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[Relay][Frontend][TF] Fix slice when begin or size is not Const #4372

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Nov 21, 2019
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[Relay][Frontend][TF] Fix slice when begin or size is not Const #4372

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e01f0d4
fix slice bug when input is param
lisiyuan643 Nov 19, 2019
4d5c015
use _infer_value rather than _infer_value_simulated
lisiyuan643 Nov 21, 2019
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10 changes: 8 additions & 2 deletions python/tvm/relay/frontend/tensorflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -626,8 +626,14 @@ def _impl(inputs, attr, params):

def _slice():
def _impl(inputs, attr, params):
begin = _get_list_param(params, inputs[1])
size = _get_list_param(params, inputs[2])
try:
begin = _get_list_param(params, inputs[1])
except (IndexError, KeyError, AttributeError):
begin = _infer_value(inputs[1], params).asnumpy().tolist()[0]
try:
size = _get_list_param(params, inputs[2])
except (IndexError, KeyError, AttributeError):
size = _infer_value(inputs[2], params).asnumpy().tolist()[0]
data_shape = attr['_input_shapes'][inputs[0]]
data_dim = len(data_shape)
end = size
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16 changes: 15 additions & 1 deletion tests/python/frontend/tensorflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2188,6 +2188,20 @@ def test_forward_transpose():
_test_forward_tranapose_axes_input((2, 3, 4, 5), (3, 0, 1, 2))


def _test_forward_slice_operation_input(input_value, begin_value, size_value):
input_data = np.array(input_value, dtype=np.float32)
with tf.Graph().as_default():
input_tensor = tf.placeholder(
shape=input_data.shape, dtype=input_data.dtype, name="input")
begin_tensor = tf.expand_dims(begin_value, axis=0)
size_tensor = tf.expand_dims(size_value, axis=0)
slice_tensor = tf.slice(input_tensor, begin_tensor, size_tensor, name='slice_output')
compare_tf_with_tvm([input_data], ['input:0'], 'slice_output:0')


def test_forward_slice():
_test_forward_slice_operation_input([1, 1], 0, 2)
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add test cases to cover begin & size as tensor

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@lsy643 lsy643 Nov 21, 2019 •
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@yongwww begin and size as tensor are already covered. In the _test_forward_slice_operation_input([1, 1], 0, 2) , the input 0 and input 2 will be changed into tensors after tf.expand_dimes

def _test_forward_slice_operation_input(input_value, begin_value, size_value):
    input_data = np.array(input_value, dtype=np.float32)
    with tf.Graph().as_default():
        input_tensor = tf.placeholder(
            shape=input_data.shape, dtype=input_data.dtype, name="input")
        begin_tensor = tf.expand_dims(begin_value, axis=0)
        size_tensor = tf.expand_dims(size_value, axis=0)
        slice_tensor = tf.slice(input_tensor, begin_tensor, size_tensor, name='slice_output')
        compare_tf_with_tvm([input_data], ['input:0'], 'slice_output:0')


def test_forward_ceil():
ishape = (1, 3, 10, 10)
inp_array = np.random.uniform(size=ishape).astype(np.float32)
Expand Down Expand Up @@ -2760,8 +2774,8 @@ def test_forward_add_n():
# Main
# ----
if __name__ == '__main__':

# Transforms
test_forward_slice()
test_forward_transpose()
test_forward_reshape()
test_forward_depthtospace()
Expand Down