def eval(name, cpu, test_data, train, arch, log_dir, model_path,
output_filename):
use_cuda = not cpu and torch.cuda.is_available()
device = "cuda" if use_cuda else "cpu"
print("Using device:", device)
model = get_model(arch)
model.to(device)
model = load_model(
model, device, model_path
) # modified to add model_path. may not be a good idea for normal use
criterion = get_criterion(device, train["loss_reduction"])
exp_logger = logger.Experiment(name)
exp_logger.add_meters("test", metrics.make_meter_matching())
gene_test = Generator("test", test_data)
gene_test.load_dataset()
test_loader = siamese_loader(gene_test, train["batch_size"],
gene_test.constant_n_vertices)
acc, loss = trainer.val_triplet(
test_loader,
model,
criterion,
exp_logger,
device,
epoch=0,
eval_score=metrics.accuracy_linear_assignment,
val_test="test",
)
key = create_key()
filename_test = os.path.join(log_dir, output_filename)
print("Saving result at: ", filename_test)
save_to_json(key, acc, loss, filename_test)
def eval(name, cpu, test_data, train, arch, log_dir, model_path,
output_filename):
use_cuda = not cpu and torch.cuda.is_available()
device = 'cuda' if use_cuda else 'cpu'
print('Using device:', device)
model = get_model(arch)
model.to(device)
model = load_model(model, device)
criterion = get_criterion(device, train['loss_reduction'])
exp_logger = logger.Experiment(name)
exp_logger.add_meters('test', metrics.make_meter_matching())
gene_test = Generator('test', test_data)
gene_test.load_dataset()
test_loader = siamese_loader(gene_test, train['batch_size'],
gene_test.constant_n_vertices)
acc, loss = trainer.val_triplet(
test_loader,
model,
criterion,
exp_logger,
device,
epoch=0,
eval_score=metrics.accuracy_linear_assignment,
val_test='test')
key = create_key()
filename_test = os.path.join(log_dir, output_filename)
print('Saving result at: ', filename_test)
save_to_json(key, acc, loss, filename_test)
def init_logger(name, _config, _run):
# set loggers
exp_logger = logger.Experiment(name, _config, run=_run)
exp_logger.add_meters("train", metrics.make_meter_matching())
exp_logger.add_meters("val", metrics.make_meter_matching())
# exp_logger.add_meters('test', metrics.make_meter_matching())
exp_logger.add_meters("hyperparams", {"learning_rate": metrics.ValueMeter()})
return exp_logger
def init_logger(args, model):
# set loggers
exp_name = args.name
exp_logger = logger.Experiment(exp_name, args.__dict__)
exp_logger.add_meters('train', metrics.make_meters(args.num_classes))
exp_logger.add_meters('val', metrics.make_meters(args.num_classes))
exp_logger.add_meters('hyperparams', {'learning_rate': metrics.ValueMeter()})
return exp_logger
def init_logger(name, _config, _run):
# set loggers
exp_logger = logger.Experiment(name, _config, run=_run)
exp_logger.add_meters('train', metrics.make_meter_matching())
exp_logger.add_meters('val', metrics.make_meter_matching())
#exp_logger.add_meters('test', metrics.make_meter_matching())
exp_logger.add_meters('hyperparams',
{'learning_rate': metrics.ValueMeter()})
return exp_logger
def init_logger(args):
# set loggers
exp_name = args['--name']
exp_logger = logger.Experiment(exp_name, args)
exp_logger.add_meters('train', metrics.make_meter_matching())
exp_logger.add_meters('val', metrics.make_meter_matching())
exp_logger.add_meters('hyperparams',
{'learning_rate': metrics.ValueMeter()})
return exp_logger
def eval_spectral(name, load_data, train, test_data, log_dir, output_filename):
exp_logger = logger.Experiment(name)
exp_logger.add_meters("test", metrics.make_meter_matching())
gene_test = Generator("test", test_data)
if load_data:
gene_test.load_dataset()
else:
gene_test.create_dataset()
test_loader = label_loader(gene_test, train["batch_size"], gene_test.constant_n_vertices)
exp_logger.reset_meters("test")
print_freq = 10
for i, (input, cluster_sizes) in enumerate(test_loader):
acc, total_n_vertices = metrics.accuracy_spectral_cluster_kmeans(input, cluster_sizes)
exp_logger.update_meter("test", "acc", acc, n=total_n_vertices)
if i % print_freq:
acc = exp_logger.get_meter("test", "acc")
print("Test set\t" "Acc {acc.avg:.3f} ({acc.val:.3f})".format(acc=acc))
def eval(name, cpu, load_data, test_data, train, arch, log_dir, model_path, output_filename, return_result=False):
use_cuda = not cpu and torch.cuda.is_available()
device = "cuda" if use_cuda else "cpu"
print("Using device:", device)
model = get_model(arch)
model.to(device)
model = load_model(model, device)
if arch["arch"] == "Simple_Node_Embedding":
criterion = cluster_embedding_loss(device=device)
elif arch["arch"] == "Similarity_Model":
criterion = cluster_similarity_loss()
exp_logger = logger.Experiment(name)
exp_logger.add_meters("test", metrics.make_meter_matching())
gene_test = Generator("test", test_data)
if load_data:
gene_test.load_dataset()
else:
gene_test.create_dataset()
test_loader = label_loader(gene_test, train["batch_size"], gene_test.constant_n_vertices)
acc, loss = trainer.val_cluster(
test_loader,
model,
criterion,
exp_logger,
device,
epoch=0,
eval_score=metrics.accuracy_cluster_kmeans,
val_test="test",
)
if not return_result:
key = create_key()
filename_test = os.path.join(log_dir, output_filename)
print("Saving result at: ", filename_test)
save_to_json(key, acc, loss, filename_test)
return acc, loss
