def show() -> None:
fig, ax = plt.subplots()
history = torch.load(GLOBAL_OPTS['input'])
if GLOBAL_OPTS['loss_history_key'] not in history:
raise ValueError('No key [%s] in history file [%s] (try using --mode=probe)' %\
(str(GLOBAL_OPTS['loss_history_key']), str(GLOBAL_OPTS['input']))
)
loss_history = history[
GLOBAL_OPTS['loss_history_key']][0:history['loss_iter']]
if GLOBAL_OPTS['acc_history_key'] in history:
acc_history = history[
GLOBAL_OPTS['acc_history_key']][0:history['loss_iter']]
else:
acc_history = None
if 'test_loss_history' in history:
test_loss_history = history['test_loss_history'][
0:history['test_loss_iter']]
if GLOBAL_OPTS['verbose']:
print('%d test loss iterations' % history['test_loss_iter'])
else:
test_loss_history = None
if GLOBAL_OPTS['test_loss_history_key'] in history:
test_loss_history = history[
GLOBAL_OPTS['test_loss_history_key']][0:history['test_loss_iter']]
else:
test_loss_history = None
if acc_history is not None:
fig, ax = vis_loss_history.get_figure_subplots(2)
else:
fig, ax = vis_loss_history.get_figure_subplots(1)
vis_loss_history.plot_train_history_2subplots(
ax,
loss_history,
test_loss_curve=test_loss_history,
acc_curve=acc_history,
title=GLOBAL_OPTS['title'],
iter_per_epoch=history['iter_per_epoch'],
cur_epoch=history['cur_epoch'])
if GLOBAL_OPTS['print_loss']:
print(str(loss_history))
if GLOBAL_OPTS['print_acc']:
print(str(acc_history))
if GLOBAL_OPTS['plot_filename'] is not None:
fig.tight_layout()
fig.savefig(GLOBAL_OPTS['plot_filename'])
else:
plt.show()
def main() -> None:
model = get_model(GLOBAL_OPTS['model'])
t = get_trainer(model, "superconverge_cifar10_", trainer_type="cifar")
lr_finder = get_lr_finder(t, 1e-7, 1.0)
find_start_time = time.time()
lr_finder.find()
lr_min, lr_max = lr_finder.get_lr_range()
find_end_time = time.time()
find_total_time = find_end_time - find_start_time
print('Found learning rate range as %.4f -> %.4f' % (lr_min, lr_max))
print('Learning rate search took %s' % str(find_total_time)) # TODO : better string format
if GLOBAL_OPTS['sched_stepsize'] > 0:
stepsize = GLOBAL_OPTS['sched_stepsize']
else:
stepsize = int(len(t.train_loader) / 2)
# get a scheduler for learning rate
lr_scheduler = get_scheduler(
lr_min,
lr_max,
stepsize,
sched_type='TriangularScheduler'
)
# get a scheduler for momentum
mtm_scheduler = get_scheduler(
lr_min,
lr_max,
stepsize,
sched_type='InvTriangularScheduler'
)
t.set_lr_scheduler(lr_scheduler)
t.set_mtm_scheduler(mtm_scheduler)
train_start_time = time.time()
t.train()
train_end_time = time.time()
train_total_time = train_end_time - train_start_time
print('Training time : %s' % str(train_total_time)) # TODO: check string formatting
# plot outputs
loss_title = 'CIFAR10 Superconvergence Loss'
acc_title = 'CIFAR10 Superconvergence Accuracy'
fig_filename = 'figures/cifar10_superconv_test.png'
train_fig, train_ax = vis_loss_history.get_figure_subplots(2)
vis_loss_history.plot_train_history_2subplots(
train_ax,
t.get_loss_history(),
acc_history = t.get_acc_history(),
cur_epoch = t.cur_epoch,
iter_per_epoch = t.iter_per_epoch,
loss_title = loss_title,
acc_title = acc_title
)
train_fig.tight_layout()
train_fig.savefig(fig_filename)
def test_train(self) -> None:
test_checkpoint_name = self.checkpoint_dir + 'resnet_trainer_train_checkpoint.pkl'
test_history_name = self.checkpoint_dir + 'resnet_trainer_train_history.pkl'
train_num_epochs = 4
train_batch_size = 128
# get a model
model = resnets.WideResnet(
depth = self.resnet_depth,
num_classes = 10, # using CIFAR-10 data
input_channels=3,
w_factor=1
