Tensorflow 2がValueErrorをスロー：as_list（）が不明なTensorShapeで定義されていません
画像とセグメンテーションマスクを入力として受け取るTensorflow 2.0でUnetモデルをトレーニングしようとしていますが、ValueError : as_list() is not defined on an unknown TensorShapeを取得しています。スタックトレースは、_get_input_from_iterator(inputs)の実行中に問題が発生したことを示しています。
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in _prepare_feed_values(model, inputs, mode)
110 for inputs will always be wrapped in lists.
111 """
--> 112 inputs, targets, sample_weights = _get_input_from_iterator(inputs)
113
114 # When the inputs are dict, then we want to flatten it in the same order as
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in _get_input_from_iterator(iterator)
147 # Validate that all the elements in x and y are of the same type and shape.
148 dist_utils.validate_distributed_dataset_inputs(
--> 149 distribution_strategy_context.get_strategy(), x, y, sample_weights)
150 return x, y, sample_weights
151
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py in validate_distributed_dataset_inputs(distribution_strategy, x, y, sample_weights)
309
310 if y is not None:
--> 311 y_values_list = validate_per_replica_inputs(distribution_strategy, y)
312 else:
313 y_values_list = None
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py in validate_per_replica_inputs(distribution_strategy, x)
354 if not context.executing_eagerly():
355 # Validate that the shape and dtype of all the elements in x are the same.
--> 356 validate_all_tensor_shapes(x, x_values)
357 validate_all_tensor_types(x, x_values)
358
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py in validate_all_tensor_shapes(x, x_values)
371 def validate_all_tensor_shapes(x, x_values):
372 # Validate that the shape of all the elements in x have the same shape
--> 373 x_shape = x_values[0].shape.as_list()
374 for i in range(1, len(x_values)):
375 if x_shape != x_values[i].shape.as_list():
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_shape.py in as_list(self)
1169 """
1170 if self._dims is None:
-> 1171 raise ValueError("as_list() is not defined on an unknown TensorShape.")
1172 return [dim.value for dim in self._dims]
1173
他のいくつかのStackoverflow投稿（ here および here ）をこのエラーで調べましたが、私の場合、自分に渡すマップ関数で問題が発生すると思いますDataSets。以下で定義するprocess_path関数をtensorflow DataSetのmap関数に呼び出します。これは画像へのパスを受け入れ、numpy fileである対応するセグメンテーションマスクへのパスを作成します。 numpyファイルの（256 256）配列は、kerasUtil.to_categoricalを使用して（256 256 10）に変換されます。ここで、10チャネルは各クラスを表します。テンソルの形状が正しいことを確認するためにcheck_shape関数を使用しましたが、model.fitを呼び出しても形状を導出できません。
# --------------------------------------------------------------------------------------
# DECODE A NUMPY .NPY FILE INTO THE REQUIRED FORMAT FOR TRAINING
# --------------------------------------------------------------------------------------
def decode_npy(npy):
filename = npy.numpy()
data = np.load(filename)
data = kerasUtils.to_categorical(data, 10)
return data
def check_shape(image, mask):
print('shape of image: ', image.get_shape())
print('shape of mask: ', mask.get_shape())
return 0.0
# --------------------------------------------------------------------------------------
# DECODE AN IMAGE (PNG) FILE INTO THE REQUIRED FORMAT FOR TRAINING
# --------------------------------------------------------------------------------------
def decode_img(img):
# convert the compressed string to a 3D uint8 tensor
img = tf.image.decode_png(img, channels=3)
# Use `convert_image_dtype` to convert to floats in the [0,1] range.
return tf.image.convert_image_dtype(img, tf.float32)
# --------------------------------------------------------------------------------------
# PROCESS A FILE PATH FOR THE DATASET
# input - path to an image file
# output - an input image and output mask
# --------------------------------------------------------------------------------------
def process_path(filePath):
parts = tf.strings.split(filePath, '/')
fileName = parts[-1]
parts = tf.strings.split(fileName, '.')
prefix = tf.convert_to_tensor(convertedMaskDir, dtype=tf.string)
suffix = tf.convert_to_tensor("-mask.npy", dtype=tf.string)
maskFileName = tf.strings.join((parts[-2], suffix))
maskPath = tf.strings.join((prefix, maskFileName), separator='/')
# load the raw data from the file as a string
img = tf.io.read_file(filePath)
img = decode_img(img)
mask = tf.py_function(decode_npy, [maskPath], tf.float32)
return img, mask
# --------------------------------------------------------------------------------------
# CREATE A TRAINING and VALIDATION DATASETS
# --------------------------------------------------------------------------------------
trainSize = int(0.7 * DATASET_SIZE)
validSize = int(0.3 * DATASET_SIZE)
allDataSet = tf.data.Dataset.list_files(str(imageDir + "/*"))
# allDataSet = allDataSet.map(process_path, num_parallel_calls=AUTOTUNE)
# allDataSet = allDataSet.map(process_path)
trainDataSet = allDataSet.take(trainSize)
trainDataSet = trainDataSet.map(process_path).batch(64)
validDataSet = allDataSet.skip(trainSize)
validDataSet = validDataSet.map(process_path).batch(64)
...
# this code throws the error!
model_history = model.fit(trainDataSet, epochs=EPOCHS,
steps_per_Epoch=stepsPerEpoch,
validation_steps=validationSteps,
validation_data=validDataSet,
callbacks=callbacks)
私はあなたと同じ問題を画像とマスクで抱えていて、特にtf.mapの間にpyfuncを呼び出すときに、前処理関数の間に両方の形状を手動で設定することで解決しました。
def process_path(filePath):
...
# load the raw data from the file as a string
img = tf.io.read_file(filePath)
img = decode_img(img)
mask = tf.py_function(decode_npy, [maskPath], tf.float32)
# TODO:
img.set_shape([MANUALLY ENTER THIS])
mask.set_shape([MANUALLY ENTER THIS])
return img, mask