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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Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/decode_heads/aspp_head.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
class ASPPModule(nn.ModuleList):
"""Atrous Spatial Pyramid Pooling (ASPP) Module.
Args:
dilations (tuple[int]): Dilation rate of each la... | 3,419 | 30.666667 | 76 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/decode_heads/psa_head.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmseg.ops import resize
from ..builder import HEADS
from .decode_head import BaseDecodeHead
try:
from mmcv.ops import PSAMask
except ModuleNotFoundError:
PSAMask = None
@HEADS.register_module()
class PSAH... | 7,484 | 36.994924 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/decode_heads/gc_head.py | import torch
from mmcv.cnn import ContextBlock
from ..builder import HEADS
from .fcn_head import FCNHead
@HEADS.register_module()
class GCHead(FCNHead):
"""GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.
This head is the implementation of `GCNet
<https://arxiv.org/abs/1904.11492>`... | 1,591 | 32.166667 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/decode_heads/point_head.py | # Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, normal_init
from mmcv.ops import point_sample
from mmseg.models.builder import HEADS
from mmseg.ops import resize
from ..lo... | 14,674 | 40.928571 | 126 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/utils/se_layer.py | import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from .make_divisible import make_divisible
class SELayer(nn.Module):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) channels of the SE layer.
ratio (int): Squeeze ratio in SELayer, the intermedi... | 2,103 | 35.275862 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/utils/res_layer.py | from mmcv.cnn import build_conv_layer, build_norm_layer
from torch import nn as nn
class ResLayer(nn.Sequential):
"""ResLayer to build ResNet style backbone.
Args:
block (nn.Module): block used to build ResLayer.
inplanes (int): inplanes of block.
planes (int): planes of block.
... | 3,315 | 33.905263 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/utils/self_attention_block.py | import torch
from mmcv.cnn import ConvModule, constant_init
from torch import nn as nn
from torch.nn import functional as F
class SelfAttentionBlock(nn.Module):
"""General self-attention block/non-local block.
Please refer to https://arxiv.org/abs/1706.03762 for details about key,
query and value.
A... | 6,125 | 37.2875 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/utils/up_conv_block.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, build_upsample_layer
class UpConvBlock(nn.Module):
"""Upsample convolution block in decoder for UNet.
This upsample convolution block consists of one upsample module
followed by one convolution block. The upsample module expands the
... | 3,968 | 37.911765 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/utils/inverted_residual.py | from mmcv.cnn import ConvModule
from torch import nn
from torch.utils import checkpoint as cp
from .se_layer import SELayer
class InvertedResidual(nn.Module):
"""InvertedResidual block for MobileNetV2.
Args:
in_channels (int): The input channels of the InvertedResidual block.
out_channels (i... | 7,005 | 32.521531 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/segmentors/base.py | import logging
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
import mmcv
import numpy as np
import torch
import torch.distributed as dist
import torch.nn as nn
from mmcv.runner import auto_fp16
class BaseSegmentor(nn.Module):
"""Base class for segmentors."""
__m... | 10,349 | 36.773723 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/segmentors/cascade_encoder_decoder.py | from torch import nn
from mmseg.core import add_prefix
from mmseg.ops import resize
from .. import builder
from ..builder import SEGMENTORS
from .encoder_decoder import EncoderDecoder
@SEGMENTORS.register_module()
class CascadeEncoderDecoder(EncoderDecoder):
"""Cascade Encoder Decoder segmentors.
CascadeEnc... | 3,668 | 36.060606 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/segmentors/encoder_decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmseg.core import add_prefix
from mmseg.ops import resize
from .. import builder
from ..builder import SEGMENTORS
from .base import BaseSegmentor
@SEGMENTORS.register_module()
class EncoderDecoder(BaseSegmentor):
"""Encoder Decoder segmentor... | 11,129 | 36.857143 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/losses/dice_loss.py | """Modified from https://github.com/LikeLy-Journey/SegmenTron/blob/master/
segmentron/solver/loss.py (Apache-2.0 License)"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def dice_loss(pred,
target,
... | 4,158 | 33.658333 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/losses/lovasz_loss.py | """Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .u... | 11,310 | 36.207237 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/losses/utils.py | import functools
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss tensor.
