def _compute_svm_params(img_feats, prim_labels, expressions, exp_idx):
# setup svm
exp_lst = expressions[exp_idx]
_logger.info("{}/{} - Training svm ...".format(exp_idx, len(expressions)))
exp_tree = dbac_expression.list2exp_parse(exp_lst)
var_dic = {p: prim_labels[:, int(p)] for p in dbac_expression.get_vars(exp_tree)}
exp_labels = dbac_expression.eval_exp(exp_tree, var_dic)
svm_object = sklearn_svm.LinearSVC(C=1e-5, class_weight={1: 2.0, 0: 1.0}, verbose=0, penalty='l2',
loss='hinge', dual=True)
svm_object.fit(img_feats, exp_labels)
train_acc = svm_object.score(img_feats, exp_labels)
_logger.info("{}/{} - Finalized svm. Positives {}, Negatives {}, Accuracy {}."
.format(exp_idx, len(expressions), np.sum(exp_labels), np.sum(np.logical_not(exp_labels)), train_acc))
svm_params = np.hstack((svm_object.intercept_.ravel(), svm_object.coef_.ravel()))
return svm_params
def _compute_statistics(db_name,
db_path,
split_file,
comb_file=None,
plot=False):
# Read data
logger.info("Loading dataset and split...")
dataset = dbac_data.IDataset.factory(db_name, db_path)
dataset.load_split(split_file, comb_file)
# extract information
train_labels = dataset.labels[dataset.images_split ==
dbac_data.DB_IMAGE_SPLITS.index('train')]
val_labels = dataset.labels[dataset.images_split ==
dbac_data.DB_IMAGE_SPLITS.index('val')]
test_labels = dataset.labels[dataset.images_split ==
dbac_data.DB_IMAGE_SPLITS.index('test')]
logger.info("Number of images (train, val, test): {}, {}, {}".format(
len(train_labels), len(val_labels), len(test_labels)))
train_exps = dataset.expressions[dataset.expressions_split ==
dbac_data.DB_EXP_SPLITS.index('train')]
test_exps = dataset.expressions[dataset.expressions_split ==
dbac_data.DB_EXP_SPLITS.index('test')]
logger.info("Number of expressions (train, test): {}, {}".format(
len(train_exps), len(test_exps)))
valid_prims = np.where(dataset.valid_primitives)[0]
logger.info("Number of primitives: {}".format(len(valid_prims)))
if comb_file:
train_combs = dataset.combinations[dataset.combinations_split ==
dbac_data.DB_COMB_SPLITS.index(
'train')]
test_combs = dataset.combinations[dataset.combinations_split ==
dbac_data.DB_COMB_SPLITS.index(
'test')]
logger.info("Number of combinations (train, test): {}, {}".format(
len(train_combs), len(test_combs)))
else:
train_combs, test_combs = None, None
# compute detailed statistics
info_exp, info_prim, info_comb = [], [], []
prim_box, exp_box, comb_box = [], [], []
for labels, combs, exps, prims in zip(
[train_labels, val_labels, test_labels],
[train_combs, train_combs, test_combs],
[train_exps, train_exps, test_exps], [valid_prims] * 3):
count_prim = np.zeros(len(prims))
for p, prim in enumerate(prims):
count_prim[p] = np.sum(labels[:, prim])
mean, std, min, max = np.mean(count_prim), np.std(count_prim), np.min(
count_prim), np.max(count_prim)
info_prim.append((mean, std, min, max))
prim_box.append(count_prim)
count_exp = np.zeros(len(exps))
for e, exp in enumerate(exps):
op, v_a, v_b = exp[0], labels[:, int(
exp[1])], labels[:,
int(exp[2])] if exp[2] is not None else None
count_exp[e] = np.sum(dbac_expression.eval_op(op, v_a, v_b))
mean, std, min, max = np.mean(count_exp), np.std(count_exp), np.min(
count_exp), np.max(count_exp)
info_exp.append((mean, std, min, max))
exp_box.append(count_exp)
if comb_file:
count_comb = np.zeros(len(combs))
for c, comb in enumerate(combs):
comb_tree = dbac_expression.list2exp_parse(comb)
var_dic = {
p: labels[:, int(p)]
for p in dbac_expression.get_vars(comb_tree)
}
count_comb[c] = dbac_expression.eval_exp(comb_tree,
var_dic).sum()
mean, std, min, max = np.mean(count_comb), np.std(
count_comb), np.min(count_comb), np.max(count_comb)
info_comb.append((mean, std, min, max))
comb_box.append(count_comb)
logger.info(
"Primitives sample density (mean, std, min, max) train, val, test: {}".
