fid_official_tf.py

"""
@Brief:
    Tensorflow implementation of FID score, should be the same as the official one
    modified from official inception score implementation
    [bioinf-jku/TTUR](https://github.com/bioinf-jku/TTUR)
@Author: lzhbrian (https://lzhbrian.me)
@Date: 2019.4.7
@Usage:
    # CMD
        # from 2 precalculated stats
        python fid_official_tf.py res/stats_tf/fid_stats_imagenet_valid.npz res/stats_tf/fid_stats_imagenet_train.npz --gpu 0

        # from 1 precalculated stats, 1 image foldername/
        python fid_official_tf.py res/stats_tf/fid_stats_imagenet_valid.npz /path/to/image/foldername/ --gpu 0

        # from 2 image foldername/
        python fid_official_tf.py /path/to/image/foldername1/ /path/to/image/foldername2/ --gpu 0

    # used in code
        ```
        import tensorflow as tf

        # load from precalculated
        f = np.load('res/stats_tf/fid_stats_imagenet_train.npz')
        mu1, sigma1 = f['mu'][:], f['sigma'][:]
        f.close()

        # calc from image ndarray
        # images should be Numpy array of dimension (N, H, W, C). images should be in 0~255
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            mu2, sigma2 = fid_official_tf.calculate_activation_statistics(images, sess, batch_size=100)
        fid_score = calculate_frechet_distance(mu1, sigma1, mu2, sigma2)
        ```

@Note:
    Need to first download stats_tf of datasets in stats_tf/, see README.md

    also, the same as inception_score_official_tf.py, the inception model used
    contains resize and normalization layers
    so the input of our images should be 0~255, and arbitrary HxW size

    For calculating mu and sigma for foldername/, see precalc_stats_official_tf.py
"""

import numpy as np
import os
import tensorflow as tf
import scipy.misc
from scipy.misc import imread
from scipy import linalg
import pathlib
import urllib
import warnings
from tqdm import tqdm


cur_dirname = os.path.dirname(os.path.abspath(__file__))

MODEL_DIR = '%s/res/' % cur_dirname

class InvalidFIDException(Exception):
    pass


def create_inception_graph(pth):
    """Creates a graph from saved GraphDef file."""
    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(pth, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='FID_Inception_Net')


# -------------------------------------------------------------------------------


# code for handling inception net derived from
#   https://github.com/openai/improved-gan/blob/master/inception_score/model.py
def _get_inception_layer(sess):
    """Prepares inception net for batched usage and returns pool_3 layer. """
    layername = 'FID_Inception_Net/pool_3:0'
    pool3 = sess.graph.get_tensor_by_name(layername)
    ops = pool3.graph.get_operations()
    for op_idx, op in enumerate(ops):
        for o in op.outputs:
            shape = o.get_shape()
            if shape._dims != []:
                shape = [s.value for s in shape]
                new_shape = []
                for j, s in enumerate(shape):
                    if s == 1 and j == 0:
                        new_shape.append(None)
                    else:
                        new_shape.append(s)
                o.__dict__['_shape_val'] = tf.TensorShape(new_shape)
    return pool3


# -------------------------------------------------------------------------------


def get_activations(images, sess, batch_size=50, verbose=False):
    """Calculates the activations of the pool_3 layer for all images.
    Params:
    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
                     must lie between 0 and 256.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the disposable hardware.
    -- verbose    : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- A numpy array of dimension (num images, 2048) that contains the
       activations of the given tensor when feeding inception with the query tensor.
    """
    inception_layer = _get_inception_layer(sess)
    d0 = images.shape[0]
    if batch_size > d0:
        print("warning: batch size is bigger than the data size. setting batch size to data size")
        batch_size = d0
    n_batches = d0 // batch_size
    n_used_imgs = n_batches * batch_size
    pred_arr = np.empty((n_used_imgs, 2048))
    for i in tqdm(range(n_batches)):
        if verbose:
            print("\rPropagating batch %d/%d" % (i + 1, n_batches))
        start = i * batch_size
        end = start + batch_size
        batch = images[start:end]
        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
        pred_arr[start:end] = pred.reshape(batch_size, -1)
    if verbose:
        print(" done")
    return pred_arr


# -------------------------------------------------------------------------------


def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
    """Numpy implementation of the Frechet Distance.
    The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
    and X_2 ~ N(mu_2, C_2) is
            d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).

