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Posted to commits@tvm.apache.org by tq...@apache.org on 2020/04/24 02:54:34 UTC
[incubator-tvm] branch master updated: [FRONTEND][MXNET] support
elemwise logic ops (#5361)
This is an automated email from the ASF dual-hosted git repository.
tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new 6c77195 [FRONTEND][MXNET] support elemwise logic ops (#5361)
6c77195 is described below
commit 6c77195e9c277d6ed3f0fbd31dae4ab94d64af25
Author: MORITA Kazutaka <mo...@gmail.com>
AuthorDate: Fri Apr 24 11:54:26 2020 +0900
[FRONTEND][MXNET] support elemwise logic ops (#5361)
---
python/tvm/relay/frontend/mxnet.py | 6 ++++++
tests/python/frontend/mxnet/test_forward.py | 12 +++++++++---
2 files changed, 15 insertions(+), 3 deletions(-)
diff --git a/python/tvm/relay/frontend/mxnet.py b/python/tvm/relay/frontend/mxnet.py
index be2f110..775eb53 100644
--- a/python/tvm/relay/frontend/mxnet.py
+++ b/python/tvm/relay/frontend/mxnet.py
@@ -1751,6 +1751,12 @@ _convert_map = {
"broadcast_greater_equal": _mx_compare(_op.greater_equal, _rename),
"broadcast_lesser" : _mx_compare(_op.less, _rename),
"broadcast_lesser_equal" : _mx_compare(_op.less_equal, _rename),
+ "_equal" : _mx_compare(_op.equal, _rename),
+ "_not_equal" : _mx_compare(_op.not_equal, _rename),
+ "_greater" : _mx_compare(_op.greater, _rename),
+ "_greater_equal" : _mx_compare(_op.greater_equal, _rename),
+ "_lesser" : _mx_compare(_op.less, _rename),
+ "_lesser_equal" : _mx_compare(_op.less_equal, _rename),
"elemwise_add" : _rename(_op.add),
"elemwise_sub" : _rename(_op.subtract),
"elemwise_mul" : _rename(_op.multiply),
diff --git a/tests/python/frontend/mxnet/test_forward.py b/tests/python/frontend/mxnet/test_forward.py
index 10edff9..5e4c137 100644
--- a/tests/python/frontend/mxnet/test_forward.py
+++ b/tests/python/frontend/mxnet/test_forward.py
@@ -328,13 +328,19 @@ def test_forward_broadcast_ops():
def test_forward_elemwise_ops():
for op in ["elemwise_add", "elemwise_sub", "elemwise_mul",
- "elemwise_div", "maximum", "minimum"]:
+ "elemwise_div", "maximum", "minimum",
+ operator.lt, operator.le, operator.eq,
+ operator.ne, operator.gt, operator.ge]:
shape = (3, 4, 5)
dtype = 'float32'
a_np = np.random.uniform(size=shape).astype(dtype)
b_np = np.random.uniform(size=shape).astype(dtype)
- mx_sym = _mx_symbol(mx.sym, op, [mx.sym.var('a'), mx.sym.var('b')])
- ref_res = _mx_symbol(mx.nd, op, [mx.nd.array(a_np), mx.nd.array(b_np)])
+ if type(op) == str:
+ mx_sym = _mx_symbol(mx.sym, op, [mx.sym.var('a'), mx.sym.var('b')])
+ ref_res = _mx_symbol(mx.nd, op, [mx.nd.array(a_np), mx.nd.array(b_np)])
+ else:
+ mx_sym = op(mx.sym.var('a'), mx.sym.var('b'))
+ ref_res = op(mx.nd.array(a_np), mx.nd.array(b_np))
shapes = {'a': shape, 'b': shape}
mod, _ = relay.frontend.from_mxnet(mx_sym, shapes, dtype)
for target, ctx in ctx_list():