import cPickle as pickle
import string
import sys
import time
from itertools import izip
import lasagne as nn
import numpy as np
import theano
from datetime import datetime, timedelta
import utils
import logger
import theano.tensor as T
import buffering
from configuration import config, set_configuration
import pathfinder
nn.random.set_rng(np.random.RandomState(317070))
theano.config.warn_float64 = 'raise'
if len(sys.argv) < 2:
sys.exit("Usage: train.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs_luna_props_patch', config_name)
expid = utils.generate_expid(config_name)
print
print "Experiment ID: %s" % expid
print
# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid
# logs
logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
sys.stdout = logger.Logger(logs_dir + '/%s.log' % expid)
sys.stderr = sys.stdout
print 'Build model'
model = config().build_model()
all_layers = nn.layers.get_all_layers(model.l_out)
all_params = nn.layers.get_all_params(model.l_out)
num_params = nn.layers.count_params(model.l_out)
print ' number of parameters: %d' % num_params
print string.ljust(' layer output shapes:', 36),
print string.ljust('#params:', 10),
print 'output shape:'
for layer in all_layers:
name = string.ljust(layer.__class__.__name__, 32)
num_param = sum([np.prod(p.get_value().shape) for p in layer.get_params()])
num_param = string.ljust(num_param.__str__(), 10)
print ' %s %s %s' % (name, num_param, layer.output_shape)
train_loss = config().build_objective(model, deterministic=False)
valid_loss = config().build_objective(model, deterministic=True)
learning_rate_schedule = config().learning_rate_schedule
learning_rate = theano.shared(np.float32(learning_rate_schedule[0]))
updates = config().build_updates(train_loss, model, learning_rate)
x_shared = nn.utils.shared_empty(dim=len(model.l_in.shape))
y_shared = nn.utils.shared_empty(dim=len(model.l_target.shape))
idx = T.lscalar('idx')
givens_train = {}
givens_train[model.l_in.input_var] = x_shared[idx * config().batch_size:(idx + 1) * config().batch_size]
givens_train[model.l_target.input_var] = y_shared[idx * config().batch_size:(idx + 1) * config().batch_size]
givens_valid = {}
givens_valid[model.l_in.input_var] = x_shared
givens_valid[model.l_target.input_var] = y_shared
# theano functions
iter_train = theano.function([idx], train_loss, givens=givens_train, updates=updates)
iter_validate = theano.function([], valid_loss, givens=givens_valid)
if config().restart_from_save:
print 'Load model parameters for resuming'
resume_metadata = utils.load_pkl(config().restart_from_save)
nn.layers.set_all_param_values(model.l_out, resume_metadata['param_values'])
start_chunk_idx = resume_metadata['chunks_since_start'] + 1
chunk_idxs = range(start_chunk_idx, config().max_nchunks)
lr = np.float32(utils.current_learning_rate(learning_rate_schedule, start_chunk_idx))
print ' setting learning rate to %.7f' % lr
learning_rate.set_value(lr)
losses_eval_train = resume_metadata['losses_eval_train']
losses_eval_valid = resume_metadata['losses_eval_valid']
else:
chunk_idxs = range(config().max_nchunks)
losses_eval_train = []
losses_eval_valid = []
start_chunk_idx = 0
train_data_iterator = config().train_data_iterator
valid_data_iterator = config().valid_data_iterator
print
print 'Data'
print 'n train: %d' % train_data_iterator.nsamples
print 'n validation: %d' % valid_data_iterator.nsamples
print 'n chunks per epoch', config().nchunks_per_epoch
print
print 'Train model'
chunk_idx = 0
start_time = time.time()
prev_time = start_time
tmp_losses_train = []
losses_train_print = []
# use buffering.buffered_gen_threaded()
for chunk_idx, (x_chunk_train, y_chunk_train, id_train) in izip(chunk_idxs, buffering.buffered_gen_threaded(
train_data_iterator.generate())):
if chunk_idx in learning_rate_schedule:
lr = np.float32(learning_rate_schedule[chunk_idx])
print ' setting learning rate to %.7f' % lr
print
learning_rate.set_value(lr)
# load chunk to GPU
x_shared.set_value(x_chunk_train)
y_shared.set_value(y_chunk_train)
# make nbatches_chunk iterations
for b in xrange(config().nbatches_chunk):
loss = iter_train(b)
# print loss
tmp_losses_train.append(loss)
losses_train_print.append(loss)
if (chunk_idx + 1) % 10 == 0:
print 'Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks), np.mean(losses_train_print)
losses_train_print = []
if ((chunk_idx + 1) % config().validate_every) == 0:
print
print 'Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks)
# calculate mean train loss since the last validation phase
mean_train_loss = np.mean(tmp_losses_train)
print 'Mean train loss: %7f' % mean_train_loss
losses_eval_train.append(mean_train_loss)
tmp_losses_train = []
# load validation data to GPU
tmp_losses_valid = []
for i, (x_chunk_valid, y_chunk_valid, ids_batch) in enumerate(
buffering.buffered_gen_threaded(valid_data_iterator.generate(),
buffer_size=2)):
x_shared.set_value(x_chunk_valid)
y_shared.set_value(y_chunk_valid)
l_valid = iter_validate()
print i, l_valid
tmp_losses_valid.append(l_valid)
# calculate validation loss across validation set
valid_loss = np.mean(tmp_losses_valid)
print 'Validation loss: ', valid_loss
losses_eval_valid.append(valid_loss)
now = time.time()
time_since_start = now - start_time
time_since_prev = now - prev_time
prev_time = now
est_time_left = time_since_start * (config().max_nchunks - chunk_idx + 1.) / (chunk_idx + 1. - start_chunk_idx)
eta = datetime.now() + timedelta(seconds=est_time_left)
eta_str = eta.strftime("%c")
print " %s since start (%.2f s)" % (utils.hms(time_since_start), time_since_prev)
print " estimated %s to go (ETA: %s)" % (utils.hms(est_time_left), eta_str)
print
if ((chunk_idx + 1) % config().save_every) == 0:
print
print 'Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks)
print 'Saving metadata, parameters'
with open(metadata_path, 'w') as f:
pickle.dump({
'configuration_file': config_name,
'git_revision_hash': utils.get_git_revision_hash(),
'experiment_id': expid,
'chunks_since_start': chunk_idx,
'losses_eval_train': losses_eval_train,
'losses_eval_valid': losses_eval_valid,
'param_values': nn.layers.get_all_param_values(model.l_out)
}, f, pickle.HIGHEST_PROTOCOL)
print ' saved to %s' % metadata_path
print
Datasets
Models
