import torch
from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, TBLRBBoxCoder,
YOLOBBoxCoder)
def test_yolo_bbox_coder():
coder = YOLOBBoxCoder()
bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.],
[22., -29.,... | 4,089 | 42.052632 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_utils/test_misc.py | import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import mask2ndarray
def dummy_raw_polygon_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (N, H, W)
R... | 3,194 | 33.354839 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_utils/test_masks.py | import numpy as np
import pytest
import torch
from mmdet.core import BitmapMasks, PolygonMasks
def dummy_raw_bitmap_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
Return:
ndarray: dummy mask
"""
return np.random.randint(0, 2, size, dtype=n... | 25,985 | 38.612805 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_utils/test_assigner.py | """Tests the Assigner objects.
CommandLine:
pytest tests/test_utils/test_assigner.py
xdoctest tests/test_utils/test_assigner.py zero
"""
import torch
from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
CenterRegionAssigner, HungarianAssigner,
... | 13,922 | 31.76 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_metrics/test_losses.py | import pytest
import torch
from mmdet.models import Accuracy, build_loss
def test_ce_loss():
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(
type='CrossEntropyLoss',
use_mask=True,
use_sigmoid=True,
... | 5,517 | 31.845238 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/tests/test_metrics/test_box_overlap.py | import numpy as np
import pytest
import torch
from mmdet.core import BboxOverlaps2D, bbox_overlaps
def test_bbox_overlaps_2d(eps=1e-7):
def _construct_bbox(num_bbox=None):
img_h = int(np.random.randint(3, 1000))
img_w = int(np.random.randint(3, 1000))
if num_bbox is None:
num... | 4,230 | 38.915094 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/demo/webcam_demo.py | import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')... | 1,260 | 25.829787 | 78 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=dict(type='... | 372 | 25.642857 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 123 | 40.333333 | 76 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=dict(type='... | 372 | 25.642857 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=... | 506 | 30.6875 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=dict(type='BN', requi... | 540 | 27.473684 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 8,295 | 34.004219 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=dict(type='BN', requi... | 540 | 27.473684 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
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,
norm_cfg=dict(type='BN', requi... | 1,438 | 32.465116 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/htc/htc_r101_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 217 | 35.333333 | 76 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 280 | 34.125 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 139 | 45.666667 | 76 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0,... | 2,027 | 28.823529 | 79 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
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,
norm... | 511 | 31 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
type='GFL',
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,
norm_c... | 534 | 28.722222 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
type='GFL',
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,
norm_c... | 410 | 24.6875 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
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=True),
norm_eval=True,
... | 346 | 25.692308 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/gfl/gfl_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 1,701 | 28.344828 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
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=True),
dcn=dict(type='D... | 469 | 30.333333 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py | _base_ = 'tridentnet_r50_caffe_1x_coco.py'
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(133... | 756 | 31.913043 | 72 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py | _base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py'
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 138 | 26.8 | 53 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='TridentFasterRCNN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
type='Tri... | 1,805 | 32.444444 | 74 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
... | 2,450 | 29.259259 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
... | 2,440 | 29.5125 | 77 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/paa/paa_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 2,082 | 28.338028 | 73 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py | _base_ = './paa_r50_fpn_mstrain_3x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 121 | 39.666667 | 76 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/paa/paa_r101_fpn_1x_coco.py | _base_ = './paa_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 113 | 37 | 76 | py |
Swin-Transformer-Object-Detection | Swin-Transformer-Object-Detection-master/configs/yolact/yolact_r50_1x8_coco.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 5,065 | 30.465839 | 77 | py |
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