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train.py
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train.py
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import argparse
import os
import copy
import pprint
from os import path
import torch
import numpy as np
from torch import nn
from gan_training import utils
from gan_training.train import Trainer, update_average
from gan_training.logger import Logger
from gan_training.checkpoints import CheckpointIO
from gan_training.inputs import get_dataset
from gan_training.distributions import get_ydist, get_zdist
from gan_training.eval import Evaluator
from gan_training.config import (load_config, get_clusterer, build_models, build_optimizers)
from seeing.pidfile import exit_if_job_done, mark_job_done
import time
from torch.autograd import Variable
torch.backends.cudnn.benchmark = True
# Arguments
parser = argparse.ArgumentParser(
description='Train a GAN with different regularization strategies.')
parser.add_argument('config', type=str, help='Path to config file.')
parser.add_argument('--outdir', type=str, help='used to override outdir (useful for multiple runs)')
parser.add_argument('--nepochs', type=int, default=250, help='number of epochs to run before terminating')
parser.add_argument('--model_it', type=int, default=-1, help='which model iteration to load from, -1 loads the most recent model')
parser.add_argument('--devices', nargs='+', type=str, default=['0'], help='devices to use')
args = parser.parse_args()
config = load_config(args.config, 'configs/default.yaml')
out_dir = config['training']['out_dir'] if args.outdir is None else args.outdir
def main():
pp = pprint.PrettyPrinter(indent=1)
pp.pprint({
'data': config['data'],
'generator': config['generator'],
'discriminator1': config['discriminator1'],
'discriminator2': config['discriminator2'],
'clusterer': config['clusterer'],
'training': config['training']
})
is_cuda = torch.cuda.is_available()
# Short hands
batch_size = config['training']['batch_size']
log_every = config['training']['log_every']
inception_every = config['training']['inception_every']
backup_every = config['training']['backup_every']
sample_nlabels = config['training']['sample_nlabels']
nlabels = config['data']['nlabels']
sample_nlabels = min(nlabels, sample_nlabels)
checkpoint_dir = path.join(out_dir, 'chkpts')
# Create missing directories
if not path.exists(out_dir):
os.makedirs(out_dir)
if not path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
# Logger
checkpoint_io = CheckpointIO(checkpoint_dir=checkpoint_dir)
device = torch.device("cuda:0" if is_cuda else "cpu")
CUDA = True if torch.cuda.is_available() else False
Tensor = torch.cuda.FloatTensor if CUDA else torch.FloatTensor
train_dataset, _ = get_dataset(
name=config['data']['type'],
data_dir=config['data']['train_dir'],
size=config['data']['img_size'],
deterministic=config['data']['deterministic'])
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
num_workers=config['training']['nworkers'],
shuffle=True,
pin_memory=True,
sampler=None,
drop_last=True)
# Create models
generator, discriminator1, discriminator2 = build_models(config)
# Put models on gpu if needed
generator = generator.to(device)
discriminator1 = discriminator1.to(device)
discriminator2 = discriminator2.to(device)
for name, module in discriminator1.named_modules():
if isinstance(module, nn.Sigmoid):
print('Found sigmoid layer in discriminator; not compatible with BCE with logits')
exit()
for name, module in discriminator2.named_modules():
if isinstance(module, nn.Sigmoid):
print('Found sigmoid layer in discriminator; not compatible with BCE with logits')
exit()
g_optimizer, d1_optimizer, d2_optimizer = build_optimizers(generator, discriminator1, discriminator2, config)
devices = [int(x) for x in args.devices]
generator = nn.DataParallel(generator, device_ids=devices)
discriminator1 = nn.DataParallel(discriminator1, device_ids=devices)
discriminator2 = nn.DataParallel(discriminator2, device_ids=devices)
# Register modules to checkpoint
checkpoint_io.register_modules(generator=generator,
discriminator1=discriminator1,
discriminator2=discriminator2,
g_optimizer=g_optimizer,
d1_optimizer=d1_optimizer,
d2_optimizer=d2_optimizer)
# Logger
logger = Logger(log_dir=path.join(out_dir, 'logs'),
img_dir=path.join(out_dir, 'imgs'),
monitoring=config['training']['monitoring'],
monitoring_dir=path.join(out_dir, 'monitoring'))
# Distributions
ydist = get_ydist(nlabels, device=device)
zdist = get_zdist(config['z_dist']['type'], config['z_dist']['dim'], device=device)
ntest = config['training']['ntest']
x_test, y_test = utils.get_nsamples(train_loader, ntest)
x_cluster, y_cluster = utils.get_nsamples(train_loader, config['clusterer']['nimgs'])
x_test, y_test = x_test.to(device), y_test.to(device)
z_test = zdist.sample((ntest, ))
utils.save_images(x_test, path.join(out_dir, 'real.png'))
logger.add_imgs(x_test, 'gt', 0)
generator_test = generator
clusterer = get_clusterer(config)(discriminator=discriminator1,
x_cluster=x_cluster,
x_labels=y_cluster,
gt_nlabels=config['data']['nlabels'],
**config['clusterer']['kwargs'])
# Load checkpoint if it exists
it = utils.get_most_recent(checkpoint_dir, 'model') if args.model_it == -1 else args.model_it
it, epoch_idx, loaded_clusterer = checkpoint_io.load_models(it=it, load_samples='supervised' != config['clusterer']['name'])
if loaded_clusterer is None:
print('Initializing new clusterer. The first clustering can be quite slow.')
