ONNX is an open representation format for machine learning models, which enables AI developers to use models across different libraries and tools. SINGA supports loading ONNX format models for training and inference, and saving models defined using SINGA APIs (e.g., Module) into ONNX format.
SINGA has been tested with the following version of ONNX.
|ONNX version||File format version||Opset version ai.onnx||Opset version ai.onnx.ml||Opset version ai.onnx.training|
Loading an ONNX Model into SINGA
After loading an ONNX model from disk by
onnx.load, You only need to update
the batch-size of input using
tensor.PlaceHolder after SINGA v3.0, the shape
of internal tensors will be inferred automatically.
Then, you should define a class inheriting from
sonnx.SONNXModel and implement
forward for forward work and
train_one_batch for training work.
After you call
model.compile, the SONNX iterates and translates all the nodes
within the ONNX model's graph into SINGA operators, loads all stored weights and
infers each intermediate tensor's shape.
import onnx from singa import device from singa import sonnx class MyModel(sonnx.SONNXModel): def __init__(self, onnx_model): super(MyModel, self).__init__(onnx_model) def forward(self, *x): y = super(MyModel, self).forward(*x) # Since SINGA model returns the output as a list, # if there is only one output, # you just need to take the first element. return y def train_one_batch(self, x, y): pass model_path = "PATH/To/ONNX/MODEL" onnx_model = onnx.load(model_path) # convert onnx model into SINGA model dev = device.create_cuda_gpu() x = tensor.PlaceHolder(INPUT.shape, device=dev) model = MyModel(onnx_model) model.compile([x], is_train=False, use_graph=True, sequential=True)
Inference SINGA model
Once the model is created, you can do inference by calling
input and output must be SINGA
x = tensor.Tensor(device=dev, data=INPUT) y = model.forward(x)
Saving SINGA model into ONNX Format
Given the input tensors and the output tensors generated by the operators the model, you can trace back all internal operations. Therefore, a SINGA model is defined by the input and outputs tensors. To export a SINGA model into ONNX format, you just need to provide the input and output tensor list.
# x is the input tensor, y is the output tensor sonnx.to_onnx([x], [y])
Re-training an ONNX model
To train (or refine) an ONNX model using SINGA, you need to implement the
sonnx.SONNXModel and mark the
from singa import opt from singa import autograd class MyModel(sonnx.SONNXModel): def __init__(self, onnx_model): super(MyModel, self).__init__(onnx_model) def forward(self, *x): y = super(MyModel, self).forward(*x) return y def train_one_batch(self, x, y, dist_option, spars): out = self.forward(x) loss = autograd.softmax_cross_entropy(out, y) if dist_option == 'fp32': self.optimizer.backward_and_update(loss) elif dist_option == 'fp16': self.optimizer.backward_and_update_half(loss) elif dist_option == 'partialUpdate': self.optimizer.backward_and_partial_update(loss) elif dist_option == 'sparseTopK': self.optimizer.backward_and_sparse_update(loss, topK=True, spars=spars) elif dist_option == 'sparseThreshold': self.optimizer.backward_and_sparse_update(loss, topK=False, spars=spars) return out, loss def set_optimizer(self, optimizer): self.optimizer = optimizer sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5) model.set_optimizer(sgd) model.compile([tx], is_train=True, use_graph=graph, sequential=True)
Transfer-learning an ONNX model
You also can append some layers to the end of the ONNX model to do
last_layers accept a negative integer indicating the
layer to cut off from. For example,
-1 means cut off after the final output(do
not cut off any layer),
-2 means you cut off after the last second layer.
from singa import opt from singa import autograd class MyModel(sonnx.SONNXModel): def __init__(self, onnx_model): super(MyModel, self).__init__(onnx_model) self.linear = layer.Linear(1000, 3) def forward(self, *x): # cut off after the last third layer # and append a linear layer y = super(MyModel, self).forward(*x, last_layers=-3) y = self.linear(y) return y def train_one_batch(self, x, y, dist_option, spars): out = self.forward(x) loss = autograd.softmax_cross_entropy(out, y) if dist_option == 'fp32': self.optimizer.backward_and_update(loss) elif dist_option == 'fp16': self.optimizer.backward_and_update_half(loss) elif dist_option == 'partialUpdate': self.optimizer.backward_and_partial_update(loss) elif dist_option == 'sparseTopK': self.optimizer.backward_and_sparse_update(loss, topK=True, spars=spars) elif dist_option == 'sparseThreshold': self.optimizer.backward_and_sparse_update(loss, topK=False, spars=spars) return out, loss def set_optimizer(self, optimizer): self.optimizer = optimizer sgd = opt.SGD(lr=0.005, momentum=0.9, weight_decay=1e-5) model.set_optimizer(sgd) model.compile([tx], is_train=True, use_graph=graph, sequential=True)
ONNX model zoo
The ONNX Model Zoo is a collection of pre-trained, state-of-the-art models in the ONNX format contributed by community members. SINGA has supported several CV and NLP models now. More models are going to be supported soon.
