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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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