def test_static_accuracy(self):
"""
Test the accuracy computation built into the optimizer, given some data.
This function tests the accuracy of a given chunk of data, with no previous data totals,
and thus only tests "static" accuracy, not "running" accuracy
"""
cfg = DefaultOptimizerConfig()
batch_size = cfg.training_cfg.batch_size
num_outputs = 5
# now, modify a subset of the network output and make that the "real" output
step = 0.05
batch_acc_vec = np.arange(0, 1 + step, step)
for batch_acc in batch_acc_vec:
random_mat = self.rso.rand(batch_size, num_outputs)
row_sum = random_mat.sum(axis=1)
# normalize the random_mat such that every row adds up to 1
# broadcast so we can divide every element in matrix by the row's sum
fake_network_output = random_mat / row_sum[:,
None] # shape: [batch_size x n_output]
network_output = np.argmax(fake_network_output,
axis=1) # the hard-decision prediction
true_output = network_output.copy()
num_indices_to_modify = int(batch_size * (1 - batch_acc))
indices_to_modify = self.rso.choice(range(batch_size),
num_indices_to_modify,
replace=False)
# create the "true" output such that the target accuracy matches the desired value
for ii in indices_to_modify:
true_output[ii] = (true_output[ii] + 1) % num_outputs
expected_balanced_acc = balanced_accuracy_score(
true_output, network_output) * 100
# convert datatypes to what is expected during operation
network_output_pt = torch.tensor(fake_network_output,
dtype=torch.float)
true_output_pt = torch.tensor(true_output, dtype=torch.long)
# now compute the accuracy
actual_acc, n_total, n_correct = \
_running_eval_acc(network_output_pt, true_output_pt, n_total=None, n_correct=None)
self.assertAlmostEqual(actual_acc, expected_balanced_acc)
def train_epoch(self, model: nn.Module, train_loader: DataLoader,
val_clean_loader: DataLoader, val_triggered_loader: DataLoader,
epoch_num: int, use_amp: bool = False):
"""
Runs one epoch of training on the specified model
:param model: the model to train for one epoch
:param train_loader: a DataLoader object pointing to the training dataset
:param val_clean_loader: a DataLoader object pointing to the validation dataset that is clean
:param val_triggered_loader: a DataLoader object pointing to the validation dataset that is triggered
:param epoch_num: the epoch number that is being trained
:param use_amp: if True, uses automated mixed precision for FP16 training.
:return: a list of statistics for batches where statistics were computed
"""
# Probability of Adversarial attack to occur in each iteration
attack_prob = self.optimizer_cfg.training_cfg.adv_training_ratio
pid = os.getpid()
train_dataset_len = len(train_loader.dataset)
loop = tqdm(train_loader, disable=self.optimizer_cfg.reporting_cfg.disable_progress_bar)
scaler = None
if use_amp:
scaler = torch.cuda.amp.GradScaler()
train_n_correct, train_n_total = None, None
# Define parameters of the adversarial attack
attack_eps = float(self.optimizer_cfg.training_cfg.adv_training_eps)
attack_iterations = int(self.optimizer_cfg.training_cfg.adv_training_iterations)
eps_iter = (2.0 * attack_eps) / float(attack_iterations)
attack = LinfPGDAttack(
predict=model,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=attack_eps,
nb_iter=attack_iterations,
eps_iter=eps_iter)
sum_batchmean_train_loss = 0
running_train_acc = 0
num_batches = len(train_loader)
model.train()
for batch_idx, (x, y_truth) in enumerate(loop):
x = x.to(self.device)
y_truth = y_truth.to(self.device)
# put network into training mode & zero out previous gradient computations
self.optimizer.zero_grad()
# get predictions based on input & weights learned so far
if use_amp:
with torch.cuda.amp.autocast():
# add adversarial noise via l_inf PGD attack
# only apply attack to attack_prob of the batches
if attack_prob and np.random.rand() <= attack_prob:
with ctx_noparamgrad_and_eval(model):
x = attack.perturb(x, y_truth)
y_hat = model(x)
# compute metrics
batch_train_loss = self._eval_loss_function(y_hat, y_truth)
else:
# add adversarial noise vis lin PGD attack
if attack_prob and np.random.rand() <= attack_prob:
with ctx_noparamgrad_and_eval(model):
x = attack.perturb(x, y_truth)
y_hat = model(x)
batch_train_loss = self._eval_loss_function(y_hat, y_truth)
sum_batchmean_train_loss += batch_train_loss.item()
running_train_acc, train_n_total, train_n_correct = default_optimizer._running_eval_acc(y_hat, y_truth,
n_total=train_n_total,
n_correct=train_n_correct,
soft_to_hard_fn=self.soft_to_hard_fn,
soft_to_hard_fn_kwargs=self.soft_to_hard_fn_kwargs)
# compute gradient
if use_amp:
