def get_different_object_loss(pixelwise_contrastive_loss, image_a_pred,
image_b_pred, blind_non_matches_a,
blind_non_matches_b):
"""
Simple wrapper for pixelwise_contrastive_loss functions. Args and return args documented above in get_loss()
"""
scale_by_hard_negatives = pixelwise_contrastive_loss.config[
"scale_by_hard_negatives_DIFFERENT_OBJECT"]
blind_non_match_loss = zero_loss()
if not (SpartanDataset.is_empty(blind_non_matches_a.data)):
M_descriptor = pixelwise_contrastive_loss.config["M_background"]
blind_non_match_loss, num_hard_negatives =\
pixelwise_contrastive_loss.non_match_loss_descriptor_only(image_a_pred, image_b_pred,
blind_non_matches_a, blind_non_matches_b,
M_descriptor=M_descriptor)
if scale_by_hard_negatives:
scale_factor = max(num_hard_negatives, 1)
else:
scale_factor = max(len(blind_non_matches_a), 1)
blind_non_match_loss = 1.0 / scale_factor * blind_non_match_loss
loss = blind_non_match_loss
return loss, zero_loss(), zero_loss(), zero_loss(), blind_non_match_loss
def get_same_object_across_scene_loss(pixelwise_contrastive_loss, image_a_pred, image_b_pred,
blind_non_matches_a, blind_non_matches_b):
"""
Simple wrapper for pixelwise_contrastive_loss functions. Args and return args documented above in get_loss()
"""
blind_non_match_loss = zero_loss()
if not (SpartanDataset.is_empty(blind_non_matches_a.data)):
blind_non_match_loss, num_hard_negatives =\
pixelwise_contrastive_loss.non_match_loss_descriptor_only(image_a_pred, image_b_pred,
blind_non_matches_a, blind_non_matches_b,
M_descriptor=pcl._config["M_masked"], invert=True)
if pixelwise_contrastive_loss._config["scale_by_hard_negatives"]:
scale_factor = max(num_hard_negatives, 1)
else:
scale_factor = max(len(blind_non_matches_a), 1)
loss = 1.0/scale_factor * blind_non_match_loss
blind_non_match_loss_scaled = 1.0/scale_factor * blind_non_match_loss
return loss, zero_loss(), zero_loss(), zero_loss(), blind_non_match_loss
def get_within_scene_loss(pixelwise_contrastive_loss, image_a_pred,
image_b_pred, matches_a, matches_b,
masked_non_matches_a, masked_non_matches_b,
background_non_matches_a, background_non_matches_b,
blind_non_matches_a, blind_non_matches_b):
"""
Simple wrapper for pixelwise_contrastive_loss functions. Args and return args documented above in get_loss()
"""
pcl = pixelwise_contrastive_loss
match_loss, masked_non_match_loss, num_masked_hard_negatives =\
pixelwise_contrastive_loss.get_loss_matched_and_non_matched_with_l2(image_a_pred, image_b_pred,
matches_a, matches_b,
masked_non_matches_a, masked_non_matches_b,
M_descriptor=pcl._config["M_masked"])
if pcl._config["use_l2_pixel_loss_on_background_non_matches"]:
background_non_match_loss, num_background_hard_negatives =\
pixelwise_contrastive_loss.non_match_loss_with_l2_pixel_norm(image_a_pred, image_b_pred, matches_b,
background_non_matches_a, background_non_matches_b, M_descriptor=pcl._config["M_background"])
else:
background_non_match_loss, num_background_hard_negatives =\
pixelwise_contrastive_loss.non_match_loss_descriptor_only(image_a_pred, image_b_pred,
background_non_matches_a, background_non_matches_b,
M_descriptor=pcl._config["M_background"])
blind_non_match_loss = zero_loss()
num_blind_hard_negatives = 1
if not (SpartanDataset.is_empty(blind_non_matches_a.data)):
blind_non_match_loss, num_blind_hard_negatives =\
pixelwise_contrastive_loss.non_match_loss_descriptor_only(image_a_pred, image_b_pred,
blind_non_matches_a, blind_non_matches_b,
M_descriptor=pcl._config["M_masked"])
total_num_hard_negatives = num_masked_hard_negatives + num_background_hard_negatives
total_num_hard_negatives = max(total_num_hard_negatives, 1)
if pcl._config["scale_by_hard_negatives"]:
scale_factor = total_num_hard_negatives
masked_non_match_loss_scaled = masked_non_match_loss * 1.0 / max(
num_masked_hard_negatives, 1)
background_non_match_loss_scaled = background_non_match_loss * 1.0 / max(
num_background_hard_negatives, 1)
blind_non_match_loss_scaled = blind_non_match_loss * 1.0 / max(
num_blind_hard_negatives, 1)
else:
# we are not currently using blind non-matches
num_masked_non_matches = max(len(masked_non_matches_a), 1)
num_background_non_matches = max(len(background_non_matches_a), 1)
num_blind_non_matches = max(len(blind_non_matches_a), 1)
scale_factor = num_masked_non_matches + num_background_non_matches
masked_non_match_loss_scaled = masked_non_match_loss * 1.0 / num_masked_non_matches
background_non_match_loss_scaled = background_non_match_loss * 1.0 / num_background_non_matches
blind_non_match_loss_scaled = blind_non_match_loss * 1.0 / num_blind_non_matches
non_match_loss = 1.0 / scale_factor * (masked_non_match_loss +
background_non_match_loss)
loss = pcl._config["match_loss_weight"] * match_loss + \
pcl._config["non_match_loss_weight"] * non_match_loss
return loss, match_loss, masked_non_match_loss_scaled, background_non_match_loss_scaled, blind_non_match_loss_scaled