632 lines
24 KiB
Python
632 lines
24 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import math
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from unittest import TestCase
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import torch
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import torch.nn.functional as F
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import torch.optim as optim
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from mmengine.optim.scheduler import (ConstantLR, CosineAnnealingLR,
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ExponentialLR, LinearLR, MultiStepLR,
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OneCycleLR, PolyLR, StepLR,
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_ParamScheduler)
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from mmengine.testing import assert_allclose
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class ToyModel(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = torch.nn.Conv2d(1, 1, 1)
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self.conv2 = torch.nn.Conv2d(1, 1, 1)
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def forward(self, x):
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return self.conv2(F.relu(self.conv1(x)))
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class TestLRScheduler(TestCase):
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def setUp(self):
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"""Setup the model and optimizer which are used in every test method.
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TestCase calls functions in this order: setUp() -> testMethod() ->
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tearDown() -> cleanUp()
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"""
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self.model = ToyModel()
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lr = 0.05
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self.layer2_mult = 10
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self.optimizer = optim.SGD([{
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'params': self.model.conv1.parameters()
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}, {
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'params': self.model.conv2.parameters(),
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'lr': lr * self.layer2_mult,
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}],
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lr=lr,
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momentum=0.01,
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weight_decay=5e-4)
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def test_base_scheduler_step(self):
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with self.assertRaises(NotImplementedError):
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_ParamScheduler(self.optimizer, param_name='lr')
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def test_invalid_optimizer(self):
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with self.assertRaisesRegex(TypeError, 'should be an Optimizer'):
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StepLR('invalid_optimizer', step_size=1)
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def test_overwrite_optimzer_step(self):
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# raise warning if the counter in optimizer.step() is overwritten
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scheduler = ExponentialLR(self.optimizer, gamma=0.9)
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def overwrite_fun():
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pass
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self.optimizer.step = overwrite_fun
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self.optimizer.step()
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self.assertWarnsRegex(UserWarning, r'how-to-adjust-learning-rate',
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scheduler.step)
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def test_resume(self):
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# test invalid case: optimizer and scheduler are not both resumed
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with self.assertRaisesRegex(KeyError,
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"param 'initial_lr' is not specified"):
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StepLR(self.optimizer, gamma=0.1, step_size=3, last_step=10)
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# test manually resume with ``last_step`` instead of load_state_dict
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epochs = 10
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targets = [0.05 * (0.9**x) for x in range(epochs)]
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scheduler = ExponentialLR(self.optimizer, gamma=0.9)
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results = []
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for epoch in range(5):
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results.append(self.optimizer.param_groups[0]['lr'])
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# The order should be
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# train_epoch() -> save_checkpoint() -> scheduler.step().
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# Break at here to simulate the checkpoint is saved before
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# the scheduler.step().
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if epoch == 4:
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break
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scheduler.step()
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scheduler2 = ExponentialLR(self.optimizer, gamma=0.9, last_step=4)
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for epoch in range(6):
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results.append(self.optimizer.param_groups[0]['lr'])
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scheduler2.step()
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for epoch in range(epochs):
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assert_allclose(
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targets[epoch],
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results[epoch],
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msg='lr is wrong in epoch {}: expected {}, got {}'.format(
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epoch, targets[epoch], results[epoch]),
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atol=1e-5,
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rtol=0)
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def test_scheduler_before_optim_warning(self):
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"""warns if scheduler is used before optimizer."""
