| import unittest |
| import functools |
| from copy import deepcopy |
| import torch |
| import torch.optim as optim |
| import torch.legacy.optim as old_optim |
| import torch.nn.functional as F |
| from torch.optim import SGD |
| from torch.autograd import Variable |
| from torch import sparse |
| from torch.optim.lr_scheduler import LambdaLR, StepLR, MultiStepLR, ExponentialLR, ReduceLROnPlateau |
| from common import TestCase, run_tests |
| |
| |
| def rosenbrock(tensor): |
| x, y = tensor |
| return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2 |
| |
| |
| def drosenbrock(tensor): |
| x, y = tensor |
| return torch.DoubleTensor((-400 * x * (y - x ** 2) - 2 * (1 - x), 200 * (y - x ** 2))) |
| |
| |
| def wrap_old_fn(old_fn, **config): |
| def wrapper(closure, params, state): |
| return old_fn(closure, params, config, state) |
| return wrapper |
| |
| |
| class TestOptim(TestCase): |
| |
| def _test_rosenbrock(self, constructor, old_fn): |
| params_t = torch.Tensor([1.5, 1.5]) |
| state = {} |
| |
| params = Variable(torch.Tensor([1.5, 1.5]), requires_grad=True) |
| optimizer = constructor([params]) |
| |
| solution = torch.Tensor([1, 1]) |
| initial_dist = params.data.dist(solution) |
| |
| def eval(): |
| optimizer.zero_grad() |
| loss = rosenbrock(params) |
| loss.backward() |
| # loss.backward() will give **slightly** different |
| # gradients, than drosenbtock, because of a different ordering |
| # of floating point operations. In most cases it doesn't matter, |
| # but some optimizers are so sensitive that they can temporarily |
| # diverge up to 1e-4, just to converge again. This makes the |
| # comparison more stable. |
| params.grad.data.copy_(drosenbrock(params.data)) |
| return loss |
| |
| for i in range(2000): |
| optimizer.step(eval) |
| old_fn(lambda _: (rosenbrock(params_t), drosenbrock(params_t)), |
| params_t, state) |
| self.assertEqual(params.data, params_t) |
| |
| self.assertLessEqual(params.data.dist(solution), initial_dist) |
| |
| def _test_rosenbrock_sparse(self, constructor, sparse_only=False): |
| params_t = torch.Tensor([1.5, 1.5]) |
| |
| params = Variable(params_t, requires_grad=True) |
| optimizer = constructor([params]) |
| if not sparse_only: |
| params_c = Variable(params_t.clone(), requires_grad=True) |
| optimizer_c = constructor([params_c]) |
| |
| solution = torch.Tensor([1, 1]) |
| initial_dist = params.data.dist(solution) |
| |
| def eval(params, sparse_grad, w): |
| # Depending on w, provide only the x or y gradient |
| optimizer.zero_grad() |
| loss = rosenbrock(params) |
| loss.backward() |
| grad = drosenbrock(params.data) |
| # NB: We torture test the optimizer by returning an |
| # uncoalesced sparse tensor |
| if w: |
| i = torch.LongTensor([[0, 0]]) |
| x = grad[0] |
| v = torch.DoubleTensor([x / 4., x - x / 4.]) |
| else: |
| i = torch.LongTensor([[1, 1]]) |
| y = grad[1] |
| v = torch.DoubleTensor([y - y / 4., y / 4.]) |
| x = sparse.DoubleTensor(i, v, torch.Size([2])) |
| if sparse_grad: |
| params.grad.data = x |
| else: |
| params.grad.data = x.to_dense() |
| return loss |
| |
| for i in range(2000): |
| # Do cyclic coordinate descent |
| w = i % 2 |
| optimizer.step(functools.partial(eval, params, True, w)) |
| if not sparse_only: |
| optimizer_c.step(functools.partial(eval, params_c, False, w)) |
| self.assertEqual(params.data, params_c.data) |
| |
| self.assertLessEqual(params.data.dist(solution), initial_dist) |
| |
| def _test_basic_cases_template(self, weight, bias, input, constructor): |
| weight = Variable(weight, requires_grad=True) |
| bias = Variable(bias, requires_grad=True) |
| input = Variable(input) |
| optimizer = constructor(weight, bias) |
| |
| def fn(): |
| optimizer.zero_grad() |
| y = weight.mv(input) |
| if y.is_cuda and bias.is_cuda and y.get_device() != bias.get_device(): |
| y = y.cuda(bias.get_device()) |
| loss = (y + bias).pow(2).sum() |
