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[Bugfix][caffe2] Fix caffe2 relay frontend #2733

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Mar 14, 2019
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[Bugfix][caffe2] Fix caffe2 relay frontend #2733

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20 changes: 5 additions & 15 deletions python/tvm/relay/frontend/caffe2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -132,25 +132,15 @@ class Elemwise(Caffe2OpConverter):
""" A helper class for elemwise op converters.
"""
name = ''
@classmethod
def _math_name_picker(cls, suffix):

def _impl(attr):
if attr.get('broadcast', 0):
return 'broadcast_' + suffix
return 'elemwise_' + suffix

return _impl

@classmethod
def _impl(cls, inputs, args, params):
assert len(inputs) == 2, "Math op take 2 inputs, {} given".format(
len(inputs))
op_name = cls._math_name_picker(cls.name)(args)
axis = int(args.get('axis', 0))
op_name = cls.name
conv_ops = ["conv2d", "conv2d_transpose"]
if op_name == 'broadcast_add' and inputs[0].attr('op_name') in conv_ops:
if args.get('broadcast', 0) and any(x in str(inputs[0]) for x in conv_ops):
# TODO(zhreshold): remove hard coded infershape
axis = int(args.get('axis', 0))
inputs[1] = _op.expand_dims(inputs[1], axis=axis, num_newaxis=2)
return get_relay_op(op_name)(*inputs)

Expand Down Expand Up @@ -214,7 +204,7 @@ def _impl(cls, inputs, args, params):
'order': ('data_layout', ("NCHW"), lambda x: x if isinstance(x, str) else x.decode('UTF-8')),
},
excludes=[],
ignores=[],
ignores=_caffe2_internal_args,
custom_check=dimension_constraint())(inputs[:2], args, params)
use_bias = len(inputs) == 3
if use_bias:
Expand Down Expand Up @@ -256,7 +246,7 @@ def _impl(cls, inputs, args, params):
mean = _op.expand_dims(inputs[1], axis=2, num_newaxis=2)
std = _op.expand_dims(inputs[2], axis=2, num_newaxis=2)

return _op.broadcast_divide(_op.subtract(inputs[0], mean), std)
return _op.divide(_op.subtract(inputs[0], mean), std)


class ResizeNearest(Caffe2OpConverter):
Expand Down
132 changes: 131 additions & 1 deletion tests/python/frontend/caffe2/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,9 @@
from tvm.relay.testing.config import ctx_list
from tvm import relay
from model_zoo import c2_squeezenet, c2_resnet50, c2_vgg19
from caffe2.python import workspace
from caffe2.python import workspace, core
from caffe2.proto import caffe2_pb2
from collections import namedtuple


def get_tvm_output(model,
Expand Down Expand Up @@ -81,7 +83,135 @@ def test_forward_vgg19():
verify_caffe2_forward_impl(c2_vgg19, (1, 3, 224, 224), (1, 1000))


Model = namedtuple('Model', ['init_net', 'predict_net'])


def test_elementwise_add():
data_shape = (1, 16, 9, 9)
init_net = caffe2_pb2.NetDef()
init_net.name = 'test_init_net'
init_net.external_output[:] = ['A', 'B']
init_net.op.extend([
core.CreateOperator(
'GivenTensorFill',
[],
['A'],
shape=data_shape,
values=np.random.uniform(size=data_shape).flatten().tolist(),
),
core.CreateOperator(
'GivenTensorFill',
[],
['B'],
shape=data_shape,
values=np.random.uniform(size=data_shape).flatten().tolist(),
),
])

predict_net = caffe2_pb2.NetDef()
predict_net.name = 'test_predict_net'
predict_net.external_input[:] = ['A', 'B']
predict_net.external_output[:] = ['C']
predict_net.op.extend([
core.CreateOperator(
'Add',
['A', 'B'],
['C'],
)
])

model = Model(init_net, predict_net)
verify_caffe2_forward_impl(model, data_shape, data_shape)


def test_elementwise_add_with_broadcast():
data_shape = (1, 16, 9, 9)
init_net = caffe2_pb2.NetDef()
init_net.name = 'test_init_net'
init_net.external_output[:] = ['A', 'B']
init_net.op.extend([
core.CreateOperator(
'GivenTensorFill',
[],
['A'],
shape=data_shape,
values=np.random.uniform(size=data_shape).flatten().tolist(),
),
core.CreateOperator(
'GivenTensorFill',
[],
['B'],
shape=(1,),
values=np.random.uniform(size=1).flatten().tolist(),
),
])

predict_net = caffe2_pb2.NetDef()
predict_net.name = 'test_predict_net'
predict_net.external_input[:] = ['A', 'B']
predict_net.external_output[:] = ['C']
predict_net.op.extend([
core.CreateOperator(
'Add',
['A', 'B'],
['C'],
broadcast=1,
)
])

model = Model(init_net, predict_net)
verify_caffe2_forward_impl(model, data_shape, data_shape)


def test_normalize_yuv():
data_shape = (1, 3, 96, 96)
init_net = caffe2_pb2.NetDef()
init_net.name = 'test_init_net'
init_net.external_output[:] = ['A', 'mean', 'std']
init_net.op.extend([
core.CreateOperator(
'GivenTensorFill',
[],
['A'],
shape=data_shape,
values=np.random.uniform(size=data_shape).flatten().tolist(),
),
core.CreateOperator(
'GivenTensorFill',
[],
['mean'],
shape=(1, 3,),
values=np.random.uniform(size=3).flatten().tolist(),
),
core.CreateOperator(
'GivenTensorFill',
[],
['std'],
shape=(1, 3,),
values=np.random.uniform(size=3).flatten().tolist(),
),
])

predict_net = caffe2_pb2.NetDef()
predict_net.name = 'test_predict_net'
predict_net.external_input[:] = ['A', 'mean', 'std']
predict_net.external_output[:] = ['C']
predict_net.op.extend([
core.CreateOperator(
'NormalizePlanarYUV',
['A', 'mean', 'std'],
['C'],
)
])

model = Model(init_net, predict_net)
verify_caffe2_forward_impl(model, data_shape, data_shape)


if __name__ == '__main__':
test_forward_squeezenet1_1()
test_forward_resnet50()
test_forward_vgg19()
test_elementwise_add()
test_elementwise_add_with_broadcast()
test_normalize_yuv()