)
# get a traner
trainer = resnet_trainer.ResnetTrainer(
model,
# training parameters
batch_size = train_batch_size,
num_epochs = train_num_epochs,
learning_rate = self.test_learning_rate,
# device
device_id = util.get_device_id(),
# checkpoint
checkpoint_dir = self.checkpoint_dir,
checkpoint_name = 'resnet_trainer_test',
# display,
print_every = self.print_every,
save_every = 5000,
verbose = self.verbose
)
if self.verbose:
print('Created %s object' % repr(trainer))
print(trainer)
print('Training model %s for %d epochs (batch size = %d)' %\
(repr(trainer), train_num_epochs, train_batch_size)
)
trainer.train()
# save the final checkpoint
trainer.save_checkpoint(test_checkpoint_name)
trainer.save_history(test_history_name)
fig, ax = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
ax,
trainer.loss_history,
acc_history = trainer.acc_history,
cur_epoch = trainer.cur_epoch,
iter_per_epoch = trainer.iter_per_epoch
)
fig.savefig('figures/resnet_trainer_train_history.png', bbox_inches='tight')
print('======== TestResnetTrainer.test_train <END>')
def generate_plot(trainer, loss_title, acc_title, fig_filename):
train_fig, train_ax = vis_loss_history.get_figure_subplots(2)
vis_loss_history.plot_train_history_2subplots(
train_ax,
trainer.get_loss_history(),
acc_history=trainer.get_acc_history(),
cur_epoch=trainer.cur_epoch,
iter_per_epoch=trainer.iter_per_epoch,
loss_title=loss_title,
acc_title=acc_title)
train_fig.tight_layout()
train_fig.savefig(fig_filename)
def test_model_param_save(self) -> None:
# get a trainer, etc
trainer = get_trainer()
lr_finder = lr_common.LogFinder(
trainer,
lr_min = GLOBAL_TEST_PARAMS['test_lr_min'],
lr_max = GLOBAL_TEST_PARAMS['test_lr_max'],
num_iter = GLOBAL_TEST_PARAMS['test_num_iter'],
num_epochs = GLOBAL_TEST_PARAMS['test_lr_num_epochs'],
acc_test = True,
max_batches = self.test_max_batches,
verbose = self.verbose
)
# shut linter up
if self.verbose:
print(lr_finder)
# make a copy of the model parameters before we start looking for a new
# learning rate.
lr_find_min, lr_find_max = lr_finder.find()
# show plot
fig1, ax1 = plt.subplots()
lr_finder.plot_lr_vs_acc(ax1)
# now check that the restored parameters match the copy of the
# parameters save earlier
if self.draw_plot is True:
plt.show()
else:
plt.savefig('figures/test_lr_range_find_lr_vs_acc.png', bbox_inches='tight')
trainer.print_every = 200
trainer.train()
fig2, ax2 = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
ax2,
trainer.get_loss_history(),
acc_curve = trainer.get_acc_history(),
iter_per_epoch = trainer.iter_per_epoch,
cur_epoch = trainer.cur_epoch
)
if self.draw_plot is True:
plt.show()
else:
plt.savefig('figures/test_lr_range_find_train_results.png', bbox_inches='tight')
def test_find_lr(self) -> None:
# get an LRFinder
trainer = get_trainer()
lr_finder = lr_common.LogFinder(
trainer,
lr_min = GLOBAL_TEST_PARAMS['test_lr_min'],
lr_max = GLOBAL_TEST_PARAMS['test_lr_max'],
num_epochs = GLOBAL_TEST_PARAMS['test_lr_num_epochs'],
explode_thresh = GLOBAL_TEST_PARAMS['test_lr_explode_thresh'],
max_batches = self.test_max_batches,
verbose = self.verbose
)
if self.verbose:
print('Created LRFinder object')
print(lr_finder)
lr_find_min, lr_find_max = lr_finder.find()
print('Found learning rate range as %.3f -> %.3f' % (lr_find_min, lr_find_max))
# show plot
finder_fig, finder_ax = vis_loss_history.get_figure_subplots(2)
lr_finder.plot_lr_vs_acc(finder_ax[0])
lr_finder.plot_lr_vs_loss(finder_ax[1])
if self.draw_plot is True:
plt.show()
else:
plt.savefig('figures/test_find_lr_plots.png', bbox_inches='tight')
