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Types & classes100 in github.com/Shank2358/TS-Conv

⨍ Functions 408◇ Types & classes 100
↓ 84 callersClassConvolutional
model/layers/convolutions.py:22
↓ 24 callersClassResidual_block
model/layers/conv_blocks.py:5
↓ 8 callersClassDCNv2Pooling
lib/DCNv2/dcn_v2.py:262
↓ 4 callersClassGGHL
model/TSConv.py:13
↓ 4 callersClassLogger
utils/log.py:25
↓ 3 callersClassDCN
lib/DCNv2/dcn_v2.py:147
↓ 3 callersClassFReLU
model/layers/activations.py:50
↓ 3 callersClassHead1
model/head/head.py:10
↓ 3 callersClassHead2
model/head/head.py:171
↓ 3 callersClassMemoryEfficientMish
model/layers/activations.py:35
↓ 3 callersClassMemoryEfficientSwish
model/layers/activations.py:16
↓ 3 callersClassMish
model/layers/activations.py:30
↓ 3 callersClassNestedTensor
utils/mics.py:283
↓ 3 callersClassSwish
model/layers/activations.py:5
↓ 2 callersClassBackbone
ResNet backbone with frozen BatchNorm.
model/backbones/resnet.py:84
↓ 2 callersClassDCNPooling
lib/DCNv2/dcn_v2.py:303
↓ 2 callersClassDCNv2
lib/DCNv2/dcn_v2.py:96
↓ 2 callersClassRoute
model/neck/neck.py:22
↓ 2 callersClassSmoothedValue
Track a series of values and provide access to smoothed values over a window or the global series average.
utils/mics.py:26
↓ 2 callersClassSobel_conv
model/layers/np_attention_blocks.py:35
↓ 2 callersClassUpsample
model/neck/neck.py:12
↓ 1 callersClassBatchSampler
batch_sampler.py:6
↓ 1 callersClassCondConv2d
model/layers/convolutions.py:121
↓ 1 callersClassCosineDecayLR
utils/cosine_lr_scheduler.py:3
↓ 1 callersClassDCNv2
lib/DCNv2/dcn_v2_amp.py:125
↓ 1 callersClassDCNv2_Circle8
lib/DCNv2/dcn_v2_amp.py:278
↓ 1 callersClassDarknet53
model/backbones/darknet53.py:8
↓ 1 callersClassDeformable_Convolutional
model/layers/convolutions.py:63
↓ 1 callersClassEvaluator
evalR/eval.py:13
↓ 1 callersClassEvaluator
evalR/evaluatorTSplot.py:17
↓ 1 callersClassEvaluator
evalR/evaluator_demo.py:17
↓ 1 callersClassFeatureExtractor
model/backbones/mobilenetv2.py:141
↓ 1 callersClassFocalLoss
model/loss/loss.py:8
↓ 1 callersClassLoss
model/loss/loss.py:26
↓ 1 callersClassNeck
model/neck/neck.py:31
↓ 1 callersClassNewRichHandler
utils/log.py:5
↓ 1 callersClassResNet
model/backbones/model_resnet.py:164
↓ 1 callersClassSPPF
model/layers/multiscale_fusion_blocks.py:22
↓ 1 callersClassSoftmaxCELoss
model/loss/loss.py:19
↓ 1 callersClassTester
test.py:12
↓ 1 callersClassTester
demo.py:15
↓ 1 callersClassTrainer
trainv2.py:66
↓ 1 callersClass_MobileNetV2
model/backbones/mobilenetv2.py:84
↓ 1 callersClass_RepeatSampler
trainv2.py:57
↓ 1 callersClassroute_func
r"""CondConv: Conditionally Parameterized Convolutions for Efficient Inference https://papers.nips.cc/paper/8412-condconv-conditionally-parameteri
model/layers/convolutions.py:100
ClassASFF
model/layers/multiscale_fusion_blocks.py:53
