siammask代码阅读（3）siammask.py和siammask_sharp.py区别

1.siammask.py里面：

class SiamMask(nn.Module):
      def __init__(self, anchors=None, o_sz=63, g_sz=127):

siammask_sharp.py里面

	class SiamMask(nn.Module):
	def __init__(self, anchors=None, o_sz=127, g_sz=127):

第一个是参数o_sz=63改成了127

2.siammask.py里面：

	def run(self, template, search, softmax=False):
	"""
	run network
	"""
	template_feature = self.feature_extractor(template)
	search_feature = self.feature_extractor(search)
	rpn_pred_cls, rpn_pred_loc = self.rpn(template_feature, search_feature)
	rpn_pred_mask = self.mask(template_feature, search_feature) # (b, 63*63, w, h)

siammask_sharp.py里面

	def run(self, template, search, softmax=False):
	"""
	run network
	"""
	template_feature = self.feature_extractor(template)
	feature, search_feature = self.features.forward_all(search)
	rpn_pred_cls, rpn_pred_loc = self.rpn(template_feature, search_feature)
	corr_feature = self.mask_model.mask.forward_corr(template_feature, search_feature) # (b, 256, w, h)
	rpn_pred_mask = self.refine_model(feature, corr_feature)
	

第二个是生成mask的不同方法，siammask.py里面是用的论文中的第一种方法，而sharp 是用的refine 的方法。
3.siammask.py里面：

	p_m = p_m.permute(0, 2, 3, 1).contiguous().view(-1, 1, o_sz, o_sz)
	p_m = torch.index_select(p_m, 0, pos)
	p_m = nn.UpsamplingBilinear2d(size=[g_sz, g_sz])(p_m)
	p_m = p_m.view(-1, g_sz * g_sz)
	
	mask_uf = F.unfold(mask, (g_sz, g_sz), padding=32, stride=8)
	mask_uf = torch.transpose(mask_uf, 1, 2).contiguous().view(-1, g_sz * g_sz)

siammask_sharp.py里面

	if len(p_m.shape) == 4:
	p_m = p_m.permute(0, 2, 3, 1).contiguous().view(-1, 1, o_sz, o_sz)
	p_m = torch.index_select(p_m, 0, pos)
	p_m = nn.UpsamplingBilinear2d(size=[g_sz, g_sz])(p_m)
	p_m = p_m.view(-1, g_sz * g_sz)
	else:
	p_m = torch.index_select(p_m, 0, pos)
	
	mask_uf = F.unfold(mask, (g_sz, g_sz), padding=0, stride=8)
	mask_uf = torch.transpose(mask_uf, 1, 2).contiguous().view(-1, g_sz * g_sz)

第三个是加了条件判断以及改了一个padding的参数，由32改到了0