def _clear_folder(self):
utils.clear_folder(self._output_path)
print(f"Logging to {self._log_file}\n")
sys.stdout = utils.StdOut(self._log_file)
print(f"PyTorch version: {torch.__version__}")
if self._cuda:
print(f"CUDA version: {torch.version.cuda}\n")
cudnn.benchmark = True
try:
import accimage
torchvision.set_image_backend('accimage')
print('Image loader backend: accimage')
except:
print('Image loader backend: PIL')
if FLAGS.train_single:
utils.clear_folder(FLAGS.out_dir)
log_file = os.path.join(FLAGS.out_dir, 'log.txt')
print("Logging to {}\n".format(log_file))
sys.stdout = utils.StdOut(log_file)
print("PyTorch version: {}".format(torch.__version__))
print("CUDA version: {}\n".format(torch.version.cuda))
print(" " * 9 + "Args" + " " * 9 + "| " + "Type" + \
" | " + "Value")
print("-" * 50)
for arg in vars(FLAGS):
arg_str = str(arg)
var_str = str(getattr(FLAGS, arg))
type_str = str(type(getattr(FLAGS, arg)).__name__)
print(" " + arg_str + " " * (20-len(arg_str)) + "|" + \
" " + type_str + " " * (10-len(type_str)) + "|" + \
" " + var_str)
BATCH_SIZE = 128 # Adjust this value according to your GPU memory
IMAGE_CHANNEL = 1
# IMAGE_CHANNEL = 3
Z_DIM = 100
G_HIDDEN = 64
X_DIM = 64
D_HIDDEN = 64
EPOCH_NUM = 25
REAL_LABEL = 1
FAKE_LABEL = 0
lr = 2e-4
seed = 1 # Change to None to get different results at each run
utils.clear_folder(OUT_PATH)
print("Logging to {}\n".format(LOG_FILE))
sys.stdout = utils.StdOut(LOG_FILE)
CUDA = CUDA and torch.cuda.is_available()
print("PyTorch version: {}".format(torch.__version__))
if CUDA:
print("CUDA version: {}\n".format(torch.version.cuda))
if seed is None:
seed = np.random.randint(1, 10000)
print("Random Seed: ", seed)
np.random.seed(seed)
torch.manual_seed(seed)
if CUDA:
torch.cuda.manual_seed(seed)
cudnn.benchmark = True # May train faster but cost more memory
dataset = dset.MNIST(root=DATA_PATH,