def main(out_dir=None,
data_dir=None,
feature_fn_format='feature-seq.pkl',
label_fn_format='label_seq.pkl',
cv_params={}):
out_dir = os.path.expanduser(out_dir)
data_dir = os.path.expanduser(data_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
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)
# Load data
trial_ids = utils.getUniqueIds(data_dir,
prefix='trial=',
suffix=feature_fn_format,
to_array=True)
dataset = utils.CvDataset(trial_ids,
data_dir,
feature_fn_format=feature_fn_format,
label_fn_format=label_fn_format,
vocab=[])
utils.saveMetadata(dataset.metadata, out_data_dir)
# Make folds
dataset_size = len(trial_ids)
cv_folds = utils.makeDataSplits(dataset_size,
metadata=dataset.metadata,
**cv_params)
save_cv_folds(cv_folds, os.path.join(out_data_dir, 'cv-folds.json'))
# Check folds
for cv_index, cv_fold in enumerate(cv_folds):
train_data, val_data, test_data = dataset.getFold(cv_fold)
train_feats, train_labels, train_ids = train_data
test_feats, test_labels, test_ids = test_data
val_feats, val_labels, val_ids = val_data
logger.info(
f'CV fold {cv_index + 1} / {len(cv_folds)}: {len(trial_ids)} total '
f'({len(train_ids)} train, {len(val_ids)} val, {len(test_ids)} test)'
)
def main(out_dir=None, data_dir=None, labels_dir=None):
out_dir = os.path.expanduser(out_dir)
data_dir = os.path.expanduser(data_dir)
labels_dir = os.path.expanduser(labels_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
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)
filenames = [
utils.stripExtension(fn)
for fn in glob.glob(os.path.join(labels_dir, '*.csv'))
]
metadata = utils.loadMetadata(data_dir)
metadata['seq_id'] = metadata.index
metadata = metadata.set_index(
'dir_name', drop=False).loc[filenames].set_index('seq_id')
seq_ids = np.sort(metadata.index.to_numpy())
logger.info(f"Loaded {len(seq_ids)} sequences from {labels_dir}")
vocab = []
for i, seq_id in enumerate(seq_ids):
seq_id_str = f"seq={seq_id}"
seq_dir_name = metadata['dir_name'].loc[seq_id]
labels_fn = os.path.join(labels_dir, f'{seq_dir_name}.csv')
event_labels = utils.loadVariable(f'{seq_id_str}_labels', data_dir)
assembly_actions = pd.read_csv(labels_fn)
label_seq = parseActions(assembly_actions, event_labels.shape[0],
vocab)
utils.saveVariable(label_seq, f'{seq_id_str}_label-seq', out_data_dir)
plotLabels(os.path.join(fig_dir, f'{seq_id_str}_labels.png'),
label_seq)
writeLabels(os.path.join(fig_dir, f'{seq_id_str}_labels.csv'),
label_seq, vocab)
utils.saveMetadata(metadata, out_data_dir)
utils.saveVariable(vocab, 'vocab', out_data_dir)
def main(out_dir=None,
data_dir=None,
annotation_dir=None,
frames_dir=None,
col_format='standard',
win_params={},
slowfast_csv_params={},
label_types=('event', 'action', 'part')):
out_dir = os.path.expanduser(out_dir)
data_dir = os.path.expanduser(data_dir)
annotation_dir = os.path.expanduser(annotation_dir)
frames_dir = os.path.expanduser(frames_dir)
annotation_dir = os.path.join(annotation_dir, 'action_annotations')
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
fig_dir = os.path.join(out_dir, 'figures')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
out_labels_dir = os.path.join(out_dir, 'labels')
if not os.path.exists(out_labels_dir):
os.makedirs(out_labels_dir)
data_dirs = {
name: os.path.join(out_dir, f"{name}-dataset")
for name in label_types
}
for name, dir_ in data_dirs.items():
if not os.path.exists(dir_):
os.makedirs(dir_)
event_vocab_df, part_vocab, action_vocab = load_vocabs(
os.path.join(data_dir, 'ANU_ikea_dataset', 'indexing_files',
'atomic_action_list.txt'))
event_vocab_df.to_csv(os.path.join(out_labels_dir, 'event-vocab.csv'))
event_vocab = event_vocab_df.index.tolist()
vocabs = {'event': event_vocab, 'action': action_vocab, 'part': part_vocab}
vocabs = {label_name: vocabs[label_name] for label_name in label_types}
for name, vocab in vocabs.items():
utils.saveVariable(vocab, 'vocab', data_dirs[name])
label_fn = os.path.join(annotation_dir, 'gt_segments.json')
seq_ids, event_labels, metadata = load_action_labels(
label_fn, event_vocab_df)
utils.saveMetadata(metadata, out_labels_dir)
for name, dir_ in data_dirs.items():
utils.saveMetadata(metadata, dir_)
logger.info(f"Loaded {len(seq_ids)} sequences from {label_fn}")
part_names = [name for name in part_vocab if name != '']
