def synchronize_between_processes(self):
if self.fixed_ap:
all_cats = dist.all_gather(self.by_cat)
self.by_cat = defaultdict(list)
for cats in all_cats:
for cat, cat_anns in cats.items():
self.by_cat[cat].extend(cat_anns)
else:
self.results = sum(dist.all_gather(self.results), [])
def merge(img_ids, eval_imgs):
all_img_ids = all_gather(img_ids)
all_eval_imgs = all_gather(eval_imgs)
merged_img_ids = []
for p in all_img_ids:
merged_img_ids.extend(p)
merged_eval_imgs = []
for p in all_eval_imgs:
merged_eval_imgs.append(p)
merged_img_ids = np.array(merged_img_ids)
merged_eval_imgs = np.concatenate(merged_eval_imgs, 2)
# keep only unique (and in sorted order) images
merged_img_ids, idx = np.unique(merged_img_ids, return_index=True)
merged_eval_imgs = merged_eval_imgs[..., idx]
return merged_img_ids, merged_eval_imgs
def synchronize_between_processes(self):
all_predictions = dist.all_gather(self.predictions)
merged_predictions = {}
for p in all_predictions:
merged_predictions.update(p)
self.predictions = merged_predictions
def synchronize_between_processes(self):
all_predictions = dist.all_gather(self.predictions)
merged_predictions = []
for p in all_predictions:
merged_predictions += p
self.predictions = merged_predictions
def synchronize_between_processes(self):
all_predictions = dist.all_gather(self.predictions)
self.predictions = sum(all_predictions, [])
self.predictions = sorted(self.predictions,
key=lambda x: x["questionId"])
self.predictions = [p["prediction"] for p in self.predictions]
def synchronize_between_processes(self):
all_predictions = dist.all_gather(self.predictions)
self.predictions = sum(all_predictions, [])