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def call_sch_before_optim():
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scheduler = StepLR(self.optimizer, gamma=0.1, step_size=3)
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scheduler.step()
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self.optimizer.step()
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# check warning doc link
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self.assertWarnsRegex(UserWarning, r'how-to-adjust-learning-rate',
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call_sch_before_optim)
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# check warning when resume
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for i, group in enumerate(self.optimizer.param_groups):
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group['initial_lr'] = 0.01
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def call_sch_before_optim_resume():
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scheduler = StepLR(
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self.optimizer, gamma=0.1, step_size=3, last_step=10)
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scheduler.step()
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self.optimizer.step()
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# check warning doc link
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self.assertWarnsRegex(UserWarning, r'how-to-adjust-learning-rate',
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call_sch_before_optim_resume)
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def test_get_last_value(self):
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epochs = 10
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single_targets = [0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005]
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = StepLR(self.optimizer, 3, gamma=0.1)
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for epoch in range(epochs):
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result = scheduler.get_last_value()
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self.optimizer.step()
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scheduler.step()
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target = [t[epoch] for t in targets]
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for t, r in zip(target, result):
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assert_allclose(
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target,
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result,
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msg='LR is wrong in epoch {}: expected {}, got {}'.format(
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epoch, t, r),
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atol=1e-5,
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rtol=0)
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def test_scheduler_step_count(self):
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iteration = 10
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scheduler = StepLR(self.optimizer, gamma=0.1, step_size=3)
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self.assertEqual(scheduler.last_step, 0)
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target = [i + 1 for i in range(iteration)]
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step_counts = []
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for i in range(iteration):
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self.optimizer.step()
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scheduler.step()
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step_counts.append(scheduler.last_step)
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self.assertEqual(step_counts, target)
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def test_effective_interval(self):
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# check invalid begin end
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with self.assertRaisesRegex(ValueError,
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'end should be larger than begin'):
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StepLR(self.optimizer, gamma=0.1, step_size=3, begin=10, end=5)
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# lr = 0.05 if epoch == 0
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# lr = 0.025 if epoch == 1
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# lr = 0.03125 if epoch == 2
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# lr = 0.0375 if epoch == 3
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# lr = 0.04375 if epoch == 4
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# lr = 0.005 if epoch > 4
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begin = 1
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epochs = 10
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start_factor = 1.0 / 2
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iters = 4
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interpolation = [
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start_factor + i * (1 - start_factor) / iters for i in range(iters)
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]
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single_targets = [0.05] * begin + [x * 0.05
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for x in interpolation] + [0.05] * (
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epochs - iters - begin)
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = LinearLR(
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self.optimizer,
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start_factor=start_factor,
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begin=begin,
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end=begin + iters + 1)
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self._test_scheduler_value(scheduler, targets, epochs)
