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[Refactor] Add pyupgrade pre-commit hook (#2078)

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* add pyupgrade hook

* run pyupgrade precommit hook
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谢昕辰 2022-09-19 14:06:29 +08:00 committed by GitHub
parent eef38883c8
commit 230246f557
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GPG Key ID: 4AEE18F83AFDEB23
101 changed files with 242 additions and 251 deletions
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3
.dev/check_urls.py
View File

@ -56,8 +56,7 @@ def main():
for model_name, yml_path in yml_list:
# Default yaml loader unsafe.
model_infos = yml.load(
open(yml_path, 'r'), Loader=yml.CLoader)['Models']
model_infos = yml.load(open(yml_path), Loader=yml.CLoader)['Models']
for model_info in model_infos:
config_name = model_info['Name']
checkpoint_url = model_info['Weights']

6
.dev/gather_models.py
View File

@ -35,7 +35,7 @@ def process_checkpoint(in_file, out_file):
# The hash code calculation and rename command differ on different system
# platform.
sha = calculate_file_sha256(out_file)
final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
final_file = out_file.rstrip('.pth') + f'-{sha[:8]}.pth'
os.rename(out_file, final_file)
# Remove prefix and suffix
@ -54,7 +54,7 @@ def get_final_iter(config):
def get_final_results(log_json_path, iter_num):
result_dict = dict()
last_iter = 0
with open(log_json_path, 'r') as f:
with open(log_json_path) as f:
for line in f.readlines():
log_line = json.loads(line)
if 'mode' not in log_line.keys():
@ -125,7 +125,7 @@ def main():
exp_dir = osp.join(work_dir, config_name)
# check whether the exps is finished
final_iter = get_final_iter(used_config)
final_model = 'iter_{}.pth'.format(final_iter)
final_model = f'iter_{final_iter}.pth'
model_path = osp.join(exp_dir, final_model)
# skip if the model is still training

2
.dev/generate_benchmark_train_script.py
View File

@ -74,7 +74,7 @@ def main():
commands.append('\n')
commands.append('\n')
with open(args.txt_path, 'r') as f:
with open(args.txt_path) as f:
model_cfgs = f.readlines()
for i, cfg in enumerate(model_cfgs):
create_train_bash_info(commands, cfg, script_name, '$PARTITION',

2
.dev/log_collector/log_collector.py
View File

@ -86,7 +86,7 @@ def main():
val_list = []
last_iter = 0
for log_name in log_list:
with open(os.path.join(preceding_path, log_name), 'r') as f:
with open(os.path.join(preceding_path, log_name)) as f:
# ignore the info line
f.readline()
all_lines = f.readlines()

6
.dev/md2yml.py
View File

@ -15,7 +15,7 @@ import sys
from lxml import etree
from mmengine.fileio import dump
MMSEG_ROOT = osp.dirname(osp.dirname((osp.dirname(__file__))))
MMSEG_ROOT = osp.dirname(osp.dirname(osp.dirname(__file__)))
COLLECTIONS = [
'ANN', 'APCNet', 'BiSeNetV1', 'BiSeNetV2', 'CCNet', 'CGNet', 'DANet',
@ -42,7 +42,7 @@ def dump_yaml_and_check_difference(obj, filename, sort_keys=False):
str_dump = dump(obj, None, file_format='yaml', sort_keys=sort_keys)
if osp.isfile(filename):
file_exists = True
with open(filename, 'r', encoding='utf-8') as f:
with open(filename, encoding='utf-8') as f:
str_orig = f.read()
else:
file_exists = False
@ -97,7 +97,7 @@ def parse_md(md_file):
# should be set with head or neck of this config file.
is_backbone = None
with open(md_file, 'r', encoding='UTF-8') as md:
with open(md_file, encoding='UTF-8') as md:
lines = md.readlines()
i = 0
current_dataset = ''

5
.pre-commit-config.yaml
View File

@ -52,6 +52,11 @@ repos:
language: python
files: ^configs/.*\.md$
require_serial: true
- repo: https://github.com/asottile/pyupgrade
rev: v2.32.1
hooks:
- id: pyupgrade
args: ["--py36-plus"]
- repo: https://github.com/open-mmlab/pre-commit-hooks
rev: v0.2.0 # Use the rev to fix revision
hooks:

2
docs/en/conf.py
View File

@ -28,7 +28,7 @@ version_file = '../../mmseg/version.py'
def get_version():
with open(version_file, 'r') as f:
with open(version_file) as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__']

12
docs/en/stat.py
View File

@ -18,13 +18,15 @@ num_ckpts = 0
for f in files:
url = osp.dirname(f.replace('../../', url_prefix))
with open(f, 'r') as content_file:
with open(f) as content_file:
content = content_file.read()
title = content.split('\n')[0].replace('#', '').strip()
ckpts = set(x.lower().strip()
for x in re.findall(r'https?://download.*\.pth', content)
if 'mmsegmentation' in x)
ckpts = {
x.lower().strip()
for x in re.findall(r'https?://download.*\.pth', content)
if 'mmsegmentation' in x
}
if len(ckpts) == 0:
continue
@ -34,7 +36,7 @@ for f in files:
assert len(_papertype) > 0
papertype = _papertype[0]
paper = set([(papertype, title)])
paper = {(papertype, title)}
titles.append(title)
num_ckpts += len(ckpts)

2
docs/zh_cn/conf.py
View File

@ -28,7 +28,7 @@ version_file = '../../mmseg/version.py'
def get_version():
with open(version_file, 'r') as f:
with open(version_file) as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__']

12
docs/zh_cn/stat.py
View File

@ -18,13 +18,15 @@ num_ckpts = 0
for f in files:
url = osp.dirname(f.replace('../../', url_prefix))
with open(f, 'r') as content_file:
with open(f) as content_file:
content = content_file.read()
title = content.split('\n')[0].replace('#', '').strip()
ckpts = set(x.lower().strip()
for x in re.findall(r'https?://download.*\.pth', content)
if 'mmsegmentation' in x)
ckpts = {
x.lower().strip()
for x in re.findall(r'https?://download.*\.pth', content)
if 'mmsegmentation' in x
}
if len(ckpts) == 0:
continue
@ -34,7 +36,7 @@ for f in files:
assert len(_papertype) > 0
papertype = _papertype[0]
paper = set([(papertype, title)])
paper = {(papertype, title)}
titles.append(title)
num_ckpts += len(ckpts)

3
mmseg/engine/optimizers/layer_decay_optimizer_constructor.py
View File

@ -204,5 +204,4 @@ class LayerDecayOptimizerConstructor(LearningRateDecayOptimizerConstructor):
warnings.warn('DeprecationWarning: Layer_decay_rate will '
'be deleted, please use decay_rate instead.')
paramwise_cfg['decay_rate'] = paramwise_cfg.pop('layer_decay_rate')
super(LayerDecayOptimizerConstructor,
self).__init__(optim_wrapper_cfg, paramwise_cfg)
super().__init__(optim_wrapper_cfg, paramwise_cfg)

2
mmseg/evaluation/metrics/iou_metric.py
View File

@ -212,7 +212,7 @@ class IoUMetric(BaseMetric):
metrics = [metrics]
allowed_metrics = ['mIoU', 'mDice', 'mFscore']
if not set(metrics).issubset(set(allowed_metrics)):
raise KeyError('metrics {} is not supported'.format(metrics))
raise KeyError(f'metrics {metrics} is not supported')
all_acc = total_area_intersect.sum() / total_area_label.sum()
ret_metrics = OrderedDict({'aAcc': all_acc})

12
mmseg/models/backbones/beit.py
View File

@ -194,7 +194,7 @@ class BEiTTransformerEncoderLayer(VisionTransformerEncoderLayer):
init_values=None):
attn_cfg.update(dict(window_size=window_size, qk_scale=None))
super(BEiTTransformerEncoderLayer, self).__init__(
super().__init__(
embed_dims=embed_dims,
num_heads=num_heads,
feedforward_channels=feedforward_channels,
@ -214,9 +214,9 @@ class BEiTTransformerEncoderLayer(VisionTransformerEncoderLayer):
self.drop_path = build_dropout(
dropout_layer) if dropout_layer else nn.Identity()
self.gamma_1 = nn.Parameter(
init_values * torch.ones((embed_dims)), requires_grad=True)
init_values * torch.ones(embed_dims), requires_grad=True)
self.gamma_2 = nn.Parameter(
init_values * torch.ones((embed_dims)), requires_grad=True)
init_values * torch.ones(embed_dims), requires_grad=True)
def build_attn(self, attn_cfg):
self.attn = BEiTAttention(**attn_cfg)
@ -287,7 +287,7 @@ class BEiT(BaseModule):
pretrained=None,
init_values=0.1,
init_cfg=None):
super(BEiT, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
if isinstance(img_size, int):
img_size = to_2tuple(img_size)
elif isinstance(img_size, tuple):
@ -505,7 +505,7 @@ class BEiT(BaseModule):
state_dict = self.resize_rel_pos_embed(checkpoint)
self.load_state_dict(state_dict, False)
elif self.init_cfg is not None:
super(BEiT, self).init_weights()
super().init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
@ -551,7 +551,7 @@ class BEiT(BaseModule):
return tuple(outs)
def train(self, mode=True):
super(BEiT, self).train(mode)
super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, nn.LayerNorm):

10
mmseg/models/backbones/bisenetv1.py
View File

@ -29,7 +29,7 @@ class SpatialPath(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(SpatialPath, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert len(num_channels) == 4, 'Length of input channels \
of Spatial Path must be 4!'
@ -98,7 +98,7 @@ class AttentionRefinementModule(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(AttentionRefinementModule, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.conv_layer = ConvModule(
in_channels=in_channels,
out_channels=out_channel,
@ -152,7 +152,7 @@ class ContextPath(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(ContextPath, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert len(context_channels) == 3, 'Length of input channels \
of Context Path must be 3!'
@ -228,7 +228,7 @@ class FeatureFusionModule(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(FeatureFusionModule, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.conv1 = ConvModule(
in_channels=in_channels,
out_channels=out_channels,
@ -304,7 +304,7 @@ class BiSeNetV1(BaseModule):
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(BiSeNetV1, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert len(spatial_channels) == 4, 'Length of input channels \
of Spatial Path must be 4!'