| loss.backward() |
| return loss |
| |
| initial_value = fn().data[0] |
| for i in range(200): |
| optimizer.step(fn) |
| self.assertLess(fn().data[0], initial_value) |
| |
| def _test_state_dict(self, weight, bias, input, constructor): |
| weight = Variable(weight, requires_grad=True) |
| bias = Variable(bias, requires_grad=True) |
| input = Variable(input) |
| |
| def fn_base(optimizer, weight, bias): |
| optimizer.zero_grad() |
| i = input_cuda if weight.is_cuda else input |
| loss = (weight.mv(i) + bias).pow(2).sum() |
| loss.backward() |
| return loss |
| |
| optimizer = constructor(weight, bias) |
| fn = functools.partial(fn_base, optimizer, weight, bias) |
| |
| # Prime the optimizer |
| for i in range(20): |
| optimizer.step(fn) |
| # Clone the weights and construct new optimizer for them |
| weight_c = Variable(weight.data.clone(), requires_grad=True) |
| bias_c = Variable(bias.data.clone(), requires_grad=True) |
| optimizer_c = constructor(weight_c, bias_c) |
| fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c) |
| # Load state dict |
| state_dict = deepcopy(optimizer.state_dict()) |
| state_dict_c = deepcopy(optimizer.state_dict()) |
| optimizer_c.load_state_dict(state_dict_c) |
| # Run both optimizations in parallel |
| for i in range(20): |
| optimizer.step(fn) |
| optimizer_c.step(fn_c) |
| self.assertEqual(weight, weight_c) |
| self.assertEqual(bias, bias_c) |
| # Make sure state dict wasn't modified |
| self.assertEqual(state_dict, state_dict_c) |
| |
| # Check that state dict can be loaded even when we cast parameters |
| # to a different type and move to a different device. |
| if not torch.cuda.is_available(): |
| return |
| |
| input_cuda = Variable(input.data.float().cuda()) |
| weight_cuda = Variable(weight.data.float().cuda(), requires_grad=True) |
| bias_cuda = Variable(bias.data.float().cuda(), requires_grad=True) |
| optimizer_cuda = constructor(weight_cuda, bias_cuda) |
| fn_cuda = functools.partial(fn_base, optimizer_cuda, weight_cuda, bias_cuda) |
| |
| state_dict = deepcopy(optimizer.state_dict()) |
| state_dict_c = deepcopy(optimizer.state_dict()) |
| optimizer_cuda.load_state_dict(state_dict_c) |
| # Make sure state dict wasn't modified |
| self.assertEqual(state_dict, state_dict_c) |
| |
| for i in range(20): |
| optimizer.step(fn) |
| optimizer_cuda.step(fn_cuda) |
| self.assertEqual(weight, weight_cuda) |
| self.assertEqual(bias, bias_cuda) |
| |
| def _test_basic_cases(self, constructor, ignore_multidevice=False): |
| self._test_state_dict( |
| torch.randn(10, 5), |
| torch.randn(10), |
| torch.randn(5), |
| constructor |
| ) |
| self._test_basic_cases_template( |
| torch.randn(10, 5), |
| torch.randn(10), |
| torch.randn(5), |
| constructor |
| ) |
| # non-contiguous parameters |
| self._test_basic_cases_template( |
| torch.randn(10, 5, 2)[..., 0], |
| torch.randn(10, 2)[..., 0], |
| torch.randn(5), |
| constructor |
| ) |
| # CUDA |
| if not torch.cuda.is_available(): |
| return |
| self._test_basic_cases_template( |
| torch.randn(10, 5).cuda(), |
| torch.randn(10).cuda(), |
| torch.randn(5).cuda(), |
| constructor |
| ) |
| # Multi-GPU |
| if not torch.cuda.device_count() > 1 or ignore_multidevice: |
| return |
| self._test_basic_cases_template( |
| torch.randn(10, 5).cuda(0), |
| torch.randn(10).cuda(1), |
| torch.randn(5).cuda(0), |
| constructor |
| ) |
| |
| def _build_params_dict(self, weight, bias, **kwargs): |
| return [dict(params=[weight]), dict(params=[bias], **kwargs)] |
| |
| def _build_params_dict_single(self, weight, bias, **kwargs): |
| return [dict(params=bias, **kwargs)] |
| |
| def test_sgd(self): |
| self._test_rosenbrock( |
| lambda params: optim.SGD(params, lr=1e-3), |
| wrap_old_fn(old_optim.sgd, learningRate=1e-3) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.SGD(params, lr=1e-3, momentum=0.9, |
| dampening=0, weight_decay=1e-4), |