    Stable version by Dougal J. Sutherland.
    Params:
    -- mu1 : Numpy array containing the activations of the pool_3 layer of the
             inception net ( like returned by the function 'get_predictions')
             for generated samples.
    -- mu2   : The sample mean over activations of the pool_3 layer, precalcualted
               on an representive data set.
    -- sigma1: The covariance matrix over activations of the pool_3 layer for
               generated samples.
    -- sigma2: The covariance matrix over activations of the pool_3 layer,
               precalcualted on an representive data set.
    Returns:
    --   : The Frechet Distance.
    """

    mu1 = np.atleast_1d(mu1)
    mu2 = np.atleast_1d(mu2)

    sigma1 = np.atleast_2d(sigma1)
    sigma2 = np.atleast_2d(sigma2)

    assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
    assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"

    diff = mu1 - mu2

    # product might be almost singular
    covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
    if not np.isfinite(covmean).all():
        msg = "fid calculation produces singular product; adding %s to diagonal of cov estimates" % eps
        warnings.warn(msg)
        offset = np.eye(sigma1.shape[0]) * eps
        covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

    # numerical error might give slight imaginary component
    if np.iscomplexobj(covmean):
        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
            m = np.max(np.abs(covmean.imag))
            raise ValueError("Imaginary component {}".format(m))
        covmean = covmean.real

    tr_covmean = np.trace(covmean)

    return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean


# -------------------------------------------------------------------------------


def calculate_activation_statistics(images, sess, batch_size=50, verbose=False):
    """Calculation of the statistics used by the FID.
    Params:
    -- images      : Numpy array of dimension (n_images, hi, wi, 3). The values
                     must lie between 0 and 255.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the available hardware.
    -- verbose     : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- mu    : The mean over samples of the activations of the pool_3 layer of
               the inception model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the inception model.
    """
    act = get_activations(images, sess, batch_size, verbose)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return mu, sigma


# ------------------
# The following methods are implemented to obtain a batched version of the activations.
# This has the advantage to reduce memory requirements, at the cost of slightly reduced efficiency.
# - Pyrestone
# ------------------


def load_image_batch(files):
    """Convenience method for batch-loading images
    Params:
    -- files    : list of paths to image files. Images need to have same dimensions for all files.
    Returns:
    -- A numpy array of dimensions (num_images,hi, wi, 3) representing the image pixel values.
    """
    return np.array([imread(str(fn)).astype(np.float32) for fn in files])


def get_activations_from_files(files, sess, batch_size=50, verbose=False):
    """Calculates the activations of the pool_3 layer for all images.
    Params:
    -- files      : list of paths to image files. Images need to have same dimensions for all files.
    -- sess       : current session
    -- batch_size : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the disposable hardware.
    -- verbose    : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- A numpy array of dimension (num images, 2048) that contains the
       activations of the given tensor when feeding inception with the query tensor.
    """
    inception_layer = _get_inception_layer(sess)
    d0 = len(files)
    if batch_size > d0:
        print("warning: batch size is bigger than the data size. setting batch size to data size")
        batch_size = d0
    n_batches = d0 // batch_size
    n_used_imgs = n_batches * batch_size
    pred_arr = np.empty((n_used_imgs, 2048))
    for i in range(n_batches):
        if verbose:
            print("\rPropagating batch %d/%d" % (i + 1, n_batches))
        start = i * batch_size
        end = start + batch_size
        batch = load_image_batch(files[start:end])
        pred = sess.run(inception_layer, {'FID_Inception_Net/ExpandDims:0': batch})
        pred_arr[start:end] = pred.reshape(batch_size, -1)
        del batch  # clean up memory
    if verbose:
        print(" done")
    return pred_arr


