-
Notifications
You must be signed in to change notification settings - Fork 13
/
fid.py
177 lines (141 loc) · 7.18 KB
/
fid.py
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
# MIT License
# Copyright (c) [2023] [Anima-Lab]
# This code is adapted from https://github.com/NVlabs/edm/blob/main/fid.py.
# The original code is licensed under a Creative Commons
# Attribution-NonCommercial-ShareAlike 4.0 International License, which is can be found at licenses/LICENSE_EDM.txt.
"""Script for calculating Frechet Inception Distance (FID)."""
import argparse
from multiprocessing import Process
import click
import tqdm
import pickle
import numpy as np
import scipy.linalg
import torch
import torch.distributed as dist
from torch.utils.data import DataLoader
from utils import *
from train_utils.datasets import ImageFolderDataset
#----------------------------------------------------------------------------
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:
dist.barrier()
# Load Inception-v3 model.
# This is a direct PyTorch translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
detector_url = 'https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/metrics/inception-2015-12-05.pkl'
mprint('Loading Inception-v3 model...')
detector_kwargs = dict(return_features=True)
feature_dim = 2048
with open(detector_url, 'rb') as f:
detector_net = pickle.load(f).to(device)
# List images.
mprint(f'Loading images from "{image_path}"...')
dataset_obj = 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:
dist.barrier()
# Divide images into batches.
num_batches = ((len(dataset_obj) - 1) // (max_batch_size * dist.get_world_size()) + 1) * dist.get_world_size()
all_batches = torch.arange(len(dataset_obj)).tensor_split(num_batches)
rank_batches = all_batches[dist.get_rank() :: dist.get_world_size()]
data_loader = DataLoader(dataset_obj, batch_sampler=rank_batches, num_workers=num_workers, prefetch_factor=prefetch_factor)
# Accumulate statistics.
mprint(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)):
dist.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.
dist.all_reduce(mu)
dist.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, _ = scipy.linalg.sqrtm(np.dot(sigma, sigma_ref), disp=False)
fid = m + np.trace(sigma + sigma_ref - s * 2)
return float(np.real(fid))
#----------------------------------------------------------------------------
def calc(image_path, ref_path, num_expected, seed, batch):
"""Calculate FID for a given set of images."""
if dist.get_rank() == 0:
logger = Logger(file_name=f'{image_path}/log_fid.txt', file_mode="a+", should_flush=True)
mprint(f'Loading dataset reference statistics from "{ref_path}"...')
ref = None
if dist.get_rank() == 0:
assert ref_path.endswith('.npz')
ref = dict(np.load(ref_path))
mu, sigma = calculate_inception_stats(image_path=image_path, num_expected=num_expected, seed=seed, max_batch_size=batch)
mprint('Calculating FID...')
fid = None
if dist.get_rank() == 0:
fid = calculate_fid_from_inception_stats(mu, sigma, ref['mu'], ref['sigma'])
print(f'{fid:g}')
dist.barrier()
if dist.get_rank() == 0:
logger.close()
return fid
#----------------------------------------------------------------------------
def ref(dataset_path, dest_path, batch):
"""Calculate dataset reference statistics needed by 'calc'."""
mu, sigma = calculate_inception_stats(image_path=dataset_path, max_batch_size=batch)
mprint(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)
dist.barrier()
mprint('Done.')
if __name__ == '__main__':
parser = argparse.ArgumentParser('fid parameters')
# ddp
parser.add_argument('--num_proc_node', type=int, default=1, help='The number of nodes in multi node env.')
parser.add_argument('--num_process_per_node', type=int, default=1, help='number of gpus')
parser.add_argument('--node_rank', type=int, default=0, help='The index of node.')
parser.add_argument('--local_rank', type=int, default=0, help='rank of process in the node')
parser.add_argument('--master_address', type=str, default='localhost', help='address for master')
# fid
parser.add_argument('--mode', type=str, required=True, choices=['calc', 'ref'], help='Calcalute FID or store reference statistics')
parser.add_argument('--image_path', type=str, required=True, help='Path to the images')
parser.add_argument('--ref_path', type=str, default='assets/fid_stats/fid_stats_imagenet256_guided_diffusion.npz', help='Dataset reference statistics')
parser.add_argument('--num_expected', type=int, default=50000, help='Number of images to use')
parser.add_argument('--seed', type=int, default=0, help='Random seed for selecting the images')
parser.add_argument('--batch', type=int, default=64, help='Maximum batch size per GPU')
args = parser.parse_args()
args.global_size = args.num_proc_node * args.num_process_per_node
size = args.num_process_per_node
func = lambda args: calc(args.image_path, args.ref_path, args.num_expected, args.seed, args.batch) \
if args.mode == 'calc' else lambda args: ref(args.image_path, args.ref_path, args.batch)
if size > 1:
processes = []
for rank in range(size):
args.local_rank = rank
args.global_rank = rank + args.node_rank * args.num_process_per_node
p = Process(target=init_processes, args=(func, args))
p.start()
processes.append(p)
for p in processes:
p.join()
else:
print('Single GPU run')
assert args.global_size == 1 and args.local_rank == 0
args.global_rank = 0
init_processes(func, args)