How to manage multilabel segmentation map for Active contour loss? - keras

Question

I am using Active contour loss (https://github.com/xuuuuuuchen/Active-Contour-Loss/blob/master/Active-Contour-Loss.py) which is as follows:
from keras import backend as K
import numpy as np
def Active_Contour_Loss(y_true, y_pred):
#y_pred = K.cast(y_pred, dtype = 'float64')
x = y_pred[:,:,1:,:] - y_pred[:,:,:-1,:] # horizontal and vertical directions
y = y_pred[:,:,:,1:] - y_pred[:,:,:,:-1]
delta_x = x[:,:,1:,:-2]**2
delta_y = y[:,:,:-2,1:]**2
delta_u = K.abs(delta_x + delta_y)
epsilon = 0.00000001 # where is a parameter to avoid square root is zero in practice.
w = 1
lenth = w * K.sum(K.sqrt(delta_u + epsilon)) # equ.(11) in the paper
"""
region term
"""
C_1 = np.ones((256, 256))
C_2 = np.zeros((256, 256))
region_in = K.abs(K.sum( y_pred[:,0,:,:] * ((y_true[:,0,:,:] - C_1)**2) ) ) # equ.(12) in the paper
region_out = K.abs(K.sum( (1-y_pred[:,0,:,:]) * ((y_true[:,0,:,:] - C_2)**2) )) # equ.(12) in the paper
lambdaP = 1 # lambda parameter could be various.
loss = lenth + lambdaP * (region_in + region_out)
return loss
However, when I use it for the U-net model. I am getting below error while compiling.
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1606 try:
-> 1607 c_op = c_api.TF_FinishOperation(op_desc)
1608 except errors.InvalidArgumentError as e:
InvalidArgumentError: Dimensions must be equal, but are 4 and 256 for 'loss_2/activation_57_loss/mul_1' (op: 'Mul') with input shapes: [?,256,4], [?,256,256].
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
12 frames
<ipython-input-33-b98b233ef3b2> in <module>()
50 # model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
51
---> 52 model.compile(optimizer='adam', loss=Active_Contour_Loss, metrics=['accuracy'])
/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
343 with K.name_scope(self.output_names[i] + '_loss'):
344 output_loss = weighted_loss(y_true, y_pred,
--> 345 sample_weight, mask)
346 if len(self.outputs) > 1:
347 self.metrics_tensors.append(output_loss)
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in weighted(y_true, y_pred, weights, mask)
426 """
427 # score_array has ndim >= 2
--> 428 score_array = fn(y_true, y_pred)
429 if mask is not None:
430 # Cast the mask to floatX to avoid float64 upcasting in Theano
<ipython-input-32-b273672af934> in Active_Contour_Loss(y_true, y_pred)
28 C_2 = np.zeros((256, 256))
29
---> 30 region_in = K.abs(K.sum( y_pred[:,0,:,:] * ((y_true[:,0,:,:] - C_1)**2) ) ) # equ.(12) in the paper
31 region_out = K.abs(K.sum( (1-y_pred[:,0,:,:]) * ((y_true[:,0,:,:] - C_2)**2) )) # equ.(12) in the paper
32
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_ops.py in binary_op_wrapper(x, y)
897 with ops.name_scope(None, op_name, [x, y]) as name:
898 if isinstance(x, ops.Tensor) and isinstance(y, ops.Tensor):
--> 899 return func(x, y, name=name)
900 elif not isinstance(y, sparse_tensor.SparseTensor):
901 try:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_ops.py in _mul_dispatch(x, y, name)
1204 is_tensor_y = isinstance(y, ops.Tensor)
1205 if is_tensor_y:
-> 1206 return gen_math_ops.mul(x, y, name=name)
1207 else:
1208 assert isinstance(y, sparse_tensor.SparseTensor) # Case: Dense * Sparse.
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/gen_math_ops.py in mul(x, y, name)
6699 # Add nodes to the TensorFlow graph.
6700 _, _, _op = _op_def_lib._apply_op_helper(
-> 6701 "Mul", x=x, y=y, name=name)
6702 _result = _op.outputs[:]
6703 _inputs_flat = _op.inputs
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/op_def_library.py in _apply_op_helper(self, op_type_name, name, **keywords)
792 op = g.create_op(op_type_name, inputs, dtypes=None, name=scope,
793 input_types=input_types, attrs=attr_protos,
--> 794 op_def=op_def)
795
796 # Conditionally invoke tfdbg v2's op callback(s).
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/util/deprecation.py in new_func(*args, **kwargs)
505 'in a future version' if date is None else ('after %s' % date),
506 instructions)
--> 507 return func(*args, **kwargs)
508
509 doc = _add_deprecated_arg_notice_to_docstring(
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in create_op(***failed resolving arguments***)
3355 raise TypeError("Input #%d is not a tensor: %s" % (idx, a))
3356 return self._create_op_internal(op_type, inputs, dtypes, input_types, name,
-> 3357 attrs, op_def, compute_device)
3358
3359 def _create_op_internal(
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _create_op_internal(self, op_type, inputs, dtypes, input_types, name, attrs, op_def, compute_device)
3424 input_types=input_types,
3425 original_op=self._default_original_op,
-> 3426 op_def=op_def)
3427 self._create_op_helper(ret, compute_device=compute_device)
3428 return ret
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in __init__(self, node_def, g, inputs, output_types, control_inputs, input_types, original_op, op_def)
1768 op_def, inputs, node_def.attr)
1769 self._c_op = _create_c_op(self._graph, node_def, grouped_inputs,
-> 1770 control_input_ops)
1771 # pylint: enable=protected-access
1772
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py in _create_c_op(graph, node_def, inputs, control_inputs)
1608 except errors.InvalidArgumentError as e:
1609 # Convert to ValueError for backwards compatibility.
-> 1610 raise ValueError(str(e))
1611
1612 return c_op
ValueError: Dimensions must be equal, but are 4 and 256 for 'loss_2/activation_57_loss/mul_1' (op: 'Mul') with input shapes: [?,256,4], [?,256,256].
I tried to assign C_1 and C_2 with shape (256,256,4). But, the segmentation mask is not generated. Am I missing something?

Answer 1

The source code you shared for Active Contours seems fine. Probably, you are feeding the loss function with wrong parameter size.
Since the code is working with tensors, you should feed the function 4-D. Try something like that:
a0 = np.zeros([1,256,256,1], dtype='float64')
a0[0,...] = np.expand_dims(C_1, -1).astype(np.float64)
a1 = np.zeros([1,256,256,1], dtype='float64')
a1[0,...] = np.expand_dims(C_2, -1).astype(np.float64)
los_ac = Active_Contour_Loss(a0, a1)
print(sess.run(los_ac))
I wrote this code quickly but I suppose it'll work.