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[Relay to onnx conversion fixes][Pool, Pad] #8435

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Merged
masahi merged 2 commits into from
Jul 12, 2021
Merged

[Relay to onnx conversion fixes][Pool, Pad] #8435

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04d6c5d
[Relay to Onnx conversion][Pool]
schilkunda-amba Jun 28, 2021
e0c2f3e
[Relay to Onnx conversion][Pad]
schilkunda-amba Jul 9, 2021
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17 changes: 11 additions & 6 deletions python/tvm/contrib/target/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,6 +147,7 @@ def convert_attributes(cls, attrs):
"pads": attrs.get_int_tuple("padding"),
"strides": attrs.get_int_tuple("strides"),
"kernel_shape": attrs.get_int_tuple("pool_size"),
"ceil_mode": 1 if attrs.ceil_mode else 0,
}


Expand Down Expand Up @@ -330,7 +331,10 @@ def convert_attributes(cls, attrs):
after.append(axis_pads[1])
pads = before + after
pads = numpy.asarray(pads, dtype=pads[0].dtype)
return {"pads": pads, "mode": attrs.get_str("pad_mode"), "constant_value": attrs.pad_value}
return {
"pads": pads,
"mode": attrs.get_str("pad_mode"),
}

@classmethod
def convert(cls, node_entry, model_container, node_dict):
Expand All @@ -341,16 +345,17 @@ def convert(cls, node_entry, model_container, node_dict):
attrs = cls.convert_attributes(node_entry["relay_node"].attrs)

name = node_entry["name"]
data = numpy.asarray(attrs["pads"], dtype=attrs["pads"][0].dtype).astype(numpy.int64)
value = numpy.dtype(node_entry["types"][0].dtype).type(attrs["constant_value"])
pad_data = numpy.asarray(attrs["pads"], dtype=attrs["pads"][0].dtype).astype(numpy.int64)

input_names = [
node_entry["input_names"][0],
add_input(data, name, "pads", model_container),
add_input(value, name, "value", model_container),
add_input(pad_data, name, "pads", model_container),
node_entry["input_names"][1],
]

node = onnx.helper.make_node(cls.__name__, input_names, node_entry["output_names"])
node = onnx.helper.make_node(
cls.__name__, input_names, node_entry["output_names"], mode=attrs["mode"]
)
model_container.add_nodes([node])


Expand Down
17 changes: 10 additions & 7 deletions tests/python/contrib/test_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@
import tvm
from tvm import relay
from tvm.contrib.target.onnx import to_onnx
from tvm.relay.testing import run_infer_type


def func_to_onnx(func, name):
Expand Down Expand Up @@ -270,14 +271,16 @@ def verify_batch_norm(axis=1):


def test_pad():
"""Pad unit test."""

def verify_pad():
for dtype in ["float16", "float32"]:
dshape = (4, 10, 7, 7)
x = relay.var("x", shape=dshape, dtype=dtype)
y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
func = relay.Function([x], y)
x_data = np.random.uniform(size=dshape).astype(dtype)
verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)
dshape = (4, 10, 7, 7)
x = relay.var("x", shape=dshape, dtype="int32")
y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
func = relay.Function([x], y)
func = run_infer_type(func)
x_data = np.random.randint(low=-255, high=255, size=dshape).astype(np.int32)
verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)

verify_pad()

Expand Down