def perturb(self, gt_obj, gt_rels, verbose=False):
gt_obj_lst = [gt_obj[s:e] for _, s, e in enumerate_by_image(gt_obj[:, 0])]
gt_rels_lst = [gt_rels[s:e] for _, s, e in enumerate_by_image(gt_rels[:, 0])]
nodes = self.sample_nodes_(gt_obj_lst, gt_rels_lst)
gt_obj_new = []
for im, objs in enumerate(gt_obj_lst): # for each image
for obj_ind, obj_rels in zip(*nodes[im]): # for each sampled node that will be perturbed
if verbose:
before = objs[obj_ind, 1]
print('\nbefore: %s' % self.obj_classes[before])
for (_, o1, o2, R) in obj_rels:
print(self.triplet2str('{}_{}_{}'.format(objs[o1, 1], R, objs[o2, 1])))
objs[obj_ind, 1] = self.perturb_object_(objs, obj_rels, obj_ind, verbose=verbose)
if verbose:
print('\nafter: %s' % self.obj_classes[objs[obj_ind, 1]])
for (_, o1, o2, R) in obj_rels:
print(self.triplet2str('{}_{}_{}'.format(objs[o1, 1], R, objs[o2, 1])))
gt_obj_new.append(objs)
gt_obj_new = torch.cat(gt_obj_new)
return gt_obj_new
def forward(self, union_pools, rois, union_inds, im_sizes):
boxes = rois[:, 1:].clone()
# scale boxes to the range [0,1]
scale = boxes.new(boxes.shape).fill_(0)
for i, s, e in enumerate_by_image(rois[:, 0].long().data):
h, w = im_sizes[i][:2]
scale[s:e, 0] = w
scale[s:e, 1] = h
scale[s:e, 2] = w
scale[s:e, 3] = h
boxes = boxes / scale
try:
rects = draw_union_boxes_my(boxes, union_inds,
self.pooling_size * 4 - 1) - 0.5
except Exception as e:
# there was a problem with bboxes being larger than images at test time, had to clip them
print(rois, boxes, im_sizes, scale)
raise
if self.concat:
return torch.cat((union_pools, self.conv(rects)), 1)
return union_pools + self.conv(rects)
def _sort_by_score(im_inds, scores):
"""
We'll sort everything scorewise from Hi->low, BUT we need to keep images together
and sort LSTM from l
:param im_inds: Which im we're on
:param scores: Goodness ranging between [0, 1]. Higher numbers come FIRST
:return: Permutation to put everything in the right order for the LSTM
Inverse permutation
Lengths for the TxB packed sequence.
"""
num_im = im_inds[-1] + 1
rois_per_image = scores.new(num_im)
lengths = []
for i, s, e in enumerate_by_image(im_inds):
rois_per_image[i] = 2 * (s - e) * num_im + i
lengths.append(e - s)
lengths = sorted(lengths, reverse=True)
inds, ls_transposed = transpose_packed_sequence_inds(lengths) # move it to TxB form
inds = torch.LongTensor(inds).cuda(im_inds.get_device())
# ~~~~~~~~~~~~~~~~
# HACKY CODE ALERT!!!
# we're sorting by confidence which is in the range (0,1), but more importantly by longest
# img....
# ~~~~~~~~~~~~~~~~
roi_order = scores - 2 * rois_per_image[im_inds]
_, perm = torch.sort(roi_order, 0, descending=True)
perm = perm[inds]
_, inv_perm = torch.sort(perm)
return perm, inv_perm, ls_transposed
def _PadRelFeats(rel_im_inds,
rel_feats_all,
num_relation,
seq_per_img,
pred_classes,
obj_classes,
rels,
freq_matrix=None):
"""
:param rel_im_inds: torch.LongTensor, [num_rels, ]
:param rel_feats_all: Variable, [num_rels, 4096]
:param num_relation:
:param seq_per_img:
:return: rel_feats, [batch_size*seq_per_img, num_relation, 4096]
"""
rel_feats = []
categories_info_all = []
for i, s, e in enumerate_by_image(rel_im_inds):
rel_feats_i = rel_feats_all[s:e, :]
pred_classes_i = pred_classes[s:e][:, None]
rels_i = rels[s:e, :]
subj_categories = obj_classes[rels_i[:, 1]][:, None]
obj_categories = obj_classes[rels_i[:, 2]][:, None]
categories_info = torch.cat(
(subj_categories, obj_categories, pred_classes_i), 1)
# compute freqency baseline: reranking based on triplet frequency
if freq_matrix is not None:
categories_info_np = categories_info.data.cpu().numpy()
freqs = []
for cat in categories_info_np:
freqs.append(freq_matrix[cat[0], cat[1], cat[2]])
sort_index = torch.from_numpy(np.argsort(
np.array(freqs) * -1)).cuda(rel_feats_all.get_device())
rel_feats_i = rel_feats_i[sort_index, :]
rels_i = rels_i[sort_index, :]
categories_info = categories_info[sort_index, :]
this_num_rel = e - s
if num_relation <= this_num_rel:
rel_feats_i = rel_feats_i[:num_relation, :]
categories_info = categories_info[:num_relation]
else: # oversample
sample_inds = torch.from_numpy(
np.random.choice(np.arange(this_num_rel, dtype=np.int32),
num_relation,
replace=True)).long().cuda(
rel_feats_all.get_device())
rel_feats_i = rel_feats_i[sample_inds]
categories_info = categories_info[sample_inds]
rel_feats += [rel_feats_i[None, :, :]] * seq_per_img
categories_info_all += [categories_info[None, :, :]] * seq_per_img
rel_feats = torch.cat(rel_feats, 0)
categories_info_all = torch.cat(categories_info_all, 0)
return rel_feats, categories_info_all
def nms_boxes(self, obj_dists, rois, box_deltas, im_sizes):
"""
Performs NMS on the boxes
:param obj_dists: [#rois, #classes], ex:[4000+, 151]
:param rois: [#rois, 5], ex:[4000+, 5]
:param box_deltas: [#rois, #classes, 4]
:param im_sizes: sizes of images [6,3]
:return
nms_inds [#nms], ex: #nms=384
nms_scores [#nms]
nms_labels [#nms]
nms_boxes_assign [#nms, 4]
nms_boxes [#nms, #classes, 4]. classid=0 is the box prior.
"""
# Now Converts "deltas" (predicted by the network) along with prior boxes into (x1, y1, x2, y2) representation.
# box deltas is (num_rois, num_classes, 4) but rois is only #(num_rois, 4)
# boxes = bbox_preds([#rois * 151, 4]) = [#rois, 151, 4]
boxes = bbox_preds(
rois[:, None, 1:].expand_as(box_deltas).contiguous().view(-1, 4),
box_deltas.view(-1, 4)).view(*box_deltas.size())
inds = rois[:, 0].long().contiguous()
dets = []
# Clip the boxes and get the best N dets per image.
for i, s, e in enumerate_by_image(inds.data):
h, w = im_sizes[i, :2]
boxes[s:e, :, 0].data.clamp_(min=0, max=w - 1)
boxes[s:e, :, 1].data.clamp_(min=0, max=h - 1)
boxes[s:e, :, 2].data.clamp_(min=0, max=w - 1)
boxes[s:e, :, 3].data.clamp_(min=0, max=h - 1)
d_filtered = filter_det(
F.softmax(obj_dists[s:e], 1),
boxes[s:e],
start_ind=s,
nms_filter_duplicates=self.nms_filter_duplicates,
max_per_img=self.max_per_img,
thresh=self.thresh,
)
if d_filtered is not None:
dets.append(d_filtered)
# dets is a list: len is 6 (images); each image has (inds, scores, labels), each len is 64
if len(dets) == 0:
print("nothing was detected", flush=True)
return None
nms_inds, nms_scores, nms_labels = [
torch.cat(x, 0) for x in zip(*dets)
] # [384]
twod_inds = nms_inds * boxes.size(1) + nms_labels.data
nms_boxes_assign = boxes.view(-1, 4)[twod_inds]
# nms_boxes: [384,151,4], the first dim of 151 is not "0" background class, it's rois
# rois[:, 1:][nms_inds][:, None].shape: [384, 1, 4]; boxes[nms_inds][:, 1:]: [384,150,4]
nms_boxes = torch.cat(
(rois[:, 1:][nms_inds][:, None], boxes[nms_inds][:, 1:]), 1)
return nms_inds, nms_scores, nms_labels, nms_boxes_assign, nms_boxes, inds[
nms_inds]
def nms_boxes(self, obj_dists, rois, box_deltas, im_sizes):
"""
Performs NMS on the boxes
:param obj_dists: [#rois, #classes]
:param rois: [#rois, 5]
:param box_deltas: [#rois, #classes, 4]
:param im_sizes: sizes of images
:return
nms_inds [#nms]
nms_scores [#nms]
nms_labels [#nms]
nms_boxes_assign [#nms, 4]
nms_boxes [#nms, #classes, 4]. classid=0 is the box prior.
