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# Copyright (c) OpenMMLab. All rights reserved.
import itertools
from unittest import TestCase
import torch
from mmengine.model import (ExponentialMovingAverage, MomentumAnnealingEMA,
StochasticWeightAverage)
from mmengine.testing import assert_allclose
class TestAveragedModel(TestCase):
"""Test the AveragedModel class.
Some test cases are referenced from https://github.com/pytorch/pytorch/blob/master/test/test_optim.py
""" # noqa: E501
def _test_swa_model(self, net_device, avg_device):
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.Linear(5, 10)).to(net_device)
averaged_model = StochasticWeightAverage(model, device=avg_device)
averaged_params = [
torch.zeros_like(param) for param in model.parameters()
]
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for i in range(n_updates):
for p, p_avg in zip(model.parameters(), averaged_params):
p.detach().add_(torch.randn_like(p))
p_avg += p.detach() / n_updates
averaged_model.update_parameters(model)
for p_avg, p_swa in zip(averaged_params, averaged_model.parameters()):
# Check that AveragedModel is on the correct device
self.assertTrue(p_swa.device == avg_device)
self.assertTrue(p.device == net_device)
assert_allclose(p_avg, p_swa.to(p_avg.device))
self.assertTrue(averaged_model.steps.device == avg_device)
def test_averaged_model_all_devices(self):
cpu = torch.device('cpu')
self._test_swa_model(cpu, cpu)
if torch.cuda.is_available():
cuda = torch.device(0)
self._test_swa_model(cuda, cpu)
self._test_swa_model(cpu, cuda)
self._test_swa_model(cuda, cuda)
def test_swa_mixed_device(self):
if not torch.cuda.is_available():
return
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10))
model[0].cuda()
model[1].cpu()
averaged_model = StochasticWeightAverage(model)
averaged_params = [
torch.zeros_like(param) for param in model.parameters()
]
n_updates = 10
for i in range(n_updates):
for p, p_avg in zip(model.parameters(), averaged_params):
p.detach().add_(torch.randn_like(p))
p_avg += p.detach() / n_updates
averaged_model.update_parameters(model)
for p_avg, p_swa in zip(averaged_params, averaged_model.parameters()):
assert_allclose(p_avg, p_swa)
# Check that AveragedModel is on the correct device
self.assertTrue(p_avg.device == p_swa.device)
def test_swa_state_dict(self):
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10))
averaged_model = StochasticWeightAverage(model)
averaged_model2 = StochasticWeightAverage(model)
n_updates = 10
for i in range(n_updates):
for p in model.parameters():
p.detach().add_(torch.randn_like(p))
averaged_model.update_parameters(model)
averaged_model2.load_state_dict(averaged_model.state_dict())
for p_swa, p_swa2 in zip(averaged_model.parameters(),
averaged_model2.parameters()):
assert_allclose(p_swa, p_swa2)
self.assertTrue(averaged_model.steps == averaged_model2.steps)
def test_ema(self):
# test invalid momentum
with self.assertRaisesRegex(AssertionError,
'momentum must be in range'):
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10))
ExponentialMovingAverage(model, momentum=3)
with self.assertWarnsRegex(
Warning,
'The value of momentum in EMA is usually a small number'):
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10))
ExponentialMovingAverage(model, momentum=0.9)
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# test EMA
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.Linear(5, 10))
momentum = 0.1
ema_model = ExponentialMovingAverage(model, momentum=momentum)
averaged_params = [
torch.zeros_like(param) for param in model.parameters()
]
n_updates = 10
for i in range(n_updates):
updated_averaged_params = []
for p, p_avg in zip(model.parameters(), averaged_params):
p.detach().add_(torch.randn_like(p))
if i == 0:
updated_averaged_params.append(p.clone())
else:
updated_averaged_params.append(
(p_avg * (1 - momentum) + p * momentum).clone())
ema_model.update_parameters(model)
averaged_params = updated_averaged_params
for p_target, p_ema in zip(averaged_params, ema_model.parameters()):
assert_allclose(p_target, p_ema)
def test_ema_update_buffers(self):
