def onnx_loss(a,model,target):
'''
input:
a graph node dictionary
model: graph model
target: label
load other nodes of onnx
'''
for i in model.graph.node:
if (i.op_type == 'Constant'):
pass
# do nothing
if (i.op_type == 'LeakyRelu'):
a[str(i.output[0])] = autograd.relu(a[str(i.input[0])])
elif (i.op_type == 'Relu'):
a[str(i.output[0])] = autograd.relu(a[str(i.input[0])])
elif (i.op_type == 'Softmax'):
a[str(i.output[0])] = autograd.softmax(a[str(i.input[0])])
elif (i.op_type == 'Add'):
if(str(i.input[1])[-1] == 'b'):
a[str(i.output[0])] = autograd.add_bias(a[str(i.input[0])], a[str(i.input[1])])
else:
a[str(i.output[0])] = autograd.add(a[str(i.input[0])],a[str(i.input[1])])
elif (i.op_type == 'MatMul'):
a[str(i.output[0])] = autograd.matmul(a[str(i.input[0])], a[str(i.input[1])])
loss = autograd.cross_entropy(a['Y'], target)
return loss
def run(model, modeldic, layer,inputs):
'''
input: input for singa model
load other nodes of onnx
'''
supportLayer = ['Linear','Conv','MaxPool','AveragePool','BatchNormalization']
#supportLayer = ['Conv', 'MaxPool', 'AveragePool', 'BatchNormalization']
oper=modeldic
for counter,i in enumerate(model.graph.input):
oper[i.name] = inputs[counter]
for i in model.graph.node:
if (i.op_type == 'Relu'):
oper[str(i.output[0])] = autograd.relu(oper[str(i.input[0])])
elif (i.op_type == 'Softmax'):
oper[str(i.output[0])] = autograd.softmax(oper[str(i.input[0])])
elif (i.op_type == 'Add'):
oper[str(i.output[0])] = autograd.add(oper[str(i.input[0])], oper[str(i.input[1])])
elif (i.op_type == 'MatMul'):
oper[str(i.output[0])] = autograd.matmul(oper[str(i.input[0])], oper[str(i.input[1])])
elif (i.op_type == 'Flatten'):
oper[str(i.output[0])] = autograd.flatten(oper[str(i.input[0])])
elif(i.op_type == 'Concat'):
oper[str(i.output[0])] = autograd.cat((oper[str(i.input[0])], oper[str(i.input[1])]),int(i.attribute[0].i))
elif(i.op_type == 'Tanh'):
oper[str(i.output[0])] = autograd.tanh(oper[str(i.input[0])])
elif (i.op_type == 'Sigmoid'):
oper[str(i.output[0])] = autograd.sigmoid(oper[str(i.input[0])])
elif (i.op_type == 'Mul'):
oper[str(i.output[0])] = autograd.mul(oper[str(i.input[0])],oper[str(i.input[1])])
elif (i.op_type in supportLayer):
oper[str(i.output[0])] = layer[str(i.output[0])](oper[str(i.input[0])])
out =[]
for counter,i in enumerate(model.graph.output):
out.append(modeldic[i.name])
return out
print("train_label_shape:", label.shape)
inputs = Tensor(data=data)
target = Tensor(data=label)
w0 = Tensor(shape=(2, 3), requires_grad=True, stores_grad=True)
w0.gaussian(0.0, 0.1)
b0 = Tensor(shape=(1, 3), requires_grad=True, stores_grad=True)
b0.set_value(0.0)
w1 = Tensor(shape=(3, 2), requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
b1.set_value(0.0)
sgd = optimizer.SGD(0.05)
# training process
for i in range(1001):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x = autograd.matmul(x, w1)
x = autograd.add_bias(x, b1)
x = autograd.softmax(x)
loss = autograd.cross_entropy(x, target)
for p, gp in autograd.backward(loss):
sgd.apply(0, gp, p, "")
if i % 100 == 0:
print("training loss = ", tensor.to_numpy(loss)[0])
print('train_label_shape:', label.shape)
inputs = Tensor(data=data)
target = Tensor(data=label)
w0 = Tensor(shape=(2, 3), requires_grad=True, stores_grad=True)
w0.gaussian(0.0, 0.1)
b0 = Tensor(shape=(1, 3), requires_grad=True, stores_grad=True)
b0.set_value(0.0)
w1 = Tensor(shape=(3, 2), requires_grad=True, stores_grad=True)
w1.gaussian(0.0, 0.1)
b1 = Tensor(shape=(1, 2), requires_grad=True, stores_grad=True)
b1.set_value(0.0)
sgd = optimizer.SGD(0.05)
# training process
for i in range(1001):
x = autograd.matmul(inputs, w0)
x = autograd.add_bias(x, b0)
x = autograd.relu(x)
x = autograd.matmul(x, w1)
x = autograd.add_bias(x, b1)
x = autograd.softmax(x)
loss = autograd.cross_entropy(x, target)
for p, gp in autograd.backward(loss):
sgd.apply(0, gp, p, '')
if (i % 100 == 0):
print('training loss = ', tensor.to_numpy(loss)[0])
label = to_categorical(label, 2).astype(np.float32)
print('train_data_shape:', data.shape)
print('train_label_shape:', label.shape)
inputs = Tensor(data=data)
target = Tensor(data=label)
linear1 = autograd.Linear(3, 2)
linear2 = autograd.Linear(2, 2)
linear3 = autograd.Linear(2, 2)
sgd = optimizer.SGD(0.00)
# training process
for i in range(1):
x = linear1(inputs)
x = autograd.relu(x)
x1 = linear2(x)
x2 = linear3(x)
x3 = autograd.add(x1, x2)
y = autograd.softmax(x3)
loss = autograd.cross_entropy(y, target)
gradient = autograd.backward(loss)
for p, gp in gradient:
sgd.apply(0, gp, p, '')
if (i % 100 == 0):
print('training loss = ', tensor.to_numpy(loss)[0])
model = sonnx.to_onnx_model([inputs], [y])
onnx.save(model, 'linear.onnx')