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Functions293 in github.com/EdenBelouadah/class-incremental-learning

⨍ Functions 293◇ Types & classes 38
↓ 80 callersMethodupdate
(self, val, n=1)
siw/utils/AverageMeter.py:14
↓ 66 callersMethodupdate
(self, val, n=1)
cil/utils/AverageMeter.py:14
↓ 42 callersMethodaccuracy
Computes the precision@k for the specified values of k
siw/utils/Utils.py:13
↓ 40 callersMethodrun
(self, manual_method)
ail/codes/classical_AFs.py:140
↓ 27 callersMethodaccuracy
Computes the precision@k for the specified values of k
cil/utils/Utils.py:13
↓ 20 callersMethodupdate
(self, val, n=1)
scail/codes/utils/AverageMeter.py:14
↓ 18 callersFunctionmerge_images_labels
(images, labels)
cil/lucir/codes/utils_dataset.py:31
↓ 12 callersMethodupdate
(self, val, n=1)
il2m/codes/utils/AverageMeter.py:14
↓ 11 callersMethodget_dataset_mean_std
(self, normalization_dataset_name, datasets_mean_std_file_path)
cil/utils/Utils.py:28
↓ 10 callersFunctiontrain_eval_MR_LF
(epochs, tg_model, ref_model, tg_optimizer, tg_lr_scheduler, \ trainloader, testloader, \
cil/lucir/codes/utils_incremental/train_eval_MR_LF.py:30
↓ 8 callersMethodaccuracy
Computes the precision@k for the specified values of k
scail/codes/utils/Utils.py:13
↓ 8 callersMethodfrom_str_to_list
(self, string, type)
il2m/codes/utils/Utils.py:40
↓ 7 callersFunctionconv2d_fixed_padding
Strided 2-D convolution with explicit padding.
cil/bic/codes/resnet.py:151
↓ 7 callersFunctionresidual_block
(inp, phase, alpha=0.0,nom='a',increase_dim=False,last=False)
siw/LwF/codes/utils_resnet.py:102
↓ 7 callersFunctionresidual_block
(inp, phase, alpha=0.0,nom='a',increase_dim=False,last=False)
cil/icarl/codes/utils_resnet.py:101
↓ 6 callersMethodaccuracy
Computes the precision@k for the specified values of k
il2m/codes/utils/Utils.py:11
↓ 6 callersFunctionbatch_norm_relu
Performs a batch normalization followed by a ReLU.
cil/bic/codes/resnet.py:112
↓ 6 callersFunctioncompute_features
(tg_feature_model, evalloader, num_samples, num_features, device=None)
cil/lucir/codes/utils_incremental/compute_features.py:7
↓ 6 callersFunctionget_variable
(name, shape, dtype, initializer, trainable=True, regularizer=None)
siw/LwF/codes/utils_resnet.py:17
↓ 6 callersFunctionget_variable
(name, shape, dtype, initializer, trainable=True, regularizer=None)
cil/icarl/codes/utils_resnet.py:16
↓ 5 callersFunctionconv
(inp, name, size, out_channels, strides=[1, 1, 1, 1], dilation=None, padding='SAME', apply_relu=True,
siw/LwF/codes/utils_resnet.py:25
↓ 5 callersFunctionconv
(inp, name, size, out_channels, strides=[1, 1, 1, 1], dilation=None, padding='SAME', apply_relu=True,
cil/icarl/codes/utils_resnet.py:24
↓ 5 callersMethodget_dataset_mean_std
(self, normalization_dataset_name, datasets_mean_std_file_path)
scail/codes/utils/Utils.py:28
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1, last_phase=False)
cil/lucir/codes/modified_resnet.py:69
↓ 4 callersMethod_make_layer
(self, block, planes, blocks, stride=1)
cil/lucir/codes/resnet.py:119
↓ 4 callersFunctionaccuracy
Computes the precision@k for the specified values of k
scail/codes/ft_init_l2.py:32
↓ 4 callersFunctionbatch_norm
(inp, name, phase, decay=0.9)
siw/LwF/codes/utils_resnet.py:61
↓ 4 callersFunctionbatch_norm
(inp, name, phase, decay=0.9)
cil/icarl/codes/utils_resnet.py:60
↓ 4 callersFunctioncompute_accuracy
(tg_model, tg_feature_model, class_means, evalloader, scale=None, print_info=True, device=None)
cil/lucir/codes/utils_incremental/compute_accuracy.py:14
↓ 4 callersFunctionsplit_images_labels
(imgs)
cil/lucir/codes/utils_dataset.py:8
↓ 4 callersFunctiontrain_eval_LF
(epochs, tg_model, ref_model, tg_optimizer, tg_lr_scheduler, \ trainloader, testloader, \
cil/lucir/codes/utils_incremental/train_eval_LF.py:21
↓ 4 callersMethodupdate
(self, val, n=1)
ail/codes/utils/AverageMeter.py:14
↓ 3 callersFunction_get_h_w
Convenience for grabbing the height and width of an image.
