Comments (2)
Here's a minimal code snippet for applying a pre-trained Spatial Transformer (non-clustering) to one image:
from models import get_stn
from utils.download import download_model
from utils.vis_tools.helpers import load_pil
from torchvision.utils import save_image
resolution = 512 # resolution the input image will be resized to (this can be any power of 2)
input_img = load_pil('my_image.png', resolution) # load the input image and resize to (1, C, resolution, resolution)
ckpt = download_model('cat') # download model weights
stn = get_stn(['similarity', 'flow'], flow_size=128, supersize=resolution).to('cuda') # instantiate STN
stn.load_state_dict(ckpt['t_ema']) # load weights
aligned_img = stn(input_img, iters=3, output_resolution=resolution) # forward pass through the STN
save_image(aligned_img, 'output.png', normalize=True, range=(-1, 1)) # save to disk
If you're using the celeba
or cub
models, use iters=1
instead. If your input image isn't square you may want to pad or crop it beforehand. Also, stn
supports batch mode, so input_img
can be an (N, C, H, W)
tensor containing multiple images, in which case aligned_image
will also be (N, C, H, W)
.
from gangealing.
thanks. : )
from gangealing.
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