def train_one_epoch(task_model, task_optimizer, data_loader, device, cycle, epoch, print_freq):
task_model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('task_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Cycle:[{}] Epoch: [{}]'.format(cycle, epoch)
task_lr_scheduler = None
if epoch == 0:
warmup_factor = 1. / 1000
warmup_iters = min(1000, len(data_loader) - 1)
task_lr_scheduler = utils.warmup_lr_scheduler(task_optimizer, warmup_iters, warmup_factor)
for images, targets in metric_logger.log_every(data_loader, print_freq, header):
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
task_loss_dict = task_model(images, targets)
task_losses = sum(loss for loss in task_loss_dict.values())
# reduce losses over all GPUs for logging purposes
task_loss_dict_reduced = utils.reduce_dict(task_loss_dict)
task_losses_reduced = sum(loss.cpu() for loss in task_loss_dict_reduced.values())
task_loss_value = task_losses_reduced.item()
losses = task_losses
if not math.isfinite(task_loss_value):
print("Loss is {}, stopping training".format(task_loss_value))
sys.exit(1)
task_optimizer.zero_grad()
losses.backward()
task_optimizer.step()
if task_lr_scheduler is not None:
task_lr_scheduler.step()
metric_logger.update(task_loss=task_losses_reduced)
metric_logger.update(task_lr=task_optimizer.param_groups[0]["lr"])
return metric_logger
def train_one_epoch(model,
optimizer,
data_loader,
device,
epoch,
print_freq=10,
writer=None):
global global_step
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
lr_scheduler = None
if epoch == 0:
warmup_factor = 1. / 500
warmup_iters = min(500, len(data_loader) - 1)
lr_scheduler = warmup_lr_scheduler(optimizer, warmup_iters,
warmup_factor)
for images, img_metas, targets in metric_logger.log_every(
data_loader, print_freq, header):
global_step += 1
images = images.to(device)
targets = [t.to(device) for t in targets]
loss_dict, _ = model(images, img_metas, targets)
losses = sum(list(loss_dict.values()))
loss_dict_reduced = dist_utils.reduce_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
optimizer.zero_grad()
losses.backward()
optimizer.step()
if lr_scheduler is not None:
lr_scheduler.step()
metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
if global_step % print_freq == 0:
if writer:
for k, v in loss_dict_reduced.items():
writer.add_scalar('losses/{}'.format(k),
v,
global_step=global_step)
writer.add_scalar('losses/total_loss',
losses_reduced,
global_step=global_step)
writer.add_scalar('lr',
optimizer.param_groups[0]['lr'],
global_step=global_step)
def train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq):
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
lr_scheduler = None
if epoch == 0:
warmup_factor = 1. / 1000
warmup_iters = min(1000, len(data_loader) - 1)
lr_scheduler = utils.warmup_lr_scheduler(optimizer, warmup_iters,
warmup_factor)
for images, targets in metric_logger.log_every(data_loader, print_freq,
header):
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
loss_dict = model(images, targets)
losses = sum(loss for loss in loss_dict.values())
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
loss_value = losses_reduced.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
losses.backward()
optimizer.step()
if lr_scheduler is not None:
lr_scheduler.step()
metric_logger.update(loss=losses_reduced.detach().cpu(),
**loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
del losses
del loss_value
del loss_dict_reduced
del losses_reduced
torch.cuda.empty_cache()
return metric_logger
def evaluate(model, data_loader, device):
n_threads = torch.get_num_threads()
# FIXME remove this and make paste_masks_in_image run on the GPU
torch.set_num_threads(1)
cpu_device = torch.device("cpu")
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
coco = get_coco_api_from_dataset(data_loader.dataset)
iou_types = _get_iou_types(model)
coco_evaluator = CocoEvaluator(coco, iou_types)
for image, targets in metric_logger.log_every(data_loader, 100, header):
image = list(img.to(device) for img in image)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
torch.cuda.synchronize()
model_time = time.time()
outputs = model(image)
outputs = [{k: v.to(cpu_device)
for k, v in t.items()} for t in outputs]
model_time = time.time() - model_time
res = {
target["image_id"].item(): output
