update doc

docs/qa.md

@@ -60,7 +60,7 @@ CycleGAN is more memory-intensive than pix2pix as it requires two generators and
 
 - During training, train CycleGAN on cropped images of the training set. Please be careful not to change the aspect ratio or the scale of the original image, as this can lead to the training/test gap. You can usually do this by using `--resize_or_crop crop` option, or `--resize_or_crop scale_width_and_crop`.
 
-- Then at test time, you can load only one generator to produce the results in a single direction. This greatly saves GPU memory as you are not loading the discriminators and the other generator in the opposite direction. You can probably take the whole image as input. You can do this using `--model test --dataroot [path to the directory that contains your test images (e.g., ./datasets/horse2zebra/trainA)] --model_suffix _A --resize_or_crop none`. You can use either `--resize_or_crop none` or `--resize_or_crop scale_width --fineSize [your_desired_image_width]. Please see [model_suffix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/test_model.py#L16) and [resize_or_crop](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9fc9eef1b0a14b77f7fad7b59ff750f29509dee0/data/base_dataset.py#L26) for more details.
+- Then at test time, you can load only one generator to produce the results in a single direction. This greatly saves GPU memory as you are not loading the discriminators and the other generator in the opposite direction. You can probably take the whole image as input. You can do this using `--model test --dataroot [path to the directory that contains your test images (e.g., ./datasets/horse2zebra/trainA)] --model_suffix _A --resize_or_crop none`. You can use either `--resize_or_crop none` or `--resize_or_crop scale_width --fineSize [your_desired_image_width]`. Please see the [model_suffix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/test_model.py#L16) and [resize_or_crop](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/data/base_dataset.py#L24) for more details.
 
 
 #### The color gets inverted from the beginning of training ([#249](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/249))