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experiment.py
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from __future__ import print_function
import argparse
import torch
import torch.optim as optim
from utils.optimizer import AdamNormGrad
import os
import datetime
from utils.load_data import load_dataset
# -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
# # # # # # # # # # #
# START EXPERIMENTS # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # #
# Training settings
parser = argparse.ArgumentParser(description='VAE+VampPrior')
# arguments for optimization
parser.add_argument('--batch_size', type=int, default=100, metavar='BStrain',
help='input batch size for training (default: 100)')
parser.add_argument('--test_batch_size', type=int, default=100, metavar='BStest',
help='input batch size for testing (default: 100)')
parser.add_argument('--epochs', type=int, default=2000, metavar='E',
help='number of epochs to train (default: 2000)')
parser.add_argument('--lr', type=float, default=0.0005, metavar='LR',
help='learning rate (default: 0.0005)')
parser.add_argument('--early_stopping_epochs', type=int, default=50, metavar='ES',
help='number of epochs for early stopping')
parser.add_argument('--warmup', type=int, default=100, metavar='WU',
help='number of epochs for warmu-up')
# cuda
parser.add_argument('--no-cuda', action='store_true', default=False,
help='enables CUDA training')
# random seed
parser.add_argument('--seed', type=int, default=14, metavar='S',
help='random seed (default: 14)')
# model: latent size, input_size, so on
parser.add_argument('--z1_size', type=int, default=40, metavar='M1',
help='latent size')
parser.add_argument('--z2_size', type=int, default=40, metavar='M2',
help='latent size')
parser.add_argument('--input_size', type=int, default=[1, 28, 28], metavar='D',
help='input size')
parser.add_argument('--activation', type=str, default=None, metavar='ACT',
help='activation function')
parser.add_argument('--number_components', type=int, default=500, metavar='NC',
help='number of pseudo-inputs')
parser.add_argument('--pseudoinputs_mean', type=float, default=-0.05, metavar='PM',
help='mean for init pseudo-inputs')
parser.add_argument('--pseudoinputs_std', type=float, default=0.01, metavar='PS',
help='std for init pseudo-inputs')
parser.add_argument('--use_training_data_init', action='store_true', default=False,
help='initialize pseudo-inputs with randomly chosen training data')
# model: model name, prior
parser.add_argument('--model_name', type=str, default='vae', metavar='MN',
help='model name: vae, hvae_2level, convhvae_2level, pixelhvae_2level')
parser.add_argument('--prior', type=str, default='vampprior', metavar='P',
help='prior: standard, vampprior')
parser.add_argument('--input_type', type=str, default='binary', metavar='IT',
help='type of the input: binary, gray, continuous')
# experiment
parser.add_argument('--S', type=int, default=5000, metavar='SLL',
help='number of samples used for approximating log-likelihood')
parser.add_argument('--MB', type=int, default=100, metavar='MBLL',
help='size of a mini-batch used for approximating log-likelihood')
# dataset
parser.add_argument('--dataset_name', type=str, default='freyfaces', metavar='DN',
help='name of the dataset: static_mnist, dynamic_mnist, omniglot, caltech101silhouettes, histopathologyGray, freyfaces, cifar10')
parser.add_argument('--dynamic_binarization', action='store_true', default=False,
help='allow dynamic binarization')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
def run(args, kwargs):
args.model_signature = str(datetime.datetime.now())[0:19]
model_name = args.dataset_name + '_' + args.model_name + '_' + args.prior + '(K_' + str(args.number_components) + ')' + '_wu(' + str(args.warmup) + ')' + '_z1_' + str(args.z1_size) + '_z2_' + str(args.z2_size)
# DIRECTORY FOR SAVING
snapshots_path = 'snapshots/'
dir = snapshots_path + args.model_signature + '_' + model_name + '/'
if not os.path.exists(dir):
os.makedirs(dir)
# LOAD DATA=========================================================================================================
print('load data')
# loading data
train_loader, val_loader, test_loader, args = load_dataset(args, **kwargs)
# CREATE MODEL======================================================================================================
print('create model')
# importing model
if args.model_name == 'vae':
from models.VAE import VAE
elif args.model_name == 'hvae_2level':
from models.HVAE_2level import VAE
elif args.model_name == 'convhvae_2level':
from models.convHVAE_2level import VAE
elif args.model_name == 'pixelhvae_2level':
from models.PixelHVAE_2level import VAE
else:
raise Exception('Wrong name of the model!')
model = VAE(args)
if args.cuda:
model.cuda()
optimizer = AdamNormGrad(model.parameters(), lr=args.lr)
# ======================================================================================================================
print(args)
with open('vae_experiment_log.txt', 'a') as f:
print(args, file=f)
# ======================================================================================================================
print('perform experiment')
from utils.perform_experiment import experiment_vae
experiment_vae(args, train_loader, val_loader, test_loader, model, optimizer, dir, model_name = args.model_name)
# ======================================================================================================================
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-')
with open('vae_experiment_log.txt', 'a') as f:
print('-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n', file=f)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
if __name__ == "__main__":
run(args, kwargs)
# # # # # # # # # # #
# END EXPERIMENTS # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# # # # # # # # # # #