for target, output in zip(targets, outputs)
}
evaluator_time = time.time()
coco_evaluator.update(res)
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time,
evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
# 下面这句引起内存溢出
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
print("coco_evaluator.accumulate()")
coco_evaluator.accumulate()
print("coco_evaluator.summarize()")
coco_evaluator.summarize()
torch.set_num_threads(n_threads)
return coco_evaluator
def evaluate(model, data_loader, device):
cpu_device = torch.device("cpu")
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
results = []
for images, targets in metric_logger.log_every(data_loader, 10, header):
images = list(img.to(device) for img in images)
torch.cuda.synchronize()
model_time = time.time()
outputs = model(images)
outputs = [{k: v.to(cpu_device)
for k, v in t.items()} for t in outputs]
non_empt_el = [i for i, t in enumerate(targets) if len(t) > 0]
targets = [t for i, t in enumerate(targets) if i in non_empt_el]
outputs = [o for i, o in enumerate(outputs) if i in non_empt_el]
for i, o in enumerate(outputs):
o['boxes'][:, 2:4] -= o['boxes'][:, 0:2]
areas = (o['boxes'][:, 2] * o['boxes'][:, 3]).tolist()
boxes = o['boxes'].tolist()
scores = o['scores'].tolist()
labels = o['labels'].tolist()
temp = [{
'bbox': b,
'area': a,
'category_id': l,
'score': s,
'image_id': targets[i]['image_id']
} for b, a, l, s in zip(boxes, areas, labels, scores)]
results = list(itertools.chain(results, temp))
evaluator_time = time.time()
model_time = time.time() - model_time
evaluator_time = time.time() - evaluator_time
metric_logger.update(model_time=model_time,
evaluator_time=evaluator_time)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
# accumulate predictions from all images
return results
def train_one_epoch(task_model, task_optimizer, ll_model, ll_optimizer,
data_loader, device, cycle, epoch, print_freq):
task_model.train()
ll_model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'task_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter(
'll_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Cycle:[{}] Epoch: [{}]'.format(cycle, epoch)
task_lr_scheduler = None
ll_lr_scheduler = None
if epoch == 0:
warmup_factor = 1. / 1000
warmup_iters = min(1000, len(data_loader) - 1)
task_lr_scheduler = utils.warmup_lr_scheduler(task_optimizer,
warmup_iters,
warmup_factor)
ll_lr_scheduler = utils.warmup_lr_scheduler(ll_optimizer, warmup_iters,
warmup_factor)
for images, targets in metric_logger.log_every(data_loader, print_freq,
header):
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
features, task_loss_dict = task_model(images, targets)
if 'faster' in args.model:
_task_losses = sum(loss for loss in task_loss_dict.values())
# print(_task_losses)
task_loss_dict['loss_objectness'] = torch.mean(
task_loss_dict['loss_objectness'])
task_loss_dict['loss_rpn_box_reg'] = torch.mean(
task_loss_dict['loss_rpn_box_reg'])
task_loss_dict['loss_classifier'] = torch.mean(
task_loss_dict['loss_classifier'])
task_loss_dict['loss_box_reg'] = torch.mean(
task_loss_dict['loss_box_reg'])
task_losses = sum(loss for loss in task_loss_dict.values())
# reduce losses over all GPUs for logging purposes
task_loss_dict_reduced = utils.reduce_dict(task_loss_dict)
task_losses_reduced = sum(
loss.cpu() for loss in task_loss_dict_reduced.values())
task_loss_value = task_losses_reduced.item()
if epoch >= args.task_epochs:
# After EPOCHL epochs, stop the gradient from the loss prediction module propagated to the target model.
features['0'] = features['0'].detach()
features['1'] = features['1'].detach()
features['2'] = features['2'].detach()
features['3'] = features['3'].detach()
ll_pred = ll_model(features).cuda()
elif 'retina' in args.model:
_task_losses = sum(
torch.stack(loss[1]) for loss in task_loss_dict.values())
task_loss_dict['classification'] = task_loss_dict[
'classification'][0]
task_loss_dict['bbox_regression'] = task_loss_dict[
'bbox_regression'][0]
# for loss in task_loss_dict.values():
# print(loss)
task_losses = sum(loss for loss in task_loss_dict.values())
task_loss_dict_reduced = utils.reduce_dict(task_loss_dict)
task_losses_reduced = sum(
loss.cpu() for loss in task_loss_dict_reduced.values())
task_loss_value = task_losses_reduced.item()
if epoch >= args.task_epochs:
