Source code for gluoncv.model_zoo.rcnn.mask_rcnn.predefined_models

"""Predefined Mask RCNN Model."""
from __future__ import absolute_import

import mxnet as mx
from mxnet.gluon import nn
from mxnet.gluon.contrib.nn import SyncBatchNorm

from ..mask_rcnn import get_mask_rcnn
from ....nn.feature import FPNFeatureExpander

__all__ = ['mask_rcnn_resnet50_v1b_coco',
           'mask_rcnn_fpn_resnet50_v1b_coco',
           'mask_rcnn_resnet101_v1d_coco',
           'mask_rcnn_fpn_resnet101_v1d_coco',
           'mask_rcnn_resnet18_v1b_coco',
           'mask_rcnn_fpn_resnet18_v1b_coco',
           'mask_rcnn_fpn_syncbn_resnet18_v1b_coco',
           'mask_rcnn_fpn_syncbn_mobilenet1_0_coco']


[docs]def mask_rcnn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_resnet50_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet50_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*mask', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_mask_rcnn( name='resnet50_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, mask_channels=256, rcnn_max_dets=1000, short=800, max_size=1333, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=16, clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, **kwargs)
[docs]def mask_rcnn_fpn_resnet50_v1b_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_resnet50_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet50_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet50_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True) features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu17_fwd', 'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base) top_features = None box_features = nn.HybridSequential() box_features.add(nn.AvgPool2D(pool_size=(3, 3), strides=2, padding=1)) # reduce to 7x7 for _ in range(2): box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '|'.join(['.*dense', '.*rpn', '.*mask', 'P', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_mask_rcnn( name='fpn_resnet50_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, mask_channels=256, rcnn_max_dets=1000, short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=(4, 8, 16, 32, 64), clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, target_roi_scale=2, num_fcn_convs=4, **kwargs)
[docs]def mask_rcnn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `Ture`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_resnet101_v1d_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet101_v1d from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=True) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*mask', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_mask_rcnn( name='resnet101_v1d', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, mask_channels=256, rcnn_max_dets=1000, short=800, max_size=1333, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=16, clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=128, pos_iou_thresh=0.5, pos_ratio=0.25, **kwargs)
[docs]def mask_rcnn_fpn_resnet101_v1d_coco(pretrained=False, pretrained_base=True, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_fpn_resnet101_v1d_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet101_v1d from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base base_network = resnet101_v1d(pretrained=pretrained_base, dilated=False, use_global_stats=True) features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu8_fwd', 'layers2_relu11_fwd', 'layers3_relu68_fwd', 'layers4_relu8_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base) top_features = None box_features = nn.HybridSequential() box_features.add(nn.AvgPool2D(pool_size=(3, 3), strides=2, padding=1)) # reduce to 7x7 for _ in range(2): box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '|'.join(['.*dense', '.*rpn', '.*mask', 'P', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_mask_rcnn( name='fpn_resnet101_v1d', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, mask_channels=256, rcnn_max_dets=1000, short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=(4, 8, 16, 32, 64), clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, target_roi_scale=2, num_fcn_convs=4, **kwargs)
[docs]def mask_rcnn_resnet18_v1b_coco(pretrained=False, pretrained_base=True, rcnn_max_dets=1000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. rcnn_max_dets : int, default is 1000 Number of rois to retain in RCNN. rpn_test_pre_nms : int, default is 6000 Filter top proposals before NMS in testing of RPN. rpn_test_post_nms : int, default is 300 Return top proposal results after NMS in testing of RPN. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_resnet18_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet18_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base rcnn_max_dets = rpn_test_post_nms if rcnn_max_dets > rpn_test_post_nms else rcnn_max_dets base_network = resnet18_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True) features = nn.HybridSequential() top_features = nn.HybridSequential() for layer in ['conv1', 'bn1', 'relu', 'maxpool', 'layer1', 'layer2', 'layer3']: features.add(getattr(base_network, layer)) for layer in ['layer4']: top_features.add(getattr(base_network, layer)) train_patterns = '|'.join(['.*dense', '.*rpn', '.*mask', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_mask_rcnn( name='resnet18_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, mask_channels=256, rcnn_max_dets=rcnn_max_dets, short=800, max_size=1333, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=16, clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=rpn_test_pre_nms, rpn_test_post_nms=rpn_test_post_nms, rpn_min_size=1, num_sample=256, pos_iou_thresh=0.5, pos_ratio=0.25, **kwargs)
