Python argscope.argscope方法代码示例

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def resnet_backbone(image, num_blocks, group_func, block_func):
    """
    Sec 5.1: We adopt the initialization of [15] for all convolutional layers.
    TensorFlow does not have the true "MSRA init". We use variance_scaling as an approximation.
    """
    with argscope(Conv2D, use_bias=False,
                  kernel_initializer=tf.variance_scaling_initializer(scale=2.0, mode='fan_out')):
        l = Conv2D('conv0', image, 64, 7, strides=2, activation=BNReLU)
        l = MaxPooling('pool0', l, pool_size=3, strides=2, padding='SAME')
        l = group_func('group0', l, block_func, 64, num_blocks[0], 1)
        l = group_func('group1', l, block_func, 128, num_blocks[1], 2)
        l = group_func('group2', l, block_func, 256, num_blocks[2], 2)
        l = group_func('group3', l, block_func, 512, num_blocks[3], 2)
        l = GlobalAvgPooling('gap', l)
        logits = FullyConnected('linear', l, 1000,
                                kernel_initializer=tf.random_normal_initializer(stddev=0.01))
    """
    Sec 5.1:
    The 1000-way fully-connected layer is initialized by
    drawing weights from a zero-mean Gaussian with standard
    deviation of 0.01.
    """
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def alexnet_backbone(image, qw=1):
    with argscope(Conv2DQuant, nl=tf.identity, use_bias=False,
                  W_init=tf.random_normal_initializer(stddev=0.01),
                  data_format=get_arg_scope()['Conv2D']['data_format'],
                  nbit=qw):
        logits = (LinearWrap(image)
                  .Conv2DQuant('conv1', 96, 11, stride=4, is_quant=False, padding='VALID')
                  .MaxPooling('pool1', shape=3, stride=2, padding='VALID')
                  .BNReLUQuant('bnquant2')
                  .Conv2DQuant('conv2', 256, 5)
                  .MaxPooling('pool2', shape=3, stride=2, padding='VALID')
                  .BNReLUQuant('bnquant3')
                  .Conv2DQuant('conv3', 384, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv4', 384, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv5', 256, 3)
                  .MaxPooling('pool5', shape=3, stride=2, padding='VALID')
                  .BNReLUQuant('bnquant6')
                  .Conv2DQuant('fc6', 4096, 6, nl=getfcBNReLUQuant, padding='VALID', W_init=tf.random_normal_initializer(stddev=0.005), use_bias=True)
                  .Conv2DQuant('fc7', 4096, 1, nl=getfcBNReLU, padding='VALID', W_init=tf.random_normal_initializer(stddev=0.005), use_bias=True)
                  .FullyConnected('fc8', out_dim=1000, nl=tf.identity, W_init=tf.random_normal_initializer(stddev=0.01))())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def densenet_backbone(image, qw=1):
    with argscope(Conv2DQuant, nl=tf.identity, use_bias=False,
                  W_init=variance_scaling_initializer(mode='FAN_IN'),
                  data_format=get_arg_scope()['Conv2D']['data_format'],
                  nbit=qw,
                  is_quant=True if qw > 0 else False):
        logits = (LinearWrap(image)
                  .Conv2DQuant('conv1', 2 * GROWTH_RATE, 7, stride=2, nl=BNReLU, is_quant=False)
                  .MaxPooling('pool1', shape=3, stride=2, padding='SAME')
                  # 56
                  .apply(add_dense_block, 'block0', 6)
                  # 28
                  .apply(add_dense_block, 'block1', 12)
                  # 14
                  .apply(add_dense_block, 'block2', 24)
                  # 7
                  .apply(add_dense_block, 'block3', 16, last=True)
                  .BNReLU('bnrelu_last')
                  .GlobalAvgPooling('gap')
                  .FullyConnected('linear', out_dim=1000, nl=tf.identity, W_init=variance_scaling_initializer(mode='FAN_IN'))())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def resnet_backbone(image, num_blocks, group_func, block_func):
    with argscope(Conv2D, use_bias=False,
                  kernel_initializer=tf.variance_scaling_initializer(
                      scale=2.0, mode='fan_out', distribution='untruncated_normal')):
        logits = (LinearWrap(image)
                  .tf.pad([[0, 0], [0, 0], [3, 3], [3, 3]])
                  .Conv2D('conv0', 64, 7, strides=2, activation=GNReLU, padding='VALID')
                  .tf.pad([[0, 0], [0, 0], [1, 1], [1, 1]])
                  .MaxPooling('pool0', shape=3, stride=2, padding='VALID')
                  .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,
                                  kernel_initializer=tf.random_normal_initializer(stddev=0.01))())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def pretrained_resnet_conv4(image, num_blocks):
    assert len(num_blocks) == 3
    with argscope([Conv2D, MaxPooling, BatchNorm], data_format='NCHW'), \
            argscope(Conv2D, nl=tf.identity, use_bias=False), \
            argscope(BatchNorm, use_local_stat=False):
        l = tf.pad(image, [[0, 0], [0, 0], [2, 3], [2, 3]])
        l = Conv2D('conv0', l, 64, 7, stride=2, nl=BNReLU, padding='VALID')
        l = tf.pad(l, [[0, 0], [0, 0], [0, 1], [0, 1]])
        l = MaxPooling('pool0', l, shape=3, stride=2, padding='VALID')
        l = resnet_group(l, 'group0', resnet_bottleneck, 64, num_blocks[0], 1)
        # TODO replace var by const to enable folding
        l = tf.stop_gradient(l)
        l = resnet_group(l, 'group1', resnet_bottleneck, 128, num_blocks[1], 2)
        l = resnet_group(l, 'group2', resnet_bottleneck, 256, num_blocks[2], 2)
    # 16x downsampling up to now
    return l 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def rpn_head(featuremap, channel, num_anchors):
    """
    Returns:
        label_logits: fHxfWxNA
        box_logits: fHxfWxNAx4
    """
    with argscope(Conv2D, data_format='NCHW',
                  W_init=tf.random_normal_initializer(stddev=0.01)):
        hidden = Conv2D('conv0', featuremap, channel, 3, nl=tf.nn.relu)

