3c35a25a42d6ad5ce4218c42d194158c4a9c990f,gluon/gluoncv2/models/hrnet.py,HRBlock,__init__,#HRBlock#Any#Any#Any#Any#Any#,72

Before Change

                            fuse_layer.add(Identity())
                        else:
                            conv3x3_seq = nn.HybridSequential(prefix="conv3x3_seq{}_".format(j + 1))
                            for k in range(i - j):
                                if k == i - j - 1:
                                    conv3x3_seq.add(conv3x3_block(
                                        in_channels=in_channels_list[j],
                                        out_channels=in_channels_list[i],
                                        strides=2,
                                        activation=None,
                                        bn_use_global_stats=bn_use_global_stats))
                                else:
                                    conv3x3_seq.add(conv3x3_block(
                                        in_channels=in_channels_list[j],
                                        out_channels=in_channels_list[j],
                                        strides=2,
                                        bn_use_global_stats=bn_use_global_stats))
                            fuse_layer.add(conv3x3_seq)
                    self.fuse_layers.add(fuse_layer)
                self.activ = nn.Activation("relu")

After Change

                                fuse_layer.add(Identity())
                            else:
                                conv3x3_seq = nn.HybridSequential(prefix="conv3x3seq{}_".format(j + 1))
                                with conv3x3_seq.name_scope():
                                    for k in range(i - j):
                                        if k == i - j - 1:
                                            conv3x3_seq.add(conv3x3_block(
                                                in_channels=in_channels_list[j],
                                                out_channels=in_channels_list[i],
                                                strides=2,
                                                activation=None,
                                                bn_use_global_stats=bn_use_global_stats))
                                        else:
                                            conv3x3_seq.add(conv3x3_block(
                                                in_channels=in_channels_list[j],
                                                out_channels=in_channels_list[j],
                                                strides=2,
                                                bn_use_global_stats=bn_use_global_stats))
                                fuse_layer.add(conv3x3_seq)
                    self.fuse_layers.add(fuse_layer)
                self.activ = nn.Activation("relu")

Instances