col_names = [f"{name}_active" for name in part_names]
integerizers = {
label_name: {name: i
for i, name in enumerate(label_vocab)}
for label_name, label_vocab in vocabs.items()
}
all_slowfast_labels_seg = collections.defaultdict(list)
all_slowfast_labels_win = collections.defaultdict(list)
counts = np.zeros((len(action_vocab), len(part_vocab)), dtype=int)
for i, seq_id in enumerate(seq_ids):
seq_id_str = f"seq={seq_id}"
seq_dir_name = metadata['dir_name'].loc[seq_id]
event_segs = event_labels[i]
if not event_segs.any(axis=None):
logger.warning(f"No event labels for sequence {seq_id}")
continue
event_data = make_event_data(
event_segs,
sorted(glob.glob(os.path.join(frames_dir, seq_dir_name,
'*.jpg'))), integerizers['event'],
integerizers['action'], integerizers['part'],
event_vocab.index('NA'), action_vocab.index('NA'), False)
event_wins = make_window_clips(event_data, vocabs['event'],
vocabs['action'], **win_params)
event_data.to_csv(os.path.join(out_labels_dir,
f"{seq_id_str}_data.csv"),
index=False)
event_segs.to_csv(os.path.join(out_labels_dir,
f"{seq_id_str}_segs.csv"),
index=False)
filenames = event_data['fn'].to_list()
label_indices = {}
for name in label_types:
if name == 'part':
label_indices[name] = event_data[col_names].to_numpy()
seg_labels_slowfast = make_slowfast_labels(
event_segs[['start', 'end']],
getActivePart(event_segs[col_names], part_names),
event_data['fn'],
integerizers[name],
col_format=col_format)
win_labels_slowfast = make_slowfast_labels(
event_wins[['start', 'end']],
getActivePart(event_wins[col_names], part_names),
event_data['fn'],
integerizers[name],
col_format=col_format)
else:
label_indices[name] = event_data[name].to_numpy()
seg_labels_slowfast = make_slowfast_labels(
event_segs[['start', 'end']],
event_segs[name],
event_data['fn'],
integerizers[name],
col_format=col_format)
win_labels_slowfast = make_slowfast_labels(
event_wins[['start', 'end']],
event_wins[name],
event_data['fn'],
integerizers[name],
col_format=col_format)
utils.saveVariable(filenames, f'{seq_id_str}_frame-fns',
data_dirs[name])
utils.saveVariable(label_indices[name], f'{seq_id_str}_labels',
data_dirs[name])
seg_labels_slowfast.to_csv(
os.path.join(data_dirs[name],
f'{seq_id_str}_slowfast-labels.csv'),
**slowfast_csv_params)
win_labels_slowfast.to_csv(
os.path.join(data_dirs[name],
f'{seq_id_str}_slowfast-labels.csv'),
**slowfast_csv_params)
all_slowfast_labels_seg[name].append(seg_labels_slowfast)
all_slowfast_labels_win[name].append(win_labels_slowfast)
plot_event_labels(os.path.join(fig_dir, f"{seq_id_str}.png"),
label_indices['event'], label_indices['action'],
label_indices['part'], event_vocab, action_vocab,
part_names)
for part_activity_row, action_index in zip(label_indices['part'],
label_indices['action']):
for i, is_active in enumerate(part_activity_row):
part_index = integerizers['part'][part_names[i]]
counts[action_index, part_index] += int(is_active)
for name, labels in all_slowfast_labels_seg.items():
pd.concat(labels, axis=0).to_csv(
os.path.join(data_dirs[name], 'slowfast-labels_seg.csv'),
**slowfast_csv_params)
for name, labels in all_slowfast_labels_win.items():
pd.concat(labels, axis=0).to_csv(
os.path.join(data_dirs[name], 'slowfast-labels_win.csv'),
**slowfast_csv_params)
utils.saveVariable(counts, 'action-part-counts', out_labels_dir)
plt.matshow(counts)
plt.xticks(ticks=range(len(part_vocab)),
labels=part_vocab,
rotation='vertical')
plt.yticks(ticks=range(len(action_vocab)), labels=action_vocab)
plt.savefig(os.path.join(fig_dir, 'action-part-coocurrence.png'),
bbox_inches='tight')
def main(
out_dir=None, data_dir=None, prefix='trial=',
feature_fn_format='feature-seq.pkl', label_fn_format='label_seq.pkl',
slowfast_labels_path=None, cv_params={}, slowfast_csv_params={}):
out_dir = os.path.expanduser(out_dir)
data_dir = os.path.expanduser(data_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
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)
# Load data
trial_ids = utils.getUniqueIds(
data_dir, prefix=prefix, suffix=feature_fn_format,
to_array=True
)
dataset = utils.CvDataset(
trial_ids, data_dir,
feature_fn_format=feature_fn_format, label_fn_format=label_fn_format,
vocab=[], prefix=prefix
)
utils.saveMetadata(dataset.metadata, out_data_dir)
# Make folds
dataset_size = len(trial_ids)