"""
# Now produce the boxes
# box deltas is (num_rois, num_classes, 4) but rois is only #(num_rois, 4)
boxes = bbox_preds(
rois[:, None, 1:].expand_as(box_deltas).contiguous().view(-1, 4),
box_deltas.view(-1, 4)).view(*box_deltas.size())
# Clip the boxes and get the best N dets per image.
inds = rois[:, 0].long().contiguous()
dets = []
for i, s, e in enumerate_by_image(inds.data):
h, w = im_sizes[i, :2]
boxes[s:e, :, 0].data.clamp_(min=0, max=w - 1)
boxes[s:e, :, 1].data.clamp_(min=0, max=h - 1)
boxes[s:e, :, 2].data.clamp_(min=0, max=w - 1)
boxes[s:e, :, 3].data.clamp_(min=0, max=h - 1)
d_filtered = filter_det(
F.softmax(obj_dists[s:e], 1),
boxes[s:e],
start_ind=s,
nms_filter_duplicates=self.nms_filter_duplicates,
max_per_img=self.max_per_img,
thresh=self.thresh,
)
if d_filtered is not None:
dets.append(d_filtered)
if len(dets) == 0:
print("nothing was detected", flush=True)
return None
nms_inds, nms_scores, nms_labels = [
torch.cat(x, 0) for x in zip(*dets)
]
twod_inds = nms_inds * boxes.size(1) + nms_labels.data
nms_boxes_assign = boxes.view(-1, 4)[twod_inds]
nms_boxes = torch.cat(
(rois[:, 1:][nms_inds][:, None], boxes[nms_inds][:, 1:]), 1)
return nms_inds, nms_scores, nms_labels, nms_boxes_assign, nms_boxes, inds[
nms_inds]
def get_scaled_boxes(self, boxes, im_inds, im_sizes):
if self.backbone == 'vgg16_old':
boxes_scaled = boxes / IM_SCALE
else:
boxes_scaled = boxes.clone()
for im_ind, s, e in enumerate_by_image(im_inds.long().data):
boxes_scaled[s:e, [0, 2]] = boxes_scaled[
s:e, [0, 2]] / im_sizes[im_ind][1] # width
boxes_scaled[s:e, [1, 3]] = boxes_scaled[
s:e, [1, 3]] / im_sizes[im_ind][0] # height
assert boxes_scaled.max() <= 1 + 1e-3, (boxes_scaled.max(),
boxes.max(), im_sizes)
return boxes_scaled
def dummy_nodes(gt_objs, gt_boxes, gt_rels):
# Add dummy nodes to scene graphs to improve message propagation
gt_objs_new, gt_boxes_new, gt_rels_new = [], [], []
gt_rels_lst = [
gt_rels[s:e] for im, s, e in enumerate_by_image(gt_rels[:, 0])
]
dummy_box = torch.Tensor([0, 0, 1, 1]).view(1, 4).to(gt_boxes)
offset = 0
for im, s, e in enumerate_by_image(gt_objs[:, 0]):
gt_objs_im = gt_objs[s:e]
n_obj = len(gt_objs_im)
rels = torch.zeros(
(n_obj * 2,
4)).to(gt_rels) # adding two way edges from/to the dummy node
rels[:, 0] = im
for i in range(n_obj):
# make edges two way as in the visual genome data loader
for j, in_out in zip([i, i + n_obj], [(1, 2), (2, 1)]):
rels[j, in_out[0]] = n_obj
rels[j, in_out[1]] = i
rels = torch.cat((gt_rels_lst[im].clone(), rels), 0)
rels[:, 1:3] += offset
gt_rels_new.append(rels)
gt_objs_new.append(
torch.cat(
(gt_objs_im, torch.Tensor([im, 0]).view(1, 2).to(gt_objs_im)),
0))
gt_boxes_new.append(torch.cat((gt_boxes[s:e], dummy_box), 0))
offset += (n_obj + 1) # +1 because 1 dummy node is added
# assert len(torch.cat(gt_objs_new)) == offset, (torch.cat(gt_objs_new).shape, offset)
return torch.cat(gt_objs_new), torch.cat(gt_boxes_new), torch.cat(
gt_rels_new)
def pack_vectors(im_inds, vec_inps):
num_im = int(im_inds[-1] + 1)
max_num_roi = 0
im2roi = []
ls_rois = []
for i, s, e in enumerate_by_image(im_inds):
im2roi.append((s, e))
max_num_roi = max(max_num_roi, e - s)
ls_rois.append(e - s)
packed_tensor = Variable(
torch.FloatTensor(num_im, max_num_roi,
vec_inps.shape[1]).fill_(0).cuda(
vec_inps.get_device()))
for i, seg in enumerate(im2roi):
packed_tensor[i, :ls_rois[i]] = vec_inps[seg[0]:seg[1], :]
return packed_tensor, np.array(ls_rois)
def forward(self, im_inds, obj_fmaps, obj_logits, rel_inds, vr, obj_labels=None, boxes_per_cls=None):
"""
Reason relationship classes using knowledge of object and relationship coccurrence.
"""
# print(rel_inds.shape)
# (num_rel, 3)
if self.mode == 'predcls':
obj_logits = Variable(onehot_logits(obj_labels.data, self.num_obj_cls))
obj_probs = F.softmax(obj_logits, 1)
obj_fmaps = self.obj_proj(obj_fmaps)
vr = self.rel_proj(vr)
rel_logits = []
obj_logits_refined = []
for (_, obj_s, obj_e), (_, rel_s, rel_e) in zip(enumerate_by_image(im_inds.data), enumerate_by_image(rel_inds[:,0])):
rl, ol = self.ggnn(rel_inds[rel_s:rel_e, 1:] - obj_s, obj_probs[obj_s:obj_e], obj_fmaps[obj_s:obj_e], vr[rel_s:rel_e])
rel_logits.append(rl)
obj_logits_refined.append(ol)
rel_logits = torch.cat(rel_logits, 0)
if self.ggnn.refine_obj_cls:
obj_logits_refined = torch.cat(obj_logits_refined, 0)
obj_logits = obj_logits_refined
obj_probs = F.softmax(obj_logits, 1)
if self.mode == 'sgdet' and not self.training:
# NMS here for baseline
nms_mask = obj_probs.data.clone()
nms_mask.zero_()
for c_i in range(1, obj_probs.size(1)):
scores_ci = obj_probs.data[:, c_i]
boxes_ci = boxes_per_cls.data[:, c_i]
keep = apply_nms(scores_ci, boxes_ci,
pre_nms_topn=scores_ci.size(0), post_nms_topn=scores_ci.size(0),
nms_thresh=0.3)
nms_mask[:, c_i][keep] = 1
obj_preds = Variable(nms_mask * obj_probs.data, volatile=True)[:,1:].max(1)[1] + 1
else:
obj_preds = obj_labels if obj_labels is not None else obj_probs[:,1:].max(1)[1] + 1
return obj_logits, obj_preds, rel_logits
def forward(self, im_inds, obj_fmaps, obj_labels):
"""
Reason object classes using knowledge of object cooccurrence
"""
if self.mode == 'predcls':
# in task 'predcls', there is no need to run GGNN_obj
obj_dists = Variable(to_onehot(obj_labels.data, self.num_obj_cls))
return obj_dists
else:
input_ggnn = self.obj_proj(obj_fmaps)
lengths = []
for i, s, e in enumerate_by_image(im_inds.data):
lengths.append(e - s)
obj_cum_add = np.cumsum([0] + lengths)
obj_dists = torch.cat([self.ggnn_obj(input_ggnn[obj_cum_add[i] : obj_cum_add[i+1]]) for i in range(len(lengths))], 0)
return obj_dists
def forward(self, union_pools, rois, union_inds, im_sizes):
if self.edge_model == 'motifs':
pair_rois = torch.cat(
(rois[:, 1:][union_inds[:, 0]], rois[:, 1:][union_inds[:, 1]]),
1).data.cpu().numpy()
rects = torch.from_numpy(
self.draw_union_boxes(pair_rois, self.pooling_size * 4 - 1) -
0.5).to(union_pools)
elif self.edge_model == 'raw_boxes':
boxes = rois[:, 1:].clone()
# scale boxes to the range [0,1]
scale = boxes.new(boxes.shape).fill_(0).float()
for i, s, e in enumerate_by_image(rois[:, 0].long().data):
h, w = im_sizes[i][:2]
scale[s:e, 0] = w
scale[s:e, 1] = h
scale[s:e, 2] = w
scale[s:e, 3] = h
boxes = boxes / scale
try:
rects = self.draw_union_boxes(boxes, union_inds,
self.pooling_size * 4 - 1) - 0.5
except Exception as e:
# there was a problem with bboxes being larger than images at test time, had to clip them
print(rois, boxes, im_sizes, scale)
raise
# to debug:
# print('rects my', rects.shape, rects.min(), rects.max())
# np.save('rects.npy', rects.data.cpu().numpy())
# pair_rois = torch.cat((rois[:, 1:][union_inds[:, 0]], rois[:, 1:][union_inds[:, 1]]), 1).data.cpu().numpy()
# rects2 = torch.from_numpy(draw_union_boxes(pair_rois, self.pooling_size * 4 - 1) - 0.5).to(union_pools)
# print('rects2', rects2.shape, rects2.min(), rects2.max())
# np.save('rects2.npy', rects2.data.cpu().numpy())
# print(union_inds)
# raise ValueError('saved')
else:
