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fid.py
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fid.py
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# This work is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License.
# You should have received a copy of the license along with this
# work. If not, see http://creativecommons.org/licenses/by-nc-sa/4.0/
"""Script for calculating Frechet Inception Distance (FID)."""
import os
import click
import tqdm
import pickle
import numpy as np
from scipy import linalg
import torch
import dnnlib
from torch_utils import distributed as dist
from training import dataset
import glob
#----------------------------------------------------------------------------
def calculate_inception_stats(
image_path, num_expected=None, seed=0, max_batch_size=64,
num_workers=3, prefetch_factor=2, device=torch.device('cuda'),
):
# Rank 0 goes first.
if dist.get_rank() != 0:
torch.distributed.barrier()
# Load Inception-v3 model.
# This is a direct PyTorch translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
dist.print0('Loading Inception-v3 model...')
detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl'
detector_kwargs = dict(return_features=True)
feature_dim = 2048
with dnnlib.util.open_url(detector_url, verbose=(dist.get_rank() == 0)) as f:
detector_net = pickle.load(f).to(device)
# List images.
dist.print0(f'Loading images from "{image_path}"...')
dataset_obj = dataset.ImageFolderDataset(path=image_path, max_size=num_expected, random_seed=seed)
if num_expected is not None and len(dataset_obj) < num_expected:
raise click.ClickException(f'Found {len(dataset_obj)} images, but expected at least {num_expected}')
if len(dataset_obj) < 2:
raise click.ClickException(f'Found {len(dataset_obj)} images, but need at least 2 to compute statistics')
# Other ranks follow.
if dist.get_rank() == 0:
torch.distributed.barrier()
# Divide images into batches.
num_batches = ((len(dataset_obj) - 1) // (max_batch_size * dist.get_world_size()) + 1) * dist.get_world_size()
# TODO: test different seeds and take the minimum
all_batches = torch.arange(len(dataset_obj)).tensor_split(num_batches)
rank_batches = all_batches[dist.get_rank() :: dist.get_world_size()]
data_loader = torch.utils.data.DataLoader(dataset_obj, batch_sampler=rank_batches, num_workers=0, prefetch_factor=prefetch_factor)
# Accumulate statistics.
dist.print0(f'Calculating statistics for {len(dataset_obj)} images...')
mu = torch.zeros([feature_dim], dtype=torch.float64, device=device)
sigma = torch.zeros([feature_dim, feature_dim], dtype=torch.float64, device=device)
for images, _labels in tqdm.tqdm(data_loader, unit='batch', disable=(dist.get_rank() != 0)):
torch.distributed.barrier()
if images.shape[0] == 0:
continue
if images.shape[1] == 1:
images = images.repeat([1, 3, 1, 1])
features = detector_net(images.to(device), **detector_kwargs).to(torch.float64)
mu += features.sum(0)
sigma += features.T @ features
# Calculate grand totals.
torch.distributed.all_reduce(mu)
torch.distributed.all_reduce(sigma)
mu /= len(dataset_obj)
sigma -= mu.ger(mu) * len(dataset_obj)
sigma /= len(dataset_obj) - 1
return mu.cpu().numpy(), sigma.cpu().numpy()
#----------------------------------------------------------------------------
def calculate_fid_from_inception_stats(mu, sigma, mu_ref, sigma_ref):
m = np.square(mu - mu_ref).sum()
s, _ = linalg.sqrtm(np.dot(sigma, sigma_ref), disp=False)
fid = m + np.trace(sigma + sigma_ref - s * 2)
return float(np.real(fid))
#----------------------------------------------------------------------------
@click.group()
def main():
"""Calculate Frechet Inception Distance (FID).