format(info_prim))
logger.info(
"Expression sample density (mean, std, min, max) train, val, test: {}".
format(info_exp))
logger.info(
"Compositions sample density (mean, std, min, max) train, val, test: {}"
.format(info_comb))
if plot:
import matplotlib.pyplot as plt
f, (ax1, ax2, ax3) = plt.subplots(1, 3)
ax1.boxplot(prim_box, labels=['Train', 'Val', 'Test'], showmeans=True)
ax1.set_title('Primitives', fontsize=16)
ax1.set_ylabel('# Positive Images', fontsize=12)
ax1.tick_params(axis='x', labelsize=12)
ax2.boxplot(exp_box, labels=['Train', 'Val', 'Test'], showmeans=True)
ax2.set_title('Expressions', fontsize=16)
ax2.tick_params(axis='x', labelsize=12)
if comb_file:
ax3.boxplot(comb_box,
labels=['Train', 'Val', 'Test'],
showmeans=True)
ax3.set_title('Compositions', fontsize=16)
ax3.tick_params(axis='x', labelsize=12)
f.tight_layout()
plt.show()
def _compute_combs(db_name,
db_path,
split_file,
complexity,
output_file,
form='D',
allow_not=False,
min_pos_comb=[100, 10, 10],
qtd_combs=[3e3, 1e3, 1e3]):
# load data
logger.info("Loading dataset and split...")
db = dbac_data.IDataset.factory(db_name, db_path)
db.load_split(split_file)
train_labels = db.labels[db.images_split ==
dbac_data.DB_IMAGE_SPLITS.index('train')]
train_exps = db.expressions[db.expressions_split ==
dbac_data.DB_EXP_SPLITS.index('train')]
val_labels = db.labels[db.images_split == dbac_data.DB_IMAGE_SPLITS.index(
'val')]
test_labels = db.labels[db.images_split == dbac_data.DB_IMAGE_SPLITS.index(
'test')]
test_exps = db.expressions[db.expressions_split ==
dbac_data.DB_EXP_SPLITS.index('test')]
# compute combinations
comb_dict = {}
train_combs, test_combs = [], []
data_iter = zip(['train', 'test'], [train_combs, test_combs], min_pos_comb,
qtd_combs, [train_exps, test_exps],
[train_labels, test_labels])
for name, combs, min_pos, qtd, exps, labels in data_iter:
logger.info("Computing combinations for {}".format(name))
for idx, exp in enumerate(
dbac_expression.exp_comb_gen(exps,
complexity,
form=form,
allow_not=allow_not)):
# compute expression positive samples
var_dic = {
p: labels[:, int(p)]
for p in dbac_expression.get_vars(exp)
}
exp_labels = dbac_expression.eval_exp(exp, var_dic)
num_pos = exp_labels.sum()
num_neg = np.logical_not(exp_labels).sum()
# filter out expressions with few samples
if num_pos > min_pos and num_neg > min_pos and not any(
[dbac_expression.exp_tree_eq(exp, comb) for comb in combs]):
if name == 'train':
var_dic = {
p: val_labels[:, int(p)]
for p in dbac_expression.get_vars(exp)
}
exp_labels = dbac_expression.eval_exp(exp, var_dic)
num_pos = exp_labels.sum()
num_neg = np.logical_not(exp_labels).sum()
if num_pos <= min_pos_comb[1] or num_neg <= min_pos_comb[1]:
continue
combs.append(copy.deepcopy(exp))
# maximum number of combinations sampled
if len(combs) > qtd:
break
if (idx + 1) % 100 == 0:
logger.info('{} - Collected {} from {} expressions'.format(
name, len(combs), (idx + 1)))
comb_dict["{}_combs".format(name)] = [
dbac_expression.exp2list_parse(comb) for comb in combs[:qtd]
]
# save to json file
with open(output_file, "w") as outfile:
json.dump(comb_dict, outfile, indent=4, sort_keys=True)
logger.info("Combination saved to {}.".format(output_file))
return comb_dict
def learning(self, images_path, labels, expressions, **kwargs):
# training parameters
batch_size, num_epochs = int(kwargs.get('batch_size', 32)), int(kwargs.get('num_epochs', 1e3))
