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## @package onnx
# Module caffe2.python.onnx.tests.conversion_test
import json
import tempfile
import textwrap
import traceback
import unittest
import zipfile
from caffe2.proto import caffe2_pb2
from caffe2.python import brew, core
from caffe2.python.model_helper import ModelHelper
from click.testing import CliRunner
import numpy as np
from onnx import helper, ModelProto, TensorProto
from caffe2.python.onnx.helper import c2_native_run_net
from caffe2.python.onnx.bin.conversion import caffe2_to_onnx, onnx_to_caffe2
import caffe2.python.onnx.backend as c2
from caffe2.python.onnx.tests.test_utils import TestCase
class TestConversion(TestCase):
def _run_command(self, cmd, *args, **kwargs):
runner = CliRunner()
result = runner.invoke(cmd, *args, **kwargs)
self.assertEqual(result.exit_code, 0, textwrap.dedent('''
Command exited with non-zero exit code:
output: {}
exception: {}
exc_info: {}
'''.format(result.output,
result.exception,
traceback.format_exception(*result.exc_info))))
return result
def test_caffe2_to_onnx(self):
caffe2_net = tempfile.NamedTemporaryFile()
caffe2_init_net = tempfile.NamedTemporaryFile()
output = tempfile.NamedTemporaryFile()
model = ModelHelper(name='caffe2-to-onnx-test')
brew.relu(model, ["X"], "Y")
caffe2_net.write(model.net.Proto().SerializeToString())
caffe2_net.flush()
init_model = ModelHelper(name='caffe2-to-onnx-init-test')
init_model.net.GivenTensorFill([], 'X', shape=[2, 2],
values=np.zeros((2, 2)).flatten().astype(float))
caffe2_init_net.write(init_model.net.Proto().SerializeToString())
caffe2_init_net.flush()
self._run_command(
caffe2_to_onnx, [
caffe2_net.name,
'--caffe2-init-net', caffe2_init_net.name,
'--output', output.name,
],
catch_exceptions=False,
)
onnx_model = ModelProto()
onnx_model.ParseFromString(output.read())
self.assertEqual(len(onnx_model.graph.node), 1)
self.assertEqual(onnx_model.graph.node[0].op_type, 'Relu')
self.assertEqual(len(onnx_model.graph.initializer), 1)
self.assertEqual(onnx_model.graph.initializer[0].name, onnx_model.graph.input[0].name)
def test_caffe2_to_onnx_value_info(self):
caffe2_net = tempfile.NamedTemporaryFile()
output = tempfile.NamedTemporaryFile()
model = ModelHelper(name='caffe2-to-onnx-test')
brew.relu(model, ["X"], "Y")
caffe2_net.write(model.net.Proto().SerializeToString())
caffe2_net.flush()
args = [caffe2_net.name, '--output', output.name]
self.assertRaisesRegex(Exception,
'value info',
self._run_command, caffe2_to_onnx, args)
args.extend([
'--value-info',
json.dumps({
'X': (TensorProto.FLOAT, (2, 2)),
})])
self._run_command(caffe2_to_onnx, args)
onnx_model = ModelProto()
onnx_model.ParseFromString(output.read())
self.assertEqual(len(onnx_model.graph.node), 1)
self.assertEqual(onnx_model.graph.node[0].op_type, 'Relu')
self.assertEqual(len(onnx_model.graph.initializer), 0)
@unittest.skip("Disabled due to onnx optimizer deprecation")
def test_onnx_to_caffe2(self):
onnx_model = tempfile.NamedTemporaryFile()
output = tempfile.NamedTemporaryFile()
init_net_output = tempfile.NamedTemporaryFile()
node_def = helper.make_node(
"Mul", ["X", "W"], ["Y"])
graph_def = helper.make_graph(
[node_def],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
helper.make_tensor_value_info("W", TensorProto.FLOAT, (1, 3))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 3))],
initializer=[helper.make_tensor("W",
TensorProto.FLOAT,
[1, 3],
np.zeros((1, 3)).flatten().astype(float))])
