def __init__(self, use_kmeans_anchors=False):
super(RPN, self).__init__()
if use_kmeans_anchors:
print 'using k-means anchors'
self.anchor_scales = self.anchor_scales_kmeans
self.anchor_ratios = self.anchor_ratios_kmeans
self.anchor_scales_region = self.anchor_scales_kmeans_region
self.anchor_ratios_region = self.anchor_ratios_kmeans_region
else:
print 'using normal anchors'
self.anchor_scales, self.anchor_ratios = \
np.meshgrid(self.anchor_scales_normal, self.anchor_ratios_normal, indexing='ij')
self.anchor_scales = self.anchor_scales.reshape(-1)
self.anchor_ratios = self.anchor_ratios.reshape(-1)
self.anchor_scales_region, self.anchor_ratios_region = \
np.meshgrid(self.anchor_scales_normal_region, self.anchor_ratios_normal_region, indexing='ij')
self.anchor_scales_region = self.anchor_scales_region.reshape(-1)
self.anchor_ratios_region = self.anchor_ratios_region.reshape(-1)
self.anchor_num = len(self.anchor_scales)
self.anchor_num_region = len(self.anchor_scales_region)
# self.features = VGG16(bn=False)
self.features = models.vgg16(pretrained=True).features
self.features.__delattr__('30') # to delete the max pooling
# by default, fix the first four layers
network.set_trainable_param(list(self.features.parameters())[:8], requires_grad=False)
# self.features = models.vgg16().features
self.conv1 = Conv2d(512, 512, 3, same_padding=True)
self.score_conv = Conv2d(512, self.anchor_num * 2, 1, relu=False, same_padding=False)
self.bbox_conv = Conv2d(512, self.anchor_num * 4, 1, relu=False, same_padding=False)
self.conv1_region = Conv2d(512, 512, 3, same_padding=True)
self.score_conv_region = Conv2d(512, self.anchor_num_region * 2, 1, relu=False, same_padding=False)
self.bbox_conv_region = Conv2d(512, self.anchor_num_region * 4, 1, relu=False, same_padding=False)
# loss
self.cross_entropy = None
self.loss_box = None
self.cross_entropy_region = None
self.loss_box_region = None
# initialize the parameters
self.initialize_parameters()
评论列表
文章目录
