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
value |
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DiffBEV | DiffBEV-main/mmseg/models/losses/dice_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
"""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 get_class_weight, weighted_loss... | 5,067 | 35.2 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/lovasz_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
"""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.funct... | 12,223 | 36.728395 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/iou.py | import torch
def iou(preds, labels, mask=None, per_class=False):
num_class = preds.shape[1]
# preds.shape:[n,c,h,w] labels.shape:[n,c,h,w] mask.shape:[n,1,h,w]
preds = preds.flatten(2, -1).permute(1, 0, 2).reshape(num_class, -1) # preds.shape:[c,n x h x w]
labels = labels.flatten(2, -1).permute(1, ... | 1,149 | 45 | 104 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
import numpy as np
import torch.nn.functional as F
def get_class_weight(class_weight):
"""Get class weight for loss function.
Args:
class_weight (list[float] | str | None): If class_weight is a str,
take it as a... | 3,738 | 29.398374 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/accuracy.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch
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): Th... | 3,604 | 37.351064 | 86 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/pyva_losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# loss['bce'] = balanced_binary_cross_entropy(seg_logit,seg_label[:,:-1,...],seg_label[:,-1,...])
def balanced_binary_cross_entropy(logits, labels, mask, weights):
weights = (logits.new(weights).view(-1, 1, 1) - 1) * labels.float() + 1.
weights... | 4,002 | 49.670886 | 153 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/cross_entropy_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import get_class_weight, weight_reduce_loss
def cross_entropy(pred,
label,
weight=None,
class_weight=None,
... | 8,261 | 36.726027 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/losses/occupancy.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def prior_uncertainty_loss(x, mask, priors):
# priors shape: [2]-->[1,2,1,1]-->[bs,2,196,200]
priors = x.new(priors).view(1, -1, 1, 1).expand_as(x)
# F.binary_cross_entropy_with_logits(x, priors, reduce=False) return a tensor with the shape... | 1,494 | 39.405405 | 122 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/hrnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule, ModuleList, Sequential
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.ops import Upsample, resize
from ..builder import BACKBO... | 25,112 | 38.055988 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/mit.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import (Conv2d, build_activation_layer, build_norm_layer,
constant_init, normal_init, trunc_normal_init)
from mmcv.cnn.bricks.drop import build_dropout
from mmcv.cnn.bricks... | 15,616 | 37.371007 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/mobilenet_v2.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidual, make_divisible
@BACKBONES.register_module()... | 7,640 | 37.590909 | 78 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/icnet.py | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from mmseg.ops import resize
from ..builder import BACKBONES, build_backbone
from ..decode_heads.psp_head import PPM
@BACKBONES.register_module()
class ICNet(BaseModule):
"""ICNet for Real-Time Semantic Segmenta... | 5,839 | 34.180723 | 76 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/swin.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_norm_layer, trunc_normal_init
from mmcv.cnn.bricks.transformer import FFN, build_dropout
from mmcv.cnn.utils.weight_init import constan... | 30,400 | 37.776786 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/fast_scnn.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import BaseModule
from mmseg.models.decode_heads.psp_head import PPM
from mmseg.ops import resize
from ..builder import BACKBONES
from ..utils import Inverte... | 15,660 | 37.197561 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
from mmcv.runner import BaseModule
from mmcv.utils.parrots_wrapper import _BatchNorm
from ..builder import BACKBONES
fro... | 25,804 | 35.090909 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/resnextv2.py | import os
import sys
import torch
import torch.nn as nn
import math
from ..utils.SynchronizedBatchNorm2d import SynchronizedBatchNorm2d
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
__all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is comin... | 7,399 | 31.45614 | 99 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/cgnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule, build_conv_layer, build_norm_layer
from mmcv.runner import BaseModule
from mmcv.utils.parrots_wrapper import _BatchNorm
from ..builder import BACKBONE... | 13,513 | 35.13369 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/resnetv2.py | import os
import sys
import torch
import torch.nn as nn
import math
from ..utils.SynchronizedBatchNorm2d import SynchronizedBatchNorm2d
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
__all__ = ['ResNet', 'resnet18', 'resnet50', 'resnet101'] # resnet101 is comin... | 7,400 | 31.460526 | 99 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import math
import warnings
import torch
import torch.nn as nn
from mmcv.cnn import (build_norm_layer, constant_init, kaiming_init,
normal_init, trunc_normal_init)
from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
from mmcv.runner impo... | 16,785 | 39.841849 | 128 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 5,321 | 34.245033 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/mobilenet_v3.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import Conv2dAdaptivePadding
from mmcv.runner import BaseModule
from torch.nn.modules.batchnorm import _BatchNorm
from ..builder import BACKBONES
from ..utils import InvertedResidualV3 as I... | 10,845 | 39.470149 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/unet.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.utils.checkpoint as cp
from mmcv.cnn import (UPSAMPLE_LAYERS, ConvModule, build_activation_layer,
build_norm_layer)
from mmcv.runner import BaseModule
from mmcv.utils.parrots_wrapper import _BatchNo... | 19,381 | 41.597802 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/resnest.py | # Copyright (c) OpenMMLab. All rights reserved.
