def __init__(self, main_classes, sub_classes, class_agnostic): super(_hierarchyFasterRCNN, self).__init__() #self.classes = classes self.main_classes = main_classes self.sub_classes = sub_classes self.n_sub_classes = len(sub_classes) self.n_main_classes = len(main_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) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_sub_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.RFCN_psroi_pool = None 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, classes, class_agnostic): super(_fasterRCNN_OCR, 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) 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.RCNN_ocr_roi_pooling = roi_pooling(2) # ocr roi_pooling self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE self.RCNN_roi_crop = _RoICrop() # rnn初始化,隐藏节点256 nh = 256 nclass = len('0123456789.') + 1 self.rnn = nn.Sequential(BidirectionalLSTM(1024, nh, nh), BidirectionalLSTM(nh, nh, nclass)) self.ctc_critition = CTCLoss().cuda()
def __init__(self, classes, class_agnostic): super(_fasterRCNN, self).__init__() # 继承父类的__init__()方法 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 # 实例化,RPN网络(self.dout_base_model)是512,vgg16子类中定义,输入rpn的维度 self.RCNN_rpn = _RPN(self.dout_base_model) 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) # grid_size = 7 * 2 = 14 self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE self.RCNN_roi_crop = _RoICrop() # dout_base_model = 512 self.RCNN_imageDA = _ImageDA(self.dout_base_model) self.RCNN_instanceDA = _InstanceDA() self.consistency_loss = torch.nn.MSELoss(size_average=False)
def __init__(self, classes, class_agnostic): super(CoupleNet, 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.box_num_classes = 1 if class_agnostic else self.n_classes # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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.RCNN_roi_crop = _RoICrop() self.RCNN_psroi_pool_cls = PSRoIPool(cfg.POOLING_SIZE, cfg.POOLING_SIZE, spatial_scale=1/16.0, group_size=cfg.POOLING_SIZE, output_dim=self.n_classes) self.RCNN_psroi_pool_loc = PSRoIPool(cfg.POOLING_SIZE, cfg.POOLING_SIZE, spatial_scale=1/16.0, group_size=cfg.POOLING_SIZE, output_dim=self.box_num_classes * 4) self.avg_pooling = nn.AvgPool2d(kernel_size=cfg.POOLING_SIZE, stride=cfg.POOLING_SIZE) self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
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) self.RCNN_rpn_t = _RPN(self.dout_base_model) 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() self.RCNN_imageDA_3 = _ImageDA(256) self.RCNN_imageDA_4 = _ImageDA(512) self.RCNN_imageDA = _ImageDA(self.dout_base_model) self.RCNN_instanceDA = _InstanceDA()
def __init__(self, classes, class_agnostic, rpn_batchsize): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.rpn_batchsize = rpn_batchsize # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model, self.rpn_batchsize) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) print 'INFO: pooling size is: ', cfg.POOLING_SIZE_H, cfg.POOLING_SIZE_W self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE_H, cfg.POOLING_SIZE_W, 1.0 / 16.0) self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE_H, cfg.POOLING_SIZE_W, 1.0 / 16.0) ## wrote by Xudong Wang self.grid_size_H = cfg.POOLING_SIZE_H * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE_H self.grid_size_W = cfg.POOLING_SIZE_W * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE_W ## end 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, classes, class_agnostic, alpha_con=None): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic # crowds alpha_con self.alpha_con = alpha_con # label source self.label_source = cfg.LABEL_SOURCE # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model, self.classes, self.n_classes) 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.RCNN_aggregation_layer = _RCNNAggregationLayer() 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, classes, class_agnostic): super(_fasterRCNN2Fc, 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) 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.RFCN_psroi_pool = None self.grid_size = cfg.POOLING_SIZE * \ 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE self.RCNN_roi_crop = _RoICrop() # attention # self.conv_new_1 = nn.Conv2d(2048, 256, 1) # self.fc_new_1 = nn.dense(name='fc1_new_1', num_hidden=1024) # self.fc_new_2 = nn.dense(name='fc2_new_2', num_hidden=1024) self.fc1 = nn.Linear(2048, 1024) self.fc2 = nn.Linear(1024, 1024) self.nongt_dim = 256 if self.training else cfg.TEST.RPN_POST_NMS_TOP_N
