开发入门
因开发者可能会在OrangePi AIpro(下称:香橙派开发板)进行自定义模型和案例开发,本章节通过基于MindSpore的手写数字识别案例,说明香橙派开发板中的开发注意事项。
环境准备
开发者拿到香橙派开发板后,首先需要进行硬件资源确认、镜像烧录以及CANN和MindSpore版本的升级,才可运行该案例,具体如下:
香橙派AIpro |
镜像 |
CANN Toolkit/Kernels |
MindSpore |
---|---|---|---|
8T 16G |
Ubuntu |
8.0.0beta1 |
2.5.0 |
镜像烧录
运行该案例需要烧录香橙派官网Ubuntu镜像,参考镜像烧录章节。
CANN升级
参考CANN升级章节。
MindSpore升级
参考MindSpore升级章节。
[1]:
import mindspore
from mindspore import mint
from mindspore.nn import Cell, SGD
from mindspore.mint import nn
from mindspore.dataset import vision, transforms
from mindspore.dataset import MnistDataset
/usr/local/miniconda3/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.
setattr(self, word, getattr(machar, word).flat[0])
/usr/local/miniconda3/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.
return self._float_to_str(self.smallest_subnormal)
/usr/local/miniconda3/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.
setattr(self, word, getattr(machar, word).flat[0])
/usr/local/miniconda3/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.
return self._float_to_str(self.smallest_subnormal)
数据集准备与加载
MindSpore提供基于Pipeline的数据引擎,通过数据集(Dataset)实现高效的数据预处理。在本案例中,我们使用Mnist数据集,自动下载完成后,使用mindspore.dataset
提供的数据变换进行预处理。
[2]:
# install download
!pip install download
Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: download in /home/HwHiAiUser/.local/lib/python3.9/site-packages (0.3.5)
Requirement already satisfied: tqdm in /home/HwHiAiUser/.local/lib/python3.9/site-packages (from download) (4.66.5)
Requirement already satisfied: six in /usr/local/miniconda3/lib/python3.9/site-packages (from download) (1.16.0)
Requirement already satisfied: requests in /home/HwHiAiUser/.local/lib/python3.9/site-packages (from download) (2.32.2)
Requirement already satisfied: urllib3<3,>=1.21.1 in /home/HwHiAiUser/.local/lib/python3.9/site-packages (from requests->download) (2.2.3)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/miniconda3/lib/python3.9/site-packages (from requests->download) (3.4)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/miniconda3/lib/python3.9/site-packages (from requests->download) (2023.11.17)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/miniconda3/lib/python3.9/site-packages (from requests->download) (2.0.4)
[3]:
# Download data from open datasets
from download import download
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/" \
"notebook/datasets/MNIST_Data.zip"
path = download(url, "./", kind="zip", replace=True)
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)
file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:02<00:00, 4.50MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./
MNIST数据集目录结构如下:
MNIST_Data
└── train
├── train-images-idx3-ubyte (60000个训练图片)
├── train-labels-idx1-ubyte (60000个训练标签)
└── test
├── t10k-images-idx3-ubyte (10000个测试图片)
├── t10k-labels-idx1-ubyte (10000个测试标签)
数据下载完成后,获得数据集对象。
[4]:
train_dataset = MnistDataset('MNIST_Data/train')
test_dataset = MnistDataset('MNIST_Data/test')
打印数据集中包含的数据列名,用于dataset的预处理。
[5]:
print(train_dataset.get_col_names())
['image', 'label']
MindSpore的dataset使用数据处理流水线(Data Processing Pipeline),需指定map、batch、shuffle等操作。这里我们使用map对图像数据及标签进行变换处理,将输入的图像缩放为1/255,根据均值0.1307和标准差值0.3081进行归一化处理,然后将处理好的数据集打包为大小为64的batch。
[6]:
def datapipe(dataset, batch_size):
image_transforms = [
vision.Rescale(1.0 / 255.0, 0),
vision.Normalize(mean=(0.1307,), std=(0.3081,)),
vision.HWC2CHW(),
transforms.TypeCast(mindspore.float16)
]
label_transform = transforms.TypeCast(mindspore.int32)
dataset = dataset.map(image_transforms, 'image')
dataset = dataset.map(label_transform, 'label')
dataset = dataset.batch(batch_size)
return dataset
[7]:
# Map vision transforms and batch dataset
train_dataset = datapipe(train_dataset, 64)
test_dataset = datapipe(test_dataset, 64)
可使用create_tuple_iterator 或create_dict_iterator对数据集进行迭代访问,查看数据和标签的shape和datatype。
[8]:
for image, label in test_dataset.create_tuple_iterator():
print(f"Shape of image [N, C, H, W]: {image.shape} {image.dtype}")
print(f"Shape of label: {label.shape} {label.dtype}")
break
Shape of image [N, C, H, W]: (64, 1, 28, 28) Float16
Shape of label: (64,) Int32
[9]:
for data in test_dataset.create_dict_iterator():
print(f"Shape of image [N, C, H, W]: {data['image'].shape} {data['image'].dtype}")
print(f"Shape of label: {data['label'].shape} {data['label'].dtype}")
break
Shape of image [N, C, H, W]: (64, 1, 28, 28) Float16
Shape of label: (64,) Int32
模型构建
[10]:
# Define model
class Network(Cell):
def __init__(self):
super().__init__()
self.flatten = mint.flatten
