This tutorial describes how to quickly develop the Vega algorithm. In this tutorial, a simple CNN network architecture search is used as an example. An operation layer and operation parameters of a small convolutional network are searched by using a random algorithm, and a search data set is Cifar-10.
Before developing an algorithm, you need to determine the data set to which the algorithm is applicable. In this example, the Cifar10 data set is used. You can directly use the Cifar10 data set class provided by Vega.
You need to set data set parameters in the configuration file. Generally, you need to adjust the location of the data set. The data set parameters are as follows:
dataset:
type: Cifar10
common:
data_path: '/cache/datasets/cifar10/'
train:
shuffle: False
num_workers: 8
batch_size: 256
train_portion: 0.9
valid:
shuffle: False
num_workers: 8
batch_size: 256
train_portion: 0.9If there is a problem of data loading memory overflow during operation, try to set num_Workers to 0 and set batch_Size to a smaller number.
Next, the search space needs to be determined. The search space is related to one or more network definitions, and the content of the search space is a parameter required for constructing the network.
The content parameters of the search space also need to be configured in the configuration file. In this example, the content of the search space is as follows:
search_space:
hyperparameters:
- key: network.backbone.blocks
type: CATEGORY
range: [1, 2, 3, 4]
- key: network.backbone.channels
type: CATEGORY
range: [32, 48, 56, 64]
model:
model_desc:
modules: [backbone]
backbone:
type: SimpleCnn
num_class: 10
fp16: FalseThe search space definition in the preceding figure is divided into two parts: search_space and model. The former describes the hyperparameter space, and the latter describes the basic network structure. The data is sampled from the hyperparameter space and combined with the network structure definition to form a complete network structure.
The search space is described as follows:
- blocks: Indicates the number range of blocks of conv+bn+relu.
- channels: Indicates the range of block's channel.
The SimpleCnn network model is defined and implemented in the simple_cnn.py file.
@ClassFactory.register(ClassType.NETWORK)
class SimpleCnn(nn.Module):
"""Simple CNN network."""
def __init__(self, **desc):
"""Initialize."""
super(SimpleCnn, self).__init__()
desc = Config(**desc)
self.num_class = desc.num_class
self.fp16 = desc.get('fp16', False)
self.blocks = desc.blocks
self.channels = desc.channels
self.conv1 = nn.Conv2d(3, 32, padding=1, kernel_size=3)
self.pool1 = nn.MaxPool2d(2, stride=2)
self.blocks = self._blocks(self.channels)
self.pool2 = nn.MaxPool2d(2, stride=2)
self.conv2 = nn.Conv2d(self.channels, 64, padding=1, kernel_size=3)
self.global_conv = nn.Conv2d(64, 64, kernel_size=8)
self.fc = nn.Linear(64, self.num_class)
def _blocks(self, out_channels):
blocks = nn.ModuleList([None] * self.blocks)
in_channels = 32
for i in range(self.blocks):
blocks[i] = nn.Sequential(
nn.Conv2d(in_channels, out_channels, padding=1, kernel_size=3),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
in_channels = out_channels
return blocks
def forward(self, x):
"""Forward."""
x = self.pool1(self.conv1(x))
for block in self.blocks:
x = block(x)
x = self.global_conv(self.conv2(self.pool2(x)))
x = self.fc(x.view(x.size(0), -1))
return xYou can use the random search mode. The configuration is as follows:
search_algorithm:
type: RandomSearch
policy:
num_sample: 50RandomSearch is a preset search algorithm of Vega.
The complete configuration file is as follows:
# my.yml
pipeline: [nas]
nas:
pipe_step:
type: SearchPipeStep
search_algorithm:
type: RandomSearch
policy:
num_sample: 50
search_space:
hyperparameters:
- key: network.backbone.blocks
type: CATEGORY
range: [1, 2, 3, 4]
- key: network.backbone.channels
type: CATEGORY
range: [32, 48, 56, 64]
model:
model_desc:
modules: [backbone]
backbone:
type: SimpleCnn
num_class: 10
fp16: False
trainer:
type: Trainer
optimizer:
type: SGD
params:
lr: 0.01
momentum: 0.9
lr_scheduler:
type: MultiStepLR
params:
warmup: False
milestones: [30]
gamma: 0.5
loss:
type: CrossEntropyLoss
metric:
type: accuracy
epochs: 3
save_steps: 250
distributed: False
num_class: 10
dataset:
type: Cifar10
common:
data_path: /cache/datasets/cifar10/
batch_size: 64
num_parallel_batches: 64
fp16: FalseThe complete code file is as follows:
import vega
import torch.nn as nn
from vega.common.config import Config
from vega.common import ClassType, ClassFactory
@ClassFactory.register(ClassType.NETWORK)
class SimpleCnn(nn.Module):
"""Simple CNN network."""
def __init__(self, **desc):
"""Initialize."""
super(SimpleCnn, self).__init__()
desc = Config(**desc)
self.num_class = desc.num_class
self.fp16 = desc.get('fp16', False)
self.blocks = desc.blocks
self.channels = desc.channels
self.conv1 = nn.Conv2d(3, 32, padding=1, kernel_size=3)
self.pool1 = nn.MaxPool2d(2, stride=2)
self.blocks = self._blocks(self.channels)
self.pool2 = nn.MaxPool2d(2, stride=2)
self.conv2 = nn.Conv2d(self.channels, 64, padding=1, kernel_size=3)
self.global_conv = nn.Conv2d(64, 64, kernel_size=8)
self.fc = nn.Linear(64, self.num_class)
def _blocks(self, out_channels):
blocks = nn.ModuleList([None] * self.blocks)
in_channels = 32
for i in range(self.blocks):
blocks[i] = nn.Sequential(
nn.Conv2d(in_channels, out_channels, padding=1, kernel_size=3),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
in_channels = out_channels
return blocks
def forward(self, x):
"""Forward."""
x = self.pool1(self.conv1(x))
for block in self.blocks:
x = block(x)
x = self.global_conv(self.conv2(self.pool2(x)))
x = self.fc(x.view(x.size(0), -1))
return x
if __name__ == "__main__":
vega.run("./my.yml")Execute the following command:
python3 ./my.pyAfter running, the tasks directory will be generated in the execution directory. There will be a subdirectory containing time content in this directory. There will be two subdirectories, output and workers under this subdirectory. The output directory will save the network structure description file, and the workers directory will save the network weight file and evaluation results.