model_def = helper.make_model(graph_def, producer_name='onnx-to-caffe2-test')
onnx_model.write(model_def.SerializeToString())
onnx_model.flush()
self._run_command(
onnx_to_caffe2, [
onnx_model.name,
'--output', output.name,
'--init-net-output', init_net_output.name,
])
caffe2_net = caffe2_pb2.NetDef()
caffe2_net.ParseFromString(output.read())
self.assertEqual(len(caffe2_net.op), 1)
self.assertEqual(caffe2_net.op[0].type, 'Mul')
caffe2_init_net = caffe2_pb2.NetDef()
caffe2_init_net.ParseFromString(init_net_output.read())
self.assertEqual(len(caffe2_init_net.op), 1)
self.assertEqual(set(sum([list(init_op.output)
for init_op in caffe2_init_net.op], [])),
{'W'})
def test_onnx_to_caffe2_zipfile(self):
buf = tempfile.NamedTemporaryFile()
onnx_model = zipfile.ZipFile(buf, 'w')
node_def = helper.make_node(
"MatMul", ["X", "W"], ["Y"])
X = np.random.rand(2, 3).astype(np.float32)
W = np.random.rand(3, 2).flatten().astype(np.float32)
graph_def = helper.make_graph(
[node_def],
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
helper.make_tensor_value_info("W", TensorProto.FLOAT, (3, 2))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 2))],
initializer=[helper.make_tensor("W",
TensorProto.FLOAT,
[3, 2],
W.tobytes(),
raw=True)])
model_def = helper.make_model(graph_def, producer_name='onnx-to-caffe2-test')
onnx_model.writestr('__MODEL_PROTO', model_def.SerializeToString())
onnx_model.writestr('W', W.tobytes())
onnx_model.close()
W = W.reshape((3, 2))
Y_expect = np.matmul(X, W)
c2_model = c2.prepare_zip_archive(buf)
Y = c2_model.run(X).Y
np.testing.assert_allclose(Y, Y_expect)
def _make_fake_if_op(self, true_nodes, false_nodes, output_types):
true = helper.make_tensor("condition", TensorProto.BOOL, (), [True])
true_graph = helper.make_graph(true_nodes, "true_graph", [], [
helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 2)),
])
false_graph = helper.make_graph(false_nodes, "false_graph", [], [
helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 2)),
])
if_inputs = ["condition"]
if_outputs = [name for _, _, name in output_types]
retval_nodes = [
helper.make_node("Constant", [], ["condition"], value=true),
helper.make_node("If", if_inputs, if_outputs, then_branch=true_graph,
else_branch=false_graph)
]
return retval_nodes
def test_onnx_to_caffe2_if(self):
true_nodes = [helper.make_node(
"MatMul", ["X", "W"], ["Y"])]
false_nodes = [helper.make_node("Slice", ["X"], ["Y"], axes=[0, 1],
starts=[0, 0], ends=[2, 2])]
nodes = self._make_fake_if_op(true_nodes, false_nodes, [(TensorProto.FLOAT, (2, 2), "Y")])
X = np.random.rand(2, 3).astype(np.float32)
W = np.random.rand(3, 2).flatten().astype(np.float32)
graph_def = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3)),
helper.make_tensor_value_info("W", TensorProto.FLOAT, (3, 2))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 2))],
initializer=[helper.make_tensor("W",
TensorProto.FLOAT,
[3, 2],
W.tolist())]
)
onnx_id = helper.make_opsetid("", 9)
model_def = helper.make_model(graph_def, producer_name='onnx-to-caffe2-test',
opset_imports=[onnx_id])
p = c2.prepare(model_def)
Y = np.matmul(X, W.reshape(3, 2))
out = p.run(X)
np.testing.assert_allclose(out.Y, Y)
# input_types and output_types are lists of triples of (name, type, shape)
def _make_fake_loop_op(self, body_nodes, input_types, output_types):
ten = helper.make_tensor("trip_count_value", TensorProto.INT64, (1,), [10])
true = helper.make_tensor("condition", TensorProto.BOOL, (1,), [True])
