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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)
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