TensorFlow 2.0, error in keras.applications (as_list() is not defined on an unknown TensorShape) - tensorflow

Question

There are several questions on SO with this error:
ValueError: as_list() is not defined on an unknown TensorShape.
and a few relevant issues on git as well: 1, 2.
However, I haven't found a consistent answer to why this message appears, or a solution for my specific issue. This entire pipeline used to work with tf2.0.0-alpha and now, after installing with Conda conda install tensorflow=2.0 python=3.6 the pipeline is broken.
In short terms, I use a generator to return image data to the tf.data.Dataset.from_generator() method. This works fine, until I try to call the model.fit() method, which results in the following error.
Train for 750 steps, validate for 100 steps
Epoch 1/5
1/750 [..............................] - ETA: 10sTraceback (most recent call last):
File "/usr/local/anaconda3/envs/tf/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/Users/tmo/Projects/casa/image/src/train.py", line 148, in <module>
Trainer().train_vgg16()
File "/Users/tmo/Projects/casa/image/src/train.py", line 142, in train_vgg16
validation_steps=100)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py", line 728, in fit
use_multiprocessing=use_multiprocessing)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 324, in fit
total_epochs=epochs)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 123, in run_one_epoch
batch_outs = execution_function(iterator)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 86, in execution_function
distributed_function(input_fn))
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 457, in __call__
result = self._call(*args, **kwds)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 503, in _call
self._initialize(args, kwds, add_initializers_to=initializer_map)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 408, in _initialize
*args, **kwds))
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 1848, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2150, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py", line 2041, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/framework/func_graph.py", line 915, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/eager/def_function.py", line 358, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 66, in distributed_function
model, input_iterator, mode)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 112, in _prepare_feed_values
inputs, targets, sample_weights = _get_input_from_iterator(inputs)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py", line 149, in _get_input_from_iterator
distribution_strategy_context.get_strategy(), x, y, sample_weights)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py", line 308, in validate_distributed_dataset_inputs
x_values_list = validate_per_replica_inputs(distribution_strategy, x)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py", line 356, in validate_per_replica_inputs
validate_all_tensor_shapes(x, x_values)
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/keras/distribute/distributed_training_utils.py", line 373, in validate_all_tensor_shapes
x_shape = x_values[0].shape.as_list()
File "/usr/local/anaconda3/envs/tf/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_shape.py", line 1171, in as_list
raise ValueError("as_list() is not defined on an unknown TensorShape.")
ValueError: as_list() is not defined on an unknown TensorShape.
This is the code that loads and reshapes each image:
def preprocess_image(self, image):
"""
"""
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize(image, self.hw)
image /= 255.0 # normalize to [0,1] range
image.set_shape([224, 224, 3])
return image
which is applied to a generator (e.g. training_generator) that runs through a list of images and yields the preprocessed result:
def make_ts_dataset(self):
AUTOTUNE = tf.data.experimental.AUTOTUNE
BATCH_SIZE = 32
image_count_training = len(self.X_train)
image_count_validation = len(self.X_test)
training_generator = GetTensor(hw=self.hw, train=True).make_tensor
training_image_ds = tf.data.Dataset.from_generator(training_generator, tf.float32, [224, 224, 3])
training_price_ds = tf.data.Dataset.from_tensor_slices(tf.cast(self.y_train, tf.float32))
validation_generator = GetTensor(hw=self.hw, test=True).make_tensor
validation_image_ds = tf.data.Dataset.from_generator(validation_generator, tf.float32, [224, 224, 3])
validation_price_ds = tf.data.Dataset.from_tensor_slices(tf.cast(self.y_test, tf.float32))
training_ds = tf.data.Dataset.zip((training_image_ds, training_price_ds))
validation_ds = tf.data.Dataset.zip((validation_image_ds, validation_price_ds))
training_ds = training_ds.shuffle(buffer_size=int(round(image_count_training)))
training_ds = training_ds.repeat()
training_ds = training_ds.batch(BATCH_SIZE)
training_ds = training_ds.prefetch(buffer_size=AUTOTUNE)
validation_ds = validation_ds.shuffle(buffer_size=int(round(image_count_validation)))
validation_ds = validation_ds.repeat()
validation_ds = validation_ds.batch(BATCH_SIZE)
validation_ds = validation_ds.prefetch(buffer_size=AUTOTUNE)
for image_batch, label_batch in training_ds.take(1):
print(label_batch.shape, image_batch.shape)
pass
return training_ds, validation_ds
At all points, the shapes look correct, i.e. (32,) (32, 224, 224, 3)
I am initialising the weights with trained weights from VGG16
def train_vgg16(self):
training_ds, validation_ds = Trainer.make_ts_dataset(self)
base_vgg = keras.applications.vgg16.VGG16(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3))
base_vgg.trainable = False
print(base_vgg.summary())
vgg_with_base = keras.Sequential([
base_vgg,
tf.keras.layers.GlobalMaxPooling2D(),
tf.keras.layers.Dense(1024, activation=tf.nn.relu),
tf.keras.layers.Dense(1024, activation=tf.nn.relu),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dense(1)])
print(base_vgg.summary())
vgg_with_base.compile(optimizer='adam',
loss='mse',
metrics=['mape'])
vgg_with_base.fit(training_ds,
epochs=5,
validation_data=validation_ds,
steps_per_epoch=750,
validation_steps=100)
However, training never initiates because x_shape = x_values[0].shape.as_list() fails.
Edit (11/12/19):
After some troubleshooting, I found that the error is initiated in the keras.applications layer.
base_vgg = keras.applications.vgg16.VGG16(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3))
Removing base_vgg from the model and initialising training works fine.

Answer 1

Explicitly defining the shape with Input from tensorflow.keras.layers in the keras.applications.vgg16.VGG16 solved the problem for me.
from tensorflow.keras.layers import Input
base_vgg = keras.applications.vgg16.VGG16(include_top=False,
weights='imagenet',
input_tensor=Input(shape=(224,224,3)),
input_shape=(224, 224, 3))
I still think this is closer to a bug than a feature though.

Answer 2

I fixed this error by tf.cast(label, tf.float32) after importing the masks.