"""
reduction_enum = ... | 3,147 | 29.862745 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/losses/accuracy.py | import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
target (torch.Tensor): The target of each prediction, shape (N, , ...)
topk (... | 2,970 | 36.607595 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/losses/cross_entropy_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
class_weight=None,
reduction='mean',
avg_factor=N... | 7,354 | 35.959799 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/hrnet.py | import torch.nn as nn
from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.ops import Upsample, resize
from mmseg.utils import get_root_logger
from ..builder impo... | 21,106 | 36.96223 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/mobilenet_v2.py | import logging
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual, make_divisible
@BACKBONES.register_module()
class MobileNe... | 6,941 | 37.353591 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/fast_scnn.py | import torch
import torch.nn as nn
from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule, constant_init,
kaiming_init)
from torch.nn.modules.batchnorm import _BatchNorm
from mmseg.models.decode_heads.psp_head import PPM
from mmseg.ops import resize
from ..builder import BACKBONES
from ..... | 14,376 | 37.236702 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/resnet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (build_conv_layer, build_norm_layer, build_plugin_layer,
constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.utils import get_root_logger
fr... | 24,210 | 34.139332 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/cgnet.py | import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (ConvModule, build_conv_layer, build_norm_layer,
constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.utils import get_root_logg... | 13,103 | 34.608696 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/resnext.py | import math
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNet
class Bottleneck(_Bottleneck):
"""Bottleneck block for ResNeXt.
If style is "pytorch", the stride-two lay... | 5,121 | 34.082192 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/mobilenet_v3.py | import logging
import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, kaiming_init
from mmcv.cnn.bricks import Conv2dAdaptivePadding
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual... | 10,302 | 39.246094 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/unet.py | import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
build_norm_layer, constant_init, kaiming_init)
from mmcv.runner import load_checkpoint
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.utils import get... | 18,189 | 41.302326 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/models/backbones/resnest.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
from .resnet import ResNetV1d
class RS... | 10,090 | 31.034921 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/datasets/custom.py | import os
import os.path as osp
from functools import reduce
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from torch.utils.data import Dataset
from mmseg.core import eval_metrics
from mmseg.utils import get_root_logger
from .builder import DATASETS
from .pipeli... | 14,413 | 36.438961 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/datasets/dataset_wrappers.py | from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
@DATASETS.register_module()
class ConcatDataset(_ConcatDataset):
"""A wrapper of concatenated dataset.
Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
concat the group flag for image aspect rati... | 1,499 | 28.411765 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/datasets/builder.py | import copy
import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from mmcv.utils.parrots_wrapper import DataLoader, PoolDataLoader
from torch.utils.data import DistributedSa... | 5,871 | 33.541176 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/datasets/pipelines/formating.py | from collections.abc import Sequence
import mmcv
import numpy as np
import torch
from mmcv.parallel import DataContainer as DC
from ..builder import PIPELINES
def to_tensor(data):
"""Convert objects of various python types to :obj:`torch.Tensor`.
Supported types are: :class:`numpy.ndarray`, :class:`torch.T... | 9,228 | 30.934256 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/ops/wrappers.py | import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
def resize(input,
size=None,
scale_factor=None,
mode='nearest',
align_corners=None,
warning=True):
if warning:
if size is not None and align_corners:
input... | 1,920 | 34.574074 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/mmseg/ops/encoding.py | import torch
from torch import nn
from torch.nn import functional as F
class Encoding(nn.Module):
"""Encoding Layer: a learnable residual encoder.
Input is of shape (batch_size, channels, height, width).
Output is of shape (batch_size, num_codes, channels).