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def _test_scheduler_value(self,
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schedulers,
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targets,
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epochs=10,
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param_name='lr'):
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if isinstance(schedulers, _ParamScheduler):
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schedulers = [schedulers]
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for epoch in range(epochs):
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for param_group, target in zip(self.optimizer.param_groups,
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targets):
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assert_allclose(
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target[epoch],
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param_group[param_name],
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msg='{} is wrong in epoch {}: expected {}, got {}'.format(
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param_name, epoch, target[epoch],
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param_group[param_name]),
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atol=1e-5,
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rtol=0)
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[scheduler.step() for scheduler in schedulers]
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def test_step_scheduler(self):
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# lr = 0.05 if epoch < 3
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# lr = 0.005 if 3 <= epoch < 6
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# lr = 0.0005 if 6 <= epoch < 9
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# lr = 0.00005 if epoch >=9
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epochs = 10
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single_targets = [0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005
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] * 3
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = StepLR(
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self.optimizer, gamma=0.1, step_size=3, verbose=True)
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_multi_step_scheduler(self):
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# lr = 0.05 if epoch < 2
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# lr = 0.005 if 2 <= epoch < 5
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# lr = 0.0005 if 5 <= epoch < 9
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# lr = 0.00005 if epoch >= 9
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epochs = 10
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single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005
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] * 3
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = MultiStepLR(
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self.optimizer, gamma=0.1, milestones=[2, 5, 9])
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_constant_scheduler(self):
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# factor should between 0~1
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with self.assertRaises(ValueError):
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ConstantLR(self.optimizer, factor=99)
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# lr = 0.025 if epoch < 5
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# lr = 0.005 if 5 <= epoch
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epochs = 10
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single_targets = [0.025] * 4 + [0.05] * 6
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = ConstantLR(self.optimizer, factor=1.0 / 2, end=5)
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_linear_scheduler(self):
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with self.assertRaises(ValueError):
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LinearLR(self.optimizer, start_factor=10, end=900)
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with self.assertRaises(ValueError):
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LinearLR(self.optimizer, start_factor=-1, end=900)
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with self.assertRaises(ValueError):
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LinearLR(self.optimizer, end_factor=1.001, end=900)
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with self.assertRaises(ValueError):
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LinearLR(self.optimizer, end_factor=-0.00001, end=900)
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# lr = 0.025 if epoch == 0
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# lr = 0.03125 if epoch == 1
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# lr = 0.0375 if epoch == 2
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# lr = 0.04375 if epoch == 3
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# lr = 0.005 if epoch >= 4
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epochs = 10
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start_factor = 1.0 / 2
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iters = 4
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interpolation = [
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start_factor + i * (1 - start_factor) / iters for i in range(iters)
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]
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single_targets = [x * 0.05 for x in interpolation] + [0.05] * (
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epochs - iters)
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = LinearLR(
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self.optimizer, start_factor=start_factor, end=iters + 1)
