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138 lines
3.8 KiB
138 lines
3.8 KiB
import torch.utils.data
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import torch.nn as nn
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from utils import make_variable
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def test_from_save(model, saved_model, data_loader):
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"""Evaluate classifier for source domain."""
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# set eval state for Dropout and BN layers
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classifier = model.load_state_dict(torch.load(saved_model))
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classifier.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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# set loss function
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criterion = nn.NLLLoss()
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# evaluate network
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for (images, labels) in data_loader:
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images = make_variable(images, volatile=True)
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labels = make_variable(labels) #labels = labels.squeeze(1)
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preds = classifier(images)
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criterion(preds, labels)
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loss += criterion(preds, labels).data[0]
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pred_cls = preds.data.max(1)[1]
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acc += pred_cls.eq(labels.data).cpu().sum()
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loss /= len(data_loader)
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acc /= len(data_loader.dataset)
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print("Avg Loss = {}, Avg Accuracy = {:.2%}".format(loss, acc))
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def eval(model, data_loader):
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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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# 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 = make_variable(images, volatile=True)
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labels = make_variable(labels) #labels = labels.squeeze(1)
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size_tgt = len(labels)
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labels_domain = make_variable(torch.ones(size_tgt).long())
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preds, domain = model(images, alpha=0)
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loss += criterion(preds, labels).data[0]
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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).cpu().sum()
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acc_domain += pred_domain.eq(labels_domain.data).cpu().sum()
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loss /= len(data_loader)
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acc /= len(data_loader.dataset)
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acc_domain /= len(data_loader.dataset)
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print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_domain))
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def eval_src(model, data_loader):
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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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# 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 = make_variable(images, volatile=True)
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labels = make_variable(labels) #labels = labels.squeeze(1)
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size_tgt = len(labels)
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labels_domain = make_variable(torch.zeros(size_tgt).long())
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preds, domain = model(images, alpha=0)
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loss += criterion(preds, labels).data[0]
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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).cpu().sum()
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acc_domain += pred_domain.eq(labels_domain.data).cpu().sum()
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loss /= len(data_loader)
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acc /= len(data_loader.dataset)
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acc_domain /= len(data_loader.dataset)
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print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}, Avg Domain Accuracy = {:2%}".format(loss, acc, acc_domain))
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def eval_src_(model, data_loader):
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"""Evaluate classifier for source domain."""
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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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# set loss function
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criterion = nn.NLLLoss()
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# evaluate network
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for (images, labels) in data_loader:
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images = make_variable(images, volatile=True)
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labels = make_variable(labels) #labels = labels.squeeze(1)
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preds = model(images)
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criterion(preds, labels)
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loss += criterion(preds, labels).data[0]
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pred_cls = preds.data.max(1)[1]
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acc += pred_cls.eq(labels.data).cpu().sum()
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loss /= len(data_loader)
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acc /= len(data_loader.dataset)
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print("Avg Loss = {:.6f}, Avg Accuracy = {:.2%}".format(loss, acc))
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