SigmoidFocalCrossEntropy loss fails to compile model #876
Description
System information
- OS Platform and Distribution:
Deep Learning Linux VM (Debian Stretch) GCE Image - TensorFlow version and how it was installed:
Binary v2.1.0 - TensorFlow-Addons version and how it was installed:
Binary v0.7.0 - Python version:
Python 3.5.3 - Is GPU used? (yes/no):
No
Describe the bug
model.compile tries to call some code with some test values for y_true and y_pred which causes a shape mismatch with tfa.losses.sigmoid_focal_crossentropy. The following line is causing the error to occur. It may also be a problem with the tf.keras models when used with custom losses altogether.
addons/tensorflow_addons/losses/focal_loss.py
Lines 125 to 127 in 1bbe67f
Also, it is notable to mention that the same model (with exact same code) is able to be compile when using tensorflow-addons v0.6.0 (even, training works perfectly fine)
Code to reproduce the issue
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import Reduction
from tensorflow_addons.losses import SigmoidFocalCrossEntropy
def create_mlp(input_size, num_classes):
model = Sequential([
Dense(100, activation='relu', input_shape=(input_size,), name='hidden'),
Dense(num_classes, activation='softmax', name='output')
], name='mlp')
loss = SigmoidFocalCrossEntropy(reduction=Reduction.SUM_OVER_BATCH_SIZE)
opt = Adam()
model.summary()
model.compile(opt, loss)
return model
mlp = create_mlp(1000, 5)Other info / logs
Model: "mlp"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
hidden (Dense) (None, 100) 100100
_________________________________________________________________
output (Dense) (None, 5) 505
=================================================================
Total params: 100,605
Trainable params: 100,605
Non-trainable params: 0
_________________________________________________________________
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-2891a1b09888> in <module>()
----> 1 mlp = create_mlp_classifier(1000, 5)
14 frames
<ipython-input-8-686d7bad7efc> in create_mlp_classifier(input_size, num_classes)
20
21 model.summary()
---> 22 model.compile(opt, loss)
23
24 return model
/tensorflow-2.1.0/python3.6/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, distribute, **kwargs)
444
445 # Creates the model loss and weighted metrics sub-graphs.
--> 446 self._compile_weights_loss_and_weighted_metrics()
447
448 # Functions for train, test and predict will
/tensorflow-2.1.0/python3.6/tensorflow_core/python/training/tracking/base.py in _method_wrapper(self, *args, **kwargs)
455 self._self_setattr_tracking = False # pylint: disable=protected-access
456 try:
--> 457 result = method(self, *args, **kwargs)
458 finally:
459 self._self_setattr_tracking = previous_value # pylint: disable=protected-access
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py in _compile_weights_loss_and_weighted_metrics(self, sample_weights)
1590 # loss_weight_2 * output_2_loss_fn(...) +
1591 # layer losses.
-> 1592 self.total_loss = self._prepare_total_loss(masks)
1593
1594 def _prepare_skip_target_masks(self):
/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py in _prepare_total_loss(self, masks)
1650
1651 if hasattr(loss_fn, 'reduction'):
-> 1652 per_sample_losses = loss_fn.call(y_true, y_pred)
1653 weighted_losses = losses_utils.compute_weighted_loss(
1654 per_sample_losses,
/usr/local/lib/python3.6/dist-packages/tensorflow_addons/losses/focal_loss.py in call(self, y_true, y_pred)
86 alpha=self.alpha,
87 gamma=self.gamma,
---> 88 from_logits=self.from_logits)
89
90 def get_config(self):
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
604 # In this case we have not created variables on the first call. So we can
605 # run the first trace but we should fail if variables are created.
--> 606 results = self._stateful_fn(*args, **kwds)
607 if self._created_variables:
608 raise ValueError("Creating variables on a non-first call to a function"
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
2360 """Calls a graph function specialized to the inputs."""
2361 with self._lock:
-> 2362 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
2363 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2364
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2701
2702 self._function_cache.missed.add(call_context_key)
-> 2703 graph_function = self._create_graph_function(args, kwargs)
2704 self._function_cache.primary[cache_key] = graph_function
2705 return graph_function, args, kwargs
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2591 arg_names=arg_names,
2592 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2593 capture_by_value=self._capture_by_value),
2594 self._function_attributes,
2595 # Tell the ConcreteFunction to clean up its graph once it goes out of
/tensorflow-2.1.0/python3.6/tensorflow_core/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
976 converted_func)
977
--> 978 func_outputs = python_func(*func_args, **func_kwargs)
979
980 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
/tensorflow-2.1.0/python3.6/tensorflow_core/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in converted code:
/usr/local/lib/python3.6/dist-packages/tensorflow_addons/losses/focal_loss.py:126 sigmoid_focal_crossentropy *
raise ValueError("Shape mismatch for y_true: {} and y_pred: {}".format(
ValueError: Shape mismatch for y_true: Tensor("Shape:0", shape=(2,), dtype=int32) and y_pred: Tensor("Shape_1:0", shape=(2,),Thanks.