Source code for imaginaire.evaluation.prdc

# Copyright (C) 2021 NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, check out
Modified from
Copyright (c) 2020-present NAVER Corp.
MIT license
import os
import torch
from imaginaire.utils.distributed import is_master
from imaginaire.utils.distributed import master_only_print as print
from .common import load_or_compute_activations, compute_pairwise_distance, \
[docs]@torch.no_grad() def compute_prdc(prdc_path, data_loader, net_G, key_real='images', key_fake='fake_images', real_act=None, fake_act=None, sample_size=None, save_act=True, k=10, **kwargs): r"""Compute precision diversity curve Args: """ print('Computing PRDC.') act_path = os.path.join( os.path.dirname(prdc_path), 'activations_real.npy' ) if save_act else None # Get the fake activations. if fake_act is None: fake_act = load_or_compute_activations(None, data_loader, key_real, key_fake, net_G, sample_size=sample_size, **kwargs) else: print(f"Using precomputed activations of size {fake_act.shape}.") # Get the ground truth activations. if real_act is None: real_act = load_or_compute_activations(act_path, data_loader, key_real, key_fake, None, sample_size=sample_size, **kwargs) else: print(f"Using precomputed activations of size {real_act.shape}.") if is_master(): prdc_data = _get_prdc(real_act, fake_act, k) return \ prdc_data['precision'], prdc_data['recall'], \ prdc_data['density'], prdc_data['coverage'] else: return None, None, None, None
[docs]def get_kth_value(unsorted, k, dim=-1): r""" Args: unsorted: numpy.ndarray of any dimensionality. k: int Returns: kth values along the designated axis. """ indices = torch.topk(unsorted, k, dim=dim, largest=False)[1] k_smallests = torch.gather(unsorted, dim=dim, index=indices) kth_values = k_smallests.max(dim=dim)[0] return kth_values
def _get_prdc(real_features, fake_features, nearest_k): r""" Computes precision, recall, density, and coverage given two manifolds. Args: real_features: numpy.ndarray([N, feature_dim], dtype=np.float32) fake_features: numpy.ndarray([N, feature_dim], dtype=np.float32) nearest_k: int. Returns: dict of precision, recall, density, and coverage. """ real_nearest_neighbour_distances, _ = compute_nn( real_features, nearest_k) real_nearest_neighbour_distances = \ real_nearest_neighbour_distances.max(dim=-1)[0].cpu() fake_nearest_neighbour_distances, _ = compute_nn( fake_features, nearest_k) fake_nearest_neighbour_distances = \ fake_nearest_neighbour_distances.max(dim=-1)[0].cpu() distance_real_fake = compute_pairwise_distance( real_features, fake_features) precision = ( distance_real_fake < torch.unsqueeze(real_nearest_neighbour_distances, dim=1) ).any(dim=0).float().mean().item() recall = ( distance_real_fake < torch.unsqueeze(fake_nearest_neighbour_distances, dim=0) ).any(dim=1).float().mean().item() density = (1. / float(nearest_k)) * ( distance_real_fake < torch.unsqueeze(real_nearest_neighbour_distances, dim=1) ).sum(dim=0).float().mean().item() # noinspection PyUnresolvedReferences coverage = ( distance_real_fake.min(dim=1)[0] < real_nearest_neighbour_distances ).float().mean().item() return dict(precision=precision, recall=recall, density=density, coverage=coverage)