Spaces:
Runtime error
Runtime error
File size: 6,970 Bytes
b6dd358 |
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 |
import cv2
import os
import sys
sys.path.insert(0, '../')
import numpy as np
import math
import glob
import pyspng
import PIL.Image
import torch
import dnnlib
import scipy.linalg
import sklearn.svm
_feature_detector_cache = dict()
def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False):
assert 0 <= rank < num_gpus
key = (url, device)
if key not in _feature_detector_cache:
is_leader = (rank == 0)
if not is_leader and num_gpus > 1:
torch.distributed.barrier() # leader goes first
with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f:
_feature_detector_cache[key] = torch.jit.load(f).eval().to(device)
if is_leader and num_gpus > 1:
torch.distributed.barrier() # others follow
return _feature_detector_cache[key]
def read_image(image_path):
with open(image_path, 'rb') as f:
if pyspng is not None and image_path.endswith('.png'):
image = pyspng.load(f.read())
else:
image = np.array(PIL.Image.open(f))
if image.ndim == 2:
image = image[:, :, np.newaxis] # HW => HWC
if image.shape[2] == 1:
image = np.repeat(image, 3, axis=2)
image = image.transpose(2, 0, 1) # HWC => CHW
image = torch.from_numpy(image).unsqueeze(0).to(torch.uint8)
return image
class FeatureStats:
def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None):
self.capture_all = capture_all
self.capture_mean_cov = capture_mean_cov
self.max_items = max_items
self.num_items = 0
self.num_features = None
self.all_features = None
self.raw_mean = None
self.raw_cov = None
def set_num_features(self, num_features):
if self.num_features is not None:
assert num_features == self.num_features
else:
self.num_features = num_features
self.all_features = []
self.raw_mean = np.zeros([num_features], dtype=np.float64)
self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64)
def is_full(self):
return (self.max_items is not None) and (self.num_items >= self.max_items)
def append(self, x):
x = np.asarray(x, dtype=np.float32)
assert x.ndim == 2
if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items):
if self.num_items >= self.max_items:
return
x = x[:self.max_items - self.num_items]
self.set_num_features(x.shape[1])
self.num_items += x.shape[0]
if self.capture_all:
self.all_features.append(x)
if self.capture_mean_cov:
x64 = x.astype(np.float64)
self.raw_mean += x64.sum(axis=0)
self.raw_cov += x64.T @ x64
def append_torch(self, x, num_gpus=1, rank=0):
assert isinstance(x, torch.Tensor) and x.ndim == 2
assert 0 <= rank < num_gpus
if num_gpus > 1:
ys = []
for src in range(num_gpus):
y = x.clone()
torch.distributed.broadcast(y, src=src)
ys.append(y)
x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples
self.append(x.cpu().numpy())
def get_all(self):
assert self.capture_all
return np.concatenate(self.all_features, axis=0)
def get_all_torch(self):
return torch.from_numpy(self.get_all())
def get_mean_cov(self):
assert self.capture_mean_cov
mean = self.raw_mean / self.num_items
cov = self.raw_cov / self.num_items
cov = cov - np.outer(mean, mean)
return mean, cov
def save(self, pkl_file):
with open(pkl_file, 'wb') as f:
pickle.dump(self.__dict__, f)
@staticmethod
def load(pkl_file):
with open(pkl_file, 'rb') as f:
s = dnnlib.EasyDict(pickle.load(f))
obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items)
obj.__dict__.update(s)
return obj
def calculate_metrics(folder1, folder2):
l1 = sorted(glob.glob(folder1 + '/*.png') + glob.glob(folder1 + '/*.jpg'))
l2 = sorted(glob.glob(folder2 + '/*.png') + glob.glob(folder2 + '/*.jpg'))
assert(len(l1) == len(l2))
print('length:', len(l1))
# l1 = l1[:3]; l2 = l2[:3];
# build detector
detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer.
device = torch.device('cuda:0')
detector = get_feature_detector(url=detector_url, device=device, num_gpus=1, rank=0, verbose=False)
detector.eval()
stat1 = FeatureStats(capture_all=True, capture_mean_cov=True, max_items=len(l1))
stat2 = FeatureStats(capture_all=True, capture_mean_cov=True, max_items=len(l1))
with torch.no_grad():
for i, (fpath1, fpath2) in enumerate(zip(l1, l2)):
print(i)
_, name1 = os.path.split(fpath1)
_, name2 = os.path.split(fpath2)
name1 = name1.split('.')[0]
name2 = name2.split('.')[0]
assert name1 == name2, 'Illegal mapping: %s, %s' % (name1, name2)
img1 = read_image(fpath1).to(device)
img2 = read_image(fpath2).to(device)
assert img1.shape == img2.shape, 'Illegal shape'
fea1 = detector(img1, **detector_kwargs)
stat1.append_torch(fea1, num_gpus=1, rank=0)
fea2 = detector(img2, **detector_kwargs)
stat2.append_torch(fea2, num_gpus=1, rank=0)
# calculate fid
mu1, sigma1 = stat1.get_mean_cov()
mu2, sigma2 = stat2.get_mean_cov()
m = np.square(mu1 - mu2).sum()
s, _ = scipy.linalg.sqrtm(np.dot(sigma1, sigma2), disp=False) # pylint: disable=no-member
fid = np.real(m + np.trace(sigma1 + sigma2 - s * 2))
# calculate pids and uids
fake_activations = stat1.get_all()
real_activations = stat2.get_all()
svm = sklearn.svm.LinearSVC(dual=False)
svm_inputs = np.concatenate([real_activations, fake_activations])
svm_targets = np.array([1] * real_activations.shape[0] + [0] * fake_activations.shape[0])
print('SVM fitting ...')
svm.fit(svm_inputs, svm_targets)
uids = 1 - svm.score(svm_inputs, svm_targets)
real_outputs = svm.decision_function(real_activations)
fake_outputs = svm.decision_function(fake_activations)
pids = np.mean(fake_outputs > real_outputs)
return fid, pids, uids
if __name__ == '__main__':
folder1 = 'path to the inpainted result'
folder2 = 'path to the gt'
fid, pids, uids = calculate_metrics(folder1, folder2)
print('fid: %.4f, pids: %.4f, uids: %.4f' % (fid, pids, uids))
with open('fid_pids_uids.txt', 'w') as f:
f.write('fid: %.4f, pids: %.4f, uids: %.4f' % (fid, pids, uids))
|