snap_int, snap_dir, log_dir = int(kwargs.get('snap_int', 250)), kwargs.get('snap_dir', None), kwargs.get(
'log_dir', None)
init_weights = kwargs.get('init_weights', None)
snapshot = kwargs.get('snapshot', None)
learning_rate = float(kwargs.get('learning_rate', 1e-5))
alphas = [float(p) for p in kwargs.get('alphas', '10.0 1.0 0.1 1.0').split()]
_logger.info("Training parameters: batch_size={}, num_epochs={}, snap_int={}, snap_dir={}, log_dir={}, "
"learning_rate={}, alphas={}, learn_feats={}, init_weights={}, norm_in={}, norm_out={},"
" snapshot={}, demorgan_reg={}".format(batch_size, num_epochs, snap_int, snap_dir, log_dir, learning_rate, alphas,
self.learn_feats, init_weights, self.norm_in, self.norm_out,
snapshot, self.demorgan_reg))
# setup training network
with self.graph.as_default() as graph:
# Loss
reg_loss = tf.reduce_mean(tf.losses.get_regularization_loss())
norm_loss = tf.reduce_mean(0.5 * tf.pow(tf.norm(self.output_tn, axis=-1), 2.0))
cls_loss = tf.losses.hinge_loss(self._ground_truth_ph, self.scores_tn, reduction=tf.losses.Reduction.MEAN)
loss_tn = alphas[0] * norm_loss + alphas[1] * cls_loss + alphas[2] * reg_loss
if self.demorgan_reg:
dem_loss = tf.reduce_mean(0.5 * tf.pow(tf.norm(self.output_tn - self.dm_output_tn, axis=-1), 2.0))
loss_tn = loss_tn + (alphas[3] * dem_loss)
dem_loss_val, dem_loss_up, dem_loss_reset = _create_reset_metric(
tf.metrics.mean, 'epoch_dem_loss', values=dem_loss)
tf.summary.scalar('dem_loss', dem_loss_val)
pred = tf.greater(self.scores_tn, 0.0)
# Metrics
reg_loss_val, reg_loss_up, reg_loss_reset = _create_reset_metric(
tf.metrics.mean, 'epoch_reg_loss', values=reg_loss)
tf.summary.scalar('reg_loss', reg_loss_val)
cls_loss_val, cls_loss_up, cls_loss_reset = _create_reset_metric(
tf.metrics.mean, 'epoch_cls_loss', values=cls_loss)
tf.summary.scalar('cls_loss', cls_loss_val)
norm_loss_val, norm_loss_up, norm_loss_reset = _create_reset_metric(
tf.metrics.mean, 'epoch_norm_loss', values=norm_loss)
tf.summary.scalar('norm_loss', norm_loss_val)
loss_val, loss_up, loss_reset = _create_reset_metric(
tf.metrics.mean, 'epoch_loss', values=loss_tn)
tf.summary.scalar('total_loss', loss_val)
prec_val, prec_up, prec_reset = _create_reset_metric(
tf.metrics.precision, 'epoch_prec', predictions=pred, labels=self._ground_truth_ph)
tf.summary.scalar('Precision', prec_val)
rec_val, rec_up, rec_reset = _create_reset_metric(
tf.metrics.recall, 'epoch_rec', predictions=pred, labels=self._ground_truth_ph)
tf.summary.scalar('Recall', rec_val)
tf.summary.scalar('Fscore', (2 * prec_val * rec_val) / (prec_val + rec_val + 1e-6))
summ_ops = tf.summary.merge_all()
summ_writer = tf.summary.FileWriter(log_dir) if log_dir else None
metrics_ops_reset = [reg_loss_reset, cls_loss_reset, norm_loss_reset, loss_reset, prec_reset, rec_reset]
metrics_ops_update = [reg_loss_up, cls_loss_up, norm_loss_up, loss_up, prec_up, rec_up]
if self.demorgan_reg:
metrics_ops_reset += [dem_loss_reset]
metrics_ops_update += [dem_loss_up]
# Optimizer
global_step_tn = tf.train.get_or_create_global_step(graph)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss_tn, global_step=global_step_tn, colocate_gradients_with_ops=True)
init = tf.global_variables_initializer()
tf_snap_tr = tf.train.Saver(max_to_keep=1)
# Decompose expressions and compute labels
_logger.info("Decomposing expressions...")
