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Add Onnx Pad v11 #5539

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May 8, 2020
Merged

Add Onnx Pad v11 #5539

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25 changes: 25 additions & 0 deletions python/tvm/relay/frontend/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -557,6 +557,31 @@ def _impl_v2(cls, inputs, attr, params):
},
)(inputs, attr, params)

@classmethod
def _impl_v11(cls, inputs, attr, params):
pad_width = []
pads = infer_value_simulated(inputs[1], params).asnumpy()
if len(inputs) == 3:
value = infer_value_simulated(inputs[2], params).asnumpy().item()
else:
value = 0
attr["pad_value"] = value
dims = int(len(pads) / 2)
for i in range(dims):
pad_width.append((pads[i], pads[i+dims]))
attr['pad_width'] = pad_width
pad_mode = attr.get('mode', b'constant').decode('utf-8')
if pad_mode in ['constant', 'edge', 'reflect']:
attr['pad_mode'] = pad_mode
attr.pop('mode', None)
else:
raise tvm.error.OpAttributeInvalid(
'Value ' + pad_mode + ' in attribute "mode" is invalid for operator Pad.')

return AttrCvt('pad')(inputs[:1], attr, params)




class ParametricSoftPlus(OnnxOpConverter):
""" Operator converter for ParametricSoftPlus.
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69 changes: 68 additions & 1 deletion tests/python/frontend/onnx/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1278,7 +1278,63 @@ def verify_pad(indata, pads, mode='constant', value=0.0):
# tvm result
for target, ctx in ctx_list():
tvm_out = get_tvm_output(
model, indata, target, ctx, outdata.shape, 'float32')
model, indata, target, ctx, outdata.shape, 'float32', opset=2)
tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)


def verify_pad_v11(indata, pads, mode='constant', value=0.0):
indata = np.array(indata).astype(np.float32)
# numpy expect result
len_dim = len(pads) // 2
np_pads = [(pads[i], pads[i+len_dim]) for i in range(len_dim)]
pads = np.array(pads)
# onnx graph
if mode in ['edge', 'reflect']:
inputs = [indata, pads]
outdata = np.pad(indata, pad_width=np_pads, mode=mode)
node = helper.make_node(
'Pad',
inputs=['input', 'pads'],
outputs=['output'],
mode=mode
)
graph = helper.make_graph([node],
'pad_test',
inputs=[helper.make_tensor_value_info("input",
TensorProto.FLOAT, list(indata.shape)),
helper.make_tensor_value_info("pads",
TensorProto.INT64,(len(pads),))],
initializer=[helper.make_tensor("pads", TensorProto.INT64, (len(pads),), pads)],
outputs=[helper.make_tensor_value_info("output",
TensorProto.FLOAT, list(outdata.shape))])
else:
inputs = [indata, pads, np.array([value])]
outdata = np.pad(indata, pad_width=np_pads,
mode='constant', constant_values=value)
node = helper.make_node(
'Pad',
inputs=['input', 'pads', 'constant_value'],
outputs=['output'],
mode='constant'
)
graph = helper.make_graph([node],
'pad_test',
inputs=[helper.make_tensor_value_info("input",
TensorProto.FLOAT, list(indata.shape)),
helper.make_tensor_value_info("pads",
TensorProto.INT64,(len(pads),)),
helper.make_tensor_value_info("constant_value",
TensorProto.INT64,(1,)),
],
initializer=[helper.make_tensor("pads", TensorProto.INT64, (len(pads),), pads),
helper.make_tensor("constant_value", TensorProto.FLOAT, (1,), [value])],
outputs=[helper.make_tensor_value_info("output",
TensorProto.FLOAT, list(outdata.shape))])
model = helper.make_model(graph, producer_name='pad_test')
# tvm result
for target, ctx in ctx_list():
tvm_out = get_tvm_output(
model, inputs, target, ctx, outdata.shape, 'float32', opset=11)
tvm.testing.assert_allclose(outdata, tvm_out, rtol=1e-5, atol=1e-5)


Expand All @@ -1294,6 +1350,17 @@ def test_pad():
verify_pad(np.random.randn(1, 3, 4, 5).astype(
np.float32), [0, 0, 1, 1, 0, 0, 1, 1], 'reflect')

verify_pad_v11(np.random.randn(2, 2).astype(
np.float32), [0, 1, 0, 0], 'constant', 0.0)
verify_pad_v11(np.random.randn(2, 3).astype(
np.float32), [1, 0, 0, 1], 'constant', 0.0)
verify_pad_v11(np.random.randn(3, 2).astype(
np.float32), [0, 0, 1, 0], 'constant', 5.0)
verify_pad_v11(np.random.randn(1, 3, 4, 5).astype(
np.float32), [0, 0, 1, 1, 0, 0, 1, 1], 'edge')
verify_pad_v11(np.random.randn(1, 3, 4, 5).astype(
np.float32), [0, 0, 1, 1, 0, 0, 1, 1], 'reflect')


def verify_reduce_x(name, indata, axis, keepdims):
indata = np.array(indata).astype(np.float32)
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