raise NotImplementedError(self.edge_model)
if self.concat:
return torch.cat((union_pools, self.conv(rects)), 1)
return union_pools + self.conv(rects)
def filter_dets_for_gcn_caption(im_inds, region_feats, obj_scores, obj_classes, rel_inds, pred_scores,
rel_rank_scores=None,
seq_labels=None, mask_labels=None, coco_ids=None):
num_box = obj_classes.size()
num_rel = rel_inds.size(0)
assert rel_inds.size(1) == 3
assert pred_scores.size(0) == num_rel
obj_scores0 = obj_scores.data[rel_inds[:, 1]]
obj_scores1 = obj_scores.data[rel_inds[:, 2]]
pred_scores_max, pred_classes_argmax = pred_scores.data[:, 1:].max(1)
pred_classes_argmax = pred_classes_argmax + 1
rel_scores_argmaxed = pred_scores_max * obj_scores0 * obj_scores1
if rel_rank_scores is not None:
rel_scores_argmaxed *= rel_rank_scores.data
# split the relations according to image
rel_im_inds = rel_inds[:, 0]
rels = []
pred_classes = []
for i, s, e in enumerate_by_image(rel_im_inds):
rels_i = rel_inds[s:e, :]
pred_classes_argmax_i = pred_classes_argmax[s:e]
rel_scores_argmaxed_i = rel_scores_argmaxed[s:e]
rel_scores_vs_i, rel_scores_idx_i = torch.sort(rel_scores_argmaxed_i.view(-1), dim=0, descending=True)
rels_i = rels_i[rel_scores_idx_i]
pred_classes_argmax_i = pred_classes_argmax_i[rel_scores_idx_i]
rels.append(rels_i)
pred_classes.append(pred_classes_argmax_i)
rels = torch.cat(rels, 0)
pred_classes = torch.cat(pred_classes, 0)
return im_inds, region_feats, pred_classes, rels, obj_classes.data, seq_labels, mask_labels, coco_ids
def filter_dets_for_caption(boxes, obj_scores, obj_classes, rel_inds, pred_scores, rel_feats, image_fmap,
rel_rank_scores=None,
seq_labels=None, mask_labels=None, coco_ids=None):
"""
Filters detections....
:param boxes: [num_box, 4]
:param obj_scores: [num_box] probabilities for the scores
:param obj_classes: [num_box] class labels for the topk
:param rel_inds: [num_rel, 3] TENSOR consisting of (rel_im_inds, box_ind0, box_ind1)
:param pred_scores: [num_rel, num_predicates]
:param use_nms: True if use NMS to filter dets.
:return:
boxes: FloatTensor
obj_classes: FloatTensor
rels: LongTensor, [num_rel, 3]
pred_classes: LongTensor, [num_rel,]
rel_feats_all: FloatTensor, [num_rel, 4096]
seq_labels: [num_img*5, 19], [im_inds, <start>, seq labels, <end>, 0, 0, ...]
mask_labels: [num_img*5, 19], [im_inds, 1, 1, ..., {1 for <end>}, 0, 0, ...]
"""
if boxes.dim() != 2:
raise ValueError("Boxes needs to be [num_box, 4] but its {}".format(boxes.size()))
num_box = boxes.size(0)
assert obj_scores.size(0) == num_box
assert obj_classes.size() == obj_scores.size()
num_rel = rel_inds.size(0)
assert rel_inds.size(1) == 3
assert pred_scores.size(0) == num_rel
obj_scores0 = obj_scores.data[rel_inds[:, 1]]
obj_scores1 = obj_scores.data[rel_inds[:, 2]]
pred_scores_max, pred_classes_argmax = pred_scores.data[:, 1:].max(1)
pred_classes_argmax = pred_classes_argmax + 1
rel_scores_argmaxed = pred_scores_max * obj_scores0 * obj_scores1
if rel_rank_scores is not None:
rel_scores_argmaxed *= rel_rank_scores.data
# split the relations according to image
rel_im_inds = rel_inds[:, 0]
rels = []
rel_feats_all = []
pred_classes = []
for i, s, e in enumerate_by_image(rel_im_inds):
rels_i = rel_inds[s:e, :]
pred_classes_argmax_i = pred_classes_argmax[s:e]
rel_feats_i = rel_feats[s:e, :]
rel_scores_argmaxed_i = rel_scores_argmaxed[s:e]
rel_scores_vs_i, rel_scores_idx_i = torch.sort(rel_scores_argmaxed_i.view(-1), dim=0, descending=True)
rels_i = rels_i[rel_scores_idx_i]
pred_classes_argmax_i = pred_classes_argmax_i[rel_scores_idx_i]
rel_feats_i = rel_feats_i[rel_scores_idx_i]
rels.append(rels_i)
rel_feats_all.append(rel_feats_i)
pred_classes.append(pred_classes_argmax_i)
rels = torch.cat(rels, 0)
rel_feats_all = torch.cat(rel_feats_all, 0)
pred_classes = torch.cat(pred_classes, 0)
return boxes, obj_classes, rels, Variable(
pred_classes), rel_feats_all, image_fmap, seq_labels, mask_labels, coco_ids
def proposal_assignments_gtbox(rois,
gt_boxes,
gt_classes,
gt_rels,
image_offset,
RELS_PER_IMG,
sample_factor=-1):
"""
Assign object detection proposals to ground-truth targets. Produces proposal
classification labels and bounding-box regression targets.
:param rpn_rois: [img_ind, x1, y1, x2, y2]
:param gt_boxes: [num_boxes, 4] array of x0, y0, x1, y1]. Not needed it seems
:param gt_classes: [num_boxes, 2] array of [img_ind, class]
Note, the img_inds here start at image_offset
:param gt_rels [num_boxes, 4] array of [img_ind, box_0, box_1, rel type].
Note, the img_inds here start at image_offset
:param Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
:return:
rois: [num_rois, 5]
labels: [num_rois] array of labels
bbox_targets [num_rois, 4] array of targets for the labels.
rel_labels: [num_rels, 4] (img ind, box0 ind, box1ind, rel type)
"""
im_inds = rois[:, 0].long()
num_im = im_inds[-1] + 1
# Offset the image indices in fg_rels to refer to absolute indices (not just within img i)
fg_rels = gt_rels.clone()
fg_rels[:, 0] -= image_offset
offset = {}
for i, s, e in enumerate_by_image(im_inds):
offset[i] = s
for i, s, e in enumerate_by_image(fg_rels[:, 0]):
fg_rels[s:e, 1:3] += offset[i]
# Try ALL things, not just intersections.
is_cand = (im_inds[:, None] == im_inds[None])
is_cand.view(-1)[diagonal_inds(is_cand)] = 0
# NOW WE HAVE TO EXCLUDE THE FGs.
is_cand.view(-1)[fg_rels[:, 1] * im_inds.size(0) + fg_rels[:, 2]] = 0
is_bgcand = torch.nonzero(is_cand)
# TODO: make this sample on a per image case
# If too many then sample
num_fg = min(fg_rels.size(0), int(RELS_PER_IMG * REL_FG_FRACTION * num_im))
if num_fg < fg_rels.size(0):
fg_rels = random_choose(fg_rels, num_fg)
# If too many then sample
is_train = num_im > 1 # assume num_im = 1 at test time (except for the det mode, which we don't use for now)
sample_bg = is_train and sample_factor > -1
num_bg = min(
is_bgcand.size(0) if is_bgcand.dim() > 0 else 0,
int(num_fg * sample_factor) if sample_bg else
(int(RELS_PER_IMG * num_im) -
num_fg)) # sample num_fg at training time
if num_bg > 0:
bg_rels = torch.cat((
im_inds[is_bgcand[:, 0]][:, None],
is_bgcand,
(is_bgcand[:, 0, None] < -10).long(),
), 1)
if num_bg < is_bgcand.size(0):
bg_rels = random_choose(
bg_rels, num_bg
) # at test time will correspond to the baseline approach
rel_labels = torch.cat((fg_rels, bg_rels), 0)
else:
rel_labels = fg_rels
# last sort by rel.
_, perm = torch.sort(rel_labels[:, 0] * (gt_boxes.size(0)**2) +
rel_labels[:, 1] * gt_boxes.size(0) +
rel_labels[:, 2])
rel_labels = rel_labels[perm].contiguous()
labels = gt_classes[:, 1].contiguous()
return rois, labels, rel_labels
def rel_proposal_target(rois, rel_proposal_inds, gt_boxes, gt_classes, gt_rels, image_offset, mode):
"""
Assign the tareget for each proposal pairs.
