How to use the tensorpack.tfutils.argscope function in tensorpack

def get_logits(self, image):

        with argscope([Conv2D, MaxPooling, AvgPooling, GlobalAvgPooling, BatchNorm], data_format='channels_first'), \
                argscope(Conv2D, use_bias=False):

            group = args.group
            if not args.v2:
                # Copied from the paper
                channels = {
                    3: [240, 480, 960],
                    4: [272, 544, 1088],
                    8: [384, 768, 1536]
                }
                mul = group * 4  # #chan has to be a multiple of this number
                channels = [int(math.ceil(x * args.ratio / mul) * mul)
                            for x in channels[group]]
                # The first channel must be a multiple of group
                first_chan = int(math.ceil(24 * args.ratio / group) * group)
            else:

def backbone_scope(freeze):
    """
    Args:
        freeze (bool): whether to freeze all the variables under the scope
    """
    def nonlin(x):
        x = get_norm()(x)
        return tf.nn.relu(x)

    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
            argscope(Conv2D, use_bias=False, activation=nonlin,
                     kernel_initializer=tf.variance_scaling_initializer(
                         scale=2.0, mode='fan_out')), \
            ExitStack() as stack:
        if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
            if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
                stack.enter_context(argscope(BatchNorm, training=False))
            else:
                stack.enter_context(argscope(
                    BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))

        if freeze:
            stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
        else:
            # the layers are not completely freezed, but we may want to only freeze the affine
            if cfg.BACKBONE.FREEZE_AFFINE:
                stack.enter_context(custom_getter_scope(freeze_affine_getter))

def get_logits(self, image):
        group_func = resnet_group
        block_func = resnet_bottleneck
        num_blocks = [3, 4, 6, 3]
        with argscope(
                [Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm],
                data_format='NCHW'), \
                argscope(Conv2D, nl=tf.identity, use_bias=False,
                          W_init=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
            logits = (LinearWrap(image)
                      .Conv2D('conv0', 64, 7, stride=2, nl=BNReLU)
                      .MaxPooling('pool0', shape=3, stride=2, padding='SAME')
                      .apply(group_func, 'group0', block_func, 64, num_blocks[0], 1)
                      .apply(group_func, 'group1', block_func, 128, num_blocks[1], 2)
                      .apply(group_func, 'group2', block_func, 256, num_blocks[2], 2)
                      .apply(group_func, 'group3', block_func, 512, num_blocks[3], 2)
                      .GlobalAvgPooling('gap')
                      .FullyConnected('linear', 1000, nl=tf.identity)())
        return logits

def backbone_scope(freeze):
    """
    Args:
        freeze (bool): whether to freeze all the variables under the scope
    """
    def nonlin(x):
        x = get_norm()(x)
        return tf.nn.relu(x)

    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
            argscope(Conv2D, use_bias=False, activation=nonlin,
                     kernel_initializer=tf.variance_scaling_initializer(
                         scale=2.0, mode='fan_out')), \
            ExitStack() as stack:
        if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
            if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
                stack.enter_context(argscope(BatchNorm, training=False))
            else:
                stack.enter_context(argscope(
                    BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))

        if freeze:
            stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
        else:
            # the layers are not completely freezed, but we may want to only freeze the affine
            if cfg.BACKBONE.FREEZE_AFFINE:

def get_logits(self, image):
        gauss_init = tf.random_normal_initializer(stddev=0.01)
        with argscope(Conv2D,
                      kernel_initializer=tf.variance_scaling_initializer(scale=2.)), \
                argscope([Conv2D, FullyConnected], activation=tf.nn.relu), \
                argscope([Conv2D, MaxPooling], data_format='channels_last'):
            # necessary padding to get 55x55 after conv1
            image = tf.pad(image, [[0, 0], [2, 2], [2, 2], [0, 0]])
            l = Conv2D('conv1', image, filters=96, kernel_size=11, strides=4, padding='VALID')
            # size: 55
            visualize_conv1_weights(l.variables.W)
            l = tf.nn.lrn(l, 2, bias=1.0, alpha=2e-5, beta=0.75, name='norm1')
            l = MaxPooling('pool1', l, 3, strides=2, padding='VALID')
            # 27
            l = Conv2D('conv2', l, filters=256, kernel_size=5, split=2)
            l = tf.nn.lrn(l, 2, bias=1.0, alpha=2e-5, beta=0.75, name='norm2')
            l = MaxPooling('pool2', l, 3, strides=2, padding='VALID')
            # 13
            l = Conv2D('conv3', l, filters=384, kernel_size=3)
            l = Conv2D('conv4', l, filters=384, kernel_size=3, split=2)
            l = Conv2D('conv5', l, filters=256, kernel_size=3, split=2)
            l = MaxPooling('pool3', l, 3, strides=2, padding='VALID')

