Python argscope.get_arg_scope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 get_arg_scope [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 get_arg_scope [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 get_arg_scope [as 别名]
def inception_block(l, name, ch_1x1, ch_3x3, ch_5x5, is_last_block=False, is_last=False):
    data_format = get_arg_scope()['Conv2DQuant']['data_format']
    with tf.variable_scope(name):
        conv1x1 = Conv2DQuant('1x1', l, ch_1x1, 1, nl=getBNReLUQuant if not is_last_block else tf.identity)
        conv3x3_reduce = Conv2DQuant('3x3_reduce', l, ch_3x3, 1, nl=getBNReLUQuant)
        conv3x3 = Conv2DQuant('3x3', conv3x3_reduce, ch_3x3, 3, nl=getBNReLUQuant if not is_last_block else tf.identity)
        conv5x5_reduce = Conv2DQuant('5x5_reduce', l, ch_5x5, 1, nl=getBNReLUQuant)
        conv5x5 = Conv2DQuant('5x5', conv5x5_reduce, ch_5x5, 5, nl=getBNReLUQuant if not is_last_block else tf.identity)
        if is_last_block and not is_last:
            conv1x1 = MaxPooling('pool_1x1', conv1x1, shape=3, stride=2, padding='SAME')
            conv1x1 = BNReLU('conv1x1_bn', conv1x1)
            conv1x1 = QuantizedActiv('conv1x1_quant', conv1x1)
            conv3x3 = MaxPooling('pool_3x3', conv3x3, shape=3, stride=2, padding='SAME')
            conv3x3 = BNReLU('conv3x3_bn', conv3x3)
            conv3x3 = QuantizedActiv('conv3x3_quant', conv3x3)
            conv5x5 = MaxPooling('pool_5x5', conv5x5, shape=3, stride=2, padding='SAME')
            conv5x5 = BNReLU('conv5x5_bn', conv5x5)
            conv5x5 = QuantizedActiv('conv5x5_quant', conv5x5)
        l = tf.concat([
            conv1x1,
            conv3x3,
            conv5x5], 1 if data_format == 'NCHW' else 3, name='concat')
        if is_last:
            l = BNReLU('output_bn', l)
    return l 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [as 别名]
def se_resnet_bottleneck(l, ch_out, stride):
    shortcut = l
    l = Conv2D('conv1', l, ch_out, 1, activation=BNReLU)
    l = Conv2D('conv2', l, ch_out, 3, strides=stride, activation=BNReLU)
    l = Conv2D('conv3', l, ch_out * 4, 1, activation=get_bn(zero_init=True))

    squeeze = GlobalAvgPooling('gap', l)
    squeeze = FullyConnected('fc1', squeeze, ch_out // 4, activation=tf.nn.relu)
    squeeze = FullyConnected('fc2', squeeze, ch_out * 4, activation=tf.nn.sigmoid)
    data_format = get_arg_scope()['Conv2D']['data_format']
    ch_ax = 1 if data_format in ['NCHW', 'channels_first'] else 3
    shape = [-1, 1, 1, 1]
    shape[ch_ax] = ch_out * 4
    l = l * tf.reshape(squeeze, shape)
    out = l + resnet_shortcut(shortcut, ch_out * 4, stride, activation=get_bn(zero_init=False))
    return tf.nn.relu(out) 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [as 别名]
def se_bottleneck(l, ch_out, stride):
    shortcut = l
    l = Conv2D('conv1', l, ch_out, 1, activation=BNReLU)
    l = Conv2D('conv2', l, ch_out, 3, strides=stride, activation=BNReLU)
    l = Conv2D('conv3', l, ch_out * 4, 1, activation=get_bn(zero_init=True))

    squeeze = GlobalAvgPooling('gap', l)
    squeeze = FullyConnected('fc1', squeeze, ch_out // 4, activation=tf.nn.relu)
    squeeze = FullyConnected('fc2', squeeze, ch_out * 4, activation=tf.nn.sigmoid)
    data_format = get_arg_scope()['Conv2D']['data_format']
    ch_ax = 1 if data_format in ['NCHW', 'channels_first'] else 3
    shape = [-1, 1, 1, 1]
    shape[ch_ax] = ch_out * 4
    l = l * tf.reshape(squeeze, shape)
    out = l + resnet_shortcut(shortcut, ch_out * 4, stride, activation=get_bn(zero_init=False))
    return tf.nn.relu(out) 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [as 别名]
def resnet_shortcut(l, n_out, stride, nl=tf.identity):
    data_format = get_arg_scope()['Conv2D']['data_format']
    n_in = l.get_shape().as_list()[1 if data_format == 'NCHW' else 3]
    if n_in != n_out:   # change dimension when channel is not the same
        return Conv2D('convshortcut', l, n_out, 1, stride=stride, nl=nl)
    else:
        return l 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [as 别名]
def se_resnet_bottleneck(l, ch_out, stride):
    shortcut = l
    l = Conv2D('conv1', l, ch_out, 1, nl=BNReLU)
    l = Conv2D('conv2', l, ch_out, 3, stride=stride, nl=BNReLU)
    l = Conv2D('conv3', l, ch_out * 4, 1, nl=get_bn(zero_init=True))

    squeeze = GlobalAvgPooling('gap', l)
    squeeze = FullyConnected('fc1', squeeze, ch_out // 4, nl=tf.nn.relu)
    squeeze = FullyConnected('fc2', squeeze, ch_out * 4, nl=tf.nn.sigmoid)
    data_format = get_arg_scope()['Conv2D']['data_format']
    ch_ax = 1 if data_format == 'NCHW' else 3
    shape = [-1, 1, 1, 1]
    shape[ch_ax] = ch_out * 4
    l = l * tf.reshape(squeeze, shape)
    return l + resnet_shortcut(shortcut, ch_out * 4, stride, nl=get_bn(zero_init=False)) 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [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 get_arg_scope [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 get_arg_scope [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 get_arg_scope [as 别名]
def is_data_format_nchw():
    data_format = get_arg_scope()['Conv2D']['data_format']
    return data_format in ['NCHW', 'channels_first'] 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [as 别名]
def resnet_shortcut(l, n_out, stride, nl=tf.identity):
    data_format = get_arg_scope()['Conv2D']['data_format']
    n_in = l.get_shape().as_list()[1 if data_format == 'NCHW' else 3]
    if n_in != n_out:   # change dimension when channel is not the same
        if stride == 2:
            l = l[:, :, :-1, :-1]
            return Conv2D('convshortcut', l, n_out, 1,
                          stride=stride, padding='VALID', nl=nl)
        else:
            return Conv2D('convshortcut', l, n_out, 1,
                          stride=stride, nl=nl)
    else:
        return l 

# 需要导入模块: from tensorpack.tfutils import argscope [as 别名]
# 或者: from tensorpack.tfutils.argscope import get_arg_scope [as 别名]
def resnet_shortcut(l, n_out, stride, activation=tf.identity):
    data_format = get_arg_scope()['Conv2D']['data_format']
    n_in = l.get_shape().as_list()[1 if data_format in ['NCHW', 'channels_first'] else 3]
    if n_in != n_out:   # change dimension when channel is not the same
        return Conv2D('convshortcut', l, n_out, 1, strides=stride, activation=activation)
    else:
        return l