This collection of models take images as input, then classifies the major objects in the images into 1000 object categories such as keyboard, mouse, pencil, and many animals.
|MobileNet||Sandler et al.||Light-weight deep neural network best suited for mobile and embedded vision applications. |
Top-5 error from paper - ~10%
|ResNet18||He et al.||A CNN model (up to 152 layers). Uses shortcut connections to achieve higher accuracy when classifying images. |
Top-5 error from paper - ~3.6%
|VGG16||Simonyan et al.||Deep CNN model(up to 19 layers). Similar to AlexNet but uses multiple smaller kernel-sized filters that provides more accuracy when classifying images. |
Top-5 error from paper - ~8%
|ShuffleNet_V2||Simonyan et al.||Extremely computation efficient CNN model that is designed specifically for mobile devices. This network architecture design considers direct metric such as speed, instead of indirect metric like FLOP. Top-1 error from paper - ~30.6%|
We also give some re-training examples by using VGG and ResNet, please check
Object detection models detect the presence of multiple objects in an image and segment out areas of the image where the objects are detected.
|Tiny YOLOv2||Redmon et al.||A real-time CNN for object detection that detects 20 different classes. A smaller version of the more complex full YOLOv2 network.|
Face detection models identify and/or recognize human faces and emotions in given images.
|ArcFace||Deng et al.||A CNN based model for face recognition which learns discriminative features of faces and produces embeddings for input face images.|
|Emotion FerPlus||Barsoum et al.||Deep CNN for emotion recognition trained on images of faces.|
This subset of natural language processing models that answer questions about a given context paragraph.
|BERT-Squad||Devlin et al.||This model answers questions based on the context of the given input paragraph.|
|RoBERTa||Devlin et al.||A large transformer-based model that predicts sentiment based on given input text.|
|GPT-2||Devlin et al.||A large transformer-based language model that given a sequence of words within some text, predicts the next word.|
The following operators are supported:
Special comments for ONNX backend
Conv, MaxPool and AveragePool
Input must be 1d
N*C*H*W) shape and
dilationmust be 1.
epsilonis 1e-05 and cannot be changed.
Only support float32 and int32, other types are casted to these two types.
Squeeze and Unsqueeze
If you encounter errors when you
Tensorand Scalar, please report to us.
Empty tensor Empty tensor is illegal in SINGA.
The code of SINGA ONNX locates at
python/singa/soonx.py. There are four main
SingaFrontend translates a SINGA model to an ONNX model;
translates an ONNX model to
SingaRep object which stores all SINGA operators
and tensors(the tensor in this doc means SINGA
SingaRep can be run
like a SINGA model.
SONNXModel inherits from
model.Model which defines a
unified API for SINGA.
The entry function of
singa_to_onnx_model which also is
singa_to_onnx_model creates the ONNX model, and it also
create a ONNX graph by using
singa_to_onnx_graph accepts the output of the model, and recursively iterate
the SINGA model's graph from the output to get all operators to form a queue.
The input and intermediate tensors, i.e, trainable weights, of the SINGA model
is picked up at the same time. The input is stored in
the output is stored in
onnx_model.graph.output; and the trainable weights are
Then the SINGA operator in the queue is translated to ONNX operators one by one.
_rename_operators defines the operators name mapping between SINGA and ONNX.
_special_operators defines which function to be used to translate the
In addition, some operators in SINGA has different definition with ONNX, that
is, ONNX regards some attributes of SINGA operators as input, so
_unhandled_operators defines which function to handle the special operator.
Since the bool type is regarded as int32 in SINGA,
_bool_operators defines the
operators to be changed as bool type.
The entry function of
prepare which checks the version of
ONNX model and call
The purpose of
_onnx_model_to_singa_ops is to get SINGA tensors and operators.
The tensors are stored in a dictionary by their name in ONNX, and operators are
stored in queue by the form of
namedtuple('SingaOps', ['node', 'operator']).
For each operator,
node is an instance from OnnxNode which is defined to store
some basic information for an ONNX node;
operator is the SINGA operator's
The first step of
_onnx_model_to_singa_ops has four steps, the first one is to
_parse_graph_params to get all tensors stored as
params. Then call
_parse_graph_inputs_outputs to get all input and output information stores as
outputs. Finally, it iterators all nodes within the ONNX graph
and parses it by
_onnx_node_to_singa_op as SIGNA operators or layers and store
outputs. Some weights are stored within an ONNX node called
Constant, SONNX can handle them by
_onnx_constant_to_np to store it into
This class finally return a
SingaRep object and stores above
SingaBackend stores all SINGA tensors and operators.
run accepts the input
of the model and runs the SINGA operators one by one following the operators'
queue. The user can use
last_layers to cut off the model after the last few
SONNXModel inherits from
sonnx.SONNXModel and implements the method
forward to provide a unified API with other SINGA models.