比较与torch.optim.Optimizer的入参设置的差异

torch.optim.Optimizer

class torch.optim.Optimizer(
    params,
    defaults
)

更多内容详见torch.optim.Optimizer。

mindspore.nn.Optimizer

class mindspore.nn.Optimizer(
    learning_rate,
    parameters,
    weight_decay=0.0,
    loss_scale=1.0
)

更多内容详见mindspore.nn.Optimizer。

使用方式

parameters设置

• 使用默认接口

MindSpore：optimizer的入参params直接使用trainable_params方法配置。

from mindspore import nn

class Net(nn.Cell):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(3, 64, 3)
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
    def construct(self, x):
        x = self.conv(x)
        x = self.bn(x)
        out = self.relu(x)
        return out

net = Net()
optim_sgd = nn.SGD(params=net.trainable_params())

PyTorch：optimizer的入参params直接使用parameters方法配置。

from torch import optim
import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv = nn.Conv2d(3, 64, 3)
        self.bn = nn.BatchNorm2d(64)
        self.relu = nn.ReLU()
    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

net = Net()
optim_sgd = optim.SGD(params=net.parameters(), lr=0.01)

• 用户自定义配置传入的参数

MindSpore：首先使用get_parameters方法获取网络中所有的参数，再根据需要，例如参数名称等，进行筛选，并传入优化器中。

from mindspore import nn

net = Net()
all_params = net.get_parameters()
no_conv_params = list(filter(lambda x: "conv" not in x.name, all_params))
optim_sgd = nn.SGD(no_conv_params)

PyTorch：首先使用named_parameters方法获取网络中所有的参数，再根据需要，例如参数名称等，进行筛选，并传入优化器中。

from torch import optim

net = Net()
all_params = net.named_parameters()
no_conv_params = []
for pname, p in all_params:
    if "conv" not in pname:
        no_conv_params.append(p)
optim_sgd = optim.SGD(no_conv_params, lr=0.01)

learning_rate设置

固定学习率：用法相同

动态学习率

mindspore：

from mindspore import nn
import mindspore as ms

# dynamic_lr
milestone = [2, 5, 10]
learning_rates = [0.1, 0.05, 0.01]
lr_dynamic = nn.dynamic_lr.piecewise_constant_lr(milestone, learning_rates)
print(lr_dynamic)

# learning_rate_schedule
lr_schedule = nn.learning_rate_schedule.PolynomialDecayLR(learning_rate=0.1,
                                   end_learning_rate=0.01,
                                   decay_steps=4,
                                   power=0.5 )

global_step = ms.Tensor(2, ms.int32)
result = lr_schedule(global_step)
print(result)

# 用作优化器入参
optimizer1 = nn.Momentum(net.trainable_params(), learning_rate=lr_dynamic, momentum=0.9, weight_decay=0.9)
optimizer2 = nn.Momentum(net.trainable_params(), learning_rate=lr_schedule, momentum=0.9, weight_decay=0.9)

[0.1, 0.1, 0.05, 0.05, 0.05, 0.01, 0.01, 0.01, 0.01, 0.01]
0.0736396

pytorch：

from torch import optim
import numpy as np

optimizer = torch.optim.SGD(net.parameters(), lr=0.1, momentum=0.9)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
loss_fn = torch.nn.MSELoss()
dataset = [(torch.tensor(np.random.rand(1, 3, 64, 32).astype(np.float32)),
            torch.tensor(np.random.rand(1, 64, 62, 30).astype(np.float32)))]
for epoch in range(5):
    for input, target in dataset:
        optimizer.zero_grad()
        output = net(input)
        loss = loss_fn(output, target)
        loss.backward()
        optimizer.step()
    scheduler.step()
    print(scheduler.get_last_lr())

[0.09000000000000001]
[0.08100000000000002]
[0.07290000000000002]
[0.06561000000000002]
[0.05904900000000002]

weight_decay设置

用法一致。

loss_scale设置

• mindspore：作为优化器的入参，配合FixedLossScaleManager使用。

• pytorch：不单独为优化器设置混合精度。

参数分组

PyTorch和MindSpore都支持对分组的参数设置不同的值，基本用法相似。但mindspore只支持’params’，’weight_decay’，’lr’，’grad_centralizaiton’分组；pytorch支持优化器的所有参数分组。

当前Mindspore和pytorch都存在个别优化器不支持参数分组，详情参考各优化器的说明。

MindSpore：

from mindspore import nn

net = Net()

conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params()))
no_conv_params = list(filter(lambda x: "conv" not in x.name, net.trainable_params()))

fix_lr = 0.01
polynomial_decay_lr = nn.learning_rate_schedule.PolynomialDecayLR(learning_rate=0.1,
                                   end_learning_rate=0.01,
                                   decay_steps=4,
                                   power=0.5 )

group_params = [{'params': conv_params, 'weight_decay': 0.01, 'lr': fix_lr},
                {'params': no_conv_params, 'lr': polynomial_decay_lr},
                {'order_params': net.trainable_params()}]

optim_sgd = nn.SGD(group_params, learning_rate=0.1)

PyTorch:

from torch import optim

net = Net()

all_params = net.parameters()
conv_params = []
no_conv_params = []

for pname, p in net.named_parameters():
    if 'conv' in pname:
        conv_params += [p]
    else:
        no_conv_params += [p]

group_params = [{'params': conv_params, 'weight_decay': 0.01, 'lr': 0.01},
                {'params': no_conv_params, 'nesterov': True}]

optim_sgd = optim.SGD(group_params, lr=0.01)