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.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]
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