def _eval_discriminative_model(self, model: Model, writer: SummaryWriter,
step, eval_title):
training = model.training
model.eval()
K = 5
totals = []
corrects_1 = []
corrects_k = []
# Accuracy of each subset
for subset_name, subset in self.subsets.items():
data = DataLoader(
subset,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
total = 0.
correct_1 = 0.
correct_k = 0.
for x, y in iter(data):
b = x.size(0)
with torch.no_grad():
logits = model(x).view(b, -1)
# [B, K]
_, pred_topk = logits.topk(K, dim=1)
correct_topk = (pred_topk.cpu() == y.view(
b, -1).expand_as(pred_topk)).float()
correct_1 += correct_topk[:, :1].view(-1).cpu().sum()
correct_k += correct_topk[:, :K].view(-1).cpu().sum()
total += x.size(0)
totals.append(total)
corrects_1.append(correct_1)
corrects_k.append(correct_k)
accuracy_1 = correct_1 / total
accuracy_k = correct_k / total
writer.add_scalar(
'accuracy_1/%s/%s/%s' % (eval_title, self.name, subset_name),
accuracy_1, step)
writer.add_scalar(
'accuracy_%d/%s/%s/%s' %
(K, eval_title, self.name, subset_name), accuracy_k, step)
# Overall accuracy
total = sum(totals)
correct_1 = sum(corrects_1)
correct_k = sum(corrects_k)
accuracy_1 = correct_1 / total
accuracy_k = correct_k / total
writer.add_scalar('accuracy_1/%s/%s/overall' % (eval_title, self.name),
accuracy_1, step)
writer.add_scalar(
'accuracy_%d/%s/%s/overall' % (K, eval_title, self.name),
accuracy_k, step)
model.train(training)
def visualize(self, model: Model, writer: SummaryWriter, epoch, step): training = model.training model.eval() vis_indices = self.config['vis_indices'] if isinstance(self.config['vis_indices'], int): # sample k data points from n data points with equal interval n = len(self) k = self.config['vis_indices'] vis_indices = torch.linspace(0, n - 1, k) \ .type(torch.IntTensor).tolist() self.visualize_data( model, writer, self, vis_indices, 'val_pc', step ) model.train(training)
def accumulate(
training_hparams: hparams.TrainingHparams,
model: Model,
train_loader: DataLoader,
data_order_seed: int = None,
suffix: str = ''
):
"""Accumulate the gradient for one training epoch.
Args:
* training_hparams: The training hyperparameters whose schema is specified in hparams.py.
* model: The model to train. Must be a models.base.Model
* train_loader: The training data. Must be a datasets.base.DataLoader
* data_order_seed: The RNG seed for data shuffling.
"""
# Adapt for FP16.
if training_hparams.apex_fp16:
if NO_APEX: raise ImportError('Must install nvidia apex to use this model.')
model = apex.amp.initialize(model, loss_scale='dynamic', verbosity=0)
# Handle parallelism if applicable.
if get_platform().is_distributed:
model = DistributedDataParallel(model, device_ids=[get_platform().rank])
elif get_platform().is_parallel:
model = DataParallel(model)
train_loader.shuffle(data_order_seed)
for it, (examples, labels) in enumerate(train_loader):
examples = examples.to(device=get_platform().torch_device)
labels = labels.to(device=get_platform().torch_device)
model.eval()
loss = model.loss_criterion(model(examples), labels)
if training_hparams.apex_fp16:
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
get_platform().barrier()
def _eval_model(self,
model: Model,
writer: SummaryWriter,
step,
t,
eval_title,
results_dict=None):
training = model.training
model.eval()
totals = []
corrects = []
# Accuracy of each subset
for subset_name, subset in self.subsets.items():
data = DataLoader(
subset,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
total = 0.
correct = 0.