When the mode is predcls or sgcls, the target is directly obtained by comparing with gt_rel.
When the mode is sgdet, the target is sampled by firstly compute iou with gt_pairs
:param rois:
:param rel_proposal_inds: [im_ind, ind1, ind2]
:param gt_boxes:
:param image_offset:
:param mode:
:return:
"""
im_inds = rois[:, 0].long()
num_im = im_inds[-1] + 1
# Offset the image indices in fg_rels to refer to absolute indices (not just within img i)
fg_rels = gt_rels.clone()
fg_rels[:, 0] -= image_offset
offset = {}
for i, s, e in enumerate_by_image(gt_classes[:, 0]):
offset[i] = s
for i, s, e in enumerate_by_image(fg_rels[:, 0]):
fg_rels[s:e, 1:3] += offset[i]
rels_to_gt = []
num_gt_rels_seen = 0
if mode in ('predcls', 'sgcls'):
rel_proposal_inds_np = rel_proposal_inds.cpu().numpy()
fg_rels_np = fg_rels.cpu().numpy() ## Ngtp, 4
# locate the proposal
locate_inds = np.where(intersect_2d(rel_proposal_inds_np, fg_rels_np[:, :-1]))
proposal_to_gt = defaultdict(list)
for ind in zip(*locate_inds):
proposal_to_gt[ind[0]].append(ind[1])
for k, v in proposal_to_gt.items():
v0 = v[0] if len(v) == 1 else np.random.choice(v)
proposal_to_gt[k] = v0
fg_proposal_inds = np.array(list(proposal_to_gt.keys())).astype(np.int32)
bg_proposal_inds = np.array(list(set(list(range(rel_proposal_inds_np.shape[0]))) - set(list(proposal_to_gt.keys())))).astype(np.int32)
rels_to_gt = np.ones(fg_proposal_inds.shape[0] + bg_proposal_inds.shape[0], dtype=np.int64) * -1
if len(fg_proposal_inds) > 0:
rels_to_gt[fg_proposal_inds] = np.array([proposal_to_gt[ind] for ind in fg_proposal_inds])
num_fg = min(fg_proposal_inds.size, int(RELS_BATCHSIZE * REL_FG_FRACTION * num_im))
if num_fg < fg_proposal_inds.size:
fg_proposal_inds = np.random.choice(fg_proposal_inds, num_fg, replace=False)
num_bg = min(bg_proposal_inds.size if bg_proposal_inds.size else 0, int(RELS_BATCHSIZE * num_im) - num_fg)
if num_bg < bg_proposal_inds.size:
bg_proposal_inds = np.random.choice(bg_proposal_inds, num_bg, replace=False)
if len(fg_proposal_inds) == 0:
bg_labels = np.zeros(bg_proposal_inds.size)
bg_rel_labels = np.hstack((rel_proposal_inds_np[bg_proposal_inds], bg_labels[:, None]))
proposal_labels = bg_rel_labels
else:
fg_labels = np.array([fg_rels[proposal_to_gt[ind], -1] for ind in fg_proposal_inds])
fg_rel_labels = np.hstack((rel_proposal_inds_np[fg_proposal_inds], fg_labels[:, None]))
bg_labels = np.zeros(bg_proposal_inds.size)
bg_rel_labels = np.hstack((rel_proposal_inds_np[bg_proposal_inds], bg_labels[:, None]))
proposal_labels = np.vstack((fg_rel_labels, bg_rel_labels))
rels_to_gt = np.hstack((rels_to_gt[fg_proposal_inds], rels_to_gt[bg_proposal_inds]))
proposal_labels = torch.LongTensor(proposal_labels).cuda(gt_rels.get_device())
rels_to_gt = torch.LongTensor(rels_to_gt).cuda(gt_rels.get_device())
else:
assert mode == 'sgdet'
gt_box_pairs = torch.cat((gt_boxes[fg_rels[:, 1]], gt_boxes[fg_rels[:, 2]]), 1)
rel_proposal_pairs = torch.cat((rois[:, 1:][rel_proposal_inds[:, 0]], rois[:, 1:][rel_proposal_inds[:, 1]]), 1)
num_pairs = np.zeros(num_im + 1).astype(np.int32)
for i, s, e in enumerate_by_image(rel_proposal_inds[:, 0]):
num_pairs[i + 1] = e - s
cumsum_num_pairs = np.cumsum(num_pairs).astype(np.int32)
fg_rel_per_image = int(RELS_BATCHSIZE * REL_FG_FRACTION)
proposal_labels = []
gt_rel_labels = fg_rels[:, -1].contiguous().view(-1)
for i in range(1, num_im + 1):
rel_proposal_inds_i = rel_proposal_inds[cumsum_num_pairs[i - 1]:cumsum_num_pairs[i]]
rel_proposal_pairs_i = rel_proposal_pairs[cumsum_num_pairs[i - 1]:cumsum_num_pairs[i]]
gt_box_pairs_i = gt_box_pairs[torch.nonzero(fg_rels[:, 0] == (i - 1)).view(-1)]
gt_box_pairs_label_i = gt_rel_labels[torch.nonzero(fg_rels[:, 0] == (i - 1)).view(-1)].view(-1).contiguous()
overlaps = co_bbox_overlaps(rel_proposal_pairs_i, gt_box_pairs_i) # Np, Ngtp
max_overlaps, gt_assignment = torch.max(overlaps, 1) # Np
fg_inds = torch.nonzero(max_overlaps >= 0.5).view(-1)
fg_num = fg_inds.numel()
bg_inds = torch.nonzero((max_overlaps < 0.5) & (max_overlaps >= 0.0)).view(-1)
bg_num = bg_inds.numel()
rels_to_gt_i = torch.LongTensor(rel_proposal_pairs_i.shape[0]).fill(-1).cuda(gt_rels.get_device())
rels_to_gt_i[fg_inds] = gt_assignment[fg_inds] + num_gt_rels_seen
if fg_num > 0 and bg_num > 0:
fg_this_image = min(fg_rel_per_image, fg_num)
rand_num = torch.from_numpy(np.random.permutation(fg_num)).long().cuda()
fg_inds = fg_inds[rand_num[:fg_this_image]]
# sampling bg
bg_this_image = RELS_BATCHSIZE - fg_this_image
rand_num = np.floor(np.random.rand(bg_this_image) * bg_num)
rand_num = torch.from_numpy(rand_num).long().cuda()
bg_inds = bg_inds[rand_num]
rels_to_gt_i = torch.cat((rels_to_gt_i[fg_inds], rels_to_gt_i[bg_inds]), 0)
elif fg_num > 0 and bg_num == 0:
rand_num = np.floor(np.random.rand(RELS_BATCHSIZE) * fg_num)
rand_num = torch.from_numpy(rand_num).long().cuda()
fg_inds = fg_inds[rand_num]
fg_this_image = RELS_BATCHSIZE
bg_this_image = 0
rels_to_gt_i = rels_to_gt_i[fg_inds]
elif bg_num > 0 and fg_num == 0:
# sampling bg
# rand_num = torch.floor(torch.rand(rois_per_image) * bg_num_rois).long().cuda()
rand_num = np.floor(np.random.rand(RELS_BATCHSIZE) * bg_num)
rand_num = torch.from_numpy(rand_num).long().cuda()
bg_inds = bg_inds[rand_num]
bg_this_image = RELS_BATCHSIZE
fg_this_image = 0
rels_to_gt_i = rels_to_gt_i[bg_inds]
else:
import pdb
pdb.set_trace()
keep_inds = torch.cat([fg_inds, bg_inds], 0)
rel_proposal_inds_i = rel_proposal_inds_i[keep_inds]
labels_i = gt_box_pairs_label_i[gt_assignment[keep_inds]]
if fg_this_image < labels_i.size(0):
labels_i[fg_this_image:] = 0
rels_to_gt.append(rels_to_gt_i)
num_gt_rels_seen += gt_box_pairs_i.shape[0]
#try:
# labels_i[fg_this_image:] = 0
#except ValueError:
# print(labels_i)
# print(fg_this_image)
# import pdb
# pdb.set_trace()
proposal_labels.append(torch.cat((rel_proposal_inds_i, labels_i[:, None]), 1))
proposal_labels = torch.cat(proposal_labels, 0)
rels_to_gt = torch.cat(rels_to_gt, 0)
# sort
_, perm = torch.sort(
proposal_labels[:, 0] * (rois.size(0) ** 2) + proposal_labels[:, 1] * rois.size(0) + proposal_labels[:, 2])
proposal_labels = proposal_labels[perm].contiguous()
rels_to_gt = rels_to_gt[perm].contiguous()
return proposal_labels, rels_to_gt
def convert_roi_to_list(rois):
rois_lst = []
for im_ind, s, e in enumerate_by_image(rois[:, 0].long().data):
rois_lst.append(rois[s:e, 1:])
return rois_lst
def forward(self,
x,
im_sizes,
image_offset,
gt_boxes=None,
gt_classes=None,
gt_rels=None,
proposals=None,
train_anchor_inds=None,
return_fmap=False):
"""
Forward pass for Relation detection
Args:
x: Images@[batch_size, 3, IM_SIZE, IM_SIZE]
im_sizes: A numpy array of (h, w, scale) for each image.
image_offset: Offset onto what image we're on for MGPU training (if single GPU this is 0)
parameters for training:
gt_boxes: [num_gt, 4] GT boxes over the batch.
gt_classes: [num_gt, 2] gt boxes where each one is (img_id, class)
gt_rels:
proposals:
train_anchor_inds: a [num_train, 2] array of indices for the anchors that will
be used to compute the training loss. Each (img_ind, fpn_idx)
return_fmap:
Returns:
If train:
scores, boxdeltas, labels, boxes, boxtargets, rpnscores, rpnboxes, rellabels
If test:
prob dists, boxes, img inds, maxscores, classes
"""
result = self.detector(x,
im_sizes,
image_offset,
gt_boxes,
gt_classes,
gt_rels,
proposals,
train_anchor_inds,
return_fmap=True)
assert not result.is_none(), 'Empty detection result'
# image_offset refer to Blob
# self.batch_size_per_gpu * index
im_inds = result.im_inds - image_offset
boxes = result.rm_box_priors
obj_scores, box_classes = F.softmax(
result.rm_obj_dists[:, 1:].contiguous(), dim=1).max(1)
box_classes += 1
num_img = im_inds[-1] + 1
# embed(header='rel_model.py before rel_assignments')
if self.training and result.rel_labels is None:
assert self.mode == 'sgdet'
# only in sgdet mode
# shapes:
# im_inds: (box_num,)
# boxes: (box_num, 4)
# rm_obj_labels: (box_num,)
# gt_boxes: (box_num, 4)
# gt_classes: (box_num, 2) maybe[im_ind, class_ind]
# gt_rels: (rel_num, 4)
# image_offset: integer
result.rel_labels = rel_assignments(im_inds.data,
boxes.data,
result.rm_obj_labels.data,
gt_boxes.data,
gt_classes.data,
gt_rels.data,
image_offset,
filter_non_overlap=True,
num_sample_per_gt=1)
rel_inds = self.get_rel_inds(result.rel_labels, im_inds, boxes)
rois = torch.cat((im_inds[:, None].float(), boxes), 1)
# union boxes feats (NumOfRels, obj_dim)
union_box_feats = self.visual_rep(result.fmap.detach(), rois,
rel_inds[:, 1:].contiguous())
# single box feats (NumOfBoxes, feats)
box_feats = self.obj_feature_map(result.fmap.detach(), rois)
# box spatial feats (NumOfBox, 4)
bboxes = Variable(center_size(boxes.data))
sub_bboxes = bboxes[rel_inds[:, 1].contiguous()]
obj_bboxes = bboxes[rel_inds[:, 2].contiguous()]
obj_bboxes[:, :2] = obj_bboxes[:, :2].contiguous(
) - sub_bboxes[:, :2].contiguous() # x-y
obj_bboxes[:, 2:] = obj_bboxes[:, 2:].contiguous(
) / sub_bboxes[:, 2:].contiguous() # w/h
obj_bboxes[:, :2] /= sub_bboxes[:, 2:].contiguous() # x-y/h
obj_bboxes[:, 2:] = torch.log(obj_bboxes[:,
2:].contiguous()) # log(w/h)
bbox_spatial_feats = self.spatial_fc(obj_bboxes)
box_word = self.classes_word_embedding(box_classes)
box_pair_word = torch.cat((box_word[rel_inds[:, 1].contiguous()],
box_word[rel_inds[:, 2].contiguous()]), 1)
box_word_feats = self.word_fc(box_pair_word)
# (NumOfRels, DIM=)
box_pair_feats = torch.cat(
(union_box_feats, bbox_spatial_feats, box_word_feats), 1)
box_pair_score = self.relpn_fc(box_pair_feats)
#embed(header='filter_rel_labels')
if self.training:
pn_rel_label = list()
pn_pair_score = list()
#print(result.rel_labels.shape)
#print(result.rel_labels[:, 0].contiguous().squeeze())
for i, s, e in enumerate_by_image(
result.rel_labels[:, 0].data.contiguous()):
im_i_rel_label = result.rel_labels[s:e].contiguous()
im_i_box_pair_score = box_pair_score[s:e].contiguous()
im_i_rel_fg_inds = torch.nonzero(
im_i_rel_label[:, -1].contiguous()).squeeze()
im_i_rel_fg_inds = im_i_rel_fg_inds.data.cpu().numpy()
im_i_fg_sample_num = min(RELEVANT_PER_IM,
im_i_rel_fg_inds.shape[0])
if im_i_rel_fg_inds.size > 0:
im_i_rel_fg_inds = np.random.choice(
im_i_rel_fg_inds,
size=im_i_fg_sample_num,
replace=False)
im_i_rel_bg_inds = torch.nonzero(
im_i_rel_label[:, -1].contiguous() == 0).squeeze()
im_i_rel_bg_inds = im_i_rel_bg_inds.data.cpu().numpy()
im_i_bg_sample_num = min(EDGES_PER_IM - im_i_fg_sample_num,
im_i_rel_bg_inds.shape[0])
if im_i_rel_bg_inds.size > 0:
im_i_rel_bg_inds = np.random.choice(
im_i_rel_bg_inds,
size=im_i_bg_sample_num,
replace=False)
#print('{}/{} fg/bg in image {}'.format(im_i_fg_sample_num, im_i_bg_sample_num, i))
result.rel_sample_pos = torch.Tensor(
[im_i_fg_sample_num]).cuda(im_i_rel_label.get_device())
result.rel_sample_neg = torch.Tensor(
[im_i_bg_sample_num]).cuda(im_i_rel_label.get_device())
im_i_keep_inds = np.append(im_i_rel_fg_inds, im_i_rel_bg_inds)
im_i_pair_score = im_i_box_pair_score[
im_i_keep_inds.tolist()].contiguous()
im_i_rel_pn_labels = Variable(
torch.zeros(im_i_fg_sample_num + im_i_bg_sample_num).type(
torch.LongTensor).cuda(x.get_device()))
im_i_rel_pn_labels[:im_i_fg_sample_num] = 1
pn_rel_label.append(im_i_rel_pn_labels)
pn_pair_score.append(im_i_pair_score)
result.rel_pn_dists = torch.cat(pn_pair_score, 0)
result.rel_pn_labels = torch.cat(pn_rel_label, 0)
box_pair_relevant = F.softmax(box_pair_score, dim=1)
box_pos_pair_ind = torch.nonzero(box_pair_relevant[:, 1].contiguous(
) > box_pair_relevant[:, 0].contiguous()).squeeze()
if box_pos_pair_ind.data.shape == torch.Size([]):
return None
#print('{}/{} trim edges'.format(box_pos_pair_ind.size(0), rel_inds.size(0)))
result.rel_trim_pos = torch.Tensor([box_pos_pair_ind.size(0)]).cuda(
box_pos_pair_ind.get_device())
result.rel_trim_total = torch.Tensor([rel_inds.size(0)
]).cuda(rel_inds.get_device())
# filtering relations
filter_rel_inds = rel_inds[box_pos_pair_ind.data]
filter_box_pair_feats = box_pair_feats[box_pos_pair_ind.data]
if self.training:
filter_rel_labels = result.rel_labels[box_pos_pair_ind.data]
result.rel_labels = filter_rel_labels
# message passing between boxes and relations
#embed(header='mp')
for _ in range(self.mp_iter_num):
box_feats = self.message_passing(box_feats, filter_box_pair_feats,
filter_rel_inds)
box_cls_scores = self.cls_fc(box_feats)
result.rm_obj_dists = box_cls_scores
obj_scores, box_classes = F.softmax(box_cls_scores[:, 1:].contiguous(),
dim=1).max(1)
box_classes += 1 # skip background
# TODO: add memory module
# filter_box_pair_feats is to be added to memory
# fbiilter_box_pair_feats = self.memory_()
# filter_box_pair_feats is to be added to memory
# RelationCNN
filter_box_pair_feats_fc1 = self.relcnn_fc1(filter_box_pair_feats)
filter_box_pair_score = self.relcnn_fc2(filter_box_pair_feats_fc1)
if not self.graph_cons:
filter_box_pair_score = filter_box_pair_score.view(
-1, 2, self.num_rels)
result.rel_dists = filter_box_pair_score
if self.training:
return result
pred_scores = F.softmax(result.rel_dists, dim=1)
"""
filter_dets
boxes: bbox regression else [num_box, 4]
obj_scores: [num_box] probabilities for the scores
obj_classes: [num_box] class labels integer
rel_inds: [num_rel, 2] TENSOR consisting of (im_ind0, im_ind1)
pred_scores: [num_rel, num_predicates] including irrelevant class(#relclass + 1)
"""
return filter_dets(boxes, obj_scores, box_classes,
filter_rel_inds[:, 1:].contiguous(), pred_scores)
def rel_assignments_sgcls(rois, gt_boxes, gt_classes, gt_rels, image_offset):
"""
sample_rels to balance proportion of positive and negative samples
:param rois: [img_ind, x1, y1, x2, y2]
:param gt_boxes: [num_boxes, 4] array of x0, y0, x1, y1]. Not needed it seems
:param gt_classes: [num_boxes, 2] array of [img_ind, class]
Note, the img_inds here start at image_offset
:param gt_rels [num_boxes, 4] array of [img_ind, box_0, box_1, rel type].