Args:
channels: dimension of the ... | 2,788 | 36.186667 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_config.py | import glob
import os
from os.path import dirname, exists, isdir, join, relpath
from mmcv import Config
from torch import nn
from mmseg.models import build_segmentor
def _get_config_directory():
"""Find the predefined segmentor config directory."""
try:
# Assume we are running in the source mmsegmen... | 6,019 | 36.391304 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_eval_hook.py | import logging
import tempfile
from unittest.mock import MagicMock, patch
import mmcv.runner
import pytest
import torch
import torch.nn as nn
from mmcv.runner import obj_from_dict
from torch.utils.data import DataLoader, Dataset
from mmseg.apis import single_gpu_test
from mmseg.core import DistEvalHook, EvalHook
cl... | 6,659 | 33.329897 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_sampler.py | import pytest
import torch
from mmseg.core import OHEMPixelSampler
from mmseg.models.decode_heads import FCNHead
def _context_for_ohem():
return FCNHead(in_channels=32, channels=16, num_classes=19)
def test_ohem_sampler():
with pytest.raises(AssertionError):
# seg_logit and seg_label must be of th... | 1,361 | 33.923077 | 73 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_forward.py | """pytest tests/test_forward.py."""
import copy
from os.path import dirname, exists, join
from unittest.mock import patch
import numpy as np
import pytest
import torch
import torch.nn as nn
from mmcv.utils.parrots_wrapper import SyncBatchNorm, _BatchNorm
def _demo_mm_inputs(input_shape=(2, 3, 8, 16), num_classes=10)... | 7,425 | 28.585657 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_unet.py | import pytest
import torch
from mmcv.cnn import ConvModule
from torch import nn
from mmseg.models.backbones.unet import (BasicConvBlock, DeconvModule,
InterpConv, UNet, UpConvBlock)
from .utils import check_norm_state
def test_unet_basic_conv_block():
with pytest.raises(A... | 30,265 | 35.641646 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_mobilenet_v3.py | import pytest
import torch
from mmseg.models.backbones import MobileNetV3
def test_mobilenet_v3():
with pytest.raises(AssertionError):
# check invalid arch
MobileNetV3('big')
with pytest.raises(AssertionError):
# check invalid reduction_factor
MobileNetV3(reduction_factor=0)
... | 1,931 | 27.835821 | 76 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_blocks.py | import mmcv
import pytest
import torch
from mmseg.models.utils import (InvertedResidual, InvertedResidualV3, SELayer,
make_divisible)
def test_make_divisible():
# test with min_value = None
assert make_divisible(10, 4) == 12
assert make_divisible(9, 4) == 12
assert mak... | 6,569 | 37.647059 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_resnet.py | import pytest
import torch
from mmcv.ops import DeformConv2dPack
from mmcv.utils.parrots_wrapper import _BatchNorm
from torch.nn.modules import AvgPool2d, GroupNorm
from mmseg.models.backbones import ResNet, ResNetV1d
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
from mmseg.models.utils import ResLa... | 20,346 | 34.447735 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_cgnet.py | import pytest
import torch
from mmseg.models.backbones import CGNet
from mmseg.models.backbones.cgnet import (ContextGuidedBlock,
GlobalContextExtractor)
def test_cgnet_GlobalContextExtractor():
block = GlobalContextExtractor(16, 16, with_cp=True)
x = torch.randn(2, ... | 5,166 | 33.218543 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/utils.py | import torch
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
from mmseg.models.backbones.resnext import Bottleneck as BottleneckX
def is_block(modules):
"""Check if is ResNet building block."""
if isinst... | 1,258 | 28.27907 | 71 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_resnest.py | import pytest
import torch
from mmseg.models.backbones import ResNeSt
from mmseg.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-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_resnext.py | import pytest
import torch
from mmseg.models.backbones import ResNeXt
from mmseg.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... | 1,934 | 30.209677 | 72 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_backbones/test_fast_scnn.py | import pytest
import torch
from mmseg.models.backbones import FastSCNN
def test_fastscnn_backbone():
with pytest.raises(AssertionError):
