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Types & classes105 in github.com/Hawkeye-FineGrained/Hawkeye

↓ 12 callersClassClassifier
model/methods/MGE_CNN/MGE.py:18
↓ 7 callersClassPerformanceMeter
Record the performance metric during training
utils/utils.py:10
↓ 6 callersClassBottleneck1x1
model/methods/Interp_Parts.py:212
↓ 5 callersClassResNet
model/methods/NTS_Net/resnet.py:94
↓ 4 callersClassFlatten
model/methods/APCNN.py:298
↓ 3 callersClassAverageMeter
Keep track of most recent, average, sum, and count of a metric.
utils/utils.py:32
↓ 3 callersClassBalancedBatchSampler
dataset/sampler.py:5
↓ 3 callersClassChannelGate
generation channel attention mask
model/methods/APCNN.py:283
↓ 3 callersClassRegularLoss
model/loss/CrossX_loss.py:6
↓ 3 callersClassSpatialGate
generation spatial attention mask
model/methods/APCNN.py:271
↓ 2 callersClassBasicConv
model/methods/APCNN.py:73
↓ 2 callersClassFGDataset
dataset/dataset.py:22
↓ 2 callersClassGradCam
model/methods/MGE_CNN/grad_cam.py:51
↓ 2 callersClassRepository
A dict format repository to register module. Repository can also manage config node.
utils/repository.py:1
↓ 2 callersClassResNet
model/methods/Interp_Parts.py:251
↓ 2 callersClassResNet
implementation of AP-CNN on ResNet
model/methods/APCNN.py:344
↓ 2 callersClassScaleLayer
model/methods/S3N.py:105
↓ 2 callersClassTqdmHandler
utils/utils.py:69
↓ 1 callersClassAPCNNTrainer
Examples/APCNN.py:14
↓ 1 callersClassAPINetLoss
model/loss/APINet_loss.py:5
↓ 1 callersClassAPINetTrainer
Examples/APINet.py:13
↓ 1 callersClassBCNNTrainer
Examples/BCNN.py:10
↓ 1 callersClassBaselineTrainer
Examples/Baseline.py:8
↓ 1 callersClassBaselineTrainer
Examples/DCL.py:14
↓ 1 callersClassBilinearPooling
model/methods/BCNN.py:8
↓ 1 callersClassBranch
model/methods/ProtoTree/branch.py:7
↓ 1 callersClassCBCNNTrainer
Examples/CBCNN.py:9
↓ 1 callersClassCINClassifier
Channel Interaction Network Classifier
model/methods/CIN.py:63
↓ 1 callersClassCINLoss
model/loss/CIN_loss.py:7
↓ 1 callersClassCINTrainer
Examples/CIN.py:13
↓ 1 callersClassChannelInteractionModule
Channel Interaction Network
model/methods/CIN.py:10
↓ 1 callersClassClassificationPresetEval
dataset/transforms.py:52
↓ 1 callersClassClassificationPresetTrain
dataset/transforms.py:14
↓ 1 callersClassCompactBilinearPooling
Compute compact bilinear pooling over two bottom inputs. Args: output_dim: output dimension for compact bilinear pooling.
model/methods/CBCNN.py:38
↓ 1 callersClassCrossXLoss
model/loss/CrossX_loss.py:31
↓ 1 callersClassCrossXTrainer
Examples/CrossX.py:12
↓ 1 callersClassDCLDataset
dataset/dataset_DCL.py:11
↓ 1 callersClassDCLLoss
model/loss/DCL_loss.py:4
↓ 1 callersClassFeatureExtractor
Class for extracting activations and registering gradients from targetted intermediate layers
model/methods/MGE_CNN/grad_cam.py:5
↓ 1 callersClassGroupingUnit
model/methods/Interp_Parts.py:25
↓ 1 callersClassInterpPartsLoss
model/loss/InterpParts_loss.py:12
↓ 1 callersClassInterpPartsNetTrainer
Examples/InterpPartsNet.py:12
↓ 1 callersClassKernelGenerator
model/methods/S3N.py:25
↓ 1 callersClassL2Conv2D
Convolutional layer that computes the squared L2 distance instead of the conventional inner product.
