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Functions | Variables
config Namespace Reference

Functions

def preprocess (dynamic_graph)
 

Variables

 CropAndResize = gs.create_plugin_node(name='roi_pooling_conv_1/CropAndResize_new', op="CropAndResize", inputs=['activation_7/Relu', 'proposal'], crop_height=7, crop_width=7)
 
 Proposal = gs.create_plugin_node(name='proposal', op='Proposal', inputs=['rpn_out_class/Sigmoid', 'rpn_out_regress/BiasAdd'], input_height=272, input_width=480, rpn_stride=16, roi_min_size=1.0, nms_iou_threshold=0.7, pre_nms_top_n=6000, post_nms_top_n=300, anchor_sizes=[32.0, 64.0, 128.0], anchor_ratios=[1.0, 0.5, 2.0])
 
dictionary namespace_plugin_map
 
 fpn_p5upsampled = gs.create_plugin_node("fpn_p5upsampled", op="ResizeNearest_TRT", dtype=tf.float32, scale=2.0)
 
 fpn_p4upsampled = gs.create_plugin_node("fpn_p4upsampled", op="ResizeNearest_TRT", dtype=tf.float32, scale=2.0)
 
 fpn_p3upsampled = gs.create_plugin_node("fpn_p3upsampled", op="ResizeNearest_TRT", dtype=tf.float32, scale=2.0)
 
 roi = gs.create_plugin_node("ROI", op="ProposalLayer_TRT", prenms_topk=1024, keep_topk=1000, iou_threshold=0.7, image_size=[3, 1024, 1024])
 
 roi_align_classifier = gs.create_plugin_node("roi_align_classifier", op="PyramidROIAlign_TRT", pooled_size=7)
 
 mrcnn_detection = gs.create_plugin_node("mrcnn_detection", op="DetectionLayer_TRT", num_classes=81, keep_topk=100, score_threshold=0.7, iou_threshold=0.3)
 
 roi_align_mask = gs.create_plugin_node("roi_align_mask_trt", op="PyramidROIAlign_TRT", pooled_size=14)
 
 mrcnn_detection_bboxes = gs.create_plugin_node("mrcnn_detection_bboxes", op="SpecialSlice_TRT")
 
list timedistributed_remove_list
 
list timedistributed_connect_pairs
 
 Input
 
 PriorBox
 
 NMS
 
 concat_priorbox = gs.create_node(name="concat_priorbox", op="ConcatV2", dtype=tf.float32, axis=2)
 
 concat_box_loc = gs.create_plugin_node("concat_box_loc", op="FlattenConcat_TRT", dtype=tf.float32, axis=1, ignoreBatch=0)
 
 concat_box_conf = gs.create_plugin_node("concat_box_conf", op="FlattenConcat_TRT", dtype=tf.float32, axis=1, ignoreBatch=0)
 

Function Documentation

◆ preprocess()

def config.preprocess (   dynamic_graph)

Variable Documentation

◆ CropAndResize

config.CropAndResize = gs.create_plugin_node(name='roi_pooling_conv_1/CropAndResize_new', op="CropAndResize", inputs=['activation_7/Relu', 'proposal'], crop_height=7, crop_width=7)

◆ Proposal

config.Proposal = gs.create_plugin_node(name='proposal', op='Proposal', inputs=['rpn_out_class/Sigmoid', 'rpn_out_regress/BiasAdd'], input_height=272, input_width=480, rpn_stride=16, roi_min_size=1.0, nms_iou_threshold=0.7, pre_nms_top_n=6000, post_nms_top_n=300, anchor_sizes=[32.0, 64.0, 128.0], anchor_ratios=[1.0, 0.5, 2.0])

◆ namespace_plugin_map

dictionary config.namespace_plugin_map
Initial value:
1 = {
2 "crop_and_resize_1/Reshape" : CropAndResize,
3 'crop_and_resize_1/CropAndResize' : CropAndResize,
4 "crop_and_resize_1/transpose" : CropAndResize,
5 "crop_and_resize_1/transpose_1" : CropAndResize
6 }

◆ fpn_p5upsampled

config.fpn_p5upsampled = gs.create_plugin_node("fpn_p5upsampled", op="ResizeNearest_TRT", dtype=tf.float32, scale=2.0)

◆ fpn_p4upsampled

config.fpn_p4upsampled = gs.create_plugin_node("fpn_p4upsampled", op="ResizeNearest_TRT", dtype=tf.float32, scale=2.0)

◆ fpn_p3upsampled

config.fpn_p3upsampled = gs.create_plugin_node("fpn_p3upsampled", op="ResizeNearest_TRT", dtype=tf.float32, scale=2.0)

◆ roi

config.roi = gs.create_plugin_node("ROI", op="ProposalLayer_TRT", prenms_topk=1024, keep_topk=1000, iou_threshold=0.7, image_size=[3, 1024, 1024])

