adgm.py 文件源码

def resnet_arg_scope(is_training=True,
                     weight_decay=cfg.TRAIN.WEIGHT_DECAY,
                     batch_norm_decay=0.997,
                     batch_norm_epsilon=1e-5,
                     batch_norm_scale=True):
    batch_norm_params = {
        # NOTE 'is_training' here does not work because inside resnet it gets reset:
        # https://github.com/tensorflow/models/blob/master/slim/nets/resnet_v1.py#L187
        'is_training': False,
        'decay': batch_norm_decay,
        'epsilon': batch_norm_epsilon,
        'scale': batch_norm_scale,
        'trainable': cfg.RESNET.BN_TRAIN,
        'updates_collections': ops.GraphKeys.UPDATE_OPS
    }

    with arg_scope(
            [slim.conv2d],
            weights_regularizer=regularizers.l2_regularizer(weight_decay),
            weights_initializer=initializers.variance_scaling_initializer(),
            trainable=is_training,
            activation_fn=nn_ops.relu,
            normalizer_fn=layers.batch_norm,
            normalizer_params=batch_norm_params):
        with arg_scope([layers.batch_norm], **batch_norm_params) as arg_sc:
            return arg_sc