def __init__(self, config):
super(Faster_Rcnn, self).__init__()
self.config = config
self.Mean = torch.tensor(config.Mean, dtype=torch.float32)
self.num_anchor = len(config.anchor_scales) * len(config.anchor_ratios)
self.anchors = []
self.num_anchor = []
for i in range(5):
self.num_anchor.append(len(config.anchor_scales[i]) * len(config.anchor_ratios[i]))
stride = 4 * 2 ** i
print(stride, self.config.anchor_scales[i], self.config.anchor_ratios[i])
anchors = get_anchors(np.ceil(self.config.img_max / stride + 1), self.config.anchor_scales[i],
self.config.anchor_ratios[i], stride=stride)
print(anchors.shape)
self.anchors.append(anchors)
self.PC = ProposalCreator(nms_thresh=config.roi_nms_thresh,
n_train_pre_nms=config.roi_train_pre_nms,
n_train_post_nms=config.roi_train_post_nms,
n_test_pre_nms=config.roi_test_pre_nms, n_test_post_nms=config.roi_test_post_nms,
min_size=config.roi_min_size)
self.features = resnet50()
self.fpn = FPN_net([256, 512, 1024, 2048], 256, extra_blocks=LastLevelMaxPool())
self.rpn = RPN_net(256, self.num_anchor[0])
self.roialign = MultiScaleRoIAlign(['feat0', 'feat1', 'feat2', 'feat3'], 7, 2)
self.fast = Fast_net(config.num_cls, 256 * 7 * 7, 1024)
self.a = 0
self.b = 0
self.c = 0
self.d = 0
self.fast_num = 0
self.fast_num_P = 0
def __init__(self, config):
super(Mask_Rcnn, self).__init__()
self.config = config
self.Mean = torch.tensor(config.Mean, dtype=torch.float32)
self.num_anchor = len(config.anchor_scales) * len(config.anchor_ratios)
self.anchors = []
self.num_anchor = []
for i in range(5):
self.num_anchor.append(
len(config.anchor_scales[i]) * len(config.anchor_ratios[i]))
stride = 4 * 2**i
print(stride, self.config.anchor_scales[i],
self.config.anchor_ratios[i])
anchors = get_anchors(np.ceil(self.config.img_max / stride + 1),
self.config.anchor_scales[i],
self.config.anchor_ratios[i],
stride=stride)
print(anchors.shape)
self.anchors.append(anchors)
self.ATC = AnchorTargetCreator(
n_sample=config.rpn_n_sample,
pos_iou_thresh=config.rpn_pos_iou_thresh,
neg_iou_thresh=config.rpn_neg_iou_thresh,
pos_ratio=config.rpn_pos_ratio)
self.PC = ProposalCreator(nms_thresh=config.roi_nms_thresh,
n_train_pre_nms=config.roi_train_pre_nms,
n_train_post_nms=config.roi_train_post_nms,
n_test_pre_nms=config.roi_test_pre_nms,
n_test_post_nms=config.roi_test_post_nms,
min_size=config.roi_min_size)
self.PTC = ProposalTargetCreator(
n_sample=config.fast_n_sample,
pos_ratio=config.fast_pos_ratio,
pos_iou_thresh=config.fast_pos_iou_thresh,
neg_iou_thresh_hi=config.fast_neg_iou_thresh_hi,
neg_iou_thresh_lo=config.fast_neg_iou_thresh_lo)
self.features = resnet101()
self.fpn = FPN_net([256, 512, 1024, 2048],
256,
extra_blocks=LastLevelMaxPool())
self.rpn = RPN_net(256, self.num_anchor[0])
self.roialign_7 = MultiScaleRoIAlign(
['feat0', 'feat1', 'feat2', 'feat3'], 7, 2)
self.roialign_14 = MultiScaleRoIAlign(
['feat0', 'feat1', 'feat2', 'feat3'], 14, 2)
self.roialign_28 = RoIAlign((28, 28), 1.0, 2)
self.fast = Fast_net(config.num_cls, 256 * 7 * 7, 1024)
self.mask_net = Mask_net(256, config.num_cls)
self.a = 0
self.b = 0
self.c = 0
self.d = 0
self.fast_num = 0
self.fast_num_P = 0
def __init__(self, config):
super(Mask_Rcnn, self).__init__()
self.config = config
self.Mean = torch.tensor(config.Mean, dtype=torch.float32)
self.num_anchor = len(config.anchor_scales) * len(config.anchor_ratios)
self.anchors = []
self.num_anchor = []
for i in range(5):
self.num_anchor.append(
len(config.anchor_scales[i]) * len(config.anchor_ratios[i]))
stride = 4 * 2**i
print(stride, self.config.anchor_scales[i],
self.config.anchor_ratios[i])
anchors = get_anchors(np.ceil(self.config.img_max / stride + 1),
self.config.anchor_scales[i],
self.config.anchor_ratios[i],
stride=stride)
print(anchors.shape)
self.anchors.append(anchors)
self.PC = ProposalCreator(nms_thresh=config.roi_nms_thresh,
n_train_pre_nms=config.roi_train_pre_nms,
n_train_post_nms=config.roi_train_post_nms,
n_test_pre_nms=config.roi_test_pre_nms,
n_test_post_nms=config.roi_test_post_nms,
min_size=config.roi_min_size)
self.features = resnet50()
self.fpn = FPN_net([256, 512, 1024, 2048],
256,
extra_blocks=LastLevelMaxPool())
self.rpn = RPN_net(256, self.num_anchor[0])
self.roialign_7 = MultiScaleRoIAlign(
['feat0', 'feat1', 'feat2', 'feat3'], 7, 2)
self.roialign_14 = MultiScaleRoIAlign(
['feat0', 'feat1', 'feat2', 'feat3'], 14, 2)
# self.roialign_28 = RoIAlign((28, 28), 1.0, 2)
self.fast = Fast_net(config.num_cls, 256 * 7 * 7, 1024)
self.fast_2 = Fast_net(config.num_cls, 256 * 7 * 7, 1024)
self.fast_3 = Fast_net(config.num_cls, 256 * 7 * 7, 1024)
self.mask_net = Mask_net(256, config.num_cls)
self.loc_std1 = [1. / 10, 1. / 10, 1. / 5, 1. / 5]
self.loc_std2 = [1. / 20, 1. / 20, 1. / 10, 1. / 10]
self.loc_std3 = [1. / 30, 1. / 30, 1. / 15, 1. / 15]
self.weights = [1.0, 1.0, 1.0]