cv_folds = utils.makeDataSplits(dataset_size, metadata=dataset.metadata, **cv_params)
save_cv_folds(cv_folds, os.path.join(out_data_dir, 'cv-folds.json'))
split_names = ('train', 'val', 'test')
# Check folds
for cv_index, cv_fold in enumerate(cv_folds):
train_data, val_data, test_data = dataset.getFold(cv_fold)
train_feats, train_labels, train_ids = train_data
test_feats, test_labels, test_ids = test_data
val_feats, val_labels, val_ids = val_data
logger.info(
f'CV fold {cv_index + 1} / {len(cv_folds)}: {len(trial_ids)} total '
f'({len(train_ids)} train, {len(val_ids)} val, {len(test_ids)} test)'
)
slowfast_labels_pattern = os.path.join(data_dir, 'slowfast-labels*.csv')
for slowfast_labels_path in glob.glob(slowfast_labels_pattern):
cv_str = f"cvfold={cv_index}"
fn = os.path.basename(slowfast_labels_path)
slowfast_labels = pd.read_csv(
slowfast_labels_path, index_col=0, keep_default_na=False,
**slowfast_csv_params
)
for split_indices, split_name in zip(cv_fold, split_names):
matches = tuple(
slowfast_labels.loc[slowfast_labels['video_name'] == vid_id]
for vid_id in dataset.metadata.iloc[split_indices]['dir_name'].to_list()
)
if matches:
split = pd.concat(matches, axis=0)
split.to_csv(
os.path.join(out_data_dir, f"{cv_str}_{split_name}_{fn}"),
**slowfast_csv_params
)
else:
logger.info(f' Skipping empty slowfast split: {split_name}')
def main(
out_dir=None, data_dir=None, use_vid_ids_from=None,
output_data=None, magnitude_centering=None, resting_from_gt=None,
remove_before_first_touch=None, include_signals=None, fig_type=None):
data_dir = os.path.expanduser(data_dir)
out_dir = os.path.expanduser(out_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
logger.info(f"Reading from: {data_dir}")
logger.info(f"Writing to: {out_dir}")
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 loadAll(seq_ids, var_name, from_dir=data_dir, prefix='trial='):
all_data = tuple(
utils.loadVariable(f"{prefix}{seq_id}_{var_name}", from_dir)
for seq_id in seq_ids
)
return all_data
def saveVariable(var, var_name, to_dir=out_data_dir):
utils.saveVariable(var, var_name, to_dir)
if fig_type is None:
fig_type = 'multi'
# Load data
if use_vid_ids_from is None:
trial_ids = utils.getUniqueIds(data_dir, prefix='trial=', to_array=True)
else:
use_vid_ids_from = os.path.expanduser(use_vid_ids_from)
trial_ids = utils.getUniqueIds(use_vid_ids_from, prefix='trial-', to_array=True)
accel_seqs = loadAll(trial_ids, 'accel-samples.pkl')
gyro_seqs = loadAll(trial_ids, 'gyro-samples.pkl')
action_seqs = loadAll(trial_ids, 'action-seq.pkl')
rgb_timestamp_seqs = loadAll(trial_ids, 'rgb-frame-timestamp-seq.pkl')
def validate_imu(seqs):
def is_valid(d):
return not any(np.isnan(x).any() for x in d.values())
return np.array([is_valid(d) for d in seqs])
imu_is_valid = validate_imu(accel_seqs) & validate_imu(gyro_seqs)
logger.info(
f"Ignoring {(~imu_is_valid).sum()} IMU sequences with NaN-valued samples "
f"(of {len(imu_is_valid)} total)"
)
def chooseValid(seq):
return tuple(x for x, is_valid in zip(seq, imu_is_valid) if is_valid)
trial_ids = np.array(list(chooseValid(trial_ids)))
accel_seqs = chooseValid(accel_seqs)
gyro_seqs = chooseValid(gyro_seqs)
action_seqs = chooseValid(action_seqs)
rgb_timestamp_seqs = chooseValid(rgb_timestamp_seqs)
vocab = []
metadata = utils.loadMetadata(data_dir, rows=trial_ids)
utils.saveMetadata(metadata, out_data_dir)
utils.saveVariable(vocab, 'vocab', out_data_dir)
def norm(x):
norm = np.linalg.norm(imu.getImuSamples(x), axis=1)[:, None]
return norm
accel_mag_seqs = tuple(map(lambda x: dictToArray(x, transform=norm), accel_seqs))
gyro_mag_seqs = tuple(map(lambda x: dictToArray(x, transform=norm), gyro_seqs))
imu_timestamp_seqs = utils.batchProcess(makeTimestamps, accel_seqs, gyro_seqs)
if remove_before_first_touch:
before_first_touch_seqs = utils.batchProcess(
beforeFirstTouch, action_seqs, rgb_timestamp_seqs, imu_timestamp_seqs
)
num_ignored = sum(b is None for b in before_first_touch_seqs)
logger.info(
f"Ignoring {num_ignored} sequences without first-touch annotations "
f"(of {len(before_first_touch_seqs)} total)"
)
trials_missing_first_touch = [
i for b, i in zip(before_first_touch_seqs, trial_ids)
if b is None
]
logger.info(f"Trials without first touch: {trials_missing_first_touch}")
def clip(signal, bool_array):
return signal[~bool_array, ...]
