Моя модель сегментации изображений дает очень высокую точность при обучении и проверке, но выводит пустые маски - PullRequest
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Моя модель сегментации изображений дает очень высокую точность при обучении и проверке, но выводит пустые маски

0 голосов
/ 11 февраля 2020

Я использовал Dice Loss и binary_crossentropy всякий раз, когда тренирую свою модель, она показывает очень высокую точность обучения и точность проверки, но всегда печатает пустые изображения. Мои маски - это черно-белые двоичные изображения, где 0 соответствует черному, а 1 соответствует белому. В моем выходном изображении почти все пиксели имеют значение 0, пожалуйста, скажите мне, где я ошибаюсь. 

def train_generator():
    while True:
        for start in range(0, len(os.listdir('/gdrive/My Drive/Train/img/images/')), 16):
            x_batch = np.empty((16,256,512,1),dtype=np.float32)
            y_batch = np.empty((16,256,512,1),dtype=np.float32)
            end = min(start + 16, len(os.listdir('/gdrive/My Drive/Train/img/images/')))
            ids_train_batch_images =os.listdir('/gdrive/My Drive/Train/img/images/')[start:end]
            ids_train_batch_mask =os.listdir('/gdrive/My Drive/Train/msk/mask/')[start:end]
            for i,id in enumerate(ids_train_batch_images):
                x_sample = cv2.imread('/gdrive/My Drive/Train/img/images/'+ids_train_batch_images[i])
                y_sample = cv2.imread('/gdrive/My Drive/Train/msk/mask/'+ids_train_batch_mask[i])
                x_sample=cv2.resize(x_sample,(512,256),interpolation = cv2.INTER_AREA)
                y_sample=cv2.resize(y_sample,(512,256),interpolation = cv2.INTER_AREA)
                x_sample=x_sample[:,:,0]
                y_sample=y_sample[:,:,0]
                x_sample=np.expand_dims(x_sample,axis=-1)
                y_sample=np.expand_dims(y_sample,axis=-1)
                x_batch[i]=x_sample
                y_batch[i]=y_sample.astype(np.bool)
            x_batch = np.array(x_batch, np.float32)/255.0
            y_batch = np.array(y_batch, np.bool)
            yield x_batch, y_batch

def val_generator():
    while True:
        for start in range(0, len(os.listdir('/gdrive/My Drive/Validation/img/images/')), 16):
            x_batch = np.empty((16,256,512,1),dtype=np.float32)
            y_batch = np.empty((16,256,512,1),dtype=np.float32)
            end = min(start + 16, len(os.listdir('/gdrive/My Drive/Validation/img/images/')))
            ids_train_batch_images =os.listdir('/gdrive/My Drive/Validation/img/images/')[start:end]
            ids_train_batch_mask =os.listdir('/gdrive/My Drive/Validation/msk/mask/')[start:end]
            for i,id in enumerate(ids_train_batch_images):
                x_sample = cv2.imread('/gdrive/My Drive/Validation/img/images/'+ids_train_batch_images[i])
                y_sample = cv2.imread('/gdrive/My Drive/Validation/msk/mask/'+ids_train_batch_mask[i])
                x_sample=cv2.resize(x_sample,(512,256),interpolation = cv2.INTER_AREA)
                y_sample=cv2.resize(y_sample,(512,256),interpolation = cv2.INTER_AREA)
                x_sample=x_sample[:,:,0]
                y_sample=y_sample[:,:,0]
                x_sample=np.expand_dims(x_sample,axis=-1)
                y_sample=np.expand_dims(y_sample,axis=-1)
                x_batch[i]=x_sample
                y_batch[i]=y_sample.astype(np.bool)
            x_batch = np.array(x_batch, np.float32)/255.0
            y_batch = np.array(y_batch, np.bool)
            yield x_batch, y_batch

train_gen=train_generator()
val_gen=val_generator()

def unet():
    inputs = tf.keras.layers.Input((256,512,1))
    s = inputs
    c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(s)
    c1 = tf.keras.layers.Dropout(0.3)(c1)
    c1 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c1)
    p1 = tf.keras.layers.MaxPooling2D((2, 2))(c1)

    c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(p1)
    c2 = tf.keras.layers.Dropout(0.3)(c2)
    c2 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c2)
    p2 = tf.keras.layers.MaxPooling2D((2, 2))(c2)

    c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p2)
    c3 = tf.keras.layers.Dropout(0.3)(c3)
    c3 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c3)
    p3 = tf.keras.layers.MaxPooling2D((2, 2))(c3)

    c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p3)
    c4 = tf.keras.layers.Dropout(0.3)(c4)
    c4 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c4)
    p4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c4)

    c6 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p4)
    c6 = tf.keras.layers.Dropout(0.3)(c6)
    c6 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c6)
    p6 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c6)