# Scales loss. Calls backward() on scaled loss to create scaled gradients.
# Backward passes under autocast are not recommended.
# Backward ops run in the same dtype autocast chose for corresponding forward ops.
scaler.scale(batch_train_loss).backward()
else:
if np.isnan(sum_batchmean_train_loss) or np.isnan(running_train_acc):
default_optimizer._save_nandata(x, y_hat, y_truth, batch_train_loss, sum_batchmean_train_loss, running_train_acc,
train_n_total, train_n_correct, model)
batch_train_loss.backward()
# perform gradient clipping if configured
if self.optimizer_cfg.training_cfg.clip_grad:
if use_amp:
# Unscales the gradients of optimizer's assigned params in-place
scaler.unscale_(self.optimizer)
if self.optimizer_cfg.training_cfg.clip_type == 'norm':
# clip_grad_norm_ modifies gradients in place
# see: https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html
torch_clip_grad.clip_grad_norm_(model.parameters(), self.optimizer_cfg.training_cfg.clip_val,
**self.optimizer_cfg.training_cfg.clip_kwargs)
elif self.optimizer_cfg.training_cfg.clip_type == 'val':
# clip_grad_val_ modifies gradients in place
# see: https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html
torch_clip_grad.clip_grad_value_(
model.parameters(), self.optimizer_cfg.training_cfg.clip_val)
else:
msg = "Unknown clipping type for gradient clipping!"
logger.error(msg)
raise ValueError(msg)
if use_amp:
# scaler.step() first unscales the gradients of the optimizer's assigned params.
# If these gradients do not contain infs or NaNs, optimizer.step() is then called,
# otherwise, optimizer.step() is skipped.
scaler.step(self.optimizer)
# Updates the scale for next iteration.
scaler.update()
else:
self.optimizer.step()
# report batch statistics to tensorflow
if self.tb_writer:
try:
batch_num = int(epoch_num * num_batches + batch_idx)
self.tb_writer.add_scalar(self.optimizer_cfg.reporting_cfg.experiment_name + '-train_loss',
batch_train_loss.item(), global_step=batch_num)
self.tb_writer.add_scalar(self.optimizer_cfg.reporting_cfg.experiment_name + '-running_train_acc',
running_train_acc, global_step=batch_num)
except:
# TODO: catch specific expcetions
pass
loop.set_description('Epoch {}/{}'.format(epoch_num + 1, self.num_epochs))
loop.set_postfix(avg_train_loss=batch_train_loss.item())
if batch_idx % self.num_batches_per_logmsg == 0:
logger.info('{}\tTrain Epoch: {} [{}/{} ({:.0f}%)]\tTrainLoss: {:.6f}\tTrainAcc: {:.6f}'.format(
pid, epoch_num, batch_idx * len(x), train_dataset_len,
100. * batch_idx / num_batches, batch_train_loss.item(), running_train_acc))
train_stats = EpochTrainStatistics(running_train_acc, sum_batchmean_train_loss / float(num_batches))
# if we have validation data, we compute on the validation dataset
num_val_batches_clean = len(val_clean_loader)
if num_val_batches_clean > 0:
logger.info('Running Validation on Clean Data')
running_val_clean_acc, _, _, val_clean_loss = \
default_optimizer._eval_acc(val_clean_loader, model, self.device,
self.soft_to_hard_fn, self.soft_to_hard_fn_kwargs, self._eval_loss_function)
else:
logger.info("No dataset computed for validation on clean dataset!")