14
mmseg/models/backbones/bisenetv2.py
View File

@ -37,7 +37,7 @@ class DetailBranch(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(DetailBranch, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
detail_branch = []
for i in range(len(detail_channels)):
if i == 0:
@ -126,7 +126,7 @@ class StemBlock(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(StemBlock, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.conv_first = ConvModule(
in_channels=in_channels,
@ -207,7 +207,7 @@ class GELayer(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(GELayer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
mid_channel = in_channels * exp_ratio
self.conv1 = ConvModule(
in_channels=in_channels,
@ -326,7 +326,7 @@ class CEBlock(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(CEBlock, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
self.gap = nn.Sequential(
@ -385,7 +385,7 @@ class SemanticBranch(BaseModule):
in_channels=3,
exp_ratio=6,
init_cfg=None):
super(SemanticBranch, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.semantic_channels = semantic_channels
self.semantic_stages = []
@ -458,7 +458,7 @@ class BGALayer(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(BGALayer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.out_channels = out_channels
self.align_corners = align_corners
self.detail_dwconv = nn.Sequential(
@ -594,7 +594,7 @@ class BiSeNetV2(BaseModule):
dict(
type='Constant', val=1, layer=['_BatchNorm', 'GroupNorm'])
]
super(BiSeNetV2, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.in_channels = in_channels
self.out_indices = out_indices
self.detail_channels = detail_channels

10
mmseg/models/backbones/cgnet.py
View File

@ -25,7 +25,7 @@ class GlobalContextExtractor(nn.Module):
"""
def __init__(self, channel, reduction=16, with_cp=False):
super(GlobalContextExtractor, self).__init__()
super().__init__()
self.channel = channel
self.reduction = reduction
assert reduction >= 1 and channel >= reduction
@ -87,7 +87,7 @@ class ContextGuidedBlock(nn.Module):
norm_cfg=dict(type='BN', requires_grad=True),
act_cfg=dict(type='PReLU'),
with_cp=False):
super(ContextGuidedBlock, self).__init__()
super().__init__()
self.with_cp = with_cp
self.downsample = downsample
@ -172,7 +172,7 @@ class InputInjection(nn.Module):
"""Downsampling module for CGNet."""
def __init__(self, num_downsampling):
super(InputInjection, self).__init__()
super().__init__()
self.pool = nn.ModuleList()
for i in range(num_downsampling):
self.pool.append(nn.AvgPool2d(3, stride=2, padding=1))
@ -230,7 +230,7 @@ class CGNet(BaseModule):
pretrained=None,
init_cfg=None):
super(CGNet, self).__init__(init_cfg)
super().__init__(init_cfg)
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be setting at the same time'
@ -364,7 +364,7 @@ class CGNet(BaseModule):
def train(self, mode=True):
"""Convert the model into training mode will keeping the normalization
layer freezed."""
super(CGNet, self).train(mode)
super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only

8
mmseg/models/backbones/erfnet.py
View File

@ -35,7 +35,7 @@ class DownsamplerBlock(BaseModule):
norm_cfg=dict(type='BN', eps=1e-3),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(DownsamplerBlock, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@ -95,7 +95,7 @@ class NonBottleneck1d(BaseModule):
norm_cfg=dict(type='BN', eps=1e-3),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(NonBottleneck1d, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
@ -168,7 +168,7 @@ class UpsamplerBlock(BaseModule):
norm_cfg=dict(type='BN', eps=1e-3),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(UpsamplerBlock, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@ -242,7 +242,7 @@ class ERFNet(BaseModule):
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(ERFNet, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert len(enc_downsample_channels) \
== len(dec_upsample_channels)+1, 'Number of downsample\
block of encoder does not \

8
mmseg/models/backbones/fast_scnn.py
View File

@ -36,7 +36,7 @@ class LearningToDownsample(nn.Module):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
dw_act_cfg=None):
super(LearningToDownsample, self).__init__()
super().__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@ -124,7 +124,7 @@ class GlobalFeatureExtractor(nn.Module):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
align_corners=False):
super(GlobalFeatureExtractor, self).__init__()
super().__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
@ -220,7 +220,7 @@ class FeatureFusionModule(nn.Module):
dwconv_act_cfg=dict(type='ReLU'),
conv_act_cfg=None,
align_corners=False):
super(FeatureFusionModule, self).__init__()
super().__init__()
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.dwconv_act_cfg = dwconv_act_cfg
@ -340,7 +340,7 @@ class FastSCNN(BaseModule):
dw_act_cfg=None,
init_cfg=None):
super(FastSCNN, self).__init__(init_cfg)
super().__init__(init_cfg)
if init_cfg is None:
self.init_cfg = [

6
mmseg/models/backbones/hrnet.py
View File

@ -30,7 +30,7 @@ class HRModule(BaseModule):
norm_cfg=dict(type='BN', requires_grad=True),
block_init_cfg=None,
init_cfg=None):
super(HRModule, self).__init__(init_cfg)
super().__init__(init_cfg)
self.block_init_cfg = block_init_cfg
self._check_branches(num_branches, num_blocks, in_channels,
num_channels)
@ -308,7 +308,7 @@ class HRNet(BaseModule):
multiscale_output=True,
pretrained=None,
init_cfg=None):
super(HRNet, self).__init__(init_cfg)
super().__init__(init_cfg)
self.pretrained = pretrained
self.zero_init_residual = zero_init_residual
@ -633,7 +633,7 @@ class HRNet(BaseModule):
def train(self, mode=True):
"""Convert the model into training mode will keeping the normalization
layer freezed."""
super(HRNet, self).train(mode)
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():

2
mmseg/models/backbones/icnet.py
View File

@ -64,7 +64,7 @@ class ICNet(BaseModule):
dict(type='Constant', val=1, layer='_BatchNorm'),
dict(type='Normal', mean=0.01, layer='Linear')
]
super(ICNet, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.align_corners = align_corners
self.backbone = MODELS.build(backbone_cfg)

4
mmseg/models/backbones/mae.py
View File

@ -100,7 +100,7 @@ class MAE(BEiT):
pretrained=None,
init_values=0.1,
init_cfg=None):
super(MAE, self).__init__(
super().__init__(
img_size=img_size,
patch_size=patch_size,
in_channels=in_channels,
@ -186,7 +186,7 @@ class MAE(BEiT):
state_dict = self.resize_abs_pos_embed(state_dict)
self.load_state_dict(state_dict, False)
elif self.init_cfg is not None:
super(MAE, self).init_weights()
super().init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501

8
mmseg/models/backbones/mit.py
View File

@ -44,7 +44,7 @@ class MixFFN(BaseModule):
ffn_drop=0.,
dropout_layer=None,
init_cfg=None):
super(MixFFN, self).__init__(init_cfg)
super().__init__(init_cfg)
self.embed_dims = embed_dims
self.feedforward_channels = feedforward_channels
@ -253,7 +253,7 @@ class TransformerEncoderLayer(BaseModule):
batch_first=True,
sr_ratio=1,
with_cp=False):
super(TransformerEncoderLayer, self).__init__()
super().__init__()
# The ret[0] of build_norm_layer is norm name.
self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
@ -357,7 +357,7 @@ class MixVisionTransformer(BaseModule):
pretrained=None,
init_cfg=None,
with_cp=False):
super(MixVisionTransformer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be set at the same time'
@ -433,7 +433,7 @@ class MixVisionTransformer(BaseModule):
normal_init(
m, mean=0, std=math.sqrt(2.0 / fan_out), bias=0)
else:
super(MixVisionTransformer, self).init_weights()
super().init_weights()
def forward(self, x):
outs = []