    # c6 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p5)
    # c6 = tf.keras.layers.Dropout(0.1)(c6)
    # c6 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c6)
    # p6 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(c6)

    c7 = tf.keras.layers.Conv2D(512, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(p6)
    c7 = tf.keras.layers.Dropout(0.3)(c7)
    c7 = tf.keras.layers.Conv2D(512, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c7)

    # u8 = tf.keras.layers.Conv2DTranspose(1024, (2, 2), strides=(2, 2), padding='same')(c7)
    # u8 = tf.keras.layers.concatenate([u8, c6])
    # c8 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u8)
    # c8 = tf.keras.layers.Dropout(0.1)(c8)
    # c8 = tf.keras.layers.Conv2D(1024, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c8)

    u9 = tf.keras.layers.Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(c7)
    u9 = tf.keras.layers.concatenate([u9, c6])
    c9 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u9)
    c9 = tf.keras.layers.Dropout(0.3)(c9)
    c9 = tf.keras.layers.Conv2D(256, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal', padding='same')(c9)

    u10 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c9)
    u10 = tf.keras.layers.concatenate([u10, c4])
    c10 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u10)
    c10 = tf.keras.layers.Dropout(0.3)(c10)
    c10 = tf.keras.layers.Conv2D(128, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c10)

    u11 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c10)
    u11 = tf.keras.layers.concatenate([u11, c3], axis=3)
    c11 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u11)
    c11 = tf.keras.layers.Dropout(0.3)(c11)
    c11 = tf.keras.layers.Conv2D(64, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c11)

    u12 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c11)
    u12 = tf.keras.layers.concatenate([u12, c2], axis=3)
    c12 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u12)
    c12 = tf.keras.layers.Dropout(0.3)(c12)
    c12 = tf.keras.layers.Conv2D(32, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c12)

    u13 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c12)
    u13 = tf.keras.layers.concatenate([u13, c1], axis=3)
    c13 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(u13)
    c13 = tf.keras.layers.Dropout(0.3)(c13)
    c13 = tf.keras.layers.Conv2D(16, (3, 3), activation=tf.keras.activations.elu, kernel_initializer='he_normal',padding='same')(c13)

    outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid')(c13)