running_val_clean_acc = None
val_clean_loss = None
num_val_batches_triggered = len(val_triggered_loader)
if num_val_batches_triggered > 0:
logger.info('Running Validation on Triggered Data')
running_val_triggered_acc, _, _, val_triggered_loss = \
default_optimizer._eval_acc(val_triggered_loader, model, self.device,
self.soft_to_hard_fn, self.soft_to_hard_fn_kwargs, self._eval_loss_function)
else:
logger.info(
"No dataset computed for validation on triggered dataset!")
running_val_triggered_acc = None
val_triggered_loss = None
validation_stats = EpochValidationStatistics(running_val_clean_acc, val_clean_loss,
running_val_triggered_acc, val_triggered_loss)
if num_val_batches_clean > 0:
logger.info('{}\tTrain Epoch: {} \tCleanValLoss: {:.6f}\tCleanValAcc: {:.6f}'.format(
pid, epoch_num, val_clean_loss, running_val_clean_acc))
if num_val_batches_triggered > 0:
logger.info('{}\tTrain Epoch: {} \tTriggeredValLoss: {:.6f}\tTriggeredValAcc: {:.6f}'.format(
pid, epoch_num, val_triggered_loss, running_val_triggered_acc))
if self.tb_writer:
try:
batch_num = int((epoch_num + 1) * num_batches)
if num_val_batches_clean > 0:
self.tb_writer.add_scalar(self.optimizer_cfg.reporting_cfg.experiment_name +
'-clean-val-loss', val_clean_loss, global_step=batch_num)
self.tb_writer.add_scalar(self.optimizer_cfg.reporting_cfg.experiment_name +
'-clean-val_acc', running_val_clean_acc, global_step=batch_num)
if num_val_batches_triggered > 0:
self.tb_writer.add_scalar(self.optimizer_cfg.reporting_cfg.experiment_name +
'-triggered-val-loss', val_triggered_loss, global_step=batch_num)
self.tb_writer.add_scalar(self.optimizer_cfg.reporting_cfg.experiment_name +
'-triggered-val_acc', running_val_triggered_acc, global_step=batch_num)
except:
pass
# update the lr-scheduler if necessary
if self.lr_scheduler is not None:
if self.optimizer_cfg.training_cfg.lr_scheduler_call_arg is None:
self.lr_scheduler.step()
elif self.optimizer_cfg.training_cfg.lr_scheduler_call_arg.lower() == 'val_acc':
val_acc = validation_stats.get_val_acc()
if val_acc is not None:
self.lr_scheduler.step(val_acc)
else:
msg = "val_clean_acc not defined b/c validation dataset is not defined! Ignoring LR step!"
logger.warning(msg)
elif self.optimizer_cfg.training_cfg.lr_scheduler_call_arg.lower() == 'val_loss':
val_loss = validation_stats.get_val_loss()
if val_loss is not None:
self.lr_scheduler.step(val_loss)
else:
msg = "val_clean_loss not defined b/c validation dataset is not defined! Ignoring LR step!"
logger.warning(msg)
else:
msg = "Unknown mode for calling lr_scheduler!"
logger.error(msg)
raise ValueError(msg)
return train_stats, validation_stats
def test_running_accuracy(self):
"""
Tests the running accuracy of the classifier, which takes the previous counts
into account when computing the updated classification.
"""
batch_size = 32
num_outputs = 5
# setup what our accumulated totals are so far
# note that the _eval_acc function takes two dictionaries. Each dictionary has the same format.
# the key is the classification label, and the value is the number of times that label has
# appeared. In the n_total dictionary, the counts represent the total number of times that
# specific label was soon. In the n_correct dictionary, the counts represent the number of
# times that label was classified correctly.
n_total_prev = defaultdict(int, {0: 13, 1: 13, 2: 13, 3: 13, 4: 12})
# test over a large variety of the number of correct predictions, per class
val_ranges = [3, 7, 11]
for zero_val in tqdm(val_ranges):
for one_val in val_ranges:
for two_val in val_ranges:
for three_val in val_ranges:
for four_val in val_ranges:
n_correct_prev = defaultdict(
int, {
0: zero_val,
1: one_val,
2: two_val,
3: three_val,
4: four_val
})