4
mmseg/models/backbones/mobilenet_v2.py
View File

@ -63,7 +63,7 @@ class MobileNetV2(BaseModule):
with_cp=False,
pretrained=None,
init_cfg=None):
super(MobileNetV2, self).__init__(init_cfg)
super().__init__(init_cfg)
self.pretrained = pretrained
assert not (init_cfg and pretrained), \
@ -189,7 +189,7 @@ class MobileNetV2(BaseModule):
param.requires_grad = False
def train(self, mode=True):
super(MobileNetV2, self).train(mode)
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():

8
mmseg/models/backbones/mobilenet_v3.py
View File

@ -81,7 +81,7 @@ class MobileNetV3(BaseModule):
with_cp=False,
pretrained=None,
init_cfg=None):
super(MobileNetV3, self).__init__(init_cfg)
super().__init__(init_cfg)
self.pretrained = pretrained
assert not (init_cfg and pretrained), \
@ -175,7 +175,7 @@ class MobileNetV3(BaseModule):
act_cfg=dict(type=act),
with_cp=self.with_cp)
in_channels = out_channels
layer_name = 'layer{}'.format(i + 1)
layer_name = f'layer{i + 1}'
self.add_module(layer_name, layer)
layers.append(layer_name)
@ -192,7 +192,7 @@ class MobileNetV3(BaseModule):
conv_cfg=self.conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=dict(type='HSwish'))
layer_name = 'layer{}'.format(len(layer_setting) + 1)
layer_name = f'layer{len(layer_setting) + 1}'
self.add_module(layer_name, layer)
layers.append(layer_name)
@ -259,7 +259,7 @@ class MobileNetV3(BaseModule):
param.requires_grad = False
def train(self, mode=True):
super(MobileNetV3, self).train(mode)
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():

6
mmseg/models/backbones/resnest.py
View File

@ -69,7 +69,7 @@ class SplitAttentionConv2d(nn.Module):
conv_cfg=None,
norm_cfg=dict(type='BN'),
dcn=None):
super(SplitAttentionConv2d, self).__init__()
super().__init__()
inter_channels = max(in_channels * radix // reduction_factor, 32)
self.radix = radix
self.groups = groups
@ -174,7 +174,7 @@ class Bottleneck(_Bottleneck):
avg_down_stride=True,
**kwargs):
"""Bottleneck block for ResNeSt."""
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
super().__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
@ -304,7 +304,7 @@ class ResNeSt(ResNetV1d):
self.radix = radix
self.reduction_factor = reduction_factor
self.avg_down_stride = avg_down_stride
super(ResNeSt, self).__init__(**kwargs)
super().__init__(**kwargs)
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``."""

14
mmseg/models/backbones/resnet.py
View File

@ -29,7 +29,7 @@ class BasicBlock(BaseModule):
dcn=None,
plugins=None,
init_cfg=None):
super(BasicBlock, self).__init__(init_cfg)
super().__init__(init_cfg)
assert dcn is None, 'Not implemented yet.'
assert plugins is None, 'Not implemented yet.'
@ -118,7 +118,7 @@ class Bottleneck(BaseModule):
dcn=None,
plugins=None,
init_cfg=None):
super(Bottleneck, self).__init__(init_cfg)
super().__init__(init_cfg)
assert style in ['pytorch', 'caffe']
assert dcn is None or isinstance(dcn, dict)
assert plugins is None or isinstance(plugins, list)
@ -418,7 +418,7 @@ class ResNet(BaseModule):
zero_init_residual=True,
pretrained=None,
init_cfg=None):
super(ResNet, self).__init__(init_cfg)
super().__init__(init_cfg)
if depth not in self.arch_settings:
raise KeyError(f'invalid depth {depth} for resnet')
@ -676,7 +676,7 @@ class ResNet(BaseModule):
def train(self, mode=True):
"""Convert the model into training mode while keep normalization layer
freezed."""
super(ResNet, self).train(mode)
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
@ -696,8 +696,7 @@ class ResNetV1c(ResNet):
"""
def __init__(self, **kwargs):
super(ResNetV1c, self).__init__(
deep_stem=True, avg_down=False, **kwargs)
super().__init__(deep_stem=True, avg_down=False, **kwargs)
@MODELS.register_module()
@ -710,5 +709,4 @@ class ResNetV1d(ResNet):
"""
def __init__(self, **kwargs):
super(ResNetV1d, self).__init__(
deep_stem=True, avg_down=True, **kwargs)
super().__init__(deep_stem=True, avg_down=True, **kwargs)

4
mmseg/models/backbones/resnext.py
View File

@ -23,7 +23,7 @@ class Bottleneck(_Bottleneck):
base_width=4,
base_channels=64,
**kwargs):
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
super().__init__(inplanes, planes, **kwargs)
if groups == 1:
width = self.planes
@ -139,7 +139,7 @@ class ResNeXt(ResNet):
def __init__(self, groups=1, base_width=4, **kwargs):
self.groups = groups
self.base_width = base_width
super(ResNeXt, self).__init__(**kwargs)
super().__init__(**kwargs)
def make_res_layer(self, **kwargs):
"""Pack all blocks in a stage into a ``ResLayer``"""

8
mmseg/models/backbones/stdc.py
View File

@ -35,7 +35,7 @@ class STDCModule(BaseModule):
num_convs=4,
fusion_type='add',
init_cfg=None):
super(STDCModule, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert num_convs > 1
assert fusion_type in ['add', 'cat']
self.stride = stride
@ -155,7 +155,7 @@ class FeatureFusionModule(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(FeatureFusionModule, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
channels = out_channels // scale_factor
self.conv0 = ConvModule(
in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=act_cfg)
@ -240,7 +240,7 @@ class STDCNet(BaseModule):
with_final_conv=False,
pretrained=None,
init_cfg=None):
super(STDCNet, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert stdc_type in self.arch_settings, \
f'invalid structure {stdc_type} for STDCNet.'
assert bottleneck_type in ['add', 'cat'],\
@ -370,7 +370,7 @@ class STDCContextPathNet(BaseModule):
align_corners=None,
norm_cfg=dict(type='BN'),
init_cfg=None):
super(STDCContextPathNet, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.backbone = MODELS.build(backbone_cfg)
self.arms = ModuleList()
self.convs = ModuleList()

6
mmseg/models/backbones/swin.py
View File

@ -326,7 +326,7 @@ class SwinBlock(BaseModule):
with_cp=False,
init_cfg=None):
super(SwinBlock, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.with_cp = with_cp
@ -561,7 +561,7 @@ class SwinTransformer(BaseModule):
else:
raise TypeError('pretrained must be a str or None')
super(SwinTransformer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
num_layers = len(depths)
self.out_indices = out_indices
@ -636,7 +636,7 @@ class SwinTransformer(BaseModule):
def train(self, mode=True):
"""Convert the model into training mode while keep layers freezed."""
super(SwinTransformer, self).train(mode)
super().train(mode)
self._freeze_stages()
def _freeze_stages(self):

2
mmseg/models/backbones/timm_backbone.py
View File

@ -37,7 +37,7 @@ class TIMMBackbone(BaseModule):
):
if timm is None:
raise RuntimeError('timm is not installed')
super(TIMMBackbone, self).__init__(init_cfg)
super().__init__(init_cfg)
if 'norm_layer' in kwargs:
kwargs['norm_layer'] = MMENGINE_MODELS.get(kwargs['norm_layer'])
self.timm_model = timm.create_model(

24
mmseg/models/backbones/twins.py
View File

@ -62,7 +62,7 @@ class GlobalSubsampledAttention(EfficientMultiheadAttention):
norm_cfg=dict(type='LN'),
sr_ratio=1,
init_cfg=None):
super(GlobalSubsampledAttention, self).__init__(
super().__init__(
embed_dims,
num_heads,
attn_drop=attn_drop,
@ -112,7 +112,7 @@ class GSAEncoderLayer(BaseModule):
norm_cfg=dict(type='LN'),
sr_ratio=1.,
init_cfg=None):
super(GSAEncoderLayer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
self.attn = GlobalSubsampledAttention(
@ -172,7 +172,7 @@ class LocallyGroupedSelfAttention(BaseModule):
proj_drop_rate=0.,
window_size=1,
init_cfg=None):
super(LocallyGroupedSelfAttention, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert embed_dims % num_heads == 0, f'dim {embed_dims} should be ' \
f'divided by num_heads ' \
@ -284,7 +284,7 @@ class LSAEncoderLayer(BaseModule):
window_size=1,
init_cfg=None):
super(LSAEncoderLayer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.norm1 = build_norm_layer(norm_cfg, embed_dims, postfix=1)[1]
self.attn = LocallyGroupedSelfAttention(embed_dims, num_heads,
@ -325,7 +325,7 @@ class ConditionalPositionEncoding(BaseModule):
"""
def __init__(self, in_channels, embed_dims=768, stride=1, init_cfg=None):
super(ConditionalPositionEncoding, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.proj = nn.Conv2d(
in_channels,
embed_dims,
@ -401,7 +401,7 @@ class PCPVT(BaseModule):
norm_after_stage=False,
pretrained=None,
init_cfg=None):
super(PCPVT, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert not (init_cfg and pretrained), \
'init_cfg and pretrained cannot be set at the same time'
if isinstance(pretrained, str):
@ -471,7 +471,7 @@ class PCPVT(BaseModule):
def init_weights(self):
if self.init_cfg is not None:
super(PCPVT, self).init_weights()
super().init_weights()
else:
for m in self.modules():
if isinstance(m, nn.Linear):
@ -563,11 +563,11 @@ class SVT(PCPVT):
norm_after_stage=True,
pretrained=None,
init_cfg=None):
super(SVT, self).__init__(in_channels, embed_dims, patch_sizes,
strides, num_heads, mlp_ratios, out_indices,
qkv_bias, drop_rate, attn_drop_rate,
drop_path_rate, norm_cfg, depths, sr_ratios,
norm_after_stage, pretrained, init_cfg)
super().__init__(in_channels, embed_dims, patch_sizes, strides,
num_heads, mlp_ratios, out_indices, qkv_bias,
drop_rate, attn_drop_rate, drop_path_rate, norm_cfg,
depths, sr_ratios, norm_after_stage, pretrained,
init_cfg)
# transformer encoder
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))