# Fast-SCNN channel constraints.
FastSCNN(
3, (32, 48),
64, (64, 96, 128), (2, 2, 1),
global_out_channels=127,
h... | 848 | 25.53125 | 66 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_losses/test_dice_loss.py | import torch
def test_dice_lose():
from mmseg.models import build_loss
# test dice loss with loss_type = 'multi_class'
loss_cfg = dict(
type='DiceLoss',
reduction='none',
class_weight=[1.0, 2.0, 3.0],
loss_weight=1.0,
ignore_index=1)
dice_loss = build_loss(loss... | 793 | 24.612903 | 51 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_losses/test_ce_loss.py | import pytest
import torch
def test_ce_loss():
from mmseg.models import build_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,
... | 1,580 | 31.265306 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_losses/test_lovasz_loss.py | import pytest
import torch
def test_lovasz_loss():
from mmseg.models import build_loss
# loss_type should be 'binary' or 'multi_class'
with pytest.raises(AssertionError):
loss_cfg = dict(
type='LovaszLoss',
loss_type='Binary',
reduction='none',
loss... | 2,003 | 30.809524 | 75 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_losses/test_utils.py | import numpy as np
import pytest
import torch
from mmseg.models.losses import Accuracy, reduce_loss, weight_reduce_loss
def test_weight_reduce_loss():
loss = torch.rand(1, 3, 4, 4)
weight = torch.zeros(1, 3, 4, 4)
weight[:, :, :2, :2] = 1
# test reduce_loss()
reduced = reduce_loss(loss, 'none')
... | 3,109 | 30.414141 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_cc_head.py | import pytest
import torch
from mmseg.models.decode_heads import CCHead
from .utils import to_cuda
def test_cc_head():
head = CCHead(in_channels=32, channels=16, num_classes=19)
assert len(head.convs) == 2
assert hasattr(head, 'cca')
if not torch.cuda.is_available():
pytest.skip('CCHead requi... | 499 | 26.777778 | 62 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_ocr_head.py | import torch
from mmseg.models.decode_heads import FCNHead, OCRHead
from .utils import to_cuda
def test_ocr_head():
inputs = [torch.randn(1, 32, 45, 45)]
ocr_head = OCRHead(
in_channels=32, channels=16, num_classes=19, ocr_channels=8)
fcn_head = FCNHead(in_channels=32, channels=16, num_classes=1... | 594 | 30.315789 | 68 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_ema_head.py | import torch
from mmseg.models.decode_heads import EMAHead
from .utils import to_cuda
def test_emanet_head():
head = EMAHead(
in_channels=32,
ema_channels=24,
channels=16,
num_stages=3,
num_bases=16,
num_classes=19)
for param in head.ema_mid_conv.parameters():
... | 604 | 25.304348 | 57 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_decode_head.py | from unittest.mock import patch
import pytest
import torch
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
from .utils import to_cuda
@patch.multiple(BaseDecodeHead, __abstractmethods__=set())
def test_decode_head():
with pytest.raises(AssertionError):
# default input_transform doesn't... | 2,731 | 34.947368 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_apc_head.py | import pytest
import torch
from mmseg.models.decode_heads import APCHead
from .utils import _conv_has_norm, to_cuda
def test_apc_head():
with pytest.raises(AssertionError):
# pool_scales must be list|tuple
APCHead(in_channels=32, channels=16, num_classes=19, pool_scales=1)
# test no norm_cf... | 1,715 | 28.084746 | 75 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_psp_head.py | import pytest
import torch
from mmseg.models.decode_heads import PSPHead
from .utils import _conv_has_norm, to_cuda
def test_psp_head():
with pytest.raises(AssertionError):
# pool_scales must be list|tuple
PSPHead(in_channels=32, channels=16, num_classes=19, pool_scales=1)
# test no norm_cf... | 1,092 | 29.361111 | 75 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_lraspp_head.py | import pytest
import torch
from mmseg.models.decode_heads import LRASPPHead
def test_lraspp_head():
with pytest.raises(ValueError):
# check invalid input_transform
LRASPPHead(
in_channels=(16, 16, 576),
in_index=(0, 1, 2),
channels=128,
input_transf... | 2,058 | 29.279412 | 77 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_gc_head.py | import torch
from mmseg.models.decode_heads import GCHead
from .utils import to_cuda
def test_gc_head():
head = GCHead(in_channels=32, channels=16, num_classes=19)
assert len(head.convs) == 2
assert hasattr(head, 'gc_block')
inputs = [torch.randn(1, 32, 45, 45)]
if torch.cuda.is_available():
... | 446 | 26.9375 | 62 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_enc_head.py | import torch
from mmseg.models.decode_heads import EncHead
from .utils import to_cuda
def test_enc_head():
# with se_loss, w.o. lateral
inputs = [torch.randn(1, 32, 21, 21)]
head = EncHead(
in_channels=[32], channels=16, num_classes=19, in_index=[-1])
if torch.cuda.is_available():
