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train_mog_different_batchnorm.py
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train_mog_different_batchnorm.py
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import argparse
import numpy as np
import os
import mimir
import theano
import theano.tensor as T
from collections import OrderedDict
from fuel.streams import DataStream
from fuel.schemes import ShuffledScheme, SequentialScheme
from fuel.datasets.toy import Spiral
import optimizers
from util import norm_weight, _p, itemlist, load_params, create_log_dir#, unzip, save_params #ortho_weight
import sys
def plot_images(X, fname):
np.savez(fname + '.npz', X=X)
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
rng = RandomStreams(12345)
from viz import plot_2D, plot_grad
sys.setrecursionlimit(10000000)
import ipdb
class ConsiderConstant(theano.compile.ViewOp):
def grad(self, args, g_outs):
return [T.zeros_like(g_out) for g_out in g_outs]
consider_constant = ConsiderConstant()
INPUT_SIZE = 2
use_conv = False
from datasets import GaussianMixture
import itertools
import numpy
MEANS = [numpy.array([i, j]) for i, j in itertools.product(range(-1, 2, 1),
range(-1, 2, 1))]
VARIANCES = [0.05 ** 2 * numpy.eye(len(mean)) for mean in MEANS]
PRIORS = None
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=500, type=int,
help='Batch size')
parser.add_argument('--lr', default=0.0001, type=float,
help='Initial learning rate. ' + \
'Will be decayed until it\'s 1e-5.')
parser.add_argument('--resume_file', default=None, type=str,
help='Name of saved model to continue training')
parser.add_argument('--suffix', default='', type=str,
help='Optional descriptive suffix for model')
parser.add_argument('--output-dir', type=str, default='./',
help='Output directory to store trained models')
parser.add_argument('--ext-every-n', type=int, default=25,
help='Evaluate training extensions every N epochs')
parser.add_argument('--model-args', type=str, default='',
help='Dictionary string to be eval()d containing model arguments.')
parser.add_argument('--dropout_rate', type=float, default=0.,
help='Rate to use for dropout during training+testing.')
parser.add_argument('--dataset', type=str, default='CIFAR10',
help='Name of dataset to use.')
parser.add_argument('--plot_before_training', type=bool, default=False,
help='Save diagnostic plots at epoch 0, before any training.')
parser.add_argument('--num_steps', type=int, default=2,
help='Number of transition steps.')
parser.add_argument('--temperature', type=float, default=1.0,
help='Standard deviation of the diffusion process.')
parser.add_argument('--alpha', type=float, default=0.5,
help='alpha factor')
parser.add_argument('--dims', default=[4096], type=int,
nargs='+')
parser.add_argument('--noise_prob', default=0.1, type=float,
help='probability for bernouli distribution of adding noise of 1 to each input')
parser.add_argument('--avg', default=0, type=float)
parser.add_argument('--std', default=1., type=float)
parser.add_argument('--noise', default='gaussian', choices=['gaussian', 'binomial'])
parser.add_argument('--reload_', type=bool, default = False,
help='Reloading the parameters')
parser.add_argument('--saveto_filename', type = str, default = None,
help='directory where parameters are stored')
parser.add_argument('--extra_steps', type = int, default = 0,
help='Number of extra steps to sample at temperature 1')
parser.add_argument('--meta_steps', type = int, default = 1,
help='Number of extra steps to sample at temperature 1')
parser.add_argument('--optimizer', type = str, default = 'sgd',
help='optimizer we are going to use!!')
parser.add_argument('--temperature_factor', type = float, default = 2.0,
help='How much temperature must be scaled')
parser.add_argument('--sigma', type = float, default = 0.01,
help='Initial variance added at first step!')
