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2c8150b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 | import argparse
from pathlib import Path
import numpy as np
from torch_fidelity import calculate_metrics
from .utils import ImgArrDataset
from torch_fidelity.feature_extractor_inceptionv3 import FeatureExtractorInceptionV3 # original torch-fidelity :contentReference[oaicite:2]{index=2}
import scipy.linalg
import torch
def _fid_from_moments(mu1, sigma1, mu2, sigma2) -> float:
# FID formula :contentReference[oaicite:3]{index=3}
mu1 = np.asarray(mu1, dtype=np.float64)
mu2 = np.asarray(mu2, dtype=np.float64)
sigma1 = np.asarray(sigma1, dtype=np.float64)
sigma2 = np.asarray(sigma2, dtype=np.float64)
diff = mu1 - mu2
covmean = scipy.linalg.sqrtm(sigma1 @ sigma2)
if np.iscomplexobj(covmean): # numerical noise
covmean = covmean.real
fid = diff.dot(diff) + np.trace(sigma1 + sigma2 - 2.0 * covmean)
return float(max(fid, 0.0))
@torch.no_grad()
def _compute_inception_moments_from_arr(arr: np.ndarray, batch_size: int, device: str) -> tuple[np.ndarray, np.ndarray]:
"""
Uses torch-fidelity's InceptionV3 feature extractor to get 2048-d pool features.
Assumes arr is [N,H,W,C] or [N,C,H,W], uint8 (0..255) or float (0..1 or 0..255).
"""
# Convert to torch in NCHW uint8 as the safest default.
x = arr
if x.ndim != 4:
raise ValueError(f"Expected 4D array, got shape {x.shape}")
if x.shape[-1] == 3: # NHWC -> NCHW
x = np.transpose(x, (0, 3, 1, 2))
if x.dtype != np.uint8:
# If float in [0,1], scale up; otherwise assume already 0..255-ish
x_f = x.astype(np.float32)
if x_f.max() <= 1.5:
x_f = x_f * 255.0
x = np.clip(x_f, 0, 255).astype(np.uint8)
xt = torch.from_numpy(x).to(device=device, dtype=torch.uint8)
fe = FeatureExtractorInceptionV3(name="inception-v3-compat", features_list=['2048']).to(device).eval() # preregistered extractor name :contentReference[oaicite:4]{index=4}
feats = []
for i in range(0, xt.shape[0], batch_size):
batch = xt[i : i + batch_size]
f = fe(batch)[0] # (B, 2048)
feats.append(f.detach().cpu())
feats = torch.cat(feats, dim=0).double().numpy() # (N, 2048) float64
mu = feats.mean(axis=0)
sigma = np.cov(feats, rowvar=False)
return mu, sigma
@torch.no_grad()
def compute_inception_features(arr: np.ndarray, batch_size: int, device: str) -> np.ndarray:
"""Returns raw 2048-d InceptionV3 features for distributed FID computation."""
x = arr
if x.ndim != 4:
raise ValueError(f"Expected 4D array, got shape {x.shape}")
if x.shape[-1] == 3: # NHWC -> NCHW
x = np.transpose(x, (0, 3, 1, 2))
if x.dtype != np.uint8:
x_f = x.astype(np.float32)
if x_f.max() <= 1.5:
x_f = x_f * 255.0
x = np.clip(x_f, 0, 255).astype(np.uint8)
xt = torch.from_numpy(x).to(device=device, dtype=torch.uint8)
fe = FeatureExtractorInceptionV3(name="inception-v3-compat", features_list=['2048']).to(device).eval()
feats = []
for i in range(0, xt.shape[0], batch_size):
batch = xt[i: i + batch_size]
f = fe(batch)[0]
feats.append(f.detach().cpu())
return torch.cat(feats, dim=0).double().numpy() # (N, 2048)
def calculate_rfid_from_features(features1: np.ndarray, features2: np.ndarray) -> float:
"""Compute FID from pre-computed inception features (for distributed use)."""
mu1 = features1.mean(axis=0)
sigma1 = np.cov(features1, rowvar=False)
mu2 = features2.mean(axis=0)
sigma2 = np.cov(features2, rowvar=False)
return _fid_from_moments(mu1, sigma1, mu2, sigma2)
def calculate_gfid(
arr1: np.ndarray,
ref_arr: dict,
batch_size: int = 64,
device: str = "cuda",
) -> float:
mu_ref, sigma_ref = ref_arr['mu'], ref_arr['sigma']
mu_gen, sigma_gen = _compute_inception_moments_from_arr(arr1, batch_size=batch_size, device=device)
return _fid_from_moments(mu_gen, sigma_gen, mu_ref, sigma_ref)
def calculate_rfid(
arr1,
arr2=None,
bs=64,
device="cuda",
fid_statistics_file=None,
):
arr1_ds = ImgArrDataset(arr1)
if fid_statistics_file is not None:
metrics_kwargs = dict(
input1=arr1_ds,
input2=None,
fid_statistics_file=fid_statistics_file,
batch_size=bs,
fid=True,
cuda=(device == "cuda"),
)
else:
if arr2 is None:
raise ValueError("Either arr2 or fid_statistics_file must be provided.")
