From 113b46ec802101f6b4933c4fc869de99a5be9312 Mon Sep 17 00:00:00 2001
From: Siva <sivar.b@huawei.com>
Date: Tue, 26 Jun 2018 22:45:53 +0530
Subject: [PATCH] [NNVM][ONNX] Shape operator support (limited/differed) -
#1297 (#1333)
---
nnvm/python/nnvm/frontend/onnx.py | 43 ++++++++-
.../python/frontend/onnx/test_forward.py | 91 ++++++++++++++++---
2 files changed, 115 insertions(+), 19 deletions(-)
diff --git a/nnvm/python/nnvm/frontend/onnx.py b/nnvm/python/nnvm/frontend/onnx.py
index d92a856d7..fb21f6b7b 100644
--- a/nnvm/python/nnvm/frontend/onnx.py
+++ b/nnvm/python/nnvm/frontend/onnx.py
@@ -258,10 +258,11 @@ class Reshape(OnnxOpConverter):
def _impl_v5(cls, inputs, attr, params):
if inputs[1].list_output_names()[0] in params:
shape = tuple(params[inputs[1].list_output_names()[0]].asnumpy())
+ out = _sym.reshape(inputs[0], shape=shape)
else:
- raise RuntimeError('Shape is not contained in graph initializer.')
- return _sym.reshape(inputs[0], shape=shape)
+ out = _sym.reshape_like(inputs[0], inputs[1])
+ return out
class Scale(OnnxOpConverter):
@@ -405,6 +406,36 @@ def _fully_connected(opset):
return _impl
+class Shape(OnnxOpConverter):
+ """ Operator converter for Shape.
+ """
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ # Result of this operator is prominently used by reshape operator.
+ # Just pass the input as it is so that reshape_like can be used there.
+ print("Shape: Differently implemented in NNVM as a bypass (dummy operator)")
+ return inputs[0]
+
+class Cast(OnnxOpConverter):
+ """ Operator converter for Cast.
+ """
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ return AttrCvt(op_name='cast', transforms={'to': 'dtype'})(inputs, attr)
+
+ @classmethod
+ def _impl_v5(cls, inputs, attr, params):
+ try:
+ from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
+ attr['to'] = TENSOR_TYPE_TO_NP_TYPE[attr['to']]
+ except ImportError as e:
+ raise ImportError(
+ "Unable to import onnx.mapping which is required {}".format(e))
+ return AttrCvt(op_name='cast', transforms={'to': 'dtype'})(inputs, attr)
+
+
# compatible operators that do NOT require any conversion.
_identity_list = []
@@ -505,7 +536,7 @@ def _get_convert_map(opset):
# 'ArgMin'
# defs/tensor
- 'Cast': AttrCvt('cast', {'to': 'dtype'}),
+ 'Cast': Cast.get_converter(opset),
'Reshape': Reshape.get_converter(opset),
'Concat': Renamer('concatenate'),
'Split': AttrCvt('split', {'split': 'indices_or_sections'}),
@@ -514,6 +545,7 @@ def _get_convert_map(opset):
# 'Gather'
# 'Squeeze'
'Pad': Pad.get_converter(opset),
+ 'Shape': Shape.get_converter(opset),
}
@@ -719,6 +751,9 @@ def from_onnx(model):
"""
g = GraphProto()
graph = model.graph
- opset = model.opset_import[0].version if model.opset_import else 1
+ try:
+ opset = model.opset_import[0].version if model.opset_import else 1
+ except AttributeError:
+ opset = 1
sym, params = g.from_onnx(graph, opset)
return sym, params
diff --git a/nnvm/tests/python/frontend/onnx/test_forward.py b/nnvm/tests/python/frontend/onnx/test_forward.py
index 9aef8b2cb..93a458a67 100644
--- a/nnvm/tests/python/frontend/onnx/test_forward.py
+++ b/nnvm/tests/python/frontend/onnx/test_forward.py
@@ -5,6 +5,23 @@ from tvm.contrib import graph_runtime
from nnvm.testing.config import ctx_list
import onnx
from model_zoo import super_resolution, squeezenet1_1, lenet, resnet18_1_0
+from onnx import helper, TensorProto
+
+def get_tvm_output(model, x, target, ctx, out_shape, dtype='float32'):
+ new_sym, params = nnvm.frontend.from_onnx(model)
+ input_name = model.graph.input[0].name
+ shape_dict = {input_name: x.shape}
+ dtype_dict = {input_name: dtype}
+ graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, dtype_dict, params=params)
