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Functions90 in github.com/DataXujing/Faster-R-CNN-Keras

⨍ Functions 90◇ Types & classes 6
↓ 39 callersFunctionconv2d_bn
Utility function to apply conv + BN. # Arguments x: input tensor. filters: filters in `Conv2D`. kernel_size: kernel size a
Transper/inception_resnet_v2/inception_resnet_v2.py:4
↓ 31 callersFunctionconv2d_bn
Utility function to apply conv + BN. # Arguments x: input tensor. filters: filters in `Conv2D`. kernel_size: kernel size a
keras_frcnn/inception_resnet_v2.py:49
↓ 23 callersFunctionconv2d_bn_td
Utility function to apply conv + BN. # Arguments x: input tensor. filters: filters in `Conv2D`. kernel_size: kernel size a
keras_frcnn/inception_resnet_v2.py:89
↓ 10 callersFunctionidentity_block
(input_tensor, kernel_size, filters, stage, block, trainable=True)
keras_frcnn/resnet.py:41
↓ 4 callersFunctioninception_resnet_block
Adds a Inception-ResNet block. This function builds 3 types of Inception-ResNet blocks mentioned in the paper, controlled by the `block_type`
Transper/inception_resnet_v2/inception_resnet_v2.py:47
↓ 3 callersFunctionconv_block
(input_tensor, kernel_size, filters, stage, block, strides=(2, 2), trainable=True)
keras_frcnn/resnet.py:99
↓ 3 callersFunctionwrite_log
(callback, names, logs, batch_no)
train_frcnn.py:24
↓ 2 callersFunctionconv_block_td
(input_tensor, kernel_size, filters, stage, block, input_shape, strides=(2, 2), trainable=True)
keras_frcnn/resnet.py:129
↓ 2 callersFunctionget_data
Parse the data from annotation file Args: input_path: annotation file path Returns: all_data: list(filepath, width, heig
keras_frcnn/get_train.py:3
↓ 2 callersFunctionget_output_length
(input_length)
keras_frcnn/xception.py:33
↓ 2 callersFunctionget_output_length
(input_length)
keras_frcnn/inception_resnet_v2.py:33
↓ 2 callersFunctionget_output_length
(input_length)
keras_frcnn/vgg.py:30
↓ 2 callersFunctionget_output_length
(input_length)
keras_frcnn/resnet.py:28
↓ 2 callersFunctionidentity_block_td
(input_tensor, kernel_size, filters, stage, block, trainable=True)
keras_frcnn/resnet.py:69
↓ 2 callersFunctioninception_resnet_block
Adds a Inception-ResNet block. This function builds 3 types of Inception-ResNet blocks mentioned in the paper, controlled by the `block_type`
keras_frcnn/inception_resnet_v2.py:129
↓ 2 callersFunctioninception_resnet_block_td
Adds a Inception-ResNet block. This function builds 3 types of Inception-ResNet blocks mentioned in the paper, controlled by the `block_type`
keras_frcnn/inception_resnet_v2.py:211
↓ 1 callersFunctionInceptionResnetV2_model
(input_shape, include_top=False, input_tensor=None, weights='imagenet', pooling=None)
Transper/inception_resnet_v2/inception_resnet_v2.py:129
↓ 1 callersFunctionapply_regr_np
(X, T)
keras_frcnn/roi_helpers.py:133
↓ 1 callersFunctioncalc_rpn
(C, img_data, width, height, resized_width, resized_height, img_length_calc_function)
keras_frcnn/data_generators.py:82
↓ 1 callersFunctionclassifier_layers
(x, input_shape, trainable=False)
keras_frcnn/xception.py:199
↓ 1 callersFunctionclassifier_layers
(x, input_shape, trainable=False)
keras_frcnn/inception_resnet_v2.py:362
↓ 1 callersFunctionclassifier_layers
(x, input_shape, trainable=False)
keras_frcnn/resnet.py:208
↓ 1 callersFunctionexport
(model_path)
Transper/inception_resnet_v2/export_imagenet.py:28
↓ 1 callersFunctionformat_img
formats an image for model prediction based on config
test_frcnn.py:86
↓ 1 callersFunctionformat_img
(img, C)
measure_map.py:116
↓ 1 callersFunctionformat_img
formats an image for model prediction based on config
test_frcnn_video.py:86
↓ 1 callersFunctionformat_img_channels
formats the image channels based on config
test_frcnn.py:74
↓ 1 callersFunctionformat_img_channels
formats the image channels based on config
test_frcnn_video.py:74
↓ 1 callersFunctionformat_img_size
formats the image size based on config
test_frcnn.py:58
↓ 1 callersFunctionformat_img_size
formats the image size based on config
test_frcnn_video.py:58
↓ 1 callersFunctionget_data
(input_path)
keras_frcnn/simple_parser_废弃.py:4
↓ 1 callersFunctionget_map
(pred, gt, f)
measure_map.py:18
↓ 1 callersFunctionget_new_img_size
(width, height, img_min_side=600)
keras_frcnn/data_generators.py:42
↓ 1 callersFunctionget_real_coordinates
(ratio, x1, y1, x2, y2)
test_frcnn.py:93
↓ 1 callersFunctionget_real_coordinates