hea... | 1,585 | 32.041667 | 69 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_da_head.py | import torch
from mmseg.models.decode_heads import DAHead
from .utils import to_cuda
def test_da_head():
inputs = [torch.randn(1, 32, 45, 45)]
head = DAHead(in_channels=32, channels=16, num_classes=19, pam_channels=8)
if torch.cuda.is_available():
head, inputs = to_cuda(head, inputs)
outputs... | 602 | 30.736842 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_uper_head.py | import pytest
import torch
from mmseg.models.decode_heads import UPerHead
from .utils import _conv_has_norm, to_cuda
def test_uper_head():
with pytest.raises(AssertionError):
# fpn_in_channels must be list|tuple
UPerHead(in_channels=32, channels=16, num_classes=19)
# test no norm_cfg
he... | 1,031 | 28.485714 | 77 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_dm_head.py | import pytest
import torch
from mmseg.models.decode_heads import DMHead
from .utils import _conv_has_norm, to_cuda
def test_dm_head():
with pytest.raises(AssertionError):
# filter_sizes must be list|tuple
DMHead(in_channels=32, channels=16, num_classes=19, filter_sizes=1)
# test no norm_cfg... | 1,718 | 28.135593 | 75 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_nl_head.py | import torch
from mmseg.models.decode_heads import NLHead
from .utils import to_cuda
def test_nl_head():
head = NLHead(in_channels=32, channels=16, num_classes=19)
assert len(head.convs) == 2
assert hasattr(head, 'nl_block')
inputs = [torch.randn(1, 32, 45, 45)]
if torch.cuda.is_available():
... | 446 | 26.9375 | 62 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_aspp_head.py | import pytest
import torch
from mmseg.models.decode_heads import ASPPHead, DepthwiseSeparableASPPHead
from .utils import _conv_has_norm, to_cuda
def test_aspp_head():
with pytest.raises(AssertionError):
# pool_scales must be list|tuple
ASPPHead(in_channels=32, channels=16, num_classes=19, dilati... | 2,542 | 32.460526 | 75 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_point_head.py | import torch
from mmcv.utils import ConfigDict
from mmseg.models.decode_heads import FCNHead, PointHead
from .utils import to_cuda
def test_point_head():
inputs = [torch.randn(1, 32, 45, 45)]
point_head = PointHead(
in_channels=[32], in_index=[0], channels=16, num_classes=19)
assert len(point_he... | 810 | 34.26087 | 73 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_ann_head.py | import torch
from mmseg.models.decode_heads import ANNHead
from .utils import to_cuda
def test_ann_head():
inputs = [torch.randn(1, 16, 45, 45), torch.randn(1, 32, 21, 21)]
head = ANNHead(
in_channels=[16, 32],
channels=16,
num_classes=19,
in_index=[-2, -1],
project_c... | 495 | 23.8 | 69 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_fcn_head.py | import pytest
import torch
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.utils.parrots_wrapper import SyncBatchNorm
from mmseg.models.decode_heads import DepthwiseSeparableFCNHead, FCNHead
from .utils import to_cuda
def test_fcn_head():
with pytest.raises(AssertionError):
# num... | 4,493 | 33.305344 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_psa_head.py | import pytest
import torch
from mmseg.models.decode_heads import PSAHead
from .utils import _conv_has_norm, to_cuda
def test_psa_head():
with pytest.raises(AssertionError):
# psa_type must be in 'bi-direction', 'collect', 'distribute'
PSAHead(
in_channels=32,
channels=16,... | 3,596 | 28.483607 | 72 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_heads/test_dnl_head.py | import torch
from mmseg.models.decode_heads import DNLHead
from .utils import to_cuda
def test_dnl_head():
# DNL with 'embedded_gaussian' mode
head = DNLHead(in_channels=32, channels=16, num_classes=19)
assert len(head.convs) == 2
assert hasattr(head, 'dnl_block')
assert head.dnl_block.temperatur... | 1,557 | 33.622222 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_necks/test_fpn.py | import torch
from mmseg.models import FPN
def test_fpn():
in_channels = [256, 512, 1024, 2048]
inputs = [
torch.randn(1, c, 56 // 2**i, 56 // 2**i)
for i, c in enumerate(in_channels)
]
fpn = FPN(in_channels, 256, len(in_channels))
outputs = fpn(inputs)
assert outputs[0].shape... | 531 | 27 | 59 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_models/test_segmentors/utils.py | import numpy as np
import torch
from torch import nn
from mmseg.models import BACKBONES, HEADS
from mmseg.models.decode_heads.cascade_decode_head import BaseCascadeDecodeHead
from mmseg.models.decode_heads.decode_head import BaseDecodeHead
def _demo_mm_inputs(input_shape=(1, 3, 8, 16), num_classes=10):
"""Create... | 3,439 | 27.666667 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/tests/test_data/test_dataset_builder.py | import math
import os.path as osp
import pytest
from torch.utils.data import (DistributedSampler, RandomSampler,
SequentialSampler)
from mmseg.datasets import (DATASETS, ConcatDataset, build_dataloader,
