def _test_net(self, net_name, input_blob_dims=(1, 3, 224, 224), decimal=7):
np.random.seed(seed=0)
model_dir = self._model_dir(net_name)
if not os.path.exists(model_dir):
self._download(net_name)
c2_predict_pb = os.path.join(model_dir, 'predict_net.pb')
c2_predict_net = caffe2_pb2.NetDef()
with open(c2_predict_pb, 'rb') as f:
c2_predict_net.ParseFromString(f.read())
c2_predict_net.name = net_name
c2_init_pb = os.path.join(model_dir, 'init_net.pb')
c2_init_net = caffe2_pb2.NetDef()
with open(c2_init_pb, 'rb') as f:
c2_init_net.ParseFromString(f.read())
c2_init_net.name = net_name + '_init'
n, c, h, w = input_blob_dims
data = np.random.randn(n, c, h, w).astype(np.float32)
inputs = [data]
_, c2_outputs = c2_native_run_net(c2_init_net, c2_predict_net, inputs)
del _
model = c2_onnx.caffe2_net_to_onnx_model(
predict_net=c2_predict_net,
init_net=c2_init_net,
value_info=json.load(
open(os.path.join(model_dir, 'value_info.json'))))
c2_ir = c2.prepare(model)
onnx_outputs = c2_ir.run(inputs)
self.assertSameOutputs(c2_outputs, onnx_outputs, decimal=decimal)
self.report_mem_usage(net_name)
def caffe2_to_onnx(caffe2_net,
caffe2_net_name,
caffe2_init_net,
value_info,
output):
c2_net_proto = caffe2_pb2.NetDef()
c2_net_proto.ParseFromString(caffe2_net.read())
if not c2_net_proto.name and not caffe2_net_name:
raise click.BadParameter(
'The input caffe2 net does not have name, '
'--caffe2-net-name must be provided')
c2_net_proto.name = caffe2_net_name or c2_net_proto.name
if caffe2_init_net:
c2_init_net_proto = caffe2_pb2.NetDef()
c2_init_net_proto.ParseFromString(caffe2_init_net.read())
c2_init_net_proto.name = '{}_init'.format(caffe2_net_name)
else:
c2_init_net_proto = None
if value_info:
value_info = json.loads(value_info)
onnx_model = c2_onnx.caffe2_net_to_onnx_model(
predict_net=c2_net_proto,
init_net=c2_init_net_proto,
value_info=value_info)
output.write(onnx_model.SerializeToString())
def test_slice(self):
X = np.random.randn(1, 2, 3).astype(np.float32)
starts = np.array([0, 1, 0], dtype=np.int32)
ends = np.array([-1, 2, 3], dtype=np.int32)
predict_net = caffe2_pb2.NetDef()
predict_net.name = 'test-slice-net'
predict_net.external_input[:] = ['X']
predict_net.external_output[:] = ['Y']
predict_net.op.extend([
core.CreateOperator(
'Slice',
inputs=['X'],
outputs=['Y'],
starts=starts,
ends=ends,
),
])
ws, (Y, ) = c2_native_run_net(init_net=None,
predict_net=predict_net,
inputs=[X])
onnx_model = c2_onnx.caffe2_net_to_onnx_model(
predict_net=predict_net,
value_info={
'X': (onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[X.dtype], X.shape)
})
Y, = c2.run_model(onnx_model, inputs=[X])
np.testing.assert_almost_equal(Y, X[:, 1:2, :])
def _test_net(self,
net_name,
input_blob_dims=(1, 3, 224, 224),
decimal=7):
np.random.seed(seed=0)
model_dir = self._model_dir(net_name)
if not os.path.exists(model_dir):
self._download(net_name)
c2_predict_pb = os.path.join(model_dir, 'predict_net.pb')
c2_predict_net = caffe2_pb2.NetDef()
with open(c2_predict_pb, 'rb') as f:
c2_predict_net.ParseFromString(f.read())
c2_predict_net.name = net_name
c2_init_pb = os.path.join(model_dir, 'init_net.pb')
c2_init_net = caffe2_pb2.NetDef()
with open(c2_init_pb, 'rb') as f:
c2_init_net.ParseFromString(f.read())
c2_init_net.name = net_name + '_init'
n, c, h, w = input_blob_dims
data = np.random.randn(n, c, h, w).astype(np.float32)
inputs = [data]
c2_outputs = c2_native_run_net(c2_init_net, c2_predict_net, inputs)
for input_name in c2_predict_net.external_input:
if input_name == 'data' or input_name == 'gpu_0/data':
break
else:
raise RuntimeError(
'Could not find input name of model {}'.format(net_name))
predict_model = c2_onnx.caffe2_net_to_onnx_model(
predict_net=c2_predict_net,
init_net=c2_init_net,
value_info={
input_name: (onnx_pb2.TensorProto.FLOAT,
(1, 3, 224, 224))
})
c2_ir = c2.prepare(predict_model)
onnx_outputs = c2_ir.run(inputs)
self.assertSameOutputs(c2_outputs, onnx_outputs, decimal=decimal)
self.report_mem_usage(net_name)
