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ONNX

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
1.6.0	6	11	2	-

General usage

Loading an ONNX Model into SINGA

After loading an ONNX model from disk by onnx.load, you need to update the model's batchsize, since for most models, they use a placeholder to represent its batchsize. We give an example here, as update_batch_size. You only need to update the batchsize of input and output, the shape of internal tensors will be inferred automatically.

Then, you can prepare the SINGA model by using sonnx.prepare. This function 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

# if the input has multiple tensors? can put this function inside prepare()?
def update_batch_size(onnx_model, batch_size):
    model_input = onnx_model.graph.input[0]
    model_input.type.tensor_type.shape.dim[0].dim_value = batch_size
    model_output = onnx_model.graph.output[0]
    model_output.type.tensor_type.shape.dim[0].dim_value = batch_size
    return onnx_model


model_path = "PATH/To/ONNX/MODEL"
onnx_model = onnx.load(model_path)

# set batch size
onnx_model = update_batch_size(onnx_model, 1)

# convert onnx graph nodes into SINGA operators
dev = device.create_cuda_gpu()
sg_ir = sonnx.prepare(onnx_model, device=dev)

Inference SINGA model

Once the model is created, you can do inference by calling sg_ir.run. The input and output must be SINGA Tensor instances. Since SINGA model returns the output as a list, if there is only one output, you just need to take the first element from the output.

# can warp the following code in prepare()
# and provide a flag training=True/False?

class Infer:


    def __init__(self, sg_ir):
        self.sg_ir = sg_ir

    def forward(self, x):
        return sg_ir.run([x])[0]


data = get_dataset()
x = tensor.Tensor(device=dev, data=data)

model = Infer(sg_ir)
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 set the internal tensors to be trainable

class Infer:

    def __init__(self, sg_ir):
        self.sg_ir = sg_ir
        ## can wrap these codes in sonnx?
        for idx, tens in sg_ir.tensor_map.items():
            # allow the tensors to be updated
            tens.requires_grad = True
            tens.stores_grad = True

    def forward(self, x):
        return sg_ir.run([x])[0]

autograd.training = False
model = Infer(sg_ir)

autograd.training = True
# then you training the model like normal
# give more details??

Transfer-learning an ONNX model

You also can append some layers to the end of ONNX model to do transfer-learning. The last_layers means you cut the ONNX layers from [0, last_layers]. Then you can append more layers by the normal SINGA model.

class Trans:

    def __init__(self, sg_ir, last_layers):
        self.sg_ir = sg_ir
        self.last_layers = last_layers
        self.append_linear1 = autograd.Linear(500, 128, bias=False)
        self.append_linear2 = autograd.Linear(128, 32, bias=False)
        self.append_linear3 = autograd.Linear(32, 10, bias=False)

    def forward(self, x):
        y = sg_ir.run([x], last_layers=self.last_layers)[0]
        y = self.append_linear1(y)
        y = autograd.relu(y)
        y = self.append_linear2(y)
        y = autograd.relu(y)
        y = self.append_linear3(y)
        y = autograd.relu(y)
        return y

autograd.training = False
model = Trans(sg_ir, -1)

# then you training the model like normal

A Full Example

This part introduces the usage of SINGA ONNX by using the mnist example. In this section, the examples of how to export, load, inference, re-training, and transfer-learning the minist model are displayed. You can try this part here.

Load dataset

Firstly, you need to import some necessary libraries and define some auxiliary functions for downloading and preprocessing the dataset:

import os
import urllib.request
import gzip
import numpy as np
import codecs

from singa import device
from singa import tensor
from singa import opt
from singa import autograd
from singa import sonnx
import onnx


def load_dataset():
    train_x_url = 'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz'
    train_y_url = 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz'
    valid_x_url = 'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz'
    valid_y_url = 'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz'
    train_x = read_image_file(check_exist_or_download(train_x_url)).astype(
        np.float32)
    train_y = read_label_file(check_exist_or_download(train_y_url)).astype(
        np.float32)
    valid_x = read_image_file(check_exist_or_download(valid_x_url)).astype(
        np.float32)
    valid_y = read_label_file(check_exist_or_download(valid_y_url)).astype(
        np.float32)
    return train_x, train_y, valid_x, valid_y


def check_exist_or_download(url):

    download_dir = '/tmp/'

    name = url.rsplit('/', 1)[-1]
    filename = os.path.join(download_dir, name)
    if not os.path.isfile(filename):
        print("Downloading %s" % url)
        urllib.request.urlretrieve(url, filename)
    return filename


def read_label_file(path):
    with gzip.open(path, 'rb') as f:
        data = f.read()
        assert get_int(data[:4]) == 2049
        length = get_int(data[4:8])
        parsed = np.frombuffer(data, dtype=np.uint8, offset=8).reshape(
            (length))
        return parsed