def calculate_activation_statistics_from_files(files, sess, batch_size=50, verbose=False):
    """Calculation of the statistics used by the FID.
    Params:
    -- files      : list of paths to image files. Images need to have same dimensions for all files.
    -- sess        : current session
    -- batch_size  : the images numpy array is split into batches with batch size
                     batch_size. A reasonable batch size depends on the available hardware.
    -- verbose     : If set to True and parameter out_step is given, the number of calculated
                     batches is reported.
    Returns:
    -- mu    : The mean over samples of the activations of the pool_3 layer of
               the inception model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the inception model.
    """
    act = get_activations_from_files(files, sess, batch_size, verbose)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)
    return mu, sigma


# -------------------------------------------------------------------------------


# -------------------------------------------------------------------------------
# The following functions aren't needed for calculating the FID
# they're just here to make this module work as a stand-alone script
# for calculating FID scores
# -------------------------------------------------------------------------------
def check_or_download_inception(inception_path):
    ''' Checks if the path to the inception file is valid, or downloads
        the file if it is not present. '''
    INCEPTION_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
    if inception_path is None:
        inception_path = MODEL_DIR
    inception_path = pathlib.Path(inception_path)
    model_file = inception_path / 'classify_image_graph_def.pb'
    if not model_file.exists():
        print("Downloading Inception model")
        from urllib import request
        import tarfile
        fn, _ = request.urlretrieve(INCEPTION_URL)
        with tarfile.open(fn, mode='r') as f:
            f.extract('classify_image_graph_def.pb', str(model_file.parent))
    return str(model_file)


def _handle_path(path, sess, low_profile=False):
    if path.endswith('.npz'):
        f = np.load(path)
        m, s = f['mu'][:], f['sigma'][:]
        f.close()
    else:
        path = pathlib.Path(path)
        files = []
        for ext in ('*.png', '*.jpg', '*.jpeg', '.bmp'):
            files.extend( list(path.glob(ext)) )

        if low_profile:
            m, s = calculate_activation_statistics_from_files(files, sess)
        else:
            # x = np.array([scipy.misc.imresize(imread(str(fn), mode='RGB'), (299, 299), interp='bilinear').astype(np.float32) for fn in files])
            x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
            m, s = calculate_activation_statistics(x, sess)
            del x  # clean up memory
    return m, s


def calculate_fid_given_paths(paths, inception_path, low_profile=False):
    ''' Calculates the FID of two paths. '''
    inception_path = check_or_download_inception(inception_path)

    for p in paths:
        if not os.path.exists(p):
            raise RuntimeError("Invalid path: %s" % p)

    create_inception_graph(str(inception_path))
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        m1, s1 = _handle_path(paths[0], sess, low_profile=low_profile)
        m2, s2 = _handle_path(paths[1], sess, low_profile=low_profile)
        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
        return fid_value


if __name__ == "__main__":
    from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter

    parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
    parser.add_argument("path", type=str, nargs=2,
                        help='Path to the generated images or to .npz statistic files')
    parser.add_argument("-i", "--inception", type=str, default=None,
                        help='Path to Inception model (will be downloaded if not provided)')
    parser.add_argument("--gpu", default="", type=str,
                        help='GPU to use (leave blank for CPU only)')
    parser.add_argument("--lowprofile", action="store_true",
                        help='Keep only one batch of images in memory at a time. This reduces memory footprint, but may decrease speed slightly.')
    args = parser.parse_args()
    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
    fid_value = calculate_fid_given_paths(args.path, args.inception, low_profile=args.lowprofile)
    print("FID: ", fid_value)