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[FRONTEND][Mxnet][nuymp] Adding _npi_advanced_indexing_multiple #7230

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[FRONTEND][Mxnet][nuymp] Adding _npi_advanced_indexing_multiple #7230

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0476ea8
Added _npi_advanced_indexing_multiple
insop Jan 7, 2021
6991f93
Add test case for _npi_advanced_indexing_multiple
insop Jan 8, 2021
4c763cd
updated based on the PR discussion
insop Jan 12, 2021
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6 changes: 6 additions & 0 deletions python/tvm/relay/frontend/mxnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2342,6 +2342,11 @@ def _mx_npi_stack(inputs, attrs):
else:
return _op.stack(tuple(inputs), axis=int(axis))

def mx_npi_advanced_indexing_multiple(inputs, attrs):
assert len(inputs) == 2
data = inputs[0]
indices = inputs[1]
return _op.adv_index([data] + indices)

def _mx_npx_reshape(inputs, attrs):
shape = attrs.get_int_tuple("newshape")
Expand Down Expand Up @@ -2709,6 +2714,7 @@ def _mx_npi_where_rscalar(inputs, attrs):
"_npi_tanh": _rename(_op.tanh),
"_npi_true_divide_scalar": _binop_scalar(_op.divide),
"_npi_stack": _mx_npi_stack,
"_npi_advanced_indexing_multiple": mx_npi_advanced_indexing_multiple,
}

# set identity list
Expand Down
37 changes: 37 additions & 0 deletions tests/python/frontend/mxnet/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1935,6 +1935,43 @@ def verify(data_shape, axis, use_length, length):
verify((2, 3, 4), 2, True, np.array([[3, 4, 2], [1, 2, 1]]).astype("int32"))


@pytest.mark.parametrize(
"data_shape, row_sel, col",
[
(
(5, 7),
(
0,
1,
2,
3,
4,
),
2,
),
],
)
@pytest.mark.parametrize("dtype", ["float64", "float32"])
@tvm.testing.parametrize_targets
@pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
def test_forward_npi_advanced_indexing_multiple(data_shape, row_sel, col, dtype, target, ctx, kind):
data_np = np.random.uniform(size=data_shape).astype(dtype)
ref_res = mx.np.array(data_np)[row_sel, col]

row_sel_sym = mx.sym.var("row_sel").as_np_ndarray()
data_sym = mx.sym.var("data").as_np_ndarray()
col_sym = mx.sym.var("col").as_np_ndarray()
mx_sym = data_sym[row_sel_sym, col_sym]

mod, _ = relay.frontend.from_mxnet(
mx_sym, shape={"data": data_shape, "row_sel": row_sel, "col": col}, dtype=dtype
)
intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
op_res = intrp.evaluate()(data_np)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)
tvm.testing.assert_allclose(ref_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)


@pytest.mark.skipif(not hasattr(mx.sym.np, "pad"), reason="mx.sym.np.pad hasn't been publish yet")
@pytest.mark.parametrize(
"data_shape, pad_width",
Expand Down