def get_int(b):
    return int(codecs.encode(b, 'hex'), 16)


def read_image_file(path):
    with gzip.open(path, 'rb') as f:
        data = f.read()
        assert get_int(data[:4]) == 2051
        length = get_int(data[4:8])
        num_rows = get_int(data[8:12])
        num_cols = get_int(data[12:16])
        parsed = np.frombuffer(data, dtype=np.uint8, offset=16).reshape(
            (length, 1, num_rows, num_cols))
        return parsed


def to_categorical(y, num_classes):
    y = np.array(y, dtype="int")
    n = y.shape[0]
    categorical = np.zeros((n, num_classes))
    categorical[np.arange(n), y] = 1
    categorical = categorical.astype(np.float32)
    return categorical

MNIST model

Then you can define a class called CNN to construct the mnist model which consists of several convolution, pooling, fully connection and relu layers. You can also define a function to calculate the accuracy of our result. Finally, you can define a train and a test function to handle the training and prediction process.

class CNN:
    def __init__(self):
        self.conv1 = autograd.Conv2d(1, 20, 5, padding=0)
        self.conv2 = autograd.Conv2d(20, 50, 5, padding=0)
        self.linear1 = autograd.Linear(4 * 4 * 50, 500, bias=False)
        self.linear2 = autograd.Linear(500, 10, bias=False)
        self.pooling1 = autograd.MaxPool2d(2, 2, padding=0)
        self.pooling2 = autograd.MaxPool2d(2, 2, padding=0)

    def forward(self, x):
        y = self.conv1(x)
        y = autograd.relu(y)
        y = self.pooling1(y)
        y = self.conv2(y)
        y = autograd.relu(y)
        y = self.pooling2(y)
        y = autograd.flatten(y)
        y = self.linear1(y)
        y = autograd.relu(y)
        y = self.linear2(y)
        return y


def accuracy(pred, target):
    y = np.argmax(pred, axis=1)
    t = np.argmax(target, axis=1)
    a = y == t
    return np.array(a, "int").sum() / float(len(t))


def train(model,
          x,
          y,
          epochs=1,
          batch_size=64,
          dev=device.get_default_device()):
    batch_number = x.shape[0] // batch_size

    for i in range(epochs):
        for b in range(batch_number):
            l_idx = b * batch_size
            r_idx = (b + 1) * batch_size

            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])

            output_batch = model.forward(x_batch)
            # onnx_model = sonnx.to_onnx([x_batch], [y])
            # print('The model is:\n{}'.format(onnx_model))

            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
                                     tensor.to_numpy(target_batch))

            sgd = opt.SGD(lr=0.001)
            for p, gp in autograd.backward(loss):
                sgd.update(p, gp)
            sgd.step()

            if b % 1e2 == 0:
                print("acc %6.2f loss, %6.2f" %
                      (accuracy_rate, tensor.to_numpy(loss)[0]))
    print("training completed")
    return x_batch, output_batch

def test(model, x, y, batch_size=64, dev=device.get_default_device()):
    batch_number = x.shape[0] // batch_size

    result = 0
    for b in range(batch_number):
        l_idx = b * batch_size
        r_idx = (b + 1) * batch_size

        x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
        target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])

        output_batch = model.forward(x_batch)
        result += accuracy(tensor.to_numpy(output_batch),
                           tensor.to_numpy(target_batch))

    print("testing acc %6.2f" % (result / batch_number))

Train mnist model and export it to onnx

Now, you can train the mnist model and export its onnx model by calling the soonx.to_onnx function.

def make_onnx(x, y):
    return sonnx.to_onnx([x], [y])

# create device
dev = device.create_cuda_gpu()
#dev = device.get_default_device()
# create model
model = CNN()
# load data
train_x, train_y, valid_x, valid_y = load_dataset()
# normalization
train_x = train_x / 255
valid_x = valid_x / 255
train_y = to_categorical(train_y, 10)
valid_y = to_categorical(valid_y, 10)
# do training
autograd.training = True
x, y = train(model, train_x, train_y, dev=dev)
onnx_model = make_onnx(x, y)
# print('The model is:\n{}'.format(onnx_model))

# Save the ONNX model
model_path = os.path.join('/', 'tmp', 'mnist.onnx')
onnx.save(onnx_model, model_path)
print('The model is saved.')

Inference

After you export the onnx model, you can find a file called mnist.onnx in the '/tmp' directory, this model, therefore, can be imported by other libraries. Now, if you want to import this onnx model into singa again and do the inference using the validation dataset, you can define a class called Infer, the forward function of Infer will be called by the test function to do inference for validation dataset. By the way, you should set the label of training to False to fix the gradient of autograd operators.