        label_logits = Conv2D('class', hidden, num_anchors, 1)
        box_logits = Conv2D('box', hidden, 4 * num_anchors, 1)
        # 1, NA(*4), im/16, im/16 (NCHW)

        label_logits = tf.transpose(label_logits, [0, 2, 3, 1])  # 1xfHxfWxNA
        label_logits = tf.squeeze(label_logits, 0)  # fHxfWxNA

        shp = tf.shape(box_logits)  # 1x(NAx4)xfHxfW
        box_logits = tf.transpose(box_logits, [0, 2, 3, 1])  # 1xfHxfWx(NAx4)
        box_logits = tf.reshape(box_logits, tf.stack([shp[2], shp[3], num_anchors, 4]))  # fHxfWxNAx4
    return label_logits, box_logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def fastrcnn_Xconv1fc_head(feature, num_convs, norm=None):
    """
    Args:
        feature (NCHW):
        num_classes(int): num_category + 1
        num_convs (int): number of conv layers
        norm (str or None): either None or 'GN'

    Returns:
        2D head feature
    """
    assert norm in [None, 'GN'], norm
    l = feature
    with argscope(Conv2D, data_format='channels_first',
                  kernel_initializer=tf.variance_scaling_initializer(
                      scale=2.0, mode='fan_out',
                      distribution='untruncated_normal' if get_tf_version_tuple() >= (1, 12) else 'normal')):
        for k in range(num_convs):
            l = Conv2D('conv{}'.format(k), l, cfg.FPN.FRCNN_CONV_HEAD_DIM, 3, activation=tf.nn.relu)
            if norm is not None:
                l = GroupNorm('gn{}'.format(k), l)
        l = FullyConnected('fc', l, cfg.FPN.FRCNN_FC_HEAD_DIM,
                           kernel_initializer=tf.variance_scaling_initializer(), activation=tf.nn.relu)
    return l 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def maskrcnn_upXconv_head(feature, num_category, num_convs, norm=None):
    """
    Args:
        feature (NxCx s x s): size is 7 in C4 models and 14 in FPN models.
        num_category(int):
        num_convs (int): number of convolution layers
        norm (str or None): either None or 'GN'

    Returns:
        mask_logits (N x num_category x 2s x 2s):
    """
    assert norm in [None, 'GN'], norm
    l = feature
    with argscope([Conv2D, Conv2DTranspose], data_format='channels_first',
                  kernel_initializer=tf.variance_scaling_initializer(
                      scale=2.0, mode='fan_out',
                      distribution='untruncated_normal' if get_tf_version_tuple() >= (1, 12) else 'normal')):
        # c2's MSRAFill is fan_out
        for k in range(num_convs):
            l = Conv2D('fcn{}'.format(k), l, cfg.MRCNN.HEAD_DIM, 3, activation=tf.nn.relu)
            if norm is not None:
                l = GroupNorm('gn{}'.format(k), l)
        l = Conv2DTranspose('deconv', l, cfg.MRCNN.HEAD_DIM, 2, strides=2, activation=tf.nn.relu)
        l = Conv2D('conv', l, num_category, 1, kernel_initializer=tf.random_normal_initializer(stddev=0.001))
    return l 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def rpn_head(featuremap, channel, num_anchors):
    """
    Returns:
        label_logits: fHxfWxNA
        box_logits: fHxfWxNAx4
    """
    with argscope(Conv2D, data_format='channels_first',
                  kernel_initializer=tf.random_normal_initializer(stddev=0.01)):
        hidden = Conv2D('conv0', featuremap, channel, 3, activation=tf.nn.relu)

        label_logits = Conv2D('class', hidden, num_anchors, 1)
        box_logits = Conv2D('box', hidden, 4 * num_anchors, 1)
        # 1, NA(*4), im/16, im/16 (NCHW)

        label_logits = tf.transpose(label_logits, [0, 2, 3, 1])  # 1xfHxfWxNA
        label_logits = tf.squeeze(label_logits, 0)  # fHxfWxNA

