| | |
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| | | args = parse_args() |
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| | | |
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| | | cudnn.enabled = True |
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| | | batch_size = 1 |
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| | | gpu = args.gpu_id |
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| | | snapshot_path = os.path.join('output/snapshots', args.snapshot + '.pkl') |
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| | | |
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| | | # ResNet101 with 3 outputs. |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 23, 3], 66) |
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| | | # ResNet50 |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66) |
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| | | # ResNet18 |
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| | | model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
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| | | # model = hopenet.Hopenet(torchvision.models.resnet.BasicBlock, [2, 2, 2, 2], 66) |
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| | | |
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| | | print 'Loading snapshot.' |
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| | | # Load snapshot |
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| | |
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| | | pose_dataset = datasets.AFLW2000_binned(args.data_dir, args.filename_list, |
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| | | transformations) |
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| | | test_loader = torch.utils.data.DataLoader(dataset=pose_dataset, |
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| | | batch_size=batch_size, |
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| | | batch_size=args.batch_size, |
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| | | num_workers=2) |
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| | | |
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| | | model.cuda(gpu) |
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| | |
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| | | pitch_error = .0 |
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| | | roll_error = .0 |
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| | | |
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| | | l1loss = torch.nn.L1Loss(size_average=False) |
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| | | |
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| | | for i, (images, labels, name) in enumerate(test_loader): |
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| | | images = Variable(images).cuda(gpu) |
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| | | total += labels.size(0) |
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| | | label_yaw = labels[:,0] |
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| | | label_pitch = labels[:,1] |
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| | | label_roll = labels[:,2] |
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| | | label_yaw = labels[:,0].float() |
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| | | label_pitch = labels[:,1].float() |
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| | | label_roll = labels[:,2].float() |
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| | | |
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| | | yaw, pitch, roll = model(images) |
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| | | |
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| | |
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| | | roll_predicted = F.softmax(roll) |
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| | | |
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| | | # Continuous predictions |
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| | | yaw_predicted = torch.sum(yaw_predicted.data[0] * idx_tensor) |
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| | | pitch_predicted = torch.sum(pitch_predicted.data[0] * idx_tensor) |
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| | | roll_predicted = torch.sum(roll_predicted.data[0] * idx_tensor) |
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| | | yaw_predicted = torch.sum(yaw_predicted.data * idx_tensor, 1) |
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| | | pitch_predicted = torch.sum(pitch_predicted.data * idx_tensor, 1) |
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| | | roll_predicted = torch.sum(roll_predicted.data * idx_tensor, 1) |
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| | | |
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| | | yaw_predicted = yaw_predicted.cpu() |
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| | | pitch_predicted = pitch_predicted.cpu() |
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| | | roll_predicted = roll_predicted.cpu() |
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| | | |
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| | | # Mean absolute error |
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| | | yaw_error += abs(yaw_predicted - label_yaw[0]) * 3 |
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| | | pitch_error += abs(pitch_predicted - label_pitch[0]) * 3 |
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| | | roll_error += abs(roll_predicted - label_roll[0]) * 3 |
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| | | yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw) * 3) |
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| | | pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch) * 3) |
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| | | roll_error += torch.sum(torch.abs(roll_predicted - label_roll) * 3) |
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| | | |
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| | | # Binned Accuracy |
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| | | # for er in xrange(n_margins): |
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| | |
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| | | # print label_yaw[0], yaw_bpred[0,0] |
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| | | |
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| | | # Save images with pose cube. |
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| | | # TODO: fix for larger batch size |
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| | | if args.save_viz: |
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| | | name = name[0] |
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| | | cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg')) |
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| | | #print os.path.join('output/images', name + '.jpg') |
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| | | #print label_yaw[0] * 3 - 99, label_pitch[0] * 3 - 99, label_roll[0] * 3 - 99 |
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| | | #print yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99 |
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| | | utils.plot_pose_cube(cv2_img, yaw_predicted * 3 - 99, pitch_predicted * 3 - 99, roll_predicted * 3 - 99) |
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| | | utils.plot_pose_cube(cv2_img, yaw_predicted[0] * 3 - 99, pitch_predicted[0] * 3 - 99, roll_predicted[0] * 3 - 99) |
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| | | cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img) |
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| | | |
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| | | print('Test error in degrees of the model on the ' + str(total) + |
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