12
mmseg/models/backbones/unet.py
View File

@ -53,7 +53,7 @@ class BasicConvBlock(nn.Module):
act_cfg=dict(type='ReLU'),
dcn=None,
plugins=None):
super(BasicConvBlock, self).__init__()
super().__init__()
assert dcn is None, 'Not implemented yet.'
assert plugins is None, 'Not implemented yet.'
@ -112,7 +112,7 @@ class DeconvModule(nn.Module):
*,
kernel_size=4,
scale_factor=2):
super(DeconvModule, self).__init__()
super().__init__()
assert (kernel_size - scale_factor >= 0) and\
(kernel_size - scale_factor) % 2 == 0,\
@ -191,7 +191,7 @@ class InterpConv(nn.Module):
padding=0,
upsample_cfg=dict(
scale_factor=2, mode='bilinear', align_corners=False)):
super(InterpConv, self).__init__()
super().__init__()
self.with_cp = with_cp
conv = ConvModule(
@ -298,7 +298,7 @@ class UNet(BaseModule):
plugins=None,
pretrained=None,
init_cfg=None):
super(UNet, self).__init__(init_cfg)
super().__init__(init_cfg)
self.pretrained = pretrained
assert not (init_cfg and pretrained), \
@ -396,7 +396,7 @@ class UNet(BaseModule):
act_cfg=act_cfg,
dcn=None,
plugins=None))
self.encoder.append((nn.Sequential(*enc_conv_block)))
self.encoder.append(nn.Sequential(*enc_conv_block))
in_channels = base_channels * 2**i
def forward(self, x):
@ -415,7 +415,7 @@ class UNet(BaseModule):
def train(self, mode=True):
"""Convert the model into training mode while keep normalization layer
freezed."""
super(UNet, self).train(mode)
super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
# trick: eval have effect on BatchNorm only

8
mmseg/models/backbones/vit.py
View File

@ -60,7 +60,7 @@ class TransformerEncoderLayer(BaseModule):
attn_cfg=dict(),
ffn_cfg=dict(),
with_cp=False):
super(TransformerEncoderLayer, self).__init__()
super().__init__()
self.norm1_name, norm1 = build_norm_layer(
norm_cfg, embed_dims, postfix=1)
@ -197,7 +197,7 @@ class VisionTransformer(BaseModule):
with_cp=False,
pretrained=None,
init_cfg=None):
super(VisionTransformer, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
if isinstance(img_size, int):
img_size = to_2tuple(img_size)
@ -315,7 +315,7 @@ class VisionTransformer(BaseModule):
load_state_dict(self, state_dict, strict=False, logger=None)
elif self.init_cfg is not None:
super(VisionTransformer, self).init_weights()
super().init_weights()
else:
# We only implement the 'jax_impl' initialization implemented at
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py#L353 # noqa: E501
@ -431,7 +431,7 @@ class VisionTransformer(BaseModule):
return tuple(outs)
def train(self, mode=True):
super(VisionTransformer, self).train(mode)
super().train(mode)
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, nn.LayerNorm):

11
mmseg/models/decode_heads/ann_head.py
View File

@ -17,7 +17,7 @@ class PPMConcat(nn.ModuleList):
"""
def __init__(self, pool_scales=(1, 3, 6, 8)):
super(PPMConcat, self).__init__(
super().__init__(
[nn.AdaptiveAvgPool2d(pool_scale) for pool_scale in pool_scales])
def forward(self, feats):
@ -58,7 +58,7 @@ class SelfAttentionBlock(_SelfAttentionBlock):
query_downsample = nn.MaxPool2d(kernel_size=query_scale)
else:
query_downsample = None
super(SelfAttentionBlock, self).__init__(
super().__init__(
key_in_channels=low_in_channels,
query_in_channels=high_in_channels,
channels=channels,
@ -100,7 +100,7 @@ class AFNB(nn.Module):
def __init__(self, low_in_channels, high_in_channels, channels,
out_channels, query_scales, key_pool_scales, conv_cfg,
norm_cfg, act_cfg):
super(AFNB, self).__init__()
super().__init__()
self.stages = nn.ModuleList()
for query_scale in query_scales:
self.stages.append(
@ -150,7 +150,7 @@ class APNB(nn.Module):
def __init__(self, in_channels, channels, out_channels, query_scales,
key_pool_scales, conv_cfg, norm_cfg, act_cfg):
super(APNB, self).__init__()
super().__init__()
self.stages = nn.ModuleList()
for query_scale in query_scales:
self.stages.append(
@ -201,8 +201,7 @@ class ANNHead(BaseDecodeHead):
query_scales=(1, ),
key_pool_scales=(1, 3, 6, 8),
**kwargs):
super(ANNHead, self).__init__(
input_transform='multiple_select', **kwargs)
super().__init__(input_transform='multiple_select', **kwargs)
assert len(self.in_channels) == 2
low_in_channels, high_in_channels = self.in_channels
self.project_channels = project_channels

4
mmseg/models/decode_heads/apc_head.py
View File

@ -25,7 +25,7 @@ class ACM(nn.Module):
def __init__(self, pool_scale, fusion, in_channels, channels, conv_cfg,
norm_cfg, act_cfg):
super(ACM, self).__init__()
super().__init__()
self.pool_scale = pool_scale
self.fusion = fusion
self.in_channels = in_channels
@ -123,7 +123,7 @@ class APCHead(BaseDecodeHead):
"""
def __init__(self, pool_scales=(1, 2, 3, 6), fusion=True, **kwargs):
super(APCHead, self).__init__(**kwargs)
super().__init__(**kwargs)
assert isinstance(pool_scales, (list, tuple))
self.pool_scales = pool_scales
self.fusion = fusion

4
mmseg/models/decode_heads/aspp_head.py
View File

@ -22,7 +22,7 @@ class ASPPModule(nn.ModuleList):
def __init__(self, dilations, in_channels, channels, conv_cfg, norm_cfg,
act_cfg):
super(ASPPModule, self).__init__()
super().__init__()
self.dilations = dilations
self.in_channels = in_channels
self.channels = channels
@ -63,7 +63,7 @@ class ASPPHead(BaseDecodeHead):
"""
def __init__(self, dilations=(1, 6, 12, 18), **kwargs):
super(ASPPHead, self).__init__(**kwargs)
super().__init__(**kwargs)
assert isinstance(dilations, (list, tuple))
self.dilations = dilations
self.image_pool = nn.Sequential(

2
mmseg/models/decode_heads/cascade_decode_head.py
View File

@ -13,7 +13,7 @@ class BaseCascadeDecodeHead(BaseDecodeHead, metaclass=ABCMeta):
:class:`CascadeEncoderDecoder."""
def __init__(self, *args, **kwargs):
super(BaseCascadeDecodeHead, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
@abstractmethod
def forward(self, inputs, prev_output):

2
mmseg/models/decode_heads/cc_head.py
View File

@ -26,7 +26,7 @@ class CCHead(FCNHead):
if CrissCrossAttention is None:
raise RuntimeError('Please install mmcv-full for '
'CrissCrossAttention ops')
super(CCHead, self).__init__(num_convs=2, **kwargs)
super().__init__(num_convs=2, **kwargs)
self.recurrence = recurrence
self.cca = CrissCrossAttention(self.channels)