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 _Bot... | 10,259 | 31.163009 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/bisenetv2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule,
build_activation_layer, build_norm_layer)
from mmcv.runner import BaseModule
from mmseg.ops import resize
from ..builder import BACKBONES
class Deta... | 23,349 | 36.181529 | 96 | py |
DiffBEV | DiffBEV-main/mmseg/models/backbones/bisenetv1.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from mmseg.ops import resize
from ..builder import BACKBONES, build_backbone
class SpatialPath(BaseModule):
"""Spatial Path to preserve the spatial size of the ori... | 12,006 | 35.057057 | 78 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/custom.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from collections import OrderedDict
import mmcv
import numpy as np
from mmcv.utils import print_log
from prettytable import PrettyTable
from torch.utils.data import Dataset
from mmseg.core import eval_metrics, intersect_and_union, p... | 17,198 | 36.552402 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/nuscenes.py | import os.path as osp
import os
import mmcv
import torch
import json
import numpy as np
from PIL import Image
from mmcv.utils import print_log
from mmseg.utils import get_root_logger
from .builder import DATASETS
from .custom import CustomDataset
from tqdm import trange
def covert_color(input):
str1 = input[1:3]... | 6,446 | 40.326923 | 146 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/kittiodometry.py | import os.path as osp
import os
import mmcv
import torch
import numpy as np
from PIL import Image
from mmcv.utils import print_log
from .builder import DATASETS
from .custom import CustomDataset
from tqdm import trange
def covert_color(input):
str1 = input[1:3]
str2 = input[3:5]
str3 = input[5:7]
r =... | 8,322 | 41.682051 | 146 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/dataset_wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
from itertools import chain
import mmcv
import numpy as np
from mmcv.utils import print_log
from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
from .builder import DATASETS
from .cityscapes import CityscapesDataset
@DATASETS.register_mo... | 7,274 | 37.089005 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/kittiobject.py | import os.path as osp
import os
import mmcv
import torch
import numpy as np
from PIL import Image
from mmcv.utils import print_log
from .builder import DATASETS
from .custom import CustomDataset
from tqdm import trange
def covert_color(input):
str1 = input[1:3]
str2 = input[3:5]
str3 = input[5:7]
r =... | 6,907 | 41.121951 | 150 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/kittiraw.py | import os.path as osp
import os
import mmcv
import torch
import numpy as np
from PIL import Image
from mmcv.utils import print_log
from .builder import DATASETS
from .custom import CustomDataset
from tqdm import trange
def covert_color(input):
str1 = input[1:3]
str2 = input[3:5]
str3 = input[5:7]
r =... | 9,177 | 40.908676 | 146 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/builder.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
from functools import partial
import pdb
import numpy as np
import torch
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg, digit_version
from torch.utils.data ... | 6,834 | 35.945946 | 119 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/pipelines/formatting.py | # Copyright (c) OpenMMLab. All rights reserved.
