import numpy as np
import os
import gzip, pickle
import tensorflow as tf
from imageio import imread
from scipy import linalg
import pathlib
import urllib
import warnings
import tqdm
from TTUR.fid import check_or_download_inception, _handle_path, calculate_frechet_distance, create_inception_graph
if (__name__ == '__main__'):
images_path = 'fashion_mnist_images'
assert os.path.exists(
images_path), 'Need to run ../datasets.save_fashion_mnist_to_samples()'
inception_path = None
inception_path = check_or_download_inception(inception_path)
create_inception_graph(str(inception_path))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
_handle_path(images_path,
sess,
low_profile=True,
stats_path='FashionMNIST.npz')
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this fucntion, you should first call <BaseModel.__init__(self, opt)>
Then, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): specify the images that you want to display and save.
-- self.visual_names (str list): define networks used in our training.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
"""
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.device = torch.device('cuda:{}'.format(
self.gpu_ids[0])) if self.gpu_ids else torch.device(
'cpu') # get device name: CPU or GPU
self.save_dir = os.path.join(
opt.checkpoints_dir,
opt.name) # save all the checkpoints to save_dir
if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
torch.backends.cudnn.benchmark = True
self.loss_names = []
self.model_names = []
self.visual_names = []
self.optimizers = []
self.image_paths = []
self.metric = 0 # used for learning rate policy 'plateau'
# scores init
if self.opt.use_pytorch_scores and self.opt.score_name is not None:
no_FID = True
no_IS = True
parallel = len(opt.gpu_ids) > 1
for name in self.opt.score_name:
if name == 'FID':
no_FID = False
if name == 'IS':
no_IS = False
self.get_inception_metrics = inception_utils.prepare_inception_metrics(
opt.dataset_name, parallel, no_IS, no_FID)
else:
for name in self.opt.score_name:
if name == 'FID':
STAT_FILE = self.opt.fid_stat_file
INCEPTION_PATH = "./inception_v3/"
print("load train stats.. ")
# load precalculated training set statistics
f = np.load(STAT_FILE)
self.mu_real, self.sigma_real = f['mu'][:], f['sigma'][:]
f.close()
print("ok")
inception_path = fid.check_or_download_inception(
INCEPTION_PATH) # download inception network
fid.create_inception_graph(
inception_path
) # load the graph into the current TF graph
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.sess.run(tf.global_variables_initializer())