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44 lines
1.3 KiB
44 lines
1.3 KiB
import torch.utils.data
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import torch.nn as nn
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def test(model, data_loader, device, loggi, flag):
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"""Evaluate model for dataset."""
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# set eval state for Dropout and BN layers
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model.eval()
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# init loss and accuracy
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loss_ = 0.0
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acc_ = 0.0
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acc_domain_ = 0.0
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n_total = 0
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# set loss function
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criterion = nn.CrossEntropyLoss()
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# evaluate network
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for (images, labels) in data_loader:
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images = images.to(device)
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labels = labels.to(device) #labels = labels.squeeze(1)
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size = len(labels)
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if flag == 'target':
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labels_domain = torch.ones(size).long().to(device)
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else:
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labels_domain = torch.zeros(size).long().to(device)
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preds, domain = model(images, alpha=0)
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loss_ += criterion(preds, labels).item()
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pred_cls = preds.data.max(1)[1]
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pred_domain = domain.data.max(1)[1]
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acc_ += pred_cls.eq(labels.data).sum().item()
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acc_domain_ += pred_domain.eq(labels_domain.data).sum().item()
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n_total += size
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loss = loss_ / n_total
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acc = acc_ / n_total
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acc_domain = acc_domain_ / n_total
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loggi.info("{}: Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, {}/{}, Avg Domain Accuracy = {:2%}".format(flag, loss, acc, acc_, n_total, acc_domain))
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return loss, acc, acc_domain
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