# lcd is a dummy loop-carried dependency that only exists because
# right now the schema checker is broken and assumes a variadic
# input needs at least one value.
graph_inputs = [helper.make_tensor_value_info("i", TensorProto.INT64, (1,)),
helper.make_tensor_value_info("cond", TensorProto.BOOL, (1,))]
for type, shape, name in input_types:
graph_inputs.append(helper.make_tensor_value_info("_" + name, type, shape))
graph_outputs = [helper.make_tensor_value_info("cond", TensorProto.BOOL, (1,))]
for type, shape, name in output_types:
graph_outputs.append(helper.make_tensor_value_info("_" + name, type, shape))
body_graph = helper.make_graph(body_nodes, "body_graph", graph_inputs,
graph_outputs)
loop_inputs = ["trip_count", "condition"]
loop_inputs.extend([name for _, _, name in input_types])
loop_outputs = [name for _, _, name in output_types]
retval_nodes = [
helper.make_node("Constant", [], ["trip_count"], value=ten),
helper.make_node("Constant", [], ["condition"], value=true),
helper.make_node("Loop", loop_inputs, loop_outputs, body=body_graph)
]
return retval_nodes
@unittest.skip("Disabled due to onnx optimizer deprecation")
def test_onnx_to_caffe2_loop(self):
body_nodes = [helper.make_node(
"MatMul", ["_X", "W"], ["_Y"])]
nodes = self._make_fake_loop_op(body_nodes,
[(TensorProto.FLOAT, (2, 2), "X")],
[(TensorProto.FLOAT, (2, 2), "Y")])
X = np.random.rand(2, 2).astype(np.float32)
W = np.random.rand(2, 2).flatten().astype(np.float32)
graph_def = helper.make_graph(
nodes,
"test",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 2)),
helper.make_tensor_value_info("W", TensorProto.FLOAT, (2, 2))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 2))],
initializer=[helper.make_tensor("W",
TensorProto.FLOAT,
[2, 2],
W.tolist())]
)
model_def = helper.make_model(graph_def, producer_name='onnx-to-caffe2-test')
Y = X
for _ in range(10):
Y = np.matmul(Y, W.reshape(2, 2))
p = c2.prepare(model_def)
out = p.run(X)
np.testing.assert_allclose(out.Y, Y)
# TODO investigate why this is failing after changing Reshape
# operator from taking the new shape as attribute to as input
@unittest.skip('Start failing after Reshape op change')
def test_convert_end2end(self):
predict_net_f = tempfile.NamedTemporaryFile()
init_net_f = tempfile.NamedTemporaryFile()
onnx_model_f = tempfile.NamedTemporaryFile()
x = 'X'
w = 'W'
b = 'b'
y = 'Y'
predict_net = caffe2_pb2.NetDef()
predict_net.name = 'test-convert-end2end'
predict_net.external_input[:] = [x, w, b]
predict_net.external_output[:] = [y]
predict_net.op.extend([
core.CreateOperator(
'FC',
inputs=[x, w, b],
outputs=[y],
axis=2,
),
])
predict_net_f.write(predict_net.SerializeToString())
predict_net_f.flush()
init_net = caffe2_pb2.NetDef()
init_net.name = 'test-convert-end2end-init'
init_net.external_output[:] = [w, b]
x_val = np.random.randn(1, 3, 2).astype(np.float32)
w_val = np.random.randn(4, 2).astype(np.float32)
b_val = np.random.randn(4).astype(np.float32)
init_net.op.extend([
core.CreateOperator(
'GivenTensorFill',
[],
[w],
values=w_val,
shape=w_val.shape,
),
core.CreateOperator(
'GivenTensorFill',
[],
[b],
values=b_val,
shape=b_val.shape,
),
])
init_net_f.write(init_net.SerializeToString())
init_net_f.flush()
y_val = np.matmul(x_val, w_val.transpose()) + b_val
for _ in range(5):
self._run_command(
caffe2_to_onnx, [
predict_net_f.name,
'--caffe2-init-net', init_net_f.name,
'--output', onnx_model_f.name,
'--value-info',
json.dumps({
x: (TensorProto.FLOAT, (1, 3, 2)),
}),
],
catch_exceptions=False,
)
onnx_model_f.seek(0)
onnx_model = ModelProto()
onnx_model.ParseFromString(onnx_model_f.read())
np.testing.assert_almost_equal(
c2.run_model(
onnx_model, {onnx_model.graph.input[0].name: x_val}),
[y_val])
self._run_command(
onnx_to_caffe2, [
onnx_model_f.name,
'--output', predict_net_f.name,
'--init-net-output', init_net_f.name,
])
predict_net_f.seek(0)
predict_net = caffe2_pb2.NetDef()
predict_net.ParseFromString(predict_net_f.read())
init_net_f.seek(0)
init_net = caffe2_pb2.NetDef()
init_net.ParseFromString(init_net_f.read())
x = predict_net.external_input[0]
np.testing.assert_almost_equal(c2_native_run_net(init_net=init_net,
predict_net=predict_net,
inputs={x: x_val})[1],
[y_val])
|