valid_exps, pos_img_ites, neg_img_ites, exp_labels = [], [], [], []
for exp_lst in expressions:
for exp_term in dbac_expression.get_terms(dbac_expression.list2exp_parse(exp_lst)):
term_lst = dbac_expression.exp2list_parse(exp_term)
var_dic = {p: labels[:, int(p)] for p in dbac_expression.get_vars(exp_term)}
term_labels = dbac_expression.eval_exp(exp_term, var_dic)
if (term_lst not in valid_exps) and (exp_term.name != dbac_expression.OPS[0]) \
and (term_labels.sum() > 0) and (np.logical_not(term_labels).sum() > 0):
valid_exps.append(term_lst)
exp_labels.append(term_labels)
pos_img_ites.append(CycleIterator(list(np.where(term_labels)[0])))
neg_img_ites.append(CycleIterator(list(np.where(np.logical_not(term_labels))[0])))
expressions = valid_exps
exp_ite = CycleIterator(np.arange(len(expressions)).tolist())
exp_labels = np.vstack(exp_labels).astype(np.float32)
_logger.info("Total of expressions decomposed: {}".format(len(expressions)))
# Initialization
_logger.info("Initializing model...")
self.tf_session.run(init)
if self.init_vgg is not None:
_logger.info("Loading features pre-trained weights")
self.init_vgg(self.tf_session)
if init_weights:
_logger.info("Loading model pre-trained weights")
self.load(init_weights)
init_epoch = 0
if snapshot:
_logger.info("Loading from training snapshot")
tf_snap_tr.restore(self.tf_session, snapshot)
init_epoch = int((self.tf_session.run(global_step_tn) * batch_size) / len(expressions))
# training loop
_logger.info("Training...")
for epoch in range(init_epoch, num_epochs):
self.tf_session.run(metrics_ops_reset)
for b in range(int(np.ceil(len(expressions) / batch_size))):
# batch sampling
b_exp_ids = [next(exp_ite) for _ in range(batch_size)]
b_img_ids = [next(pos_img_ites[exp_id]) for _ in range(5) for exp_id in b_exp_ids]
b_img_ids += [next(neg_img_ites[exp_id]) for _ in range(5) for exp_id in b_exp_ids]
# compute image features
if self.learn_feats:
b_img_feats = images_path[b_img_ids]
else:
b_img_feats = self.feat_ext.compute(images_path[b_img_ids])
# compute operations
b_prims_rpr, b_op_switch = [], []
for exp_id in b_exp_ids:
exp_tree = dbac_expression.list2exp_parse(expressions[exp_id])
b_op_switch.append({'NOT': 0, 'AND': 1, 'OR': 2}[exp_tree.name])
operand_a, operand_b = exp_tree.children if np.random.rand() > 0.5 else exp_tree.children[::-1]
b_prims_rpr.append(dbac_expression.exp2list_parse(operand_a))
b_prims_rpr.append(dbac_expression.exp2list_parse(operand_b))
b_op_switch = np.array(b_op_switch)
b_prims_rpr = self.inference(b_prims_rpr).reshape((len(b_exp_ids), 2 * self.dim))
# compute labels
b_exp_labels = exp_labels[b_exp_ids, :]
b_exp_labels = b_exp_labels[:, b_img_ids]
# run model
self.tf_session.run(
[train_op, loss_tn] + metrics_ops_update,
feed_dict={self.is_training_ph: True,
self._images_ph: b_img_feats,
self._prims_rpr_ph: b_prims_rpr,
self._switch_ph: b_op_switch,
self._ground_truth_ph: b_exp_labels})
if (epoch + 1) % 2 == 0:
loss, prec, rec, summary = self.tf_session.run([loss_val, prec_val, rec_val, summ_ops])
_logger.info("Epoch {}: Loss={:.4f}, Prec={:.2f}, Rec={:.2f}, Fsc={:.2f}"
.format((epoch + 1), loss, prec, rec, (2 * prec * rec) / (prec + rec + 1e-6)))
if summ_writer:
summ_writer.add_summary(summary, global_step=epoch + 1)
if snap_dir and (epoch + 1) % snap_int == 0:
snap_file = os.path.join(snap_dir, 'nba_mlp_snap_E{}.npz'.format((epoch + 1)))
self.save(snap_file)
tf_snap_tr.save(self.tf_session, os.path.join(snap_dir, 'train.chk'), latest_filename='checkpoint.TRAIN')
