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train.py
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train.py
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import torch
from torch.autograd import Variable
#import cPickle as pickle
import pickle
import string
import sys
import time
import numpy as np
from datetime import datetime, timedelta
import buffering
import utils
import logger
from configuration import config, set_configuration
import pathfinder
import app
print('train.py importing complete')
if len(sys.argv) < 2:
sys.exit("Usage: CUDA_VISIBLE_DEVICES=<gpu_number> python train.py <configuration_name>")
config_name = sys.argv[1]
set_configuration('configs', config_name)
expid = utils.generate_expid(config_name)
print("\nExperiment ID: %s\n" % expid)
# metadata
metadata_dir = utils.get_dir_path('models', pathfinder.METADATA_PATH)
metadata_path = metadata_dir + '/%s.pkl' % expid
metadata_best_path = metadata_dir + '/%s-best.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()
print(model.l_out)
model.l_out.cuda() # move to gpu
criterion = config().build_objective()
criterion2 = config().build_objective2()
learning_rate_schedule = config().learning_rate_schedule
learning_rate =(learning_rate_schedule[0])
optimizer = config().build_updates(model.l_out, learning_rate)
chunk_idxs = range(config().max_nchunks)
losses_eval_train = []
losses_eval_valid = []
losses_eval_train2 = []
losses_eval_valid2 = []
start_chunk_idx = 0
train_data_iterator = config().train_data_iterator
valid_data_iterator = config().valid_data_iterator
print('\nData')
print('\nn train: %d' % train_data_iterator.nsamples)
print('\n n validation: %d' % valid_data_iterator.nsamples)
print('\n n chunks per epoch', config().nchunks_per_epoch)
print('\nTrain model')
chunk_idx = 0
start_time = time.time()
prev_time = start_time
tmp_preds = []
tmp_gts = []
tmp_losses_train = []
tmp_losses_train2 = []
tmp_preds_train = []
tmp_gts_train = []
losses_train_print = []
losses_train_print2 = []
preds_train_print = []
gts_train_print = []
losses_time_print = []
best_valid_f2_score = 0
best_threshold = 0.91
# use buffering.buffered_gen_threaded()
for chunk_idx, (x_chunk_train, y_chunk_train, id_train) in zip(chunk_idxs, buffering.buffered_gen_threaded(
train_data_iterator.generate(), buffer_size=128)):
if chunk_idx in learning_rate_schedule:
lr = learning_rate_schedule[chunk_idx]
print(' setting learning rate to %.7f' % lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# for gt in y_chunk_train:
# tmp_gts.append(gt)
# tmp_gts_train.append(gt)
# gts_train_print.append(gt)
# make nbatches_chunk iterations
model.l_out.train()
for b in range(config().nbatches_chunk):
losses_time_print.append(time.time())
# wrap them in Variable
inputs, labels = Variable(torch.from_numpy(x_chunk_train).cuda()), \
Variable(torch.from_numpy(y_chunk_train).cuda())
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model.l_out(inputs)
loss = criterion(outputs, labels)
loss2 = criterion2(outputs, labels)
loss.backward()
optimizer.step()
# pr=outputs.cpu().data.numpy()
# tmp_preds.append(pr)
# tmp_preds_train.append(pr)
# preds_train_print.append(pr)
loss_out = loss.cpu().data.numpy()[0]
loss2_out = loss2.cpu().data.numpy()[0]
tmp_losses_train.append(loss_out)
tmp_losses_train2.append(loss2_out)
losses_train_print.append(loss_out)
losses_train_print2.append(loss2_out)
if (chunk_idx + 1) % 10 == 0:
print('Chunk %d/%d %.1fHz' % (chunk_idx + 1, config().max_nchunks,
10. * config().nbatches_chunk * config().batch_size / (
time.time() - losses_time_print[0])),)
print(np.mean(losses_train_print), np.mean(losses_train_print2))
# print('score', config().score(np.vstack(preds_train_print), gts_train_print))
preds_train_print = []
gts_train_print = []
losses_train_print = []
losses_time_print = []
losses_train_print2 = []
losses_time_print2 = []
if ((chunk_idx + 1) % config().validate_every) == 0:
print('\nChunk %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)
mean_train_loss2 = np.mean(tmp_losses_train2)
# mean_train_score = np.mean(config().score(np.vstack(tmp_preds_train), tmp_gts_train))
mean_train_score = -1
print('\nMean train loss: %7f' % mean_train_loss, mean_train_loss2, mean_train_score)
losses_eval_train.append(mean_train_loss)
tmp_losses_train = []
tmp_losses_train2 = []
tmp_preds_train = []
tmp_gts_train = []
# load validation data to GPU
tmp_losses_valid = []
tmp_losses_valid2 = []
tmp_preds_valid = []
tmp_gts_valid = []
tmp_xs_valid = []
model.l_out.eval()
for i, (x_chunk_valid, y_chunk_valid, ids_batch) in enumerate(
buffering.buffered_gen_threaded(valid_data_iterator.generate(),
buffer_size=2)):
inputs, labels = Variable(torch.from_numpy(x_chunk_valid).cuda(),volatile=True), Variable(
torch.from_numpy(y_chunk_valid).cuda(),volatile=True)
outputs = model.l_out(inputs)
loss = criterion(outputs, labels)
loss2 = criterion2(outputs, labels)
pr = outputs.cpu().data.numpy()
tmp_preds_valid.append(pr)
tmp_losses_valid.append(loss.cpu().data.numpy()[0])
tmp_losses_valid2.append(loss2.cpu().data.numpy()[0])
for gt in y_chunk_valid:
tmp_gts_valid.append(gt)
for xx in x_chunk_valid:
tmp_xs_valid.append(xx)
# calculate validation loss across validation set
valid_loss = np.mean(tmp_losses_valid)
valid_loss2 = np.mean(tmp_losses_valid2)
# valid_score = np.mean(config().score(np.vstack(tmp_preds_valid), tmp_gts_valid))
valid_score = -1
print('\nValidation loss: ', valid_loss, valid_loss2, valid_score)
losses_eval_valid.append(valid_loss)
losses_eval_valid2.append(valid_loss2)
# do something with the intermediate valid predictions, like saving to image
config().intermediate_valid_predictions(tmp_xs_valid, tmp_gts_valid, tmp_preds_valid, expid, chunk_idx)
if valid_score > best_threshold and valid_score > best_valid_f2_score:
with open(metadata_best_path, 'wb') 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': model.l_out.state_dict(),
#'optimizer_values': optimizer.state_dict(),
}, f, pickle.HIGHEST_PROTOCOL)
print('\n saved to %s\n' % metadata_best_path)
best_valid_f2_score = valid_score
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)\n" % (utils.hms(est_time_left), eta_str))
if ((chunk_idx + 1) % config().save_every) == 0:
print('Chunk %d/%d' % (chunk_idx + 1, config().max_nchunks))
print('Saving metadata, parameters')
with open(metadata_path, 'wb') 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': model.l_out.state_dict(),
'optimizer_values': optimizer.state_dict(),
}, f, pickle.HIGHEST_PROTOCOL)
print(' saved to %s\n' % metadata_path)