Resnet-Tensorflow 在Mask-Rcnn 中的结构

阅读Mask-Rcnn代码时，观察resnet结构。
其中重要的有identity_block和conv_block。

Identity Block:

1*1，3*3，1*1的三层stride=(1,1)卷积，最后直接输出x+input作为输出。

def identity_block(input_tensor, kernel_size, filters, stage, block,                   use_bias=True):    """The identity_block is the block that has no conv layer at shortcut    # Arguments        input_tensor: input tensor        kernel_size: defualt 3, the kernel size of middle conv layer at main path        filters: list of integers, the nb_filters of 3 conv layer at main path        stage: integer, current stage label, used for generating layer names        block: 'a','b'..., current block label, used for generating layer names    """    nb_filter1, nb_filter2, nb_filter3 = filters    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a',                  use_bias=use_bias)(input_tensor)    x = BatchNorm(axis=3, name=bn_name_base + '2a')(x)    x = KL.Activation('relu')(x)    x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',                  name=conv_name_base + '2b', use_bias=use_bias)(x)    x = BatchNorm(axis=3, name=bn_name_base + '2b')(x)    x = KL.Activation('relu')(x)    x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base + '2c',                  use_bias=use_bias)(x)    x = BatchNorm(axis=3, name=bn_name_base + '2c')(x)    x = KL.Add()([x, input_tensor])    x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)    return x

Conv Block

1*1 3*3 1*1 的三层卷积模块,stride=(2*2)，与identity block不同的是，conv block创建了shortcut = bn(conv(input) )+ 输出x，作为输出。而indentity block直接加上input，没有再次conv和bn的过程。

def conv_block(input_tensor, kernel_size, filters, stage, block,               strides=(2, 2), use_bias=True):    """conv_block is the block that has a conv layer at shortcut    # Arguments        input_tensor: input tensor        kernel_size: defualt 3, the kernel size of middle conv layer at main path        filters: list of integers, the nb_filters of 3 conv layer at main path        stage: integer, current stage label, used for generating layer names        block: 'a','b'..., current block label, used for generating layer names    Note that from stage 3, the first conv layer at main path is with subsample=(2,2)    And the shortcut should have subsample=(2,2) as well    """    nb_filter1, nb_filter2, nb_filter3 = filters    conv_name_base = 'res' + str(stage) + block + '_branch'    bn_name_base = 'bn' + str(stage) + block + '_branch'    x = KL.Conv2D(nb_filter1, (1, 1), strides=strides,                  name=conv_name_base + '2a', use_bias=use_bias)(input_tensor)    x = BatchNorm(axis=3, name=bn_name_base + '2a')(x)    x = KL.Activation('relu')(x)    x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',                  name=conv_name_base + '2b', use_bias=use_bias)(x)    x = BatchNorm(axis=3, name=bn_name_base + '2b')(x)    x = KL.Activation('relu')(x)    x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +                  '2c', use_bias=use_bias)(x)    x = BatchNorm(axis=3, name=bn_name_base + '2c')(x)    shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides,                         name=conv_name_base + '1', use_bias=use_bias)(input_tensor)    shortcut = BatchNorm(axis=3, name=bn_name_base + '1')(shortcut)    x = KL.Add()([x, shortcut])    x = KL.Activation('relu', name='res' + str(stage) + block + '_out')(x)    return x

identityblock = 3层
convblock = 3层
stage1 :padding(3,3) + conv(7,7) + bn + relu + pool
stage2 : convblock + identityblock * 2
stage3 : convblock + identityblock * 3
stage4 : convblock + identity_block * 5 or 22 (resnet50 or resnet 101)
stage5 : if ture: convblock + identityblock * 2
if stage5, padding+conv + convblock * 4 + identityblock * 12=resnet 50

def resnet_graph(input_image, architecture, stage5=False):    assert architecture in ["resnet50", "resnet101"]    # Stage 1    x = KL.ZeroPadding2D((3, 3))(input_image)    x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1', use_bias=True)(x)    x = BatchNorm(axis=3, name='bn_conv1')(x)    x = KL.Activation('relu')(x)    C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)    # Stage 2    x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))    x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')    C2 = x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')    # Stage 3    x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')    x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')    x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')    C3 = x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')    # Stage 4    x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')    block_count = {"resnet50": 5, "resnet101": 22}[architecture]    for i in range(block_count):        x = identity_block(x, 3, [256, 256, 1024], stage=4, block=chr(98 + i))    C4 = x    # Stage 5    if stage5:        x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')        x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')        C5 = x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')    else:        C5 = None    return [C1, C2, C3, C4, C5]