        shp = tf.shape(box_logits)  # 1x(NAx4)xfHxfW
        box_logits = tf.transpose(box_logits, [0, 2, 3, 1])  # 1xfHxfWx(NAx4)
        box_logits = tf.reshape(box_logits, tf.stack([shp[2], shp[3], num_anchors, 4]))  # fHxfWxNAx4
    return label_logits, box_logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def resnet_backbone(image, num_blocks, group_func, block_func):
    with 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', NUM_CLASSES, nl=tf.identity)())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def googlenet_backbone(image, qw=1):
    with argscope(Conv2DQuant, nl=tf.identity, use_bias=False,
                  W_init=variance_scaling_initializer(mode='FAN_IN'),
                  data_format=get_arg_scope()['Conv2D']['data_format'],
                  nbit=qw,
                  is_quant=True if qw > 0 else False):
        logits = (LinearWrap(image)
                  .Conv2DQuant('conv1', 64, 7, stride=2, is_quant=False)
                  .MaxPooling('pool1', shape=3, stride=2, padding='SAME')
                  .BNReLUQuant('pool1/out')
                  .Conv2DQuant('conv2/3x3_reduce', 192, 1, nl=getBNReLUQuant)
                  .Conv2DQuant('conv2/3x3', 192, 3)
                  .MaxPooling('pool2', shape=3, stride=2, padding='SAME')
                  .BNReLUQuant('pool2/out')
                  .apply(inception_block, 'incpetion_3a', 96, 128, 32)
                  .apply(inception_block, 'incpetion_3b', 192, 192, 96, is_last_block=True)
                  .apply(inception_block, 'incpetion_4a', 256, 208, 48)
                  .apply(inception_block, 'incpetion_4b', 224, 224, 64)
                  .apply(inception_block, 'incpetion_4c', 192, 256, 64)
                  .apply(inception_block, 'incpetion_4d', 176, 288, 64)
                  .apply(inception_block, 'incpetion_4e', 384, 320, 128, is_last_block=True)
                  .apply(inception_block, 'incpetion_5a', 384, 320, 128)
                  .apply(inception_block, 'incpetion_5b', 512, 384, 128, is_last_block=True, is_last=True)
                  .GlobalAvgPooling('pool5')
                  .FullyConnected('linear', out_dim=1000, nl=tf.identity)())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def vgg_backbone(image, qw=1):
    with argscope(Conv2DQuant, nl=tf.identity, use_bias=False,
                  W_init=variance_scaling_initializer(mode='FAN_IN'),
                  data_format=get_arg_scope()['Conv2D']['data_format'],
                  nbit=qw):
        logits = (LinearWrap(image)
                  .Conv2DQuant('conv1', 96, 7, stride=2, nl=tf.nn.relu, is_quant=False)
                  .MaxPooling('pool1', shape=2, stride=2, padding='VALID')
                  # 56
                  .BNReLUQuant('bnquant2_0')
                  .Conv2DQuant('conv2_1', 256, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv2_2', 256, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv2_3', 256, 3)
                  .MaxPooling('pool2', shape=2, stride=2, padding='VALID')
                  # 28
                  .BNReLUQuant('bnquant3_0')
                  .Conv2DQuant('conv3_1', 512, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv3_2', 512, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv3_3', 512, 3)
                  .MaxPooling('pool3', shape=2, stride=2, padding='VALID')
                  # 14
                  .BNReLUQuant('bnquant4_0')
                  .Conv2DQuant('conv4_1', 512, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv4_2', 512, 3, nl=getBNReLUQuant)
                  .Conv2DQuant('conv4_3', 512, 3)
                  .MaxPooling('pool4', shape=2, stride=2, padding='VALID')
                  # 7
                  .BNReLUQuant('bnquant5')
                  .Conv2DQuant('fc5', 4096, 7, nl=getfcBNReLUQuant, padding='VALID', use_bias=True)
                  .Conv2DQuant('fc6', 4096, 1, nl=getfcBNReLU, padding='VALID', use_bias=True)
                  .FullyConnected('fc7', out_dim=1000, nl=tf.identity, W_init=variance_scaling_initializer(mode='FAN_IN'))())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def resnet_backbone(image, num_blocks, group_func, block_func, qw=1):
    with argscope(Conv2DQuant, nl=tf.identity, use_bias=False,
                  W_init=variance_scaling_initializer(mode='FAN_OUT'),
                  data_format=get_arg_scope()['Conv2D']['data_format'],
                  nbit=qw):
        logits = (LinearWrap(image)
                  .Conv2DQuant('conv0', 64, 7, stride=2, nl=BNReLU, is_quant=False)
                  .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, is_last=True)
                  .GlobalAvgPooling('gap')
                  .FullyConnected('linear', 1000, nl=tf.identity)())
    return logits 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [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.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import argscope [as 别名]
def resnet_conv5(image, num_block):
    with argscope([Conv2D, BatchNorm], data_format='NCHW'), \
            argscope(Conv2D, nl=tf.identity, use_bias=False), \
            argscope(BatchNorm, use_local_stat=False):
        # 14x14:
        l = resnet_group(image, 'group3', resnet_bottleneck, 512, num_block, stride=2)
        return l