74 lines
3 KiB
Python
74 lines
3 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from torchmetrics import Metric
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class Dice(Metric):
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def __init__(self, n_class, brats):
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super().__init__(dist_sync_on_step=False)
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self.n_class = n_class
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self.brats = brats
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self.add_state("steps", default=torch.zeros(1), dist_reduce_fx="sum")
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self.add_state("dice", default=torch.zeros((n_class,)), dist_reduce_fx="sum")
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self.add_state("loss", default=torch.zeros(1), dist_reduce_fx="sum")
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def update(self, preds, target, loss):
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self.steps += 1
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self.dice += self.compute_stats_brats(preds, target) if self.brats else self.compute_stats(preds, target)
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self.loss += loss
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def compute(self):
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return 100 * self.dice / self.steps, self.loss / self.steps
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def compute_stats_brats(self, p, y):
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scores = torch.zeros(self.n_class, device=p.device, dtype=torch.float32)
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p = (torch.sigmoid(p) > 0.5).int()
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y_wt, y_tc, y_et = y > 0, ((y == 1) + (y == 3)) > 0, y == 3
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y = torch.stack([y_wt, y_tc, y_et], dim=1)
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for i in range(self.n_class):
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p_i, y_i = p[:, i], y[:, i]
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if (y_i != 1).all():
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# no foreground class
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scores[i - 1] += 1 if (p_i != 1).all() else 0
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continue
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tp, fn, fp = self.get_stats(p_i, y_i, 1)
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denom = (2 * tp + fp + fn).to(torch.float)
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score_cls = (2 * tp).to(torch.float) / denom if torch.is_nonzero(denom) else 0.0
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scores[i - 1] += score_cls
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return scores
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def compute_stats(self, preds, target):
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scores = torch.zeros(self.n_class, device=preds.device, dtype=torch.float32)
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preds = torch.argmax(preds, dim=1)
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for i in range(1, self.n_class + 1):
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if (target != i).all():
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# no foreground class
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scores[i - 1] += 1 if (preds != i).all() else 0
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continue
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tp, fn, fp = self.get_stats(preds, target, i)
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denom = (2 * tp + fp + fn).to(torch.float)
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score_cls = (2 * tp).to(torch.float) / denom if torch.is_nonzero(denom) else 0.0
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scores[i - 1] += score_cls
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return scores
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@staticmethod
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def get_stats(preds, target, class_idx):
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tp = torch.logical_and(preds == class_idx, target == class_idx).sum()
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fn = torch.logical_and(preds != class_idx, target == class_idx).sum()
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fp = torch.logical_and(preds == class_idx, target != class_idx).sum()
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return tp, fn, fp
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