(ratio, x1, y1, x2, y2)
test_frcnn_video.py:93
↓ 1 callersFunctionintersection
(ai, bi)
keras_frcnn/data_generators.py:18
↓ 1 callersFunctioniou
(a, b)
keras_frcnn/data_generators.py:29
↓ 1 callersMethodload_data
(self)
data/data_pro.py:26
↓ 1 callersFunctionnon_max_suppression_fast
(boxes, probs, overlap_thresh=0.9, max_boxes=300)
keras_frcnn/roi_helpers.py:165
↓ 1 callersMethodskip_sample_for_balanced_class
(self, img_data)
keras_frcnn/data_generators.py:63
↓ 1 callersFunctionunion
(au, bu, area_intersection)
keras_frcnn/data_generators.py:11
Method__init__
(self,data_path)
data/data_pro.py:20
Method__init__
(self)
keras_frcnn/config.py:9
Method__init__
(self, class_count)
keras_frcnn/data_generators.py:57
Method__init__
(self, it)
keras_frcnn/data_generators.py:256
Method__init__
(self, pool_size, num_rois, **kwargs)
keras_frcnn/RoiPoolingConv.py:30
Method__init__
(self, epsilon=1e-3, axis=-1, weights=None, beta_init='zero', gamma_init='one',
keras_frcnn/FixedBatchNormalization.py:8
Method__iter__
(self)
keras_frcnn/data_generators.py:260
Functionapply_regr
(x, y, w, h, tx, ty, tw, th)
keras_frcnn/roi_helpers.py:107
Functionaugment
(img_data, config, augment=True)
keras_frcnn/data_augment.py:9
Methodbuild
(self, input_shape)
keras_frcnn/RoiPoolingConv.py:40
Methodbuild
(self, input_shape)
keras_frcnn/FixedBatchNormalization.py:22
Functioncalc_iou
(R, img_data, C, class_mapping)
keras_frcnn/roi_helpers.py:9
Methodcall
(self, x, mask=None)
keras_frcnn/RoiPoolingConv.py:52
Methodcall
(self, x, mask=None)
keras_frcnn/FixedBatchNormalization.py:49
Functionclass_loss_cls
(y_true, y_pred)
keras_frcnn/losses.py:54
Functionclass_loss_regr
(num_classes)
keras_frcnn/losses.py:45
Functionclass_loss_regr_fixed_num
(y_true, y_pred)
keras_frcnn/losses.py:46
Functionclassifier
(base_layers, input_rois, num_rois, nb_classes=21, trainable=False)
keras_frcnn/xception.py:232
Functionclassifier
(base_layers, input_rois, num_rois, nb_classes=21, trainable=False)
keras_frcnn/inception_resnet_v2.py:411
Functionclassifier
(base_layers, input_rois, num_rois, nb_classes = 21, trainable=False)
keras_frcnn/vgg.py:99
Functionclassifier
(base_layers, input_rois, num_rois, nb_classes=21, trainable=False)
keras_frcnn/resnet.py:234
Methodcompute_output_shape
(self, input_shape)
keras_frcnn/RoiPoolingConv.py:46
Functiong
(*a, **kw)
keras_frcnn/data_generators.py:271
Functionget_anchor_gt
(all_img_data, class_count, C, img_length_calc_function, backend, mode='train')
keras_frcnn/data_generators.py:276
Methodget_config
(self)
keras_frcnn/FixedBatchNormalization.py:77
Functionget_data
(input_path)
keras_frcnn/pascal_voc_parser_废弃.py:7
Functionget_img_output_length
(width, height)
keras_frcnn/xception.py:32
Functionget_img_output_length
(width, height)
keras_frcnn/inception_resnet_v2.py:32
Functionget_img_output_length
(width, height)
keras_frcnn/vgg.py:29
Functionget_img_output_length
(width, height)
keras_frcnn/resnet.py:27
Functionget_weight_path
()
keras_frcnn/xception.py:25
Functionget_weight_path
()
keras_frcnn/inception_resnet_v2.py:25
Functionget_weight_path
()
keras_frcnn/vgg.py:21
Functionget_weight_path
()
keras_frcnn/resnet.py:20
Methodnext
(self)
keras_frcnn/data_generators.py:263
Functionnn_base
(input_tensor=None, trainable=False)
keras_frcnn/xception.py:47
Functionnn_base
(input_tensor=None, trainable=False)
keras_frcnn/inception_resnet_v2.py:293
Functionnn_base
(input_tensor=None, trainable=False)
keras_frcnn/vgg.py:36
Functionnn_base
(input_tensor=None, trainable=False)
keras_frcnn/resnet.py:161
Functionrpn
(base_layers, num_anchors)
keras_frcnn/xception.py:222
Functionrpn
(base_layers, num_anchors)
keras_frcnn/inception_resnet_v2.py:401
Functionrpn
(base_layers, num_anchors)
keras_frcnn/vgg.py:89
Functionrpn
(base_layers, num_anchors)
keras_frcnn/resnet.py:224
Functionrpn_loss_cls
(num_anchors)
keras_frcnn/losses.py:35
Functionrpn_loss_cls_fixed_num
(y_true, y_pred)
keras_frcnn/losses.py:36
Functionrpn_loss_regr
(num_anchors)
keras_frcnn/losses.py:16
Functionrpn_loss_regr_fixed_num
(y_true, y_pred)
keras_frcnn/losses.py:17
Functionrpn_to_roi
(rpn_layer, regr_layer, C, dim_ordering, use_regr=True, max_boxes=300,overlap_thresh=0.9)
keras_frcnn/roi_helpers.py:234
Functionthreadsafe_generator
A decorator that takes a generator function and makes it thread-safe.
keras_frcnn/data_generators.py:268