def gather_pool_logits(pool, logits):
from detectron2.utils import comm
pool_list = comm.gather(data=pool)
logits_list = comm.gather(data=logits)
if len(pool_list) > 0:
pool = torch.cat(pool_list, dim=0).to('cuda')
if len(logits_list) > 0:
logits = torch.cat(logits_list, dim=0).to('cuda')
return pool, logits
def get_sample_imgs_list_ddp(sample_func,
num_imgs=50000,
as_numpy=True,
stdout=sys.stdout):
"""
:param sample_func:
:param num_imgs:
:param stdout:
:return:
"""
import torch
from template_lib.d2.utils import comm
ws = comm.get_world_size()
num_imgs = num_imgs // ws
imgs = get_sample_imgs_list(sample_func=sample_func,
num_imgs=num_imgs,
as_numpy=as_numpy,
stdout=stdout)
imgs_list = comm.gather(imgs)
if len(imgs_list) > 0:
if as_numpy:
imgs = np.concatenate(imgs_list, axis=0)
else:
imgs = torch.cat(imgs_list, dim=0)
return imgs
def _gather_data(self, data, is_numpy=False):
data_list = comm.gather(data=data)
if len(data_list) > 0:
if is_numpy:
data = np.concatenate(data_list, axis=0)
else:
data = torch.cat(data_list, dim=0).to('cuda')
return data
def gather_tensor(data):
from template_lib.d2.utils import comm
data_list = comm.gather(data=data)
if len(data_list) > 0:
if isinstance(data, np.ndarray):
data = np.concatenate(data_list, axis=0)
else:
data = torch.cat(data_list, dim=0).to(device=data.device)
else:
data = None
return data
def _gather_numpy_array(self, data):
data_list = comm.gather(data=data)
if len(data_list) > 0:
data = np.concatenate(data_list, axis=0)
return data