Python models.BatchNorm方法代码示例

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def BNNoReLU(x, name=None):
    """
    A shorthand of BatchNormalization.
    """
    if name is None:
        x = BatchNorm('bn', x)
    else:
        x = BatchNorm(name, x)
    return x 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def resnet(input_, option):
    mode = option.mode
    DEPTH = option.depth
    bottleneck = {'se': se_resnet_bottleneck}[mode]

    cfg = {
        50: ([3, 4, 6, 3], bottleneck),
    }
    defs, block_func = cfg[DEPTH]
    group_func = resnet_group

    with argscope(Conv2D, use_bias=False, kernel_initializer= \
            tf.variance_scaling_initializer(scale=2.0, mode='fan_out')), \
         argscope([Conv2D, MaxPooling, GlobalAvgPooling, BatchNorm],
                  data_format='channels_first'):

        l = Conv2D('conv0', input_, 64, 7, strides=2, activation=BNReLU)
        if option.gating_position[0]: l = gating_op(l, option)

        l = MaxPooling('pool0', l, 3, strides=2, padding='SAME')
        if option.gating_position[1]: l = gating_op(l, option)

        l = group_func('group0', l, block_func, 64, defs[0], 1, option)
        if option.gating_position[2]: l = gating_op(l, option)

        l = group_func('group1', l, block_func, 128, defs[1], 2, option)
        if option.gating_position[3]: l = gating_op(l, option)

        l = group_func('group2', l, block_func, 256, defs[2], 2, option)
        if option.gating_position[4]: l = gating_op(l, option)

        l = group_func('group3', l, block_func, 512, defs[3],
                       1, option)
        if option.gating_position[5]: l = gating_op(l, option)

        p_logits = GlobalAvgPooling('gap', l)
        logits = FullyConnected('linearnew', p_logits, option.number_of_class)

    return logits, l 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def get_bn(zero_init=False):
    if zero_init:
        return lambda x, name=None: BatchNorm(
            'bn', x, gamma_initializer=tf.zeros_initializer())
    else:
        return lambda x, name=None: BatchNorm('bn', x) 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def get_bn(zero_init=False):
    """
    Zero init gamma is good for resnet. See https://arxiv.org/abs/1706.02677.
    """
    if zero_init:
        return lambda x, name=None: BatchNorm('bn', x, gamma_initializer=tf.zeros_initializer())
    else:
        return lambda x, name=None: BatchNorm('bn', x)


# ----------------- pre-activation resnet ---------------------- 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
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))
        yield 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def get_norm(zero_init=False):
    if cfg.BACKBONE.NORM == 'None':
        return lambda x: x
    if cfg.BACKBONE.NORM == 'GN':
        Norm = GroupNorm
        layer_name = 'gn'
    else:
        Norm = BatchNorm
        layer_name = 'bn'
    return lambda x: Norm(layer_name, x, gamma_initializer=tf.zeros_initializer() if zero_init else None) 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def RegionNorm(x, h_group_num, w_group_num, gamma_initializer=tf.constant_initializer(1.)):
    # 1. pad so that h % h_group_num == 0, w % w_group_num == 0
    orig_shape = x.get_shape().as_list()
    h, w = orig_shape[1], orig_shape[2]
    new_h = get_pad_num(h, h_group_num)
    new_w = get_pad_num(w, w_group_num)
    x_resized = tf.image.resize_images(x, [new_h, new_w], align_corners=False)

    # 2. split and stack all grid
    assert new_h % h_group_num == 0
    sub_h = new_h // h_group_num
    assert new_w % w_group_num == 0
    sub_w = new_w // w_group_num

    sub_grids = []
    for i in range(0, new_h, sub_h):
        for j in range(0, new_w, sub_w):
            x_sub_grid = x_resized[:, i:i + sub_h, j:j + sub_w, :, None]
            sub_grids.append(x_sub_grid)

    sub_grids = tf.concat(sub_grids, axis=4)
    sub_grids_shape = sub_grids.get_shape().as_list()
    feed2bn = tf.reshape(sub_grids,
                         [-1, sub_grids_shape[1], sub_grids_shape[2] * sub_grids_shape[3],
                          sub_grids_shape[4]])
    # 3. normalization
    bn_output = BatchNorm('bn', feed2bn, axis=3, gamma_initializer=gamma_initializer,
                          internal_update=True, sync_statistics='nccl')
    # 4. go back to original shape
    new_sub_grids = tf.reshape(bn_output,
                               [-1, sub_grids_shape[1], sub_grids_shape[2], sub_grids_shape[3],
                                sub_grids_shape[4]])
    counter = 0
    new_rows = []
    for i in range(0, new_h, sub_h):
        new_row = []
        for j in range(0, new_w, sub_w):
            new_row.append(new_sub_grids[:, :, :, :, counter])
            counter += 1
        new_row = tf.concat(new_row, axis=2)
        new_rows.append(new_row)
    new_x_resized = tf.concat(new_rows, axis=1)
    # 5. resize back
    new_x = tf.image.resize_images(new_x_resized, [h, w], align_corners=False)
    return new_x 

# 需要导入模块: from tensorpack import models [as 别名]
# 或者: from tensorpack.models import BatchNorm [as 别名]
def vgg_gap(image, option):
    with argscope(Conv2D, use_bias=True,
                  kernel_initializer=tf.variance_scaling_initializer(scale=2.)), \
         argscope([Conv2D, MaxPooling, BatchNorm, GlobalAvgPooling],
                  data_format='channels_first'):

        l = convnormrelu(image, 'conv1_1', 64)
        if option.gating_position[11]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv1_2', 64)
        if option.gating_position[12]: l = gating_op(l, option)
        l = MaxPooling('pool1', l, 2)
        if option.gating_position[1]: l = gating_op(l, option)

        l = convnormrelu(l, 'conv2_1', 128)
        if option.gating_position[21]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv2_2', 128)
        if option.gating_position[22]: l = gating_op(l, option)
        l = MaxPooling('pool2', l, 2)
        if option.gating_position[2]: l = gating_op(l, option)

        l = convnormrelu(l, 'conv3_1', 256)
        if option.gating_position[31]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv3_2', 256)
        if option.gating_position[32]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv3_3', 256)
        if option.gating_position[33]: l = gating_op(l, option)
        l = MaxPooling('pool3', l, 2)
        if option.gating_position[3]: l = gating_op(l, option)

        l = convnormrelu(l, 'conv4_1', 512)
        if option.gating_position[41]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv4_2', 512)
        if option.gating_position[42]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv4_3', 512)
        if option.gating_position[43]: l = gating_op(l, option)
        l = MaxPooling('pool4', l, 2)
        if option.gating_position[4]: l = gating_op(l, option)

        l = convnormrelu(l, 'conv5_1', 512)
        if option.gating_position[51]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv5_2', 512)
        if option.gating_position[52]: l = gating_op(l, option)
        l = convnormrelu(l, 'conv5_3', 512)
        if option.gating_position[53]: l = gating_op(l, option)

        convmaps = convnormrelu(l, 'new', 1024)
        if option.gating_position[6]: convmaps = gating_op(l, option)

        p_logits = GlobalAvgPooling('gap', convmaps)
        logits = FullyConnected('linear',
                                p_logits, option.number_of_class,
                                kernel_initializer=tf.random_normal_initializer(
                                    stddev=0.01))

    return logits, convmaps