# After EPOCHL epochs, stop the gradient from the loss prediction module propagated to the target model.
_features = dict()
_features['0'] = features[0].detach()
_features['1'] = features[1].detach()
_features['2'] = features[2].detach()
_features['3'] = features[3].detach()
else:
_features = dict()
_features['0'] = features[0]
_features['1'] = features[1]
_features['2'] = features[2]
_features['3'] = features[3]
ll_pred = ll_model(_features).cuda()
ll_pred = ll_pred.view(ll_pred.size(0))
ll_loss = args.ll_weight * LossPredLoss(
ll_pred, _task_losses, margin=MARGIN)
losses = task_losses + ll_loss
if not math.isfinite(task_loss_value):
print("Loss is {}, stopping training".format(task_loss_value))
print(task_loss_dict_reduced)
sys.exit(1)
task_optimizer.zero_grad()
ll_optimizer.zero_grad()
losses.backward()
task_optimizer.step()
ll_optimizer.step()
if task_lr_scheduler is not None:
task_lr_scheduler.step()
if ll_lr_scheduler is not None:
ll_lr_scheduler.step()
metric_logger.update(task_loss=task_losses_reduced)
metric_logger.update(task_lr=task_optimizer.param_groups[0]["lr"])
metric_logger.update(ll_loss=ll_loss.item())
metric_logger.update(ll_lr=ll_optimizer.param_groups[0]["lr"])
return metric_logger
def train_one_epoch(task_model, task_optimizer, vae, vae_optimizer,
discriminator, discriminator_optimizer, labeled_dataloader,
unlabeled_dataloader, device, cycle, epoch, print_freq):
def read_unlabeled_data(dataloader):
while True:
for images, _ in dataloader:
yield list(image.to(device) for image in images)
labeled_data = read_unlabeled_data(labeled_dataloader)
unlabeled_data = read_unlabeled_data(unlabeled_dataloader)
task_model.train()
vae.train()
discriminator.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'task_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Cycle:[{}] Epoch: [{}]'.format(cycle, epoch)
task_lr_scheduler = None
vae_lr_scheduler = None
discriminator_lr_scheduler = None
if epoch == 0:
warmup_factor = 1. / 1000
warmup_iters = min(1000, len(labeled_dataloader) - 1)
task_lr_scheduler = utils.warmup_lr_scheduler(task_optimizer,
warmup_iters,
warmup_factor)
vae_lr_scheduler = utils.warmup_lr_scheduler(vae_optimizer,
warmup_iters,
warmup_factor)
discriminator_lr_scheduler = utils.warmup_lr_scheduler(
discriminator_optimizer, warmup_iters, warmup_factor)
for images, targets in metric_logger.log_every(labeled_dataloader,
print_freq, header):
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
task_loss_dict = task_model(images, targets)
task_losses = sum(loss for loss in task_loss_dict.values())
# reduce losses over all GPUs for logging purposes
task_loss_dict_reduced = utils.reduce_dict(task_loss_dict)
task_losses_reduced = sum(loss.cpu()
for loss in task_loss_dict_reduced.values())
task_loss_value = task_losses_reduced.item()
losses = task_losses
if not math.isfinite(task_loss_value):
print("Loss is {}, stopping training".format(task_loss_value))
print(task_loss_dict_reduced)
sys.exit(1)
task_optimizer.zero_grad()
losses.backward()
task_optimizer.step()
if task_lr_scheduler is not None:
task_lr_scheduler.step()
metric_logger.update(task_loss=task_losses_reduced)
metric_logger.update(task_lr=task_optimizer.param_groups[0]["lr"])
for i in range(len(labeled_dataloader)):
unlabeled_imgs = next(unlabeled_data)
labeled_imgs = next(labeled_data)
recon, z, mu, logvar = vae(labeled_imgs)
unsup_loss = vae_loss(labeled_imgs, recon, mu, logvar, 1)
unlab_recon, unlab_z, unlab_mu, unlab_logvar = vae(unlabeled_imgs)
transductive_loss = vae_loss(unlabeled_imgs, unlab_recon, unlab_mu,
unlab_logvar, 1)
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)
lab_real_preds = torch.ones(len(labeled_imgs)).cuda()
unlab_real_preds = torch.ones(len(unlabeled_imgs)).cuda()
if not len(labeled_preds.shape) == len(lab_real_preds.shape):
dsc_loss = bce_loss(
labeled_preds, lab_real_preds.unsqueeze(1)) + bce_loss(
unlabeled_preds, unlab_real_preds.unsqueeze(1))
else:
dsc_loss = bce_loss(labeled_preds, lab_real_preds) + bce_loss(
unlabeled_preds, unlab_real_preds)
total_vae_loss = unsup_loss + transductive_loss + dsc_loss