accel_mag_seqs = tuple(
clip(signal, b) for signal, b in zip(accel_mag_seqs, before_first_touch_seqs)
if b is not None
)
gyro_mag_seqs = tuple(
clip(signal, b) for signal, b in zip(gyro_mag_seqs, before_first_touch_seqs)
if b is not None
)
imu_timestamp_seqs = tuple(
clip(signal, b) for signal, b in zip(imu_timestamp_seqs, before_first_touch_seqs)
if b is not None
)
trial_ids = tuple(
x for x, b in zip(trial_ids, before_first_touch_seqs)
if b is not None
)
action_seqs = tuple(
x for x, b in zip(action_seqs, before_first_touch_seqs)
if b is not None
)
rgb_timestamp_seqs = tuple(
x for x, b in zip(rgb_timestamp_seqs, before_first_touch_seqs)
if b is not None
)
assembly_seqs = utils.batchProcess(
parseActions,
action_seqs, rgb_timestamp_seqs, imu_timestamp_seqs
)
if output_data == 'components':
accel_feat_seqs = accel_mag_seqs
gyro_feat_seqs = gyro_mag_seqs
unique_components = {frozenset(): 0}
imu_label_seqs = zip(
*tuple(
labels.componentLabels(*args, unique_components)
for args in zip(action_seqs, rgb_timestamp_seqs, imu_timestamp_seqs)
)
)
saveVariable(unique_components, 'unique_components')
elif output_data == 'pairwise components':
imu_label_seqs = utils.batchProcess(
labels.pairwiseComponentLabels, assembly_seqs,
static_kwargs={'lower_tri_only': True, 'include_action_labels': False}
)
accel_feat_seqs = tuple(map(imu.pairwiseFeats, accel_mag_seqs))
gyro_feat_seqs = tuple(map(imu.pairwiseFeats, gyro_mag_seqs))
else:
raise AssertionError()
signals = {'accel': accel_feat_seqs, 'gyro': gyro_feat_seqs}
if include_signals is None:
include_signals = tuple(signals.keys())
signals = tuple(signals[key] for key in include_signals)
imu_feature_seqs = tuple(np.stack(x, axis=-1).squeeze(axis=-1) for x in zip(*signals))
video_seqs = tuple(zip(imu_feature_seqs, imu_label_seqs, trial_ids))
imu.plot_prediction_eg(video_seqs, fig_dir, fig_type=fig_type, output_data=output_data)
video_seqs = tuple(
zip(assembly_seqs, imu_feature_seqs, imu_timestamp_seqs, imu_label_seqs, trial_ids)
)
for assembly_seq, feature_seq, timestamp_seq, label_seq, trial_id in video_seqs:
id_string = f"trial={trial_id}"
saveVariable(assembly_seq, f'{id_string}_assembly-seq')
saveVariable(feature_seq, f'{id_string}_feature-seq')
saveVariable(timestamp_seq, f'{id_string}_timestamp-seq')
saveVariable(label_seq, f'{id_string}_label-seq')
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)
def main(out_dir=None,
data_dir=None,
model_name=None,
predict_mode='classify',
gpu_dev_id=None,
batch_size=None,
learning_rate=None,
independent_signals=None,
active_only=None,
output_dim_from_vocab=False,
prefix='trial=',
feature_fn_format='feature-seq.pkl',
label_fn_format='label_seq.pkl',
dataset_params={},
model_params={},
cv_params={},
train_params={},
viz_params={},
metric_names=['Loss', 'Accuracy', 'Precision', 'Recall', 'F1'],
plot_predictions=None,
results_file=None,
sweep_param_name=None):
data_dir = os.path.expanduser(data_dir)
out_dir = os.path.expanduser(out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_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):
return utils.saveVariable(var, var_name, to_dir)
# Load data
device = torchutils.selectDevice(gpu_dev_id)
trial_ids = utils.getUniqueIds(data_dir,
prefix=prefix,
suffix=feature_fn_format,
to_array=True)
dataset = utils.CvDataset(
trial_ids,
data_dir,
prefix=prefix,
feature_fn_format=feature_fn_format,
label_fn_format=label_fn_format,
)
utils.saveMetadata(dataset.metadata, out_data_dir)
utils.saveVariable(dataset.vocab, 'vocab', out_data_dir)
# Define cross-validation folds
cv_folds = utils.makeDataSplits(len(trial_ids), **cv_params)
utils.saveVariable(cv_folds, 'cv-folds', out_data_dir)
if predict_mode == 'binary multiclass':
# criterion = torchutils.BootstrappedCriterion(
# 0.25, base_criterion=torch.nn.functional.binary_cross_entropy_with_logits,
# )
criterion = torch.nn.BCEWithLogitsLoss()
labels_dtype = torch.float
elif predict_mode == 'multiclass':
criterion = torch.nn.CrossEntropyLoss()
labels_dtype = torch.long
elif predict_mode == 'classify':
criterion = torch.nn.CrossEntropyLoss()
labels_dtype = torch.long
else:
raise AssertionError()
def make_dataset(feats, labels, ids, shuffle=True):
dataset = torchutils.SequenceDataset(feats,
labels,
device=device,
labels_dtype=labels_dtype,
seq_ids=ids,
**dataset_params)
loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True)
return dataset, loader
for cv_index, cv_fold in enumerate(cv_folds):
train_data, val_data, test_data = dataset.getFold(cv_fold)
if independent_signals:
train_data = splitSeqs(*train_data, active_only=active_only)
val_data = splitSeqs(*val_data, active_only=active_only)
test_data = splitSeqs(*test_data, active_only=False)
train_set, train_loader = make_dataset(*train_data, shuffle=True)
test_set, test_loader = make_dataset(*test_data, shuffle=False)
val_set, val_loader = make_dataset(*val_data, shuffle=True)
logger.info(
f'CV fold {cv_index + 1} / {len(cv_folds)}: {len(dataset.trial_ids)} total '
f'({len(train_set)} train, {len(val_set)} val, {len(test_set)} test)'
)
