def pdc_train(dataset_config, train_config, dataset_name, logging_dir, num_iterations, dimension):
# print("training args")
# print(dataset_config)
# print(train_config)
# print(dataset_name)
# print(logging_dir)
# print(num_iterations)
# print(dimension)
print('dataset_name')
print(dataset_name)
dataset = SpartanDataset(config=dataset_config)
d = dimension # the descriptor dimension
name = dataset_name.split('/')[-1] + "_%d" %(d)
train_config["training"]["logging_dir_name"] = name
print('logging dir name')
print(name)
train_config["training"]["logging_dir"] = logging_dir
train_config["dense_correspondence_network"]["descriptor_dimension"] = d
train_config["training"]["num_iterations"] = num_iterations
print "training descriptor of dimension %d" %(d)
start_time = time.time()
train = DenseCorrespondenceTraining(dataset=dataset, config=train_config)
train.run()
end_time = time.time()
print "finished training descriptor of dimension %d using time %.2f seconds" %(d, end_time-start_time)
def train(self):
# This should take about ~12-15 minutes with a GTX 1080 Ti
# All of the saved data for this network will be located in the
# code/data_volume/pdc/trained_models/tutorials/caterpillar_3 folder
descr_dim = self.train_config["dense_correspondence_network"][
"descriptor_dimension"]
print("training descriptor of dimension %d" % (descr_dim))
train = DenseCorrespondenceTraining(dataset=self.dataset,
config=self.train_config)
train.run()
print("finished training descriptor of dimension %d" % (descr_dim))
from dense_correspondence.dataset.spartan_dataset_masked import SpartanDataset
logging.basicConfig(level=logging.INFO)
from dense_correspondence.evaluation.evaluation import DenseCorrespondenceEvaluation
config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'dataset', 'composite', 'toy.yaml')
config = utils.getDictFromYamlFilename(config_filename)
train_config_file = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'training', 'toy_training.yaml')
train_config = utils.getDictFromYamlFilename(train_config_file)
dataset = SpartanDataset(config=config)
logging_dir = "/home/zhouxian/git/pytorch-dense-correspondence/pdc/trained_models/tutorials"
d = 3 # the descriptor dimension
name = "toy_hacker_%d" %(d)
train_config["training"]["logging_dir_name"] = name
train_config["training"]["logging_dir"] = logging_dir
train_config["dense_correspondence_network"]["descriptor_dimension"] = d
TRAIN = True
EVALUATE = True
if TRAIN:
print "training descriptor of dimension %d" %(d)
train = DenseCorrespondenceTraining(dataset=dataset, config=train_config)
train.run()
# train.run_from_pretrained('/home/zhouxian/git/pytorch-dense-correspondence/pdc/trained_models/tutorials/backup/toy_hack_3')
print "finished training descriptor of dimension %d" %(d)
# train.run()
# print "finished training descriptor of dimension %d" %(d)
# quit()
### ITERATIVE
# First
config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'dataset', 'composite', 'shoe_train_1_green_nike.yaml')
config = utils.getDictFromYamlFilename(config_filename)
dataset = SpartanDataset(config=config)
print("training descriptor of dimension %d" %(d))
train = DenseCorrespondenceTraining(dataset=dataset, config=train_config)
train.run()
print("finished training descriptor of dimension %d" %(d))
# Second
config_filename = os.path.join(utils.getDenseCorrespondenceSourceDir(), 'config', 'dense_correspondence',
'dataset', 'composite', 'shoe_train_1_gray_nike.yaml')
config = utils.getDictFromYamlFilename(config_filename)
dataset = SpartanDataset(config=config)
print("training descriptor of dimension %d" %(d))
train_config["training"]["logging_dir_name"] = name+"1"
train = DenseCorrespondenceTraining(dataset=dataset, config=train_config)
train.run_from_pretrained("2018-10-15/"+name)
print("finished training descriptor of dimension %d" %(d))
'training.yaml')
train_config = utils.getDictFromYamlFilename(train_config_file)
dataset = SpartanDataset(config=config)
logging_dir = "/home/priya/code/data_volume/pdc_synthetic_2/trained_models/tutorials"
num_iterations = args.iters
d = args.dim # the descriptor dimension
name = args.name
train_config["training"]["logging_dir_name"] = name
train_config["training"]["logging_dir"] = logging_dir
train_config["dense_correspondence_network"]["descriptor_dimension"] = d
train_config["training"]["num_iterations"] = num_iterations
if args.normalization == "unit":
train_config["dense_correspondence_network"]["normalize"] = True
print("Using unit normalization")
else:
assert args.normalization == "standard" # By default, if "normalize" is not in the config, it defaults to False
if args.depth_invariant == True:
train_config["dense_correspondence_network"]["depth_invariant"] = True
print("Using depth invariant... ")
train = DenseCorrespondenceTraining(dataset=dataset, config=train_config)
if args.resume:
train.run_from_pretrained(
"/home/priya/code/data_volume/pdc_synthetic_2/trained_models/tutorials/{}"
.format(args.name))
else:
train.run()
train_config_file = os.path.join(utils.getDenseCorrespondenceSourceDir(),
'config', 'dense_correspondence', 'training',
'training.yaml')
train_config = utils.getDictFromYamlFilename(train_config_file)
dataset = SpartanDataset(config=config)
logging_dir = "/home/priya/code/data_volume/pdc_synthetic_2/trained_models/tutorials"
num_iterations = args.iters
d = args.dim # the descriptor dimension
name = args.name
train_config["training"]["logging_dir_name"] = name
train_config["training"]["logging_dir"] = logging_dir
train_config["dense_correspondence_network"]["descriptor_dimension"] = d
train_config["training"]["num_iterations"] = num_iterations
if args.normalization == "unit":
train_config["dense_correspondence_network"]["normalize"] = True
print("Using unit normalization")
else:
assert args.normalization == "standard" # By default, if "normalize" is not in the config, it defaults to False
if args.depth_invariant == True:
train_config["dense_correspondence_network"]["depth_invariant"] = True
print("Using depth invariant... ")
train = DenseCorrespondenceTraining(dataset=dataset, config=train_config)
if args.resume:
train.run_from_pretrained("simulated/{}".format(args.name))
else:
train.run()