def __init__(self, num_classes, pretrained=None):
super(MaskRCNN, self).__init__()
if pretrained is not None:
assert pretrained in ['imagenet', 'voc2007', 'coco']
assert pretrained not in ['voc2007', 'coco'], ("VOC2007 and COCO pretrained weights "
"will be added soon.")
self.config = ConfigParser()
self.config.read(os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.ini"))
self.num_classes = num_classes
self.pooling_size_clsbbox = (7, 7)
self.pooling_size_mask = (14, 14)
self.use_fpn = False # FPN backbone feature is debugging right now.
self.train_rpn_only = False
if self.use_fpn:
self.backbone_fpn = ResNet_101_FPN(pretrained=pretrained)
self.depth = 256
else:
self.backbone = ResNet_101(pretrained=pretrained)
self.depth = 1024
self.rpn = RPN(dim=self.depth, use_fpn=self.use_fpn)
if not self.train_rpn_only:
# RoiAlign for cls and bbox head, pooling size 7x7
self.roi_align_clsbbox = RoiAlign(grid_size=self.pooling_size_clsbbox)
# RoiAlign for mask head, pooling size 14x14
self.roi_align_mask = RoiAlign(grid_size=self.pooling_size_mask)
self.clsbbox_head = ClsBBoxHead(depth=self.depth, pool_size=self.pooling_size_clsbbox,
num_classes=num_classes)
self.mask_head = MaskHead(depth=self.depth, pool_size=self.pooling_size_mask,
num_classes=num_classes)
self.img_height = None
self.img_width = None
self.batch_size = None
def __init__(self, num_classes, pretrained=None):
"""
Args:
num_classes(int): number of classes, background should be counted in.
e.g: there are 100 foreground objects, num_classes should be 101.
pretrained(str): 'imagenet' or 'coco', set 'imagenet' indicate just
backbone use imagenet pretrained weights, 'coco' indicate whole
Mask R-CNN model use pretrained weights on COCO dataset.
"""
super(MaskRCNN, self).__init__()
if pretrained is not None:
assert pretrained in ['imagenet', 'coco']
assert pretrained not in [
'coco'
], "COCO pretrained weights is not available yet."
self.config = ConfigParser()
self.config.read(
os.path.abspath(os.path.join(__file__, "../", "config.ini")))
self.num_classes = num_classes
self.pooling_size_clsbbox = (7, 7)
self.pooling_size_mask = (14, 14)
self.validating = False # when True output loss and predict results.
self.fpn_on = bool(int(self.config['BACKBONE']['FPN_ON']))
self.mask_head_on = bool(int(self.config['HEAD']['MASK_HEAD_ON']))
self.train_rpn_only = bool(int(self.config['TRAIN']['TRAIN_RPN_ONLY']))
resnet_layer = int(self.config['BACKBONE']['RESNET_LAYER'])
if self.fpn_on:
self.backbone_fpn = ResNetFPN(resnet_layer, pretrained=pretrained)
self.depth = 256
else:
self.backbone = ResNet(resnet_layer, pretrained=pretrained)
self.depth = 1024
self.rpn = RPN(self.depth, self.fpn_on)
if not self.train_rpn_only:
# RoiAlign for cls and bbox head, pooling size 7x7
self.roi_align_clsbbox = RoiAlign(
grid_size=self.pooling_size_clsbbox)
# RoiAlign for mask head, pooling size 14x14
self.roi_align_mask = RoiAlign(grid_size=self.pooling_size_mask)
self.clsbbox_head = ClsBBoxHead(
depth=self.depth,
pool_size=self.pooling_size_clsbbox,
num_classes=num_classes)
if self.mask_head_on:
self.mask_head = MaskHead(depth=self.depth,
pool_size=self.pooling_size_mask,
num_classes=num_classes)
self.img_height = None
self.img_width = None
self.batch_size = None
def __init__(self, num_classes):
super(MaskRCNN, self).__init__()
self.num_classes = num_classes
self.fpn = ResNet_101_FPN()
self.rpn = RPN(dim=512)
self.roi_align = RoiAlign(grid_size=(14, 14))
self.cls_box_head = ClsBBoxHead(depth=512,
pool_size=14,
num_classes=num_classes)
self.mask_head = MaskHead(depth=512,
pool_size=14,
num_classes=num_classes)
def __init__(self, num_classes, img_size):
super(MaskRCNN, self).__init__()
self.config = ConfigParser()
self.config.read(
os.path.join(os.path.dirname(os.path.realpath(__file__)),
"config.ini"))
self.num_classes = num_classes
self.fpn = ResNet_101_FPN()
self.rpn = RPN(dim=256)
self.roi_align = RoiAlign(grid_size=(14, 14))
self.cls_box_head = ClsBBoxHead(depth=256,
pool_size=14,
num_classes=num_classes)
self.mask_head = MaskHead(depth=256,
pool_size=14,
num_classes=num_classes,
img_size=img_size)
def __init__(self, num_classes, pretrained=None):
super(MaskRCNN, self).__init__()
if pretrained is not None:
assert pretrained in ['imagenet', 'voc2007', 'coco']
assert pretrained not in ['voc2007', 'coco'], ("VOC2007 and COCO pretrained weights "
"will be added soon.")