parser.add_argument('--infusion_rate', type = float, default = 0.0,
help='Infusion rate')
args = parser.parse_args()
model_args = eval('dict(' + args.model_args + ')')
print model_args
if not os.path.exists(args.output_dir):
raise IOError("Output directory '%s' does not exist. "%args.output_dir)
return args, model_args
def init_tparams(params):
tparams = OrderedDict()
for kk, pp in params.iteritems():
tparams[kk] = theano.shared(params[kk], name=kk)
print kk
return tparams
layers = {'ff': ('param_init_fflayer', 'fflayer')}
def get_layer(name):
fns = layers[name]
return (eval(fns[0]), eval(fns[1]))
import logging
logger = logging.getLogger('UDGAN.layer')
def param_init_fflayer(options, params, prefix='ff', prefix_bnorm='bnorm', nin=None, nout=None, ortho=True, batch_norm=False):
if prefix in params:
print 'this layer is already present'
else:
params[_p(prefix, 'W')] = norm_weight(nin, nout)
params[_p(prefix, 'b')] = np.zeros((nout,)).astype('float32')
return params
def fflayer(tparams,
state_below,
options,
index,
prefix='rconv',
prefix_bnorm='bnorm',
activ='lambda x: tensor.tanh(x)',
batch_norm = False,
**kwargs):
logger.debug('Forming layer with name {}'.format(prefix))
preactivation = T.dot(state_below, tparams[_p(prefix, 'W')]) + tparams[_p(prefix, 'b')]
if batch_norm:
preactivation = (preactivation - preactivation.mean(axis=0)) / (0.0001 + preactivation.std(axis=0))
preactivation = (tparams[_p(prefix_bnorm, 'newmu')][index] + preactivation* tparams[_p(prefix_bnorm, 'newsigma')][index])
return preactivation
def init_params(options):
params = OrderedDict()
params = get_layer('ff')[0](options, params, prefix='layer_1', nin=INPUT_SIZE,nout=args.dims[0], ortho=False, batch_norm=True)
params = get_layer('ff')[0](options, params, prefix='layer_2', nin=args.dims[0], nout=args.dims[0], ortho=False, batch_norm=True)
params[_p('layer1_bnorm', 'newmu')] = np.zeros(shape=(args.num_steps *args.meta_steps , args.dims[0])).astype('float32')
params[_p('layer1_bnorm', 'newsigma')] = np.ones(shape=(args.num_steps *args.meta_steps, args.dims[0])).astype('float32')
params[_p('layer2_bnorm', 'newmu')] = np.zeros(shape=(args.num_steps *args.meta_steps , args.dims[0])).astype('float32')
params[_p('layer2_bnorm', 'newsigma')] = np.ones(shape=(args.num_steps *args.meta_steps, args.dims[0])).astype('float32')
if len(args.dims) == 1:
params = get_layer('ff')[0](options, params, prefix='mu_0',
nin=args.dims[0], nout=INPUT_SIZE,
ortho=False)
if args.noise == 'gaussian':
params = get_layer('ff')[0](options, params, prefix='sigma_0',
nin=args.dims[0], nout=INPUT_SIZE,
ortho=False)
return params
def join(a, b=None):
if b==None:
return a
else:
return T.concatenate([a,b],axis=1)
def ln(inp):
return (inp - T.mean(inp,axis=1,keepdims=True)) / (0.001 + T.std(inp,axis=1,keepdims=True))
srng = theano.tensor.shared_randomstreams.RandomStreams(42)
def transition_operator(tparams, options, x, temperature, num_step):
h1 = T.nnet.relu(fflayer(tparams, x, options, index = num_step, prefix='layer_1', prefix_bnorm='layer1_bnorm', batch_norm=True))
h2 = T.nnet.relu(fflayer(tparams, h1, options, index = num_step, prefix='layer_2', prefix_bnorm='layer2_bnorm', batch_norm=True))
h = h2
for i in range(len(args.dims)):
if i == 0:
mu = fflayer(tparams, h, options, index = num_step, prefix='mu_0')
