def anchor_preds(self, preds, train_anchor_inds, image_offset):
"""
Get predictions for the training indices
:param preds: [batch_size, IM_SIZE/16, IM_SIZE/16, A, 6]
:param train_anchor_inds: [num_train, 4] indices into the predictions
:return: class_preds: [num_train, 2] array of yes/no
box_preds: [num_train, 4] array of predicted boxes
"""
assert train_anchor_inds.size(1) == 4
tai = train_anchor_inds.data.clone()
tai[:, 0] -= image_offset
train_regions = gather_nd(preds, tai)
class_preds = train_regions[:, :2]
box_preds = train_regions[:, 2:]
return class_preds, box_preds
def anchor_preds(self, preds, train_anchor_inds, image_offset):
"""
Get [1536,2] scores and [1536,4] deltas from [6*37*37*20,6]
1536 location bases are provided by train_anchor_inds
:param preds: [batch_size, IM_SIZE/16, IM_SIZE/16, A, 6]
:param train_anchor_inds: [nusm_train, 4] indices into the predictions
:return: class_preds: [num_train, 2] array of yes/no
box_preds: [num_train, 4] array of predicted boxes
"""
assert train_anchor_inds.size(1) == 4
#ipdb.set_trace()
tai = train_anchor_inds.data.clone()
tai[:, 0] -= image_offset
train_regions = gather_nd(preds, tai)
class_preds = train_regions[:, :2]
box_preds = train_regions[:, 2:]
return class_preds, box_preds