21
mmseg/models/decode_heads/da_head.py
View File

@ -21,7 +21,7 @@ class PAM(_SelfAttentionBlock):
"""
def __init__(self, in_channels, channels):
super(PAM, self).__init__(
super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
@ -43,7 +43,7 @@ class PAM(_SelfAttentionBlock):
def forward(self, x):
"""Forward function."""
out = super(PAM, self).forward(x, x)
out = super().forward(x, x)
out = self.gamma(out) + x
return out
@ -53,7 +53,7 @@ class CAM(nn.Module):
"""Channel Attention Module (CAM)"""
def __init__(self):
super(CAM, self).__init__()
super().__init__()
self.gamma = Scale(0)
def forward(self, x):
@ -86,7 +86,7 @@ class DAHead(BaseDecodeHead):
"""
def __init__(self, pam_channels, **kwargs):
super(DAHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.pam_channels = pam_channels
self.pam_in_conv = ConvModule(
self.in_channels,
@ -173,15 +173,12 @@ class DAHead(BaseDecodeHead):
loss = dict()
loss.update(
add_prefix(
super(DAHead, self).loss_by_feat(pam_cam_seg_logit,
batch_data_samples),
super().loss_by_feat(pam_cam_seg_logit, batch_data_samples),
'pam_cam'))
loss.update(
add_prefix(
super(DAHead, self).loss_by_feat(pam_seg_logit,
batch_data_samples), 'pam'))
add_prefix(super().loss_by_feat(pam_seg_logit, batch_data_samples),
'pam'))
loss.update(
add_prefix(
super(DAHead, self).loss_by_feat(cam_seg_logit,
batch_data_samples), 'cam'))
add_prefix(super().loss_by_feat(cam_seg_logit, batch_data_samples),
'cam'))
return loss

2
mmseg/models/decode_heads/decode_head.py
View File

@ -97,7 +97,7 @@ class BaseDecodeHead(BaseModule, metaclass=ABCMeta):
align_corners=False,
init_cfg=dict(
type='Normal', std=0.01, override=dict(name='conv_seg'))):
super(BaseDecodeHead, self).__init__(init_cfg)
super().__init__(init_cfg)
self._init_inputs(in_channels, in_index, input_transform)
self.channels = channels
self.num_classes = num_classes

4
mmseg/models/decode_heads/dm_head.py
View File

@ -24,7 +24,7 @@ class DCM(nn.Module):
def __init__(self, filter_size, fusion, in_channels, channels, conv_cfg,
norm_cfg, act_cfg):
super(DCM, self).__init__()
super().__init__()
self.filter_size = filter_size
self.fusion = fusion
self.in_channels = in_channels
@ -105,7 +105,7 @@ class DMHead(BaseDecodeHead):
"""
def __init__(self, filter_sizes=(1, 3, 5, 7), fusion=False, **kwargs):
super(DMHead, self).__init__(**kwargs)
super().__init__(**kwargs)
assert isinstance(filter_sizes, (list, tuple))
self.filter_sizes = filter_sizes
self.fusion = fusion

2
mmseg/models/decode_heads/dnl_head.py
View File

@ -111,7 +111,7 @@ class DNLHead(FCNHead):
mode='embedded_gaussian',
temperature=0.05,
**kwargs):
super(DNLHead, self).__init__(num_convs=2, **kwargs)
super().__init__(num_convs=2, **kwargs)
self.reduction = reduction
self.use_scale = use_scale
self.mode = mode

8
mmseg/models/decode_heads/dpt_head.py
View File

@ -30,7 +30,7 @@ class ReassembleBlocks(BaseModule):
readout_type='ignore',
patch_size=16,
init_cfg=None):
super(ReassembleBlocks, self).__init__(init_cfg)
super().__init__(init_cfg)
assert readout_type in ['ignore', 'add', 'project']
self.readout_type = readout_type
@ -116,7 +116,7 @@ class PreActResidualConvUnit(BaseModule):
stride=1,
dilation=1,
init_cfg=None):
super(PreActResidualConvUnit, self).__init__(init_cfg)
super().__init__(init_cfg)
self.conv1 = ConvModule(
in_channels,
@ -168,7 +168,7 @@ class FeatureFusionBlock(BaseModule):
expand=False,
align_corners=True,
init_cfg=None):
super(FeatureFusionBlock, self).__init__(init_cfg)
super().__init__(init_cfg)
self.in_channels = in_channels
self.expand = expand
@ -242,7 +242,7 @@ class DPTHead(BaseDecodeHead):
act_cfg=dict(type='ReLU'),
norm_cfg=dict(type='BN'),
**kwargs):
super(DPTHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.in_channels = self.in_channels
self.expand_channels = expand_channels

4
mmseg/models/decode_heads/ema_head.py
View File

@ -30,7 +30,7 @@ class EMAModule(nn.Module):
"""
def __init__(self, channels, num_bases, num_stages, momentum):
super(EMAModule, self).__init__()
super().__init__()
assert num_stages >= 1, 'num_stages must be at least 1!'
self.num_bases = num_bases
self.num_stages = num_stages
@ -99,7 +99,7 @@ class EMAHead(BaseDecodeHead):
concat_input=True,
momentum=0.1,
**kwargs):
super(EMAHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.ema_channels = ema_channels
self.num_bases = num_bases
self.num_stages = num_stages

8
mmseg/models/decode_heads/enc_head.py
View File

@ -26,7 +26,7 @@ class EncModule(nn.Module):
"""
def __init__(self, in_channels, num_codes, conv_cfg, norm_cfg, act_cfg):
super(EncModule, self).__init__()
super().__init__()
self.encoding_project = ConvModule(
in_channels,
in_channels,
@ -90,8 +90,7 @@ class EncHead(BaseDecodeHead):
use_sigmoid=True,
loss_weight=0.2),
**kwargs):
super(EncHead, self).__init__(
input_transform='multiple_select', **kwargs)
super().__init__(input_transform='multiple_select', **kwargs)
self.use_se_loss = use_se_loss
self.add_lateral = add_lateral
self.num_codes = num_codes
@ -188,8 +187,7 @@ class EncHead(BaseDecodeHead):
"""Compute segmentation and semantic encoding loss."""
seg_logit, se_seg_logit = seg_logit
loss = dict()
loss.update(
super(EncHead, self).loss_by_feat(seg_logit, batch_data_samples))
loss.update(super().loss_by_feat(seg_logit, batch_data_samples))
seg_label = self._stack_batch_gt(batch_data_samples)
se_loss = self.loss_se_decode(

2
mmseg/models/decode_heads/fcn_head.py
View File

@ -31,7 +31,7 @@ class FCNHead(BaseDecodeHead):
self.num_convs = num_convs
self.concat_input = concat_input
self.kernel_size = kernel_size
super(FCNHead, self).__init__(**kwargs)
super().__init__(**kwargs)
if num_convs == 0:
assert self.in_channels == self.channels

3
mmseg/models/decode_heads/fpn_head.py
View File

@ -22,8 +22,7 @@ class FPNHead(BaseDecodeHead):
"""
def __init__(self, feature_strides, **kwargs):
super(FPNHead, self).__init__(
input_transform='multiple_select', **kwargs)
super().__init__(input_transform='multiple_select', **kwargs)
assert len(feature_strides) == len(self.in_channels)
assert min(feature_strides) == feature_strides[0]
self.feature_strides = feature_strides

2
mmseg/models/decode_heads/gc_head.py
View File

@ -26,7 +26,7 @@ class GCHead(FCNHead):
pooling_type='att',
fusion_types=('channel_add', ),
**kwargs):
super(GCHead, self).__init__(num_convs=2, **kwargs)
super().__init__(num_convs=2, **kwargs)
self.ratio = ratio
self.pooling_type = pooling_type
self.fusion_types = fusion_types

6
mmseg/models/decode_heads/isa_head.py
View File

@ -22,7 +22,7 @@ class SelfAttentionBlock(_SelfAttentionBlock):
"""
def __init__(self, in_channels, channels, conv_cfg, norm_cfg, act_cfg):
super(SelfAttentionBlock, self).__init__(
super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
@ -51,7 +51,7 @@ class SelfAttentionBlock(_SelfAttentionBlock):
def forward(self, x):
"""Forward function."""
context = super(SelfAttentionBlock, self).forward(x, x)
context = super().forward(x, x)
return self.output_project(context)
@ -68,7 +68,7 @@ class ISAHead(BaseDecodeHead):
"""
def __init__(self, isa_channels, down_factor=(8, 8), **kwargs):
super(ISAHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.down_factor = down_factor
self.in_conv = ConvModule(

4
mmseg/models/decode_heads/knet_head.py
View File

@ -48,7 +48,7 @@ class KernelUpdator(nn.Module):
norm_cfg=dict(type='LN'),
act_cfg=dict(type='ReLU', inplace=True),
):
super(KernelUpdator, self).__init__()
super().__init__()
self.in_channels = in_channels
self.feat_channels = feat_channels
self.out_channels_raw = out_channels
@ -213,7 +213,7 @@ class KernelUpdateHead(nn.Module):
out_channels=256,
act_cfg=dict(type='ReLU', inplace=True),
norm_cfg=dict(type='LN'))):
super(KernelUpdateHead, self).__init__()
super().__init__()
self.num_classes = num_classes
self.in_channels = in_channels
self.out_channels = out_channels

2
mmseg/models/decode_heads/lraspp_head.py
View File

@ -22,7 +22,7 @@ class LRASPPHead(BaseDecodeHead):
"""
def __init__(self, branch_channels=(32, 64), **kwargs):
super(LRASPPHead, self).__init__(**kwargs)
super().__init__(**kwargs)
if self.input_transform != 'multiple_select':
raise ValueError('in Lite R-ASPP (LRASPP) head, input_transform '
f'must be \'multiple_select\'. But received '

2
mmseg/models/decode_heads/nl_head.py
View File

@ -26,7 +26,7 @@ class NLHead(FCNHead):
use_scale=True,
mode='embedded_gaussian',
**kwargs):
super(NLHead, self).__init__(num_convs=2, **kwargs)
super().__init__(num_convs=2, **kwargs)
self.reduction = reduction
self.use_scale = use_scale
self.mode = mode

9
mmseg/models/decode_heads/ocr_head.py
View File

@ -18,7 +18,7 @@ class SpatialGatherModule(nn.Module):
"""
def __init__(self, scale):
super(SpatialGatherModule, self).__init__()
super().__init__()
self.scale = scale
def forward(self, feats, probs):
@ -46,7 +46,7 @@ class ObjectAttentionBlock(_SelfAttentionBlock):
query_downsample = nn.MaxPool2d(kernel_size=scale)
else:
query_downsample = None
super(ObjectAttentionBlock, self).__init__(
super().__init__(
key_in_channels=in_channels,
query_in_channels=in_channels,
channels=channels,
@ -73,8 +73,7 @@ class ObjectAttentionBlock(_SelfAttentionBlock):
def forward(self, query_feats, key_feats):
"""Forward function."""
context = super(ObjectAttentionBlock,
self).forward(query_feats, key_feats)
context = super().forward(query_feats, key_feats)
output = self.bottleneck(torch.cat([context, query_feats], dim=1))
if self.query_downsample is not None:
output = resize(query_feats)
@ -96,7 +95,7 @@ class OCRHead(BaseCascadeDecodeHead):
"""
def __init__(self, ocr_channels, scale=1, **kwargs):
super(OCRHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.ocr_channels = ocr_channels
self.scale = scale
self.object_context_block = ObjectAttentionBlock(

2
mmseg/models/decode_heads/point_head.py
View File

@ -74,7 +74,7 @@ class PointHead(BaseCascadeDecodeHead):
norm_cfg=None,
act_cfg=dict(type='ReLU', inplace=False),
**kwargs):
super(PointHead, self).__init__(
super().__init__(
input_transform='multiple_select',
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,

2
mmseg/models/decode_heads/psa_head.py
View File

@ -43,7 +43,7 @@ class PSAHead(BaseDecodeHead):
**kwargs):
if PSAMask is None:
raise RuntimeError('Please install mmcv-full for PSAMask ops')
super(PSAHead, self).__init__(**kwargs)
super().__init__(**kwargs)
assert psa_type in ['collect', 'distribute', 'bi-direction']
self.psa_type = psa_type
self.compact = compact

4
mmseg/models/decode_heads/psp_head.py
View File

@ -24,7 +24,7 @@ class PPM(nn.ModuleList):
def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg,
act_cfg, align_corners, **kwargs):
super(PPM, self).__init__()
super().__init__()
self.pool_scales = pool_scales
self.align_corners = align_corners
self.in_channels = in_channels
@ -72,7 +72,7 @@ class PSPHead(BaseDecodeHead):
"""
def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
super(PSPHead, self).__init__(**kwargs)
super().__init__(**kwargs)
assert isinstance(pool_scales, (list, tuple))
self.pool_scales = pool_scales
self.psp_modules = PPM(

3
mmseg/models/decode_heads/segmenter_mask_head.py
View File

@ -61,8 +61,7 @@ class SegmenterMaskTransformerHead(BaseDecodeHead):
init_std=0.02,
**kwargs,
):
super(SegmenterMaskTransformerHead, self).__init__(
in_channels=in_channels, **kwargs)
super().__init__(in_channels=in_channels, **kwargs)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, num_layers)]
self.layers = ModuleList()

4
mmseg/models/decode_heads/sep_aspp_head.py
View File

@ -13,7 +13,7 @@ class DepthwiseSeparableASPPModule(ASPPModule):
conv."""
def __init__(self, **kwargs):
super(DepthwiseSeparableASPPModule, self).__init__(**kwargs)
super().__init__(**kwargs)
for i, dilation in enumerate(self.dilations):
if dilation > 1:
self[i] = DepthwiseSeparableConvModule(
@ -41,7 +41,7 @@ class DepthwiseSeparableASPPHead(ASPPHead):
"""
def __init__(self, c1_in_channels, c1_channels, **kwargs):
super(DepthwiseSeparableASPPHead, self).__init__(**kwargs)
super().__init__(**kwargs)
assert c1_in_channels >= 0
self.aspp_modules = DepthwiseSeparableASPPModule(
dilations=self.dilations,

2
mmseg/models/decode_heads/sep_fcn_head.py
View File

@ -32,7 +32,7 @@ class DepthwiseSeparableFCNHead(FCNHead):
"""
def __init__(self, dw_act_cfg=None, **kwargs):
super(DepthwiseSeparableFCNHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.convs[0] = DepthwiseSeparableConvModule(
self.in_channels,
self.channels,

3
mmseg/models/decode_heads/setr_mla_head.py
View File

@ -21,8 +21,7 @@ class SETRMLAHead(BaseDecodeHead):
"""
def __init__(self, mla_channels=128, up_scale=4, **kwargs):
super(SETRMLAHead, self).__init__(
input_transform='multiple_select', **kwargs)
super().__init__(input_transform='multiple_select', **kwargs)
self.mla_channels = mla_channels
num_inputs = len(self.in_channels)

2
mmseg/models/decode_heads/setr_up_head.py
View File

@ -41,7 +41,7 @@ class SETRUPHead(BaseDecodeHead):
assert kernel_size in [1, 3], 'kernel_size must be 1 or 3.'
super(SETRUPHead, self).__init__(init_cfg=init_cfg, **kwargs)
super().__init__(init_cfg=init_cfg, **kwargs)
assert isinstance(self.in_channels, int)

5
mmseg/models/decode_heads/stdc_head.py
View File

@ -21,7 +21,7 @@ class STDCHead(FCNHead):
"""
def __init__(self, boundary_threshold=0.1, **kwargs):
super(STDCHead, self).__init__(**kwargs)
super().__init__(**kwargs)
self.boundary_threshold = boundary_threshold
# Using register buffer to make laplacian kernel on the same
# device of `seg_label`.
@ -93,6 +93,5 @@ class STDCHead(FCNHead):
seg_data_sample.gt_sem_seg = PixelData(data=label)
batch_sample_list.append(seg_data_sample)
loss = super(STDCHead, self).loss_by_feat(seg_logits,
batch_sample_list)
loss = super().loss_by_feat(seg_logits, batch_sample_list)
return loss

3
mmseg/models/decode_heads/uper_head.py
View File

@ -22,8 +22,7 @@ class UPerHead(BaseDecodeHead):
"""
def __init__(self, pool_scales=(1, 2, 3, 6), **kwargs):
super(UPerHead, self).__init__(
input_transform='multiple_select', **kwargs)
super().__init__(input_transform='multiple_select', **kwargs)
# PSP Module
self.psp_modules = PPM(
pool_scales,

2
mmseg/models/losses/cross_entropy_loss.py
View File

@ -223,7 +223,7 @@ class CrossEntropyLoss(nn.Module):
loss_weight=1.0,
loss_name='loss_ce',
avg_non_ignore=False):
super(CrossEntropyLoss, self).__init__()
super().__init__()
assert (use_sigmoid is False) or (use_mask is False)
self.use_sigmoid = use_sigmoid
self.use_mask = use_mask

2
mmseg/models/losses/dice_loss.py
View File

@ -80,7 +80,7 @@ class DiceLoss(nn.Module):
ignore_index=255,
loss_name='loss_dice',
**kwards):
super(DiceLoss, self).__init__()
super().__init__()
self.smooth = smooth
self.exponent = exponent
self.reduction = reduction

2
mmseg/models/losses/focal_loss.py
View File

@ -172,7 +172,7 @@ class FocalLoss(nn.Module):
loss item to be included into the backward graph, `loss_` must
be the prefix of the name. Defaults to 'loss_focal'.
"""
super(FocalLoss, self).__init__()
super().__init__()
assert use_sigmoid is True, \
'AssertionError: Only sigmoid focal loss supported now.'
assert reduction in ('none', 'mean', 'sum'), \

2
mmseg/models/losses/lovasz_loss.py
View File

@ -257,7 +257,7 @@ class LovaszLoss(nn.Module):
class_weight=None,
loss_weight=1.0,
loss_name='loss_lovasz'):
super(LovaszLoss, self).__init__()
super().__init__()
assert loss_type in ('binary', 'multi_class'), "loss_type should be \
'binary' or 'multi_class'."