logger.info(f'{train_set.num_label_types} unique labels in train set; '
f'vocab size is {len(dataset.vocab)}')
input_dim = train_set.num_obsv_dims
output_dim = train_set.num_label_types
if output_dim_from_vocab:
output_dim = len(dataset.vocab)
if model_name == 'linear':
model = torchutils.LinearClassifier(
input_dim, output_dim, **model_params).to(device=device)
elif model_name == 'conv':
model = ConvClassifier(input_dim, output_dim,
**model_params).to(device=device)
elif model_name == 'TCN':
if predict_mode == 'multiclass':
num_multiclass = train_set[0][1].shape[-1]
output_dim = max([
train_set.num_label_types, test_set.num_label_types,
val_set.num_label_types
])
else:
num_multiclass = None
model = TcnClassifier(input_dim,
output_dim,
num_multiclass=num_multiclass,
**model_params).to(device=device)
elif model_name == 'LSTM':
if predict_mode == 'multiclass':
num_multiclass = train_set[0][1].shape[-1]
output_dim = max([
train_set.num_label_types, test_set.num_label_types,
val_set.num_label_types
])
else:
num_multiclass = None
model = LstmClassifier(input_dim,
output_dim,
num_multiclass=num_multiclass,
**model_params).to(device=device)
else:
raise AssertionError()
optimizer_ft = torch.optim.Adam(model.parameters(),
lr=learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=0,
amsgrad=False)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer_ft,
step_size=1,
gamma=1.00)
train_epoch_log = collections.defaultdict(list)
val_epoch_log = collections.defaultdict(list)
metric_dict = {name: metrics.makeMetric(name) for name in metric_names}
model, last_model_wts = torchutils.trainModel(
model,
criterion,
optimizer_ft,
lr_scheduler,
train_loader,
val_loader,
device=device,
metrics=metric_dict,
train_epoch_log=train_epoch_log,
val_epoch_log=val_epoch_log,
**train_params)
# Test model
metric_dict = {name: metrics.makeMetric(name) for name in metric_names}
test_io_history = torchutils.predictSamples(
model.to(device=device),
test_loader,
criterion=criterion,
device=device,
metrics=metric_dict,
data_labeled=True,
update_model=False,
seq_as_batch=train_params['seq_as_batch'],
return_io_history=True)
if independent_signals:
test_io_history = tuple(joinSeqs(test_io_history))
logger.info('[TST] ' +
' '.join(str(m) for m in metric_dict.values()))
utils.writeResults(results_file,
{k: v.value
for k, v in metric_dict.items()}, sweep_param_name,
model_params)
if plot_predictions:
io_fig_dir = os.path.join(fig_dir, 'model-io')
if not os.path.exists(io_fig_dir):
os.makedirs(io_fig_dir)
label_names = ('gt', 'pred')
preds, scores, inputs, gt_labels, ids = zip(*test_io_history)
for batch in test_io_history:
batch = tuple(
x.cpu().numpy() if isinstance(x, torch.Tensor) else x
for x in batch)
for preds, _, inputs, gt_labels, seq_id in zip(*batch):
fn = os.path.join(io_fig_dir,
f"{prefix}{seq_id}_model-io.png")
utils.plot_array(inputs, (gt_labels.T, preds.T),
label_names,
fn=fn)
for batch in test_io_history:
batch = tuple(x.cpu().numpy() if isinstance(x, torch.Tensor) else x
for x in batch)
for pred_seq, score_seq, feat_seq, label_seq, trial_id in zip(
*batch):
saveVariable(pred_seq, f'{prefix}{trial_id}_pred-label-seq')
saveVariable(score_seq, f'{prefix}{trial_id}_score-seq')
saveVariable(label_seq, f'{prefix}{trial_id}_true-label-seq')
saveVariable(model, f'cvfold={cv_index}_{model_name}-best')
train_fig_dir = os.path.join(fig_dir, 'train-plots')
if not os.path.exists(train_fig_dir):
os.makedirs(train_fig_dir)
if train_epoch_log:
torchutils.plotEpochLog(train_epoch_log,
subfig_size=(10, 2.5),
title='Training performance',
fn=os.path.join(
train_fig_dir,
f'cvfold={cv_index}_train-plot.png'))
if val_epoch_log:
torchutils.plotEpochLog(val_epoch_log,
subfig_size=(10, 2.5),
title='Heldout performance',
fn=os.path.join(
train_fig_dir,
f'cvfold={cv_index}_val-plot.png'))
def main(out_dir=None,
data_dir=None,
actions_dir=None,
parts_dir=None,
events_dir=None,
edges_dir=None,
prefix='seq=',
feature_fn_format='score-seq',
label_fn_format='true-label-seq',
stop_after=None,
only_fold=None,
plot_io=None,
dataset_params={},
model_params={},
cv_params={}):
data_dir = os.path.expanduser(data_dir)
actions_dir = os.path.expanduser(actions_dir)
parts_dir = os.path.expanduser(parts_dir)
events_dir = os.path.expanduser(events_dir)
edges_dir = os.path.expanduser(edges_dir)
out_dir = os.path.expanduser(out_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
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)
# vocab = utils.loadVariable(
# 'assembly-action-vocab',
# os.path.join(data_dir, 'event-dataset')
# )
# vocab = [BlockAssembly()] + list(abs(x) for x in vocab)
dataset = FusionDataset(
actions_dir,
parts_dir,
events_dir,
edges_dir,
prefix=prefix,
**dataset_params,
# vocab=vocab,
)
utils.saveMetadata(dataset.metadata, out_data_dir)
utils.saveVariable(dataset.vocab, 'vocab', out_data_dir)
seq_ids = dataset.trial_ids
logger.info(f"Loaded scores for {len(seq_ids)} sequences from {data_dir}")
# Define cross-validation folds
# cv_folds = utils.makeDataSplits(len(seq_ids), **cv_params)
# utils.saveVariable(cv_folds, 'cv-folds', out_data_dir)
for i, seq_id in enumerate(seq_ids):
