U-NET中每个Conv2D层上的输入大小和过滤器数量之间的关系

debugcn 发表于 Dev

范思法兰

我有这个U-NET实现：

import numpy as np 
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.models import *
from tensorflow.python.keras.layers import *
from tensorflow.python.keras.optimizers import *
from tensorflow.python.keras.callbacks import ModelCheckpoint, LearningRateScheduler
from tensorflow.python.keras import backend as keras

def unet(pretrained_weights = None,input_size = (240, 240, 1)):
    inputs = Input(input_size)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
    conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
    conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
    conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    drop5 = Dropout(0.5)(conv5)

    up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
    merge6 = concatenate([drop4,up6], axis = 3)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
    conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)

    up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
    merge7 = concatenate([conv3,up7], axis = 3)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
    conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)

    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
    merge8 = concatenate([conv2,up8], axis = 3)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)

    up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
    conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)

    model = Model(inputs = inputs, outputs = conv10)

    model.compile(tf.keras.optimizers.Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])

    #model.summary()

    if(pretrained_weights):
        model.load_weights(pretrained_weights)

    return model

当我将其input_size参数更改为(200, 200, 1)它失败时，出现以下错误：

A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 25, 25, 512), (None, 24, 24, 512)]

在这一行：

merge6 = concatenate([drop4,up6], axis = 3)

我认为问题与Conv2D图层中过滤器的大小有关。

input_size在所有Conv2D图层中，和过滤器大小之间是否存在任何关系？

如果有任何关系，我可以解决我的问题。

马克·斯奈德

问题是与之间的相互作用MaxPooling2D和UpSampling2D层，实际上。使用input_sizeof时(200, 200, 1)，图层输出的边长从200-> 100-> 50-> 25->开始12，因为MaxPooling2D将尺寸向下舍入。使用时UpSampling2D(size = (2,2))，它只会将尺寸加倍并发送12-> 24，与兼容25。

您需要做的是使用一个上采样层，该层将上采样到特定的形状，而不是按特定的因子。我这样做的方式是包裹tf.image.resize在Lambda一层。

my_upsampling_layer = Lambda(lambda image: tf.image.resize(image,...
    tf.convert_to_tensor(enc_layer.shape[1:3])),output_shape=list(enc_layer.shape[1:]))

enc_layer 将是U形下坡上的相应图层，因为在上U形时需要匹配其大小。

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