2
mmseg/models/losses/tversky_loss.py
View File

@ -88,7 +88,7 @@ class TverskyLoss(nn.Module):
alpha=0.3,
beta=0.7,
loss_name='loss_tversky'):
super(TverskyLoss, self).__init__()
super().__init__()
self.smooth = smooth
self.class_weight = get_class_weight(class_weight)
self.loss_weight = loss_weight

2
mmseg/models/necks/featurepyramid.py
View File

@ -23,7 +23,7 @@ class Feature2Pyramid(nn.Module):
embed_dim,
rescales=[4, 2, 1, 0.5],
norm_cfg=dict(type='SyncBN', requires_grad=True)):
super(Feature2Pyramid, self).__init__()
super().__init__()
self.rescales = rescales
self.upsample_4x = None
for k in self.rescales:

2
mmseg/models/necks/fpn.py
View File

@ -80,7 +80,7 @@ class FPN(BaseModule):
upsample_cfg=dict(mode='nearest'),
init_cfg=dict(
type='Xavier', layer='Conv2d', distribution='uniform')):
super(FPN, self).__init__(init_cfg)
super().__init__(init_cfg)
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels

4
mmseg/models/necks/ic_neck.py
View File

@ -42,7 +42,7 @@ class CascadeFeatureFusion(BaseModule):
act_cfg=dict(type='ReLU'),
align_corners=False,
init_cfg=None):
super(CascadeFeatureFusion, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.align_corners = align_corners
self.conv_low = ConvModule(
low_channels,
@ -108,7 +108,7 @@ class ICNeck(BaseModule):
act_cfg=dict(type='ReLU'),
align_corners=False,
init_cfg=None):
super(ICNeck, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert len(in_channels) == 3, 'Length of input channels \
must be 3!'

2
mmseg/models/necks/jpu.py
View File

@ -51,7 +51,7 @@ class JPU(BaseModule):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
init_cfg=None):
super(JPU, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
assert isinstance(in_channels, tuple)
assert isinstance(dilations, tuple)
self.in_channels = in_channels

4
mmseg/models/necks/mla_neck.py
View File

@ -12,7 +12,7 @@ class MLAModule(nn.Module):
out_channels=256,
norm_cfg=None,
act_cfg=None):
super(MLAModule, self).__init__()
super().__init__()
self.channel_proj = nn.ModuleList()
for i in range(len(in_channels)):
self.channel_proj.append(
@ -83,7 +83,7 @@ class MLANeck(nn.Module):
norm_layer=dict(type='LN', eps=1e-6, requires_grad=True),
norm_cfg=None,
act_cfg=None):
super(MLANeck, self).__init__()
super().__init__()
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels

2
mmseg/models/necks/multilevel_neck.py
View File

@ -29,7 +29,7 @@ class MultiLevelNeck(nn.Module):
scales=[0.5, 1, 2, 4],
norm_cfg=None,
act_cfg=None):
super(MultiLevelNeck, self).__init__()
super().__init__()
assert isinstance(in_channels, list)
self.in_channels = in_channels
self.out_channels = out_channels

2
mmseg/models/segmentors/base.py
View File

@ -27,7 +27,7 @@ class BaseSegmentor(BaseModel, metaclass=ABCMeta):
def __init__(self,
data_preprocessor: OptConfigType = None,
init_cfg: OptMultiConfig = None):
super(BaseSegmentor, self).__init__(
super().__init__(
data_preprocessor=data_preprocessor, init_cfg=init_cfg)
@property

2
mmseg/models/segmentors/cascade_encoder_decoder.py
View File

@ -48,7 +48,7 @@ class CascadeEncoderDecoder(EncoderDecoder):
pretrained: Optional[str] = None,
init_cfg: OptMultiConfig = None):
self.num_stages = num_stages
super(CascadeEncoderDecoder, self).__init__(
super().__init__(
backbone=backbone,
decode_head=decode_head,
neck=neck,

2
mmseg/models/segmentors/encoder_decoder.py
View File

@ -78,7 +78,7 @@ class EncoderDecoder(BaseSegmentor):
data_preprocessor: OptConfigType = None,
pretrained: Optional[str] = None,
init_cfg: OptMultiConfig = None):
super(EncoderDecoder, self).__init__(
super().__init__(
data_preprocessor=data_preprocessor, init_cfg=init_cfg)
if pretrained is not None:
assert backbone.get('pretrained') is None, \

4
mmseg/models/utils/embed.py
View File

@ -42,7 +42,7 @@ class AdaptivePadding(nn.Module):
def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
super(AdaptivePadding, self).__init__()
super().__init__()
assert padding in ('same', 'corner')
@ -120,7 +120,7 @@ class PatchEmbed(BaseModule):
norm_cfg=None,
input_size=None,
init_cfg=None):
super(PatchEmbed, self).__init__(init_cfg=init_cfg)
super().__init__(init_cfg=init_cfg)
self.embed_dims = embed_dims
if stride is None:

2
mmseg/models/utils/encoding.py
View File

@ -16,7 +16,7 @@ class Encoding(nn.Module):
"""
def __init__(self, channels, num_codes):
super(Encoding, self).__init__()
super().__init__()
# init codewords and smoothing factor
self.channels, self.num_codes = channels, num_codes
std = 1. / ((num_codes * channels)**0.5)

4
mmseg/models/utils/inverted_residual.py
View File

@ -40,7 +40,7 @@ class InvertedResidual(nn.Module):
act_cfg=dict(type='ReLU6'),
with_cp=False,
**kwargs):
super(InvertedResidual, self).__init__()
super().__init__()
self.stride = stride
assert stride in [1, 2], f'stride must in [1, 2]. ' \
f'But received {stride}.'
@ -138,7 +138,7 @@ class InvertedResidualV3(nn.Module):
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'),
with_cp=False):
super(InvertedResidualV3, self).__init__()
super().__init__()
self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
assert stride in [1, 2]
self.with_cp = with_cp

2
mmseg/models/utils/res_layer.py
View File

@ -93,4 +93,4 @@ class ResLayer(Sequential):
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
**kwargs))
super(ResLayer, self).__init__(*layers)
super().__init__(*layers)

2
mmseg/models/utils/se_layer.py
View File

@ -30,7 +30,7 @@ class SELayer(nn.Module):
conv_cfg=None,
act_cfg=(dict(type='ReLU'),
dict(type='HSigmoid', bias=3.0, divisor=6.0))):
super(SELayer, self).__init__()
super().__init__()
if isinstance(act_cfg, dict):
act_cfg = (act_cfg, act_cfg)
assert len(act_cfg) == 2

2
mmseg/models/utils/self_attention_block.py
View File

@ -36,7 +36,7 @@ class SelfAttentionBlock(nn.Module):
key_downsample, key_query_num_convs, value_out_num_convs,
key_query_norm, value_out_norm, matmul_norm, with_out,
conv_cfg, norm_cfg, act_cfg):
super(SelfAttentionBlock, self).__init__()
super().__init__()
if share_key_query:
assert key_in_channels == query_in_channels
self.key_in_channels = key_in_channels

2
mmseg/models/utils/up_conv_block.py
View File

@ -57,7 +57,7 @@ class UpConvBlock(nn.Module):
upsample_cfg=dict(type='InterpConv'),
dcn=None,
plugins=None):
super(UpConvBlock, self).__init__()
super().__init__()
assert dcn is None, 'Not implemented yet.'
assert plugins is None, 'Not implemented yet.'

2
mmseg/models/utils/wrappers.py
View File

@ -34,7 +34,7 @@ class Upsample(nn.Module):
scale_factor=None,
mode='nearest',
align_corners=None):
super(Upsample, self).__init__()
super().__init__()
self.size = size
if isinstance(scale_factor, tuple):
self.scale_factor = tuple(float(factor) for factor in scale_factor)

2
mmseg/structures/sampler/ohem_pixel_sampler.py
View File

@ -23,7 +23,7 @@ class OHEMPixelSampler(BasePixelSampler):
"""
def __init__(self, context, thresh=None, min_kept=100000):
super(OHEMPixelSampler, self).__init__()
super().__init__()
self.context = context
assert min_kept > 1
self.thresh = thresh

2
mmseg/utils/collect_env.py
View File

@ -15,4 +15,4 @@ def collect_env():
if __name__ == '__main__':
for name, val in collect_env().items():
print('{}: {}'.format(name, val))
print(f'{name}: {val}')

4
mmseg/utils/misc.py
View File

@ -52,12 +52,12 @@ def stack_batch(inputs: List[torch.Tensor],
"""
assert isinstance(inputs, list), \
f'Expected input type to be list, but got {type(inputs)}'
assert len(set([tensor.ndim for tensor in inputs])) == 1, \
assert len({tensor.ndim for tensor in inputs}) == 1, \
f'Expected the dimensions of all inputs must be the same, ' \
f'but got {[tensor.ndim for tensor in inputs]}'
assert inputs[0].ndim == 3, f'Expected tensor dimension to be 3, ' \
f'but got {inputs[0].ndim}'
assert len(set([tensor.shape[0] for tensor in inputs])) == 1, \
assert len({tensor.shape[0] for tensor in inputs}) == 1, \
f'Expected the channels of all inputs must be the same, ' \
f'but got {[tensor.shape[0] for tensor in inputs]}'