try:
labels = dataset.loadTargets(seq_id)
features = dataset.loadInputs(seq_id)
except AssertionError as e:
logger.warning(f'Skipping sequence {seq_id}: {e}')
continue
logger.info(f"Processing sequence {seq_id}")
if labels.shape[0] != features.shape[0]:
message = f'Label shape {labels.shape} != feature shape {features.shape}'
raise AssertionError(message)
seq_prefix = f"seq={seq_id}"
utils.saveVariable(features, f'{seq_prefix}_feature-seq', out_data_dir)
utils.saveVariable(labels, f'{seq_prefix}_label-seq', out_data_dir)
if plot_io:
fn = os.path.join(fig_dir, f'{seq_prefix}.png')
utils.plot_array(features.T, (labels.T, ), ('gt', ), fn=fn)
def main(
out_dir=None, data_dir=None, results_file=None, cv_file=None,
take_log=False, col_format=None, win_params={}, slowfast_csv_params={}):
out_dir = os.path.expanduser(out_dir)
data_dir = os.path.expanduser(data_dir)
results_file = os.path.expanduser(results_file)
cv_file = os.path.expanduser(cv_file)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
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)
vocab = utils.loadVariable('vocab', data_dir)
metadata = utils.loadMetadata(data_dir)
slowfast_labels = pd.read_csv(
cv_file, keep_default_na=False, index_col=0,
**slowfast_csv_params
)
seg_ids = slowfast_labels.index.to_numpy()
vid_names = slowfast_labels['video_name'].unique().tolist()
metadata['seq_id'] = metadata.index
vid_ids = metadata.set_index('dir_name').loc[vid_names].set_index('seq_id').index
metadata = metadata.drop('seq_id', axis=1)
with open(results_file, 'rb') as file_:
model_probs, gt_labels = pickle.load(file_)
model_probs = model_probs.numpy()
gt_labels = gt_labels.numpy()
if len(model_probs) != len(seg_ids):
err_str = f"{len(model_probs)} segment scores != {slowfast_labels.shape[0]} CSV rows"
raise AssertionError(err_str)
logger.info(f"Loaded {len(seg_ids)} segments, {len(vid_ids)} videos")
for vid_id, vid_name in zip(vid_ids, vid_names):
matches_video = (slowfast_labels['video_name'] == vid_name).to_numpy()
win_labels = gt_labels[matches_video]
win_probs = model_probs[matches_video, :]
if win_labels.shape == win_probs.shape:
win_preds = (win_probs > 0.5).astype(int)
else:
win_preds = win_probs.argmax(axis=1)
if take_log:
win_probs = np.log(win_probs)
seq_id_str = f"seq={vid_id}"
utils.saveVariable(win_probs, f'{seq_id_str}_score-seq', out_data_dir)
utils.saveVariable(win_labels, f'{seq_id_str}_true-label-seq', out_data_dir)
utils.saveVariable(win_preds, f'{seq_id_str}_pred-label-seq', out_data_dir)
utils.plot_array(
win_probs.T, (win_labels.T, win_preds.T), ('true', 'pred'),
tick_names=vocab,
fn=os.path.join(fig_dir, f"{seq_id_str}.png"),
subplot_width=12, subplot_height=5
)
utils.saveVariable(vocab, 'vocab', out_data_dir)
utils.saveMetadata(metadata, out_data_dir)
def main(
out_dir=None, modalities=['rgb', 'imu'], gpu_dev_id=None, plot_io=None,
rgb_data_dir=None, rgb_attributes_dir=None, imu_data_dir=None, imu_attributes_dir=None):
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)
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'))
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)
# 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)
utils.saveVariable(dataset.vocab, 'vocab', out_data_dir)
for i, trial_id in enumerate(trial_ids):
logger.info(f"Processing sequence {trial_id}...")
true_label_seq = dataset.loadTargets(trial_id)
attribute_feats = dataset.loadInputs(trial_id)
# (Process the samples here if we need to)
attribute_feats = attribute_feats.cpu().numpy()
true_label_seq = true_label_seq.cpu().numpy()
trial_prefix = f"trial={trial_id}"
utils.saveVariable(attribute_feats, f'{trial_prefix}_feature-seq', out_data_dir)
utils.saveVariable(true_label_seq, f'{trial_prefix}_label-seq', out_data_dir)
if plot_io:
fn = os.path.join(fig_dir, f'{trial_prefix}.png')
utils.plot_array(
attribute_feats.T,
(true_label_seq,),
('gt',),
fn=fn
)
def main(out_dir=None,
metadata_file=None,
corpus_name=None,
default_annotator=None,
metadata_criteria={},
win_params={},
slowfast_csv_params={},
label_types=('event', 'action', 'part')):
out_dir = os.path.expanduser(out_dir)
metadata_file = os.path.expanduser(metadata_file)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
fig_dir = os.path.join(out_dir, 'figures')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
out_labels_dir = os.path.join(out_dir, 'labels')
if not os.path.exists(out_labels_dir):
os.makedirs(out_labels_dir)
data_dirs = {
name: os.path.join(out_dir, f"{name}-dataset")
for name in label_types
}
for name, dir_ in data_dirs.items():
if not os.path.exists(dir_):
os.makedirs(dir_)
assembly_data_dir = os.path.join(out_dir, 'assembly-dataset')
if not os.path.exists(assembly_data_dir):
os.makedirs(assembly_data_dir)
(seq_ids, event_labels, assembly_seqs, frame_fn_seqs, frame_fn_idx_seqs,
vocabs, assembly_action_vocab,
metadata) = load_all_labels(corpus_name,
default_annotator,
metadata_file,
metadata_criteria,
start_video_from_first_touch=True,
subsample_period=None,
use_coarse_actions=True)
assembly_vocab = []