model/methods/ProtoTree/l2conv.py:6
↓ 1 callersClassLeaf
model/methods/ProtoTree/leaf.py:8
↓ 1 callersClassLocalCamNet
model/methods/MGE_CNN/MGE.py:75
↓ 1 callersClassMAMCLoss
model/loss/MAMC_loss.py:6
↓ 1 callersClassMELayer
model/methods/CrossX.py:47
↓ 1 callersClassMGE_CNNTrainer
Examples/MGE_CNN.py:11
↓ 1 callersClassMPNCOV
Matrix power normalized Covariance pooling (MPNCOV) implementation of fast MPN-COV (i.e.,iSQRT-COV) https://arxiv.org/abs/1712.01034
model/methods/MPNCOV.py:41
↓ 1 callersClassMPNTrainer
Examples/MPN.py:9
↓ 1 callersClassModelOutputs
Class for making a forward pass, and getting: 1. The network output. 2. Activations from intermeddiate targetted layers. 3. Gradients fro
model/methods/MGE_CNN/grad_cam.py:30
↓ 1 callersClassMultiSmoothLoss
Multi smooth loss.
model/loss/S3N_loss.py:6
↓ 1 callersClassMyTrainer
Examples/ProtoTreeNet.py:12
↓ 1 callersClassNPairsLoss
N-pairs loss as explained in equation 11 of MAMC paper. Reference: Multi-Attention Multi-Class Constraint for Fine-grained Image Recognit
model/loss/MAMC_loss.py:24
↓ 1 callersClassNTSLoss
model/loss/NTS_loss.py:6
↓ 1 callersClassNTSTrainer
Examples/NTSNet.py:11
↓ 1 callersClassOSME
model/methods/OSME.py:27
↓ 1 callersClassOSMENetTrainer
Examples/OSMENet.py:13
↓ 1 callersClassOSME_block
model/methods/OSME.py:8
↓ 1 callersClassPCResNetTrainer
Examples/PairConfusion.py:10
↓ 1 callersClassPLTrainer
Examples/PeerLearning.py:16
↓ 1 callersClassPairwiseConfusionLoss
model/loss/pair_confusion.py:8
↓ 1 callersClassProposalNet
model/methods/NTS_Net/NTSNet.py:63
↓ 1 callersClassProtoTree
model/methods/ProtoTree/prototree.py:18
↓ 1 callersClassPyramidAttentions
Attention pyramid module with bottom-up attention pathway
model/methods/APCNN.py:236
↓ 1 callersClassPyramidFeatures
Feature pyramid module with top-down feature pathway
model/methods/APCNN.py:202
↓ 1 callersClassRandomCutmix
Randomly apply Cutmix to the provided batch and targets. The class implements the data augmentations as described in the paper `"CutMix: Regul
dataset/transforms.py:152
↓ 1 callersClassRandomMixup
Randomly apply Mixup to the provided batch and targets. The class implements the data augmentations as described in the paper `"mixup: Beyond
dataset/transforms.py:76
↓ 1 callersClassRandomSwap
dataset/transforms.py:243
↓ 1 callersClassResNet
model/backbone/resnet.py:147
↓ 1 callersClassResNet
model/methods/CrossX.py:126
↓ 1 callersClassS3NTrainer
Examples/S3N.py:12
↓ 1 callersClassSimpleFPA
model/methods/APCNN.py:170
↓ 1 callersClassTester
Test a model from a config which could be a training config.
test.py:14
↓ 1 callersClassTimer
utils/utils.py:79
↓ 1 callersClassTrainer
Base trainer
train.py:37
↓ 1 callersClassVGG
model/backbone/vgg.py:25
ClassAPINet
model/methods/APINet.py:10
ClassBCNN
model/methods/BCNN.py:31
ClassBasicBlock
model/backbone/resnet.py:40
ClassBasicBlock
model/methods/Interp_Parts.py:179
ClassBasicBlock
model/methods/APCNN.py:99
ClassBasicBlock
model/methods/NTS_Net/resnet.py:23
ClassBottleneck
model/backbone/resnet.py:89
ClassBottleneck
model/methods/Interp_Parts.py:139
ClassBottleneck
model/methods/APCNN.py:131
ClassBottleneck
model/methods/CrossX.py:73
ClassBottleneck
model/methods/NTS_Net/resnet.py:55
ClassCBCNN
model/methods/CBCNN.py:13
ClassCIN
model/methods/CIN.py:85
ClassCovpool
model/methods/MPNCOV.py:105
ClassDCL
model/methods/DCL.py:9
ClassMPN
model/methods/MPNCOV.py:24
ClassMixupCutmixCollateFn
dataset/collate_fn.py:8
ClassNTSNet
model/methods/NTS_Net/NTSNet.py:11
ClassNode
model/methods/ProtoTree/node.py:4
ClassOSMENet
model/methods/OSME.py:48
ClassPeakStimulation
model/methods/S3N.py:57
ClassPeerLearningNet
model/methods/PeerLearningNet.py:9
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