◆ roi_align_classifier

config.roi_align_classifier = gs.create_plugin_node("roi_align_classifier", op="PyramidROIAlign_TRT", pooled_size=7)

◆ mrcnn_detection

config.mrcnn_detection = gs.create_plugin_node("mrcnn_detection", op="DetectionLayer_TRT", num_classes=81, keep_topk=100, score_threshold=0.7, iou_threshold=0.3)

◆ roi_align_mask

config.roi_align_mask = gs.create_plugin_node("roi_align_mask_trt", op="PyramidROIAlign_TRT", pooled_size=14)

◆ mrcnn_detection_bboxes

config.mrcnn_detection_bboxes = gs.create_plugin_node("mrcnn_detection_bboxes", op="SpecialSlice_TRT")

◆ timedistributed_remove_list

list config.timedistributed_remove_list
Initial value:
1 = [
2  "mrcnn_class_conv1/Reshape/shape", "mrcnn_class_conv1/Reshape", "mrcnn_class_conv1/Reshape_1/shape", "mrcnn_class_conv1/Reshape_1",
3  "mrcnn_class_bn1/Reshape/shape", "mrcnn_class_bn1/Reshape", "mrcnn_class_bn1/Reshape_5/shape", "mrcnn_class_bn1/Reshape_5",
4  "mrcnn_class_conv2/Reshape/shape", "mrcnn_class_conv2/Reshape", "mrcnn_class_conv2/Reshape_1/shape", "mrcnn_class_conv2/Reshape_1",
5  "mrcnn_class_bn2/Reshape/shape", "mrcnn_class_bn2/Reshape", "mrcnn_class_bn2/Reshape_5/shape", "mrcnn_class_bn2/Reshape_5",
6  "mrcnn_class_logits/Reshape/shape", "mrcnn_class_logits/Reshape","mrcnn_class_logits/Reshape_1/shape", "mrcnn_class_logits/Reshape_1",
7  "mrcnn_class/Reshape/shape", "mrcnn_class/Reshape","mrcnn_class/Reshape_1/shape", "mrcnn_class/Reshape_1",
8  "mrcnn_bbox_fc/Reshape/shape", "mrcnn_bbox_fc/Reshape","mrcnn_bbox_fc/Reshape_1/shape", "mrcnn_bbox_fc/Reshape_1",
9 
10  "mrcnn_mask_conv1/Reshape/shape", "mrcnn_mask_conv1/Reshape", "mrcnn_mask_conv1/Reshape_1/shape", "mrcnn_mask_conv1/Reshape_1",
11  "mrcnn_mask_bn1/Reshape/shape", "mrcnn_mask_bn1/Reshape", "mrcnn_mask_bn1/Reshape_5/shape", "mrcnn_mask_bn1/Reshape_5",
12  "mrcnn_mask_conv2/Reshape/shape", "mrcnn_mask_conv2/Reshape", "mrcnn_mask_conv2/Reshape_1/shape", "mrcnn_mask_conv2/Reshape_1",
13  "mrcnn_mask_bn2/Reshape/shape", "mrcnn_mask_bn2/Reshape", "mrcnn_mask_bn2/Reshape_5/shape", "mrcnn_mask_bn2/Reshape_5",
14  "mrcnn_mask_conv3/Reshape/shape", "mrcnn_mask_conv3/Reshape", "mrcnn_mask_conv3/Reshape_1/shape", "mrcnn_mask_conv3/Reshape_1",
15  "mrcnn_mask_bn3/Reshape/shape", "mrcnn_mask_bn3/Reshape", "mrcnn_mask_bn3/Reshape_5/shape", "mrcnn_mask_bn3/Reshape_5",
16  "mrcnn_mask_conv4/Reshape/shape", "mrcnn_mask_conv4/Reshape", "mrcnn_mask_conv4/Reshape_1/shape", "mrcnn_mask_conv4/Reshape_1",
17  "mrcnn_mask_bn4/Reshape/shape", "mrcnn_mask_bn4/Reshape", "mrcnn_mask_bn4/Reshape_5/shape", "mrcnn_mask_bn4/Reshape_5",
18  "mrcnn_mask_deconv/Reshape/shape", "mrcnn_mask_deconv/Reshape", "mrcnn_mask_deconv/Reshape_1/shape", "mrcnn_mask_deconv/Reshape_1",
19  "mrcnn_mask/Reshape/shape", "mrcnn_mask/Reshape", "mrcnn_mask/Reshape_1/shape", "mrcnn_mask/Reshape_1",
20  ]