for x, y in iter(data):
with torch.no_grad():
pred = model(x).view(x.size(0), -1).argmax(dim=1)
total += x.size(0)
correct += (pred.cpu() == y).float().sum()
totals.append(total)
corrects.append(correct)
accuracy = correct / total
writer.add_scalar(
'accuracy/%s/%s/%s' % (eval_title, self.name, subset_name),
accuracy, step)
# Overall accuracy
total = sum(totals)
correct = sum(corrects)
accuracy = correct / total
writer.add_scalar('accuracy/%s/%s/overall' % (eval_title, self.name),
accuracy, step)
model.train(training)
def _eval_generative_model(self, model: Model, writer: SummaryWriter, step,
eval_title):
# change the model to eval mode
training = model.training
z_samples = model.config['z_samples']
model.eval()
model.config['z_samples'] = 16
# evaluate generative model on each subset
subset_counts = []
subset_cumulative_bpds = []
for subset_name, subset in self.subsets.items():
data = DataLoader(
subset,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
subset_count = 0
subset_cumulative_bpd = 0
# evaluate on a subset
for x, _ in iter(data):
dim = reduce(lambda x, y: x * y, x.size()[1:])
with torch.no_grad():
ll = model(x)
bpd = -ll / math.log(2) / dim
subset_count += x.size(0)
subset_cumulative_bpd += bpd.sum()
# append the subset evaluation result
subset_counts.append(subset_count)
subset_cumulative_bpds.append(subset_cumulative_bpd)
subset_bpd = subset_cumulative_bpd / subset_count
writer.add_scalar(
'bpd/%s/%s/%s' % (eval_title, self.name, subset_name),
subset_bpd, step)
# Overall accuracy
overall_bpd = sum(subset_cumulative_bpds) / sum(subset_counts)
writer.add_scalar('bpd/%s/%s/overall' % (eval_title, self.name),
overall_bpd, step)
# roll back the mode
model.train(training)
model.config['z_samples'] = z_samples
def visualize(self, options, model: Model, writer: SummaryWriter, step):
training = model.training
model.eval()
vis_config = self.config['vis']
if vis_config.get('num_scene_samples'):
# sample k data points from n data points with equal interval
n = len(self)
k = vis_config.get('num_scene_samples')
vis_indices = torch.linspace(0, n - 1, k) \
.type(torch.IntTensor).tolist()
else:
vis_indices = [self.dir2idx[i] for i in vis_config.get('scene_names')]
if self.config['overfit_one_ex']:
vis_scene = self.config['overfit_one_ex']
vis_indices = [self.dir2idx[vis_scene]]
vis_indices = list(set(vis_indices))
for i in vis_indices:
coords, feats, labels, _ = self[i]
coords, feats, = sparse_collate([coords], [feats])
x = SparseTensor(feats, coords)
x = x.to(model.device)
with torch.no_grad():
y_hat = model(x)
embs = y_hat
insts = labels[:, 1]
for option in options:
# visualize tsne
if option == 'tsne':
tsne_img = visualization.visualize_tsne(
embs.cpu(), insts.cpu(),
config=self.config['vis']['tsne']
)
writer.add_image('tsne/{}'.format(self.idx2dir[i]), tsne_img, step)
elif option == 'embs':
vis_config = self.config['vis']['embs']
# visualize embs with background
emb_imgs, axis_range = visualization.visualize_embs(
embs.cpu(), insts.cpu(),
remove_bg=False, max_sample=vis_config['max_sample'],
num_view=vis_config['num_view']
)
for view_num, img in enumerate(emb_imgs):
writer.add_image(
'emb/with_bg/{}_{}'.format(self.idx2dir[i], view_num),
img, step
)
# visualize embs without background
not_bg_emb_imgs, _ = visualization.visualize_embs(
embs.cpu(), insts.cpu(),
remove_bg=True, max_sample=vis_config['max_sample'],
num_view=vis_config['num_view'], axis_range=axis_range
)
for view_num, img in enumerate(not_bg_emb_imgs):
writer.add_image(
'emb/no_bg/{}_{}'.format(self.idx2dir[i], view_num),
img, step
)
model.train(training)
def eval(self, model: Model, writer: SummaryWriter, step):
training = model.training
model.eval()
scalar_summaries = defaultdict(list)
list_summaries = defaultdict(list)
data_loader = DataLoader(
self,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['num_workers'],
collate_fn=self.collate_fn,
drop_last=True,
)
print('')
for eval_step, data in enumerate(data_loader):
x, y = data[0], data[1]