clusterer.recluster(discriminator=discriminator1)
checkpoint_io.save_clusterer(clusterer, it=0)
np.savez(os.path.join(checkpoint_dir, 'cluster_samples.npz'), x=x_cluster)
else:
print('Using loaded clusterer')
clusterer = loaded_clusterer
# Evaluator
evaluator = Evaluator(
generator_test,
zdist,
ydist,
train_loader=train_loader,
clusterer=clusterer,
batch_size=batch_size,
device=device,
inception_nsamples=config['training']['inception_nsamples'])
# Trainer
trainer = Trainer(generator,
discriminator1,
discriminator2,
g_optimizer,
d1_optimizer,
d2_optimizer,
gan_type=config['training']['gan_type'],
reg_type=config['training']['reg_type'],
reg_param=config['training']['reg_param'],
train_loader= train_loader,
Tensor=Tensor,
dim=config['z_dist']['dim'],
alpha=0.0,
beta=0.0)
# Training loop
print('Start training...')
time_mark = time.time()
while it < args.nepochs * len(train_loader):
epoch_idx += 1
for x_real, y in train_loader:
it += 1
x_real, y = x_real.to(device), y.to(device)
z = zdist.sample((batch_size, ))
y = clusterer.get_labels(x_real, y).to(device)
# dualLoss param adjust
dual_alpha, dual_beta = trainer.update_dual_param(epoch_idx,args.nepochs,'all',config['training'])
# Discriminator updates
d1loss, dual_D1 = trainer.discriminator1_trainstep(x_real, z)
logger.add('losses', 'discriminator1', d1loss, it=it)
logger.add('losses', 'dual_D1', dual_D1, it=it)
d2loss, dual_D2 = trainer.discriminator2_trainstep(x_real, z)
logger.add('losses', 'discriminator2', d2loss, it=it)
logger.add('losses', 'dual_D2', dual_D2, it=it)
# Generators updates
gloss = trainer.generator_trainstep(x_real, z)
logger.add('losses', 'generator', gloss, it=it)
# D2GAN training
# gloss, d1loss, d2loss = trainer.d2gan_trainstep(x_real, z)
# logger.add('losses', 'generator', gloss, it=it)
# logger.add('losses', 'discriminator1', d1loss, it=it)
# logger.add('losses', 'discriminator2', d2loss, it=it)
# # DCGAN training
# gloss, d1loss, d2loss = trainer.dcgan_trainstep(x_real, z)
# logger.add('losses', 'generator', gloss, it=it)
# logger.add('losses', 'discriminator1', d1loss, it=it)
# logger.add('losses', 'discriminator2', d2loss, it=it)
# # WGAN training
# gloss, d1loss, d2loss = trainer.wgan_trainstep(x_real, z)
# logger.add('losses', 'generator', gloss, it=it)
# logger.add('losses', 'discriminator1', d1loss, it=it)
# logger.add('losses', 'discriminator2', d2loss, it=it)
# DCGAN with 2 d training
# gloss, d1loss, d2loss = trainer.dcgan_w2d_trainstep(x_real, z)
# logger.add('losses', 'generator', gloss, it=it)
# logger.add('losses', 'discriminator1', d1loss, it=it)
# logger.add('losses', 'discriminator2', d2loss, it=it)
# Print stats
if it % log_every == 0:
time_eplase = time.time() - time_mark
time_mark = time.time()
g_loss_last = logger.get_last('losses', 'generator')
d1_loss_last = logger.get_last('losses', 'discriminator1')
# d1_dual_last = logger.get_last('losses', 'dual_D1')
d2_loss_last = logger.get_last('losses', 'discriminator2')
# d2_dual_last = logger.get_last('losses', 'dual_D2')
# print('[epoch %0d, it %4d, time %4f] g_loss = %.4f, d1_loss = %.4f, D1_dual=%.4f, d2_loss = %.4f, D2_dual=%.4f'
# % (epoch_idx, it, time_eplase, g_loss_last, d1_loss_last, d1_dual_last, d2_loss_last, d2_dual_last))
# print('dual_alpha %.4f, dual_beta %.4f' % (dual_alpha, dual_beta))
print('[epoch %0d, it %4d, time %4f] g_loss = %.4f, d1_loss = %.4f, d2_loss = %.4f'
% (epoch_idx, it, time_eplase, g_loss_last, d1_loss_last, d2_loss_last))
# (i) Sample if necessary
if it % config['training']['sample_every'] == 0:
print('Creating samples...')
x = evaluator.create_samples(z_test, y_test)
x = evaluator.create_samples(z_test, clusterer.get_labels(x_test, y_test).to(device))
logger.add_imgs(x, 'all', it)
for y_inst in range(sample_nlabels):
x = evaluator.create_samples(z_test, y_inst)
logger.add_imgs(x, '%04d' % y_inst, it)
# (ii) Backup if necessary
if it % backup_every == 0:
checkpoint_io.save('model_%08d.pt' % it, it=it)
checkpoint_io.save_clusterer(clusterer, int(it))
logger.save_stats('stats_%08d.p' % it)
if it > 0:
checkpoint_io.save('model.pt', it=it)
if __name__ == '__main__':
exit_if_job_done(out_dir)
main()
mark_job_done(out_dir)