# train the network with the discovered parameters
trainer.print_every = 200
trainer.train()
train_fig, train_ax = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
train_ax,
trainer.get_loss_history(),
acc_curve = trainer.get_acc_history(),
iter_per_epoch = trainer.iter_per_epoch,
cur_epoch = trainer.cur_epoch
)
if self.draw_plot is True:
plt.show()
else:
train_fig.savefig('figures/test_find_lr_train_results.png', bbox_inches='tight')
def test_lr_range_find(self) -> None:
trainer = get_trainer()
lr_finder = lr_common.LogFinder(
trainer,
lr_min = GLOBAL_TEST_PARAMS['test_lr_min'],
lr_max = GLOBAL_TEST_PARAMS['test_lr_max'],
num_iter = GLOBAL_TEST_PARAMS['test_num_iter'],
num_epochs = GLOBAL_TEST_PARAMS['test_lr_num_epochs'],
acc_test = True,
max_batches = self.test_max_batches,
verbose = self.verbose
)
# shut linter up
if self.verbose:
print(lr_finder)
lr_find_min, lr_find_max = lr_finder.find()
# show plot
fig1, ax1 = plt.subplots()
lr_finder.plot_lr_vs_acc(ax1)
if self.draw_plot is True:
plt.show()
else:
plt.savefig('figures/test_lr_range_find_lr_vs_acc.png', bbox_inches='tight')
trainer.print_every = 200
trainer.train()
fig2, ax2 = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
ax2,
trainer.get_loss_history(),
acc_curve = trainer.get_acc_history(),
iter_per_epoch = trainer.iter_per_epoch,
cur_epoch = trainer.cur_epoch
)
if self.draw_plot is True:
plt.show()
else:
plt.savefig('figures/test_lr_range_find_train_results.png', bbox_inches='tight')
def test_train(self) -> None:
test_checkpoint = 'checkpoint/trainer_train_test.pkl'
model = cifar.CIFAR10Net()
# Get trainer object
trainer = cifar_trainer.CIFAR10Trainer(
model,
save_every = 0,
print_every = 50,
device_id = util.get_device_id(),
# loader options,
num_epochs = self.test_num_epochs,
learning_rate = 3e-4,
batch_size = 128,
num_workers = self.test_num_workers,
)
if self.verbose:
print('Created trainer object')
print(trainer)
# train for one epoch
trainer.train()
trainer.save_checkpoint(test_checkpoint)
fig, ax = get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
ax,
trainer.loss_history,
acc_history = trainer.acc_history,
cur_epoch = trainer.cur_epoch,
iter_per_epoch = trainer.iter_per_epoch,
loss_title = 'CIFAR-10 Trainer Test loss',
acc_title = 'CIFAR-10 Trainer Test accuracy'
)
fig.savefig('figures/trainer_train_test_history.png', bbox_inches='tight')
def main() -> None:
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_dataset_transform = transforms.Compose([
transforms.RandomRotation(5),
transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(224,
scale=(0.96, 1.0),
ratio=(0.95, 1.05)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
test_dataset_transform = transforms.Compose([
transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# HDF5 Datasets
if GLOBAL_USE_HDF5 is True:
cvd_train_dataset = hdf5_dataset.HDF5Dataset(
GLOBAL_OPTS['train_dataset'],
feature_name='images',
label_name='labels',
label_max_dim=1,
transform=normalize)
cvd_val_dataset = hdf5_dataset.HDF5Dataset(GLOBAL_OPTS['test_dataset'],
feature_name='images',
label_name='labels',
label_max_dim=1,
transform=normalize)
else:
cvd_train_dir = '/home/kreshnik/ml-data/cats-vs-dogs/train'
# ImageFolder dataset
cvd_train_dataset = datasets.ImageFolder(cvd_train_dir,
train_dataset_transform)
csv_val_dir = '/home/kreshnik/ml-data/cats-vs-dogs/test'
cvd_val_dataset = datasets.ImageFolder(csv_val_dir,
test_dataset_transform)
# get a network
model = cvdnet.CVDNet2()
cvd_train = cvd_trainer.CVDTrainer(
model,