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_exp_scheduler(self):
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epochs = 10
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single_targets = [0.05 * (0.9**x) for x in range(epochs)]
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = ExponentialLR(self.optimizer, gamma=0.9)
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_cos_anneal_scheduler(self):
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epochs = 12
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t = 10
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eta_min = 1e-10
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single_targets = [
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eta_min + (0.05 - eta_min) * (1 + math.cos(math.pi * x / t)) / 2
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for x in range(epochs)
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]
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = CosineAnnealingLR(self.optimizer, T_max=t, eta_min=eta_min)
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_poly_scheduler(self):
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epochs = 10
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power = 0.9
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min_lr = 0.001
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iters = 4
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targets_layer1 = [
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min_lr + (0.05 - min_lr) * (1 - i / iters)**power
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for i in range(iters)
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] + [min_lr] * (
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epochs - iters)
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targets_layer2 = [
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min_lr + (0.05 * self.layer2_mult - min_lr) *
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(1 - i / iters)**power for i in range(iters)
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] + [min_lr] * (
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epochs - iters)
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targets = [targets_layer1, targets_layer2]
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scheduler = PolyLR(
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self.optimizer, power=power, eta_min=min_lr, end=iters + 1)
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self._test_scheduler_value(scheduler, targets, epochs=10)
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def _check_scheduler_state_dict(self, construct, construct2, epochs=10):
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scheduler = construct()
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for _ in range(epochs):
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scheduler.optimizer.step()
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scheduler.step()
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scheduler_copy = construct2()
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scheduler_copy.load_state_dict(scheduler.state_dict())
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for key in scheduler.__dict__.keys():
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if key != 'optimizer':
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self.assertEqual(scheduler.__dict__[key],
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scheduler_copy.__dict__[key])
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self.assertEqual(scheduler.get_last_value(),
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scheduler_copy.get_last_value())
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def test_step_scheduler_state_dict(self):
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self._check_scheduler_state_dict(
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lambda: StepLR(self.optimizer, gamma=0.1, step_size=3),
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lambda: StepLR(self.optimizer, gamma=0.01 / 2, step_size=1))
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def test_multi_step_scheduler_state_dict(self):
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self._check_scheduler_state_dict(
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lambda: MultiStepLR(
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self.optimizer, gamma=0.1, milestones=[2, 5, 9]),
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lambda: MultiStepLR(
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self.optimizer, gamma=0.01, milestones=[1, 4, 6]))
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def test_exp_scheduler_state_dict(self):
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self._check_scheduler_state_dict(
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lambda: ExponentialLR(self.optimizer, gamma=0.1),
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lambda: ExponentialLR(self.optimizer, gamma=0.01))
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def test_cosine_scheduler_state_dict(self):
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epochs = 10
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eta_min = 1e-10
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self._check_scheduler_state_dict(
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lambda: CosineAnnealingLR(
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self.optimizer, T_max=epochs, eta_min=eta_min),
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lambda: CosineAnnealingLR(
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self.optimizer, T_max=epochs // 2, eta_min=eta_min / 2),
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epochs=epochs)
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def test_linear_scheduler_state_dict(self):
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epochs = 10