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) 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.RCNN_roi_align_32 = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 32.0) self.RCNN_roi_align_16 = RoIAlignAvg(14, 14, 1.0 / 16.0) self.RCNN_roi_align_8 = RoIAlignAvg(28, 28, 1.0 / 8.0) self.RCNN_roi_align_4 = RoIAlignAvg(56, 56, 1.0 / 4.0) self.RCNN_roi_align_2 = RoIAlignAvg(112, 112, 1.0 / 2.0) self.RCNN_roi_align_1 = RoIAlignAvg(224, 224, 1.0 / 1.0) 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, classes, class_agnostic, meta_train, meta_test=None, meta_loss=None): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.meta_train = meta_train self.meta_test = meta_test self.meta_loss = meta_loss # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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, classes, num_ways, class_agnostic, meta_train, meta_test=None, meta_loss=None, transductive=None, visualization=None): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.meta_train = meta_train self.meta_test = meta_test self.meta_loss = meta_loss self.simloss = True self.dis_simloss = True self.transductive = transductive self.visualization = visualization # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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() self.num_layers_g = 3 self.num_ways = num_ways self.alpha = 0.5
def __init__(self, classes, class_agnostic): super(CoupleNet, 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.box_num_classes = 1 if class_agnostic else self.n_classes # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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.RCNN_roi_crop = _RoICrop() self.RCNN_psroi_pool_cls = PSRoIPool(cfg.POOLING_SIZE, cfg.POOLING_SIZE, spatial_scale=1 / 16.0, group_size=cfg.POOLING_SIZE, output_dim=self.n_classes) self.RCNN_psroi_pool_loc = PSRoIPool(cfg.POOLING_SIZE, cfg.POOLING_SIZE, spatial_scale=1 / 16.0, group_size=cfg.POOLING_SIZE, output_dim=self.box_num_classes * 4) self.avg_pooling = nn.AvgPool2d(kernel_size=cfg.POOLING_SIZE, stride=cfg.POOLING_SIZE) self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE
def __init__(self, classes, class_agnostic, context, S_agent, T_agent, ts, tt, select_num, candidate_num): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.select_num = select_num self.candidate_num = candidate_num print("self.select_num: %d self.candidate_num: %d " % (self.select_num, self.candidate_num)) # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 self.context = context # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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.epsilon_by_epoch = lambda epoch_idx: cfg.epsilon_final + (cfg.epsilon_start - \ cfg.epsilon_final) * math.exp(-1. * epoch_idx / cfg.epsilon_decay) self.iter_dqn = 0 self.epsilon_by_epoch_T = lambda epoch_idx: cfg.epsilon_final + (cfg.epsilon_start - \ cfg.epsilon_final) * math.exp(-1. * epoch_idx / cfg.epsilon_decay) self.iter_dqn_T = 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, 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) 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.RCNN_deform_roi_pool_1 = DeformRoIFunction(pool_height=7, pool_width=7, spatial_scale=1.0 / 16.0, no_trans=True, trans_std=0.1, sample_per_part=4, output_dim=256, group_size=1, part_size=7) self.RCNN_deform_roi_pool_2 = DeformRoIFunction(pool_height=7, pool_width=7, spatial_scale=1.0 / 16.0, no_trans=False, trans_std=0.1, sample_per_part=4, output_dim=256, group_size=1, part_size=7) 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, classes, class_agnostic, lighthead=False, compact_mode=False): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.lighthead = lighthead # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define Large Separable Convolution Layer if self.lighthead: self.lh_mode = 'S' if compact_mode else 'L' self.lsconv = LargeSeparableConv2d( self.dout_lh_base_model, bias=False, bn=False, setting=self.lh_mode) self.lh_relu = nn.ReLU(inplace=True) # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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() self.rpn_time = None self.pre_roi_time = None self.roi_pooling_time = None self.subnet_time = None