self.dense1 = nn.Linear(28*28, 512, dtype=mindspore.float16)
self.dense2 = nn.Linear(512, 512, dtype=mindspore.float16)
self.dense3 = nn.Linear(512, 10, dtype=mindspore.float16)
self.relu = nn.ReLU()
def construct(self, x):
x = self.flatten(x, start_dim=1)
x = self.dense1(x)
x = self.relu(x)
x = self.dense2(x)
x = self.relu(x)
logits = self.dense3(x)
return logits
model = Network()
print(model)
Network<
(dense1): Linear<input_features=784, output_features=512, has_bias=True>
(dense2): Linear<input_features=512, output_features=512, has_bias=True>
(dense3): Linear<input_features=512, output_features=10, has_bias=True>
(relu): ReLU<>
>
模型训练
在模型训练中,一个完整的训练过程(step)需要实现以下三步:
正向计算:模型预测结果(logits),并与正确标签(label)求预测损失(loss)。
反向传播:利用自动微分机制,自动求模型参数(parameters)对于loss的梯度(gradients)。
参数优化:将梯度更新到参数上。
MindSpore使用函数式自动微分机制,因此针对上述步骤需要实现:
定义正向计算函数。
使用value_and_grad通过函数变换获得梯度计算函数。
定义训练函数,使用set_train设置为训练模式,执行正向计算、反向传播和参数优化。
[11]:
# Instantiate loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = SGD(model.trainable_params(), 1e-2)
# 1. Define forward function
def forward_fn(data, label):
logits = model(data)
loss = loss_fn(logits, label)
return loss, logits
# 2. Get gradient function
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
# 3. Define function of one-step training
def train_step(data, label):
(loss, _), grads = grad_fn(data, label)
optimizer(grads)
return loss
def train(model, dataset):
size = dataset.get_dataset_size()
model.set_train()
for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):
loss = train_step(data, label)
if batch % 100 == 0:
loss, current = loss.asnumpy(), batch
print(f"loss: {loss:>7f} [{current:>3d}/{size:>3d}]")
除训练外,我们定义测试函数,用来评估模型的性能。
[12]:
def test(model, dataset, loss_fn):
num_batches = dataset.get_dataset_size()
model.set_train(False)
total, test_loss, correct = 0, 0, 0
for data, label in dataset.create_tuple_iterator():
pred = model(data)
total += len(data)
test_loss += loss_fn(pred, label).asnumpy()
correct += (pred.argmax(1) == label).asnumpy().sum()
test_loss /= num_batches
correct /= total
print(f"Test: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
训练过程需多次迭代数据集,一次完整的迭代称为一轮(epoch)。在每一轮,遍历训练集进行训练,结束后使用测试集进行预测。打印每一轮的loss值和预测准确率(Accuracy),可以看到loss在不断下降,Accuracy在不断提高。
[13]:
epochs = 3
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train(model, train_dataset)
test(model, test_dataset, loss_fn)
print("Done!")
Epoch 1
-------------------------------
loss: 2.298828 [ 0/938]
loss: 1.756836 [100/938]
loss: 0.783691 [200/938]
loss: 0.732910 [300/938]
loss: 0.426514 [400/938]
loss: 0.547363 [500/938]
loss: 0.283203 [600/938]
loss: 0.833496 [700/938]
loss: 0.241455 [800/938]
loss: 0.342773 [900/938]
.Test:
Accuracy: 90.7%, Avg loss: 0.321171
Epoch 2
-------------------------------
loss: 0.275879 [ 0/938]
loss: 0.311035 [100/938]
loss: 0.294189 [200/938]
loss: 0.458740 [300/938]
loss: 0.292725 [400/938]
loss: 0.177612 [500/938]
loss: 0.367920 [600/938]
loss: 0.219482 [700/938]
loss: 0.226685 [800/938]
loss: 0.230103 [900/938]
Test:
Accuracy: 92.8%, Avg loss: 0.253441
Epoch 3
-------------------------------
loss: 0.310791 [ 0/938]
loss: 0.213379 [100/938]
loss: 0.247925 [200/938]
loss: 0.227783 [300/938]
loss: 0.518066 [400/938]
loss: 0.197266 [500/938]
loss: 0.199219 [600/938]
loss: 0.143188 [700/938]
loss: 0.383545 [800/938]
loss: 0.290283 [900/938]
Test:
Accuracy: 93.8%, Avg loss: 0.215057
Done!
保存模型
模型训练完成后,需要将其参数进行保存。
[14]:
# Save checkpoint
mindspore.save_checkpoint(model, "model.ckpt")
print("Saved Model to model.ckpt")
Saved Model to model.ckpt
权重加载
加载保存的权重分为两步:
重新实例化模型对象,构造模型。
加载模型参数,并将其加载至模型上。
[15]:
# Instantiate a random initialized model
model = Network()
# Load checkpoint and load parameter to model
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
print(param_not_load)
[]
param_not_load
是未被加载的参数列表,为空时代表所有参数均加载成功。
模型推理
加载后的模型可以直接用于预测推理。
[16]:
import matplotlib.pyplot as plt
model.set_train(False)
for data, label in test_dataset:
pred = model(data)
predicted = pred.argmax(1)
print(f'Predicted: "{predicted[:6]}", Actual: "{label[:6]}"')
# 显示数字及数字的预测值
plt.figure()
for i in range(6):
plt.subplot(2, 3, i + 1)
# 若预测正确,显示为蓝色;若预测错误,显示为红色
color = 'blue' if predicted[i] == label[i] else 'red'
plt.title('Predicted:{}'.format(predicted[i]), color=color)
plt.imshow(data.asnumpy()[i][0], interpolation="None", cmap="gray")
plt.axis('off')
plt.show()
break
Predicted: "[6 9 4 8 9 3]", Actual: "[6 9 4 8 9 3]"

本案例已同步上线GitHub仓,更多案例可参考该仓库。
本案例运行所需环境:
香橙派AIpro |
镜像 |
CANN Toolkit/Kernels |
MindSpore |
---|---|---|---|
8T 16G |
Ubuntu |
8.0.0beta1 |
2.5.0 |