vae_optimizer.zero_grad()
total_vae_loss.backward()
vae_optimizer.step()
# Discriminator step
with torch.no_grad():
_, _, mu, _ = vae(labeled_imgs)
_, _, unlab_mu, _ = vae(unlabeled_imgs)
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)
lab_real_preds = torch.ones(len(labeled_imgs)).cuda()
unlab_fake_preds = torch.zeros(len(unlabeled_imgs)).cuda()
if not len(labeled_preds.shape) == len(lab_real_preds.shape):
dsc_loss = bce_loss(
labeled_preds, lab_real_preds.unsqueeze(1)) + bce_loss(
unlabeled_preds, unlab_fake_preds.unsqueeze(1))
else:
dsc_loss = bce_loss(labeled_preds, lab_real_preds) + bce_loss(
unlabeled_preds, unlab_fake_preds)
discriminator_optimizer.zero_grad()
dsc_loss.backward()
discriminator_optimizer.step()
if vae_lr_scheduler is not None:
vae_lr_scheduler.step()
if discriminator_lr_scheduler is not None:
discriminator_lr_scheduler.step()
if i == len(labeled_dataloader) - 1:
print('vae_loss: {} dis_loss:{}'.format(total_vae_loss, dsc_loss))
return metric_logger
def train_one_epoch(model,
optimizer,
data_loader,
device,
epoch,
print_freq=10):
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
lr_scheduler = None
if epoch == 0:
warmup_factor = 1. / 1000
warmup_iters = min(1000, len(data_loader) - 1)
lr_scheduler = utils.warmup_lr_scheduler(optimizer, warmup_iters,
warmup_factor)
writer = SummaryWriter(log_dir=log_dir)
i = 0
for images, targets in metric_logger.log_every(data_loader, print_freq,
header):
if epoch == 27:
print('')
images = list(image.to(device) for image in images)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
i += 1
print("epoch {}/70, iteration{}/{}".format(epoch, i, len(data_loader)))
loss_dict = model(images, targets)
# losses = sum(loss for loss in loss_dict.values())
# # reduce losses over all GPUs for logging purposes
# loss_dict_reduced = utils.reduce_dict(loss_dict)
# losses_reduced = sum(loss for loss in loss_dict_reduced.values())
# loss_value = losses_reduced.item()
# if not math.isfinite(loss_value):
# print("Loss is {}, stopping training".format(loss_value))
# print(loss_dict_reduced)
# sys.exit(1)
# optimizer.zero_grad()
# losses.backward()
# optimizer.step()
# if lr_scheduler is not None:
# lr_scheduler.step()
# writer.add_scalars('train', {'loss_all': loss_value,
# 'loss_box_reg': loss_dict_reduced['loss_box_reg'].item(),
# 'loss_classifier': loss_dict_reduced['loss_classifier'].item(),
# 'loss_objectness': loss_dict_reduced['loss_objectness'].item(),
# 'loss_rpn_box_reg': loss_dict_reduced['loss_rpn_box_reg'].item()},
# epoch*len(data_loader)+i)
losses_reduced = torch.tensor([0.])
loss_dict_reduced = {'1': 0.0}
metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
def train_one_epoch(model,
optimizer,
train_loader,
target_loader,
device,
epoch,
dis_model,
dis_optimizer,
print_freq=10,
writer=None,
test_func=None,
save_func=None):
global global_step
model.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
'lr', utils.SmoothedValue(window_size=1, fmt='{value:.0e}'))
metric_logger.add_meter(
'LAMBDA', utils.SmoothedValue(window_size=1, fmt='{value:.3f}'))
header = 'Epoch: [{}]'.format(epoch)
lr_schedulers = []
if epoch == 0:
warmup_factor = 1. / 500
warmup_iters = min(500, len(train_loader) - 1)
# lr_schedulers = [
# warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor),
# warmup_lr_scheduler(dis_optimizer, warmup_iters, warmup_factor),
# ]
target_loader_iter = iter(target_loader)
for images, img_metas, targets in metric_logger.log_every(
train_loader, print_freq, header):
global_step += 1
images = images.to(device)
targets = [t.to(device) for t in targets]
try:
t_images, t_img_metas, _ = next(target_loader_iter)
except StopIteration:
target_loader_iter = iter(target_loader)
t_images, t_img_metas, _ = next(target_loader_iter)
t_images = t_images.to(device)
loss_dict, outputs = model(images, img_metas, targets, t_images,
t_img_metas)
adv_loss = loss_dict.pop('adv_loss')
loss_dict_for_log = dict(**loss_dict, **adv_loss)
det_loss = sum(list(loss_dict.values()))
ada_loss = sum(list(adv_loss.values()))
LAMBDA = cosine_scheduler(cfg.ADV.LAMBDA_FROM, cfg.ADV.LAMBDA_TO,
