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Enhance Transpose optimizer "Mul" handler #978

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merged 2 commits into from
Jun 19, 2020
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Enhance Transpose optimizer "Mul" handler #978

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04220ba
Add support for Keras resnet50 pretrained model testing.
jignparm Jun 19, 2020
e332323
Add unit test for mul with 1D tensor
jignparm Jun 19, 2020
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2 changes: 1 addition & 1 deletion tests/test_optimizers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -691,7 +691,7 @@ def test_trans_can_be_replaced_with_reshape2(self):
model_proto, remaining_transpose_num=0)

def test_two_transposes_switch_with_mul(self):
const_node = self._make_onnx_const(np.array(10, dtype=np.float32), "const_10")
const_node = self._make_onnx_const(np.array(np.random.random(6), dtype=np.float32), "const_10")
node0 = helper.make_node("Transpose", ["u1"], ["v1"], perm=[0, 2, 3, 1], name="trans_0")
node1 = helper.make_node("Transpose", ["u2"], ["v2"], perm=[0, 2, 3, 1], name="trans_1")

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17 changes: 15 additions & 2 deletions tf2onnx/optimizer/transpose_optimizer.py
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Expand Up @@ -482,8 +482,21 @@ def _mul_handler(self, trans, node):
self._g.remove_node(node.name)
return True

# if the shape is () or (1), we just move transpose after the mul
if not multiplier.shape or (len(multiplier.shape) == 1 and multiplier.shape[0] == 1):
# if the shape is (), we just move transpose after the mul
if not multiplier.shape:
return self._switch_transpose_and_node(node, trans)

# if multiplier is 1-D
if len(multiplier.shape) == 1:
if multiplier.shape[0] == 1:
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@RandySheriffH RandySheriffH Jun 19, 2020 •
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Is it possible for "if..." at line 491 be merged to 486 like:
"if not multiplier.shape or multipier.shape == [1]"
Plus shall we have any UT for the enhancement?

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Updated unit test to see if 1D const tensors can be jumped over.

For the 'if' merging, it seems cleaner to separate handling of scalars and 1D tensors into 2 blocks (mostly for readability)

# shape is (1)
return self._switch_transpose_and_node(node, trans)

# shape is (N). reshape so that trans(shape) = 1,1,...,N
perm = list(trans.get_attr('perm').ints)
new_shape = np.ones(len(perm), dtype=np.int32)
new_shape[perm[-1]] = multiplier.shape[0]
multiplier_input_node.set_tensor_value(multiplier.reshape(new_shape))
return self._switch_transpose_and_node(node, trans)

return False
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