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 ty... | 9,290 | 31.037931 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/pipelines/loading.py | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from cv2 import decomposeProjectionMatrix
import cv2
import mmcv
import numpy as np
import json
import torch
import torchvision
from PIL import Image
from ..builder import PIPELINES
import os
# from tools import heatmap_vis
@PIPELINES.register_modu... | 9,573 | 42.518182 | 122 | py |
DiffBEV | DiffBEV-main/mmseg/datasets/pipelines/transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
from mmcv.utils import deprecated_api_warning, is_tuple_of
from numpy import random
import cv2
from ..builder import PIPELINES
import pdb
import os
import torch
import torch.nn.functional as F
@PIPELINES.register_module()
class ResizeToMult... | 50,771 | 35.369628 | 109 | py |
DiffBEV | DiffBEV-main/mmseg/ops/wrappers.py | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
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 a... | 1,875 | 35.076923 | 79 | py |
DiffBEV | DiffBEV-main/mmseg/ops/encoding.py | # Copyright (c) OpenMMLab. All rights reserved.
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)... | 2,836 | 36.328947 | 78 | py |
DiffBEV | DiffBEV-main/docs_zh-CN/conf.py | # Copyright (c) OpenMMLab. All rights reserved.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup -----------------------... | 5,960 | 29.569231 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_config.py | # Copyright (c) OpenMMLab. All rights reserved.
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:
... | 6,067 | 36.45679 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_eval_hook.py | # Copyright (c) OpenMMLab. All rights reserved.
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
fr... | 7,237 | 34.307317 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_sampler.py | # Copyright (c) OpenMMLab. All rights reserved.
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):... | 1,409 | 34.25 | 73 | py |
DiffBEV | DiffBEV-main/tests/test_metrics.py | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmseg.core.evaluation import (eval_metrics, mean_dice, mean_fscore,
mean_iou)
from mmseg.core.evaluation.metrics import f_score
def get_confusion_matrix(pred_label, label, num_classes, ignore_index):
"""Int... | 13,239 | 36.720798 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_forward.py | # Copyright (c) OpenMMLab. All rights reserved.
"""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.cnn.utils import revert_sync_batchnorm
def _demo_mm_inputs(input_shap... | 6,534 | 26.690678 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_vit.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.backbones.vit import VisionTransformer
from .utils import check_norm_state
def test_vit_backbone():
with pytest.raises(TypeError):
# pretrained must be a string path
model = VisionTransformer()
mo... | 3,657 | 29.231405 | 77 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_unet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.cnn import ConvModule
from mmseg.models.backbones.unet import (BasicConvBlock, DeconvModule,
InterpConv, UNet, UpConvBlock)
from mmseg.ops import Upsample
from .utils import check_norm_state
... | 30,122 | 35.601458 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_mobilenet_v3.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 1,979 | 28.117647 | 76 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_blocks.py | # Copyright (c) OpenMMLab. All rights reserved.
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
... | 6,617 | 37.701754 | 78 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_swin.py | import pytest
import torch
from mmseg.models.backbones.swin import SwinBlock, SwinTransformer
def test_swin_block():
# test SwinBlock structure and forward
block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
assert block.ffn.embed_dims == 64
assert block.attn.w_msa.num_heads == 4
... | 3,163 | 30.64 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_resnet.py | # Copyright (c) OpenMMLab. All rights reserved.
