dlpy.layers.RegionProposal

class dlpy.layers.RegionProposal(anchor_ratio, anchor_scale, name=None, act='AUTO', anchor_num_to_sample=256, base_anchor_size=16, coord_type='COCO', do_RPN_only=False, max_label_per_image=200, proposed_roi_num_score=300, proposed_roi_num_train=2000, roi_train_sample_num=128, src_layers=None, **kwargs)

Region proposal layer

Parameters

namestring, optional

Specifies the name of the layer.

anchor_ratioiter-of-float

Specifies the anchor height and width ratios (h/w) used.

anchor_scaleiter-of-float

Specifies the anchor scales used based on base_anchor_size

actstring, optional

Specifies the activation function. Valid Values: AUTO, IDENTITY, LOGISTIC, SOFTMAX Default: AUTO

anchor_num_to_sampleint, optional

Specifies the number of anchors to sample for training the region proposal network Default: 256

base_anchor_sizeint, optional

Specifies the basic anchor size in width and height (in pixels) in the original input image dimension Default: 16

coord_typeint, optional

Specifies the coordinates format type in the input label and detection result. Valid Values: RECT, COCO, YOLO Default: COCO

do_RPN_onlyBoolean, optional

Specifies that in the model, only Region Proposal task is to be done in the model, not including the Fast RCNN task Default: FALSE

max_label_per_image: int, optional

Specifies the maximum number of labels per training image. Default: 200

proposed_roi_num_score: int, optional

Specifies the number of ROI (Region of Interest) to propose in the scoring phase Default: 300

proposed_roi_num_train: int, optional

Specifies the number of ROI (Region of Interest) to propose used for RPN training, and also the pool to sample from for FastRCNN Training in the training phase Default: 2000

roi_train_sample_num: int, optional

Specifies the number of ROIs(Regions of Interests) to sample after NMS(Non-maximum Suppression) is performed in the training phase. Default: 128

src_layersiter-of-Layers, optional

Specifies the layers directed to this layer.

Returns

RegionProposal

__init__(anchor_ratio, anchor_scale, name=None, act='AUTO', anchor_num_to_sample=256, base_anchor_size=16, coord_type='COCO', do_RPN_only=False, max_label_per_image=200, proposed_roi_num_score=300, proposed_roi_num_train=2000, roi_train_sample_num=128, src_layers=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(anchor_ratio, anchor_scale[, name, …])	Initialize self.
count_instances()
format_name([block_num, local_count])	Format the name of the layer
get_number_of_instances()
to_model_params()	Convert the model configuration to CAS action parameters

Attributes

can_be_last_layer
kernel_size
layer_id
num_bias
num_weights
number_of_instances
output_size
rnn_summary	Return a DataFrame containing the layer information for rnn models
summary	Return a DataFrame containing the layer information
type
type_desc
type_label