# dataset options
train_dataset=cvd_train_dataset,
val_dataset=cvd_val_dataset,
# training options
loss_function='CrossEntropyLoss',
learning_rate=GLOBAL_OPTS['learning_rate'],
weight_decay=GLOBAL_OPTS['weight_decay'],
momentum=GLOBAL_OPTS['momentum'],
num_epochs=GLOBAL_OPTS['num_epochs'],
batch_size=GLOBAL_OPTS['batch_size'],
val_batch_size=GLOBAL_OPTS['val_batch_size'],
# checkpoint
checkpoint_name=GLOBAL_OPTS['checkpoint_name'],
save_every=GLOBAL_OPTS['save_every'],
# device
device_id=GLOBAL_OPTS['device_id'],
# other
print_every=GLOBAL_OPTS['print_every'],
verbose=GLOBAL_OPTS['verbose'])
# Add a tensorboard writer
if GLOBAL_OPTS['tensorboard_dir'] is not None:
if not os.path.isdir(GLOBAL_OPTS['tensorboard_dir']):
os.mkdir(GLOBAL_OPTS['tensorboard_dir'])
writer = tensorboard.SummaryWriter(
log_dir=GLOBAL_OPTS['tensorboard_dir'])
cvd_train.set_tb_writer(writer)
# Optionally do a search pass here and add a scheduler
if GLOBAL_OPTS['find_lr']:
lr_finder = expr_util.get_lr_finder(cvd_train)
lr_find_start_time = time.time()
lr_finder.find()
lr_find_min, lr_find_max = lr_finder.get_lr_range()
lr_find_end_time = time.time()
lr_find_total_time = lr_find_end_time - lr_find_start_time
print('Found learning rate range %.4f -> %.4f' %
(lr_find_min, lr_find_max))
print('Total find time [%s] ' %\
str(timedelta(seconds = lr_find_total_time))
)
# Now get a scheduler
stepsize = cvd_train.get_num_epochs() * len(trainer.train_loader) // 2
# get scheduler
lr_scheduler = expr_util.get_scheduler(
lr_find_min,
lr_find_max,
stepsize,
sched_type='TriangularScheduler')
cvd_train.set_lr_scheduler(lr_scheduler)
# train the model
train_start_time = time.time()
cvd_train.train()
train_end_time = time.time()
train_total_time = train_end_time - train_start_time
print('Total scheduled training time [%s] (%d epochs) %s' %\
(repr(cvd_train), cvd_train.cur_epoch,
str(timedelta(seconds = train_total_time)))
)
# Show results
fig, ax = vis_loss_history.get_figure_subplots(num_subplots=2)
vis_loss_history.plot_train_history_2subplots(
ax,
cvd_train.get_loss_history(),
acc_history=cvd_train.get_acc_history(),
iter_per_epoch=cvd_train.iter_per_epoch,
loss_title='CVD Loss',
acc_title='CVD Acc',
cur_epoch=cvd_train.cur_epoch)
fig.savefig('figures/cvd_train.png')
def main() -> None:
train_dataset, val_dataset = get_datasets(GLOBAL_OPTS['data_dir'])
# get some models
encoder = denoise_ae.DAEEncoder(num_channels=1)
decoder = denoise_ae.DAEDecoder(num_channels=1)
trainer = dae_trainer.DAETrainer(
encoder,
decoder,
# datasets
train_dataset=train_dataset,
val_dataset=val_dataset,
device_id=GLOBAL_OPTS['device_id'],
# trainer params
batch_size=GLOBAL_OPTS['batch_size'],
num_epochs=GLOBAL_OPTS['num_epochs'],
learning_rate=GLOBAL_OPTS['learning_rate'],
# checkpoints, saving, etc
checkpoint_dir=GLOBAL_OPTS['checkpoint_dir'],
checkpoint_name=GLOBAL_OPTS['checkpoint_name'],
save_every=GLOBAL_OPTS['save_every'],
print_every=GLOBAL_OPTS['print_every'],
verbose=GLOBAL_OPTS['verbose'])
# Add a summary writer
if GLOBAL_OPTS['tensorboard_dir'] is not None:
if GLOBAL_OPTS['verbose']:
print('Adding tensorboard writer to [%s]' % repr(trainer))
writer = tensorboard.SummaryWriter(
log_dir=GLOBAL_OPTS['tensorboard_dir'])
trainer.set_tb_writer(writer)
# train the model
train_start_time = time.time()
trainer.train()
train_end_time = time.time()
train_total_time = train_end_time - train_start_time
print('Trained [%s] for %d epochs, total time : %s' %\
(repr(trainer), trainer.cur_epoch, str(timedelta(seconds = train_total_time)))
)
