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| # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. | |
| import sys | |
| import time | |
| import math | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torch.nn.modules.loss import _WeightedLoss | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| map_loc = None if torch.cuda.is_available() else 'cpu' | |
| class MaskedCrossEntropyCriterion(_WeightedLoss): | |
| def __init__(self, ignore_index=[-100], reduce=None): | |
| super(MaskedCrossEntropyCriterion, self).__init__() | |
| self.padding_idx = ignore_index | |
| self.reduce = reduce | |
| def forward(self, outputs, targets): | |
| lprobs = nn.functional.log_softmax(outputs, dim=-1) | |
| lprobs = lprobs.view(-1, lprobs.size(-1)) | |
| for idx in self.padding_idx: | |
| # remove padding idx from targets to allow gathering without error (padded entries will be suppressed later) | |
| targets[targets == idx] = 0 | |
| nll_loss = -lprobs.gather(dim=-1, index=targets.unsqueeze(1)) | |
| if self.reduce: | |
| nll_loss = nll_loss.sum() | |
| return nll_loss.squeeze() | |
| def softIoU(out, target, e=1e-6, sum_axis=1): | |
| num = (out*target).sum(sum_axis, True) | |
| den = (out+target-out*target).sum(sum_axis, True) + e | |
| iou = num / den | |
| return iou | |
| def update_error_types(error_types, y_pred, y_true): | |
| error_types['tp_i'] += (y_pred * y_true).sum(0).cpu().data.numpy() | |
| error_types['fp_i'] += (y_pred * (1-y_true)).sum(0).cpu().data.numpy() | |
| error_types['fn_i'] += ((1-y_pred) * y_true).sum(0).cpu().data.numpy() | |
| error_types['tn_i'] += ((1-y_pred) * (1-y_true)).sum(0).cpu().data.numpy() | |
| error_types['tp_all'] += (y_pred * y_true).sum().item() | |
| error_types['fp_all'] += (y_pred * (1-y_true)).sum().item() | |
| error_types['fn_all'] += ((1-y_pred) * y_true).sum().item() | |
| def compute_metrics(ret_metrics, error_types, metric_names, eps=1e-10, weights=None): | |
| if 'accuracy' in metric_names: | |
| ret_metrics['accuracy'].append(np.mean((error_types['tp_i'] + error_types['tn_i']) / (error_types['tp_i'] + error_types['fp_i'] + error_types['fn_i'] + error_types['tn_i']))) | |
| if 'jaccard' in metric_names: | |
| ret_metrics['jaccard'].append(error_types['tp_all'] / (error_types['tp_all'] + error_types['fp_all'] + error_types['fn_all'] + eps)) | |
| if 'dice' in metric_names: | |
| ret_metrics['dice'].append(2*error_types['tp_all'] / (2*(error_types['tp_all'] + error_types['fp_all'] + error_types['fn_all']) + eps)) | |
| if 'f1' in metric_names: | |
| pre = error_types['tp_i'] / (error_types['tp_i'] + error_types['fp_i'] + eps) | |
| rec = error_types['tp_i'] / (error_types['tp_i'] + error_types['fn_i'] + eps) | |
| f1_perclass = 2*(pre * rec) / (pre + rec + eps) | |
| if 'f1_ingredients' not in ret_metrics.keys(): | |
| ret_metrics['f1_ingredients'] = [np.average(f1_perclass, weights=weights)] | |
| else: | |
| ret_metrics['f1_ingredients'].append(np.average(f1_perclass, weights=weights)) | |
| pre = error_types['tp_all'] / (error_types['tp_all'] + error_types['fp_all'] + eps) | |
| rec = error_types['tp_all'] / (error_types['tp_all'] + error_types['fn_all'] + eps) | |
| f1 = 2*(pre * rec) / (pre + rec + eps) | |
| ret_metrics['f1'].append(f1) | |