Comparing the function differences between torch.optim.lr_scheduler.CosineAnnealingLR and torch.optim.lr_scheduler.cosine_decay_lr

torch.optim.lr_scheduler.CosineAnnealingLR

torch.optim.lr_scheduler.CosineAnnealingLR(
    optimizer,
    T_max,
    eta_min=0,
    last_epoch=-1,
    verbose=False
)

For more information, seetorch.optim.lr_scheduler.CosineAnnealingLR.

mindspore.nn.cosine_decay_lr

mindspore.nn.cosine_decay_lr(
    min_lr,
    max_lr,
    total_step,
    step_per_epoch,
    decay_epoch
)

For more information, seemindspore.nn.cosine_decay_lr。

Differences

PyTorch (torch.optim.lr_scheduler.CosineAnnealingLR): torch.optim.lr_scheduler.CosineAnnealingLR is used to periodically adjust the learning rate, where the input parameter T_max represents 1/2 of the period. Assuming the initial learning rate is lr, in each period of 2*T_max, the learning rate changes according to the specified calculation logic, for the formula detail, see the API docs; after the period ends, the learning rate returns to the initial value lr , and keep looping. When verbose is True, the relevant information is printed for each update.

MindSpore (mindspore.nn.cosine_decay_lr): the learning rate adjustment of mindspore.nn.cosine_decay_lr has no periodic changes, and the learning rate value changes according to the specified calculation logic. The formula calculation logic is the same as that of torch.optim.lr_scheduler.CosineAnnealingLR.

Categories	Subcategories	PyTorch	MindSpore	Differences
Parameter	Parameter 1	optimizer		Optimizer for PyTorch applications. MindSpore does not have this parameter
	Parameter 2	T_max	decay_steps	The step to perform decay. The function is the same, and the parameter name is different
	Parameter 3	eta_min	min_lr	Minimum learning rate, same function, different parameter names
	Parameter 4	last_epoch		MindSpore does not have this parameter
	Parameter 5	verbose		PyTorch prints information about each update when verbose is True. MindSpore does not have this parameter
	Parameter 6		max_lr	Maximum learning rate. PyTorch is set to initial lr, and MindSpore is set to max_lr

Code Example

# In MindSpore：
import mindspore.nn as nn

min_lr = 0.01
max_lr = 0.1
total_step = 6
step_per_epoch = 2
decay_epoch = 2
output = nn.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch)
print(output)
# out: [0.1, 0.1, 0.05500000000000001, 0.05500000000000001, 0.01, 0.01]


# In PyTorch:
import torch
import numpy as np
from torch import optim

model = torch.nn.Sequential(torch.nn.Linear(20, 1))
optimizer = optim.SGD(model.parameters(), 0.1)

scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=1, eta_min=0.002)


myloss = torch.nn.MSELoss()
dataset = [(torch.tensor(np.random.rand(1, 20).astype(np.float32)), torch.tensor([1.]))]

for epoch in range(6):
    for input, target in dataset:
        optimizer.zero_grad()
        output = model(input)
        loss = myloss(output.view(-1), target)
        loss.backward()
        optimizer.step()
    scheduler.step()
    print(scheduler.get_last_lr())
# out:
# [0.002]
# [0.1]
# [0.002]
# [0.1]
# [0.002]
# [0.1]