def start_evaluating(run_config, train_config, data_config, model_config):
# hack to prevent the data loader from going on GPU 0
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(run_config['cuda_device'])
# torch.cuda.set_device(run_config['cuda_device'])
torch.cuda.set_device(0)
logger = Logger(run_config)
# load the data
train_data, val_data, test_data = load_data(data_config,
train_config['batch_size'])
if test_data is None:
test_data = val_data
# load the model
print('Loading model...')
model = load_model(model_config)
assert run_config[
'resume_path'] is not None, 'Resume path must be set for evaluation.'
print('Loading checkpoint ' + run_config['resume_path'])
model = logger.load_best(model)
# model = logger.load_epoch(model, 500)
# load the training batch size (needed to evaluate AVF)
sys.path.insert(
0,
os.path.join(run_config['log_root_path'], run_config['resume_path'],
'source', 'config'))
import train_config as tc
reload(tc)
batch_size = tc.train_config['batch_size']
print('Putting the model on the GPU...')
model.cuda()
model.eval()
output = eval_model(test_data,
model,
train_config,
training_batch_size=batch_size)
path = os.path.join(run_config['log_root_path'], run_config['log_dir'])
with open(path, 'wb') as f:
pickle.dump(output, f)
# load the data
train_data, val_data, test_data = load_data(data_config, train_config['batch_size'])
if val_data is None:
val_data = test_data
data = val_data
# load the model, optimizers
print('Loading model...')
model = load_model(model_config)
assert run_config['resume_path'] is not None, 'Model must be resumed from checkpoint.'
if run_config['resume_path']:
print('Resuming checkpoint ' + run_config['resume_path'])
model = logger.load_best(model)
print('Putting the model on the GPU...')
model.cuda()
out = visualize(data, model, train_config, data_config)
################################################################################
## plot data, predictions, and reconstructions
batch_index = 5
plt.figure()
data_shape = out['output_mean'].shape
if len(data_shape) == 6: