def compute_metric(self, preds, data):
'''
compute f1 and extract match scores for output
'''
m = collections.defaultdict(list)
for ex, feat in tqdm.tqdm(data, ncols=80, desc='compute_metric'):
# if 'repeat_idx' in ex: ex = self.load_task_json(self.args, ex, None)[0]
key = (ex['task_id'], ex['repeat_idx'])
# feat should already contain the following, since all AlfredDataset s which are fed into this function have test_mode=False
# feat = self.featurize(ex, self.args, False, load_mask=True, load_frames=True)
# Evaluate low level actions.
label = ' '.join(self.vocab['action_low'].index2word(
feat['action_low'].tolist()))
pred = ' '.join(self.vocab['action_low'].index2word(
preds[key]['action_low']))
label_lower = label.lower()
pred_lower = pred.lower()
m['action_low_f1'].append(compute_f1(label_lower, pred_lower))
m['action_low_em'].append(compute_exact(label_lower, pred_lower))
m['action_low_gold_length'].append(len(label.split()))
m['action_low_pred_length'].append(len(pred.split()))
m['action_low_edit_distance'].append(
compute_edit_distance(label_lower, pred_lower))
# Evaluate high-level controller.
# Get indexes of predicted transitions.
stop_idxs = np.argwhere(
np.array(preds[key]['action_low'])[:-1] == 2).flatten()
high_idxs = np.append([0], stop_idxs + 1).astype(np.int32)
# Get predicted submodule transitions
pred_high_idx = np.array(preds[key]['controller_attn'])[high_idxs]
label_high_idx = feat['module_idxs'][np.nonzero(
feat['transition_mask'])]
pred = ' '.join(self.vocab['high_level'].index2word(
pred_high_idx.tolist()))
label = ' '.join(self.vocab['high_level'].index2word(
label_high_idx.tolist()))
label_lower = label.lower()
pred_lower = pred.lower()
m['action_high_f1'].append(compute_f1(label_lower, pred_lower))
m['action_high_em'].append(compute_exact(label_lower, pred_lower))
m['action_high_gold_length'].append(len(label.split()))
m['action_high_pred_length'].append(len(pred.split()))
m['action_high_edit_distance'].append(
compute_edit_distance(label_lower, pred_lower))
return {k: sum(v) / len(v) for k, v in m.items()}
def compute_metric(self, preds, data):
'''
compute f1 and extract match scores for output
'''
m = collections.defaultdict(list)
for task in data:
ex = self.load_task_json(task)
i = self.get_task_and_ann_id(ex)
label = ' '.join([
a['discrete_action']['action']
for a in ex['plan']['low_actions']
])
m['action_low_f1'].append(
compute_f1(label.lower(), preds[i]['action_low'].lower()))
m['action_low_em'].append(
compute_exact(label.lower(), preds[i]['action_low'].lower()))
return {k: sum(v) / len(v) for k, v in m.items()}
def compute_metric(self, preds, data):
'''
compute f1 and extract match scores for output
'''
m = collections.defaultdict(list)
for ex in data:
# if 'repeat_idx'in ex: ex = self.load_task_json(ex, None)[0]
key = (ex['task_id'], ex['repeat_idx'])
label = ' '.join([
a['discrete_action']['action']
for a in ex['plan']['low_actions']
])
m['action_low_f1'].append(
compute_f1(label.lower(), preds[key]['action_low'].lower()))
m['action_low_em'].append(
compute_exact(label.lower(), preds[key]['action_low'].lower()))
return {k: sum(v) / len(v) for k, v in m.items()}
