Method__init__(self, cls_loss_fn=None, loc_loss_fn=None, \
cls_loss_weight=1, loc_loss_weight=2, \
modelling/criterion.py:77
Method__init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm
modelling/backbones/resnet.py:47
Method__init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm
modelling/backbones/resnet.py:87
Method__init__(self, alpha=0.25, gamma=2.0, ignore_label=-1, eps=1e-7, few_sample=False)
modelling/loss_modules/loss.py:30
Method__init__(self, backbone, fpn, pred_net, pred_net_1, fp_context=None, phase='training', out_bb_ft=False)
modelling/architectures/widerface_basenet.py:51
Method__init__(self, backbone, fpn, pred_net, phase='training', out_bb_ft=False)
modelling/architectures/widerface_basenet.py:94
Method__init__(self, weight_init_fn=None, c2_out_ch=256, c3_out_ch=512, c4_out_ch=1024, c5_out_ch=2048, \
c
modelling/neck_modules/fpn.py:118
Method__init__(self, num_anchor_per_pixel=1, num_classes=1, \
input_ch_list=[256, 256, 256, 256, 256, 256],
modelling/pred_modules/pred_net.py:121
Method__init__(self, num_anchor_per_pixel=1, num_classes=1, \
input_ch_list=[256, 256, 256, 256, 256, 256],
modelling/pred_modules/pred_net.py:187
Method__init__(self, num_anchor_per_pixel=1, num_classes=1, \
input_ch_list=[256, 256, 256, 256, 256, 256],
modelling/pred_modules/pred_net.py:318
Method__init__(self, num_anchor_per_pixel=1, num_classes=1, \
input_ch_list=[256, 256, 256, 256, 256, 256],
modelling/pred_modules/pred_net.py:414
Method__init__(self, das_scale_list=[16, 32, 64, 128, 256, 512], \
p2_scale_range=None, p3_scale_range=None,
data/data_aug_settings.py:16