# Take the models and load them into an inferrer
if GLOBAL_OPTS['infer']:
trainer.drop_last = True
# make the number of squares in the output figure settable later
subplot_square = 8
trainer.set_batch_size(
subplot_square *
subplot_square) # So that we have an NxN grid of outputs
# Get some figure stuff
out_fig, out_ax_list = vis_img.get_grid_subplots(subplot_square)
noise_fig, noise_ax_list = vis_img.get_grid_subplots(subplot_square)
# Get an inferrer
inferrer = dae_inferrer.DAEInferrer(
trainer.encoder,
trainer.decoder,
noise_bias=GLOBAL_OPTS['noise_bias'],
noise_factor=GLOBAL_OPTS['noise_factor'],
device_id=GLOBAL_OPTS['device_id'])
if GLOBAL_OPTS['verbose']:
print('Created [%s] object and attached models [%s], [%s]' %\
(repr(trainer), repr(trainer.encoder), repr(trainer.decoder))
)
infer_start_time = time.time()
for batch_idx, (data, _) in enumerate(trainer.val_loader):
print('Inferring batch [%d / %d]' %
(batch_idx + 1, len(trainer.val_loader)),
end='\r')
noise_batch = inferrer.get_noise(data)
out_batch = inferrer.forward(data)
# Plot noise
plot_denoise(noise_ax_list, noise_batch)
noise_fig_fname = 'figures/dae/mnist_dae_batch_%d_noise.png' % int(
batch_idx)
noise_fig.tight_layout()
noise_fig.savefig(noise_fig_fname)
# Plot outputs
plot_denoise(out_ax_list, out_batch)
out_fig_fname = 'figures/dae/mnist_dae_batch_%d_output.png' % int(
batch_idx)
out_fig.tight_layout()
out_fig.savefig(out_fig_fname)
print('\n OK')
infer_end_time = time.time()
infer_total_time = infer_end_time - infer_start_time
print('Inferrer [%s] inferrer %d batches of size %d, total time : %s' %\
(repr(inferrer), len(trainer.val_loader), trainer.batch_size, str(timedelta(seconds = infer_total_time)))
)
# Plot the loss history
hist_fig, hist_ax = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
hist_ax,
trainer.get_loss_history(),
cur_epoch=trainer.cur_epoch,
iter_per_epoch=trainer.iter_per_epoch,
loss_title='Denoising AE MNIST Training loss')
hist_fig.savefig(GLOBAL_OPTS['loss_history_file'], bbox_inches='tight')
def test_save_load_checkpoint(self) -> None:
test_checkpoint_name = self.checkpoint_dir + 'resnet_trainer_checkpoint.pkl'
test_history_name = self.checkpoint_dir + 'resnet_trainer_history.pkl'
# get a model
model = resnets.WideResnet(
depth = self.resnet_depth,
num_classes = 10, # using CIFAR-10 data
input_channels=3,
w_factor = 1
)
# get a traner
src_tr = resnet_trainer.ResnetTrainer(
model,
# training parameters
batch_size = self.test_batch_size,
num_epochs = self.test_num_epochs,
learning_rate = self.test_learning_rate,
# device
device_id = util.get_device_id(),
# display,
print_every = self.print_every,
save_every = 0,
verbose = self.verbose
)
if self.verbose:
print('Created %s object' % repr(src_tr))
print(src_tr)
print('Training model %s for %d epochs' % (repr(src_tr), self.test_num_epochs))
src_tr.train()
# save the final checkpoint
src_tr.save_checkpoint(test_checkpoint_name)
src_tr.save_history(test_history_name)
# get a new trainer and load checkpoint
dst_tr = resnet_trainer.ResnetTrainer(
model
)
dst_tr.load_checkpoint(test_checkpoint_name)
# Test object parameters
assert src_tr.num_epochs == dst_tr.num_epochs
assert src_tr.learning_rate == dst_tr.learning_rate
assert src_tr.weight_decay == dst_tr.weight_decay
assert src_tr.print_every == dst_tr.print_every
assert src_tr.save_every == dst_tr.save_every
print('\t Comparing model parameters ')
src_model_params = src_tr.get_model_params()
dst_model_params = dst_tr.get_model_params()
assert len(src_model_params.items()) == len(dst_model_params.items())
