Unet和Keras在图像分割中的误差分析

Question

我使用一个Unet模型进行卫星图像分割，输入为512x512x3。但在执行模型时，我得到了以下错误： 值错误：无法为张量'conv2d_19输入形状（3，512，512）的值_目标：0'有形状'（？, ?, ?, ?)'. Unet模型的代码是：

from __future__ import print_function
import os
from skimage.transform import resize
from skimage.io import imsave
import numpy as np
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D,     Conv2DTranspose
from keras.optimizers import Adam

from keras.callbacks import ModelCheckpoint
from keras import backend as K
from data import load_train_data, load_test_data

K.set_image_data_format('channels_last')  # TF dimension ordering in this code

img_rows = 512
img_cols = 512
image_channels=3
smooth = 1.
OUTPUT_MASK_CHANNELS = 1


def dice_coef(y_true, y_pred):
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) +    K.sum(y_pred_f) + smooth)

def dice_coef_loss(y_true, y_pred):
   return -dice_coef(y_true, y_pred)


def get_unet():
   inputs = Input((img_rows, img_cols, 3))
   conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
   conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
   pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

   conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
   conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
   pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

   conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
   conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
   pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

   conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
   conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
   pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

   conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
   conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

   up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5), conv4], axis=3)
   conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
   conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

   up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6), conv3], axis=3)
   conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
   conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)

   up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7), conv2], axis=3)
   conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
   conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)

   up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8), conv1], axis=3)
   conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
   conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

   conv_final = Conv2D(OUTPUT_MASK_CHANNELS, (1, 1),activation='sigmoid')(conv9)
   #conv_final = Activation('sigmoid')(conv_final)

   model = Model(inputs, conv_final, name="ZF_UNET_224")

   #conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
   #model = Model(inputs=[inputs], outputs=[conv10])

   model.compile(optimizer=Adam(lr=1e-5), loss=dice_coef_loss, metrics=[dice_coef])

   return model


def preprocess(imgs):
   imgs_p = np.ndarray((imgs.shape[0], img_rows, img_cols),       dtype=np.uint8)
for i in range(imgs.shape[0]):
    imgs_p[i] = resize(imgs[i], (img_cols, img_rows), preserve_range=True)

imgs_p = imgs_p[..., np.newaxis]
return imgs_p


def train_and_predict():
   print('-'*30)
   print('Loading and preprocessing train data...')
   print('-'*30)
   imgs_train, imgs_mask_train = load_train_data()

   #imgs_train = preprocess(imgs_train)
   #imgs_mask_train = preprocess(imgs_mask_train)

   imgs_train = imgs_train.astype('float32')
   mean = np.mean(imgs_train)  # mean for data centering
   std = np.std(imgs_train)  # std for data normalization

   imgs_train -= mean
   imgs_train /= std

   imgs_mask_train = imgs_mask_train.astype('float32')
   imgs_mask_train /= 255.  # scale masks to [0, 1]

   print('-'*30)
   print('Creating and compiling model...')
   print('-'*30)
   model = get_unet()
   model_checkpoint = ModelCheckpoint('weights.h5', monitor='val_loss',    save_best_only=True)

   print('-'*30)
   print('Fitting model...')
   print('-'*30)
   model.fit(imgs_train, imgs_mask_train, batch_size=3, epochs=20,   verbose=2, shuffle=True,
          validation_split=0.2,
          callbacks=[model_checkpoint])

   print('-'*30)
   print('Loading and preprocessing test data...')
   print('-'*30)
   imgs_test, imgs_id_test = load_test_data()
   imgs_test = preprocess(imgs_test)

   imgs_test = imgs_test.astype('float32')
   imgs_test -= mean
   imgs_test /= std

   print('-'*30)
   print('Loading saved weights...')
   print('-'*30)
   model.load_weights('weights.h5')

   print('-'*30)
   print('Predicting masks on test data...')
   print('-'*30)
   imgs_mask_test = model.predict(imgs_test, verbose=1)
   np.save('imgs_mask_test.npy', imgs_mask_test)

   print('-' * 30)
   print('Saving predicted masks to files...')
   print('-' * 30)
   pred_dir = 'preds'
   if not os.path.exists(pred_dir):
       os.mkdir(pred_dir)
for image, image_id in zip(imgs_mask_test, imgs_id_test):
    image = (image[:, :, 0] * 255.).astype(np.uint8)
    imsave(os.path.join(pred_dir, str(image_id) + '_pred.png'), image)

if __name__ == '__main__':
train_and_predict()

错误回溯如下：

^{pr2}$

帮我找出问题所在

Answer 1

您设置了K.set_image_data_format('channels_last')，但是您的输入图像(3 X 512 X 512)首先有通道。更改为K.set_image_data_format('channels_first')（这可能不适用于UNET），或者使用^{}置换输入图像的维度，使其具有输入形状(512,512,3)。在