When import the onnx model, you need to call onnx.load to load the onnx model firstly. Then the onnx model will be fed into the soonx.prepare to parse and initiate to a singa model(sg_ir in the code). The sg_ir contains a singa graph within it, and then you can run an step of inference by feeding input to its run function.

class Infer:
    def __init__(self, sg_ir):
        self.sg_ir = sg_ir
        for idx, tens in sg_ir.tensor_map.items():
            # allow the tensors to be updated
            tens.requires_grad = True
            tens.stores_grad= True
            sg_ir.tensor_map[idx] = tens

    def forward(self, x):
        return sg_ir.run([x])[0] # we can run one step of inference by feeding input

# load the ONNX model
onnx_model = onnx.load(model_path)
sg_ir = sonnx.prepare(onnx_model, device=dev) # parse and initiate to a singa model

# inference
autograd.training = False
print('The inference result is:')
test(Infer(sg_ir), valid_x, valid_y, dev=dev)

Re-training

Assume after import the model, you want to re-train the model again, we can define a function called re_train. Before we call this re_train function, we should set the label of training to True to make the autograde operators update their gradient. And after we finish the training, we set it as False again to call the test function doing inference.

def re_train(sg_ir,
             x,
             y,
             epochs=1,
             batch_size=64,
             dev=device.get_default_device()):
    batch_number = x.shape[0] // batch_size

    new_model = Infer(sg_ir)

    for i in range(epochs):
        for b in range(batch_number):
            l_idx = b * batch_size
            r_idx = (b + 1) * batch_size

            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])

            output_batch = new_model.forward(x_batch)

            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
                                     tensor.to_numpy(target_batch))

            sgd = opt.SGD(lr=0.01)
            for p, gp in autograd.backward(loss):
                sgd.update(p, gp)
            sgd.step()

            if b % 1e2 == 0:
                print("acc %6.2f loss, %6.2f" %
                      (accuracy_rate, tensor.to_numpy(loss)[0]))
    print("re-training completed")
    return new_model

# load the ONNX model
onnx_model = onnx.load(model_path)
sg_ir = sonnx.prepare(onnx_model, device=dev)

# re-training
autograd.training = True
new_model = re_train(sg_ir, train_x, train_y, dev=dev)
autograd.training = False
test(new_model, valid_x, valid_y, dev=dev)

Transfer learning

Finally, if we want to do transfer-learning, we can define a function called Trans to append some layers after the onnx model. For demonstration, the code only appends several linear(fully connection) and relu after the onnx model. You can define a transfer_learning function to handle the training process of the transfer-learning model. And the label of training is the same as the previous one.

class Trans:
    def __init__(self, sg_ir, last_layers):
        self.sg_ir = sg_ir
        self.last_layers = last_layers
        self.append_linear1 = autograd.Linear(500, 128, bias=False)
        self.append_linear2 = autograd.Linear(128, 32, bias=False)
        self.append_linear3 = autograd.Linear(32, 10, bias=False)

    def forward(self, x):
        y = sg_ir.run([x], last_layers=self.last_layers)[0]
        y = self.append_linear1(y)
        y = autograd.relu(y)
        y = self.append_linear2(y)
        y = autograd.relu(y)
        y = self.append_linear3(y)
        y = autograd.relu(y)
        return y

def transfer_learning(sg_ir,
             x,
             y,
             epochs=1,
             batch_size=64,
             dev=device.get_default_device()):
    batch_number = x.shape[0] // batch_size

    trans_model = Trans(sg_ir, -1)

    for i in range(epochs):
        for b in range(batch_number):
            l_idx = b * batch_size
            r_idx = (b + 1) * batch_size

            x_batch = tensor.Tensor(device=dev, data=x[l_idx:r_idx])
            target_batch = tensor.Tensor(device=dev, data=y[l_idx:r_idx])
            output_batch = trans_model.forward(x_batch)

            loss = autograd.softmax_cross_entropy(output_batch, target_batch)
            accuracy_rate = accuracy(tensor.to_numpy(output_batch),
                                     tensor.to_numpy(target_batch))

            sgd = opt.SGD(lr=0.07)
            for p, gp in autograd.backward(loss):
                sgd.update(p, gp)
            sgd.step()

            if b % 1e2 == 0:
                print("acc %6.2f loss, %6.2f" %
                      (accuracy_rate, tensor.to_numpy(loss)[0]))
    print("transfer-learning completed")
    return trans_mode

# load the ONNX model
onnx_model = onnx.load(model_path)
sg_ir = sonnx.prepare(onnx_model, device=dev)

# transfer-learning
autograd.training = True
new_model = transfer_learning(sg_ir, train_x, train_y, dev=dev)
autograd.training = False
test(new_model, valid_x, valid_y, dev=dev)

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.

