extractor = Extractor()
s1_classifier = Classifier(num_classes=num_classes)
s2_classifier = Classifier(num_classes=num_classes)
s3_classifier = Classifier(num_classes=num_classes)
s1_t_discriminator = Discriminator()
s2_t_discriminator = Discriminator()
s3_t_discriminator = Discriminator()
extractor.load_state_dict(
torch.load(
osp.join(
MAIN_DIR,
"MSDA/A_W_2_D_Open/bvlc_A_W_2_D/pretrain/bvlc_extractor.pth")))
extractor = nn.DataParallel(extractor)
extractor = extractor.cuda()
s1_classifier.load_state_dict(
torch.load(
osp.join(
MAIN_DIR,
"MSDA/A_W_2_D_Open/bvlc_A_W_2_D/pretrain/office-home/bvlc_s1_cls.pth"
)))
s2_classifier.load_state_dict(
torch.load(
osp.join(
MAIN_DIR,
"MSDA/A_W_2_D_Open/bvlc_A_W_2_D/pretrain/office-home/bvlc_s2_cls.pth"
)))
s3_classifier.load_state_dict(
torch.load(
pbar.update(1)
im, ann, im_fns = data[0], data[1], data[2]
im = im.cuda()
feat = model(im)
x = feat.cpu().numpy()
y = ann.numpy()
precision = precision + clf.score(x, y)
pbar.close()
print("\nAverage Precision is {}".format(precision / len(loader)))
if __name__ == '__main__':
icdar_patches = "./data/icdar2015/patches"
dataset = ImageFolder(root=icdar_patches, transform=transforms)
loader = torch.utils.data.DataLoader(dataset, batch_size=20, shuffle=True)
# extractor for deep features
model = Extractor()
model = model.cuda()
for params in model.parameters():
params.requires_grad = False
# vanilla svm
clf = svm.SVC(kernel="rbf", gamma=10)
train(loader=loader, model=model, clf=clf)