def make_signatures(unique_assemblies):
signatures = np.stack([
labels.inSameComponent(a, lower_tri_only=True)
for a in unique_assemblies
])
signatures = 2 * signatures.astype(float) - 1
return signatures
def make_signatures(unique_assemblies):
signatures = torch.stack([
torch.tensor(labels.inSameComponent(a, lower_tri_only=True))
for a in unique_assemblies
])
signatures = 2 * signatures.float() - 1
return signatures
def components_equivalent(x, y):
return (labels.inSameComponent(x) == labels.inSameComponent(y)).all()
def main(out_dir=None,
rgb_data_dir=None,
rgb_attributes_dir=None,
rgb_vocab_dir=None,
imu_data_dir=None,
imu_attributes_dir=None,
modalities=['rgb', 'imu'],
gpu_dev_id=None,
plot_predictions=None,
results_file=None,
sweep_param_name=None,
model_params={},
cv_params={},
train_params={},
viz_params={}):
out_dir = os.path.expanduser(out_dir)
rgb_data_dir = os.path.expanduser(rgb_data_dir)
rgb_attributes_dir = os.path.expanduser(rgb_attributes_dir)
rgb_vocab_dir = os.path.expanduser(rgb_vocab_dir)
imu_data_dir = os.path.expanduser(imu_data_dir)
imu_attributes_dir = os.path.expanduser(imu_attributes_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
if results_file is None:
results_file = os.path.join(out_dir, 'results.csv')
else:
results_file = os.path.expanduser(results_file)
fig_dir = os.path.join(out_dir, 'figures')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
out_data_dir = os.path.join(out_dir, 'data')
if not os.path.exists(out_data_dir):
os.makedirs(out_data_dir)
def saveVariable(var, var_name, to_dir=out_data_dir):
utils.saveVariable(var, var_name, to_dir)
# Load data
if modalities == ['rgb']:
trial_ids = utils.getUniqueIds(rgb_data_dir,
prefix='trial=',
to_array=True)
logger.info(f"Processing {len(trial_ids)} videos")
else:
rgb_trial_ids = utils.getUniqueIds(rgb_data_dir,
prefix='trial=',
to_array=True)
imu_trial_ids = utils.getUniqueIds(imu_data_dir,
prefix='trial=',
to_array=True)
trial_ids = np.array(
sorted(set(rgb_trial_ids.tolist()) & set(imu_trial_ids.tolist())))
logger.info(
f"Processing {len(trial_ids)} videos common to "
f"RGB ({len(rgb_trial_ids)} total) and IMU ({len(imu_trial_ids)} total)"
)
device = torchutils.selectDevice(gpu_dev_id)
dataset = FusionDataset(trial_ids,
rgb_attributes_dir,
rgb_data_dir,
imu_attributes_dir,
imu_data_dir,
device=device,
modalities=modalities)
utils.saveMetadata(dataset.metadata, out_data_dir)
saveVariable(dataset.vocab, 'vocab')
# parts_vocab = loadVariable('parts-vocab')
edge_labels = {
'rgb':
utils.loadVariable('part-labels', rgb_vocab_dir),
'imu':
np.stack([
labels.inSameComponent(a, lower_tri_only=True)
for a in dataset.vocab
])
}
# edge_labels = revise_edge_labels(edge_labels, input_seqs)
attribute_labels = tuple(edge_labels[name] for name in modalities)
logger.info('Making transition probs...')
transition_probs = make_transition_scores(dataset.vocab)
saveVariable(transition_probs, 'transition-probs')
model = AttributeModel(*attribute_labels, device=device)
if plot_predictions:
figsize = (12, 3)
fig, axis = plt.subplots(1, figsize=figsize)
axis.imshow(edge_labels['rgb'].T, interpolation='none', aspect='auto')
plt.savefig(os.path.join(fig_dir, "edge-labels.png"))
plt.close()
for i, trial_id in enumerate(trial_ids):
logger.info(f"Processing sequence {trial_id}...")
trial_prefix = f"trial={trial_id}"
true_label_seq = dataset.loadTargets(trial_id)
attribute_feats = dataset.loadInputs(trial_id)
score_seq = model(attribute_feats)
pred_label_seq = model.predict(score_seq)
attribute_feats = attribute_feats.cpu().numpy()
score_seq = score_seq.cpu().numpy()
true_label_seq = true_label_seq.cpu().numpy()
pred_label_seq = pred_label_seq.cpu().numpy()
saveVariable(score_seq.T, f'{trial_prefix}_score-seq')
saveVariable(true_label_seq.T, f'{trial_prefix}_label-seq')
if plot_predictions:
fn = os.path.join(fig_dir, f'{trial_prefix}.png')
utils.plot_array(attribute_feats.T,
(true_label_seq, pred_label_seq, score_seq),
('gt', 'pred', 'scores'),
fn=fn)
metric_dict = eval_metrics(pred_label_seq, true_label_seq)
for name, value in metric_dict.items():
logger.info(f" {name}: {value * 100:.2f}%")
utils.writeResults(results_file, metric_dict, sweep_param_name,
model_params)