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Fix wrong shapes in loop body inputs if shape invariances are set in TF #2203

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Merged
merged 4 commits into from
Jul 26, 2023
Merged

Fix wrong shapes in loop body inputs if shape invariances are set in TF #2203

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c9ee19e
fix wrong shapes in loop body inputs if shape invariances are set in TF
f-salvetti Jul 7, 2023
6858c4d
fix and enable test for TF2
f-salvetti Jul 24, 2023
d27a102
skip tflite due to infinite loop
f-salvetti Jul 24, 2023
37419db
Merge branch 'main' into fix-loop-body-wrong-input-shapes
f-salvetti Jul 25, 2023
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10 changes: 4 additions & 6 deletions tests/test_loops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,8 @@
import tensorflow as tf

from backend_test_base import Tf2OnnxBackendTestBase
from common import unittest_main, check_tf_min_version, check_tf_max_version, \
check_onnxruntime_min_version, check_tfjs_max_version
from common import unittest_main, check_tf_min_version, \
check_onnxruntime_min_version, check_tfjs_max_version, skip_tflite
from tf2onnx.tf_loader import is_tf2


Expand Down Expand Up @@ -286,15 +286,13 @@ def func(x, y):
self.run_test_case(func, feed_dict, input_names_with_port, output_names_with_port, rtol=1e-5)

@check_tf_min_version("1.9")
@check_tf_max_version("1.15")
@skip_tflite("infinite loop with tflite")
def test_simple_while_loop_var_shape(self):
# test for while_loop with variant shape variables
# may not meet ONNX Loop spec
# Note: this is not working on tf2 itself.
def func(i):
const = tf.constant(np.array([2], dtype=np.int32))
c = lambda i: tf.reduce_all(tf.shape(i) < 10)
b = lambda i: tf.concat([i, const], 0)
b = lambda i: [tf.concat([i, const], 0)]
r = tf.while_loop(c, b, [i], shape_invariants=[tf.TensorShape([None])])
return tf.identity(r, name="output")
input_names_with_port = ["input_1:0"]
Expand Down
3 changes: 2 additions & 1 deletion tf2onnx/onnx_opset/controlflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -571,7 +571,8 @@ def wire_while_body(parent_g, g, loop_node, body_input_to_state_var, cond_input_
g.set_dtype(func_inputs[0], onnx_pb.TensorProto.INT64)
g.inputs = [g.get_node_by_output(inp) for inp in func_inputs]

for p, c in zip(loop_node.input, func_inputs):
# we should use outputs shape, not inputs, since there may be shape invariants
for p, c in zip(loop_node.output, func_inputs[2:]):
g.copy_shape(p, c)

for i, node in enumerate(g.inputs):
Expand Down