| | |
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| | | |
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| | | print 'Loading data.' |
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| | | |
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| | | # transformations = transforms.Compose([transforms.Scale(224), |
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| | | # transforms.RandomCrop(224), transforms.ToTensor()]) |
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| | | |
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| | | transformations = transforms.Compose([transforms.Scale(224), |
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| | | transforms.CenterCrop(224), transforms.ToTensor(), |
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| | | transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) |
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| | |
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| | | # Test the Model |
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| | | model.eval() # Change model to 'eval' mode (BN uses moving mean/var). |
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| | | total = 0 |
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| | | n_margins = 20 |
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| | | yaw_correct = np.zeros(n_margins) |
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| | | pitch_correct = np.zeros(n_margins) |
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| | | roll_correct = np.zeros(n_margins) |
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| | | |
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| | | idx_tensor = [idx for idx in xrange(66)] |
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| | | idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu) |
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| | |
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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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| | | # yaw_bpred[er] += (label_yaw[0] in range(yaw_bpred[0,0] - er, yaw_bpred[0,0] + er + 1)) |
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| | | # pitch_bpred[er] += (label_pitch[0] in range(pitch_bpred[0,0] - er, pitch_bpred[0,0] + er + 1)) |
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| | | # roll_bpred[er] += (label_roll[0] in range(roll_bpred[0,0] - er, roll_bpred[0,0] + er + 1)) |
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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[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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| | | ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total, |
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| | | pitch_error / total, roll_error / total)) |
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| | | |
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| | | # Binned accuracy |
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| | | # for idx in xrange(len(yaw_correct)): |
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| | | # print yaw_correct[idx] / total, pitch_correct[idx] / total, roll_correct[idx] / total |
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