File size: 3,840 Bytes
b910c09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | import torch
import torch.nn as nn
from torchmetrics import CatMetric
from torchmetrics import SumMetric # sum over devices
from torchmetrics.image.fid import FrechetInceptionDistance
from torchmetrics.multimodal.clip_score import CLIPScore
from jutils.nn import DINOv2, preprocess_for_dinov2
from jutils.nn.metric_kid import kid_features_to_metric
def un_normalize_ims(ims):
"""Convert from [-1, 1] to [0, 255]"""
ims = ((ims * 127.5) + 127.5).clip(0, 255).to(torch.uint8)
return ims
class ImageMetricTracker(nn.Module):
def __init__(self):
super().__init__()
self.total_samples = SumMetric()
self.fid = FrechetInceptionDistance(
feature=2048, reset_real_features=True, normalize=False, sync_on_compute=True
)
def __call__(self, target, pred):
"""Assumes target and pred in [-1, 1] range"""
bs = target.shape[0]
real_ims = un_normalize_ims(target)
fake_ims = un_normalize_ims(pred)
self.fid.update(real_ims, real=True)
self.fid.update(fake_ims, real=False)
self.total_samples.update(bs)
def reset(self):
self.fid.reset()
self.total_samples.reset()
def aggregate(self):
"""Compute the final metrics (automatically synced across devices)"""
n_total_samples = int(self.total_samples.compute())
return {
f"fid-{n_total_samples}": self.fid.compute(),
"n_metric_samples": n_total_samples,
}
class Text2ImageMetricTracker(nn.Module):
def __init__(self, kid_subsets: int = 100, kid_subset_size: int = 200):
super().__init__()
self.total_samples = SumMetric()
self.fid = FrechetInceptionDistance(
feature=2048, reset_real_features=True, normalize=False, sync_on_compute=True
)
self.clip = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16")
self.dino = DINOv2(pretrained=True).eval()
self.dino_features_real = CatMetric()
self.dino_features_fake = CatMetric()
self.kid_subsets = kid_subsets
self.kid_subset_size = kid_subset_size
for p in self.parameters():
p.requires_grad = False
@torch.no_grad()
def __call__(self, target, pred, txt):
"""Assumes target and pred in [-1, 1] range"""
bs = target.shape[0]
real_ims = un_normalize_ims(target)
fake_ims = un_normalize_ims(pred)
self.fid.update(real_ims, real=True)
self.fid.update(fake_ims, real=False)
txt = [t.decode() if isinstance(t, bytes) else t for t in txt]
self.clip.update(fake_ims, list(txt))
real_fts = self.dino(preprocess_for_dinov2(target, safe_mode=False))
fake_fts = self.dino(preprocess_for_dinov2(pred, safe_mode=False))
self.dino_features_real.update(real_fts)
self.dino_features_fake.update(fake_fts)
self.total_samples.update(bs)
def reset(self):
self.fid.reset()
self.clip.reset()
self.dino_features_real.reset()
self.dino_features_fake.reset()
self.total_samples.reset()
def aggregate(self):
"""Compute the final metrics (automatically synced across devices)"""
n_total_samples = int(self.total_samples.compute())
# compute KDD
real_fts = self.dino_features_real.compute()
fake_fts = self.dino_features_fake.compute()
kdd = kid_features_to_metric(
real_fts,
fake_fts,
kid_subsets=self.kid_subsets,
kid_subset_size=self.kid_subset_size,
verbose=False,
)
return {
f"fid-{n_total_samples}": self.fid.compute(),
f"clip": self.clip.compute(),
**kdd,
"n_metric_samples": n_total_samples,
}
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