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# Copyright 2020 - 2022 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch
def dice(x, y):
intersect = np.sum(np.sum(np.sum(x * y)))
y_sum = np.sum(np.sum(np.sum(y)))
if y_sum == 0:
return 0.0
x_sum = np.sum(np.sum(np.sum(x)))
return 2 * intersect / (x_sum + y_sum)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = np.where(self.count > 0, self.sum / self.count, self.sum)
def distributed_all_gather(
tensor_list, valid_batch_size=None, out_numpy=False, world_size=None, no_barrier=False, is_valid=None
):
if world_size is None:
world_size = torch.distributed.get_world_size()
if valid_batch_size is not None:
valid_batch_size = min(valid_batch_size, world_size)
elif is_valid is not None:
is_valid = torch.tensor(bool(is_valid), dtype=torch.bool, device=tensor_list[0].device)
if not no_barrier:
torch.distributed.barrier()
tensor_list_out = []
with torch.no_grad():
if is_valid is not None:
is_valid_list = [torch.zeros_like(is_valid) for _ in range(world_size)]
torch.distributed.all_gather(is_valid_list, is_valid)
is_valid = [x.item() for x in is_valid_list]
for tensor in tensor_list:
gather_list = [torch.zeros_like(tensor) for _ in range(world_size)]
torch.distributed.all_gather(gather_list, tensor)
if valid_batch_size is not None:
gather_list = gather_list[:valid_batch_size]
elif is_valid is not None:
gather_list = [g for g, v in zip(gather_list, is_valid_list) if v]
if out_numpy:
gather_list = [t.cpu().numpy() for t in gather_list]
tensor_list_out.append(gather_list)
return tensor_list_out
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