+ m = graph_runtime.create(graph, lib, ctx)
+ # set inputs
+ m.set_input(input_name, tvm.nd.array(x.astype(dtype)))
+ m.set_input(**params)
+ m.run()
+ # get outputs
+ out = m.get_output(0, tvm.nd.empty(out_shape, dtype))
+ return out.asnumpy()
+
def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
import caffe2.python.onnx.backend
@@ -14,26 +31,12 @@ def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
c2_out = prepared_backend.run(W)[0]
return c2_out
- def get_tvm_output(model, x, target, ctx, dtype='float32'):
- new_sym, params = nnvm.frontend.from_onnx(model)
- input_name = model.graph.input[0].name
- shape_dict = {input_name: x.shape}
- graph, lib, params = nnvm.compiler.build(new_sym, target, shape_dict, params=params)
- m = graph_runtime.create(graph, lib, ctx)
- # set inputs
- m.set_input(input_name, tvm.nd.array(x.astype(dtype)))
- m.set_input(**params)
- m.run()
- # get outputs
- out = m.get_output(0, tvm.nd.empty(out_shape, dtype))
- return out.asnumpy()
-
dtype = 'float32'
x = np.random.uniform(size=data_shape)
model = onnx.load(graph_file)
c2_out = get_caffe2_output(model, x, dtype)
for target, ctx in ctx_list():
- tvm_out = get_tvm_output(model, x, target, ctx, dtype)
+ tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype)
np.testing.assert_allclose(c2_out, tvm_out, rtol=1e-5, atol=1e-5)
def verify_super_resolution_example():
@@ -48,8 +51,66 @@ def verify_lenet():
def verify_resnet18():
verify_onnx_forward_impl(resnet18_1_0, (1, 3, 224, 224), (1, 1000))
+
+def test_reshape():
+ in_shape = (4, 3, 3, 4)
+ ref_shape = (3, 4, 4, 3)
+
+ ref_array = np.array(ref_shape)
+ ref_node = onnx.helper.make_node('Constant',
+ inputs=[],
+ outputs=['ref_in'],
+ value=onnx.helper.make_tensor(name = 'const_tensor',
+ data_type = onnx.TensorProto.INT32,
+ dims = ref_array.shape,
+ vals = ref_array.flatten().astype(int)))
+ reshape_node = helper.make_node("Reshape", ["in", "ref_in"], ["out"])
+
+ graph = helper.make_graph([ref_node, reshape_node],
+ "reshape_test",
+ inputs = [helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
+ outputs = [helper.make_tensor_value_info("out", TensorProto.FLOAT, list(ref_shape))])
+
+ model = helper.make_model(graph, producer_name='reshape_test')
+
+ for target, ctx in ctx_list():
+ x = np.random.uniform(size=in_shape)
+ tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
+
+ np.testing.assert_allclose(ref_shape, tvm_out.shape)
+
+def test_reshape_like():
+ in_shape = (4, 3, 3, 4)
+ ref_shape = (3, 4, 4, 3)
+
+ ref_array = np.random.uniform(size=ref_shape).astype('float32')
+ ref_node = onnx.helper.make_node('Constant',
+ inputs=[],
+ outputs=['ref_in'],
+ value=onnx.helper.make_tensor(name = 'const_tensor',
+ data_type = onnx.TensorProto.FLOAT,
+ dims = ref_array.shape,
+ vals = ref_array.flatten().astype(float)))
+ copy_node = helper.make_node("Identity", ["ref_in"], ["copy_in"])
+ reshape_node = helper.make_node("Reshape", ["in", "copy_in"], ["out"])
+
+ graph = helper.make_graph([ref_node, copy_node, reshape_node],
+ "reshape_like_test",
+ inputs = [helper.make_tensor_value_info("in", TensorProto.FLOAT, list(in_shape))],
+ outputs = [helper.make_tensor_value_info("out", TensorProto.FLOAT, list(ref_shape))])
+
+ model = helper.make_model(graph, producer_name='reshape_like_test')
+
+ for target, ctx in ctx_list():
+ x = np.random.uniform(size=in_shape)
+ tvm_out = get_tvm_output(model, x, target, ctx, ref_shape, 'float32')
+
+ np.testing.assert_allclose(ref_shape, tvm_out.shape)
+
if __name__ == '__main__':
# verify_super_resolution_example()
# verify_squeezenet1_1()
# verify_lenet()
verify_resnet18()
+ test_reshape()
+ test_reshape_like()
--
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