__call__ not supported
#24
Comments
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Suppose we have this input code stored in import tensorflow as tf
# Create an override model to classify pictures
class SequentialModel(tf.keras.Model):
def __init__(self, **kwargs):
super(SequentialModel, self).__init__(**kwargs)
self.flatten = tf.keras.layers.Flatten(input_shape=(28, 28))
# Add a lot of small layers
num_layers = 100
self.my_layers = [tf.keras.layers.Dense(64, activation="relu")
for n in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(0.2)
self.dense_2 = tf.keras.layers.Dense(10)
def __call__(self, x):
x = self.flatten(x)
for layer in self.my_layers:
x = layer(x)
x = self.dropout(x)
x = self.dense_2(x)
return x
if __name__ == '__main__':
input_data = tf.random.uniform([20, 28, 28])
print("Input:")
print(type(input_data))
print(input_data)
model = SequentialModel()
result = model(input_data)
print("Output:")
print(type(input_data))
print(result)The problematic expression above is |
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If I change the input file to use |
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And, the diff between the two: 7c7
< Node: synthetic < PythonLoader, Lwala/builtin/type, do()LRoot; > Context: CallStringContext: [ script A.py.do()LRoot;@122 ]
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> Node: synthetic < PythonLoader, Lwala/builtin/type, do()LRoot; > Context: CallStringContext: [ script A.py.do()LRoot;@123 ]
13a14
> Node: synthetic < PythonLoader, L$script A.py/SequentialModel/__call__, trampoline2()LRoot; > Context: CallStringContext: [ script A.py.do()LRoot;@120 ]
14a16
> Node: <Code body of function Lscript A.py/SequentialModel/__call__> Context: CallStringContext: [ $script A.py.SequentialModel.__call__.trampoline2()LRoot;@2 ]The first difference just looks like a difference is the IR with the values, probably because there's one more value corresponding to the "new" function invocation. |
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I would think that |
Adding tests for testing calling a method. We add four tests that include: 1. Calling model indirectly using `call` and `__call__` (2 tests) 2. Calling a model directly using `call` and `__call__` (2 tests) Related to #24
I don't think so. Specifically, |
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I was able to switch the target to the correct However, The "receiver" is stil The "receiver" here is: |
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In the working case (test 4), by the time we hit |
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Thus, at the point of adding the "new" node, we've already know that |
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The problem may have something to do with |
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Ah, because this isn't a "static" method (not sure what that means for Python), |
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When we get to |
__call__doesn't show up in the call graph.The text was updated successfully, but these errors were encountered: