Блог

Я работаю над семантической сегментацией с использованием segmentation-models библиотеки. Я изменил это руководство, чтобы рассмотреть 8 классов для сегментации вместо 2, рассмотренных в примере. При обучении модели я получил ConcatOp : Dimensions of inputs should match: ошибку. Знаете ли вы, что вызвало эту ошибку?

 # define optomizer
optim = keras.optimizers.Adam(LR)

# Segmentation models losses can be combined together by ' ' and scaled by integer or float factor
# set class weights for dice_loss (car: 1.; pedestrian: 2.; background: 0.5;)
dice_loss = sm.losses.DiceLoss(class_weights=np.array([1, 2, 2, 2, 2, 2, 1, 1, 0.5])) 
focal_loss = sm.losses.BinaryFocalLoss() if n_classes == 1 else sm.losses.CategoricalFocalLoss()
# total_loss = dice_loss   (1 * focal_loss)

# actulally total_loss can be imported directly from library, above example just show you how to manipulate with losses
total_loss = sm.losses.categorical_focal_dice_loss # or sm.losses.categorical_focal_dice_loss 

metrics = [sm.metrics.IOUScore(threshold=0.5), sm.metrics.FScore(threshold=0.5)]

# compile keras model with defined optimozer, loss and metrics
model.compile(optim, total_loss, metrics)
 

 # Dataset for train images
train_dataset = Dataset(
    x_train_dir, 
    y_train_dir, 
    classes=CLASSES, 
    augmentation=get_training_augmentation(),
    preprocessing=get_preprocessing(preprocess_input),
)

# Dataset for validation images
valid_dataset = Dataset(
    x_valid_dir, 
    y_valid_dir, 
    classes=CLASSES, 
    augmentation=get_validation_augmentation(),
    preprocessing=get_preprocessing(preprocess_input),
)

train_dataloader = Dataloder(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
valid_dataloader = Dataloder(valid_dataset, batch_size=1, shuffle=False)

# check shapes for errors
assert train_dataloader[0][0].shape == (BATCH_SIZE, 320, 320, 3)
assert train_dataloader[0][1].shape == (BATCH_SIZE, 320, 320, n_classes)

# define callbacks for learning rate scheduling and best checkpoints saving
callbacks = [
    keras.callbacks.ModelCheckpoint('./best_model.h5', save_weights_only=True, save_best_only=True, mode='min'),
    keras.callbacks.ReduceLROnPlateau(),
]

# train model
history = model.fit_generator(
    train_dataloader, 
    steps_per_epoch=len(train_dataloader), 
    epochs=EPOCHS, 
    callbacks=callbacks, 
    validation_data=valid_dataloader, 
    validation_steps=len(valid_dataloader),
)
 

 Epoch 1/40
61/62 [============================>.] - ETA: 0s - loss: 0.9784 - iou_score: 0.0108 - f1-score: 0.0124

---------------------------------------------------------------------------

InvalidArgumentError                      Traceback (most recent call last)

<ipython-input-59-859d1e145522> in <module>()
      6     callbacks=callbacks,
      7     validation_data=valid_dataloader,
----> 8     validation_steps=len(valid_dataloader),
      9 )

13 frames

/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `'   object_name   '` call to the '  
     90                               'Keras 2 API: '   signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1730             use_multiprocessing=use_multiprocessing,
   1731             shuffle=shuffle,
-> 1732             initial_epoch=initial_epoch)
   1733 
   1734     @interfaces.legacy_generator_methods_support

/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
    240                             validation_steps,
    241                             callbacks=callbacks,
--> 242                             workers=0)
    243                     else:
    244                         # No need for try/except because

/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `'   object_name   '` call to the '  
     90                               'Keras 2 API: '   signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in evaluate_generator(self, generator, steps, callbacks, max_queue_size, workers, use_multiprocessing, verbose)
   1789             workers=workers,
   1790             use_multiprocessing=use_multiprocessing,
-> 1791             verbose=verbose)
   1792 
   1793     @interfaces.legacy_generator_methods_support

/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py in evaluate_generator(model, generator, steps, callbacks, max_queue_size, workers, use_multiprocessing, verbose)
    399             outs = model.test_on_batch(x, y,
    400                                        sample_weight=sample_weight,
--> 401                                        reset_metrics=False)
    402             outs = to_list(outs)
    403             outs_per_batch.append(outs)

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in test_on_batch(self, x, y, sample_weight, reset_metrics)
   1557             ins = x   y   sample_weights
   1558         self._make_test_function()
-> 1559         outputs = self.test_function(ins)
   1560 
   1561         if reset_metrics:

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py in __call__(self, inputs)
   3725         value = math_ops.cast(value, tensor.dtype)
   3726       converted_inputs.append(value)
-> 3727     outputs = self._graph_fn(*converted_inputs)
   3728 
   3729     # EagerTensor.numpy() will often make a copy to ensure memory safety.

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
   1549       TypeError: For invalid positional/keyword argument combinations.
   1550     """
-> 1551     return self._call_impl(args, kwargs)
   1552 
   1553   def _call_impl(self, args, kwargs, cancellation_manager=None):

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in _call_impl(self, args, kwargs, cancellation_manager)
   1589       raise TypeError("Keyword arguments {} unknown. Expected {}.".format(
   1590           list(kwargs.keys()), list(self._arg_keywords)))
-> 1591     return self._call_flat(args, self.captured_inputs, cancellation_manager)
   1592 
   1593   def _filtered_call(self, args, kwargs):

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1690       # No tape is watching; skip to running the function.
   1691       return self._build_call_outputs(self._inference_function.call(
-> 1692           ctx, args, cancellation_manager=cancellation_manager))
   1693     forward_backward = self._select_forward_and_backward_functions(
   1694         args,

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    543               inputs=args,
    544               attrs=("executor_type", executor_type, "config_proto", config),
--> 545               ctx=ctx)
    546         else:
    547           outputs = execute.execute_with_cancellation(

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     keras_symbolic_tensors = [

/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)

InvalidArgumentError:  ConcatOp : Dimensions of inputs should match: shape[0] = [1,1536,44,64] vs. shape[1] = [1,816,43,64]
     [[node decoder_stage0_concat_4/concat (defined at /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3009) ]] [Op:__inference_keras_scratch_graph_508722]

Function call stack:
keras_scratch_graph