# in order to compute the actual accuracy we should expect, we update the dictionaries with
# the network simulation, convert to a list, and then use sklearn to get the baseline
# accuracy that our default optimizer should be reporting.
# We then input the same information into the _eval_acc in the
# required format (i.e. cur_batch information, and all accumulated prev_information),
# and check whether they match
step = 0.1
batch_acc_vec = np.arange(0, 1 + step, step)
for batch_acc in batch_acc_vec:
random_mat = self.rso.rand(
batch_size, num_outputs)
row_sum = random_mat.sum(axis=1)
# normalize the random_mat such that every row adds up to 1
# broadcast so we can divide every element in matrix by the row's sum
fake_network_output = random_mat / row_sum[:,
None]
network_output = np.argmax(fake_network_output,
axis=1)
# now, modify a subset of the netowrk output and make that the "real" output
true_output = network_output.copy()
num_indices_to_modify = int(batch_size *
(1 - batch_acc))
indices_to_modify = self.rso.choice(
range(batch_size),
num_indices_to_modify,
replace=False)
for ii in indices_to_modify:
true_output[ii] = (true_output[ii] +
1) % num_outputs
# convert datatypes to what is expected during operation
network_output_pt = torch.tensor(
fake_network_output, dtype=torch.float)
true_output_pt = torch.tensor(true_output,
dtype=torch.long)
# update the totals dictionaries to reflect the new batch of data
n_total_expected = n_total_prev.copy()
n_correct_expected = n_correct_prev.copy()
for to in true_output:
n_total_expected[to] += 1
# compute how many the network got right, and update the necessary output
indices_not_modified = set(
range(batch_size)).symmetric_difference(
set(indices_to_modify))
for ii in indices_not_modified:
n_correct_expected[network_output[ii]] += 1
# update the true & fake_network_output to aggregate both the previous call to _eval_acc
# and the current call to _eval_acc
true_output_prev_and_cur = list(true_output)
network_output_prev_and_cur = list(
network_output)
for k, v in n_total_prev.items():
true_output_prev_and_cur.extend([k] * v)
# simulate network outputs to keep the correct & total counts according to the
# previously defined dictionaries
for k, v in n_correct_prev.items():
num_correct = v
num_incorrect = n_total_prev[
k] - num_correct
network_output_prev_and_cur.extend(
[k] * num_correct)
network_output_prev_and_cur.extend(
[((k + 1) % num_outputs)] *
num_incorrect)
expected_balanced_acc = balanced_accuracy_score(
true_output_prev_and_cur,
network_output_prev_and_cur) * 100
actual_acc, n_total_actual, n_correct_actual = \
_running_eval_acc(network_output_pt, true_output_pt, n_total=n_total_prev,
n_correct=n_correct_prev)
self.assertAlmostEqual(actual_acc,
expected_balanced_acc)
self.assertEqual(n_total_expected,
n_total_actual)
self.assertEqual(n_correct_expected,
n_correct_actual)
def test_eval_binary_one_output_accuracy(self):
batch_size = 32
num_outputs = 1
sigmoid_fn = lambda x: 1. / (1. + np.exp(-x))
soft_to_hard_fn = lambda x: torch.round(torch.sigmoid(x)).int()
step = 0.05
batch_acc_vec = np.arange(0, 1 + step, step)
for batch_acc in batch_acc_vec:
true_output = (self.rso.rand(batch_size, num_outputs) * 4) - 2
true_output_binary = np.expand_dims(np.asarray(
[0 if x < 0 else 1 for x in true_output], dtype=np.int),
axis=1)
# now, modify a subset of the netowrk output and make that the "real" output
network_output = true_output.copy()
num_indices_to_modify = int(batch_size * (1 - batch_acc))
num_indices_unmodified = batch_size - num_indices_to_modify
indices_to_modify = self.rso.choice(range(batch_size),
num_indices_to_modify,
replace=False)
for ii in indices_to_modify:
# flip pos to neg, neg to pos
if network_output[ii][0] >= 0:
network_output[ii][0] = network_output[ii][0] - 10
else:
network_output[ii][0] = network_output[ii][0] + 10
# convert datatypes to what is expected during operation
network_output_pt = torch.tensor(network_output, dtype=torch.float)