if args.noise == 'gaussian':
sigma = fflayer(tparams, h, options, index = num_step, prefix='sigma_0')
else:
mu = fflayer(tparams, mu, options, index = num_step,prefix='mu_' + str(i))
if args.noise == 'gaussian':
sigma = fflayer(tparams, sigma, options, index = num_step, prefix='sigma_' + str(i))
if args.noise == 'gaussian':
'''
sigma = T.nnet.softplus(sigma)
sigma = args.sigma * sigma * T.sqrt(temperature)
epsilon = rng.normal(size=(args.batch_size, INPUT_SIZE), avg=args.avg, std=args.std, dtype=theano.config.floatX)
x_hat = consider_constant((args.alpha)*x + (1-args.alpha) * (mu) + sigma * epsilon)
mean_ = ((args.alpha)*x + (1-args.alpha) * (mu))
log_p_reverse = -0.5 * T.sum(1.0 * (T.log(2 * np.pi) + T.log(sigma) + (x - mean_) ** 2 / (sigma)),[1])
log_p_reverse_2 = -0.5 * T.sum(1.0 * (T.log(2 * np.pi) + T.log(sigma) + (x_hat - mean_) ** 2 / (sigma)),[1])
return x_hat, log_p_reverse, sigma, mean_
'''
sigma = T.nnet.softplus(sigma)
sigma *= temperature
sigma = T.sqrt(sigma)
epsilon = rng.normal(size=(args.batch_size, INPUT_SIZE), avg=args.avg, std=args.std, dtype=theano.config.floatX)
epsilon = epsilon + 0.0 * num_step * epsilon
alpha = args.alpha + num_step*args.infusion_rate
x_hat = consider_constant(x*(alpha) + (1-alpha) * (mu) + T.sqrt(args.sigma * sigma) * epsilon)#.clip(0.0,1.0)
mean_ = ((alpha)*x + (1-alpha) * (mu))
#log_p_reverse = -0.5 * T.sum(T.sqr(x - mu) + 0.0 * num_step * sigma,[1])
log_p_reverse = -0.5 * T.sum(1.0 * (T.log(2 * np.pi) + T.log(args.sigma * sigma) + (x - mean_) ** 2 / (args.sigma * sigma)) + 0.0 * num_step * sigma,[1])
#log_p_reverse_2 = -0.5 * T.sum(1.0 * (T.log(2 * np.pi) + T.log(sigma) + (x_hat - mean_) ** 2 / (sigma)),[1])
return x_hat, log_p_reverse, sigma, mean_
def sample(tparams, options):
x_data = T.matrix('x_sample', dtype='float32')
temperature = T.scalar('temperature_sample', dtype='float32')
num_step = T.scalar('num_step', dtype='int32')
x_tilde, _, sampled, sampled_activation = transition_operator(tparams, options, x_data, temperature, num_step)
f = theano.function([x_data, temperature, num_step], [x_tilde, sampled], on_unused_input='warn')
return f
#from distributions import log_normal1
def compute_loss(x, options, tparams, start_temperature, num_step):
temperature = start_temperature
step_factor = num_step
x_tilde, log_p_reverse, _, _ = transition_operator(tparams, options, x, temperature, step_factor)
states = [x_tilde]
log_p_reverse_list = [log_p_reverse]
print args.num_steps
for _ in range(args.num_steps - 1):
temperature *= args.temperature_factor
step_factor += 1
x_tilde, log_p_reverse, _,_ = transition_operator(tparams, options, states[-1], temperature, step_factor)
states.append(x_tilde)
log_p_reverse_list.append(log_p_reverse)
#mean_ = x_tilde.mean(axis=0)
#var_ = x_tilde.mean(axis=0)
#log_loss = log_normal1(x_tilde, T.addbroadcast(mean_, 1), T.addbroadcast(var_, 1))
#log_p_reverse_list.append(log_loss)
loss = -T.mean(sum(log_p_reverse_list, 0.0))
return loss
def one_step_diffusion(x, options, tparams, temperature, num_step):
x_tilde, log_p_reverse, sampled, sampled_activation = transition_operator(tparams, options, x, temperature, num_step)
forward_diffusion = theano.function([x, temperature, num_step], [x_tilde, sampled, sampled_activation, sampled_activation], on_unused_input='warn')
