I am trying to train network for Bounding Box Regression. I've created pd.DataFrame that looks like this:
Here are my train and validation image generators:
image_generator = tf.keras.preprocessing.image.ImageDataGenerator(
rescale = 1./255,
rotation_range = 25,
zoom_range=[0.8, 1.2],
vertical_flip=True,
horizontal_flip=True,
)
train_generator = image_generator.flow_from_dataframe(
dataframe=train_df,
directory=cbis_ddsm_train_images_dir,
x_col="image file path",
y_col="coordinates",
class_mode="raw",
batch_size=BATCH_SIZE,
shuffle=True,
seed=1,
color_mode="grayscale",
target_size=(1024, 1024))
val_gen = tf.keras.preprocessing.image.ImageDataGenerator(
rescale = 1./255,
)
val_generator = val_gen.flow_from_dataframe(
dataframe=val_df,
directory=cbis_ddsm_train_images_dir,
x_col="image file path",
y_col=None,
class_mode="raw",
batch_size=BATCH_SIZE,
shuffle=False,
seed=1,
color_mode="grayscale",
target_size=(1024, 1024))
Please note that I've converted Y column (i.e. bbox coordinates) from lists into numpy dimensional array's as follows:
for idx, row in train_df.iterrows():
height, width = row['size']
row['coordinates'] = np.asarray([normalize_bbox(c, height, width) for c in row['coordinates']][0]).astype('float32')
When I try to execute code below:
opt = Adam(lr=INIT_LR)
final_model.compile(optimizer=opt, loss="mse")
final_model.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=3,
validation_data=val_generator, validation_steps=val_steps, verbose=1)
I am getting following error message:
ValueError Traceback (most recent call last)
<ipython-input-46-90f3a1cd9c87> in <module>()
8
9 final_model.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=3,
---> 10 validation_data=val_generator, validation_steps=val_steps, verbose=1)
11 final_model.save(os.path.join(experiment1_dir, "resnet_fine-tuned-head.h5"))
12
14 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1061 use_multiprocessing=use_multiprocessing,
1062 model=self,
-> 1063 steps_per_execution=self._steps_per_execution)
1064
1065 # Container that configures and calls `tf.keras.Callback`s.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
1115 use_multiprocessing=use_multiprocessing,
1116 distribution_strategy=ds_context.get_strategy(),
-> 1117 model=model)
1118
1119 strategy = ds_context.get_strategy()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, shuffle, workers, use_multiprocessing, max_queue_size, model, **kwargs)
914 max_queue_size=max_queue_size,
915 model=model,
--> 916 **kwargs)
917
918 @staticmethod
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
786 peek, x = self._peek_and_restore(x)
787 peek = self._standardize_batch(peek)
--> 788 peek = _process_tensorlike(peek)
789
790 # Need to build the Model on concrete input shapes.
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _process_tensorlike(inputs)
1019 return x
1020
-> 1021 inputs = nest.map_structure(_convert_numpy_and_scipy, inputs)
1022 return nest.list_to_tuple(inputs)
1023
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py in map_structure(func, *structure, **kwargs)
633
634 return pack_sequence_as(
--> 635 structure[0], [func(*x) for x in entries],
636 expand_composites=expand_composites)
637
/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py in <listcomp>(.0)
633
634 return pack_sequence_as(
--> 635 structure[0], [func(*x) for x in entries],
636 expand_composites=expand_composites)
637
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _convert_numpy_and_scipy(x)
1014 if issubclass(x.dtype.type, np.floating):
1015 dtype = backend.floatx()
-> 1016 return ops.convert_to_tensor(x, dtype=dtype)
1017 elif scipy_sparse and scipy_sparse.issparse(x):
1018 return _scipy_sparse_to_sparse_tensor(x)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1497
1498 if ret is None:
-> 1499 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1500
1501 if ret is NotImplemented:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_conversion_registry.py in _default_conversion_function(***failed resolving arguments***)
50 def _default_conversion_function(value, dtype, name, as_ref):
51 del as_ref # Unused.
---> 52 return constant_op.constant(value, dtype, name=name)
53
54
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name)
262 """
263 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 264 allow_broadcast=True)
265
266
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
273 with trace.Trace("tf.constant"):
274 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 275 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
276
277 g = ops.get_default_graph()
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
298 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
299 """Implementation of eager constant."""
--> 300 t = convert_to_eager_tensor(value, ctx, dtype)
301 if shape is None:
302 return t
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
96 dtype = dtypes.as_dtype(dtype).as_datatype_enum
97 ctx.ensure_initialized()
---> 98 return ops.EagerTensor(value, ctx.device_name, dtype)
99
100
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).
Any sort of help would be GREATLY appreciated. Thanks!