def __init__(self, classes, class_agnostic): super(_Deconv, 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.maxpool2d = nn.MaxPool2d(1, stride=2) #define rpn # if USE_ONE_FEATURE == 0: self.RCNN_rpn = _RPN_Deconv(self.dout_base_model) # else: # self.RCNN_rpn = _RPN(self.dout_base_model) 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, classes, class_agnostic): super(_FPN, 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.maxpool2d = nn.MaxPool2d(1, stride=2) # define rpn self.RCNN_rpn = _RPN_FPN(self.dout_base_model) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) # NOTE: the original paper used pool_size = 7 for cls branch, and 14 for mask branch, to save the # computation time, we first use 14 as the pool_size, and then do stride=2 pooling for cls branch. 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.RCNN_roi_prroi = PrRoIPool2D( cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0) ######## add precision roi pooling ##### 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, classes, class_agnostic): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic # define rpn 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() self.spaCNN = SpaConv() self.spa_cls_score = nn.Sequential(nn.Linear(5408, 1024), nn.LeakyReLU(), nn.Dropout(p=0.5), nn.Linear(1024, self.n_classes)) self.obj_cls_score = nn.Sequential(nn.Linear(300, 512), nn.LeakyReLU(), nn.Linear(512, self.n_classes)) self.obj_attention = nn.Sequential(nn.Linear(300, 512), nn.LeakyReLU(), nn.Linear(512, 6))
def __init__(self, classes, class_agnostic, transfer): 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) 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() #transfer setting self.transfer = transfer if self.transfer: self.transfer_weight = Variable(torch.Tensor([cfg.TRANSFER_WEIGHT ]).cuda(), requires_grad=True) self.grl = cfg.TRANSFER_GRL self.weight = Variable(torch.zeros(0).cuda(), requires_grad=False) self.transfer_select = cfg.TRANSFER_SELECT self.transfer_gamma = cfg.TRANSFER_GAMMA
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) #for p in self.RCNN_rpn.parameters(): p.requires_grad = False self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0) # roi pooling in vgg16 # stride = 16 self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0) # roi pooling in Generator Network # stride = 2 self.RCNN_roi_pool_conv1 = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 2.0) 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, classes, class_agnostic, num_class=20): super(_FPN, self).__init__() self.num_classes = num_class 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.maxpool2d = nn.MaxPool2d(1, stride=2) # define rpn self.RCNN_rpn = _RPN_FPN(self.dout_base_model) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) # NOTE: the original paper used pool_size = 7 for cls branch, and 14 for mask branch, to save the # computation time, we first use 14 as the pool_size, and then do stride=2 pooling for cls branch. 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() self.top = nn.Sequential( nn.Linear(in_features=12544, out_features=4096, bias=True), nn.ReLU(inplace=True), nn.Dropout(0.5), nn.Linear(in_features=4096, out_features=4096, bias=True), nn.ReLU(inplace=True), nn.Dropout(0.5)) self.fc8c = nn.Linear(4096, self.num_classes) self.fc8d = nn.Linear(4096, self.num_classes)
def __init__(self, classes, class_agnostic, shrink=1, mimic=False, rois=None): super(_fasterRCNN, self).__init__() self.shrink = shrink self.student = True if shrink >= 2 else False self.mimic = mimic 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) 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_modules(self, load_model=True): resnet = resnet101() self.RCNN_base = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1, resnet.layer2, resnet.layer3) self.RCNN_top = nn.Sequential(resnet.layer4) self.Linear_top = nn.Linear(2048, self.embedding_dim) if load_model: state_dict = torch.load(self.oneshot_model_path)['model'] self.load_state_dict({ k: v for k, v in state_dict.items() if k in self.state_dict() }) self.det_module = pseudo_siamese_det(self.det_model_path) self.det_module.create_architecture(load_det_model=load_model) self.det_module.training = False self.RCNN_roi_crop = _RoICrop() self.RCNN_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0) self.RCNN_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 16.0)