Note, the img_inds here start at image_offset
:param Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
:return:
rois: [num_rois, 5]
labels: [num_rois] array of labels
rel_labels: [num_rels, 4] (img ind, box0 ind, box1ind, rel type)
"""
im_inds = rois[:,0].long()
num_im = im_inds[-1] + 1
# Offset the image indices in fg_rels to refer to absolute indices (not just within img i)
fg_rels = gt_rels.clone()
fg_rels[:,0] -= image_offset
offset = {}
for i, s, e in enumerate_by_image(im_inds):
offset[i] = s
for i, s, e in enumerate_by_image(fg_rels[:, 0]):
fg_rels[s:e, 1:3] += offset[i]
# Try ALL things, not just intersections.
is_cand = (im_inds[:, None] == im_inds[None])
is_cand.view(-1)[diagonal_inds(is_cand)] = 0
# # Compute salience
# gt_inds = fg_rels[:, 1:3].contiguous().view(-1)
# labels_arange = labels.data.new(labels.size(0))
# torch.arange(0, labels.size(0), out=labels_arange)
# salience_labels = ((gt_inds[:, None] == labels_arange[None]).long().sum(0) > 0).long()
# labels = torch.stack((labels, salience_labels), 1)
# Add in some BG labels
# NOW WE HAVE TO EXCLUDE THE FGs.
# TODO: check if this causes an error if many duplicate GTs havent been filtered out
is_cand.view(-1)[fg_rels[:,1]*im_inds.size(0) + fg_rels[:,2]] = 0
is_bgcand = is_cand.nonzero()
# TODO: make this sample on a per image case
# If too many then sample
num_fg = min(fg_rels.size(0), int(RELS_PER_IMG * REL_FG_FRACTION * num_im))
if num_fg < fg_rels.size(0):
fg_rels = random_choose(fg_rels, num_fg)
# If too many then sample
num_bg = min(is_bgcand.size(0) if is_bgcand.dim() > 0 else 0,
int(RELS_PER_IMG * num_im) - num_fg)
if num_bg > 0:
bg_rels = torch.cat((
im_inds[is_bgcand[:, 0]][:, None],
is_bgcand,
(is_bgcand[:, 0, None] < -10).long(),
), 1)
if num_bg < is_bgcand.size(0):
bg_rels = random_choose(bg_rels, num_bg)
rel_labels = torch.cat((fg_rels, bg_rels), 0)
else:
rel_labels = fg_rels
# last sort by rel.
_, perm = torch.sort(rel_labels[:, 0]*(gt_boxes.size(0)**2) +
rel_labels[:,1]*gt_boxes.size(0) + rel_labels[:,2])
rel_labels = rel_labels[perm].contiguous()
labels = gt_classes[:,1].contiguous()
return rois, labels, rel_labels
def forward(self,
x,
im_sizes,
image_offset,
gt_boxes=None,
gt_classes=None,
gt_rels=None,
proposals=None,
train_anchor_inds=None,
return_fmap=False):
"""
Forward pass for Relation detection
Args:
x: Images@[batch_size, 3, IM_SIZE, IM_SIZE]
im_sizes: A numpy array of (h, w, scale) for each image.
image_offset: Offset onto what image we're on for MGPU training (if single GPU this is 0)
parameters for training:
gt_boxes: [num_gt, 4] GT boxes over the batch.
gt_classes: [num_gt, 2] gt boxes where each one is (img_id, class)
gt_rels:
proposals:
train_anchor_inds: a [num_train, 2] array of indices for the anchors that will
be used to compute the training loss. Each (img_ind, fpn_idx)
return_fmap:
Returns:
If train:
scores, boxdeltas, labels, boxes, boxtargets, rpnscores, rpnboxes, rellabels
If test:
prob dists, boxes, img inds, maxscores, classes
"""
s_t = time.time()
verbose = False
def check(sl, een, sst=s_t):
if verbose:
print('{}{}'.format(sl, een - sst))
result = self.detector(x,
im_sizes,
image_offset,
gt_boxes,
gt_classes,
gt_rels,
proposals,
train_anchor_inds,
return_fmap=True)
check('detector', tt())
assert not result.is_none(), 'Empty detection result'
# image_offset refer to Blob
# self.batch_size_per_gpu * index
im_inds = result.im_inds - image_offset
boxes = result.rm_box_priors
obj_scores, box_classes = F.softmax(
result.rm_obj_dists[:, 1:].contiguous(), dim=1).max(1)
box_classes += 1
# TODO: predcls implementation obj_scores and box_classes
num_img = im_inds[-1] + 1
# embed(header='rel_model.py before rel_assignments')
if self.training and result.rel_labels is None:
assert self.mode == 'sgdet'
# only in sgdet mode
# shapes:
# im_inds: (box_num,)
# boxes: (box_num, 4)
# rm_obj_labels: (box_num,)
# gt_boxes: (box_num, 4)
# gt_classes: (box_num, 2) maybe[im_ind, class_ind]
# gt_rels: (rel_num, 4)
# image_offset: integer
result.rel_labels = rel_assignments(im_inds.data,
boxes.data,
result.rm_obj_labels.data,
gt_boxes.data,
gt_classes.data,
gt_rels.data,
image_offset,
filter_non_overlap=True,
num_sample_per_gt=1)
rel_inds = self.get_rel_inds(result.rel_labels, im_inds, boxes)
rois = torch.cat((im_inds[:, None].float(), boxes), 1)
# union boxes feats (NumOfRels, obj_dim)
union_box_feats = self.visual_rep(result.fmap.detach(), rois,
rel_inds[:, 1:].contiguous())
# single box feats (NumOfBoxes, feats)
box_feats = self.obj_feature_map(result.fmap.detach(), rois)
# box spatial feats (NumOfBox, 4)
box_pair_feats = self.fuse_message(union_box_feats, boxes, box_classes,
rel_inds)
box_pair_score = self.relpn_fc(box_pair_feats)
if self.training:
# sampling pos and neg relations here for training
rel_sample_pos, rel_sample_neg = 0, 0
pn_rel_label, pn_pair_score = list(), list()
for i, s, e in enumerate_by_image(
result.rel_labels[:, 0].data.contiguous()):
im_i_rel_label = result.rel_labels[s:e].contiguous()
im_i_box_pair_score = box_pair_score[s:e].contiguous()
im_i_rel_fg_inds = torch.nonzero(
im_i_rel_label[:, -1].contiguous()).squeeze()
im_i_rel_fg_inds = im_i_rel_fg_inds.data.cpu().numpy()
im_i_fg_sample_num = min(RELEVANT_PER_IM,
im_i_rel_fg_inds.shape[0])
if im_i_rel_fg_inds.size > 0:
im_i_rel_fg_inds = np.random.choice(
im_i_rel_fg_inds,
size=im_i_fg_sample_num,
replace=False)
im_i_rel_bg_inds = torch.nonzero(
im_i_rel_label[:, -1].contiguous() == 0).squeeze()
im_i_rel_bg_inds = im_i_rel_bg_inds.data.cpu().numpy()
im_i_bg_sample_num = min(EDGES_PER_IM - im_i_fg_sample_num,
im_i_rel_bg_inds.shape[0])
if im_i_rel_bg_inds.size > 0:
im_i_rel_bg_inds = np.random.choice(
im_i_rel_bg_inds,
size=im_i_bg_sample_num,
replace=False)
#print('{}/{} fg/bg in image {}'.format(im_i_fg_sample_num, im_i_bg_sample_num, i))
rel_sample_pos += im_i_fg_sample_num
rel_sample_neg += im_i_bg_sample_num
im_i_keep_inds = np.append(im_i_rel_fg_inds, im_i_rel_bg_inds)
im_i_pair_score = im_i_box_pair_score[
im_i_keep_inds.tolist()].contiguous()
im_i_rel_pn_labels = Variable(
torch.zeros(im_i_fg_sample_num + im_i_bg_sample_num).type(
torch.LongTensor).cuda(x.get_device()))
im_i_rel_pn_labels[:im_i_fg_sample_num] = 1
pn_rel_label.append(im_i_rel_pn_labels)
pn_pair_score.append(im_i_pair_score)
result.rel_pn_dists = torch.cat(pn_pair_score, 0)
result.rel_pn_labels = torch.cat(pn_rel_label, 0)
result.rel_sample_pos = torch.Tensor([rel_sample_pos]).cuda(
im_i_rel_label.get_device())
result.rel_sample_neg = torch.Tensor([rel_sample_neg]).cuda(
im_i_rel_label.get_device())
box_pair_relevant = F.softmax(box_pair_score, dim=1)
box_pos_pair_ind = torch.nonzero(box_pair_relevant[:, 1].contiguous(
) > box_pair_relevant[:, 0].contiguous()).squeeze()
if box_pos_pair_ind.data.shape == torch.Size([]):
return None
#print('{}/{} trim edges'.format(box_pos_pair_ind.size(0), rel_inds.size(0)))
result.rel_trim_pos = torch.Tensor([box_pos_pair_ind.size(0)]).cuda(
box_pos_pair_ind.get_device())
result.rel_trim_total = torch.Tensor([rel_inds.size(0)
]).cuda(rel_inds.get_device())
if self.trim_graph:
# filtering relations
filter_rel_inds = rel_inds[box_pos_pair_ind.data]
filter_box_pair_feats = box_pair_feats[box_pos_pair_ind.data]
else:
filter_rel_inds = rel_inds
filter_box_pair_feats = box_pair_feats
if self.training:
if self.trim_graph:
filter_rel_labels = result.rel_labels[box_pos_pair_ind.data]
else:
filter_rel_labels = result.rel_labels
num_gt_filtered = torch.nonzero(filter_rel_labels[:, -1])
if num_gt_filtered.shape == torch.Size([]):
num_gt_filtered = 0
else:
num_gt_filtered = num_gt_filtered.size(0)
num_gt_orignial = torch.nonzero(result.rel_labels[:, -1]).size(0)
result.rel_pn_recall = torch.Tensor(
[num_gt_filtered / num_gt_orignial]).cuda(x.get_device())
result.rel_labels = filter_rel_labels
check('trim', tt())
# message passing between boxes and relations
if self.mode in ('sgcls', 'sgdet'):
for _ in range(self.mp_iter_num):
box_feats = self.message_passing(box_feats,
filter_box_pair_feats,
filter_rel_inds)
box_cls_scores = self.cls_fc(box_feats)
result.rm_obj_dists = box_cls_scores
obj_scores, box_classes = F.softmax(
box_cls_scores[:, 1:].contiguous(), dim=1).max(1)
box_classes += 1 # skip background
check('mp', tt())
# RelationCNN
filter_box_pair_feats_fc1 = self.relcnn_fc1(filter_box_pair_feats)
filter_box_pair_score = self.relcnn_fc2(filter_box_pair_feats_fc1)
result.rel_dists = filter_box_pair_score
pred_scores_stage_one = F.softmax(result.rel_dists, dim=1).data
# filter_box_pair_feats is to be added to memory
if self.training:
padded_filter_feats, pack_lengths, re_filter_rel_inds, padded_rel_labels = \
self.pad_sequence(
filter_rel_inds,
filter_box_pair_feats_fc1,
rel_labels=result.rel_labels
)
else:
padded_filter_feats, pack_lengths, re_filter_rel_inds, padded_rel_inds = \
self.pad_sequence(
filter_rel_inds,
filter_box_pair_feats_fc1
)
# trimming zeros to avoid no rel in image
trim_pack_lengths = np.trim_zeros(pack_lengths)
trim_padded_filter_feats = padded_filter_feats[:trim_pack_lengths.
shape[0]]
packed_filter_feats = pack_padded_sequence(trim_padded_filter_feats,
trim_pack_lengths,
batch_first=True)
if self.training:
trim_padded_rel_labels = padded_rel_labels[:trim_pack_lengths.
shape[0]]
packed_rel_labels = pack_padded_sequence(trim_padded_rel_labels,
trim_pack_lengths,
batch_first=True)
rel_mem_dists = self.mem_module(inputs=packed_filter_feats,
rel_labels=packed_rel_labels)
rel_mem_dists = self.re_order_packed_seq(rel_mem_dists,
filter_rel_inds,
re_filter_rel_inds)
result.rel_mem_dists = rel_mem_dists
else:
trim_padded_rel_inds = padded_rel_inds[:trim_pack_lengths.shape[0]]
packed_rel_inds = pack_padded_sequence(trim_padded_rel_inds,
trim_pack_lengths,
batch_first=True)
rel_mem_dists = self.mem_module(inputs=packed_filter_feats,
rel_inds=packed_rel_inds,
obj_classes=box_classes)
rel_mem_probs = self.re_order_packed_seq(rel_mem_dists,
filter_rel_inds,
re_filter_rel_inds)
rel_mem_probs = rel_mem_probs.data
check('mem', tt())
if self.training:
return result
# pad stage one output in rel_mem_probs if it sums zero
for rel_i in range(rel_mem_probs.size(0)):
rel_i_probs = rel_mem_probs[rel_i]
if rel_i_probs.sum() == 0:
rel_mem_probs[rel_i] = pred_scores_stage_one[rel_i]
"""
filter_dets
boxes: bbox regression else [num_box, 4]
obj_scores: [num_box] probabilities for the scores
obj_classes: [num_box] class labels integer
rel_inds: [num_rel, 2] TENSOR consisting of (im_ind0, im_ind1)
pred_scores: [num_rel, num_predicates] including irrelevant class(#relclass + 1)
"""
check('mem processing', tt())
return filter_dets(boxes, obj_scores, box_classes,
filter_rel_inds[:, 1:].contiguous(), rel_mem_probs)
def message_passing(self, box_feats, rel_feats, edges):
"""Integrate box feats to each other
update box feats by decending out-degree order, that is, the node with largest out-degree update first
suppose node i and j are neighbours, and has connection i->j
feature of i and j are fi and fj, feature of union box ij are fij
fi = sigma(W1*fi + sum_neighbour(V1 * alpha * fij))
fj = sigma(W2*fj + sum_neighbour(V2 * alpha * fij))
alpha = attention(fi, fij)
V1, V2, W1, W2 are parameters to be learned, sigma is acitvation function, alpha is attention
Args:
box_feats: Variable, box features with shape of (NumOfBoxes, FEAT_DIM)
rel_feats: Variable, edge features with shape of (NumOfRels, REL_FEAT_DIM)
edges: Variable, scene graph edges(pruned), with shape of (NumOfRels, 3)
e.g. edges[0, :] = [1, 0, 5] means box 0 and box 5 in image 1 had an affair~
Returns:
box_feats: Variable, box features combining relation features
"""
# embed(header='mp ')
im_inds = edges[:, 0].contiguous()
num_img = im_inds[-1] + 1
# list of dict: record the number of boxes per image
count_dic = [{} for _ in range(num_img)]
for im_i, s, e in enumerate_by_image(im_inds):
im_i_edges = edges[s:e, :].contiguous()
for rel in im_i_edges:
box0, box1 = rel[1:]
count_dic[im_i][box0] = 1 + count_dic[im_i].get(box0, 0)
# list of Variable
box_nodes_feats = list()
for box_feat in box_feats:
box_nodes_feats.append(box_feat) #.clone())
for im_i, s, e in enumerate_by_image(im_inds):
im_i_edges = edges[s:e, :].contiguous()
im_i_rel_feats = rel_feats[s:e, :].contiguous()
for box_id, v in \
sorted(
count_dic[im_i].items(),
key=lambda kv: kv[1],
reverse=True
):
# update passing message
# subject message from rel feats
choose_sub_edges_ind = torch.nonzero(
im_i_edges[:, 1].contiguous() == box_id).squeeze()
choose_sub_edges = im_i_edges[choose_sub_edges_ind]
choose_sub_rel_feats = im_i_rel_feats[choose_sub_edges_ind]
box_id_feats = box_nodes_feats[box_id]
# attention on subject reltions
num_sub_neigh = choose_sub_edges.size(0)
sub_cat_att_feats = torch.cat((box_id_feats.expand(
[num_sub_neigh, -1]), choose_sub_rel_feats), 1)
sub_atten = self.mp_atten_fc(sub_cat_att_feats)
sub_alpha = F.softmax(sub_atten, dim=0)
sub_feats = (sub_alpha *
self.sub_rel_mp_fc(choose_sub_rel_feats)).sum(0)
# object message from rel feats(may be null)
choose_obj_edges_ind = torch.nonzero(
im_i_edges[:, 2].contiguous() == box_id).squeeze()
if choose_obj_edges_ind.size() == torch.Size([]):
box_id_feats = self.box_mp_fc(box_id_feats) + sub_feats
box_id_feats = F.relu(box_id_feats, inplace=True)
box_nodes_feats[box_id] = box_id_feats
continue
choose_obj_edges = im_i_edges[choose_obj_edges_ind]
choose_obj_rel_feats = im_i_rel_feats[choose_obj_edges_ind]
box_id_feats = box_nodes_feats[box_id]
# attention on object reltions
num_obj_neigh = choose_obj_edges.size(0)
obj_cat_att_feats = torch.cat((box_id_feats.expand(
[num_obj_neigh, -1]), choose_obj_rel_feats), 1)
obj_atten = self.mp_atten_fc(obj_cat_att_feats)
obj_alpha = F.softmax(obj_atten, dim=0)
obj_feats = (obj_alpha *
self.obj_rel_mp_fc(choose_obj_rel_feats)).sum(0)
# add back to box feature
box_id_feats = self.box_mp_fc(
box_id_feats) + obj_feats + sub_feats
box_id_feats = F.relu(box_id_feats, inplace=True)
box_nodes_feats[box_id] = box_id_feats
mp_box_feats = torch.stack(box_nodes_feats)
return mp_box_feats
def rel_anchor_target(rois, gt_boxes, gt_classes, scores, gt_rels,
image_offset):
"""
use all roi pairs and sample some pairs to train relation proposal module
Note: ONLY for mode SGDET!!!!