x, y = x.to(self.config['device']), y.to(self.config['device'])
with torch.no_grad():
y_hat = model(x)
loss, scalar_summary, list_summary = model.compute_loss(x, y, y_hat, step)
print('\r[Evaluating, Step {:7}, Loss {:5}]'.format(
eval_step, '%.3f' %loss), end=''
)
for (k, v) in scalar_summary.items():
scalar_summaries[k].append(v)
for (k, v) in list_summary.items():
list_summaries[k] += v
# write summaries
for (k, v) in scalar_summaries.items():
v = np.array(v).mean().item()
writer.add_scalar(k, v, step)
for (k, v) in list_summaries.items():
v = np.array(v)
if k[:4] == 'mIoU':
num_classes = self.config['y_c']
confusion_matrix = v.reshape(-1, num_classes ** 2)
confusion_matrix = confusion_matrix.sum(axis=0) \
.reshape(num_classes, num_classes)
mious = []
for i in range(num_classes):
true_positive = confusion_matrix[i, i].item()
false_positive = (confusion_matrix[i, :].sum() - true_positive).item()
false_negative = (confusion_matrix[:, i].sum() - true_positive).item()
denom = true_positive + false_positive + false_negative
mious.append(0 if denom == 0 else float(true_positive) / denom)
if hasattr(self, 'class_id2label'):
writer.add_scalar(k + self.class_id2label[i], mious[-1], step)
writer.add_scalar(k + 'mIoU/overall', sum(mious) / len(mious), step)
else:
bins = np.linspace(0., 1.1, num=12)
counts, limits = np.histogram(v, bins=bins)
sum_sq = v.dot(v)
writer.add_histogram_raw(
tag=k,
min=v.min(), max=v.max(),
num=len(v), sum=v.sum(),
sum_squares=sum_sq,
bucket_limits=limits[1:].tolist(),
bucket_counts=counts.tolist(),
global_step=step
)
model.train(training)
def distill(
training_hparams: hparams.TrainingHparams,
distill_hparams: hparams.DistillHparams,
student: Model,
teacher: Model,
train_loader: DataLoader,
output_location: str,
callbacks: typing.List[typing.Callable] = [],
start_step: Step = None,
end_step: Step = None
):
"""The main training loop for this framework.
Args:
* training_hparams: The training hyperparameters whose schema is specified in hparams.py.
* distll_hparams: The knowledge distillation hyperparameters whose schema is specified in hparams.py.
* student: The student model to train. Must be a models.base.Model
* teacher: The teacher model to distill the knowledge. Must be a models.base.Model
* train_loader: The training data. Must be a datasets.base.DataLoader
* output_location: The string path where all outputs should be stored.
* callbacks: A list of functions that are called before each training step and once more
after the last training step. Each function takes five arguments: the current step,
the output location, the model, the optimizer, and the logger.
Callbacks are used for running the test set, saving the logger, saving the state of the
model, etc. The provide hooks into the training loop for customization so that the
training loop itself can remain simple.
* start_step: The step at which the training data and learning rate schedule should begin.
Defaults to step 0.
* end_step: The step at which training should cease. Otherwise, training will go for the
full `training_hparams.training_steps` steps.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
# Create the output location if it doesn't already exist.
if not get_platform().exists(output_location) and get_platform().is_primary_process:
get_platform().makedirs(output_location)
# Get the optimizer and learning rate schedule.
student.to(get_platform().torch_device)
teacher.to(get_platform().torch_device)
optimizer = optimizers.get_optimizer(training_hparams, student)
step_optimizer = optimizer
lr_schedule = optimizers.get_lr_schedule(training_hparams, optimizer, train_loader.iterations_per_epoch)
ce_loss_fct = nn.KLDivLoss(reduction="batchmean")
if distill_hparams.alpha_mse > 0.0:
mse_loss_fct = nn.MSELoss(reduction='sum')
if distill_hparams.alpha_cos > 0.0:
cos_loss_fct = nn.CosineEmbeddingLoss(reduction='mean')
# Adapt for FP16.
if training_hparams.apex_fp16:
if NO_APEX: raise ImportError('Must install nvidia apex to use this model.')
