I am trying to use the tf.map_fn() , where my elems should be pointing to the channel dimension of my inputs(shape = 100,24,24,6), so my elems should be a list/tuple of tensors, pointing or accessing the values of the channel dimension(6) of the inputs .I am trying to do it by making a for loop in such a way :
@tf.function def call(self, inputs, training=True):
elems = []
for b in inputs:
for h in b:
for w in h:
for c in w:
elems.append(c)
changed_inputs = tf.map_fn(self.do_mapping, elems)
return changed_inputs
What i am trying to achieve in the self.do_mapping is that it is doing a dictionary look up for the values of a dictionary (vmap) using the keys and the return the values. the dictionary vmap is made by accessing the output of a layer and appending only the similar values of the channel dimension of the output of layer so the keys in dictionary are tuple of 6 (as the size of channel dimension) tf.tensorobjects and values of dictionary is the count which i keep. This is how the dictionary is made :
value = list(self.get_values())
vmap = {}
cnt = 0
for v0 in value:
for v1 in v0:
for v2 in v1:
for v3 in v2:
v = tuple(v3)
if v not in vmap:
vmap[v]=cnt
cnt+=1
the do_mapping function is :
@tf.function
def do_mapping(self,pixel):
if self._compression :
pixel = tuple(pixel)
enumerated_value=self._vmap.get(pixel)
print(enumerated_value)
print(tf.shape(pixel))
exit()
return enumerated_value
If i try to use the tf.map_fn now where i try to point the elems to the channel dimension then i get the following error :(ValueError: elements in elems must be 1+ dimensional Tensors, not scalars ). Please help me to understand how can i use the tf.map_fn for my case ? Thank you in advance
First, instead of doing a for loop (try to avoid for efficiency), you can just reshape that way:
elems = tf.reshape(inputs,-1)
Second, what do you want to do exactly? What do you mean by "it doesn't work"? What is the error message? What is self.do_mapping?
Best,
Keivan