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import numpy as np
import scipy.linalg
from . import metric_utils
import sklearn.svm
#----------------------------------------------------------------------------
def compute_ids(opts, max_real, num_gen):
# Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
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.
real_activations = metric_utils.compute_feature_stats_for_dataset(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all()
fake_activations = metric_utils.compute_feature_stats_for_generator(
opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all()
if opts.rank != 0:
return float('nan')
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('Fitting ...')
svm.fit(svm_inputs, svm_targets)
u_ids = 1 - svm.score(svm_inputs, svm_targets)
real_outputs = svm.decision_function(real_activations)
fake_outputs = svm.decision_function(fake_activations)
p_ids = np.mean(fake_outputs > real_outputs)
return float(u_ids), float(p_ids)
#----------------------------------------------------------------------------
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