Tensorflowlite on android缓冲区大小错误
Tensorflowlite on android缓冲区大小错误
提问于 2021-07-23 14:34:29
我正在尝试建立一个图像分类器android应用程序。我已经使用keras构建了我的模型。模型如下：
model.add(MobileNetV2(include_top=False, weights='imagenet',input_shape=(224, 224, 3)))
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(3, activation='softmax'))
model.layers[0].trainable = False
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()输出：
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280) 2257984
_________________________________________________________________
global_average_pooling2d_2 ( (None, 1280) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 1280) 0
_________________________________________________________________
dense_1 (Dense) (None, 3) 3843
=================================================================
Total params: 2,261,827
Trainable params: 3,843
Non-trainable params: 2,257,984训练完成后，我使用以下命令转换模型
model = tf.keras.models.load_model('model.h5')
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open(f"myModel.tflite", "wb").write(tflite_model)对于android，代码如下：
make_prediction.setOnClickListener(View.OnClickListener {
var resized = Bitmap.createScaledBitmap(bitmap, 224, 224, true)
val model = MyModel.newInstance(this)
var tbuffer = TensorImage.fromBitmap(resized)
var byteBuffer = tbuffer.buffer
// Creates inputs for reference.
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 224, 224, 3), DataType.FLOAT32)
inputFeature0.loadBuffer(byteBuffer)
// Runs model inference and gets result.
val outputs = model.process(inputFeature0)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer
var max = getMax(outputFeature0.floatArray)
text_view.setText(labels[max])
// Releases model resources if no longer used.
model.close()
})但是每当我尝试运行我的应用程序时，它就会关闭，并且我在logcat中得到这个错误。
java.lang.IllegalArgumentException: The size of byte buffer and the shape do not match.如果我将我的图像的输入形状从224改为300，并在300输入形状上训练我的模型，并将其插入android，我得到了anthor错误。
java.lang.IllegalArgumentException: Cannot convert between a TensorFlowLite buffer with 1080000 bytes and a Java Buffer with 150528 bytes任何形式的帮助都将是非常感谢的。
回答 1
Stack Overflow用户
回答已采纳
发布于 2021-07-26 12:22:49
像这样使用它：
make_prediction.setOnClickListener(View.OnClickListener {
var resized = Bitmap.createScaledBitmap(bitmap, 224, 224, true)
val model = MyModel.newInstance(this)
var tImage = TensorImage(DataType.FLOAT32)
var tensorImage = tImage.load(resized)
var byteBuffer = tensorImage.buffer
// Creates inputs for reference.
//val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 224, 224, 3), DataType.FLOAT32)
//inputFeature0.loadBuffer(byteBuffer)
// Runs model inference and gets result.
val outputs = model.process(byteBuffer)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer
var max = getMax(outputFeature0.floatArray)
text_view.setText(labels[max])
// Releases model resources if no longer used.
model.close()
})如果问题仍然存在，请使用调试器进行检查，或者
val outputFeature0 = outputs.outputFeature0AsTensorBuffer导致了另一个问题。
如果您需要更多帮助，请联系我
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原文链接：
https://stackoverflow.com/questions/68494971
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