cil/bic/codes/vgg_preprocessing.py:52
↓ 3 callersFunctionaccuracy
Computes the precision@k for the specified values of k
cil/FT_bal/codes/ft_bal_herd.py:25
↓ 3 callersMethodaccuracy
Computes the precision@k for the specified values of k
ail/codes/utils/Utils.py:18
↓ 3 callersFunctionfrom_list_to_dict
(images_list, shuffle=False)
scail/codes/prepare_imbalanced_list_files.py:21
↓ 3 callersMethodget_dataset_mean_std
(self, normalization_dataset_name, datasets_mean_std_file_path)
il2m/codes/utils/Utils.py:26
↓ 3 callersMethodget_dataset_mean_std
(self, normalization_dataset_name, datasets_mean_std_file_path)
siw/utils/Utils.py:28
↓ 3 callersMethodprint_parameters
(self, cp)
ail/codes/utils/Utils.py:13
↓ 3 callersFunctionth_calibration
(scores, N, n)
ail/codes/main.py:42
↓ 2 callersMethod__init__
(self, block, layers, num_classes=1000)
cil/lucir/codes/resnet.py:97
↓ 2 callersMethod__repr__
(self)
il2m/codes/utils/MyImageFolder.py:106
↓ 2 callersMethod__repr__
(self)
deesil/codes/utils/MyImageFolder.py:106
↓ 2 callersMethod__repr__
(self)
siw/utils/MyImageFolder.py:106
↓ 2 callersMethod__repr__
(self)
ail/codes/utils/MyImageFolder.py:106
↓ 2 callersMethod__repr__
(self)
scail/codes/utils/MyImageFolder.py:106
↓ 2 callersMethod__repr__
(self)
cil/utils/MyImageFolder.py:106
↓ 2 callersFunctioncompute_rebuffi_herding_faster
(img_list, img_feats, exem_dir, m, file_name)
cil/FT_bal/codes/ft_bal_herd.py:67
↓ 2 callersMethodcompute_rebuffi_herding_faster
(self, exemplars_dir, img_list_path, img_feats_path, per_class, out_file_name)
scail/codes/utils/Herding.py:10
↓ 2 callersFunctionconv3x3
3x3 convolution with padding
cil/lucir/codes/modified_resnet.py:6
↓ 2 callersFunctionconv3x3
3x3 convolution with padding
cil/lucir/codes/resnet.py:19
↓ 2 callersFunctionget_dataset_N_n
(dsets, model_num_classes)
ail/codes/main.py:50
↓ 2 callersMethodget_dataset_mean_std
(self, normalization_dataset_name, datasets_mean_std_file_path)
deesil/codes/utils/Utils.py:26
↓ 2 callersMethodget_dataset_mean_std
(self, normalization_dataset_name, datasets_mean_std_file_path)
ail/codes/utils/Utils.py:33
↓ 2 callersFunctionload_class_paths
(class_number, batch_paths)
cil/FT_bal/codes/ft_bal_herd.py:56
↓ 2 callersFunctionloss_filter_fn
(name)
cil/bic/codes/resnet.py:616
↓ 2 callersFunctionpool
(inp, name, kind, size, stride, padding='SAME')
siw/LwF/codes/utils_resnet.py:85
↓ 2 callersFunctionpool
(inp, name, kind, size, stride, padding='SAME')
cil/icarl/codes/utils_resnet.py:84
↓ 2 callersFunctionprojection_shortcut
(inputs)
cil/bic/codes/resnet.py:273
↓ 2 callersMethodreduce_exemplars
(self, exemplars_dir, old_classes_number, per_class, batch_number, file_names_suffix)
scail/codes/utils/Herding.py:176
↓ 2 callersFunctionsave_protosets
(current_eval_set, b, output_dir)
cil/lucir/codes/utils_dataset.py:43
↓ 2 callersFunctiontrain_eval
(epochs, tg_model, ref_model, tg_optimizer, tg_lr_scheduler, \ trainloader, testloader, \
cil/lucir/codes/utils_incremental/train_eval.py:12
↓ 2 callersFunctiontrain_eval_MS
(epochs, tg_model, ref_model, tg_optimizer, tg_lr_scheduler, \ trainloader, testloader, \
cil/lucir/codes/utils_incremental/train_eval_MS.py:18
↓ 2 callersMethodupdate
(self, val, n=1)
deesil/codes/utils/AverageMeter.py:14
↓ 2 callersMethodupdate
(self, annotated_img_idx, L2_annotated_features, annotated_classes, annotated_paths)
ail/codes/classical_AFs.py:156
↓ 1 callersMethod__init__
Creates a model for classifying an image. Args: resnet_size: A single integer for the size of the ResNet model. num_classes: The numb
cil/bic/codes/resnet.py:292
↓ 1 callersMethod__init__
(self, block, layers, num_classes=1000)
cil/lucir/codes/modified_resnet.py:47
↓ 1 callersMethod__init__
(self, in_features, out_features, sigma=True)
cil/lucir/codes/modified_linear.py:9
↓ 1 callersMethod__len__
(self)
il2m/codes/utils/MyImageFolder.py:103
↓ 1 callersMethod__len__
(self)
il2m/codes/utils/MyImageFolder.py:207
↓ 1 callersMethod__len__
(self)
deesil/codes/utils/MyImageFolder.py:103
↓ 1 callersMethod__len__
(self)
deesil/codes/utils/MyImageFolder.py:207