global_step)
losses = det_loss + ada_loss * LAMBDA
optimizer.zero_grad()
losses.backward()
optimizer.step()
loss_dict_reduced = dist_utils.reduce_dict(loss_dict_for_log)
losses_reduced = sum(loss for loss in loss_dict_reduced.values())
for lr_scheduler in lr_schedulers:
lr_scheduler.step()
metric_logger.update(loss=losses_reduced, **loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(LAMBDA=LAMBDA)
if global_step % print_freq == 0:
if writer:
for k, v in loss_dict_reduced.items():
writer.add_scalar('losses/{}'.format(k),
v,
global_step=global_step)
writer.add_scalar('losses/total_loss',
losses_reduced,
global_step=global_step)
writer.add_scalar('lr',
optimizer.param_groups[0]['lr'],
global_step=global_step)
writer.add_scalar('LAMBDA', LAMBDA, global_step=global_step)
if global_step % (2000 // max(1, (dist_utils.get_world_size() // 2))
) == 0 and test_func is not None:
updated = test_func()
if updated:
save_func('best.pth', 'mAP: {:.4f}'.format(best_mAP))
print('Best mAP: {:.4f}'.format(best_mAP))
def do_evaluation(model, data_loader, device, types, output_dir, iteration=None, viz=False):
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
dataset = data_loader.dataset
header = 'Testing {}:'.format(dataset.dataset_name)
results_dict = {}
has_mask = False
for images, img_metas, targets in metric_logger.log_every(data_loader, 10, header):
assert len(targets) == 1
images = images.to(device)
model_time = time.time()
det = model(images, img_metas)[0]
boxes, scores, labels = det['boxes'], det['scores'], det['labels']
model_time = time.time() - model_time
img_meta = img_metas[0]
scale_factor = img_meta['scale_factor']
img_info = img_meta['img_info']
if viz:
import matplotlib.pyplot as plt
import matplotlib.patches as patches
plt.switch_backend('TKAgg')
image = de_normalize(images[0], img_meta)
plt.subplot(122)
plt.imshow(image)
plt.title('Predict')
for i, ((x1, y1, x2, y2), label) in enumerate(zip(boxes.tolist(), labels.tolist())):
if scores[i] > 0.65:
rect = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, facecolor='none', edgecolor='g')
category_id = dataset.label2cat[label]
plt.text(x1, y1, '{}:{:.2f}'.format(dataset.CLASSES[category_id], scores[i]), color='r')
plt.gca().add_patch(rect)
plt.subplot(121)
plt.imshow(image)
plt.title('GT')
for i, ((x1, y1, x2, y2), label) in enumerate(zip(targets[0]['boxes'].tolist(), targets[0]['labels'].tolist())):
category_id = dataset.label2cat[label]
rect = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, facecolor='none', edgecolor='g')
plt.text(x1, y1, '{}'.format(dataset.CLASSES[category_id]))
plt.gca().add_patch(rect)
plt.show()
boxes /= scale_factor
result = {}
if 'masks' in det:
has_mask = True
(w, h) = img_meta['origin_img_shape']
masks = paste_masks_in_image(det['masks'], boxes, (h, w))
rles = []
for mask in masks.cpu().numpy():
mask = mask >= 0.5
mask = mask_util.encode(np.array(mask[0][:, :, None], order='F', dtype='uint8'))[0]
# "counts" is an array encoded by mask_util as a byte-stream. Python3's
# json writer which always produces strings cannot serialize a bytestream
# unless you decode it. Thankfully, utf-8 works out (which is also what
# the pycocotools/_mask.pyx does).
mask['counts'] = mask['counts'].decode('utf-8')
rles.append(mask)
result['masks'] = rles
boxes = boxes.tolist()
labels = labels.tolist()
labels = [dataset.label2cat[label] for label in labels]
scores = scores.tolist()
result['boxes'] = boxes
result['scores'] = scores
result['labels'] = labels
# save_visualization(dataset, img_meta, result, output_dir)
results_dict.update({
img_info['id']: result
})
metric_logger.update(model_time=model_time)
if get_world_size() > 1:
dist.barrier()
predictions = _accumulate_predictions_from_multiple_gpus(results_dict)
if not is_main_process():
return {}
results = {}
if has_mask:
result = coco_evaluation(dataset, predictions, output_dir, iteration=iteration)
results.update(result)
if 'voc' in types:
result = voc_evaluation(dataset, predictions, output_dir, iteration=iteration, use_07_metric=False)
results.update(result)
return results
