def initialize(self, outchn, stride): self.stride = stride self.outchn = outchn self.bn0 = M.BatchNorm() self.c1 = M.ConvLayer(3, outchn, activation=M.PARAM_PRELU, usebias=False, batch_norm=True) self.c2 = M.ConvLayer(3, outchn, stride=stride, usebias=False, batch_norm=True) # se module #self.c3 = M.ConvLayer(1, outchn//16, activation=M.PARAM_PRELU) #self.c4 = M.ConvLayer(1, outchn, activation=M.PARAM_SIGMOID) # shortcut self.sc = M.ConvLayer(1, outchn, stride=stride, usebias=False, batch_norm=True)
def initialize(self, channel_list, blocknum_list, embedding_size, embedding_bn=True): self.head = HeadBlock(channel_list[0]) self.body = nn.ModuleList() for num, chn in zip(blocknum_list, channel_list[1:]): for i in range(num): self.body.append(ResBlock_v1(chn, 2 if i==0 else 1)) self.emb_bn = M.BatchNorm() self.embedding = M.Dense(embedding_size, batch_norm=embedding_bn, affine=False)
def initialize(self, channel_list, blocknum_list, embedding_size, embedding_bn=True): self.c1 = M.ConvLayer(3, channel_list[0], 1, usebias=False, activation=M.PARAM_PRELU, batch_norm=True) # self.u1 = ResBlock_v1(channel_list[1], stride=2) self.stage1 = Stage(channel_list[1], blocknum_list[0]) self.stage2 = Stage(channel_list[2], blocknum_list[1]) self.stage3 = Stage(channel_list[3], blocknum_list[2]) self.stage4 = Stage(channel_list[4], blocknum_list[3]) self.bn1 = M.BatchNorm() self.fc1 = M.Dense(512, usebias=False, batch_norm=True)