return forward_diffusion
def build_model(tparams, model_options):
x = T.matrix('x', dtype='float32')
start_temperature = T.scalar('start_temperature', dtype='float32')
num_step = T.scalar('num_step', dtype='int32')
loss = compute_loss(x, model_options, tparams, start_temperature, num_step)
return x, loss, start_temperature, num_step
def train(args,
model_args):
model_id = '/data/lisatmp4/anirudhg/mog_walk_back/walkback_'
model_dir = create_log_dir(args, model_id)
model_id2 = 'logs/walkback_'
model_dir2 = create_log_dir(args, model_id2)
print model_dir
print model_dir2 + '/' + 'log.jsonl.gz'
logger = mimir.Logger(filename=model_dir2 + '/log.jsonl.gz', formatter=None)
# TODO batches_per_epoch should not be hard coded
lrate = args.lr
import sys
sys.setrecursionlimit(10000000)
args, model_args = parse_args()
#trng = RandomStreams(1234)
if args.resume_file is not None:
print "Resuming training from " + args.resume_file
from blocks.scripts import continue_training
continue_training(args.resume_file)
## load the training data
if args.dataset == 'MNIST':
print 'loading MNIST'
from fuel.datasets import MNIST
dataset_train = MNIST(['train'], sources=('features',))
dataset_test = MNIST(['test'], sources=('features',))
n_colors = 1
spatial_width = 28
elif args.dataset == 'CIFAR10':
from fuel.datasets import CIFAR10
dataset_train = CIFAR10(['train'], sources=('features',))
dataset_test = CIFAR10(['test'], sources=('features',))
n_colors = 3
spatial_width = 32
elif args.dataset == "lsun" or args.dataset == "lsunsmall":
print "loading lsun class!"
from load_lsun import load_lsun
print "loading lsun data!"
if args.dataset == "lsunsmall":
dataset_train, dataset_test = load_lsun(args.batch_size, downsample=True)
spatial_width=32
else:
dataset_train, dataset_test = load_lsun(args.batch_size, downsample=False)
spatial_width=64
n_colors = 3
elif args.dataset == "celeba":
print "loading celeba data"
from fuel.datasets.celeba import CelebA
dataset_train = CelebA(which_sets = ['train'], which_format="64", sources=('features',), load_in_memory=False)
dataset_test = CelebA(which_sets = ['test'], which_format="64", sources=('features',), load_in_memory=False)
spatial_width = 64
n_colors = 3
tr_scheme = SequentialScheme(examples=dataset_train.num_examples, batch_size=args.batch_size)
ts_scheme = SequentialScheme(examples=dataset_test.num_examples, batch_size=args.batch_size)
train_stream = DataStream.default_stream(dataset_train, iteration_scheme = tr_scheme)
test_stream = DataStream.default_stream(dataset_test, iteration_scheme = ts_scheme)
dataset_train = train_stream
dataset_test = test_stream
#epoch_it = train_stream.get_epoch_iterator()
elif args.dataset == 'Spiral':
print 'loading SPIRAL'
train_set = Spiral(num_examples=20000, classes=1, cycles=1., noise=0.01,
sources=('features',))
dataset_train = DataStream.default_stream(train_set,
iteration_scheme=ShuffledScheme(
train_set.num_examples, args.batch_size))
elif args.dataset == 'MOG':
print 'loading GOM'
dataset = GaussianMixture(num_examples=20000,
means=MEANS, variances=VARIANCES, priors=None,
rng=None, sources=('features', 'label'))
dataset_train = DataStream.default_stream(dataset,
iteration_scheme=ShuffledScheme(
dataset.num_examples, args.batch_size))