| wrap_old_fn(old_optim.sgd, learningRate=1e-3, momentum=0.9, |
| dampening=0, weightDecay=1e-4) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.SGD([weight, bias], lr=1e-3) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.SGD( |
| self._build_params_dict(weight, bias, lr=1e-2), |
| lr=1e-3) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.SGD( |
| self._build_params_dict_single(weight, bias, lr=1e-2), |
| lr=1e-3) |
| ) |
| |
| def test_sgd_sparse(self): |
| self._test_rosenbrock_sparse( |
| lambda params: optim.SGD(params, lr=5e-3) |
| ) |
| |
| def test_adam(self): |
| self._test_rosenbrock( |
| lambda params: optim.Adam(params, lr=1e-2), |
| wrap_old_fn(old_optim.adam, learningRate=1e-2) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adam(params, lr=1e-2, weight_decay=1e-2), |
| wrap_old_fn(old_optim.adam, learningRate=1e-2, weightDecay=1e-2) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adam([weight, bias], lr=1e-3) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adam( |
| self._build_params_dict(weight, bias, lr=1e-2), |
| lr=1e-3) |
| ) |
| |
| def test_sparse_adam(self): |
| self._test_rosenbrock_sparse( |
| lambda params: optim.SparseAdam(params, lr=4e-2), |
| True |
| ) |
| |
| def test_adadelta(self): |
| self._test_rosenbrock( |
| lambda params: optim.Adadelta(params), |
| wrap_old_fn(old_optim.adadelta) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adadelta(params, rho=0.95), |
| wrap_old_fn(old_optim.adadelta, rho=0.95) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adadelta(params, weight_decay=1e-2), |
| wrap_old_fn(old_optim.adadelta, weightDecay=1e-2) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adadelta([weight, bias]) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adadelta( |
| self._build_params_dict(weight, bias, rho=0.95)) |
| ) |
| |
| def test_adagrad(self): |
| self._test_rosenbrock( |
| lambda params: optim.Adagrad(params, lr=1e-1), |
| wrap_old_fn(old_optim.adagrad, learningRate=1e-1) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adagrad(params, lr=1e-1, lr_decay=1e-3), |
| wrap_old_fn(old_optim.adagrad, learningRate=1e-1, learningRateDecay=1e-3) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adagrad(params, lr=1e-1, weight_decay=1e-2), |
| wrap_old_fn(old_optim.adagrad, learningRate=1e-1, weightDecay=1e-2) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adagrad([weight, bias], lr=1e-1) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adagrad( |
| self._build_params_dict(weight, bias, lr=1e-2), |
| lr=1e-1) |
| ) |
| |
| def test_adagrad_sparse(self): |
| self._test_rosenbrock_sparse( |
| lambda params: optim.Adagrad(params, lr=1e-1) |
| ) |
| |
| def test_adamax(self): |
| self._test_rosenbrock( |
| lambda params: optim.Adamax(params, lr=1e-1), |
| wrap_old_fn(old_optim.adamax, learningRate=1e-1) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adamax(params, lr=1e-1, weight_decay=1e-2), |
| wrap_old_fn(old_optim.adamax, learningRate=1e-1, weightDecay=1e-2) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Adamax(params, lr=1e-1, betas=(0.95, 0.998)), |
| wrap_old_fn(old_optim.adamax, learningRate=1e-1, beta1=0.95, beta2=0.998) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adagrad([weight, bias], lr=1e-1) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adagrad( |
| self._build_params_dict(weight, bias, lr=1e-2), |
| lr=1e-1) |
| ) |
| |
| def test_rmsprop(self): |
| self._test_rosenbrock( |
| lambda params: optim.RMSprop(params, lr=1e-2), |
| wrap_old_fn(old_optim.rmsprop, learningRate=1e-2) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.RMSprop(params, lr=1e-2, weight_decay=1e-2), |
| wrap_old_fn(old_optim.rmsprop, learningRate=1e-2, weightDecay=1e-2) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.RMSprop(params, lr=1e-2, alpha=0.95), |
