Tensorflow 2 throwing ValueError: as_list() is not defined on an unknown TensorShape
Question:
I’m trying to train a Unet model in Tensorflow 2.0 which takes as input an image and a segmentation mask, but I’m getting a ValueError : as_list() is not defined on an unknown TensorShape. The stack trace shows the problem occurs during _get_input_from_iterator(inputs):
/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
I’ve looked through a couple of other Stackoverflow posts (here and here) with this error but in my case I think the problem arises in the map function I pass to my DataSets. I call the process_path function defined below to the map function of tensorflow DataSet. This accepts a path to the image and constructs a path to the corresponding segmentation mask which is a numpy file. The (256 256) array in the numpy file is then converted to (256 256 10), using kerasUtil.to_categorical where the 10 channels represents each class. I used the check_shape function to confirm that the tensor shapes are correct but still when I call model.fit the shape cannot be derived.
# --------------------------------------------------------------------------------------
# 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)
Answers:
I had the same problem as you with image and mask and solved it by setting both their shapes during the preprocessing function manually, in particular when calling a pyfunc during the tf.map.
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
I had a similar issue and used dataset.padded_batch with explicit padded_shapes solved my problem!
I was facing the same issue, also with a py_func mapping inside my pipeline.
Turns out that the issue was related to the compatibility between loss function and metrics. I was trying to use "Categorical Crossentropy" loss with "accuracy" metric.
Solved my problem by changing the metric to "categorical accuracy".
I’m trying to train a Unet model in Tensorflow 2.0 which takes as input an image and a segmentation mask, but I’m getting a ValueError : as_list() is not defined on an unknown TensorShape. The stack trace shows the problem occurs during _get_input_from_iterator(inputs):
/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
I’ve looked through a couple of other Stackoverflow posts (here and here) with this error but in my case I think the problem arises in the map function I pass to my DataSets. I call the process_path function defined below to the map function of tensorflow DataSet. This accepts a path to the image and constructs a path to the corresponding segmentation mask which is a numpy file. The (256 256) array in the numpy file is then converted to (256 256 10), using kerasUtil.to_categorical where the 10 channels represents each class. I used the check_shape function to confirm that the tensor shapes are correct but still when I call model.fit the shape cannot be derived.
# --------------------------------------------------------------------------------------
# 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)
I had the same problem as you with image and mask and solved it by setting both their shapes during the preprocessing function manually, in particular when calling a pyfunc during the tf.map.
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
I had a similar issue and used dataset.padded_batch with explicit padded_shapes solved my problem!
I was facing the same issue, also with a py_func mapping inside my pipeline.
Turns out that the issue was related to the compatibility between loss function and metrics. I was trying to use "Categorical Crossentropy" loss with "accuracy" metric.
Solved my problem by changing the metric to "categorical accuracy".