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import sys
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import time
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn.modules.loss import _WeightedLoss
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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map_loc = None if torch.cuda.is_available() else 'cpu'
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class MaskedCrossEntropyCriterion(_WeightedLoss):
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def __init__(self, ignore_index=[-100], reduce=None):
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super(MaskedCrossEntropyCriterion, self).__init__()
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self.padding_idx = ignore_index
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self.reduce = reduce
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def forward(self, outputs, targets):
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lprobs = nn.functional.log_softmax(outputs, dim=-1)
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lprobs = lprobs.view(-1, lprobs.size(-1))
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for idx in self.padding_idx:
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targets[targets == idx] = 0
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nll_loss = -lprobs.gather(dim=-1, index=targets.unsqueeze(1))
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if self.reduce:
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nll_loss = nll_loss.sum()
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return nll_loss.squeeze()
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def softIoU(out, target, e=1e-6, sum_axis=1):
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num = (out*target).sum(sum_axis, True)
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den = (out+target-out*target).sum(sum_axis, True) + e
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iou = num / den
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return iou
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def update_error_types(error_types, y_pred, y_true):
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error_types['tp_i'] += (y_pred * y_true).sum(0).cpu().data.numpy()
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error_types['fp_i'] += (y_pred * (1-y_true)).sum(0).cpu().data.numpy()
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error_types['fn_i'] += ((1-y_pred) * y_true).sum(0).cpu().data.numpy()
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error_types['tn_i'] += ((1-y_pred) * (1-y_true)).sum(0).cpu().data.numpy()
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error_types['tp_all'] += (y_pred * y_true).sum().item()
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error_types['fp_all'] += (y_pred * (1-y_true)).sum().item()
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error_types['fn_all'] += ((1-y_pred) * y_true).sum().item()
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def compute_metrics(ret_metrics, error_types, metric_names, eps=1e-10, weights=None):
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if 'accuracy' in metric_names:
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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'])))
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if 'jaccard' in metric_names:
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ret_metrics['jaccard'].append(error_types['tp_all'] / (error_types['tp_all'] + error_types['fp_all'] + error_types['fn_all'] + eps))
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if 'dice' in metric_names:
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ret_metrics['dice'].append(2*error_types['tp_all'] / (2*(error_types['tp_all'] + error_types['fp_all'] + error_types['fn_all']) + eps))
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if 'f1' in metric_names:
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pre = error_types['tp_i'] / (error_types['tp_i'] + error_types['fp_i'] + eps)
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rec = error_types['tp_i'] / (error_types['tp_i'] + error_types['fn_i'] + eps)
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f1_perclass = 2*(pre * rec) / (pre + rec + eps)
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if 'f1_ingredients' not in ret_metrics.keys():
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ret_metrics['f1_ingredients'] = [np.average(f1_perclass, weights=weights)]
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else:
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ret_metrics['f1_ingredients'].append(np.average(f1_perclass, weights=weights))
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pre = error_types['tp_all'] / (error_types['tp_all'] + error_types['fp_all'] + eps)
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rec = error_types['tp_all'] / (error_types['tp_all'] + error_types['fn_all'] + eps)
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f1 = 2*(pre * rec) / (pre + rec + eps)
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ret_metrics['f1'].append(f1)
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