def __init__(self, main_classes, sub_classes, class_agnostic, casecade_type='add_score', alpha=0.5): super(_hierarchyAttentionFasterRCNN, self).__init__() #self.classes = classes # type: add_score, add_prob, mul_score, mul_prob self.casecade_type = casecade_type self.alpha = alpha self.main_classes = main_classes self.sub_classes = sub_classes self.n_sub_classes = len(sub_classes) self.n_main_classes = len(main_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) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_sub_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.RFCN_psroi_pool = None self.grid_size = cfg.POOLING_SIZE * \ 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE self.RCNN_roi_crop = _RoICrop() self.main2sub_idx_dict = defaultdict(list) for key, val in sub2main_dict.items(): try: # not all cls in dict are in this imdb self.main2sub_idx_dict[self.main_classes.index( val)].append(self.sub_classes.index(key)) except: print("key:{}, val:{} may not in this imdb".format(key, val)) # attention self.fc1 = nn.Linear(2048, 1024) self.fc2 = nn.Linear(1024, 1024) self.nongt_dim = 300 if self.training else cfg.TEST.RPN_POST_NMS_TOP_N self.attention_1 = attention_module_multi_head(nongt_dim=self.nongt_dim, fc_dim=16, feat_dim=1024, index=1, group=16, dim=(1024, 1024, 1024)) self.attention_2 = attention_module_multi_head(nongt_dim=self.nongt_dim, fc_dim=16, feat_dim=1024, index=2, group=16, dim=(1024, 1024, 1024))
def __init__(self, classes, class_agnostic): super(_fasterRCNN, self).__init__() self.n_classes = 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) 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.RCNN_roi_crop = _RoICrop()
def __init__(self, classes, class_agnostic, tb=None): super(_OICR, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.param_groups = [[], [], [], []] self.OICR_roi_pool = _RoIPooling(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 8.0) self.OICR_roi_align = RoIAlignAvg(cfg.POOLING_SIZE, cfg.POOLING_SIZE, 1.0 / 8.0) self.grid_size = cfg.POOLING_SIZE * 2 if cfg.CROP_RESIZE_WITH_MAX_POOL else cfg.POOLING_SIZE self.OICR_roi_crop = _RoICrop() self.ic_layers = [] self.tb = tb
def __init__(self, det_model_path): super(pseudo_siamese_det, self).__init__() self.det_model_path = det_model_path self.dout_base_model = 1024 # self.resnet101_path = '/home/huangyucheng/MYDATA/git-cores/FasterRCNN/data/pretrained_model/resnet101_caffe.pth' # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) # 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, classes): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model) 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_grid_gen = _AffineGridGen(self.grid_size, self.grid_size) self.RCNN_roi_crop = _RoICrop()
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) 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, classes, class_agnostic): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic # define rpn # self.RCNN_rpn = _RPN(self.dout_base_model) # 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() self.spa_cls_CNN = SpaConv() self.spa_bin_CNN = SpaConv()
def __init__(self, classes, class_agnostic, K=-1): super(_fasterRCNN, self).__init__() self.classes = classes self.n_classes = len(classes) self.class_agnostic = class_agnostic self.K = K # if K > 1, transform FasterRCNN to take a stack of K images as input # loss self.RCNN_loss_cls = 0 self.RCNN_loss_bbox = 0 # define rpn self.RCNN_rpn = _RPN(self.dout_base_model, K=self.K) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes, K=K) 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, classes, class_agnostic): super(_fasterRCNN, self).__init__() #继承Module的舒适化 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) 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 #def larger(num1, num2): return num1 if num1 > num2 else num2, this variable may be declare in other place self.RCNN_roi_crop = _RoICrop()
def __init__(self, classes, class_agnostic): super(_FPN, 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_FPN(self.dout_base_model) self.RCNN_proposal_target = _ProposalTargetLayer(self.n_classes) # NOTE: the original paper used pool_size = 7 for cls branch, and 14 for mask branch, to save the # computation time, we first use 14 as the pool_size, and then do stride=2 pooling for cls branch. 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()