Examples:
\b
# Generate 50000 images and save them as fid-tmp/*/*.png
torchrun --standalone --nproc_per_node=1 generate.py --outdir=fid-tmp --seeds=0-49999 --subdirs \\
--network=https://nvlabs-fi-cdn.nvidia.com/edm/pretrained/edm-cifar10-32x32-cond-vp.pkl
\b
# Calculate FID
torchrun --standalone --nproc_per_node=1 fid.py calc --images=fid-tmp \\
--ref=https://nvlabs-fi-cdn.nvidia.com/edm/fid-refs/cifar10-32x32.npz
\b
# Compute dataset reference statistics
python fid.py ref --data=datasets/my-dataset.zip --dest=fid-refs/my-dataset.npz
"""
#----------------------------------------------------------------------------
@main.command()
@click.option('--images', 'image_path', help='Path to the images', metavar='PATH|ZIP', type=str, required=True)
@click.option('--ref', 'ref_path', help='Dataset reference statistics ', metavar='NPZ|URL', type=str, required=True)
@click.option('--num', 'num_expected', help='Number of images to use', metavar='INT', type=click.IntRange(min=2), default=50000, show_default=True)
@click.option('--seed', help='Random seed for selecting the images', metavar='INT', type=int, default=0, show_default=True)
@click.option('--ckpt', help='begin ckpt', metavar='INT', type=int, default=0, show_default=True)
@click.option('--end_ckpt', help='begin ckpt', metavar='INT', type=int, default=1000000, show_default=True)
@click.option('--batch', help='Maximum batch size', metavar='INT', type=click.IntRange(min=1), default=64, show_default=True)
@click.option('--gen_seed', help='generate seeds', metavar='INT', type=click.IntRange(min=1), default=1, show_default=True)
@click.option('--steps', help='load varying steps', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--alpha', help='load varying alpha', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--prune', help='load varying alpha', metavar='BOOL', type=bool, default=False, show_default=True)
@click.option('--quant', help='load varying alpha', metavar='BOOL', type=bool, default=False, show_default=True)
def calc(image_path, ref_path, num_expected, seed, ckpt, end_ckpt, batch, gen_seed, steps, alpha, prune, quant):
"""Calculate FID for a given set of images."""
torch.multiprocessing.set_start_method('spawn')
dist.init()
dist.print0(f'Loading dataset reference statistics from "{ref_path}"...')
ref = None
if dist.get_rank() == 0:
with dnnlib.util.open_url(ref_path) as f:
ref = dict(np.load(f))
print("seed config:", gen_seed)
if gen_seed == 1:
# 0 ~ 49999
if steps:
stats = glob.glob(os.path.join(image_path, "ckpt_[0-9]*_steps_[0-9]*"))
elif alpha:
stats = glob.glob(os.path.join(image_path, "ckpt_[0-9]*_alpha_0.[0-9]*"))
elif prune:
stats = glob.glob(os.path.join(image_path, "ckpt_[0-9]*_prune_0.[0-9]*"))
elif quant:
stats = glob.glob(os.path.join(image_path, "ckpt_[0-9]*_quant_[0-9]*"))
else:
stats = glob.glob(os.path.join(image_path, "ckpt_[0-9]*"))
elif gen_seed == 2:
# 50000 ~ 99999
if prune:
stats = glob.glob(os.path.join(image_path, "ckpt_2_[0-9]*_prune_0.[0-9]*"))
elif quant:
stats = glob.glob(os.path.join(image_path, "ckpt_2_[0-9]*_quant_[0-9]*"))
else:
stats = glob.glob(os.path.join(image_path, "ckpt_2_*"))
elif gen_seed == 3:
# 100000 ~ 149999
stats = glob.glob(os.path.join(image_path, "ckpt_3_*"))
else:
raise NotImplementedError
done_list = []
for path in stats:
print("path:", path)
if not steps and not alpha and not prune and not quant:
ckpt_num = path[-6:]
if ckpt_num[0] == '_':
continue
ckpt_num = ckpt_num[1:]
ckpt_num = int(ckpt_num)
# print(ckpt_num)
if ckpt_num < ckpt or ckpt_num > end_ckpt or ckpt_num in done_list:
continue
mu, sigma = calculate_inception_stats(image_path=path, num_expected=num_expected, seed=seed,
max_batch_size=batch)
dist.print0('Calculating FID... ')
if dist.get_rank() == 0:
fid = calculate_fid_from_inception_stats(mu, sigma, ref['mu'], ref['sigma'])
print(f'path:{path}, {fid:g}')
torch.distributed.barrier()
#----------------------------------------------------------------------------
@main.command()
@click.option('--data', 'dataset_path', help='Path to the dataset', metavar='PATH|ZIP', type=str, required=True)
@click.option('--dest', 'dest_path', help='Destination .npz file', metavar='NPZ', type=str, required=True)
@click.option('--batch', help='Maximum batch size', metavar='INT', type=click.IntRange(min=1), default=64, show_default=True)
def ref(dataset_path, dest_path, batch):
"""Calculate dataset reference statistics needed by 'calc'."""
torch.multiprocessing.set_start_method('spawn')
dist.init()
mu, sigma = calculate_inception_stats(image_path=dataset_path, max_batch_size=batch)
dist.print0(f'Saving dataset reference statistics to "{dest_path}"...')
if dist.get_rank() == 0:
if os.path.dirname(dest_path):
os.makedirs(os.path.dirname(dest_path), exist_ok=True)
np.savez(dest_path, mu=mu, sigma=sigma)
torch.distributed.barrier()
dist.print0('Done.')
#----------------------------------------------------------------------------
if __name__ == "__main__":
main()
#----------------------------------------------------------------------------