prdc.py

"""
Copyright (c) 2020-present NAVER Corp.

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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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"""

### Reliable Fidelity and Diversity Metrics for Generative Models (https://arxiv.org/abs/2002.09797)
### Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, Jaejun Yoo
### https://github.com/clovaai/generative-evaluation-prdc


from tqdm import tqdm
import math

import torch
import numpy as np
import sklearn.metrics

import utils.sample as sample
import utils.losses as losses

__all__ = ["compute_prdc"]


def compute_real_embeddings(data_loader, batch_size, eval_model, quantize, world_size, DDP, disable_tqdm):
    data_iter = iter(data_loader)
    num_batches = int(math.ceil(float(len(data_loader.dataset)) / float(batch_size)))
    if DDP: num_batches = num_batches = int(math.ceil(float(len(data_loader.dataset)) / float(batch_size*world_size)))

    real_embeds = []
    for i in tqdm(range(num_batches), disable=disable_tqdm):
        try:
            real_images, real_labels = next(data_iter)
        except StopIteration:
            break

        real_images, real_labels = real_images.to("cuda"), real_labels.to("cuda")

        with torch.no_grad():
            real_embeddings, _ = eval_model.get_outputs(real_images, quantize=quantize)
            real_embeds.append(real_embeddings)

    real_embeds = torch.cat(real_embeds, dim=0)
    if DDP: real_embeds = torch.cat(losses.GatherLayer.apply(real_embeds), dim=0)
    real_embeds = np.array(real_embeds.detach().cpu().numpy(), dtype=np.float64)
    return real_embeds[:len(data_loader.dataset)]


def calculate_pr_dc(real_feats, fake_feats, data_loader, eval_model, num_generate, cfgs, quantize, nearest_k,
                    world_size, DDP, disable_tqdm):
    eval_model.eval()

    if real_feats is None:
        real_embeds = compute_real_embeddings(data_loader=data_loader,
                                              batch_size=cfgs.OPTIMIZATION.batch_size,
                                              eval_model=eval_model,
                                              quantize=quantize,
                                              world_size=world_size,
                                              DDP=DDP,
                                              disable_tqdm=disable_tqdm)

    real_embeds = real_feats
    fake_embeds = np.array(fake_feats.detach().cpu().numpy(), dtype=np.float64)[:num_generate]

    metrics = compute_prdc(real_features=real_embeds, fake_features=fake_embeds, nearest_k=nearest_k)

    prc, rec, dns, cvg = metrics["precision"], metrics["recall"], metrics["density"], metrics["coverage"]
    return prc, rec, dns, cvg


def compute_pairwise_distance(data_x, data_y=None):
    """
    Args:
        data_x: numpy.ndarray([N, feature_dim], dtype=np.float32)
        data_y: numpy.ndarray([N, feature_dim], dtype=np.float32)
    Returns:
        numpy.ndarray([N, N], dtype=np.float32) of pairwise distances.
    """
    if data_y is None:
        data_y = data_x
    dists = sklearn.metrics.pairwise_distances(
        data_x, data_y, metric='euclidean', n_jobs=8)
    return dists


def get_kth_value(unsorted, k, axis=-1):
    """
    Args:
        unsorted: numpy.ndarray of any dimensionality.
        k: int
    Returns:
        kth values along the designated axis.
    """
    indices = np.argpartition(unsorted, k, axis=axis)[..., :k]
    k_smallests = np.take_along_axis(unsorted, indices, axis=axis)
    kth_values = k_smallests.max(axis=axis)
    return kth_values


def compute_nearest_neighbour_distances(input_features, nearest_k):
    """
    Args:
        input_features: numpy.ndarray([N, feature_dim], dtype=np.float32)
        nearest_k: int
    Returns:
        Distances to kth nearest neighbours.
    """
    distances = compute_pairwise_distance(input_features)
    radii = get_kth_value(distances, k=nearest_k + 1, axis=-1)
    return radii


def compute_prdc(real_features, fake_features, nearest_k):
    """
    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_nearest_neighbour_distances(
        real_features, nearest_k)
    fake_nearest_neighbour_distances = compute_nearest_neighbour_distances(
        fake_features, nearest_k)
    distance_real_fake = compute_pairwise_distance(
        real_features, fake_features)

    precision = (
            distance_real_fake <
            np.expand_dims(real_nearest_neighbour_distances, axis=1)
    ).any(axis=0).mean()

    recall = (
            distance_real_fake <
            np.expand_dims(fake_nearest_neighbour_distances, axis=0)
    ).any(axis=1).mean()

    density = (1. / float(nearest_k)) * (
            distance_real_fake <
            np.expand_dims(real_nearest_neighbour_distances, axis=1)
    ).sum(axis=0).mean()

    coverage = (
            distance_real_fake.min(axis=1) <
            real_nearest_neighbour_distances
    ).mean()

    return dict(precision=precision, recall=recall,
                density=density, coverage=coverage)