(student, teacher), step_optimizer = apex.amp.initialize(
[student, teacher], optimizer, loss_scale='dynamic', verbosity=0
)
# Handle parallelism if applicable.
if get_platform().is_distributed:
student = DistributedDataParallel(student, device_ids=[get_platform().rank])
teacher = DistributedDataParallel(teacher, device_ids=[get_platform().rank])
elif get_platform().is_parallel:
student = DataParallel(student)
teacher = DataParallel(teacher)
# Get the random seed for the data order.
data_order_seed = training_hparams.data_order_seed
# Restore the model from a saved checkpoint if the checkpoint exists.
cp_step, cp_logger = restore_checkpoint(output_location, student, optimizer, train_loader.iterations_per_epoch)
start_step = cp_step or start_step or Step.zero(train_loader.iterations_per_epoch)
logger = cp_logger or MetricLogger()
with warnings.catch_warnings(): # Filter unnecessary warning.
warnings.filterwarnings("ignore", category=UserWarning)
for _ in range(start_step.iteration): lr_schedule.step()
# Determine when to end training.
end_step = end_step or Step.from_str(training_hparams.training_steps, train_loader.iterations_per_epoch)
if end_step <= start_step: return
# The training loop.
for ep in range(start_step.ep, end_step.ep + 1):
# Ensure the data order is different for each epoch.
train_loader.shuffle(None if data_order_seed is None else (data_order_seed + ep))
for it, (examples, labels) in enumerate(train_loader):
# Advance the data loader until the start epoch and iteration.
if ep == start_step.ep and it < start_step.it: continue
# Run the callbacks.
step = Step.from_epoch(ep, it, train_loader.iterations_per_epoch)
for callback in callbacks: callback(output_location, step, student, optimizer, logger)
# Exit at the end step.
if ep == end_step.ep and it == end_step.it: return
# Otherwise, train.
examples = examples.to(device=get_platform().torch_device)
labels = labels.to(device=get_platform().torch_device)
loss = 0.0
step_optimizer.zero_grad()
student.train()
teacher.eval()
student_outputs = student(examples)
with torch.no_grad():
teacher_outputs = teacher(examples)
s_logits = student_outputs
t_logits = teacher_outputs
# KL Divergence loss for the knowledge distillation
loss_ce = ce_loss_fct(
F.log_softmax(s_logits / distill_hparams.temperature, dim=-1),
F.softmax(t_logits / distill_hparams.temperature, dim=-1),
) * distill_hparams.temperature**2
loss += distill_hparams.alpha_ce * loss_ce
if distill_hparams.alpha_cls > 0.0:
loss_cls = student.loss_criterion(student_outputs, labels)
loss += distill_hparams.alpha_cls * loss_cls
if distill_hparams.alpha_mse > 0.0:
loss_mse = mse_loss_fct(s_logits, t_logits) / s_logits.size(0)
loss += distill_hparams.alpha_mse * loss_mse
if training_hparams.apex_fp16:
with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Step forward. Ignore extraneous warnings that the lr_schedule generates.
step_optimizer.step()
with warnings.catch_warnings(): # Filter unnecessary warning.
warnings.filterwarnings("ignore", category=UserWarning)
lr_schedule.step()
get_platform().barrier()
def grasp(
training_hparams: hparams.TrainingHparams,
model: Model,
parameter_list: list,
train_loader: DataLoader,
data_order_seed: int = None,
suffix: str = ''
):
"""For the implementation of GraSP.
Args:
* training_hparams: The training hyperparameters whose schema is specified in hparams.py.
* model: The model to train. Must be a models.base.Model
* train_loader: The training data. Must be a datasets.base.DataLoader
* data_order_seed: The RNG seed for data shuffling.
"""
# Adapt for FP16.
if training_hparams.apex_fp16:
if NO_APEX: raise ImportError('Must install nvidia apex to use this model.')
model = apex.amp.initialize(model, loss_scale='dynamic', verbosity=0)
