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| # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # This work is licensed under the Creative Commons Attribution-NonCommercial | |
| # 4.0 International License. To view a copy of this license, visit | |
| # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to | |
| # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. | |
| """Frechet Inception Distance (FID).""" | |
| import os | |
| import numpy as np | |
| import scipy | |
| import tensorflow as tf | |
| import dnnlib.tflib as tflib | |
| from metrics import metric_base | |
| from training import misc | |
| #---------------------------------------------------------------------------- | |
| class FID(metric_base.MetricBase): | |
| def __init__(self, num_images, minibatch_per_gpu, **kwargs): | |
| super().__init__(**kwargs) | |
| self.num_images = num_images | |
| self.minibatch_per_gpu = minibatch_per_gpu | |
| def _evaluate(self, Gs, num_gpus): | |
| minibatch_size = num_gpus * self.minibatch_per_gpu | |
| inception = misc.load_pkl('https://drive.google.com/uc?id=1MzTY44rLToO5APn8TZmfR7_ENSe5aZUn') # inception_v3_features.pkl | |
| activations = np.empty([self.num_images, inception.output_shape[1]], dtype=np.float32) | |
| # Calculate statistics for reals. | |
| cache_file = self._get_cache_file_for_reals(num_images=self.num_images) | |
| os.makedirs(os.path.dirname(cache_file), exist_ok=True) | |
| if os.path.isfile(cache_file): | |
| mu_real, sigma_real = misc.load_pkl(cache_file) | |
| else: | |
| for idx, images in enumerate(self._iterate_reals(minibatch_size=minibatch_size)): | |
| begin = idx * minibatch_size | |
| end = min(begin + minibatch_size, self.num_images) | |
| activations[begin:end] = inception.run(images[:end-begin], num_gpus=num_gpus, assume_frozen=True) | |
| if end == self.num_images: | |
| break | |
| mu_real = np.mean(activations, axis=0) | |
| sigma_real = np.cov(activations, rowvar=False) | |
| misc.save_pkl((mu_real, sigma_real), cache_file) | |
| # Construct TensorFlow graph. | |
| result_expr = [] | |
| for gpu_idx in range(num_gpus): | |
| with tf.device('/gpu:%d' % gpu_idx): | |
| Gs_clone = Gs.clone() | |
| inception_clone = inception.clone() | |
| latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:]) | |
| images = Gs_clone.get_output_for(latents, None, is_validation=True, randomize_noise=True) | |
| images = tflib.convert_images_to_uint8(images) | |
| result_expr.append(inception_clone.get_output_for(images)) | |
| # Calculate statistics for fakes. | |
| for begin in range(0, self.num_images, minibatch_size): | |
| end = min(begin + minibatch_size, self.num_images) | |
| activations[begin:end] = np.concatenate(tflib.run(result_expr), axis=0)[:end-begin] | |
| mu_fake = np.mean(activations, axis=0) | |
| sigma_fake = np.cov(activations, rowvar=False) | |
| # Calculate FID. | |
| m = np.square(mu_fake - mu_real).sum() | |
| s, _ = scipy.linalg.sqrtm(np.dot(sigma_fake, sigma_real), disp=False) # pylint: disable=no-member | |
| dist = m + np.trace(sigma_fake + sigma_real - 2*s) | |
| self._report_result(np.real(dist)) | |
| #---------------------------------------------------------------------------- | |