def backbone_scope(freeze):
    """
    Args:
        freeze (bool): whether to freeze all the variables under the scope
    """
    def nonlin(x):
        x = get_norm()(x)
        return tf.nn.relu(x)

    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
            argscope(Conv2D, use_bias=False, activation=nonlin,
                     kernel_initializer=tf.variance_scaling_initializer(
                         scale=2.0, mode='fan_out')), \
            ExitStack() as stack:
        if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
            if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
                stack.enter_context(argscope(BatchNorm, training=False))
            else:
                stack.enter_context(argscope(
                    BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))

        if freeze:
            stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
        else:
            # the layers are not completely freezed, but we may want to only freeze the affine
            if cfg.BACKBONE.FREEZE_AFFINE:
                stack.enter_context(custom_getter_scope(freeze_affine_getter))

def backbone_scope(freeze):
    """
    Args:
        freeze (bool): whether to freeze all the variables under the scope
    """
    def nonlin(x):
        x = get_norm()(x)
        return tf.nn.relu(x)

    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
            argscope(Conv2D, use_bias=False, activation=nonlin,
                     kernel_initializer=tf.variance_scaling_initializer(
                         scale=2.0, mode='fan_out')), \
            ExitStack() as stack:
        if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
            if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
                stack.enter_context(argscope(BatchNorm, training=False))
            else:
                stack.enter_context(argscope(
                    BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))

        if freeze:
            stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
        else:
            # the layers are not completely freezed, but we may want to only freeze the affine
            if cfg.BACKBONE.FREEZE_AFFINE:

freeze (bool): whether to freeze all the variables under the scope
    """
    def nonlin(x):
        x = get_norm()(x)
        return tf.nn.relu(x)

    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
            argscope(Conv2D, use_bias=False, activation=nonlin,
                     kernel_initializer=tf.variance_scaling_initializer(
                         scale=2.0, mode='fan_out')), \
            ExitStack() as stack:
        if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
            if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
                stack.enter_context(argscope(BatchNorm, training=False))
            else:
                stack.enter_context(argscope(
                    BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))

        if freeze:
            stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
        else:
            # the layers are not completely freezed, but we may want to only freeze the affine
            if cfg.BACKBONE.FREEZE_AFFINE:
                stack.enter_context(custom_getter_scope(freeze_affine_getter))
        yield

"""
    Args:
        freeze (bool): whether to freeze all the variables under the scope
    """
    def nonlin(x):
        x = get_norm()(x)
        return tf.nn.relu(x)

    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
            argscope(Conv2D, use_bias=False, activation=nonlin,
                     kernel_initializer=tf.variance_scaling_initializer(
                         scale=2.0, mode='fan_out')), \
            ExitStack() as stack:
        if cfg.BACKBONE.NORM in ['FreezeBN', 'SyncBN']:
            if freeze or cfg.BACKBONE.NORM == 'FreezeBN':
                stack.enter_context(argscope(BatchNorm, training=False))
            else:
                stack.enter_context(argscope(
                    BatchNorm, sync_statistics='nccl' if cfg.TRAINER == 'replicated' else 'horovod'))

        if freeze:
            stack.enter_context(freeze_variables(stop_gradient=False, skip_collection=True))
        else:
            # the layers are not completely freezed, but we may want to only freeze the affine
            if cfg.BACKBONE.FREEZE_AFFINE:
                stack.enter_context(custom_getter_scope(freeze_affine_getter))
        yield