    model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
    return model

Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_4 (InputLayer)            [(None, 256, 512, 1) 0                                            
__________________________________________________________________________________________________
conv2d_69 (Conv2D)              (None, 256, 512, 16) 160         input_4[0][0]                    
__________________________________________________________________________________________________
dropout_33 (Dropout)            (None, 256, 512, 16) 0           conv2d_69[0][0]                  
__________________________________________________________________________________________________
conv2d_70 (Conv2D)              (None, 256, 512, 16) 2320        dropout_33[0][0]                 
__________________________________________________________________________________________________
max_pooling2d_15 (MaxPooling2D) (None, 128, 256, 16) 0           conv2d_70[0][0]                  
__________________________________________________________________________________________________
conv2d_71 (Conv2D)              (None, 128, 256, 32) 4640        max_pooling2d_15[0][0]           
__________________________________________________________________________________________________
dropout_34 (Dropout)            (None, 128, 256, 32) 0           conv2d_71[0][0]                  
__________________________________________________________________________________________________
conv2d_72 (Conv2D)              (None, 128, 256, 32) 9248        dropout_34[0][0]                 
__________________________________________________________________________________________________
max_pooling2d_16 (MaxPooling2D) (None, 64, 128, 32)  0           conv2d_72[0][0]                  
__________________________________________________________________________________________________
conv2d_73 (Conv2D)              (None, 64, 128, 64)  18496       max_pooling2d_16[0][0]           
__________________________________________________________________________________________________
dropout_35 (Dropout)            (None, 64, 128, 64)  0           conv2d_73[0][0]                  
__________________________________________________________________________________________________
conv2d_74 (Conv2D)              (None, 64, 128, 64)  36928       dropout_35[0][0]                 
__________________________________________________________________________________________________
max_pooling2d_17 (MaxPooling2D) (None, 32, 64, 64)   0           conv2d_74[0][0]                  
__________________________________________________________________________________________________
conv2d_75 (Conv2D)              (None, 32, 64, 128)  73856       max_pooling2d_17[0][0]           
__________________________________________________________________________________________________
dropout_36 (Dropout)            (None, 32, 64, 128)  0           conv2d_75[0][0]                  
__________________________________________________________________________________________________
conv2d_76 (Conv2D)              (None, 32, 64, 128)  147584      dropout_36[0][0]                 
__________________________________________________________________________________________________
max_pooling2d_18 (MaxPooling2D) (None, 16, 32, 128)  0           conv2d_76[0][0]                  
__________________________________________________________________________________________________
conv2d_77 (Conv2D)              (None, 16, 32, 256)  295168      max_pooling2d_18[0][0]           
__________________________________________________________________________________________________
dropout_37 (Dropout)            (None, 16, 32, 256)  0           conv2d_77[0][0]                  
__________________________________________________________________________________________________
conv2d_78 (Conv2D)              (None, 16, 32, 256)  590080      dropout_37[0][0]                 
__________________________________________________________________________________________________
max_pooling2d_19 (MaxPooling2D) (None, 8, 16, 256)   0           conv2d_78[0][0]                  
__________________________________________________________________________________________________
conv2d_79 (Conv2D)              (None, 8, 16, 512)   1180160     max_pooling2d_19[0][0]           
__________________________________________________________________________________________________
dropout_38 (Dropout)            (None, 8, 16, 512)   0           conv2d_79[0][0]                  
__________________________________________________________________________________________________
conv2d_80 (Conv2D)              (None, 8, 16, 512)   2359808     dropout_38[0][0]                 
__________________________________________________________________________________________________
conv2d_transpose_15 (Conv2DTran (None, 16, 32, 256)  524544      conv2d_80[0][0]                  
__________________________________________________________________________________________________
concatenate_15 (Concatenate)    (None, 16, 32, 512)  0           conv2d_transpose_15[0][0]        
                                                                 conv2d_78[0][0]                  
__________________________________________________________________________________________________
conv2d_81 (Conv2D)              (None, 16, 32, 256)  1179904     concatenate_15[0][0]             
__________________________________________________________________________________________________
dropout_39 (Dropout)            (None, 16, 32, 256)  0           conv2d_81[0][0]                  
__________________________________________________________________________________________________
conv2d_82 (Conv2D)              (None, 16, 32, 256)  590080      dropout_39[0][0]                 
__________________________________________________________________________________________________
conv2d_transpose_16 (Conv2DTran (None, 32, 64, 128)  131200      conv2d_82[0][0]                  
__________________________________________________________________________________________________
concatenate_16 (Concatenate)    (None, 32, 64, 256)  0           conv2d_transpose_16[0][0]        
                                                                 conv2d_76[0][0]                  
__________________________________________________________________________________________________
conv2d_83 (Conv2D)              (None, 32, 64, 128)  295040      concatenate_16[0][0]             
__________________________________________________________________________________________________
dropout_40 (Dropout)            (None, 32, 64, 128)  0           conv2d_83[0][0]                  
__________________________________________________________________________________________________
conv2d_84 (Conv2D)              (None, 32, 64, 128)  147584      dropout_40[0][0]                 
__________________________________________________________________________________________________
conv2d_transpose_17 (Conv2DTran (None, 64, 128, 64)  32832       conv2d_84[0][0]                  
__________________________________________________________________________________________________
concatenate_17 (Concatenate)    (None, 64, 128, 128) 0           conv2d_transpose_17[0][0]        
                                                                 conv2d_74[0][0]                  
__________________________________________________________________________________________________
conv2d_85 (Conv2D)              (None, 64, 128, 64)  73792       concatenate_17[0][0]             
__________________________________________________________________________________________________
dropout_41 (Dropout)            (None, 64, 128, 64)  0           conv2d_85[0][0]                  
__________________________________________________________________________________________________
conv2d_86 (Conv2D)              (None, 64, 128, 64)  36928       dropout_41[0][0]                 
__________________________________________________________________________________________________
conv2d_transpose_18 (Conv2DTran (None, 128, 256, 32) 8224        conv2d_86[0][0]                  
__________________________________________________________________________________________________
concatenate_18 (Concatenate)    (None, 128, 256, 64) 0           conv2d_transpose_18[0][0]        
                                                                 conv2d_72[0][0]                  
__________________________________________________________________________________________________
conv2d_87 (Conv2D)              (None, 128, 256, 32) 18464       concatenate_18[0][0]             
__________________________________________________________________________________________________
dropout_42 (Dropout)            (None, 128, 256, 32) 0           conv2d_87[0][0]                  
__________________________________________________________________________________________________
conv2d_88 (Conv2D)              (None, 128, 256, 32) 9248        dropout_42[0][0]                 
__________________________________________________________________________________________________
conv2d_transpose_19 (Conv2DTran (None, 256, 512, 16) 2064        conv2d_88[0][0]                  
__________________________________________________________________________________________________
concatenate_19 (Concatenate)    (None, 256, 512, 32) 0           conv2d_transpose_19[0][0]        
                                                                 conv2d_70[0][0]                  
__________________________________________________________________________________________________
conv2d_89 (Conv2D)              (None, 256, 512, 16) 4624        concatenate_19[0][0]             
__________________________________________________________________________________________________
dropout_43 (Dropout)            (None, 256, 512, 16) 0           conv2d_89[0][0]                  
__________________________________________________________________________________________________
conv2d_90 (Conv2D)              (None, 256, 512, 16) 2320        dropout_43[0][0]                 
__________________________________________________________________________________________________
conv2d_91 (Conv2D)              (None, 256, 512, 1)  17          conv2d_90[0][0]                  
==================================================================================================
Total params: 7,775,313
Trainable params: 7,775,313
Non-trainable params: 0
_________________________________________________________