↓ 1 callersMethod__len__
(self)
siw/utils/MyImageFolder.py:103
↓ 1 callersMethod__len__
(self)
siw/utils/MyImageFolder.py:207
↓ 1 callersMethod__len__
(self)
ail/codes/utils/MyImageFolder.py:103
↓ 1 callersMethod__len__
(self)
ail/codes/utils/MyImageFolder.py:207
↓ 1 callersMethod__len__
(self)
scail/codes/utils/MyImageFolder.py:103
↓ 1 callersMethod__len__
(self)
scail/codes/utils/MyImageFolder.py:207
↓ 1 callersMethod__len__
(self)
cil/utils/MyImageFolder.py:103
↓ 1 callersMethod__len__
(self)
cil/utils/MyImageFolder.py:207
↓ 1 callersFunction_aspect_preserving_resize
Resize images preserving the original aspect ratio. Args: image: A 3-D image `Tensor`. smallest_side: A python integer or scalar `Tensor` i
cil/bic/codes/vgg_preprocessing.py:172
↓ 1 callersFunction_get_block_sizes
The number of block layers used for the Resnet model varies according to the size of the model. This helper grabs the layer set we want, throwing
cil/bic/codes/main_no_herding.py:319
↓ 1 callersFunction_get_block_sizes
The number of block layers used for the Resnet model varies according to the size of the model. This helper grabs the layer set we want, throwing
cil/bic/codes/main.py:319
↓ 1 callersFunction_mean_image_subtraction
Subtracts the given means from each image channel. For example: means = [123.68, 116.779, 103.939] image = _mean_image_subtraction(image, m
cil/bic/codes/vgg_preprocessing.py:110
↓ 1 callersFunction_smallest_size_at_least
Computes new shape with the smallest side equal to `smallest_side`. Computes new shape with the smallest side equal to `smallest_side` while pres
cil/bic/codes/vgg_preprocessing.py:143
↓ 1 callersFunctionaccuracy
Computes the precision@k for the specified values of k
cil/FT_bal/codes/ft_bal.py:19
↓ 1 callersFunctionaccuracy
Computes the precision@k for the specified values of k
cil/FR/codes/no_mem_fr_train_val.py:25
↓ 1 callersFunctionaccuracy
Computes the precision@k for the specified values of k
cil/FT_nem/ift_nem.py:31
↓ 1 callersFunctionaccuracy
Computes the precision@k for the specified values of k
cil/FT_nem/ift_nem_herd.py:29
↓ 1 callersMethodaccuracy
Computes the precision@k for the specified values of k
deesil/codes/utils/Utils.py:11
↓ 1 callersFunctionactive_learning
Annotate a new batch of images Return the oracle (class oracle_annotation)
ail/codes/main.py:64
↓ 1 callersFunctionbcore
DESCRIPTION: balancing method which selects the points which maximize the difference between classes having the least number of images and the maxi
ail/codes/balancing_driven_AFs.py:189
↓ 1 callersFunctionblock_layer
Creates one layer of blocks for the ResNet model. Args: inputs: A tensor of size [batch, channels, height_in, width_in] or [batch, height
cil/bic/codes/resnet.py:249
↓ 1 callersFunctioncompute_accuracy
compute accuracy for batches of synsets whose size increases from 100 to 1000 by step of 100 for each batch, we evaluate only the examples of i
deesil/codes/eval.py:35
↓ 1 callersFunctioncompute_accuracy
function that reads the models created for the current regularization parameter and computes the classification accuracy using a validation se
deesil/codes/calibrate_svms.py:126
↓ 1 callersFunctioncompute_predictions
function that reads the models created for the current regularization parameter and computes the classification accuracy using a test set.
deesil/codes/compute_predictions.py:48
↓ 1 callersMethodcompute_rebuffi_herding_faster
(self, exemplars_dir, img_list_path, img_feats_path, oracle_annotated_paths, per_class, out_file_name)
ail/codes/utils/Herding_AL.py:11
↓ 1 callersFunctioncreate_calibration_models
function that creates linear models for ImageNet synsets with the set of parameters given to the script
deesil/codes/calibrate_svms.py:46
↓ 1 callersFunctioncreate_dist_matrix
Compute and return the L2 distance matrix of features array
ail/codes/data_utils.py:25
↓ 1 callersFunctioncreate_model
function that creates linear models for ImageNet synsets with the set of parameters given to the script
deesil/codes/train_svms.py:56
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