build_dataset)
@DATASETS.register_module()
class ToyDat... | 6,087 | 30.544041 | 78 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/pspnet/pspnet_r101b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 154 | 30 | 52 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/pspnet/pspnet_r50b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 133 | 43.666667 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/pspnet/pspnet_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 155 | 30.2 | 53 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/pspnet/pspnet_r50b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 134 | 44 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/pspnet/pspnet_r18b-d8_769x769_80k_cityscapes.py | _base_ = './pspnet_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 283 | 27.4 | 54 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/pspnet/pspnet_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './pspnet_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 284 | 27.5 | 54 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_d6_r50b-d16_512x1024_80k_cityscapes.py | _base_ = './fcn_d6_r50-d16_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 135 | 44.333333 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 152 | 29.6 | 50 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_r50b-d8_769x769_80k_cityscapes.py | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 130 | 42.666667 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py | _base_ = './fcn_d6_r50b-d16_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 157 | 30.6 | 55 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_d6_r50b-d16_769x769_80k_cityscapes.py | _base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 134 | 44 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_d6_r101b-d16_769x769_80k_cityscapes.py | _base_ = './fcn_d6_r50b-d16_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 156 | 30.4 | 54 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_r18b-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 281 | 27.2 | 54 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_r101b-d8_769x769_80k_cityscapes.py | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(type='ResNet', depth=101))
| 151 | 29.4 | 49 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_r18b-d8_769x769_80k_cityscapes.py | _base_ = './fcn_r50-d8_769x769_80k_cityscapes.py'
model = dict(
pretrained='torchvision://resnet18',
backbone=dict(type='ResNet', depth=18),
decode_head=dict(
in_channels=512,
channels=128,
),
auxiliary_head=dict(in_channels=256, channels=64))
| 280 | 27.1 | 54 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/fcn/fcn_r50b-d8_512x1024_80k_cityscapes.py | _base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
model = dict(pretrained='torchvision://resnet50', backbone=dict(type='ResNet'))
| 131 | 43 | 79 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/ccnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,258 | 26.977778 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/ann_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,346 | 27.659574 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/gcnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,326 | 27.234043 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/encnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,435 | 28.306122 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/danet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,261 | 27.044444 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/dnl_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,316 | 27.021277 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/pspnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,271 | 27.266667 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/upernet_r50.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 1, 1),
strides=... | 1,301 | 27.933333 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/apcnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,302 | 27.955556 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/psanet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,406 | 27.14 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/deeplabv3plus_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,343 | 27.595745 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/emanet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,329 | 26.708333 | 74 | py |
Swin-Transformer-Semantic-Segmentation-mmseg | Swin-Transformer-Semantic-Segmentation-mmseg-master/configs/_base_/models/dmnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
pretrained='open-mmlab://resnet50_v1c',
backbone=dict(
type='ResNetV1c',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
dilations=(1, 1, 2, 4),
strides=... | 1,302 | 27.955556 | 74 | py |
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