ClassASPP
model/layers/multiscale_fusion_blocks.py:35
ClassBackboneBase
model/backbones/resnet.py:49
ClassBasicBlock
model/backbones/model_resnet.py:57
ClassBatchSampler
dataloadR/batch_sampler.py:6
ClassBottleneck
model/backbones/model_resnet.py:106
ClassCSP_stage
model/layers/conv_blocks.py:19
ClassCond_Convolutional
model/layers/convolutions.py:169
ClassConstruct_Dataset
datasetsv2.py:11
ClassConstruct_Dataset
dataloadR/datasetsv2.py:11
ClassContextBlock
GCNet
model/layers/attention_blocks.py:64
ClassDCN
lib/DCNv2/dcn_v2_onnx.py:125
ClassDCN
lib/DCNv2/dcn_v2_amp.py:400
ClassDCNPooling
lib/DCNv2/dcn_v2_onnx.py:264
ClassDCNPooling
lib/DCNv2/dcn_v2_amp.py:590
ClassDCNv2
lib/DCNv2/dcn_v2_onnx.py:85
ClassDCNv2Pooling
lib/DCNv2/dcn_v2_onnx.py:228
ClassDCNv2Pooling
lib/DCNv2/dcn_v2_amp.py:549
ClassDCNv2_Circle
lib/DCNv2/dcn_v2_amp.py:184
ClassDataLoaderX
trainv2.py:38
ClassEvaluator
evalR/evaluatorTS.py:17
ClassEvaluator
evalR/evaluatorGGHL.py:17
ClassF
model/layers/activations.py:17
ClassF
model/layers/activations.py:36
ClassFeatureAdaption
model/layers/multiscale_fusion_blocks.py:114
ClassFeatures_Fusion
model/layers/multiscale_fusion_blocks.py:127
ClassFrozenBatchNorm2d
BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt,
model/backbones/resnet.py:10
ClassHardswish
model/layers/activations.py:10
ClassInfiniteDataLoader
trainv2.py:43
ClassInvertedResidual
model/backbones/mobilenetv2.py:44
ClassInvertedResidual_block
model/layers/conv_blocks.py:49
ClassMSR_L
model/layers/msr_blocks.py:4
ClassMSR_M
model/layers/msr_blocks.py:33
ClassMSR_S
model/layers/msr_blocks.py:59
ClassMetricLogger
utils/mics.py:158
ClassMobilenetV2
model/backbones/mobilenetv2.py:161
ClassModelEMA
Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models Keeps a moving average of everything in the model
utils/utils_basic.py:599
ClassNPAttention
model/layers/np_attention_blocks.py:100
ClassNPAttention1
model/layers/np_attention_blocks.py:164
ClassNPAttention2
model/layers/np_attention_blocks.py:227
ClassNonLocalBlock
Non-local Network
model/layers/attention_blocks.py:32
ClassResidual_block_CSP
model/layers/conv_blocks.py:34
ClassSELayer
SENet
model/layers/attention_blocks.py:9
ClassSPP
model/layers/multiscale_fusion_blocks.py:6
ClassSobel_Edge_Block
model/layers/np_attention_blocks.py:85
ClassSobel_Edge_Block_Aux
model/layers/np_attention_blocks.py:69
ClassSpatialCGNL
Spatial CGNL block with dot production kernel for image classfication.
model/layers/attention_blocks.py:143
Class_DCNv2
lib/DCNv2/dcn_v2_onnx.py:17
Class_DCNv2
lib/DCNv2/dcn_v2.py:15
Class_DCNv2
lib/DCNv2/dcn_v2_amp.py:23
Class_DCNv2Pooling
lib/DCNv2/dcn_v2_onnx.py:173
Class_DCNv2Pooling
lib/DCNv2/dcn_v2.py:195
Class_DCNv2Pooling
lib/DCNv2/dcn_v2_amp.py:456
Classhsigmoid
model/layers/attention_blocks.py:4
Classroute_func
lib/DCNv2/dcn_v2_amp.py:263