[docs]def mask_rcnn_fpn_resnet18_v1b_coco(pretrained=False, pretrained_base=True, rcnn_max_dets=1000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. rcnn_max_dets : int, default is 1000 Number of rois to retain in RCNN. rpn_test_pre_nms : int, default is 6000 Filter top proposals before NMS in testing of RPN. rpn_test_post_nms : int, default is 300 Return top proposal results after NMS in testing of RPN. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_fpn_resnet18_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet18_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base rcnn_max_dets = rpn_test_post_nms if rcnn_max_dets > rpn_test_post_nms else rcnn_max_dets base_network = resnet18_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=True) features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu3_fwd', 'layers2_relu3_fwd', 'layers3_relu3_fwd', 'layers4_relu3_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base) top_features = None box_features = nn.HybridSequential() for _ in range(2): box_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '|'.join(['.*dense', '.*rpn', '.*mask', 'P', '.*down(2|3|4)_conv', '.*layers(2|3|4)_conv']) return get_mask_rcnn( name='fpn_resnet18_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, mask_channels=256, rcnn_max_dets=rcnn_max_dets, short=800, max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7), strides=(4, 8, 16, 32, 64), clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=rpn_test_pre_nms, rpn_test_post_nms=rpn_test_post_nms, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, target_roi_scale=2, num_fcn_convs=2, **kwargs)
[docs]def mask_rcnn_fpn_syncbn_resnet18_v1b_coco(pretrained=False, pretrained_base=True, num_devices=0, rcnn_max_dets=1000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. num_devices : int, default is 0 Number of devices for sync batch norm layer. if less than 1, use all devices available. rcnn_max_dets : int, default is 1000 Number of rois to retain in RCNN. rpn_test_pre_nms : int, default is 6000 Filter top proposals before NMS in testing of RPN. rpn_test_post_nms : int, default is 300 Return top proposal results after NMS in testing of RPN. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_fpn_syncbn_resnet18_v1b_coco(pretrained=True) >>> print(model) """ from ....model_zoo.resnetv1b import resnet18_v1b from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base rcnn_max_dets = rpn_test_post_nms if rcnn_max_dets > rpn_test_post_nms else rcnn_max_dets gluon_norm_kwargs = {'num_devices': num_devices} if num_devices >= 1 else {} sym_norm_kwargs = {'ndev': num_devices} if num_devices >= 1 else {} base_network = resnet18_v1b(pretrained=pretrained_base, dilated=False, use_global_stats=False, norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs) features = FPNFeatureExpander( network=base_network, outputs=['layers1_relu3_fwd', 'layers2_relu3_fwd', 'layers3_relu3_fwd', 'layers4_relu3_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base, norm_layer=mx.sym.contrib.SyncBatchNorm, norm_kwargs=sym_norm_kwargs) top_features = None box_features = nn.HybridSequential() box_features.add(nn.Conv2D(256, 3, padding=1), SyncBatchNorm(**gluon_norm_kwargs), nn.Activation('relu'), nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '(?!.*moving)' return get_mask_rcnn( name='fpn_syncbn_resnet18_v1b', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, mask_channels=256, rcnn_max_dets=rcnn_max_dets, short=(640, 800), max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(7, 7), strides=(4, 8, 16, 32, 64), clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=rpn_test_pre_nms, rpn_test_post_nms=rpn_test_post_nms, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, target_roi_scale=2, num_fcn_convs=2, norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs)
[docs]def mask_rcnn_fpn_syncbn_mobilenet1_0_coco(pretrained=False, pretrained_base=True, num_devices=0, rcnn_max_dets=1000, rpn_test_pre_nms=6000, rpn_test_post_nms=1000, **kwargs): r"""Mask RCNN model from the paper "He, K., Gkioxari, G., Doll&ar, P., & Girshick, R. (2017). Mask R-CNN" Parameters ---------- pretrained : bool or str Boolean value controls whether to load the default pretrained weights for model. String value represents the hashtag for a certain version of pretrained weights. pretrained_base : bool or str, optional, default is True Load pretrained base network, the extra layers are randomized. Note that if pretrained is `True`, this has no effect. num_devices : int, default is 0 Number of devices for sync batch norm layer. if less than 1, use all devices available. rcnn_max_dets : int, default is 1000 Number of rois to retain in RCNN. rpn_test_pre_nms : int, default is 6000 Filter top proposals before NMS in testing of RPN. rpn_test_post_nms : int, default is 300 Return top proposal results after NMS in testing of RPN. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. Examples -------- >>> model = mask_rcnn_fpn_syncbn_mobilenet1_0_coco(pretrained=True) >>> print(model) """ from ....model_zoo.mobilenet import mobilenet1_0 from ....data import COCODetection classes = COCODetection.CLASSES pretrained_base = False if pretrained else pretrained_base rcnn_max_dets = rpn_test_post_nms if rcnn_max_dets > rpn_test_post_nms else rcnn_max_dets gluon_norm_kwargs = {'num_devices': num_devices} if num_devices >= 1 else {} sym_norm_kwargs = {'ndev': num_devices} if num_devices >= 1 else {} base_network = mobilenet1_0(pretrained=pretrained_base, norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs) features = FPNFeatureExpander( network=base_network, outputs=['relu6_fwd', 'relu10_fwd', 'relu22_fwd', 'relu26_fwd'], num_filters=[256, 256, 256, 256], use_1x1=True, use_upsample=True, use_elewadd=True, use_p6=True, no_bias=False, pretrained=pretrained_base, norm_layer=mx.sym.contrib.SyncBatchNorm, norm_kwargs=sym_norm_kwargs) top_features = None box_features = nn.HybridSequential() box_features.add(nn.AvgPool2D(pool_size=(3, 3), strides=2, padding=1)) # reduce to 7x7 box_features.add(nn.Conv2D(256, 3, padding=1), SyncBatchNorm(**gluon_norm_kwargs), nn.Activation('relu'), nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)), nn.Activation('relu')) train_patterns = '(?!.*moving)' return get_mask_rcnn( name='fpn_syncbn_mobilenet1_0', dataset='coco', pretrained=pretrained, features=features, top_features=top_features, classes=classes, box_features=box_features, mask_channels=256, rcnn_max_dets=rcnn_max_dets, short=(640, 800), max_size=1333, min_stage=2, max_stage=6, train_patterns=train_patterns, nms_thresh=0.5, nms_topk=-1, post_nms=-1, roi_mode='align', roi_size=(14, 14), strides=(4, 8, 16, 32, 64), clip=4.42, rpn_channel=1024, base_size=16, scales=(2, 4, 8, 16, 32), ratios=(0.5, 1, 2), alloc_size=(384, 384), rpn_nms_thresh=0.7, rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=rpn_test_pre_nms, rpn_test_post_nms=rpn_test_post_nms, rpn_min_size=1, num_sample=512, pos_iou_thresh=0.5, pos_ratio=0.25, target_roi_scale=2, num_fcn_convs=2, norm_layer=SyncBatchNorm, norm_kwargs=gluon_norm_kwargs, **kwargs)