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Functions94 in github.com/TachibanaYoshino/Remote-sensing-image-semantic-segmentation

⨍ Functions 94◇ Types & classes 3
↓ 43 callersFunctionconv_block
(input_tensor, filters, kernel_size, strides, name, padding='same', dila=1)
net/model6_5.py:14
↓ 43 callersFunctionconv_block
(input_tensor, filters, kernel_size, strides, name, padding='same', dila=1)
net/model6_4.py:14
↓ 43 callersFunctionconv_block
(input_tensor, filters, kernel_size, strides, name, padding='same', dila=1)
net/model6_6.py:14
↓ 41 callersFunctionconv_block
(input_tensor, filters, kernel_size, name, strides, padding='same', dila=1)
net/model6.py:17
↓ 36 callersFunctionconv_block
(input_tensor, filters, kernel_size, name, strides, padding='same', dila=1)
net/model5.py:17
↓ 34 callersFunctionconv_block
(input_tensor, filters, kernel_size, name, strides, padding='same', dila=1)
net/model6_3.py:17
↓ 34 callersFunctionconv_block
(input_tensor, filters, kernel_size, name, strides, padding='same', dila=1)
net/model6_2.py:17
↓ 34 callersFunctionconv_block
(input_tensor, filters, kernel_size, name, strides, padding='same', dila=1)
net/model6_1.py:17
↓ 22 callersFunctionconv_block
(input_tensor, filters, kernel_size, name, strides, padding='same', dila=1)
net/model0.py:17
↓ 11 callersMethodcheck_folder
(self, log_dir)
config.py:26
↓ 10 callersFunctionValImggenerator
(data_path, img_size, batch_size)
data_loader.py:101
↓ 9 callersFunctionTrainImggenerator
(train_data_path, img_size, batch_size, augment)
data_loader.py:19
↓ 5 callersFunctionsave
(img,label,s, count, file)
data_preprocess.py:66
↓ 4 callersFunctiondata_augment
(x, y)
data_preprocess.py:56
↓ 3 callersFunctionrandom_crop
(img1, img2, crop_H, crop_W)
data_preprocess.py:12
↓ 2 callersFunctionduconv
(x,name='duconv')
net/model6_3.py:30
↓ 2 callersFunctionduconv
(x,name='duconv')
net/model6_2.py:30
↓ 2 callersFunctionflatten_binary_scores
Flattens predictions in the batch (binary case) Remove labels equal to 'ignore'
Loss.py:78
↓ 2 callersFunctionflatten_probas
Flattens predictions in the batch
Loss.py:153
↓ 2 callersFunctionlovasz_grad
Computes gradient of the Lovasz extension w.r.t sorted errors See Alg. 1 in paper
Loss.py:9
↓ 2 callersFunctionlovasz_hinge_flat
Binary Lovasz hinge loss logits: [P] Variable, logits at each prediction (between -\infty and +\infty) labels: [P] Tensor, binary
Loss.py:50
↓ 2 callersFunctionlovasz_softmax_flat
Multi-class Lovasz-Softmax loss probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1) labels: [P] Tens
Loss.py:121
↓ 2 callersFunctionmain
(path)
create_train_val_label.py:21
↓ 2 callersFunctionudconv
(x,name='udconv')
net/model6_3.py:26
↓ 2 callersFunctionudconv
(x,name='udconv')
net/model6_2.py:26
↓ 1 callersFunctionNet
(n_label,img_input)
net/model6_3.py:35
↓ 1 callersFunctionNet
(n_label,img_input, drop_rate=0.1)
net/model6_5.py:57
↓ 1 callersFunctionNet
(n_label,img_input)
net/model0.py:26
↓ 1 callersFunctionNet
(n_label,img_input)
net/model6_2.py:35
↓ 1 callersFunctionNet
(n_label,img_input)
net/model5.py:26
↓ 1 callersFunctionNet
(n_label,img_input, drop_rate=0.2)
net/model6_4.py:57
↓ 1 callersFunctionNet
(n_label,img_input, drop_rate=0.2)
net/model6.py:26
↓ 1 callersFunctionNet
(n_label,img_input)
net/model6_1.py:26
↓ 1 callersFunctionNet
(n_label,img_input, drop_rate=0.1)
net/model6_6.py:57
↓ 1 callersFunctionValImggenerator
(data_path, img_size, batch_size)
data_loader5.py:108
↓ 1 callersFunctionValImggenerator
(data_path, img_size, batch_size)
data_loader6_1.py:94