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,... | 20,418 | 34.449653 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_cgnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.backbones import CGNet
from mmseg.models.backbones.cgnet import (ContextGuidedBlock,
GlobalContextExtractor)
def test_cgnet_GlobalContextExtractor():
block = GlobalContextExtract... | 5,214 | 33.309211 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_bisenetv2.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.cnn import ConvModule
from mmseg.models.backbones import BiSeNetV2
from mmseg.models.backbones.bisenetv2 import (BGALayer, DetailBranch,
SemanticBranch)
def test_bisenetv2_backbone():
# Test BiSeN... | 1,859 | 31.068966 | 73 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
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):
"""Che... | 1,306 | 28.704545 | 71 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_hrnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.utils.parrots_wrapper import _BatchNorm
from mmseg.models.backbones.hrnet import HRModule, HRNet
from mmseg.models.backbones.resnet import BasicBlock, Bottleneck
@pytest.mark.parametrize('block', [BasicBlock, Bottleneck])
def test_h... | 4,270 | 28.455172 | 68 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_bisenetv1.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.backbones import BiSeNetV1
from mmseg.models.backbones.bisenetv1 import (AttentionRefinementModule,
ContextPath, FeatureFusionModule,
... | 3,450 | 30.372727 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_resnest.py | # Copyright (c) OpenMMLab. All rights reserved.
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']
Bot... | 1,468 | 31.644444 | 76 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_resnext.py | # Copyright (c) OpenMMLab. All rights reserved.
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 ['pyt... | 1,982 | 30.47619 | 72 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_mit.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.backbones import MixVisionTransformer
from mmseg.models.backbones.mit import EfficientMultiheadAttention, MixFFN
def test_mit():
with pytest.raises(TypeError):
# Pretrained represents pretrain url and must be str... | 1,843 | 30.793103 | 74 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_fast_scnn.py | # Copyright (c) OpenMMLab. All rights reserved.
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),
... | 896 | 26.181818 | 66 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_backbones/test_icnet.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.backbones import ICNet
def test_icnet_backbone():
with pytest.raises(TypeError):
# Must give backbone dict in config file.
ICNet(
in_channels=3,
layer_channels=(512, 2048),
... | 1,495 | 29.530612 | 74 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_losses/test_dice_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
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,
... | 2,206 | 27.294872 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_losses/test_ce_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
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_m... | 2,944 | 32.089888 | 78 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_losses/test_lovasz_loss.py | # Copyright (c) OpenMMLab. All rights reserved.
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'... | 3,631 | 29.779661 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_losses/test_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 3,157 | 30.58 | 74 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_cc_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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.i... | 547 | 27.842105 | 62 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_ocr_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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 = FC... | 642 | 31.15 | 68 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_ema_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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)
... | 652 | 26.208333 | 57 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_decode_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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(AssertionE... | 6,151 | 36.060241 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_segformer_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.decode_heads import SegformerHead
def test_segformer_head():
with pytest.raises(AssertionError):
# `in_channels` must have same length as `in_index`
SegformerHead(
in_channels=(1, 2, 3), in_in... | 1,204 | 28.390244 | 78 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_apc_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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_c... | 1,763 | 28.4 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_psp_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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_c... | 1,140 | 29.837838 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_lraspp_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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),
... | 2,106 | 29.536232 | 77 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_gc_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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,... | 494 | 28.117647 | 62 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_setr_up_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.decode_heads import SETRUPHead
from .utils import to_cuda
def test_setr_up_head(capsys):
with pytest.raises(AssertionError):
# kernel_size must be [1/3]
SETRUPHead(num_classes=19, kernel_size=2)
wit... | 1,628 | 27.578947 | 69 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_enc_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 1,633 | 32.346939 | 69 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_da_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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():
... | 650 | 31.55 | 78 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_isa_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmseg.models.decode_heads import ISAHead
from .utils import to_cuda
def test_isa_head():
inputs = [torch.randn(1, 32, 45, 45)]
isa_head = ISAHead(
in_channels=32,
channels=16,
num_classes=19,
isa_channels=16,
... | 525 | 24.047619 | 60 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_uper_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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... | 1,079 | 29 | 77 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_dm_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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_cla... | 1,766 | 28.45 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_nl_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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,... | 494 | 28.117647 | 62 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_dpt_head.py | import pytest
import torch
from mmseg.models.decode_heads import DPTHead
def test_dpt_head():
with pytest.raises(AssertionError):
# input_transform must be 'multiple_select'
head = DPTHead(
in_channels=[768, 768, 768, 768],
channels=256,
num_classes=19,
... | 1,328 | 26.122449 | 54 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_aspp_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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_... | 2,590 | 32.649351 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_point_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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], chan... | 858 | 34.791667 | 73 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_ann_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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=... | 543 | 24.904762 | 69 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_fcn_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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():
w... | 4,541 | 33.409091 | 78 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_psa_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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(
... | 3,644 | 28.634146 | 72 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_setr_mla_head.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.decode_heads import SETRMLAHead
from .utils import to_cuda
def test_setr_mla_head(capsys):
with pytest.raises(AssertionError):