# p1, p2 are k,v tuple pairs of each model parameters
# k = str
# v = torch.Tensor
for n, (p1, p2) in enumerate(zip(src_model_params.items(), dst_model_params.items())):
assert p1[0] == p2[0]
print('Checking parameter %s [%d/%d] \t\t' % (str(p1[0]), n+1, len(src_model_params.items())), end='\r')
assert torch.equal(p1[1], p2[1]) == True
print('\n ...done')
# test history
dst_tr.load_history(test_history_name)
# loss history
assert len(src_tr.loss_history) == len(dst_tr.loss_history)
assert src_tr.loss_iter == dst_tr.loss_iter
for n in range(len(src_tr.loss_history)):
assert src_tr.loss_history[n] == dst_tr.loss_history[n]
# test loss history
assert len(src_tr.val_loss_history) == len(dst_tr.val_loss_history)
assert src_tr.val_loss_iter == dst_tr.val_loss_iter
for n in range(len(src_tr.val_loss_history)):
assert src_tr.val_loss_history[n] == dst_tr.val_loss_history[n]
# test acc history
assert len(src_tr.acc_history) == len(dst_tr.acc_history)
assert src_tr.acc_iter == dst_tr.acc_iter
for n in range(len(src_tr.acc_history)):
assert src_tr.acc_history[n] == dst_tr.acc_history[n]
fig, ax = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
ax,
src_tr.loss_history,
acc_history = src_tr.acc_history,
cur_epoch = src_tr.cur_epoch,
iter_per_epoch = src_tr.iter_per_epoch
)
fig.savefig('figures/resnet_trainer_train_test_history.png', bbox_inches='tight')
def main() -> None:
# Get a model
model = cifar.CIFAR10Net()
# Get a trainer
trainer = cifar_trainer.CIFAR10Trainer(
model,
# training parameters
batch_size=GLOBAL_OPTS['batch_size'],
num_epochs=GLOBAL_OPTS['num_epochs'],
learning_rate=GLOBAL_OPTS['learning_rate'],
momentum=GLOBAL_OPTS['momentum'],
weight_decay=GLOBAL_OPTS['weight_decay'],
# device
device_id=GLOBAL_OPTS['device_id'],
# checkpoint
checkpoint_dir=GLOBAL_OPTS['checkpoint_dir'],
checkpoint_name=GLOBAL_OPTS['checkpoint_name'],
# display,
print_every=GLOBAL_OPTS['print_every'],
save_every=GLOBAL_OPTS['save_every'],
verbose=GLOBAL_OPTS['verbose'])
if GLOBAL_OPTS['tensorboard_dir'] is not None:
writer = tensorboard.SummaryWriter(
log_dir=GLOBAL_OPTS['tensorboard_dir'])
trainer.set_tb_writer(writer)
# Optionally do a search pass here and add a scheduler
if GLOBAL_OPTS['find_lr']:
lr_finder = expr_util.get_lr_finder(trainer)
lr_find_start_time = time.time()
lr_finder.find()
lr_find_min, lr_find_max = lr_finder.get_lr_range()
lr_find_end_time = time.time()
lr_find_total_time = lr_find_end_time - lr_find_start_time
print('Found learning rate range %.4f -> %.4f' %
(lr_find_min, lr_find_max))
print('Total find time [%s] ' %\
str(timedelta(seconds = lr_find_total_time))
)
# Now get a scheduler
stepsize = trainer.get_num_epochs() * len(trainer.train_loader) // 2
# get scheduler
lr_scheduler = expr_util.get_scheduler(
lr_find_min,
lr_find_max,
stepsize,
sched_type='TriangularScheduler')
trainer.set_lr_scheduler(lr_scheduler)
# train the model
train_start_time = time.time()
trainer.train()
train_end_time = time.time()
train_total_time = train_end_time - train_start_time
print('Total training time [%s] (%d epochs) %s' %\
(repr(trainer), trainer.cur_epoch,
str(timedelta(seconds = train_total_time)))
)
# Visualise the output
train_fig, train_ax = vis_loss_history.get_figure_subplots()
vis_loss_history.plot_train_history_2subplots(
train_ax,
trainer.get_loss_history(),
acc_history=trainer.get_acc_history(),
cur_epoch=trainer.cur_epoch,
iter_per_epoch=trainer.iter_per_epoch,
loss_title='CIFAR-10 Training Loss',
acc_title='CIFAR-10 Training Accuracy ')
train_fig.savefig(GLOBAL_OPTS['fig_name'], bbox_inches='tight')
