def __init__(self, dim_in, spatial_scales): super().__init__() self.dim_in = dim_in self.spatial_scales = spatial_scales self.dim_out = self.dim_in num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS) # Create conv ops shared by all FPN levels self.FPN_RPN_conv = nn.Conv2d(dim_in, self.dim_out, 3, 1, 1) dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \ else num_anchors self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0) self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1, 0) self.GenerateProposals_modules = nn.ModuleList() k_max = cfg.FPN.RPN_MAX_LEVEL # coarsest level of pyramid k_min = cfg.FPN.RPN_MIN_LEVEL # finest level of pyramid for lvl in range(k_min, k_max + 1): sc = self.spatial_scales[k_max - lvl] # in reversed order lvl_anchors = generate_anchors( stride = 2. ** lvl, sizes = (cfg.FPN.RPN_ANCHOR_START_SIZE * 2. ** (lvl - k_min),), aspect_ratios = cfg.FPN.RPN_ASPECT_RATIOS ) self.GenerateProposals_modules.append(GenerateProposalsOp(lvl_anchors, sc)) self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp() self._init_weights()
def __init__(self, dim_in, spatial_scales):
super().__init__()
self.dim_in = dim_in
self.spatial_scales = spatial_scales
self.dim_out = self.dim_in
num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)
# Create conv ops shared by all FPN levels
# 12/25,RPN 3D CONTEXT TEST
if cfg.LESION.CONCAT_BEFORE_RPN:
#self.cbam = CBAM(self.dim_in*cfg.LESION.NUM_IMAGES_3DCE, 16)
#self.FPN_RPN_conv_embedding = nn.Conv2d(self.dim_in*cfg.LESION.NUM_IMAGES_3DCE, self.dim_in, 1)
self.FPN_RPN_conv = nn.Conv2d(self.dim_in*cfg.LESION.NUM_IMAGES_3DCE, self.dim_in, 1)
else:
self.FPN_RPN_conv = nn.Conv2d(self.dim_in, self.dim_out, 3, 1, 1)
#elif cfg.LESION.SUM_BEFORE_RPN:
#self.FPN_RPN_conv_embedding = nn.Conv2d(self.dim_in, self.dim_in, 1)
#self.FPN_RPN_conv = nn.Conv2d(self.dim_in*cfg.LESION.NUM_IMAGES_3DCE, self.dim_out, 3, 1, 1)
dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0)
self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1, 0)
self.GenerateProposals_modules = nn.ModuleList()
k_max = cfg.FPN.RPN_MAX_LEVEL # coarsest level of pyramid
k_min = cfg.FPN.RPN_MIN_LEVEL # finest level of pyramid
#anchor_scale = [0,0,2,3,4,6,12]
#anchor_scale = [0,0,1,2,4,8,16]
#anchor_scale = [0,0,1,2,3,6,12]
for lvl in range(k_min, k_max + 1):
sc = self.spatial_scales[k_max - lvl] # in reversed order
lvl_anchors = generate_anchors(
stride=2.**lvl,
sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2.**(lvl - k_min), ),
#sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * anchor_scale[lvl], ),
aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS
)
self.GenerateProposals_modules.append(GenerateProposalsOp(lvl_anchors, sc))
self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp()
self._init_weights()
def __init__(self, dim_in, spatial_scale):
super().__init__()
self.dim_in = dim_in
self.dim_out = dim_in if cfg.RPN.OUT_DIM_AS_IN_DIM else cfg.RPN.OUT_DIM
anchors = generate_anchors(stride=1. / spatial_scale,
sizes=cfg.RPN.SIZES,
aspect_ratios=cfg.RPN.ASPECT_RATIOS)
num_anchors = anchors.shape[0]
# RPN hidden representation
self.RPN_conv = nn.Conv2d(self.dim_in, self.dim_out, 3, 1, 1)
# Proposal classification scores
self.n_score_out = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.RPN_cls_score = nn.Conv2d(self.dim_out, self.n_score_out, 1, 1, 0)
# Proposal bbox regression deltas
self.RPN_bbox_pred = nn.Conv2d(self.dim_out, num_anchors * 4, 1, 1, 0)
self.RPN_GenerateProposals = GenerateProposalsOp(
anchors, spatial_scale)
self.RPN_GenerateProposalLabels = GenerateProposalLabelsOp()
self._init_weights()
def __init__(self, dim_in, spatial_scales):
super().__init__()
self.dim_in = dim_in
self.spatial_scales = spatial_scales
self.dim_out = self.dim_in
num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)
# Create conv ops shared by all FPN levels
'''i think this is the head part, they go across a conv with equal-input-output-size, and then
go through the score and bbox subnet separately'''
self.FPN_RPN_conv = nn.Conv2d(dim_in, self.dim_out, 3, 1, 1)
dim_score = num_anchors * 2 if cfg.RPN.CLS_ACTIVATION == 'softmax' \
else num_anchors
self.FPN_RPN_cls_score = nn.Conv2d(self.dim_out, dim_score, 1, 1, 0)
self.FPN_RPN_bbox_pred = nn.Conv2d(self.dim_out, 4 * num_anchors, 1, 1,
0)
self.GenerateProposals_modules = nn.ModuleList()
k_max = cfg.FPN.RPN_MAX_LEVEL # coarsest level of pyramid
k_min = cfg.FPN.RPN_MIN_LEVEL # finest level of pyramid
for lvl in range(k_min, k_max + 1):
'''3..0'''
sc = self.spatial_scales[k_max - lvl] # in reversed order
'''FIXME the part to work with!!'''
lvl_anchors = generate_anchors(
stride=2.**lvl,
sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2.**(lvl - k_min), ),
aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS)
self.GenerateProposals_modules.append(
GenerateProposalsOp(lvl_anchors, sc))
self.CollectAndDistributeFpnRpnProposals = CollectAndDistributeFpnRpnProposalsOp(
)
self._init_weights()
