Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[Frontend][MXNet] add _npi_stack, issue #7186 #7209

Merged
merged 1 commit into from
Jan 6, 2021
Merged

[Frontend][MXNet] add _npi_stack, issue #7186 #7209

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 9 additions & 0 deletions python/tvm/relay/frontend/mxnet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2335,6 +2335,14 @@ def _mx_npi_concatenate(inputs, attrs):
return _op.concatenate(tuple(inputs), axis=int(axis))


def _mx_npi_stack(inputs, attrs):
axis = attrs.get_str("axis", "0")
if axis == "None":
return _op.reshape(_op.stack(tuple(inputs), axis=0), (-1,))
else:
return _op.stack(tuple(inputs), axis=int(axis))


def _mx_npx_reshape(inputs, attrs):
shape = attrs.get_int_tuple("newshape")
reverse = attrs.get_bool("reverse", False)
Expand Down Expand Up @@ -2700,6 +2708,7 @@ def _mx_npi_where_rscalar(inputs, attrs):
"_npi_less_equal": _mx_compare(_op.less_equal, _rename),
"_npi_tanh": _rename(_op.tanh),
"_npi_true_divide_scalar": _binop_scalar(_op.divide),
"_npi_stack": _mx_npi_stack,
}

# set identity list
Expand Down
28 changes: 28 additions & 0 deletions tests/python/frontend/mxnet/test_forward.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2012,6 +2012,34 @@ def test_forward_npi_concatenate(data_shape1, data_shape2, axis, dtype, target,
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)


@pytest.mark.parametrize(
"data_shape1, data_shape2, axis",
[
((3,), (3,), 0),
((3,), (3,), -1),
((1, 3, 2), (1, 3, 2), 2),
((1, 3, 3), (1, 3, 3), 1),
((1, 3), (1, 3), 0),
],
)
@pytest.mark.parametrize("dtype", ["float64", "float32", "int64", "int32"])
@tvm.testing.parametrize_targets
@pytest.mark.parametrize("kind", ["graph", "vm", "debug"])
def test_forward_npi_stack(data_shape1, data_shape2, axis, dtype, target, ctx, kind):
data_np1 = np.random.uniform(size=data_shape1).astype(dtype)
data_np2 = np.random.uniform(size=data_shape2).astype(dtype)
data1 = mx.sym.var("data1")
data2 = mx.sym.var("data2")
ref_res = mx.np.stack([mx.np.array(data_np1), mx.np.array(data_np2)], axis=axis)
mx_sym = mx.sym.np.stack([data1.as_np_ndarray(), data2.as_np_ndarray()], axis=axis)
mod, _ = relay.frontend.from_mxnet(
mx_sym, shape={"data1": data_shape1, "data2": data_shape2}, dtype=dtype
)
intrp = relay.create_executor(kind, mod=mod, ctx=ctx, target=target)
op_res = intrp.evaluate()(data_np1, data_np2)
tvm.testing.assert_allclose(op_res.asnumpy(), ref_res.asnumpy(), rtol=1e-5)


@pytest.mark.parametrize("data_shape", [(2, 2, 2), (2, 7, 2), (2, 2, 2, 1, 2, 3, 1), (1, 8)])
@pytest.mark.parametrize("dtype", ["float64", "float32", "int64", "int32", "bool"])
@tvm.testing.parametrize_targets
Expand Down