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from pathlib import Path
import torch
from PIL import Image
from sklearn.decomposition import PCA
import sinder
from .singular_defect import singular_defect_directions
def pca_array(tokens, whiten=False):
h, w, c = tokens.shape
tokens = tokens.detach().cpu()
pca = PCA(n_components=3, whiten=whiten)
pca.fit(tokens.reshape(-1, c))
projected_tokens = pca.transform(tokens.reshape(-1, c))
t = torch.tensor(projected_tokens)
t_min = t.min(dim=0, keepdim=True).values
t_max = t.max(dim=0, keepdim=True).values
normalized_t = (t - t_min) / (t_max - t_min)
array = (normalized_t * 255).byte().numpy()
array = array.reshape(h, w, 3)
return Image.fromarray(array).resize((w * 7, h * 7), 0)
def get_tokens(model, image, blocks=1):
model.eval()
with torch.no_grad():
image_batch = image.unsqueeze(0).cuda()
image_batch = image_batch.cuda()
H = image_batch.shape[2]
W = image_batch.shape[3]
print(f'{W=} {H=}')
tokens = model.get_intermediate_layers(
image_batch, blocks, return_class_token=True, norm=False
)
tokens = [
(
t.reshape(
(H // model.patch_size, W // model.patch_size, t.size(-1))
),
tc,
)
for t, tc in tokens
]
return tokens
def load_model(model_name, checkpoint=None):
print(f'using {model_name} model')
model = torch.hub.load(
repo_or_dir=Path(sinder.__file__).parent.parent,
source='local',
model=model_name,
)
if checkpoint is not None:
states = torch.load(checkpoint, map_location='cpu')
model.load_state_dict(states, strict=False)
model = model.cuda()
model.eval()
model.interpolate_antialias = True
model.singular_defects = singular_defect_directions(model)
print(f'model loaded. patch size: {model.patch_size}')
return model
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