def singa_to_onnx(epochs, use_cpu=False, batchsize=32):
sgd = opt.SGD(lr=0.1)
# operations initialization
conv1 = autograd.Conv2d(1, 8, 3, 2, padding=1) # 28 - 14
conv2 = autograd.Conv2d(8, 4, 3, 2, padding=1) # 14 - 7
pooling = autograd.MaxPool2d(3, 2, padding=1) # 7 - 4
linear = autograd.Linear(64, 10)
def forward(x, t):
y = conv1(x)
y = autograd.relu(y)
y = conv2(y)
y = autograd.relu(y)
y = pooling(y)
y = autograd.flatten(y)
y = linear(y)
loss = autograd.softmax_cross_entropy(y, t)
return loss, y
autograd.training = True
(x_train, y_train), (x_test, y_test), dev = common(use_cpu)
niter = 1 # x_train.shape[0] // batchsize
for epoch in range(epochs):
accuracy_rate = 0.0
loss_rate = 0.0
for i in range(niter):
inputs = tensor.Tensor(
device=dev,
data=x_train[i * batchsize : (i + 1) * batchsize],
stores_grad=False,
name="input",
)
targets = tensor.Tensor(
device=dev,
data=y_train[i * batchsize : (i + 1) * batchsize],
requires_grad=False,
stores_grad=False,
name="target",
)
loss, y = forward(inputs, targets)
accuracy_rate += accuracy(
tensor.to_numpy(y), y_train[i * batchsize : (i + 1) * batchsize]
)
loss_rate += tensor.to_numpy(loss)[0]
for p, gp in autograd.backward(loss):
sgd.update(p, gp)
print( "accuracy is {}, loss is {}".format( accuracy_rate / niter, loss_rate / niter))
model = sonnx.to_onnx_model([inputs], [y])
sonnx.save(model, "cnn.onnx")
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')