features, targets = dataset.indexables
else:
raise ValueError("Unknown dataset %s."%args.dataset)
model_options = locals().copy()
train_stream = dataset_train
shp = next(train_stream.get_epoch_iterator())[0].shape
print "got epoch iterator"
# make the training data 0 mean and variance 1
# TODO compute mean and variance on full dataset, not minibatch
Xbatch = next(train_stream.get_epoch_iterator())[0]
scl = 1./np.sqrt(np.mean((Xbatch-np.mean(Xbatch))**2))
shft = -np.mean(Xbatch*scl)
# scale is applied before shift
#train_stream = ScaleAndShift(train_stream, scl, shft)
#test_stream = ScaleAndShift(test_stream, scl, shft)
print 'Building model'
params = init_params(model_options)
if args.reload_:
print "Trying to reload parameters"
if os.path.exists(args.saveto_filename):
print 'Reloading Parameters'
print args.saveto_filename
params = load_params(args.saveto_filename, params)
tparams = init_tparams(params)
print tparams
x, cost, start_temperature, step_chain = build_model(tparams, model_options)
inps = [x, start_temperature, step_chain]
x_Data = T.matrix('x_Data', dtype='float32')
temperature = T.scalar('temperature', dtype='float32')
step_chain_part = T.scalar('step_chain_part', dtype='int32')
forward_diffusion = one_step_diffusion(x_Data, model_options, tparams, temperature, step_chain_part)
print tparams
grads = T.grad(cost, wrt=itemlist(tparams), disconnected_inputs = 'ignore')
'''
import lasagne
clip_grad = 1
max_norm = 5
mgrads = lasagne.updates.total_norm_constraint(grads,max_norm=max_norm)
cgrads = [T.clip(g,-clip_grad, clip_grad) for g in mgrads]
'''
for j in range(0, len(grads)):
grads[j] = T.switch(T.isnan(grads[j]), T.zeros_like(grads[j]), grads[j])
# compile the optimizer, the actual computational graph is compiled here
lr = T.scalar(name='lr')
print 'Building optimizers...',
optimizer = args.optimizer
f_grad_shared, f_update = getattr(optimizers, optimizer)(lr, tparams, grads, inps, cost)
print 'Done'
print 'Buiding Sampler....'
f_sample = sample(tparams, model_options)
print 'Done'
uidx = 0
estop = False
bad_counter = 0
max_epochs = 1000
batch_index = 0
print 'Number of steps....', args.num_steps
print 'Done'
count_sample = 1
batch_index = 0
for eidx in xrange(max_epochs):
#if eidx%20==0:
# params = unzip(tparams)
# name = 'params_' + str(eidx) + 'num_steps_' + str(args.num_steps) + '_meta_steps_' + str(args.meta_steps) + '_alpha_' + str(args.alpha) + '_temperature_factor_' + str(args.temperature_factor) + '_sigma_' + str(args.sigma) + '_infusion_rate_' + str(args.infusion_rate) + '_learning_rate_' + str(args.lr) + '.npz'
# save_params(params, model_dir + '/' + name)
n_samples = 0
print 'Starting Next Epoch ', eidx
for data in train_stream.get_epoch_iterator():
batch_index += 1
n_samples += len(data[0])
uidx += 1
if data[0] is None:
print 'No data '
uidx -= 1
continue
data_run = data[0]
temperature_forward = args.temperature
meta_cost = []
for meta_step in range(0, args.meta_steps):
meta_cost.append(f_grad_shared(data_run, temperature_forward, meta_step))
f_update(lrate)
if args.meta_steps > 1:
data_run, sigma, _, _ = forward_diffusion(data_run, temperature_forward, meta_step)
temperature_forward *= args.temperature_factor
cost = sum(meta_cost) / len(meta_cost)
if np.isnan(cost) or np.isinf(cost):
print 'NaN detected'
return 1.