self.config = ConfigParser()
self.config.read(os.path.abspath(os.path.join(__file__, "../", "config.ini")))
self.num_classes = num_classes
self.pooling_size_clsbbox = (7, 7)
self.pooling_size_mask = (14, 14)
use_fpn = bool(int(self.config['TRAIN']['USE_FPN']))
assert not use_fpn, "FPN backbone is under debugging right now."
self.use_fpn = use_fpn
self.train_rpn_only = bool(int(self.config['TRAIN']['TRAIN_RPN_ONLY']))
if self.use_fpn:
self.backbone_fpn = ResNet_101_FPN(pretrained=pretrained)
self.depth = 256
else:
self.backbone = ResNet_101(pretrained=pretrained)
self.depth = 1024
self.rpn = RPN(self.depth, self.use_fpn)
if not self.train_rpn_only:
# RoiAlign for cls and bbox head, pooling size 7x7
self.roi_align_clsbbox = RoiAlign(grid_size=self.pooling_size_clsbbox)
# RoiAlign for mask head, pooling size 14x14
self.roi_align_mask = RoiAlign(grid_size=self.pooling_size_mask)
self.clsbbox_head = ClsBBoxHead(depth=self.depth, pool_size=self.pooling_size_clsbbox,
num_classes=num_classes)
self.mask_head = MaskHead(depth=self.depth, pool_size=self.pooling_size_mask,
num_classes=num_classes)
self.img_height = None
self.img_width = None
self.batch_size = None
def __init__(self, num_classes, pretrained=None):
"""
Args:
num_classes(int): number of classes, background should be counted in.
e.g: there are 100 foreground objects, num_classes should be 101.
pretrained(str): 'imagenet' or 'coco', set 'imagenet' indicate just
backbone use imagenet pretrained weights, 'coco' indicate whole
Mask R-CNN model use pretrained weights on COCO dataset.
"""
super(MaskRCNN, self).__init__()
if pretrained is not None:
assert pretrained in ['imagenet', 'coco']
assert pretrained not in [
'coco'
], "COCO pretrained weights is not available yet."
self.config = ConfigParser()
config_path = os.path.abspath(
os.path.join(__file__, "../", "config.ini"))
assert os.path.exists(config_path), "config.ini not exists!"
self.config.read(config_path)
self.num_classes = num_classes
self.validating = False # when validating output loss and predict results.
self.fpn_on = bool(int(self.config['BACKBONE']['FPN_ON']))
self.mask_head_on = bool(int(self.config['HEAD']['MASK_HEAD_ON']))
self.train_rpn_only = bool(int(self.config['TRAIN']['TRAIN_RPN_ONLY']))
resnet_layer = int(self.config['BACKBONE']['RESNET_LAYER'])
self.pooling_size_clsbbox = int(
self.config['POOLING']['POOLING_SIZE_CLSBBOX'])
self.pooling_size_mask = int(
self.config['POOLING']['POOLING_SIZE_MASK'])
if self.fpn_on:
self.backbone_fpn = ResNetFPN(resnet_layer, pretrained=pretrained)
self.depth_for_rpn = 256
self.depth_for_head = 2048
else:
backbone_type = self.config['BACKBONE']['BACKBONE_TYPE']
assert backbone_type in ['resnet', 'vgg16']
if backbone_type == 'resnet':
self.backbone = ResNet(resnet_layer, pretrained=pretrained)
self.depth_rpn = 1024
self.depth_head = 2048
elif backbone_type == 'vgg16':
self.backbone = VGG16(pretrained=pretrained)
self.depth_rpn = 512
self.depth_head = 4096
self.rpn = RPN(self.depth_rpn, self.fpn_on)
self.head_common = HeadCommon(pretrained=pretrained)
if not self.train_rpn_only:
# RoiAlign for cls and bbox head
self.roi_align_clsbbox = RoiAlign(
grid_size=(self.pooling_size_clsbbox,
self.pooling_size_clsbbox))
self.clsbbox_head = ClsBBoxHead(depth=self.depth_head,
num_classes=num_classes)
if self.mask_head_on:
# RoiAlign for mask head
self.roi_align_mask = RoiAlign(
grid_size=(self.pooling_size_mask, self.pooling_size_mask))
self.mask_head = MaskHead(depth=self.depth_head,
pool_size=(self.pooling_size_mask,
self.pooling_size_mask),
num_classes=num_classes)
self.img_height = None
self.img_width = None
self.batch_size = None