| wrap_old_fn(old_optim.rmsprop, learningRate=1e-2, alpha=0.95) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adagrad([weight, bias], lr=1e-2) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Adagrad( |
| self._build_params_dict(weight, bias, lr=1e-3), |
| lr=1e-2) |
| ) |
| |
| def test_asgd(self): |
| self._test_rosenbrock( |
| lambda params: optim.ASGD(params, lr=1e-3), |
| wrap_old_fn(old_optim.asgd, eta0=1e-3) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.ASGD(params, lr=1e-3, alpha=0.8), |
| wrap_old_fn(old_optim.asgd, eta0=1e-3, alpha=0.8) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.ASGD(params, lr=1e-3, t0=1e3), |
| wrap_old_fn(old_optim.asgd, eta0=1e-3, t0=1e3) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.ASGD([weight, bias], lr=1e-3, t0=100) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.ASGD( |
| self._build_params_dict(weight, bias, lr=1e-2), |
| lr=1e-3, t0=100) |
| ) |
| |
| def test_rprop(self): |
| self._test_rosenbrock( |
| lambda params: optim.Rprop(params, lr=1e-3), |
| wrap_old_fn(old_optim.rprop, stepsize=1e-3) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Rprop(params, lr=1e-3, etas=(0.6, 1.1)), |
| wrap_old_fn(old_optim.rprop, stepsize=1e-3, etaminus=0.6, etaplus=1.1) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.Rprop(params, lr=1e-3, step_sizes=(1e-4, 3)), |
| wrap_old_fn(old_optim.rprop, stepsize=1e-3, stepsizemin=1e-4, stepsizemax=3) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Rprop([weight, bias], lr=1e-3) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.Rprop( |
| self._build_params_dict(weight, bias, lr=1e-2), |
| lr=1e-3) |
| ) |
| |
| def test_lbfgs(self): |
| self._test_rosenbrock( |
| lambda params: optim.LBFGS(params), |
| wrap_old_fn(old_optim.lbfgs) |
| ) |
| self._test_rosenbrock( |
| lambda params: optim.LBFGS(params, lr=5e-2, max_iter=5), |
| wrap_old_fn(old_optim.lbfgs, learningRate=5e-2, maxIter=5) |
| ) |
| self._test_basic_cases( |
| lambda weight, bias: optim.LBFGS([weight, bias]), |
| ignore_multidevice=True |
| ) |
| |
| def test_invalid_param_type(self): |
| with self.assertRaises(TypeError): |
| optim.SGD(Variable(torch.randn(5, 5)), lr=3) |
| |
| |
| class SchedulerTestNet(torch.nn.Module): |
| def __init__(self): |
| super(SchedulerTestNet, self).__init__() |
| self.conv1 = torch.nn.Conv2d(1, 1, 1) |
| self.conv2 = torch.nn.Conv2d(1, 1, 1) |
| |
| def forward(self, x): |
| return self.conv2(F.relu(self.conv1(x))) |
| |
| |
| class TestLRScheduler(TestCase): |
| def setUp(self): |
| self.net = SchedulerTestNet() |
| self.opt = SGD( |
| [{'params': self.net.conv1.parameters()}, {'params': self.net.conv2.parameters(), 'lr': 0.5}], |
| lr=0.05) |
| |
| def test_step_lr(self): |
| # lr = 0.05 if epoch < 3 |
| # lr = 0.005 if 30 <= epoch < 6 |
| # lr = 0.0005 if epoch >= 9 |
| single_targets = [0.05] * 3 + [0.005] * 3 + [0.0005] * 3 + [0.00005] * 3 |
| targets = [single_targets, list(map(lambda x: x * 10, single_targets))] |
| scheduler = StepLR(self.opt, gamma=0.1, step_size=3) |
| epochs = 10 |
| self._test(scheduler, targets, epochs) |
| |
| def test_multi_step_lr(self): |
| # lr = 0.05 if epoch < 2 |
| # lr = 0.005 if 2 <= epoch < 5 |
| # lr = 0.0005 if epoch < 9 |
| # lr = 0.00005 if epoch >= 9 |
| single_targets = [0.05] * 2 + [0.005] * 3 + [0.0005] * 4 + [0.00005] * 3 |
| targets = [single_targets, list(map(lambda x: x * 10, single_targets))] |
| scheduler = MultiStepLR(self.opt, gamma=0.1, milestones=[2, 5, 9]) |
| epochs = 10 |
| self._test(scheduler, targets, epochs) |
| |
| def test_exp_lr(self): |
| single_targets = [0.05 * (0.9 ** x) for x in range(10)] |
| targets = [single_targets, list(map(lambda x: x * 10, single_targets))] |
| scheduler = ExponentialLR(self.opt, gamma=0.9) |
| epochs = 10 |
| self._test(scheduler, targets, epochs) |
| |
| def test_reduce_lr_on_plateau1(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 20] |