assembly_idx_seqs = tuple(
tuple(labels.gen_eq_classes(state_seq, assembly_vocab))
for state_seq in assembly_seqs)
utils.saveVariable(assembly_vocab, 'vocab', assembly_data_dir)
utils.saveVariable(assembly_action_vocab, 'assembly-action-vocab',
data_dirs['event'])
vocabs = {label_name: vocabs[label_name] for label_name in label_types}
for name, vocab in vocabs.items():
utils.saveVariable(vocab, 'vocab', data_dirs[name])
utils.saveMetadata(metadata, out_labels_dir)
for name, dir_ in data_dirs.items():
utils.saveMetadata(metadata, dir_)
assembly_attrs = labels.blockConnectionsSeq(assembly_vocab)
utils.saveVariable(assembly_attrs, 'assembly-attrs', assembly_data_dir)
plt.matshow(assembly_attrs.T)
plt.savefig(os.path.join(fig_dir, 'assembly-attrs.png'))
logger.info(
f"Loaded {len(seq_ids)} sequence labels from {corpus_name} dataset")
part_names = [name for name in vocabs['part'] if name != '']
col_names = [f"{name}_active" for name in part_names]
integerizers = {
label_name: {name: i
for i, name in enumerate(label_vocab)}
for label_name, label_vocab in vocabs.items()
}
all_slowfast_labels_seg = collections.defaultdict(list)
all_slowfast_labels_win = collections.defaultdict(list)
counts = np.zeros((len(vocabs['action']), len(vocabs['part'])), dtype=int)
for i, seq_id in enumerate(seq_ids):
logger.info(f"Processing sequence {i + 1} / {len(seq_ids)}")
seq_id_str = f"seq={seq_id}"
event_segs = event_labels[i]
frame_fns = frame_fn_seqs[i]
frame_fn_idxs = frame_fn_idx_seqs[i]
assembly_seq = assembly_seqs[i]
assembly_label_seq = make_assembly_labels(assembly_seq,
assembly_idx_seqs[i],
**win_params)
# video_dir = os.path.dirname(frame_fns[0]).split('/')[-1]
video_dir = f"{seq_id}"
event_data = make_event_data(event_segs, frame_fns, frame_fn_idxs,
integerizers['event'],
integerizers['action'],
integerizers['part'],
vocabs['event'].index(''),
vocabs['action'].index(''), False)
# Redefining event segments from the sequence catches background segments
# that are not annotated in the source labels
event_segs = make_clips(event_data,
vocabs['event'],
vocabs['action'],
clip_type='segment')
event_wins = make_clips(event_data,
vocabs['event'],
vocabs['action'],
clip_type='window',
**win_params)
for name in ('event', 'action'):
event_segs[f'{name}_id'] = [
integerizers[name][n] for n in event_segs[name]
]
event_wins[f'{name}_id'] = [
integerizers[name][n] for n in event_wins[name]
]
event_data.to_csv(os.path.join(out_labels_dir,
f"{seq_id_str}_data.csv"),
index=False)
event_segs.to_csv(os.path.join(out_labels_dir,
f"{seq_id_str}_segs.csv"),
index=False)
event_wins.to_csv(os.path.join(out_labels_dir,
f"{seq_id_str}_wins.csv"),
index=False)
utils.saveVariable(assembly_label_seq, f'seq={seq_id}_label-seq',
assembly_data_dir)
filenames = event_data['fn'].to_list()
label_indices = {}
bound_keys = ['start', 'end']
for name in label_types:
if name == 'part':
label_indices[name] = event_data[col_names].to_numpy()
label_keys = col_names
else:
label_indices[name] = event_data[name].to_numpy()
label_keys = [f'{name}_id']
seg_labels_slowfast = make_slowfast_labels(
event_segs[bound_keys], event_segs[label_keys],
[video_dir for _ in range(event_segs.shape[0])])
win_labels_slowfast = make_slowfast_labels(
event_wins[bound_keys], event_wins[label_keys],
[video_dir for _ in range(event_wins.shape[0])])
utils.saveVariable(filenames, f'{seq_id_str}_frame-fns',
data_dirs[name])
utils.saveVariable(label_indices[name], f'{seq_id_str}_labels',
data_dirs[name])
seg_labels_slowfast.to_csv(os.path.join(
data_dirs[name], f'{seq_id_str}_slowfast-labels.csv'),
index=False,
**slowfast_csv_params)
win_labels_slowfast.to_csv(os.path.join(
data_dirs[name], f'{seq_id_str}_slowfast-labels.csv'),
index=False,
**slowfast_csv_params)
all_slowfast_labels_seg[name].append(seg_labels_slowfast)
all_slowfast_labels_win[name].append(win_labels_slowfast)
plot_event_labels(os.path.join(fig_dir, f"{seq_id_str}.png"),
label_indices['event'], label_indices['action'],
label_indices['part'], vocabs['event'],
vocabs['action'], part_names)
plot_assembly_labels(
os.path.join(fig_dir, f"{seq_id_str}_assembly.png"),
assembly_label_seq, label_indices['event'], vocabs['event'])
for part_activity_row, action_index in zip(label_indices['part'],
label_indices['action']):
for i, is_active in enumerate(part_activity_row):
part_index = integerizers['part'][part_names[i]]
counts[action_index, part_index] += int(is_active)
for name, sf_labels in all_slowfast_labels_seg.items():
pd.concat(sf_labels, axis=0).to_csv(
os.path.join(data_dirs[name], 'slowfast-labels_seg.csv'),
**slowfast_csv_params)
for name, sf_labels in all_slowfast_labels_win.items():
pd.concat(sf_labels, axis=0).to_csv(
os.path.join(data_dirs[name], 'slowfast-labels_win.csv'),
**slowfast_csv_params)
utils.saveVariable(counts, 'action-part-counts', out_labels_dir)
plt.matshow(counts)
plt.xticks(ticks=range(len(vocabs['part'])),
labels=vocabs['part'],
rotation='vertical')
plt.yticks(ticks=range(len(vocabs['action'])), labels=vocabs['action'])
plt.savefig(os.path.join(fig_dir, 'action-part-coocurrence.png'),