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self._check_scheduler_state_dict(
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lambda: LinearLR(self.optimizer, start_factor=1 / 3),
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lambda: LinearLR(self.optimizer, start_factor=0, end_factor=0.3),
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epochs=epochs)
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def test_poly_scheduler_state_dict(self):
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self._check_scheduler_state_dict(
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lambda: PolyLR(self.optimizer, power=0.5, eta_min=0.001),
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lambda: PolyLR(self.optimizer, power=0.8, eta_min=0.002),
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epochs=10)
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def test_step_scheduler_convert_iterbased(self):
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# invalid epoch_length
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with self.assertRaises(AssertionError):
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scheduler = StepLR.build_iter_from_epoch(
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self.optimizer, gamma=0.1, step_size=2, epoch_length=-1)
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# lr = 0.05 if epoch < 2
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# lr = 0.005 if 2 <= epoch < 4
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epochs = 4
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epoch_length = 7
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single_targets = [0.05] * 2 * epoch_length + [0.005] * 2 * epoch_length
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = StepLR.build_iter_from_epoch(
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self.optimizer, gamma=0.1, step_size=2, epoch_length=epoch_length)
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self._test_scheduler_value(
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scheduler, targets, epochs * epoch_length, param_name='lr')
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def test_multi_step_scheduler_convert_iterbased(self):
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# lr = 0.05 if epoch < 2
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# lr = 0.005 if 2 <= epoch < 5
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# lr = 0.0005 if 5 <= epoch < 9
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# lr = 0.00005 if epoch >= 9
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epochs = 10
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epoch_length = 7
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single_targets = [0.05
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] * 2 * epoch_length + [0.005] * 3 * epoch_length + [
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0.0005
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] * 4 * epoch_length + [0.00005] * 3 * epoch_length
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = MultiStepLR.build_iter_from_epoch(
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self.optimizer,
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gamma=0.1,
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milestones=[2, 5, 9],
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epoch_length=epoch_length)
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self._test_scheduler_value(scheduler, targets, epochs * epoch_length)
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def test_constant_scheduler_convert_iterbased(self):
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# lr = 0.025 if epoch < 5
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# lr = 0.005 if 5 <= epoch
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epochs = 10
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epoch_length = 7
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single_targets = [0.025] * (5 * epoch_length -
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1) + [0.05] * (5 * epoch_length + 1)
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = ConstantLR.build_iter_from_epoch(
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self.optimizer, factor=1.0 / 2, end=5, epoch_length=epoch_length)
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self._test_scheduler_value(scheduler, targets, epochs * epoch_length)
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def test_linear_scheduler_convert_iterbased(self):
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epochs = 10
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start_factor = 1.0 / 2
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end = 5
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epoch_length = 11
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iters = end * epoch_length - 1
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interpolation = [
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start_factor + i * (1 - start_factor) / iters for i in range(iters)
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]
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single_targets = [x * 0.05 for x in interpolation] + [0.05] * (
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epochs * epoch_length - iters)
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targets = [
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single_targets, [x * self.layer2_mult for x in single_targets]
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]
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scheduler = LinearLR.build_iter_from_epoch(
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self.optimizer,
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start_factor=start_factor,
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end=end,
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epoch_length=epoch_length)
