'tensorboard.compat.tensorflow_stub.io.gfile' has no attribute 'FastGFile' 与 Tensorflow 错误

Question

我在 Docker 20.12 中使用 TensorRT，在 Ubuntu 18 中使用 Tensorflow-gpu 2.4

我有下面的代码

N=1024  
target_size = (N, N)

warnings.filterwarnings('ignore')

LR= 1e-4
E, BS = 2,4 


def get_unet(img_rows, img_cols):
    inputs = Input((img_rows, img_cols, 1))
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
    conv1 = BatchNormalization()(conv1)
    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
    conv1 = BatchNormalization()(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

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

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

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

    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
    conv5 = BatchNormalization()(conv5)
    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)
    conv5 = BatchNormalization()(conv5)
    
    conv5 = Dropout(0.5)(conv5)

    up6 = concatenate([UpSampling2D(size=(2, 2))(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([UpSampling2D(size=(2, 2))(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([UpSampling2D(size=(2, 2))(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([UpSampling2D(size=(2, 2))(conv8), conv1], axis=3)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)


    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)
      
    
    model = Model(inputs=[inputs], outputs=[conv10])
    
    model.compile(optimizer=Adam(lr=LR), 
                      loss='binary_crossentropy',
                      metrics=['binary_crossentropy'])


    return model


train_images=np.zeros([1, N,N,1], dtype=float)
annot_train=np.zeros([1, N,N,1], dtype=float)
test_images=np.zeros([1, N,N,1], dtype=float)
annot_test=np.zeros([1, N,N,1], dtype=float)

img = cv2.imread('owlResized.bmp', 0)/255.0 
label = cv2.imread('owlResized.bmp', 0)/255.0
train_images[0,:,:,0], annot_train[0,:,:,0] =img, label
test_images[0,:,:,0], annot_test[0,:,:,0] =img, label

C = np.concatenate([annot_test, annot_train])
I = np.concatenate([test_images, train_images])

unet = get_unet(N, N)
history = unet.fit(I, C, verbose=2, epochs=E, batch_size=BS, validation_split=0.1)

tf.compat.v1.disable_eager_execution()

with tf.Session() as sess:


    y1 = tf.image.resize_bilinear(C, target_size, align_corners=True, half_pixel_centers=False)
    _1 = tf.identity(y1, name="output")

    y2 = tf.image.resize_bilinear(I, target_size, align_corners=True, half_pixel_centers=False)
    _2 = tf.identity(y1, name="output")

    init_op = tf.global_variables_initializer()

    sess.run(init_op)
    constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph_def, ['output'])
    with tf.io.gfile.FastGFile('tfmodel.pb', mode='wb') as f:
        f.write(constant_graph.SerializeToString())
    os.system("python3 -m tf2onnx.convert --opset 11 --input tfmodel.pb --inputs input:0 --outputs output:0 --output tfmodel.onnx")

然后我得到了错误

with tf.io.gfile.FastGFile('tfmodel.pb', mode='wb') as f:
module 'tensorflow._api.v2.io.gfile' has no attribute 'FastGFile'

然后我添加了tensorboard，如下：

import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile

我现在明白了

    with tf.io.gfile.FastGFile('tfmodel.pb', mode='wb') as f:
AttributeError: module 'tensorboard.compat.tensorflow_stub.io.gfile' has no attribute 'FastGFile'

谁能帮忙？

Answer 1

使用tf.io.gfile.GFile 代替tf.io.gfile.FastGFile

示例工作代码

with open("/tmp/x", "w") as f:
  f.write("asdfqwer")
f = tf.io.gfile.GFile("/tmp/x")
f.read(3)