↓ 1 callersFunctionValImggenerator
(data_path, img_size, batch_size)
data_loader0.py:104
↓ 1 callersFunctionargs_parse
()
test.py:21
↓ 1 callersFunctionargs_parse
()
train6_2.py:84
↓ 1 callersFunctionargs_parse
()
train6_4.py:73
↓ 1 callersFunctionargs_parse
()
train6_6.py:73
↓ 1 callersFunctionargs_parse
()
train5.py:72
↓ 1 callersFunctionargs_parse
()
train0.py:73
↓ 1 callersFunctionargs_parse
()
train6_3.py:89
↓ 1 callersFunctionargs_parse
()
train6_1.py:73
↓ 1 callersFunctionargs_parse
()
train6_5.py:73
↓ 1 callersFunctionargs_parse
()
train6.py:73
↓ 1 callersFunctioncreat_train_dataset
(train_path, crop_size,prepro_train_path)
data_preprocess.py:135
↓ 1 callersFunctionflip
(x,y)
data_preprocess.py:46
↓ 1 callersFunctionget_label_from_palette
(label_img, palette_file='Palette.json')
create_train_val_label.py:7
↓ 1 callersFunctionglobal_context_block
(x, channels)
net/model6_5.py:29
↓ 1 callersFunctionglobal_context_block
(x, channels)
net/model6_4.py:29
↓ 1 callersFunctionglobal_context_block
(x, channels)
net/model6_6.py:29
↓ 1 callersFunctionlovasz_softmax
Multi-class Lovasz-Softmax loss probas: [B, H, W, C] or [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
Loss.py:96
↓ 1 callersFunctionout_mask_to_color_pic
(mask, palette_file='Palette.json')
test.py:33
↓ 1 callersFunctionpredict
(args)
test.py:44
↓ 1 callersFunctionrotate
(x, y, angle)
data_preprocess.py:34
↓ 1 callersFunctiontrain
(args)
train6_2.py:21
↓ 1 callersFunctiontrain
(args)
train6_4.py:21
↓ 1 callersFunctiontrain
(args)
train6_6.py:21
↓ 1 callersFunctiontrain
(args)
train5.py:21
↓ 1 callersFunctiontrain
(args)
train0.py:22
↓ 1 callersFunctiontrain
(args)
train6_3.py:19
↓ 1 callersFunctiontrain
(args)
train6_1.py:22
↓ 1 callersFunctiontrain
(args)
train6_5.py:21
↓ 1 callersFunctiontrain
(args)
train6.py:21
FunctionTrainImggenerator
(train_data_path, img_size, batch_size, augment)
data_loader5.py:19
FunctionTrainImggenerator
(train_data_path, img_size, batch_size, augment)
data_loader6_1.py:19
FunctionTrainImggenerator
(train_data_path, img_size, batch_size, augment)
data_loader0.py:19
Method__init__
(self)
config.py:3
Method__init__
(self, groups=32, axis=-1, epsilon=1e-5,
net/group_norm.py:46
Method__init__
(self, gamma_init=20, **kwargs)
net/keras_layer_L2Normalization.py:46
Function_cumsum
(x, axis=None)
Loss.py:5
Functionbackend_dot
(x)
net/model6_5.py:26
Functionbackend_dot
(x)
net/model6_4.py:26
Functionbackend_dot
(x)
net/model6_6.py:26
Functionbackend_expand_dims_1
(x)
net/model6_5.py:20
Functionbackend_expand_dims_1
(x)
net/model6_4.py:20
Functionbackend_expand_dims_1
(x)
net/model6_6.py:20
Functionbackend_expand_dims_last
(x)
net/model6_5.py:23
Functionbackend_expand_dims_last
(x)
net/model6_4.py:23
Functionbackend_expand_dims_last
(x)
net/model6_6.py:23
Methodbuild
(self, input_shape)
net/group_norm.py:73
Methodbuild
(self, input_shape)
net/keras_layer_L2Normalization.py:54
Methodcall
(self, inputs, **kwargs)
net/group_norm.py:114
Methodcall
(self, x, mask=None)
net/keras_layer_L2Normalization.py:61
Functioncompute_loss
()
Loss.py:58
Methodcompute_output_shape
(self, input_shape)
net/group_norm.py:177
Functioncreat_train_dataset1
(train_path, crop_size,prepro_train_path)
data_preprocess.py:79
Methodget_config
(self)
net/group_norm.py:160
Functionlovasz_hinge
Binary Lovasz hinge loss logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty) labels: [B, H, W] Tensor,
Loss.py:28
Functionsplit
(x)
net/model6_3.py:23
Functionsplit
(x)
net/model6_2.py:23
Functiontreat_image
(log_lab)
Loss.py:37