# Test EMA and update_buffers as True.
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.BatchNorm2d(5, momentum=0.3), torch.nn.Linear(5, 10))
momentum = 0.1
ema_model = ExponentialMovingAverage(
model, momentum=momentum, update_buffers=True)
averaged_params = [
torch.zeros_like(param)
for param in itertools.chain(model.parameters(), model.buffers())
if param.size() != torch.Size([])
]
n_updates = 10
for i in range(n_updates):
updated_averaged_params = []
params = [
param for param in itertools.chain(model.parameters(),
model.buffers())
if param.size() != torch.Size([])
]
for p, p_avg in zip(params, averaged_params):
p.detach().add_(torch.randn_like(p))
if i == 0:
updated_averaged_params.append(p.clone())
else:
updated_averaged_params.append(
(p_avg * (1 - momentum) + p * momentum).clone())
ema_model.update_parameters(model)
averaged_params = updated_averaged_params
ema_params = [
param for param in itertools.chain(ema_model.module.parameters(),
ema_model.module.buffers())
if param.size() != torch.Size([])
]
for p_target, p_ema in zip(averaged_params, ema_params):
assert_allclose(p_target, p_ema)
def test_momentum_annealing_ema(self):
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.BatchNorm2d(5, momentum=0.3), torch.nn.Linear(5, 10))
# Test invalid gamma
with self.assertRaisesRegex(AssertionError,
'gamma must be greater than 0'):
MomentumAnnealingEMA(model, gamma=-1)
# Test EMA with momentum annealing.
momentum = 0.1
gamma = 4
ema_model = MomentumAnnealingEMA(
model, gamma=gamma, momentum=momentum, update_buffers=True)
averaged_params = [
torch.zeros_like(param)
for param in itertools.chain(model.parameters(), model.buffers())
if param.size() != torch.Size([])
]
n_updates = 10
for i in range(n_updates):
updated_averaged_params = []
params = [
param for param in itertools.chain(model.parameters(),
model.buffers())
if param.size() != torch.Size([])
]
for p, p_avg in zip(params, averaged_params):
p.add(torch.randn_like(p))
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if i == 0:
updated_averaged_params.append(p.clone())
else:
m = max(momentum, gamma / (gamma + i))
updated_averaged_params.append(
(p_avg * (1 - m) + p * m).clone())
ema_model.update_parameters(model)
averaged_params = updated_averaged_params
ema_params = [
param for param in itertools.chain(ema_model.module.parameters(),
ema_model.module.buffers())
if param.size() != torch.Size([])
]
for p_target, p_ema in zip(averaged_params, ema_params):
assert_allclose(p_target, p_ema)
def test_momentum_annealing_ema_with_interval(self):
# Test EMA with momentum annealing and interval
model = torch.nn.Sequential(
torch.nn.Conv2d(1, 5, kernel_size=3),
torch.nn.BatchNorm2d(5, momentum=0.3), torch.nn.Linear(5, 10))
momentum = 0.1
gamma = 4
interval = 3
ema_model = MomentumAnnealingEMA(
model,
gamma=gamma,
momentum=momentum,
interval=interval,
update_buffers=True)
averaged_params = [
torch.zeros_like(param)
for param in itertools.chain(model.parameters(), model.buffers())
if param.size() != torch.Size([])
]
n_updates = 10
for i in range(n_updates):
updated_averaged_params = []
params = [
param for param in itertools.chain(model.parameters(),
model.buffers())
if param.size() != torch.Size([])
]
for p, p_avg in zip(params, averaged_params):
p.add(torch.randn_like(p))
if i == 0:
updated_averaged_params.append(p.clone())
elif i % interval == 0:
m = max(momentum, gamma / (gamma + i))
updated_averaged_params.append(
(p_avg * (1 - m) + p * m).clone())
else:
updated_averaged_params.append(p_avg.clone())
ema_model.update_parameters(model)
averaged_params = updated_averaged_params
ema_params = [
param for param in itertools.chain(ema_model.module.parameters(),
ema_model.module.buffers())
if param.size() != torch.Size([])
]
for p_target, p_ema in zip(averaged_params, ema_params):
assert_allclose(p_target, p_ema)