bbox_inches='tight')
def main(
out_dir=None, annotation_dir=None, frames_dir=None,
win_params={}, slowfast_csv_params={}, label_types=('event', 'action', 'part')):
out_dir = os.path.expanduser(out_dir)
annotation_dir = os.path.expanduser(annotation_dir)
frames_dir = os.path.expanduser(frames_dir)
logger = utils.setupRootLogger(filename=os.path.join(out_dir, 'log.txt'))
fig_dir = os.path.join(out_dir, 'figures')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
out_labels_dir = os.path.join(out_dir, 'labels')
if not os.path.exists(out_labels_dir):
os.makedirs(out_labels_dir)
data_dirs = {name: os.path.join(out_dir, f"{name}-dataset") for name in label_types}
for name, dir_ in data_dirs.items():
if not os.path.exists(dir_):
os.makedirs(dir_)
seq_ids, event_labels, vocabs, metadata = load_all_labels(annotation_dir)
vocabs = {label_name: vocabs[label_name] for label_name in label_types}
for name, vocab in vocabs.items():
utils.saveVariable(vocab, 'vocab', data_dirs[name])
utils.saveMetadata(metadata, out_labels_dir)
for name, dir_ in data_dirs.items():
utils.saveMetadata(metadata, dir_)
logger.info(f"Loaded {len(seq_ids)} sequence labels from {annotation_dir}")
part_names = [name for name in vocabs['part'] if name != '']
col_names = [f"{name}_active" for name in part_names]
integerizers = {
label_name: {name: i for i, name in enumerate(label_vocab)}
for label_name, label_vocab in vocabs.items()
}
all_slowfast_labels_seg = collections.defaultdict(list)
all_slowfast_labels_win = collections.defaultdict(list)
counts = np.zeros((len(vocabs['action']), len(vocabs['part'])), dtype=int)
for i, seq_id in enumerate(seq_ids):
logger.info(f"Processing sequence {i + 1} / {len(seq_ids)}")
seq_id_str = f"seq={seq_id}"
event_segs = event_labels[i]
# Ignore 'check booklet' events because they don't have an impact on construction
event_segs = event_segs.loc[event_segs['event'] != 'check_booklet']
event_data = make_event_data(
event_segs, sorted(glob.glob(os.path.join(frames_dir, f'{seq_id}', '*.jpg'))),
integerizers['event'], integerizers['action'], integerizers['part'],
vocabs['event'].index(''), vocabs['action'].index(''), False
)
# Redefining event segments from the sequence catches background segments
# that are not annotated in the source labels
event_segs = make_clips(
event_data, vocabs['event'], vocabs['action'],
clip_type='segment'
)
event_wins = make_clips(
event_data, vocabs['event'], vocabs['action'],
clip_type='window', **win_params
)
for name in ('event', 'action'):
event_segs[f'{name}_id'] = [integerizers[name][n] for n in event_segs[name]]
event_wins[f'{name}_id'] = [integerizers[name][n] for n in event_wins[name]]
event_data.to_csv(os.path.join(out_labels_dir, f"{seq_id_str}_data.csv"), index=False)
event_segs.to_csv(os.path.join(out_labels_dir, f"{seq_id_str}_segs.csv"), index=False)
event_wins.to_csv(os.path.join(out_labels_dir, f"{seq_id_str}_wins.csv"), index=False)
filenames = event_data['fn'].to_list()
label_indices = {}
bound_keys = ['start', 'end']
for name in label_types:
if name == 'part':
label_indices[name] = event_data[col_names].to_numpy()
label_keys = col_names
else:
label_indices[name] = event_data[name].to_numpy()
label_keys = [f'{name}_id']
seg_labels_slowfast = make_slowfast_labels(
event_segs[bound_keys], event_segs[label_keys], event_data['fn']
)
win_labels_slowfast = make_slowfast_labels(
event_wins[bound_keys], event_wins[label_keys], event_data['fn']
)
utils.saveVariable(filenames, f'{seq_id_str}_frame-fns', data_dirs[name])
utils.saveVariable(label_indices[name], f'{seq_id_str}_labels', data_dirs[name])
seg_labels_slowfast.to_csv(
os.path.join(data_dirs[name], f'{seq_id_str}_slowfast-labels.csv'),
index=False, **slowfast_csv_params
)
win_labels_slowfast.to_csv(
os.path.join(data_dirs[name], f'{seq_id_str}_slowfast-labels.csv'),
index=False, **slowfast_csv_params
)
all_slowfast_labels_seg[name].append(seg_labels_slowfast)
all_slowfast_labels_win[name].append(win_labels_slowfast)
plot_event_labels(
os.path.join(fig_dir, f"{seq_id_str}.png"),
label_indices['event'], label_indices['action'], label_indices['part'],
vocabs['event'], vocabs['action'], part_names
)
for part_activity_row, action_index in zip(label_indices['part'], label_indices['action']):
for i, is_active in enumerate(part_activity_row):
part_index = integerizers['part'][part_names[i]]
counts[action_index, part_index] += int(is_active)
for name, labels in all_slowfast_labels_seg.items():
pd.concat(labels, axis=0).to_csv(
os.path.join(data_dirs[name], 'slowfast-labels_seg.csv'),
**slowfast_csv_params
)
for name, labels in all_slowfast_labels_win.items():
pd.concat(labels, axis=0).to_csv(
os.path.join(data_dirs[name], 'slowfast-labels_win.csv'),
**slowfast_csv_params
)
utils.saveVariable(counts, 'action-part-counts', out_labels_dir)
plt.matshow(counts)
plt.xticks(ticks=range(len(vocabs['part'])), labels=vocabs['part'], rotation='vertical')
plt.yticks(ticks=range(len(vocabs['action'])), labels=vocabs['action'])
plt.savefig(os.path.join(fig_dir, 'action-part-coocurrence.png'), bbox_inches='tight')