inception_score.py

import torch
from torch import nn
from torch.autograd import Variable
from torch.nn import functional as F
import torch.utils.data

from torchvision.models.inception import inception_v3

import numpy as np
from scipy.stats import entropy

from glob import glob
import os
from scipy import misc
from scipy.misc import imread, imresize

IMAGE_EXTS = ['.png', '.jpg', '.jpeg']

def inception_score(imgs, cuda=True, batch_size=32, resize=False, splits=10):
    """Computes the inception score of the generated images imgs

    imgs -- Torch dataset of (3xHxW) numpy images normalized in the range [-1, 1]
    cuda -- whether or not to run on GPU
    batch_size -- batch size for feeding into Inception v3
    splits -- number of splits
    """
    
    os.environ['CUDA_VISIBLE_DEVICES']='1'
    
    N = len(imgs)

    assert batch_size > 0
    assert N > batch_size

    # Set up dtype
    if cuda:
        dtype = torch.cuda.FloatTensor
    else:
        if torch.cuda.is_available():
            print("WARNING: You have a CUDA device, so you should probably set cuda=True")
        dtype = torch.FloatTensor

    # Set up dataloader
    dataloader = torch.utils.data.DataLoader(imgs, batch_size=batch_size)

    # Load inception model
    inception_model = inception_v3(pretrained=True, transform_input=False).type(dtype)
    inception_model.eval();
    up = nn.Upsample(size=(299, 299), mode='bilinear').type(dtype)
    def get_pred(x):
        if resize:
            x = up(x)
        x = inception_model(x)
        return F.softmax(x).data.cpu().numpy()

    # Get predictions
    preds = np.zeros((N, 1000))

    for i, batch in enumerate(dataloader, 0):
        batch = batch.type(dtype)
        batchv = Variable(batch)
        batch_size_i = batch.size()[0]

        preds[i*batch_size:i*batch_size + batch_size_i] = get_pred(batchv)

    # Now compute the mean kl-div
    split_scores = []

    for k in range(splits):
        part = preds[k * (N // splits): (k+1) * (N // splits), :]
        py = np.mean(part, axis=0)
        scores = []
        for i in range(part.shape[0]):
            pyx = part[i, :]
            scores.append(entropy(pyx, py))
        split_scores.append(np.exp(np.mean(scores)))

    return np.mean(split_scores), np.std(split_scores)

def load_images(image_dir):
  print('Loading images from ', image_dir)
  images = []
  for fn in os.listdir(image_dir):
    ext = os.path.splitext(fn)[1].lower()
    if ext not in IMAGE_EXTS:
      continue
    img_path = os.path.join(image_dir, fn)
    img = imread(img_path)
    img = ((imresize(img, (32, 32)).astype('float'))-127.5)/127.5
    images.append(img)
  print('Found %d images' % len(images))
  return images

if __name__ == '__main__':
    class IgnoreLabelDataset(torch.utils.data.Dataset):
        def __init__(self, orig):
            self.orig = orig

        def __getitem__(self, index):
            return self.orig[index][0]

        def __len__(self):
            return len(self.orig)

    import torchvision.datasets as dset
    import torchvision.transforms as transforms

#    cifar = dset.CIFAR10(root='data/', download=True,
#                             transform=transforms.Compose([
#                                 transforms.Scale(32),
#                                 transforms.ToTensor(),
#                                 transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
#                                 #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
#                             ])
#    )
#
#    IgnoreLabelDataset(cifar)    
    
    images = load_images('./cifar')
    images = np.transpose(images, axes=[0, 3, 1, 2])

    print ("Calculating Inception Score...")
    #print (inception_score(IgnoreLabelDataset(cifar), cuda=True, batch_size=32, resize=True, splits=10))
    print (inception_score(images, cuda=True, batch_size=32, resize=True, splits=10))