true_output_pt = torch.tensor(true_output_binary, dtype=torch.long)
actual_acc, n_total, n_correct = \
_running_eval_acc(network_output_pt, true_output_pt,
n_total=None, n_correct=None,
soft_to_hard_fn=soft_to_hard_fn)
expected_n_total = defaultdict(int)
for ii in range(len(true_output_binary)):
to = true_output_binary[ii][0]
expected_n_total[to] += 1
expected_n_correct = defaultdict(int)
for ii in range(batch_size):
expected_n_correct[true_output_binary[ii][0]] += int(
np.round(sigmoid_fn(network_output[ii][0])) ==
true_output_binary[ii][0])
expected_acc = balanced_accuracy_score(
true_output_binary, np.round(sigmoid_fn(network_output))) * 100
self.assertAlmostEqual(actual_acc, expected_acc)
self.assertEqual(n_total, expected_n_total)
self.assertEqual(n_correct, expected_n_correct)
n_total_prev = defaultdict(int, {0: 40, 1: 24})
val_ranges = [10, 15, 20]
# test over a large variety of the number of correct predictions, per class
for zero_val in tqdm(val_ranges):
for one_val in val_ranges:
n_correct_prev = defaultdict(int, {0: zero_val, 1: one_val})
# in order to compute the actual accuracy we should expect, we update the dictionaries with
# the network simulation, convert to a list, and then use sklearn to get the baseline
# accuracy that our default optimizer should be reporting.
# We then input the same information into the _eval_acc in the
# required format (i.e. cur_batch information, and all accumulated prev_information),
# and check whether they match
step = 0.1
batch_acc_vec = np.arange(0, 1 + step, step)
for batch_acc in batch_acc_vec:
true_output = (self.rso.rand(batch_size, num_outputs) *
4) - 2
true_output_binary = np.expand_dims(
np.asarray([0 if x < 0 else 1 for x in true_output],
dtype=np.int),
axis=1)
# now, modify a subset of the netowrk output and make that the "real" output
network_output = true_output.copy()
num_indices_to_modify = int(batch_size * (1 - batch_acc))
num_indices_unmodified = batch_size - num_indices_to_modify
indices_to_modify = self.rso.choice(range(batch_size),
num_indices_to_modify,
replace=False)
for ii in indices_to_modify:
# flip pos to neg, neg to pos
if network_output[ii][0] >= 0:
network_output[ii][0] = network_output[ii][0] - 10
else:
network_output[ii][0] = network_output[ii][0] + 10
# convert datatypes to what is expected during operation
network_output_pt = torch.tensor(network_output,
dtype=torch.float)
true_output_pt = torch.tensor(true_output_binary,
dtype=torch.long)
n_total_expected = n_total_prev.copy()
n_correct_expected = n_correct_prev.copy()
for to in np.squeeze(true_output_binary):
n_total_expected[to] += 1
# compute how many the network got right, and update the necessary output
indices_not_modified = set(
range(batch_size)).symmetric_difference(
set(indices_to_modify))
for ii in indices_not_modified:
n_correct_expected[np.round(
sigmoid_fn(network_output[ii][0]))] += 1
true_output_prev_and_cur = []
for k, v, in n_total_expected.items():
true_output_prev_and_cur.extend([k] * v)
# simulate network outputs to keep the correct & total counts according to the
# previously defined dictionaries
network_output_prev_and_cur = []
for k, v in n_correct_expected.items():
num_correct = v
num_incorrect = n_total_expected[k] - num_correct
network_output_prev_and_cur.extend([k] * num_correct)
network_output_prev_and_cur.extend(
[((k + 1) % 2)] *
num_incorrect) # hard-code mod to 2,
# b/c it is binary output and 1% 1 = 0, 0 % 1 = 0
expected_balanced_acc = balanced_accuracy_score(
true_output_prev_and_cur,
network_output_prev_and_cur) * 100
actual_acc, n_total, n_correct = \
_running_eval_acc(network_output_pt, true_output_pt,
n_total=n_total_prev, n_correct=n_correct_prev,
soft_to_hard_fn=soft_to_hard_fn)
self.assertAlmostEqual(actual_acc, expected_balanced_acc)
self.assertEqual(n_total_expected, n_total)
self.assertEqual(n_correct_expected, n_correct)