232 lines
9.9 KiB
Python
232 lines
9.9 KiB
Python
# Lightning modules, see https://pytorch-lightning.readthedocs.io/en/latest/lightning_module.html
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import torch
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from torch import nn
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from transformers import AdamW
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import torch.nn.functional as F
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from torch.autograd import Variable
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import pytorch_lightning as pl
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from pytorch_lightning.metrics import Metric, F1, Accuracy
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from torch.optim.lr_scheduler import StepLR
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from models.helpers import init_embeddings
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from util.common import is_true, is_false
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from util.evaluation import evaluate
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class RecurrentModel(pl.LightningModule):
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"""
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Check out for logging insight https://www.learnopencv.com/tensorboard-with-pytorch-lightning/
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"""
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def __init__(self, lPretrained, langs, output_size, hidden_size, lVocab_size, learnable_length,
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drop_embedding_range, drop_embedding_prop, gpus=None):
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super().__init__()
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self.gpus = gpus
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self.langs = langs
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self.lVocab_size = lVocab_size
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self.learnable_length = learnable_length
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self.output_size = output_size
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self.hidden_size = hidden_size
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self.drop_embedding_range = drop_embedding_range
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self.drop_embedding_prop = drop_embedding_prop
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self.loss = torch.nn.BCEWithLogitsLoss()
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# self.microf1 = F1(num_classes=output_size, multilabel=True, average='micro')
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# self.macrof1 = F1(num_classes=output_size, multilabel=True, average='macro')
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self.accuracy = Accuracy()
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self.customMicroF1 = CustomF1(num_classes=output_size, average='micro', device=self.gpus)
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self.customMacroF1 = CustomF1(num_classes=output_size, average='macro', device=self.gpus)
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self.lPretrained_embeddings = nn.ModuleDict()
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self.lLearnable_embeddings = nn.ModuleDict()
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self.n_layers = 1
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self.n_directions = 1
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self.dropout = nn.Dropout(0.6)
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lstm_out = 256
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ff1 = 512
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ff2 = 256
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lpretrained_embeddings = {}
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llearnable_embeddings = {}
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for lang in self.langs:
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pretrained = lPretrained[lang] if lPretrained else None
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pretrained_embeddings, learnable_embeddings, embedding_length = init_embeddings(
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pretrained, self.lVocab_size[lang], self.learnable_length)
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lpretrained_embeddings[lang] = pretrained_embeddings
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llearnable_embeddings[lang] = learnable_embeddings
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self.embedding_length = embedding_length
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self.lPretrained_embeddings.update(lpretrained_embeddings)
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self.lLearnable_embeddings.update(llearnable_embeddings)
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self.rnn = nn.GRU(self.embedding_length, hidden_size)
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self.linear0 = nn.Linear(hidden_size * self.n_directions, lstm_out)
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self.linear1 = nn.Linear(lstm_out, ff1)
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self.linear2 = nn.Linear(ff1, ff2)
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self.label = nn.Linear(ff2, self.output_size)
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lPretrained = None # TODO: setting lPretrained to None, letting it to its original value will bug first
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# validation step (i.e., checkpoint will store also its ++ value, I guess, making the saving process too slow)
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self.save_hyperparameters()
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def forward(self, lX):
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_tmp = []
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for lang in sorted(lX.keys()):
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doc_embedding = self.transform(lX[lang], lang)
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_tmp.append(doc_embedding)
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embed = torch.cat(_tmp, dim=0)
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logits = self.label(embed)
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return logits
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def transform(self, X, lang):
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batch_size = X.shape[0]
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X = self.embed(X, lang)
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X = self.embedding_dropout(X, drop_range=self.drop_embedding_range, p_drop=self.drop_embedding_prop,
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training=self.training)
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X = X.permute(1, 0, 2)
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h_0 = Variable(torch.zeros(self.n_layers * self.n_directions, batch_size, self.hidden_size).to(self.device))
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output, _ = self.rnn(X, h_0)
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output = output[-1, :, :]
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output = F.relu(self.linear0(output))
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output = self.dropout(F.relu(self.linear1(output)))
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output = self.dropout(F.relu(self.linear2(output)))
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return output
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def training_step(self, train_batch, batch_idx):
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lX, ly = train_batch
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logits = self.forward(lX)
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_ly = []
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for lang in sorted(lX.keys()):
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_ly.append(ly[lang])
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ly = torch.cat(_ly, dim=0)
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loss = self.loss(logits, ly)
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# Squashing logits through Sigmoid in order to get confidence score
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predictions = torch.sigmoid(logits) > 0.5
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accuracy = self.accuracy(predictions, ly)
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microF1 = self.customMicroF1(predictions, ly)
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macroF1 = self.customMacroF1(predictions, ly)
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self.log('train-loss', loss, on_step=True, on_epoch=True, prog_bar=False, logger=True)
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self.log('train-accuracy', accuracy, on_step=True, on_epoch=True, prog_bar=False, logger=True)
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self.log('microF1', microF1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
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self.log('macroF1', macroF1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
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return {'loss': loss}
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def validation_step(self, val_batch, batch_idx):
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lX, ly = val_batch
