eval_fid.py

#  MIT License

# Copyright (c) Facebook, Inc. and its affiliates.

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import models
import argparse
import os
import torch
from torch.autograd import Variable
import numpy as np
import csv
import glob

parser = argparse.ArgumentParser()
parser.add_argument('input')
args = parser.parse_args()

INPUT_PATH = args.input
CUDA = True
BATCH_SIZE = 100
N_CHANNEL = 3
RESOLUTION = 32
NUM_SAMPLES = 50000
DEVICE = 'cpu'

checkpoint = torch.load(INPUT_PATH, map_location=DEVICE)
args = argparse.Namespace(**checkpoint['args'])

MODEL = args.model
N_LATENT = args.num_latent
N_FILTERS_G = args.num_filters_gen
BATCH_NORM_G = True

print "Init..."
import tflib.fid as fid
import tensorflow as tf
stats_path = 'tflib/data/fid_stats_cifar10_train.npz'
inception_path = fid.check_or_download_inception('tflib/model')
f = np.load(stats_path)
mu_real, sigma_real = f['mu'][:], f['sigma'][:]
f.close()

fid.create_inception_graph(inception_path)  # load the graph into the current TF graph

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
def get_fid_score():
    all_samples = []
    samples = torch.randn(NUM_SAMPLES, N_LATENT)
    for i in xrange(0, NUM_SAMPLES, BATCH_SIZE):
        samples_100 = samples[i:i+BATCH_SIZE]
        if CUDA:
            samples_100 = samples_100.cuda(0)
        all_samples.append(gen(samples_100).cpu().data.numpy())

    all_samples = np.concatenate(all_samples, axis=0)
    all_samples = np.multiply(np.add(np.multiply(all_samples, 0.5), 0.5), 255).astype('int32')
    all_samples = all_samples.reshape((-1, N_CHANNEL, RESOLUTION, RESOLUTION)).transpose(0, 2, 3, 1)

    with tf.Session(config=config) as sess:
        sess.run(tf.global_variables_initializer())
        mu_gen, sigma_gen = fid.calculate_activation_statistics(all_samples, sess, batch_size=BATCH_SIZE)

    fid_value = fid.calculate_frechet_distance(mu_gen, sigma_gen, mu_real, sigma_real)
    return fid_value

if MODEL == "resnet":
    gen = models.ResNet32Generator(N_LATENT, N_CHANNEL, N_FILTERS_G, BATCH_NORM_G)
elif MODEL == "dcgan":
    gen = models.DCGAN32Generator(N_LATENT, N_CHANNEL, N_FILTERS_G, batchnorm=BATCH_NORM_G)

print "Eval..."
gen.load_state_dict(checkpoint['state_gen'])
if CUDA:
    gen.cuda(0)
inception_score = get_fid_score()

for j, param in enumerate(gen.parameters()):
    param.data = checkpoint['gen_param_avg'][j]
if CUDA:
    gen = gen.cuda(0)
inception_score_avg = get_fid_score()

for j, param in enumerate(gen.parameters()):
    param.data = checkpoint['gen_param_ema'][j]
if CUDA:
    gen = gen.cuda(0)
inception_score_ema = get_fid_score()


print 'IS: %.2f, IS Avg: %.2f, IS EMA: %.2f'%(inception_score, inception_score_avg, inception_score_ema)