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self._test_scheduler_value(scheduler, targets, epochs)
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def test_exp_scheduler_convert_iterbased(self):
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epochs = 10
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epoch_length = 7
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single_targets = [
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0.05 * (0.9**x) for x in range(epochs * epoch_length)
|
|
]
|
|
targets = [
|
|
single_targets, [x * self.layer2_mult for x in single_targets]
|
|
]
|
|
scheduler = ExponentialLR.build_iter_from_epoch(
|
|
self.optimizer, gamma=0.9, epoch_length=epoch_length)
|
|
self._test_scheduler_value(scheduler, targets, epochs * epoch_length)
|
|
|
|
def test_cos_anneal_scheduler_convert_iterbased(self):
|
|
epochs = 12
|
|
t = 10
|
|
eta_min = 1e-10
|
|
epoch_length = 11
|
|
single_targets = [
|
|
eta_min + (0.05 - eta_min) *
|
|
(1 + math.cos(math.pi * x / t / epoch_length)) / 2
|
|
for x in range(epochs * epoch_length)
|
|
]
|
|
targets = [
|
|
single_targets, [x * self.layer2_mult for x in single_targets]
|
|
]
|
|
scheduler = CosineAnnealingLR.build_iter_from_epoch(
|
|
self.optimizer,
|
|
T_max=t,
|
|
eta_min=eta_min,
|
|
epoch_length=epoch_length)
|
|
self._test_scheduler_value(scheduler, targets, epochs)
|
|
|
|
def test_poly_scheduler_convert_iterbased(self):
|
|
epochs = 10
|
|
power = 0.9
|
|
min_lr = 0.001
|
|
end = 5
|
|
epoch_length = 11
|
|
|
|
iters = end * epoch_length - 1
|
|
targets_layer1 = [
|
|
min_lr + (0.05 - min_lr) * (1 - i / iters)**power
|
|
for i in range(iters)
|
|
] + [min_lr] * (
|
|
epochs - iters)
|
|
targets_layer2 = [
|
|
min_lr + (0.05 * self.layer2_mult - min_lr) *
|
|
(1 - i / iters)**power for i in range(iters)
|
|
] + [min_lr] * (
|
|
epochs - iters)
|
|
targets = [targets_layer1, targets_layer2]
|
|
scheduler = PolyLR.build_iter_from_epoch(
|
|
self.optimizer,
|
|
power=power,
|
|
eta_min=min_lr,
|
|
end=end,
|
|
epoch_length=epoch_length)
|
|
self._test_scheduler_value(scheduler, targets, epochs=10)
|
|
|
|
def test_multi_scheduler_without_overlap_linear_multi_step(self):
|
|
# use Linear in the first 5 epochs and then use MultiStep
|
|
epochs = 12
|
|
single_targets = [0.025, 0.03125, 0.0375, 0.04375
|
|
] + [0.05] * 4 + [0.005] * 3 + [0.0005] * 1
|
|
targets = [
|
|
single_targets, [x * self.layer2_mult for x in single_targets]
|
|
]
|
|
scheduler1 = LinearLR(
|
|
self.optimizer, start_factor=1 / 2, begin=0, end=5)
|
|
scheduler2 = MultiStepLR(
|
|
self.optimizer, gamma=0.1, milestones=[3, 6], begin=5, end=12)
|
|
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
|
|
|
|
def test_multi_scheduler_without_overlap_exp_cosine(self):
|
|
# in the first 5 epochs use Exp and then use Cosine
|
|
epochs = 10
|
|
single_targets1 = [0.05 * (0.9**x) for x in range(5)]
|
|
scheduler1 = ExponentialLR(self.optimizer, gamma=0.9, begin=0, end=5)
|
|
|
|
eta_min = 1e-10
|
|
single_targets2 = [
|
|
eta_min + (single_targets1[-1] - eta_min) *
|
|
(1 + math.cos(math.pi * x / 5)) / 2 for x in range(5)
|
|
]
|
|
single_targets = single_targets1 + single_targets2
|
|
targets = [
|
|
single_targets, [x * self.layer2_mult for x in single_targets]
|
|
]
|
|
scheduler2 = CosineAnnealingLR(
|
|
self.optimizer, T_max=5, eta_min=eta_min, begin=5, end=10)
|
|
|
|
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
|
|
|
|
def test_multi_scheduler_with_overlap(self):
|
|
# use Exp in the first 5 epochs and then use Cosine
|
|
epochs = 10
|
|
single_targets = [0.025, 0.03125, 0.0375, 0.004375
|
|
] + [0.005] * 2 + [0.0005] * 3 + [0.00005] * 1
|
|
targets = [
|
|
single_targets, [x * self.layer2_mult for x in single_targets]
|
|
]
|
|
scheduler1 = LinearLR(
|
|
self.optimizer, start_factor=1 / 2, begin=0, end=5)
|
|
scheduler2 = MultiStepLR(
|
|
self.optimizer, gamma=0.1, milestones=[3, 6, 9])
|
|
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
|
|
|
|
def test_multi_scheduler_with_gap(self):
|
|
# use Exp in the first 5 epochs and the last 5 epochs use Cosine
|
|
# no scheduler in the middle 5 epochs
|
|
epochs = 15
|
|
single_targets1 = [0.05 * (0.9**x) for x in range(5)]
|
|
scheduler1 = ExponentialLR(self.optimizer, gamma=0.9, begin=0, end=5)
|
|
|
|
eta_min = 1e-10
|
|
single_targets2 = [
|
|
eta_min + (single_targets1[-1] - eta_min) *
|
|
(1 + math.cos(math.pi * x / 5)) / 2 for x in range(5)
|
|
]
|
|
single_targets = single_targets1 + [single_targets1[-1]
|
|
] * 5 + single_targets2
|
|
targets = [
|
|
single_targets, [x * self.layer2_mult for x in single_targets]
|
|
]
|
|
scheduler2 = CosineAnnealingLR(
|
|
self.optimizer, T_max=5, eta_min=eta_min, begin=10, end=15)
|
|
|
|
self._test_scheduler_value([scheduler1, scheduler2], targets, epochs)
|
|
|
|
def test_onecycle_lr(self):
|
|
# test linear annealing
|
|
target = [1, 13, 25, 21.5, 18, 14.5, 11, 7.5, 4, 0.5]
|
|
scheduler = OneCycleLR(
|
|
self.optimizer,
|
|
eta_max=25,
|
|
final_div_factor=2,
|
|
total_steps=10,
|
|
anneal_strategy='linear')
|
|
self._test_scheduler_value(scheduler, [target], 10)
|
|
# test linear annealing three phase
|
|
target = [1, 9, 17, 25, 17, 9, 1, 0.75, 0.5, 0.25]
|
|
scheduler = OneCycleLR(
|
|
self.optimizer,
|
|
eta_max=25,
|
|
div_factor=25,
|
|
total_steps=10,
|
|
anneal_strategy='linear',
|
|
pct_start=0.4,
|
|
final_div_factor=4,
|
|
three_phase=True)
|
|
self._test_scheduler_value(scheduler, [target], 10)
|
|
|
|
# test cosine annealing
|
|
def annealing_cos(start, end, pct):
|
|
cos_out = math.cos(math.pi * pct) + 1
|
|
return end + (start - end) / 2.0 * cos_out
|
|
|
|
target = [
|
|
1, 13, 25,
|
|
annealing_cos(25, 0.5, 1 / 7.0),
|
|
annealing_cos(25, 0.5, 2 / 7.0),
|
|
annealing_cos(25, 0.5, 3 / 7.0),
|
|
annealing_cos(25, 0.5, 4 / 7.0),
|
|
annealing_cos(25, 0.5, 5 / 7.0),
|
|
annealing_cos(25, 0.5, 6 / 7.0), 0.5
|
|
]
|
|
scheduler = OneCycleLR(
|
|
self.optimizer, eta_max=25, final_div_factor=2, total_steps=10)
|
|
self._test_scheduler_value(scheduler, [target], 10)
|