Image Classification

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.

Model Class	Reference	Description	Link
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%

Object Detection

Object detection models detect the presence of multiple objects in an image and segment out areas of the image where the objects are detected.

Model Class	Reference	Description	Link
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 Analysis

Face detection models identify and/or recognize human faces and emotions in given images.

Model Class	Reference	Description	Link
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.

Machine Comprehension

This subset of natural language processing models that answer questions about a given context paragraph.

Model Class	Reference	Description	Link
BERT-Squad	Devlin et al.	This model answers questions based on the context of the given input paragraph.

Supported operators

The following operators are supported:

• Conv
• Relu
• Constant
• MaxPool
• AveragePool
• Softmax
• Sigmoid
• Add
• MatMul
• BatchNormalization
• Concat
• Flatten
• Add
• Gemm
• Reshape
• Sum
• Cos
• Cosh
• Sin
• Sinh
• Tan
• Tanh
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Selu
• Elu
• Equal
• Less
• Sign
• Div
• Sub
• Sqrt
• Log
• Greater
• HardSigmoid
• Identity
• Softplus
• Softsign
• Mean
• Pow
• Clip
• PRelu
• Mul
• Transpose
• Max
• Min
• Shape
• And
• Or
• Xor
• Not
• Neg
• Reciprocal
• LeakyRelu
• GlobalAveragePool
• ConstantOfShape
• Dropout
• ReduceSum
• ReduceMean
• LeakyRelu
• GlobalAveragePool
• Squeeze
• Unsqueeze
• Slice
• Ceil
• Split
• Gather
• Tile
• NonZero
• Cast
• OneHot

Special comments for ONNX backend

• Conv, MaxPool and AveragePool

  Input must be 1d(N*C*H) and 2d(N*C*H*W) shape and dilation must be 1.

• BatchNormalization

  epsilon is 1e-05 and cannot be changed.

• Cast

  Only support float32 and int32, other types are casted to these two types.

• Squeeze and Unsqueeze

  If you encounter errors when you Squeeze or Unsqueeze between Tensor and Scalar, please report to us.

• Empty tensor Empty tensor is illegal in SINGA.

Implementation

The code of SINGA ONNX locates at python/singa/soonx.py. There are three main class, SingaFrontend and SingaBackend and SingaRep. SingaFrontend translates a SINGA model to ONNX model; SingaBackend translates a ONNX model to SingaRep object which stores all SINGA operators and tensors(the tensor in this doc means SINGA Tensor); SingaRep can be run like a SINGA model.

SingaFrontend

The entry function of SingaFrontend is singa_to_onnx_model which also is called to_onnx. singa_to_onnx_model creates the ONNX model, and it also create a ONNX graph by using singa_to_onnx_graph.

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 onnx_model.graph.input; the output is stored in onnx_model.graph.output; and the trainable weights are stored in onnx_model.graph.initializer.

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 operator.

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.

SingaBackend

The entry function of SingaBackend is prepare which checks the version of ONNX model and call _onnx_model_to_singa_net then.

The purpose of _onnx_model_to_singa_net 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', ['name', 'op', 'handle', 'forward']). For each operator, name is its ONNX node name; op is the ONNX node; forward is the SINGA operator's forward function; handle is prepared for some special operators such as Conv and Pooling which has handle object.

The first step of _onnx_model_to_singa_net is to call _init_graph_parameter to get all tensors within the model. For trainable weights, it can init SINGA Tensor from onnx_model.graph.initializer. Please note, the weights may also be stored within graph's input or a ONNX node called Constant, SINGA can also handle these.

Though all weights are stored within ONNX model, the input of the model is unknown but its shape and type. So SINGA support two ways to init input, 1, generate random tensor by its shape and type, 2, allow the user to assign the input. The first way works fine for most models, however, for some model such as bert, the indices of matrix cannot be random generated otherwise it will incurs errors.

Then, _onnx_model_to_singa_net iterators all nodes within ONNX graph to translate it to SIGNA operators. Also, _rename_operators defines the operators name mapping between SINGA and ONNX. _special_operators defines which function to be used to translate the operator. _run_node runs the generated SINGA model by its input tensors and store its output tensors for being used by later operators.

This class finally return a SingaRep object and stores all SINGA tensors and operators within it.

SingaRep

SingaBackend stores all SINGA tensors and operators. run accepts the input of the model and run the SINGA operators one by one following the operators queue. The user can use last_layers to decide to run the model till the last few layers. Set all_outputs as False to get only the final output, True to also get all the intermediate output.