ml-ai-agents-py/EBMs/fid.py

#!/usr/bin/env python3
"""Calculates the Frechet Inception Distance (FID) to evalulate GANs.

The FID metric calculates the distance between two distributions of images.
Typically, we have summary statistics (mean & covariance matrix) of one
of these distributions, while the 2nd distribution is given by a GAN.

When run as a stand-alone program, it compares the distribution of
images that are stored as PNG/JPEG at a specified location with a
distribution given by summary statistics (in pickle format).

The FID is calculated by assuming that X_1 and X_2 are the activations of
the pool_3 layer of the inception net for generated samples and real world
samples respectivly.

See --help to see further details.
"""

from __future__ import absolute_import, division, print_function

import os
import pathlib
import tarfile
import urllib
import warnings

import numpy as np
import tensorflow as tf
from scipy import linalg
from scipy.misc import imread

MODEL_DIR = "/tmp/imagenet"
DATA_URL = (
    "http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz"
)
pool3 = None


class InvalidFIDException(Exception):
    pass


# -------------------------------------------------------------------------------
def get_fid_score(images, images_gt):
    images = np.stack(images, 0)
    images_gt = np.stack(images_gt, 0)

    with tf.Session() as sess:
        m1, s1 = calculate_activation_statistics(images, sess)
        m2, s2 = calculate_activation_statistics(images_gt, sess)
        fid_value = calculate_frechet_distance(m1, s1, m2, s2)

    print("Obtained fid value of {}".format(fid_value))
    return fid_value


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 range(n_batches):
        if verbose:
            print("\rPropagating batch %d/%d" % (i + 1, n_batches), end="", flush=True)
        start = i * batch_size
        end = start + batch_size
        batch = images[start:end]
        pred = sess.run(pool3, {"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 incption model.
    -- sigma : The covariance matrix of the activations of the pool_3 layer of
               the incption 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 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 = "/tmp"
    inception_path = pathlib.Path(inception_path)
    model_file = inception_path / "classify_image_graph_def.pb"
    if not model_file.exists():
        print("Downloading Inception model")
        import tarfile
        from urllib import request

        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):
    if path.endswith(".npz"):
        f = np.load(path)
        m, s = f["mu"][:], f["sigma"][:]
        f.close()
    else:
        path = pathlib.Path(path)
        files = list(path.glob("*.jpg")) + list(path.glob("*.png"))
        x = np.array([imread(str(fn)).astype(np.float32) for fn in files])
        m, s = calculate_activation_statistics(x, sess)
    return m, s


def calculate_fid_given_paths(paths, inception_path):
    """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)
        m2, s2 = _handle_path(paths[1], sess)
        fid_value = calculate_frechet_distance(m1, s1, m2, s2)
        return fid_value


def _init_inception():
    global pool3
    if not os.path.exists(MODEL_DIR):
        os.makedirs(MODEL_DIR)
    filename = DATA_URL.split("/")[-1]
    filepath = os.path.join(MODEL_DIR, filename)
    if not os.path.exists(filepath):

        def _progress(count, block_size, total_size):
            sys.stdout.write(
                "\r>> Downloading %s %.1f%%"
                % (filename, float(count * block_size) / float(total_size) * 100.0)
            )
            sys.stdout.flush()

        filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)
        print()
        statinfo = os.stat(filepath)
        print("Succesfully downloaded", filename, statinfo.st_size, "bytes.")
    tarfile.open(filepath, "r:gz").extractall(MODEL_DIR)
    with tf.gfile.FastGFile(
        os.path.join(MODEL_DIR, "classify_image_graph_def.pb"), "rb"
    ) as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name="")
    # Works with an arbitrary minibatch size.
    with tf.Session() as sess:
        pool3 = sess.graph.get_tensor_by_name("pool_3:0")
        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)


if pool3 is None:
    _init_inception()