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Zero
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# Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from unittest import TestCase
import torch
from fvcore.common.param_scheduler import (
CosineParamScheduler,
MultiStepParamScheduler,
StepWithFixedGammaParamScheduler,
)
from torch import nn
from detectron2.solver import LRMultiplier, WarmupParamScheduler, build_lr_scheduler
class TestScheduler(TestCase):
def test_warmup_multistep(self):
p = nn.Parameter(torch.zeros(0))
opt = torch.optim.SGD([p], lr=5)
multiplier = WarmupParamScheduler(
MultiStepParamScheduler(
[1, 0.1, 0.01, 0.001],
milestones=[10, 15, 20],
num_updates=30,
),
0.001,
5 / 30,
)
sched = LRMultiplier(opt, multiplier, 30)
# This is an equivalent of:
# sched = WarmupMultiStepLR(
# opt, milestones=[10, 15, 20], gamma=0.1, warmup_factor=0.001, warmup_iters=5)
p.sum().backward()
opt.step()
lrs = [0.005]
for _ in range(30):
sched.step()
lrs.append(opt.param_groups[0]["lr"])
self.assertTrue(np.allclose(lrs[:5], [0.005, 1.004, 2.003, 3.002, 4.001]))
self.assertTrue(np.allclose(lrs[5:10], 5.0))
self.assertTrue(np.allclose(lrs[10:15], 0.5))
self.assertTrue(np.allclose(lrs[15:20], 0.05))
self.assertTrue(np.allclose(lrs[20:], 0.005))
def test_warmup_cosine(self):
p = nn.Parameter(torch.zeros(0))
opt = torch.optim.SGD([p], lr=5)
multiplier = WarmupParamScheduler(
CosineParamScheduler(1, 0),
0.001,
5 / 30,
)
sched = LRMultiplier(opt, multiplier, 30)
p.sum().backward()
opt.step()
self.assertEqual(opt.param_groups[0]["lr"], 0.005)
lrs = [0.005]
for _ in range(30):
sched.step()
lrs.append(opt.param_groups[0]["lr"])
for idx, lr in enumerate(lrs):
expected_cosine = 2.5 * (1.0 + math.cos(math.pi * idx / 30))
if idx >= 5:
self.assertAlmostEqual(lr, expected_cosine)
else:
self.assertNotAlmostEqual(lr, expected_cosine)
def test_warmup_cosine_end_value(self):
from detectron2.config import CfgNode, get_cfg
def _test_end_value(cfg_dict):
cfg = get_cfg()
cfg.merge_from_other_cfg(CfgNode(cfg_dict))
p = nn.Parameter(torch.zeros(0))
opt = torch.optim.SGD([p], lr=cfg.SOLVER.BASE_LR)
scheduler = build_lr_scheduler(cfg, opt)
p.sum().backward()
opt.step()
self.assertEqual(
opt.param_groups[0]["lr"], cfg.SOLVER.BASE_LR * cfg.SOLVER.WARMUP_FACTOR
)
lrs = []
for _ in range(cfg.SOLVER.MAX_ITER):
scheduler.step()
lrs.append(opt.param_groups[0]["lr"])
self.assertAlmostEqual(lrs[-1], cfg.SOLVER.BASE_LR_END)
_test_end_value(
{
"SOLVER": {
"LR_SCHEDULER_NAME": "WarmupCosineLR",
"MAX_ITER": 100,
"WARMUP_ITERS": 10,
"WARMUP_FACTOR": 0.1,
"BASE_LR": 5.0,
"BASE_LR_END": 0.0,
}
}
)
_test_end_value(
{
"SOLVER": {
"LR_SCHEDULER_NAME": "WarmupCosineLR",
"MAX_ITER": 100,
"WARMUP_ITERS": 10,
"WARMUP_FACTOR": 0.1,
"BASE_LR": 5.0,
"BASE_LR_END": 0.5,
}
}
)
def test_warmup_stepwithfixedgamma(self):
p = nn.Parameter(torch.zeros(0))
opt = torch.optim.SGD([p], lr=5)
multiplier = WarmupParamScheduler(
StepWithFixedGammaParamScheduler(
base_value=1.0,
gamma=0.1,
num_decays=4,
num_updates=30,
),
0.001,
5 / 30,
rescale_interval=True,
)
sched = LRMultiplier(opt, multiplier, 30)
p.sum().backward()
opt.step()
lrs = [0.005]
for _ in range(29):
sched.step()
lrs.append(opt.param_groups[0]["lr"])
self.assertTrue(np.allclose(lrs[:5], [0.005, 1.004, 2.003, 3.002, 4.001]))
self.assertTrue(np.allclose(lrs[5:10], 5.0))
self.assertTrue(np.allclose(lrs[10:15], 0.5))
self.assertTrue(np.allclose(lrs[15:20], 0.05))
self.assertTrue(np.allclose(lrs[20:25], 0.005))
self.assertTrue(np.allclose(lrs[25:], 0.0005))
# Calling sche.step() after the last training iteration is done will trigger IndexError
with self.assertRaises(IndexError, msg="list index out of range"):
sched.step()
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