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Functions25 in github.com/ZhuangPeiyu/Dense-FCN-for-tampering-localization

⨍ Functions 25◇ Types & classes 1
↓ 4 callersFunctionget_tn_tp_fn_fp
(y_true, y_pred)
utils/metrics.py:3
↓ 3 callersFunctionbilinear_upsample_weights
Create weights matrix for transposed convolution with bilinear filter initialization.
denseFCN.py:202
↓ 2 callersFunctionget_file_list
(data_dir, pattern)
utils/read_dataset.py:11
↓ 2 callersMethodwrite
(self,msg,end='\n')
utils/tee_print.py:15
↓ 1 callersFunctionmodel
(images, weight_decay, is_training, num_classes=2)
test_withoutComputeMetrics.py:90
↓ 1 callersFunctionmodel
(images, weight_decay, is_training, num_classes=2)
train_demo.py:83
↓ 1 callersFunctionread_image_without_mask
(image_path,image_index)
test_withoutComputeMetrics.py:120
Method__del__
(self)
utils/tee_print.py:11
Method__init__
(self,filename=None,mode='a')
utils/tee_print.py:7
FunctiondenseFCN
(inputs, is_training, weight_decay=5e-4,num_classes=2)
denseFCN.py:12
Functionf1_score
(y_true, y_pred)
utils/metrics.py:18
Functionfocal_loss
(logits, labels, gamma=2.0, num_classes=2)
utils/losses.py:28
Functionget_metrics
(y_true, y_pred)
utils/metrics.py:30
Functioniou_measure
(y_true, y_pred)
utils/metrics.py:26
Functionmain
(argv=None)
test_withoutComputeMetrics.py:135
Functionmain
(argv=None)
train_demo.py:86
Functionmap_func
(*args)
train_demo.py:134
Functionmatthews_corrcoef
(y_true, y_pred)
utils/metrics.py:10
Functionquasi_f1_loss
(logits, labels, num_classes=2)
utils/losses.py:43
Functionread_dataset
(label_value, data_dir, pattern='*', shuffle_seed=None, subset=None, begin=0)
utils/read_dataset.py:24
Functionread_dataset_withmsk
(data_dir, pattern, msk_replace, shuffle_seed=None, subset=None)
utils/read_dataset.py:40
Functionread_image
(image_path, mask_path, image_index)
test_withoutComputeMetrics.py:93
Functionread_image_withmsk
(image_name,label_name,outputsize=None,random_flip=False)
utils/read_dataset.py:58
Functionsparse_softmax_cross_entropy_with_logits
(logits, labels)
utils/losses.py:54
Functionsparse_weighted_softmax_cross_entropy_with_logits
(logits, labels, num_classes=2)
utils/losses.py:10