| metrics = [10 - i * 0.0167 for i in range(20)] |
| scheduler = ReduceLROnPlateau(self.opt, threshold_mode='abs', mode='min', |
| threshold=0.01, patience=5, cooldown=5) |
| epochs = 10 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau2(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 6 + [0.05] * 7 + [0.005] * 7 + [0.0005] * 2] |
| metrics = [10 - i * 0.0165 for i in range(22)] |
| scheduler = ReduceLROnPlateau(self.opt, patience=5, cooldown=0, threshold_mode='abs', |
| mode='min', threshold=0.1) |
| epochs = 22 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau3(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * (2 + 6) + [0.05] * (5 + 6) + [0.005] * 4] |
| metrics = [-0.8] * 2 + [-0.234] * 20 |
| scheduler = ReduceLROnPlateau(self.opt, mode='max', patience=5, cooldown=5, |
| threshold_mode='abs') |
| epochs = 22 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau4(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 20] |
| metrics = [1.5 * (1.025 ** i) for i in range(20)] # 1.025 > 1.1**0.25 |
| scheduler = ReduceLROnPlateau(self.opt, mode='max', patience=3, |
| threshold_mode='rel', threshold=0.1) |
| epochs = 20 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau5(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 6 + [0.05] * (5 + 6) + [0.005] * 4] |
| metrics = [1.5 * (1.005 ** i) for i in range(20)] |
| scheduler = ReduceLROnPlateau(self.opt, mode='max', threshold_mode='rel', |
| threshold=0.1, patience=5, cooldown=5) |
| epochs = 20 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau6(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 20] |
| metrics = [1.5 * (0.85 ** i) for i in range(20)] |
| scheduler = ReduceLROnPlateau(self.opt, mode='min', threshold_mode='rel', |
| threshold=0.1) |
| epochs = 20 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau7(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 6 + [0.05] * (5 + 6) + [0.005] * 4] |
| metrics = [1] * 7 + [0.6] + [0.5] * 12 |
| scheduler = ReduceLROnPlateau(self.opt, mode='min', threshold_mode='rel', |
| threshold=0.1, patience=5, cooldown=5) |
| epochs = 20 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_reduce_lr_on_plateau8(self): |
| for param_group in self.opt.param_groups: |
| param_group['lr'] = 0.5 |
| targets = [[0.5] * 6 + [0.4] * 14, [0.5] * 6 + [0.3] * 14] |
| metrics = [1.5 * (1.005 ** i) for i in range(20)] |
| scheduler = ReduceLROnPlateau(self.opt, mode='max', threshold_mode='rel', min_lr=[0.4, 0.3], |
| threshold=0.1, patience=5, cooldown=5) |
| epochs = 20 |
| self._test_reduce_lr_on_plateau(scheduler, targets, metrics, epochs) |
| |
| def test_lambda_lr(self): |
| self.opt.param_groups[0]['lr'] = 0.05 |
| self.opt.param_groups[1]['lr'] = 0.4 |
| targets = [[0.05 * (0.9 ** x) for x in range(10)], [0.4 * (0.8 ** x) for x in range(10)]] |
| scheduler = LambdaLR(self.opt, |
| lr_lambda=[lambda x1: 0.9 ** x1, lambda x2: 0.8 ** x2]) |
| epochs = 10 |
| self._test(scheduler, targets, epochs) |
| |
| def _test(self, scheduler, targets, epochs=10): |
| for epoch in range(epochs): |
| scheduler.step(epoch) |
| for param_group, target in zip(self.opt.param_groups, targets): |
| self.assertAlmostEqual(target[epoch], param_group['lr'], |
| msg='LR is wrong in epoch {}: expected {}, got {}'.format( |
| epoch, target[epoch], param_group['lr']), delta=1e-5) |
| |
| def _test_reduce_lr_on_plateau(self, scheduler, targets, metrics, epochs=10, verbose=False): |
| for epoch in range(epochs): |
| scheduler.step(metrics[epoch]) |
| if verbose: |
| print('epoch{}:\tlr={}'.format(epoch, self.opt.param_groups[0]['lr'])) |
| for param_group, target in zip(self.opt.param_groups, targets): |
| self.assertAlmostEqual(target[epoch], param_group['lr'], |
| msg='LR is wrong in epoch {}: expected {}, got {}'.format( |
| epoch, target[epoch], param_group['lr']), delta=1e-5) |
| |
| if __name__ == '__main__': |
| run_tests() |
