def __init__(self, classes, class_agnostic):
super(_fasterRCNN, self).__init__()
self.classes = classes
self.n_classes = len(classes)
self.class_agnostic = class_agnostic
# loss
self.RCNN_loss_cls = 0
self.RCNN_loss_bbox = 0
# define rpn
self.RCNN_rpn = _RPN(self.dout_base_model,
anchor_scales=cfg.RCNN_COMMON.ANCHOR_SCALES,
anchor_ratios=cfg.RCNN_COMMON.ANCHOR_RATIOS,
feat_stride=cfg.RCNN_COMMON.FEAT_STRIDE[0])
self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
self.RCNN_roi_pool = _RoIPooling(cfg.RCNN_COMMON.POOLING_SIZE,
cfg.RCNN_COMMON.POOLING_SIZE,
1.0 / 16.0)
self.RCNN_roi_align = RoIAlignAvg(cfg.RCNN_COMMON.POOLING_SIZE,
cfg.RCNN_COMMON.POOLING_SIZE,
1.0 / 16.0)
self.grid_size = cfg.RCNN_COMMON.POOLING_SIZE * 2 if cfg.RCNN_COMMON.CROP_RESIZE_WITH_MAX_POOL else cfg.RCNN_COMMON.POOLING_SIZE
self.RCNN_roi_crop = _RoICrop()
def __init__(self, classes, class_agnostic, feat_name, feat_list=('conv4',), pretrained = True):
super(fasterRCNN, self).__init__(classes, class_agnostic, feat_name, feat_list, pretrained)
##### Important to set model to eval mode before evaluation ####
self.FeatExt.eval()
rand_img = torch.Tensor(1, 3, 224, 224)
rand_feat = self.FeatExt(rand_img)
self.FeatExt.train()
self.dout_base_model = rand_feat.size(1)
self.RCNN_loss_cls = 0
self.RCNN_loss_bbox = 0
# define rpn
self.RCNN_rpn = _RPN(self.dout_base_model,
anchor_scales=cfg.RCNN_COMMON.ANCHOR_SCALES,
anchor_ratios=cfg.RCNN_COMMON.ANCHOR_RATIOS,
feat_stride=cfg.RCNN_COMMON.FEAT_STRIDE[0])
self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
self.RCNN_roi_pool = _RoIPooling(cfg.RCNN_COMMON.POOLING_SIZE, cfg.RCNN_COMMON.POOLING_SIZE, 1.0/16.0)
self.RCNN_roi_align = RoIAlignAvg(cfg.RCNN_COMMON.POOLING_SIZE, cfg.RCNN_COMMON.POOLING_SIZE, 1.0/16.0)
self.grid_size = cfg.RCNN_COMMON.POOLING_SIZE * 2 if cfg.RCNN_COMMON.CROP_RESIZE_WITH_MAX_POOL else cfg.RCNN_COMMON.POOLING_SIZE
self.RCNN_roi_crop = _RoICrop()
self.iter_counter = 0
def __init__(self, classes, class_agnostic):
super(_All_in_One, self).__init__()
self.classes = classes
self.n_classes = len(classes)
self.class_agnostic = class_agnostic
self._fs = cfg.FCGN.FEAT_STRIDE[0]
# for resnet
if self.dout_base_model is None:
if self._fs == 16:
self.dout_base_model = 256 * self.expansions
elif self._fs == 32:
self.dout_base_model = 512 * self.expansions
# loss
self.VMRN_obj_loss_cls = 0
self.VMRN_obj_loss_bbox = 0
# define rpn
self.VMRN_obj_rpn = _RPN(self.dout_base_model,
anchor_scales=cfg.RCNN_COMMON.ANCHOR_SCALES,
anchor_ratios=cfg.RCNN_COMMON.ANCHOR_RATIOS,
feat_stride=self._fs)
self.VMRN_obj_proposal_target = _ProposalTargetLayer(self.n_classes)
self.VMRN_obj_roi_pool = _RoIPooling(cfg.RCNN_COMMON.POOLING_SIZE, cfg.RCNN_COMMON.POOLING_SIZE, 1.0 / 16.0)
self.VMRN_obj_roi_align = RoIAlignAvg(cfg.RCNN_COMMON.POOLING_SIZE, cfg.RCNN_COMMON.POOLING_SIZE, 1.0 / 16.0)
self.grid_size = cfg.RCNN_COMMON.POOLING_SIZE * 2 if cfg.RCNN_COMMON.CROP_RESIZE_WITH_MAX_POOL else cfg.RCNN_COMMON.POOLING_SIZE
self.VMRN_obj_roi_crop = _RoICrop()
self._isex = cfg.TRAIN.VMRN.ISEX
self.VMRN_rel_op2l = _OP2L(cfg.VMRN.OP2L_POOLING_SIZE, cfg.VMRN.OP2L_POOLING_SIZE, 1.0 / 16.0, self._isex)
self._train_iter_conter = 0
self._MGN_as = cfg.FCGN.ANCHOR_SCALES
self._MGN_ar = cfg.FCGN.ANCHOR_RATIOS
self._MGN_aa = cfg.FCGN.ANCHOR_ANGLES
# grasp detection components
self.MGN_classifier = _Classifier(self.dout_base_model, 5, self._MGN_as,
self._MGN_ar, self._MGN_aa)
self.MGN_proposal_target = _GraspTargetLayer(self._fs, self._MGN_ar,
self._MGN_as, self._MGN_aa)
self._MGN_anchors = torch.from_numpy(generate_oriented_anchors(base_size=self._fs,
scales=np.array(self._MGN_as),
ratios=np.array(self._MGN_ar),
angles=np.array(self._MGN_aa))).float()
self._MGN_num_anchors = self._MGN_anchors.size(0)
# [x1, y1, x2, y2] -> [xc, yc, w, h]
self._MGN_anchors = torch.cat([
0 * self._MGN_anchors[:, 0:1],
0 * self._MGN_anchors[:, 1:2],
self._MGN_anchors[:, 2:3] - self._MGN_anchors[:, 0:1] + 1,
self._MGN_anchors[:, 3:4] - self._MGN_anchors[:, 1:2] + 1,
self._MGN_anchors[:, 4:5]
], dim=1)
self._MGN_USE_POOLED_FEATS = cfg.MGN.USE_POOLED_FEATS
def __init__(self, classes, class_agnostic):
super(_fastRCNN, self).__init__()
self.classes = classes
self.n_classes = len(classes)
self.class_agnostic = class_agnostic
# loss
self.RCNN_loss_cls = 0
self.RCNN_loss_bbox = 0
self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes)
self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE,
1.0 / 16.0)
self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE,
1.0 / 16.0)
self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
self.RCNN_roi_crop = _RoICrop()
def __init__(self, model_path = None):
super(LVRN, self).__init__()
self.up_sample = nn.UpsamplingBilinear2d(scale_factor = 2)
self.maxpooling = nn.MaxPool2d(2,2)
self.grid_size = 14
self.roi_crop = _RoICrop()
self.features = self.load_model(model_path)
self.fc_score = nn.Sequential(
nn.Linear(512 * 49, 1024),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(1024, 512),
nn.ReLU(True),
nn.Dropout(),
nn.Linear(512, 1)
)
self.param_init()
print(self)