def proposal_target_layer(rpn_rois, gt_boxes, gt_ishard, dontcare_areas,
num_classes):
"""
----------
rpn_rois: (1 x H x W x A, 5) [0, x1, y1, x2, y2]
gt_boxes: (G, 5) [x1 ,y1 ,x2, y2, class] int
# gt_ishard: (G, 1) {0 | 1} 1 indicates hard
dontcare_areas: (D, 4) [ x1, y1, x2, y2]
num_classes
----------
Returns
----------
rois: (1 x H x W x A, 5) [0, x1, y1, x2, y2]
labels: (1 x H x W x A, 1) {0,1,...,_num_classes-1}
bbox_targets: (1 x H x W x A, K x4) [dx1, dy1, dx2, dy2]
bbox_inside_weights: (1 x H x W x A, Kx4) 0, 1 masks for the computing loss
bbox_outside_weights: (1 x H x W x A, Kx4) 0, 1 masks for the computing loss
"""
rpn_rois = rpn_rois.data.cpu().numpy()
rois, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights = \
proposal_target_layer_py(rpn_rois, gt_boxes, gt_ishard, dontcare_areas, num_classes)
# print labels.shape, bbox_targets.shape, bbox_inside_weights.shape
rois = network.np_to_variable(rois, is_cuda=True)
labels = network.np_to_variable(labels,
is_cuda=True,
dtype=torch.LongTensor)
bbox_targets = network.np_to_variable(bbox_targets, is_cuda=True)
bbox_inside_weights = network.np_to_variable(bbox_inside_weights,
is_cuda=True)
bbox_outside_weights = network.np_to_variable(bbox_outside_weights,
is_cuda=True)
return rois, labels, bbox_targets, bbox_inside_weights, bbox_outside_weights
def __init__(self, classes=None, debug=False):
super(FasterRCNN, self).__init__()
self.classes = np.asarray(classes)
self.n_classes = len(classes)
self.rpn = RPN(debug=debug)
self.proposal_target_layer = proposal_target_layer_py(self.n_classes)
if cfg.POOLING_MODE == 'align':
self.roi_pool = RoIAlign(7, 7, 1.0 / 16)
elif cfg.POOLING_MODE == 'pool':
self.roi_pool = RoIPool(7, 7, 1.0 / 16)
self.score_fc = FC(4096, self.n_classes, relu=False)
self.bbox_fc = FC(4096, self.n_classes * 4, relu=False)
# loss
self.cross_entropy = 0
self.loss_box = 0
self.triplet_loss = 0
# for log
self.debug = debug
if cfg.TRIPLET.IS_TRUE:
self.fc_sim = FC(512 * 7 * 7, 4096, relu=False)
pos_weight = torch.ones(3)
pos_weight[0] = 2.0
if self.debug:
self.set = 0
self.match = 0
if cfg.TRIPLET.LOSS == 'euc':
self.loss_triplet = tpl.euclidean_distance_loss
elif cfg.TRIPLET.LOSS == 'log':
self.loss_triplet = tpl.cross_entropy_l2_dist
self.relu = nn.ReLU(inplace=True)
self.BCELoss = nn.BCELoss(weight=pos_weight,
size_average=False)
elif cfg.TRIPLET.LOSS == 'cls':
self.loss_triplet = tpl.cross_entropy_cosine_sim
self.relu = nn.ReLU(inplace=True)
self.BCELoss = nn.BCELoss(weight=pos_weight,
size_average=False)
self.init_module = self._init_faster_rcnn_vgg16