if batch_index % 100 ==0:
print 'Cost', cost, batch_index
empty = []
spiral_x = [empty for i in range(args.num_steps)]
spiral_corrupted = []
spiral_sampled = []
grad_forward = []
grad_back = []
x_data_time = []
x_tilt_time = []
if batch_index%8==0:
count_sample += 1
temperature = args.temperature * (args.temperature_factor ** (args.num_steps*args.meta_steps -1 ))
temperature_forward = args.temperature
for num_step in range(args.num_steps *args.meta_steps):
if num_step == 0:
x_data_time.append(data[0])
plot_images(data[0], model_dir + '/' + 'orig_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index))
x_data, mu_data, _, _ = forward_diffusion(data[0], temperature_forward, num_step)
#plot_images(x_data, model_dir + '/' + 'corrupted_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index) + '_time_step_' + str(num_step))
x_data_time.append(x_data)
temp_grad = np.concatenate((x_data_time[-2], x_data_time[-1]), axis=1)
grad_forward.append(temp_grad)
x_data = np.asarray(x_data).astype('float32').reshape(args.batch_size, INPUT_SIZE)
spiral_corrupted.append(x_data)
mu_data = np.asarray(mu_data).astype('float32').reshape(args.batch_size, INPUT_SIZE)
mu_data = mu_data.reshape(args.batch_size, 2)
else:
x_data_time.append(x_data)
x_data, mu_data, _, _ = forward_diffusion(x_data, temperature_forward, num_step)
#plot_images(x_data, model_dir + '/' + 'corrupted_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index) + '_time_step_' + str(num_step))
x_data = np.asarray(x_data).astype('float32').reshape(args.batch_size, INPUT_SIZE)
spiral_corrupted.append(x_data)
mu_data = np.asarray(mu_data).astype('float32').reshape(args.batch_size, INPUT_SIZE)
mu_data = mu_data.reshape(args.batch_size, 2)
x_data_time.append(x_data)
temp_grad = np.concatenate((x_data_time[-2], x_data_time[-1]), axis=1)
grad_forward.append(temp_grad)
temperature_forward = temperature_forward * args.temperature_factor;
mean_sampled = x_data.mean()
var_sampled = x_data.var()
x_temp2 = data[0].reshape(args.batch_size, 2)
plot_2D(spiral_corrupted, args.num_steps*args.meta_steps, model_dir + '/' + 'corrupted_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index))
plot_2D(x_temp2, 1, model_dir + '/' + 'orig_' + 'epoch_' + str(count_sample) + '_batch_index_' + str(batch_index))
plot_grad(grad_forward, model_dir + '/' + 'grad_forward_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index))
for i in range(args.num_steps*args.meta_steps + args.extra_steps):
x_tilt_time.append(x_data)
x_data, sampled_mean = f_sample(x_data, temperature, args.num_steps*args.meta_steps -i - 1)
#plot_images(x_data, model_dir + '/' + 'sampled_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index) + '_time_step_' + str(i))
x_tilt_time.append(x_data)
temp_grad = np.concatenate((x_tilt_time[-2], x_tilt_time[-1]), axis=1)
grad_back.append(temp_grad)
###print 'Recons, On step number, using temperature', i, temperature
x_data = np.asarray(x_data).astype('float32')
x_data = x_data.reshape(args.batch_size, INPUT_SIZE)
if temperature == args.temperature:
temperature = temperature
else:
temperature /= args.temperature_factor
plot_grad(grad_back, model_dir + '/' + 'grad_back_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index))
plot_2D(x_tilt_time, args.num_steps*args.meta_steps , model_dir + '/' + 'sampled_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index))
s = np.random.normal(mean_sampled, var_sampled, [args.batch_size, 2])
x_sampled = s
temperature = args.temperature * (args.temperature_factor ** (args.num_steps*args.meta_steps -1 ))
x_data = np.asarray(x_sampled).astype('float32')
for i in range(args.num_steps*args.meta_steps + args.extra_steps):
x_data, sampled_mean = f_sample(x_data, temperature, args.num_steps*args.meta_steps -i - 1)
spiral_sampled.append(x_data)
x_data = np.asarray(x_data).astype('float32')
x_data = x_data.reshape(args.batch_size, INPUT_SIZE)
if temperature == args.temperature:
temperature = temperature
else:
temperature /= args.temperature_factor
plot_2D(spiral_sampled, args.num_steps*args.meta_steps, model_dir + '/' + 'inference_' + 'epoch_' + str(count_sample) + '_batch_' + str(batch_index))
ipdb.set_trace()
if __name__ == '__main__':
args, model_args = parse_args()
train(args, model_args)
pass