| | |
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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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| | | # Binned predictions |
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| | | _, yaw_bpred = torch.max(yaw.data, 1) |
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| | | _, pitch_bpred = torch.max(pitch.data, 1) |
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| | | _, roll_bpred = torch.max(roll.data, 1) |
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| | | |
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| | | # Continuous predictions |
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| | | yaw_predicted = utils.softmax_temperature(yaw.data, 1) |
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| | | pitch_predicted = utils.softmax_temperature(pitch.data, 1) |
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| | | roll_predicted = utils.softmax_temperature(roll.data, 1) |
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| | | |
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| | | yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() |
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| | | pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() |
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| | | roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() |
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| | | pre_yaw, pre_pitch, pre_roll, angles = model(images) |
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| | | yaw = angles[:,0].cpu().data |
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| | | pitch = angles[:,1].cpu().data |
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| | | roll = angles[:,2].cpu().data |
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| | | |
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| | | # Mean absolute error |
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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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| | | # 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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| | | yaw_error += torch.sum(torch.abs(yaw - label_yaw) * 3) |
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| | | pitch_error += torch.sum(torch.abs(pitch - label_pitch) * 3) |
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| | | roll_error += torch.sum(torch.abs(roll - label_roll) * 3) |
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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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| | | utils.plot_pose_cube(cv2_img, yaw[0] * 3 - 99, pitch[0] * 3 - 99, roll[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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