arr2_ds = ImgArrDataset(arr2)
metrics_kwargs = dict(
input1=arr1_ds,
input2=arr2_ds,
batch_size=bs,
fid=True,
cuda=(device == "cuda"),
)
metrics = calculate_metrics(**metrics_kwargs)
return metrics["frechet_inception_distance"]
if __name__ == "__main__":
import argparse
from pathlib import Path
import numpy as np
import torch
parser = argparse.ArgumentParser(description="Compute FID using original torch-fidelity")
parser.add_argument("--arr1", type=str, required=True,
help="Path to generated images array (.npy or .npz). Array should be N x H x W x 3 or N x 3 x H x W.")
parser.add_argument("--arr2", type=str, default=None,
help="Optional path to reference images array (.npy or .npz). If set, uses torch_fidelity.calculate_metrics.")
# reference stats path (npz with mu/sigma keys)
parser.add_argument("--ref", "--fid-statistics-file", dest="ref", type=str, default=None,
help="Path to reference stats (.npz with mu/sigma, mu_s/sigma_s, mu_clip/sigma_clip, etc.). "
"If set, computes moments for arr1 using torch-fidelity InceptionV3 and FID vs these stats.")
# which keys in the ref npz to use
parser.add_argument("--ref-mu-key", type=str, default="mu",
help="Key in --ref .npz for reference mean (default: mu).")
parser.add_argument("--ref-sigma-key", type=str, default="sigma",
help="Key in --ref .npz for reference covariance (default: sigma).")
parser.add_argument("--batch-size", type=int, default=64)
parser.add_argument("--device", type=str, default="cuda", choices=["cuda", "cpu"])
parser.add_argument("--no-scipy", action="store_true",
help="Do not use scipy.linalg.sqrtm; use torch eig fallback (slower/less identical).")
args = parser.parse_args()
# exactly one of arr2 or ref must be provided
if (args.arr2 is None) == (args.ref is None):
parser.error("Specify exactly one of --arr2 or --ref/--fid-statistics-file")
def load_array(path: str) -> np.ndarray:
p = Path(path)
if not p.exists():
raise FileNotFoundError(f"File not found: {p}")
if p.suffix == ".npy":
return np.load(p)
elif p.suffix == ".npz":
z = np.load(p)
# common conventions: 'arr_0' or 'images'
if "arr_0" in z.files:
return z["arr_0"]
if "images" in z.files:
return z["images"]
raise KeyError(f"{p} is .npz but has no 'arr_0' or 'images'. Keys={z.files}")
else:
raise ValueError(f"Unsupported array file type: {p.suffix} (expected .npy or .npz)")
arr1 = load_array(args.arr1)
print("[INFO] arr1:", arr1.shape, arr1.dtype)
if args.arr2 is not None:
arr2 = load_array(args.arr2)
print("[INFO] arr2:", arr2.shape, arr2.dtype)
fid = calculate_rfid(
arr1=arr1,
arr2=arr2,
bs=args.batch_size,
device=args.device,
fid_statistics_file=None, # unused in upstream path
)
print(f"[RESULT] FID: {fid:.6f}")
raise SystemExit(0)
# stats mode: arr1 vs ref npz moments
ref_path = Path(args.ref)
if not ref_path.exists():
raise FileNotFoundError(f"Ref stats not found: {ref_path}")
if ref_path.suffix != ".npz":
raise ValueError(
f"--ref must be a .npz containing mu/sigma. Got: {ref_path.suffix}. "
"Original torch-fidelity cannot use .pt stats without input2."
)
stats = np.load(ref_path)
if args.ref_mu_key not in stats.files or args.ref_sigma_key not in stats.files:
raise KeyError(
f"Missing '{args.ref_mu_key}'/'{args.ref_sigma_key}' in {ref_path}. "
f"Available keys: {list(stats.files)}"
)
print(f"[INFO] ref stats: {ref_path}")
# If you want to respect --no-scipy, thread it through:
# (edit your fid_arr_vs_npzstats / _fid_from_moments accordingly)
fid = calculate_gfid(
arr1=arr1,
ref_arr=stats,
batch_size=args.batch_size,
device=args.device,
# optionally add: use_scipy=(not args.no_scipy)
)
print(f"[RESULT] FID: {fid:.6f}") |