◆ timedistributed_connect_pairs

list config.timedistributed_connect_pairs
Initial value:
1 = [
2  ("mrcnn_mask_deconv/Relu", "mrcnn_mask/convolution"), # mrcnn_mask_deconv -> mrcnn_mask
3  ("activation_74/Relu", "mrcnn_mask_deconv/conv2d_transpose"), #active74 -> mrcnn_mask_deconv
4  ("mrcnn_mask_bn4/batchnorm/add_1","activation_74/Relu"), # mrcnn_mask_bn4 -> active74
5  ("mrcnn_mask_conv4/BiasAdd", "mrcnn_mask_bn4/batchnorm/mul_1"), #mrcnn_mask_conv4 -> mrcnn_mask_bn4
6  ("activation_73/Relu", "mrcnn_mask_conv4/convolution"), #active73 -> mrcnn_mask_conv4
7  ("mrcnn_mask_bn3/batchnorm/add_1","activation_73/Relu"), #mrcnn_mask_bn3 -> active73
8  ("mrcnn_mask_conv3/BiasAdd", "mrcnn_mask_bn3/batchnorm/mul_1"), #mrcnn_mask_conv3 -> mrcnn_mask_bn3
9  ("activation_72/Relu", "mrcnn_mask_conv3/convolution"), #active72 -> mrcnn_mask_conv3
10  ("mrcnn_mask_bn2/batchnorm/add_1","activation_72/Relu"), #mrcnn_mask_bn2 -> active72
11  ("mrcnn_mask_conv2/BiasAdd", "mrcnn_mask_bn2/batchnorm/mul_1"), #mrcnn_mask_conv2 -> mrcnn_mask_bn2
12  ("activation_71/Relu", "mrcnn_mask_conv2/convolution"), #active71 -> mrcnn_mask_conv2
13  ("mrcnn_mask_bn1/batchnorm/add_1","activation_71/Relu"), #mrcnn_mask_bn1 -> active71
14  ("mrcnn_mask_conv1/BiasAdd", "mrcnn_mask_bn1/batchnorm/mul_1"), #mrcnn_mask_conv1 -> mrcnn_mask_bn1
15  ("roi_align_mask_trt", "mrcnn_mask_conv1/convolution"), #roi_align_mask -> mrcnn_mask_conv1
16 
17 
18  ("mrcnn_class_bn2/batchnorm/add_1","activation_69/Relu"), # mrcnn_class_bn2 -> active 69
19  ("mrcnn_class_conv2/BiasAdd", "mrcnn_class_bn2/batchnorm/mul_1"), # mrcnn_class_conv2 -> mrcnn_class_bn2
20  ("activation_68/Relu", "mrcnn_class_conv2/convolution"), # active 68 -> mrcnn_class_conv2
21  ("mrcnn_class_bn1/batchnorm/add_1","activation_68/Relu"), # mrcnn_class_bn1 -> active 68
22  ("mrcnn_class_conv1/BiasAdd", "mrcnn_class_bn1/batchnorm/mul_1"), # mrcnn_class_conv1 -> mrcnn_class_bn1
23  ("roi_align_classifier", "mrcnn_class_conv1/convolution"), # roi_align_classifier -> mrcnn_class_conv1
24  ]

◆ Input

config.Input
Initial value:
1 = gs.create_node("Input",
2  op="Placeholder",
3  dtype=tf.float32,
4  shape=[1, 3, 300, 300])

◆ PriorBox

config.PriorBox
Initial value:
1 = gs.create_plugin_node(name="GridAnchor", op="GridAnchor_TRT",
2  numLayers=6,
3  minSize=0.2,
4  maxSize=0.95,
5  aspectRatios=[1.0, 2.0, 0.5, 3.0, 0.33],
6  variance=[0.1,0.1,0.2,0.2],
7  featureMapShapes=[19, 10, 5, 3, 2, 1])

◆ NMS

config.NMS
Initial value:
1 = gs.create_plugin_node(name="NMS", op="NMS_TRT",
2  shareLocation=1,
3  varianceEncodedInTarget=0,
4  backgroundLabelId=0,
5  confidenceThreshold=1e-8,
6  nmsThreshold=0.6,
7  topK=100,
8  keepTopK=100,
9  numClasses=91,
10  inputOrder=[0, 2, 1],
11  confSigmoid=1,
12  isNormalized=1)

◆ concat_priorbox

config.concat_priorbox = gs.create_node(name="concat_priorbox", op="ConcatV2", dtype=tf.float32, axis=2)

◆ concat_box_loc

config.concat_box_loc = gs.create_plugin_node("concat_box_loc", op="FlattenConcat_TRT", dtype=tf.float32, axis=1, ignoreBatch=0)

◆ concat_box_conf

config.concat_box_conf = gs.create_plugin_node("concat_box_conf", op="FlattenConcat_TRT", dtype=tf.float32, axis=1, ignoreBatch=0)