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logits = self.forward(lX)
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_ly = []
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for lang in sorted(lX.keys()):
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_ly.append(ly[lang])
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ly = torch.cat(_ly, dim=0)
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loss = self.loss(logits, ly)
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predictions = torch.sigmoid(logits) > 0.5
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accuracy = self.accuracy(predictions, ly)
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self.log('val-loss', loss, on_step=True, on_epoch=True, prog_bar=False, logger=True)
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self.log('val-accuracy', accuracy, on_step=True, on_epoch=True, prog_bar=False, logger=True)
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return {'loss': loss}
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def test_step(self, test_batch, batch_idx):
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lX, ly = test_batch
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logits = self.forward(lX)
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_ly = []
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for lang in sorted(lX.keys()):
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_ly.append(ly[lang])
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ly = torch.cat(_ly, dim=0)
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predictions = torch.sigmoid(logits) > 0.5
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accuracy = self.accuracy(predictions, ly)
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self.log('test-accuracy', accuracy, on_step=False, on_epoch=True, prog_bar=False, logger=True)
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return
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def embed(self, X, lang):
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input_list = []
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if self.lPretrained_embeddings[lang]:
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input_list.append(self.lPretrained_embeddings[lang](X))
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if self.lLearnable_embeddings[lang]:
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input_list.append(self.lLearnable_embeddings[lang](X))
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return torch.cat(tensors=input_list, dim=2)
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def embedding_dropout(self, X, drop_range, p_drop=0.5, training=True):
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if p_drop > 0 and training and drop_range is not None:
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p = p_drop
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drop_from, drop_to = drop_range
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m = drop_to - drop_from # length of the supervised embedding
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l = X.shape[2] # total embedding length
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corr = (1 - p)
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X[:, :, drop_from:drop_to] = corr * F.dropout(X[:, :, drop_from:drop_to], p=p)
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X /= (1 - (p * m / l))
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return X
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def configure_optimizers(self):
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optimizer = AdamW(self.parameters(), lr=1e-3)
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scheduler = StepLR(optimizer, step_size=25, gamma=0.5)
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return [optimizer], [scheduler]
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class CustomF1(Metric):
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def __init__(self, num_classes, device, average='micro'):
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"""
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Custom F1 metric.
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Scikit learn provides a full set of evaluation metrics, but they treat special cases differently.
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I.e., when the number of true positives, false positives, and false negatives amount to 0, all
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affected metrics (precision, recall, and thus f1) output 0 in Scikit learn.
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We adhere to the common practice of outputting 1 in this case since the classifier has correctly
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classified all examples as negatives.
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:param num_classes:
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:param device:
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:param average:
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"""
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super().__init__()
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self.num_classes = num_classes
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self.average = average
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self.device = 'cuda' if device else 'cpu'
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self.add_state('true_positive', default=torch.zeros(self.num_classes))
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self.add_state('true_negative', default=torch.zeros(self.num_classes))
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self.add_state('false_positive', default=torch.zeros(self.num_classes))
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self.add_state('false_negative', default=torch.zeros(self.num_classes))
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def update(self, preds, target):
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true_positive, true_negative, false_positive, false_negative = self._update(preds, target)
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self.true_positive += true_positive
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self.true_negative += true_negative
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self.false_positive += false_positive
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self.false_negative += false_negative
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def _update(self, pred, target):
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assert pred.shape == target.shape
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# preparing preds and targets for count
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true_pred = is_true(pred, self.device)
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false_pred = is_false(pred, self.device)
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true_target = is_true(target, self.device)
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false_target = is_false(target, self.device)
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tp = torch.sum(true_pred * true_target, dim=0)
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tn = torch.sum(false_pred * false_target, dim=0)
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fp = torch.sum(true_pred * false_target, dim=0)
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fn = torch.sum(false_pred * target, dim=0)
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return tp, tn, fp, fn
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def compute(self):
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if self.average == 'micro':
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num = 2.0 * self.true_positive.sum()
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den = 2.0 * self.true_positive.sum() + self.false_positive.sum() + self.false_negative.sum()
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if den > 0:
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return (num / den).to(self.device)
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return torch.FloatTensor([1.]).to(self.device)
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if self.average == 'macro':
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class_specific = []
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for i in range(self.num_classes):
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class_tp = self.true_positive[i]
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# class_tn = self.true_negative[i]
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class_fp = self.false_positive[i]
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class_fn = self.false_negative[i]
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num = 2.0 * class_tp
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den = 2.0 * class_tp + class_fp + class_fn
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if den > 0:
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class_specific.append(num / den)
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else:
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class_specific.append(1.)
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average = torch.sum(torch.Tensor(class_specific))/self.num_classes
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return average.to(self.device)
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