# MLA requires input multiple stage feature information.
SETRMLAHead(in_chan... | 1,913 | 28.90625 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_heads/test_dnl_head.py | # Copyright (c) OpenMMLab. All rights reserved.
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, '... | 1,605 | 33.913043 | 74 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_utils/test_embed.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.utils.embed import AdaptivePadding, PatchEmbed, PatchMerging
def test_adaptive_padding():
for padding in ('same', 'corner'):
kernel_size = 16
stride = 16
dilation = 1
input = torch.rand(1... | 12,979 | 27.095238 | 78 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_necks/test_multilevel_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmseg.models import MultiLevelNeck
def test_multilevel_neck():
# Test init_weights
MultiLevelNeck([266], 256).init_weights()
# Test multi feature maps
in_channels = [256, 512, 1024, 2048]
inputs = [torch.randn(1, c, 14, 14) for i... | 1,066 | 31.333333 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_necks/test_fpn.py | # Copyright (c) OpenMMLab. All rights reserved.
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))
o... | 579 | 28 | 59 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_necks/test_mla_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmseg.models import MLANeck
def test_mla():
in_channels = [1024, 1024, 1024, 1024]
mla = MLANeck(in_channels, 256)
inputs = [torch.randn(1, c, 24, 24) for i, c in enumerate(in_channels)]
outputs = mla(inputs)
assert outputs[0].sha... | 535 | 30.529412 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_necks/test_jpu.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.necks import JPU
def test_fastfcn_neck():
# Test FastFCN Standard Forward
model = JPU()
model.init_weights()
model.train()
batch_size = 1
input = [
torch.randn(batch_size, 512, 64, 128),
... | 1,287 | 30.414634 | 75 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_necks/test_ic_neck.py | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.necks import ICNeck
from mmseg.models.necks.ic_neck import CascadeFeatureFusion
from ..test_heads.utils import _conv_has_norm, to_cuda
def test_ic_neck():
# test with norm_cfg
neck = ICNeck(
in_channels=(64, ... | 1,598 | 28.611111 | 59 | py |
DiffBEV | DiffBEV-main/tests/test_models/test_segmentors/utils.py | # Copyright (c) OpenMMLab. All rights reserved.
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_sha... | 4,174 | 28.609929 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_apis/test_single_gpu.py | import shutil
from unittest.mock import MagicMock
import numpy as np
import pytest
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset, dataloader
from mmseg.apis import single_gpu_test
class ExampleDataset(Dataset):
def __getitem__(self, idx):
results = dict(img=torch.t... | 1,884 | 24.821918 | 70 | py |
DiffBEV | DiffBEV-main/tests/test_data/test_dataset.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import shutil
import tempfile
from typing import Generator
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
import torch
from PIL import Image
from mmseg.core.evaluation import get_classes, get_palette
from mmse... | 26,547 | 35.821082 | 79 | py |
DiffBEV | DiffBEV-main/tests/test_data/test_dataset_builder.py | # Copyright (c) OpenMMLab. All rights reserved.
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_dat... | 6,207 | 30.673469 | 78 | py |
DiffBEV | DiffBEV-main/configs/_base_/models/icnet_r50-d8.py | # model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
type='EncoderDecoder',
backbone=dict(
type='ICNet',
backbone_cfg=dict(
type='ResNetV1c',
in_channels=3,
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 2,154 | 27.733333 | 78 | py |
DiffBEV | DiffBEV-main/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 |
DiffBEV | DiffBEV-main/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 |
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