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domain_adaptation.py
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domain_adaptation.py
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import sys
import os
import argparse
from datetime import datetime
from shutil import copyfile
import ntpath
import json
import torch
import torch.optim as optim
from torch.optim import lr_scheduler
from experiments import da_exp
from ml_utils import trainer, losses, miners, clustering
from dataset_utils import dataloaders
import models
import utils
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str,
help='Path to the configuration file', default='config/da_config.json')
return parser.parse_args(argv)
def path_leaf(path):
head, tail = ntpath.split(path)
return tail or ntpath.basename(head)
def generate_experiment_name(config):
if config.debug:
experiment_name = 'debug'
else:
experiment_name = 'da_{}_to_{}_L'.format(config.source_dataset, config.target_dataset)
if config.hyperparameters.lamda[0] > 0.0:
experiment_name += '1'
if config.hyperparameters.lamda[1] > 0.0:
experiment_name += '2'
experiment_name += '_m{}'.format(config.hyperparameters.margin)
if config.miner == 'supervised_dualtriplet':
experiment_name += '_supervised'
if os.path.isdir(os.path.join(os.path.expanduser(config.output.output_dir), experiment_name)):
dir_count = 1
experiment_name += '_1'
while os.path.isdir(os.path.join(os.path.expanduser(config.output.output_dir), experiment_name)):
dir_count += 1
experiment_name = experiment_name[:-2] + '_{}'.format(dir_count)
return experiment_name
def main(args):
print('Feature extractor training.')
print('CONFIGURATION:\t{}'.format(args.config))
with open(args.config) as json_config_file:
config = utils.AttrDict(json.load(json_config_file))
# Set up output directory
experiment_name = generate_experiment_name(config)
model_dir = os.path.join(os.path.expanduser(config.output.output_dir), experiment_name)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
print('Model saved at {}'.format(model_dir))
config_filename = path_leaf(args.config)
copyfile(args.config, os.path.join(model_dir, config_filename))
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
source_loader = dataloaders.get_traindataloaders(config.source_dataset,
config)
target_loader = dataloaders.get_traindataloaders(config.target_dataset,
config)
evaluators_list = dataloaders.get_evaluators(config.evaluation_datasets,
config)
# Set up training model
print('Building training model')
if config.model.checkpoint:
checkpoint_path = config.model.checkpoint_path
else:
checkpoint_path = None
model = models.load_model(config.model.model_arch,
device,
checkpoint_path=checkpoint_path,
embedding_size=config.model.embedding_size,
imgnet_pretrained=config.model.pretrained_imagenet)
optimizer = optim.SGD(model.parameters(), lr=config.hyperparameters.learning_rate, momentum=0.9, nesterov=True, weight_decay=2e-4)
scheduler = lr_scheduler.ExponentialLR(optimizer, config.hyperparameters.learning_rate_decay_factor)
model = model.to(device)
plotter = utils.VisdomPlotter(config.visdom.server ,env_name=experiment_name, port=config.visdom.port)
print('Fitting source dataset.')
gmixture = clustering.distance_supervised_gaussian_mixture(source_loader,
model,
device,
_plotter=plotter,
name='Source Gaussians')
print('Fitting target dataset.')
clustering.update_gaussian_mixture(gmixture,
target_loader,
model,
device,
_plotter=plotter,
name='Target Gaussians')
print('DualTriplet loss training mode.')
miner = miners.get_miner(config.miner,
config.hyperparameters.margin,
config.hyperparameters.people_per_batch,
plotter,
deadzone_ratio=config.hyperparameters.deadzone_ratio)
miner.gmixture = gmixture
loss = losses.DualtripletLoss(config.hyperparameters.margin,
config.hyperparameters.lamda,
plotter)
model_trainer = trainer.Dualtriplet_Trainer(model,
miner,
loss,
optimizer,
scheduler,
device,
plotter,
config.hyperparameters.margin,
config.model.embedding_size,
batch_size=config.hyperparameters.batch_size)
if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
# Loop over epochs
epoch = 0
print('Training Launched.')
while epoch < config.hyperparameters.n_epochs:
# Validation
for evaluator in evaluators_list:
print('\nEvaluation on {}'.format(evaluator.test_name))
evaluator.evaluate(model,
device,
plotter=plotter,
epoch=epoch)
# Training
print('\nExperimentation {}'.format(config.experiment))
print('Train Epoch {}'.format(epoch))
model_trainer.Train_Epoch(source_loader, target_loader, epoch)
# Save model
# if not (epoch + 1) % config.output.save_interval:
#
# model_file_path = os.path.join(model_dir, 'model_{}.pth'.format(epoch))
# print('\nSave model at {}'.format(model_file_path))
# torch.save({'epoch': epoch,
# 'model_state_dict': utils.state_dict_to_cpu(model.state_dict()),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'scheduler_state_dict': scheduler.state_dict(),
# 'embedding_size': config.model.embedding_size
# }, model_file_path)
epoch += 1
model_file_path = os.path.join(model_dir, 'model_{}.pth'.format(epoch))
print('\nSave model at {}'.format(model_file_path))
torch.save({'epoch': epoch,
'model_state_dict': utils.state_dict_to_cpu(model.state_dict()),
'optimizer_state_dict': optimizer.state_dict(),
'scheduler_state_dict': scheduler.state_dict(),
'embedding_size': config.model.embedding_size
}, model_file_path)
print('Finish.')
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))