from keras import backend as K
def dice_coef(y_true, y_pred, smooth=1):
    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 1-dice_coef(y_true, y_pred)

from tensorflow.keras.callbacks import ModelCheckpoint
from tensorflow.keras.callbacks import CSVLogger
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.optimizers import Adam
NO_OF_TRAINING_IMAGES = len(os.listdir('/gdrive/My Drive/Train/img/images/'))
NO_OF_VAL_IMAGES = len(os.listdir('/gdrive/My Drive/Validation/img/images/'))
NO_OF_EPOCHS = 1
BATCH_SIZE = 32
filepath="weights-improvement-{epoch:02d}-{val_accuracy:.2f}.hdf5"
m = unet()
opt = Adam(lr=1E-5, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
m.compile(optimizer=opt,loss=dice_coef_loss, metrics=[dice_coef])
checkpoint = ModelCheckpoint(filepath, monitor=dice_coef_loss, 
                             verbose=1, save_best_only=True, mode='min')
earlystopping = EarlyStopping(monitor = dice_coef_loss, verbose = 1,
                              min_delta = 0.01, patience = 1, mode ='min')
callbacks_list = [checkpoint,earlystopping]
results = m.fit_generator(train_gen, epochs=NO_OF_EPOCHS, 
                          steps_per_epoch = (NO_OF_TRAINING_IMAGES//BATCH_SIZE),
                          validation_data=val_gen, 
                          validation_steps=(NO_OF_VAL_IMAGES//BATCH_SIZE), 
                          use_multiprocessing=False,
                          workers=1)
m.save('Model.h5')
418/418 [==============================] - 9828s 24s/step - loss: 0.0700 - dice_coef: 0.9300 - val_loss: 0.0299 - val_dice_coef: 0.9701

, но когда я беру вывод, все просто пусто. Я масштабирую вывод, умножая его на 255 перед визуализацией, и нормализация партии также отключена

1 Ответ

0 голосов
/ 11 февраля 2020

Ваш вывод, вероятно, нормализован между целочисленными значениями 0-20 ~, что требует масштабирования этих значений до диапазона 0-255 перед визуализацией. Кроме того, обязательно отключите нормализацию партии, указав, что модель работает в режиме вывода. Предположим, что ваш вывод из модели 

img1 = out[0,:,:,:] # select first element from our batch
img1 = img1.permute(1,2,0) # model outputs channel in first dim but to visualize we need it in last dim
matplotlib.imshow( img1 )
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