# Handle parallelism if applicable.
if get_platform().is_distributed:
model = DistributedDataParallel(model, device_ids=[get_platform().rank])
elif get_platform().is_parallel:
model = DataParallel(model)
train_loader.shuffle(data_order_seed)
# First gradient without computational graph
stopped_grads = 0
for it, (examples, labels) in enumerate(train_loader):
examples = examples.to(device=get_platform().torch_device)
labels = labels.to(device=get_platform().torch_device)
model.eval()
output = model(examples) / 200.0 # temp = 200
loss = model.loss_criterion(output, labels)
# if training_hparams.apex_fp16:
# with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
# else:
# loss.backward()
grads = torch.autograd.grad(loss, parameter_list, create_graph=False)
flatten_grads = torch.cat([g.reshape(-1) for g in grads if g is not None])
stopped_grads += flatten_grads
train_loader.shuffle(None if data_order_seed is None else (data_order_seed + 1))
# Second gradient vector with computational graph
for it, (examples, labels) in enumerate(train_loader):
examples = examples.to(device=get_platform().torch_device)
labels = labels.to(device=get_platform().torch_device)
model.eval()
output = model(examples) / 200.0 # temp = 200
loss = model.loss_criterion(output, labels)
# if training_hparams.apex_fp16:
# with apex.amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
# else:
# loss.backward()
grads = torch.autograd.grad(loss, parameter_list, create_graph=True)
flatten_grads = torch.cat([g.reshape(-1) for g in grads if g is not None])
gnorm = (stopped_grads * flatten_grads).sum()
gnorm.backward()
get_platform().barrier()
def _eval_model(self, model: Model, writer: SummaryWriter, step, t,
eval_title, results_dict):
training = model.training
model.eval()
if t in self.config['schedule_simple']:
t_idx = self.config['schedule_simple'].index(t)
else:
t_idx = len(self.config['schedule_simple']) - 1
# for calculating total performance
targets_total = []
probs_total = []
# Accuracy of each subset
for order_i, t_i in enumerate(self.config['schedule_simple'][:t_idx +
1]):
subset_name = t_i
last_id = self.config['schedule_simple'][
-1] # XXX should be -1. -2 for debugging.
subset = self.subsets[t_i]
data = DataLoader(
subset,
batch_size=self.config['eval_batch_size'],
num_workers=self.config['eval_num_workers'],
collate_fn=self.collate_fn,
)
# results is dict. {method: group_averagemeter_object}
results, targets, probs = validate(subset_name, model, data,
self.category_map, results_dict,
last_id, self.split_cats_dict)
targets_total.append(targets)
probs_total.append(probs)
if subset_name in results_dict:
results_dict[subset_name].append(results)
else:
results_dict[subset_name] = [results]
for metric in results.keys():
results[metric].write_to_excel(
os.path.join(writer.logdir,
'results_{}.xlsx'.format(metric)),
sheet_name='task {}'.format(subset_name),
column_name='task {}'.format(
self.config['schedule_simple'][t_idx]),
info='avg')
# =================================================================================================================
# calculate scores for trained tasks.
prefix = 'tally_' # prefix for tensorboard plotting and csv filename
targets_total = torch.cat(targets_total, axis=0)
probs_total = torch.cat(probs_total, axis=0)
predicts_total = probs_total > 0.5 # BCE style predicts
total_metric = ['CP', 'CR', 'CF1', 'OP', 'OR', 'OF1', 'mAP']
results = dict() # reset results
CP, CR, CF1, OP, OR, OF1, mAP = (AverageMeter()
for _ in range(len(total_metric)))
ncats = targets_total.sum(axis=0)
# ignore classes in future tasks
cats_in_task_idx = ncats > 0
cats_in_task_name = self.category_map[cats_in_task_idx].tolist()
targets_total = targets_total
probs_total = probs_total
predicts_total = predicts_total
# calculate score
precision_pc = torch.mean(
precision_score_per_class(targets_total[:, cats_in_task_idx],
predicts_total[:, cats_in_task_idx],
zero_division=0))
recall_pc = torch.mean(
recall_score_per_class(targets_total[:, cats_in_task_idx],
predicts_total[:, cats_in_task_idx],
zero_division=0))
# CF1. note that CF1 is not a mean value of categories' f1_score
f1_pc = ((2 * precision_pc * recall_pc) / (precision_pc + recall_pc)
) if (precision_pc + recall_pc) > 0 else torch.tensor([0.])
precision_oa = precision_score_overall(
targets_total[:, cats_in_task_idx],
predicts_total[:, cats_in_task_idx],
zero_division=0)
recall_oa = recall_score_overall(targets_total[:, cats_in_task_idx],
predicts_total[:, cats_in_task_idx],
zero_division=0)
f1_oa = f1_score_overall(targets_total[:, cats_in_task_idx],
predicts_total[:, cats_in_task_idx],
zero_division=0)
map_ = mean_average_precision(targets_total[:, cats_in_task_idx],
probs_total[:, cats_in_task_idx])
# save to AverageMeter
CP.update(precision_pc.item())
CR.update(recall_pc.item())
CF1.update(f1_pc.item())
OP.update(precision_oa.item())
OR.update(recall_oa.item())
OF1.update(f1_oa.item())
mAP.update(map_.item())
results[prefix + 'CP'] = CP
results[prefix + 'CR'] = CR
results[prefix + 'CF1'] = CF1
results[prefix + 'OP'] = OP
results[prefix + 'OR'] = OR
results[prefix + 'OF1'] = OF1
results[prefix + 'mAP'] = mAP
# for reporting major, moderate, minor cateogory performances
for report_name in self.split_cats_dict.keys():
reporter = Group_AverageMeter()
# get report category idxes
all_cats = self.category_map.tolist()
task_cats = set(cats_in_task_name)
report_cats = task_cats & set(self.split_cats_dict[report_name])
report_cats_idx = torch.tensor(
[all_cats.index(cat) for cat in report_cats], dtype=torch.long)