7
setup.py
View File

@ -18,7 +18,7 @@ version_file = 'mmseg/version.py'
def get_version():
with open(version_file, 'r') as f:
with open(version_file) as f:
exec(compile(f.read(), version_file, 'exec'))
return locals()['__version__']
@ -74,12 +74,11 @@ def parse_requirements(fname='requirements.txt', with_version=True):
yield info
def parse_require_file(fpath):
with open(fpath, 'r') as f:
with open(fpath) as f:
for line in f.readlines():
line = line.strip()
if line and not line.startswith('#'):
for info in parse_line(line):
yield info
yield from parse_line(line)
def gen_packages_items():
if exists(require_fpath):

19
tests/test_config.py
View File

@ -31,7 +31,7 @@ def test_config_build_segmentor():
"""Test that all segmentation models defined in the configs can be
initialized."""
config_dpath = _get_config_directory()
print('Found config_dpath = {!r}'.format(config_dpath))
print(f'Found config_dpath = {config_dpath!r}')
config_fpaths = []
# one config each sub folder
@ -42,20 +42,20 @@ def test_config_build_segmentor():
config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
config_names = [relpath(p, config_dpath) for p in config_fpaths]
print('Using {} config files'.format(len(config_names)))
print(f'Using {len(config_names)} config files')
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
config_mod = Config.fromfile(config_fpath)
config_mod.model
print('Building segmentor, config_fpath = {!r}'.format(config_fpath))
print(f'Building segmentor, config_fpath = {config_fpath!r}')
# Remove pretrained keys to allow for testing in an offline environment
if 'pretrained' in config_mod.model:
config_mod.model['pretrained'] = None
print('building {}'.format(config_fname))
print(f'building {config_fname}')
segmentor = build_segmentor(config_mod.model)
assert segmentor is not None
@ -72,19 +72,18 @@ def test_config_data_pipeline():
register_all_modules()
config_dpath = _get_config_directory()
print('Found config_dpath = {!r}'.format(config_dpath))
print(f'Found config_dpath = {config_dpath!r}')
import glob
config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
config_names = [relpath(p, config_dpath) for p in config_fpaths]
print('Using {} config files'.format(len(config_names)))
print(f'Using {len(config_names)} config files')
for config_fname in config_names:
config_fpath = join(config_dpath, config_fname)
print(
'Building data pipeline, config_fpath = {!r}'.format(config_fpath))
print(f'Building data pipeline, config_fpath = {config_fpath!r}')
config_mod = Config.fromfile(config_fpath)
# remove loading pipeline
@ -112,7 +111,7 @@ def test_config_data_pipeline():
gt_seg_map=seg)
results['seg_fields'] = ['gt_seg_map']
print('Test training data pipeline: \n{!r}'.format(train_pipeline))
print(f'Test training data pipeline: \n{train_pipeline!r}')
output_results = train_pipeline(results)
assert output_results is not None
@ -123,7 +122,7 @@ def test_config_data_pipeline():
img_shape=img.shape,
ori_shape=img.shape,
)
print('Test testing data pipeline: \n{!r}'.format(test_pipeline))
print(f'Test testing data pipeline: \n{test_pipeline!r}')
output_results = test_pipeline(results)
assert output_results is not None

2
tests/test_datasets/test_dataset_builder.py
View File

@ -11,7 +11,7 @@ register_all_modules()
@DATASETS.register_module()
class ToyDataset(object):
class ToyDataset:
def __init__(self, cnt=0):
self.cnt = cnt

2
tests/test_datasets/test_loading.py
View File

@ -10,7 +10,7 @@ from mmcv.transforms import LoadImageFromFile
from mmseg.datasets.transforms import LoadAnnotations, LoadImageFromNDArray
class TestLoading(object):
class TestLoading:
@classmethod
def setup_class(cls):

4
tests/test_models/test_backbones/test_resnet.py
View File

@ -331,7 +331,7 @@ def test_resnet_backbone():
for param in layer.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, 'layer{}'.format(i))
layer = getattr(model, f'layer{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
@ -347,7 +347,7 @@ def test_resnet_backbone():
for param in model.stem.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, 'layer{}'.format(i))
layer = getattr(model, f'layer{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False

2
tools/analysis_tools/analyze_logs.py
View File

@ -101,7 +101,7 @@ def load_json_logs(json_logs):
log_dicts = [dict() for _ in json_logs]
prev_step = 0
for json_log, log_dict in zip(json_logs, log_dicts):
with open(json_log, 'r') as log_file:
with open(json_log) as log_file:
for line in log_file:
log = json.loads(line.strip())
# the final step in json file is 0.

2
tools/dataset_converters/chase_db1.py
View File

@ -47,7 +47,7 @@ def main():
print('Generating training dataset...')
assert len(os.listdir(tmp_dir)) == CHASE_DB1_LEN, \
'len(os.listdir(tmp_dir)) != {}'.format(CHASE_DB1_LEN)
f'len(os.listdir(tmp_dir)) != {CHASE_DB1_LEN}'
for img_name in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(tmp_dir, img_name))

4
tools/dataset_converters/hrf.py
View File

@ -63,7 +63,7 @@ def main():
zip_file.extractall(tmp_dir)
assert len(os.listdir(tmp_dir)) == HRF_LEN, \
'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)
f'len(os.listdir(tmp_dir)) != {HRF_LEN}'
for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(tmp_dir, filename))
@ -85,7 +85,7 @@ def main():
zip_file.extractall(tmp_dir)
assert len(os.listdir(tmp_dir)) == HRF_LEN, \
'len(os.listdir(tmp_dir)) != {}'.format(HRF_LEN)
f'len(os.listdir(tmp_dir)) != {HRF_LEN}'
for filename in sorted(os.listdir(tmp_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(tmp_dir, filename))

8
tools/dataset_converters/isaid.py
View File

@ -188,17 +188,17 @@ def main():
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'test'))
assert os.path.exists(os.path.join(dataset_path, 'train')), \
'train is not in {}'.format(dataset_path)
f'train is not in {dataset_path}'
assert os.path.exists(os.path.join(dataset_path, 'val')), \
'val is not in {}'.format(dataset_path)
f'val is not in {dataset_path}'
assert os.path.exists(os.path.join(dataset_path, 'test')), \
'test is not in {}'.format(dataset_path)
f'test is not in {dataset_path}'
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
for dataset_mode in ['train', 'val', 'test']:
# for dataset_mode in [ 'test']:
print('Extracting {}ing.zip...'.format(dataset_mode))
print(f'Extracting {dataset_mode}ing.zip...')
img_zipp_list = glob.glob(
os.path.join(dataset_path, dataset_mode, 'images', '*.zip'))
print('Find the data', img_zipp_list)

6
tools/dataset_converters/loveda.py
View File

@ -38,11 +38,11 @@ def main():
mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
assert 'Train.zip' in os.listdir(dataset_path), \
'Train.zip is not in {}'.format(dataset_path)
f'Train.zip is not in {dataset_path}'
assert 'Val.zip' in os.listdir(dataset_path), \
'Val.zip is not in {}'.format(dataset_path)
f'Val.zip is not in {dataset_path}'
assert 'Test.zip' in os.listdir(dataset_path), \
'Test.zip is not in {}'.format(dataset_path)
f'Test.zip is not in {dataset_path}'
with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
for dataset in ['Train', 'Val', 'Test']:

6
tools/dataset_converters/stare.py
View File

@ -68,7 +68,7 @@ def main():
now_dir = osp.join(tmp_dir, 'files')
assert len(os.listdir(now_dir)) == STARE_LEN, \
'len(os.listdir(now_dir)) != {}'.format(STARE_LEN)
f'len(os.listdir(now_dir)) != {STARE_LEN}'
for filename in sorted(os.listdir(now_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(now_dir, filename))
@ -103,7 +103,7 @@ def main():
now_dir = osp.join(tmp_dir, 'files')
assert len(os.listdir(now_dir)) == STARE_LEN, \
'len(os.listdir(now_dir)) != {}'.format(STARE_LEN)
f'len(os.listdir(now_dir)) != {STARE_LEN}'
for filename in sorted(os.listdir(now_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(now_dir, filename))
@ -142,7 +142,7 @@ def main():
now_dir = osp.join(tmp_dir, 'files')
assert len(os.listdir(now_dir)) == STARE_LEN, \
'len(os.listdir(now_dir)) != {}'.format(STARE_LEN)
f'len(os.listdir(now_dir)) != {STARE_LEN}'
for filename in sorted(os.listdir(now_dir))[:TRAINING_LEN]:
img = mmcv.imread(osp.join(now_dir, filename))

2
tools/deployment/pytorch2torchscript.py
View File

@ -126,7 +126,7 @@ def pytorch2libtorch(model,
print(traced_model.graph)
traced_model.save(output_file)
print('Successfully exported TorchScript model: {}'.format(output_file))
print(f'Successfully exported TorchScript model: {output_file}')
def parse_args():

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