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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/01/13 06:31:36 UTC
[GitHub] [tvm] blackkker commented on a change in pull request #9840: [Relay/Frontend][TFLite] Change the output shape calculation based on keep_dim option in fully connected
blackkker commented on a change in pull request #9840:
URL: https://github.com/apache/tvm/pull/9840#discussion_r783663051
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File path: python/tvm/relay/frontend/tflite.py
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@@ -1911,6 +1911,14 @@ def convert_fully_connected(self, op):
weight_expr = self.exp_tab.new_const(weight_value, dtype=weight_tensor_type_str)
weight_shape = _infer_shape(weight_expr)
+ # Change the output shape calculation based on keep_dim option
Review comment:
As the document said, I constructed the keras model with tf.keras.Input(shape=(1, 1, 768)) and tf.keras.layers.Dense(1001).
And then i converted it to TFLite but got a fully connected op(keep_num_dims : False).
The specific implment in TFLite is to reshape into 2 dims before fullyconnect, and then reshape the output into 4dims.
BTW, I tried tensorflow_v1 and tensorflow_v2, both failed.
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