_logger.info("Model epoch {} snapshoted to {}".format(epoch + 1, snap_file))
def _test(db_name, db_dir, db_split_file, db_comb_file, primitives_file, model_name, model_file, output_dir, kwargs_str=None):
# processing kwargs
kwargs_dic = dbac_util.get_kwargs_dic(kwargs_str)
logger.info("Kwargs dictionary: {}".format(kwargs_dic))
# read dataset and partitions
logger.info("Reading dataset and split")
db = dbac_data.IDataset.factory(db_name, db_dir)
db.load_split(db_split_file, db_comb_file)
train_imgs_path = db.images_path[db.images_split == dbac_data.DB_IMAGE_SPLITS.index('train')]
train_labels = db.labels[db.images_split == dbac_data.DB_IMAGE_SPLITS.index('train')]
val_imgs_path = db.images_path[db.images_split == dbac_data.DB_IMAGE_SPLITS.index('val')]
val_labels = db.labels[db.images_split == dbac_data.DB_IMAGE_SPLITS.index('val')]
test_imgs_path = db.images_path[db.images_split == dbac_data.DB_IMAGE_SPLITS.index('test')]
test_labels = db.labels[db.images_split == dbac_data.DB_IMAGE_SPLITS.index('test')]
if db_comb_file:
logger.info("Loading compositions...")
train_exps = db.combinations[db.combinations_split == dbac_data.DB_COMB_SPLITS.index('train')]
test_exps = db.combinations[db.combinations_split == dbac_data.DB_COMB_SPLITS.index('test')]
else:
logger.info("Loading single expressions...")
train_exps = db.expressions[db.expressions_split == dbac_data.DB_EXP_SPLITS.index('train')]
test_exps = db.expressions[db.expressions_split == dbac_data.DB_EXP_SPLITS.index('test')]
# Set up feature extractor
logger.info("Configuring Features Extractor")
feat_extractor = dbac_feature_ext.IFeatureExtractor.factory(dbac_feature_ext.FEAT_TYPE[1], **kwargs_dic)
feat_extractor.load()
# set up primitive collection
logger.info("Configuring Primitive Collection")
prim_collection = dbac_primitives.IPrimitiveCollection.factory(dbac_primitives.PRIMITIVE_TYPES[0], **kwargs_dic)
prim_collection.load(primitives_file)
# setup model
logger.info("Configuring Model")
model = dbac_model.IModel.factory(model_name, feat_extractor, prim_collection, **kwargs_dic, is_train=False)
model.load(model_file)
# test model
logger.info("Testing on seen expressions on training images...")
train_scores = model.score(train_imgs_path, train_exps, **kwargs_dic)
logger.info("Testing on seen expressions on validation images...")
val_scores = model.score(val_imgs_path, train_exps, **kwargs_dic)
logger.info("Testing on unseen expressions on test images...")
test_scores = model.score(test_imgs_path, test_exps, **kwargs_dic)
# save results
logger.info("Computing results.")
report_dic = dict()
results_iter = zip(['train', 'val', 'test'], [train_exps, train_exps, test_exps],
[train_labels, val_labels, test_labels], [train_scores, val_scores, test_scores],
[train_imgs_path, val_imgs_path, test_imgs_path])
for key, exps, labels, scores, images in results_iter:
# compute ground truth labels
ground_truth = np.zeros_like(scores)
for idx, exp_lst in enumerate(exps):
exp_tree = dbac_expression.list2exp_parse(exp_lst)
var_dic = {p: labels[:, int(p)] for p in dbac_expression.get_vars(exp_tree)}
ground_truth[idx] = dbac_expression.eval_exp(exp_tree, var_dic)
# fill report dictionary
report_dic['_'.join([key, 'exps'])] = exps
report_dic['_'.join([key, 'imgs'])] = images
report_dic['_'.join([key, 'gt'])] = ground_truth
report_dic['_'.join([key, 'pred'])] = scores
result_file = os.path.join(output_dir, 'results.npy')
np.save(result_file, report_dic)
logger.info("Results file saved to {}.".format(result_file))