# CP, CR, CF1 performance of report_categories.
_class_precision = precision_score_per_class(
targets_total[:, report_cats_idx],
predicts_total[:, report_cats_idx],
zero_division=0)
_class_recall = recall_score_per_class(
targets_total[:, report_cats_idx],
predicts_total[:, report_cats_idx],
zero_division=0)
_class_precision = torch.mean(_class_precision)
_class_recall = torch.mean(_class_recall)
# CF1 bias. note that CF1 is not a mean value of categories' f1_score
_class_f1 = ((2*_class_precision*_class_recall)/(_class_precision+_class_recall)) \
if (_class_precision+_class_recall)>0 else torch.tensor([0.])
# OP, OR, OF1 performance of report_categories.
_overall_precision = precision_score_overall(
targets_total[:, report_cats_idx],
predicts_total[:, report_cats_idx],
zero_division=0)
_overall_recall = recall_score_overall(
targets_total[:, report_cats_idx],
predicts_total[:, report_cats_idx],
zero_division=0)
_overall_f1 = f1_score_overall(targets_total[:, report_cats_idx],
predicts_total[:, report_cats_idx],
zero_division=0)
# mAP performance of report_categories.
_mAP = mean_average_precision(targets_total[:, report_cats_idx],
probs_total[:, report_cats_idx])
reporter.update(['CP'], [_class_precision.item()], [1])
reporter.update(['CR'], [_class_recall.item()], [1])
reporter.update(['CF1'], [_class_f1.item()], [1])
reporter.update(['OP'], [_overall_precision.item()], [1])
reporter.update(['OR'], [_overall_recall.item()], [1])
reporter.update(['OF1'], [_overall_f1.item()], [1])
reporter.update(['mAP'], [_mAP.item()], [1])
reporter.total.reset()
results[prefix + report_name] = reporter
# write to tensorboard and csv.
task_len = t_idx + 1
for metric in results.keys():
if not metric in [
prefix + 'CP', prefix + 'CR', prefix + 'OP', prefix + 'OR'
]:
results[metric].write(
writer,
'%s/%s/%s/task_len(%d)' %
(metric, eval_title, self.name, task_len),
step,
info='avg')
results[metric].write_to_excel(
os.path.join(writer.logdir, 'results_{}.xlsx'.format(metric)),
sheet_name=prefix,
column_name='task {}'.format(
self.config['schedule_simple'][t_idx]),
info='avg')
# =================================================================================================================
# print performances at the end
if t_idx == len(self.config['schedule_simple']) - 1:
src = writer.logdir
csv_files = ['major', 'moderate', 'minor', 'OF1', 'CF1', 'mAP', \
prefix+'major', prefix+'moderate', prefix+'minor', prefix+'CF1', prefix+'OF1', prefix+'mAP', \
'forget']
for csv_file in csv_files:
try:
csv = pd.read_csv(os.path.join(
src, 'results_{}.csv'.format(csv_file)),
index_col=0)
# print performance after training last task
pd.set_option('display.max_rows', None)
print(
colorful.bold_green(
'\n{:10} result'.format(csv_file)).styled_string)
print(csv.round(4).iloc[:, -1])
# save as txt
with open(os.path.join(src, 'summary.txt'),
'a') as summary_txt:
summary_txt.write('\n')
summary_txt.write('{:10} result\n'.format(csv_file))
summary_txt.write(csv.round(4).iloc[:, -1].to_string())
summary_txt.write('\n')
except FileNotFoundError:
print("This excperiment doesn't have {} file!! continue.".
format(csv_file))
continue
model.train(training)
return results_dict
