def __init__(self, vgg16_image_net=True):
super(SSH, self).__init__()
vgg16_features = tv.models.vgg16(pretrained=vgg16_image_net).features
self.conv4_3 = nn.Sequential(*list(vgg16_features.children())[:23])
self.conv5_3 = nn.Sequential(*list(vgg16_features.children())[23:30])
self.m3_module = net.M3(512)
self.m2_module = net.M2(512)
self.m1_module = net.M1(128)
self.conv5_128 = net.Conv2D(512, 128, 1, False, 1, True)
self.conv5_128_up = nn.ConvTranspose2d(128, 128, 4, 2, 1, 1, 128,
False)
self.con4_128 = net.Conv2D(512, 128, 1, False, 1, True)
self.con4_fuse_final = net.Conv2D(128, 128, 3, True, 1, True)
self.pool6 = nn.MaxPool2d(2, 2)
self.m3_anchor_target_layer = _AnchorTargetLayer(32,
np.array([16, 32]),
np.array([
1,
]),
512,
name='m3')
self.m2_anchor_target_layer = _AnchorTargetLayer(16,
np.array([4, 8]),
np.array([
1,
]),
0,
name='m2')
self.m1_anchor_target_layer = _AnchorTargetLayer(8,
np.array([1, 2]),
np.array([
1,
]),
0,
name='m1')
self.m3_proposal_layer = _Proposallayer(32, np.array([16, 32]),
np.array([
1,
]))
self.m2_proposal_layer = _Proposallayer(16, np.array([4, 8]),
np.array([
1,
]))
self.m1_proposal_layer = _Proposallayer(8, np.array([1, 2]),
np.array([
1,
]))
self.m3_soft_max = nn.Softmax(1)
self.m2_soft_max = nn.Softmax(1)
self.m1_soft_max = nn.Softmax(1)
def __init__(self, din):
super(_Stereo_RPN, self).__init__()
self.din = din # get depth of input feature map, e.g., 512
self.anchor_ratios = cfg.ANCHOR_RATIOS
self.feat_stride = cfg.FEAT_STRIDE[0]
# define the convrelu layers processing input feature map
self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)
# define bg/fg classifcation score layer
self.nc_score_out = 1 * len(
self.anchor_ratios
) * 2 # 2(bg/fg) * 3 (anchor ratios) * 1 (anchor scale)
self.RPN_cls_score = nn.Conv2d(512 * 2, self.nc_score_out, 1, 1, 0)
# define anchor box offset prediction layer
self.nc_bbox_out = 1 * len(
self.anchor_ratios
) * 6 # 6(coords) * 3 (anchors) * 1 (anchor scale)
self.RPN_bbox_pred_left_right = nn.Conv2d(512 * 2, self.nc_bbox_out, 1,
1, 0)
# define proposal layer
self.RPN_proposal = _ProposalLayer(self.feat_stride,
self.anchor_ratios)
# define anchor target layer
self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride,
self.anchor_ratios)
self.rpn_loss_cls = 0
self.rpn_loss_box_left_right = 0
def __init__(self, din, use_gpu=False):
super(_RPN, self).__init__()
self.use_gpu = use_gpu
self.din = din # input channel num
self.anchor_scales = [8, 16, 32] # anchor_scales
self.anchor_ratios = [0.5, 1, 2] # anchor_ratios
self.feat_stride = [16] # feat_stride
# define the convrelu layers processing input feature map
# in_channels, out_channels, kernel_size=3, stride=1, padding=1
self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)
# 2(bg/fg) * 9(anchor)
self.nc_score_out = len(self.anchor_scales) * len(
self.anchor_ratios) * 2
self.RPN_cls_score = nn.Conv2d(512, self.nc_score_out, 1, 1, 0)
self.nc_bbox_out = len(self.anchor_scales) * len(
self.anchor_ratios) * 4
self.RPN_bbox_pred = nn.Conv2d(512, self.nc_bbox_out, 1, 1, 0)
self.RPN_proposal = _ProposalLayer(self.feat_stride,
self.anchor_scales,
self.anchor_ratios, self.use_gpu)
self.RPN_anchor_target = _AnchorTargetLayer(self.feat_stride,
self.anchor_scales,
self.anchor_ratios)
self.rpn_loss_cls = 0
self.rpn_loss_bbox = 0
def __init__(self,
in_channels=512,
mid_channels=512,
sliding_window_size=3):
super().__init__()
self.anchor_scales = cfg.ANCHOR_SCALES
self.anchor_ratios = cfg.ANCHOR_RATIOS
self.feat_stride = cfg.FEAT_STRIDE[0]
self.num_classes = 21
self.RPN_Conv = nn.Conv2d(in_channels,
mid_channels,
sliding_window_size,
stride=1,
padding=1)
self.num_anchors = len(self.anchor_ratios) * len(self.anchor_scales)
self.cls_out_channels = self.num_anchors * 2
self.reg_out_channels = self.num_anchors * 4
self.RPN_cls_score = nn.Conv2d(mid_channels,
self.cls_out_channels,
1,
stride=1,
padding=0)
self.RPN_bbox_pred = nn.Conv2d(mid_channels,
self.reg_out_channels,
1,
stride=1,
padding=0)
self.proposal_layer = ProposalLayer(scales=(8, 16, 32),
aspect_ratios=(0.5, 1, 2))
self.anchor_generator = AnchorGenerator(self.anchor_scales,
self.anchor_ratios)
self.anchor_target_layer = _AnchorTargetLayer(self.feat_stride,
self.anchor_scales,
self.anchor_ratios)