def proposal_target_layer(object_rois, region_rois, gt_objects, gt_relationships,
gt_regions, n_classes_obj, voc_sign, is_training=False, graph_generation=False):
"""
----------
object_rois: (1 x H x W x A, 5) [0, x1, y1, x2, y2]
region_rois: (1 x H x W x A, 5) [0, x1, y1, x2, y2]
gt_objects: (G_obj, 5) [x1 ,y1 ,x2, y2, obj_class] int
gt_relationships: (G_obj, G_obj) [pred_class] int (-1 for no relationship)
gt_regions: (G_region, 4+40) [x1, y1, x2, y2, word_index] (-1 for padding)
# gt_ishard: (G_region, 4+40) {0 | 1} 1 indicates hard
# dontcare_areas: (D, 4) [ x1, y1, x2, y2]
n_classes_obj
n_classes_pred
is_training to indicate whether in training scheme
----------
Returns
----------
rois: (1 x H x W x A, 5) [0, x1, y1, x2, y2]
labels: (1 x H x W x A, 1) {0,1,...,_num_classes-1}
bbox_targets: (1 x H x W x A, K x4) [dx1, dy1, dx2, dy2]
bbox_inside_weights: (1 x H x W x A, Kx4) 0, 1 masks for the computing loss
bbox_outside_weights: (1 x H x W x A, Kx4) 0, 1 masks for the computing loss
"""
object_rois = object_rois.data.cpu().numpy()
region_rois = region_rois.data.cpu().numpy()
object_labels, object_rois, bbox_targets, bbox_inside_weights, bbox_outside_weights, mat_object, \
phrase_label, phrase_rois, mat_phrase, region_seq, region_rois, \
bbox_targets_region, bbox_inside_weights_region, bbox_outside_weights_region, mat_region= \
proposal_target_layer_py(object_rois, region_rois, gt_objects, gt_relationships,
gt_regions, n_classes_obj, voc_sign, is_training, graph_generation=graph_generation)
# print labels.shape, bbox_targets.shape, bbox_inside_weights.shape
if is_training:
object_labels = network.np_to_variable(object_labels, is_cuda=True, dtype=torch.LongTensor)
bbox_targets = network.np_to_variable(bbox_targets, is_cuda=True)
bbox_inside_weights = network.np_to_variable(bbox_inside_weights, is_cuda=True)
bbox_outside_weights = network.np_to_variable(bbox_outside_weights, is_cuda=True)
phrase_label = network.np_to_variable(phrase_label, is_cuda=True, dtype=torch.LongTensor)
region_seq = network.np_to_variable(region_seq, is_cuda=True, dtype=torch.LongTensor)
bbox_targets_region = network.np_to_variable(bbox_targets_region, is_cuda=True)
bbox_inside_weights_region = network.np_to_variable(bbox_inside_weights_region, is_cuda=True)
bbox_outside_weights_region = network.np_to_variable(bbox_outside_weights_region, is_cuda=True)
object_rois = network.np_to_variable(object_rois, is_cuda=True)
phrase_rois = network.np_to_variable(phrase_rois, is_cuda=True)
region_rois = network.np_to_variable(region_rois, is_cuda=True)
return (object_rois, object_labels, bbox_targets, bbox_inside_weights, bbox_outside_weights), \
(phrase_rois, phrase_label), \
(region_rois, region_seq, bbox_targets_region, bbox_inside_weights_region, bbox_outside_weights_region), \
mat_object, mat_phrase, mat_region
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