rois are from RPN,
We take the CO_Overlap strategy from Graph-RCNN to sample fg and bg rels
:param rois: N, 5
:param scores: N, N
:param gt_rels:
:return:
"""
im_inds = rois[:, 0].long()
num_im = im_inds[-1] + 1
# Offset the image indices in fg_rels to refer to absolute indices (not just within img i)
fg_rels = gt_rels.clone()
fg_rels[:, 0] -= image_offset
offset = {}
for i, s, e in enumerate_by_image(gt_classes[:, 0]):
offset[i] = s
for i, s, e in enumerate_by_image(fg_rels[:, 0]):
fg_rels[s:e, 1:3] += offset[i]
gt_box_pairs = torch.cat(
(gt_boxes[fg_rels[:, 1]], gt_boxes[fg_rels[:, 2]]), 1) # Ngtp, 8
# get all potential pairs
is_cand = (im_inds[:, None] == im_inds[None])
is_cand.view(-1)[diagonal_inds(is_cand)] = 0
all_pair_inds = torch.nonzero(is_cand)
all_box_pairs = torch.cat(
(rois[:, 1:][all_pair_inds[:, 0]], rois[:, 1:][all_pair_inds[:, 1]]),
1)
num_pairs = np.zeros(num_im + 1).astype(np.int32)
id_to_iminds = {}
for i, s, e in enumerate_by_image(im_inds):
num_pairs[i + 1] = (e - s) * (e - s - 1)
id_to_iminds[i] = im_inds[s]
cumsum_num_pairs = np.cumsum(num_pairs).astype(np.int32)
all_rel_inds = []
for i in range(1, num_im + 1):
all_pair_inds_i = all_pair_inds[
cumsum_num_pairs[i - 1]:cumsum_num_pairs[i]]
all_box_pairs_i = all_box_pairs[
cumsum_num_pairs[i - 1]:cumsum_num_pairs[i]]
gt_box_pairs_i = gt_box_pairs[torch.nonzero(
fg_rels[:, 0] == (i - 1)).view(-1)]
labels = gt_rels.new(all_box_pairs_i.size(0)).fill_(-1)
overlaps = co_bbox_overlaps(all_box_pairs_i,
gt_box_pairs_i) ## Np, Ngtp
max_overlaps, argmax_overlaps = torch.max(overlaps, 1) ## Np
gt_max_overlaps, _ = torch.max(overlaps, 0) ## Ngtp
labels[max_overlaps < 0.15] = 0
gt_max_overlaps[gt_max_overlaps == 0] = 1e-5
# fg rel: for each gt pair, the max overlap anchor is fg
keep = torch.sum(
overlaps.eq(gt_max_overlaps.view(1, -1).expand_as(overlaps)),
1) # Np
if torch.sum(keep) > 0:
labels[keep > 0] = 1
# fg rel: above thresh
labels[max_overlaps >= 0.25] = 1
num_fg = int(RELPN_BATCHSIZE * RELPN_FG_FRACTION)
sum_fg = torch.sum((labels == 1).int())
sum_bg = torch.sum((labels == 0).int())
if sum_fg > num_fg:
fg_inds = torch.nonzero(labels == 1).view(-1)
rand_num = torch.from_numpy(np.random.permutation(
fg_inds.size(0))).type_as(gt_boxes).long()
disable_inds = fg_inds[rand_num[:fg_inds.size(0) - num_fg]]
labels[disable_inds] = -1
num_bg = RELPN_BATCHSIZE - torch.sum((labels == 1).int())
if sum_bg > num_bg:
bg_inds = torch.nonzero(labels == 0).view(-1)
rand_num = torch.from_numpy(np.random.permutation(
bg_inds.size(0))).type_as(gt_boxes).long()
disable_inds = bg_inds[rand_num[:bg_inds.size(0) - num_bg]]
labels[disable_inds] = -1
keep_inds = torch.nonzero(labels >= 0).view(-1)
labels = labels[keep_inds]
all_pair_inds_i = all_pair_inds_i[keep_inds]
im_inds_i = torch.LongTensor([id_to_iminds[i - 1]] *
keep_inds.size(0)).view(-1, 1).cuda(
all_pair_inds.get_device())
all_pair_inds_i = torch.cat(
(im_inds_i, all_pair_inds_i, labels.view(-1, 1)), 1)
all_rel_inds.append(all_pair_inds_i)
all_rel_inds = torch.cat(all_rel_inds, 0)
# sort by rel
_, perm = torch.sort(all_rel_inds[:, 0] * (rois.size(0)**2) +
all_rel_inds[:, 1] * rois.size(0) +
all_rel_inds[:, 2])
all_rel_inds = all_rel_inds[perm].contiguous()
return all_rel_inds
def faster_rcnn(self, x, gt_boxes, gt_classes, gt_rels):
targets, x_lst, original_image_sizes = [], [], []
device = self.rel_fc.weight.get_device(
) if self.rel_fc.weight.is_cuda else 'cpu'
for i, s, e in enumerate_by_image(gt_classes[:, 0].long().data):
targets.append({
'boxes': copy.deepcopy(gt_boxes[s:e]),
'labels': gt_classes[s:e, 1].long()
})
x_lst.append(x[i].to(device).squeeze())
original_image_sizes.append(x[i].shape[-2:])
images, targets = self.detector.transform(x_lst, targets)
fmap_multiscale = self.detector.backbone(images.tensors)
if isinstance(fmap_multiscale, torch.Tensor):
fmap_multiscale = OrderedDict([('0', fmap_multiscale)])
if self.mode != 'sgdet':
rois, obj_labels, rel_labels = self.gt_labels(
gt_boxes, gt_classes, gt_rels)
rm_box_priors, rm_box_priors_org = [], []
for i, s, e in enumerate_by_image(gt_classes[:, 0].long().data):
rm_box_priors.append(targets[i]['boxes'])
rm_box_priors_org.append(gt_boxes[s:e])
im_inds = rois[:, 0]
result = Result(
od_box_targets=None,
rm_box_targets=None,
od_obj_labels=obj_labels,
rm_box_priors=torch.cat(rm_box_priors),
rm_obj_labels=obj_labels,
rpn_scores=None,
rpn_box_deltas=None,
rel_labels=rel_labels,
im_inds=im_inds.long(),
)
result.rm_box_priors_org = torch.cat(rm_box_priors_org)
else:
proposals, _ = self.detector.rpn(images, fmap_multiscale, targets)
detections, _ = self.detector.roi_heads(fmap_multiscale, proposals,
images.image_sizes,
targets)
boxes = copy.deepcopy(detections)
boxes_all_dict = self.detector.transform.postprocess(
detections, images.image_sizes, original_image_sizes)
rm_box_priors, rm_box_priors_org, im_inds, obj_labels = [], [], [], []
for i in range(len(proposals)):
if len(boxes[i]['boxes']) <= 1:
raise ValueError(
'at least two objects must be detected to build relationships, make sure the detector is properly pretrained',
boxes)
rm_box_priors.append(boxes[i]['boxes'])
rm_box_priors_org.append(boxes_all_dict[i]['boxes'])
obj_labels.append(boxes_all_dict[i]['labels'])
im_inds.append(torch.zeros(len(detections[i]['boxes'])) + i)
im_inds = torch.cat(im_inds).to(device)
result = Result(rm_obj_labels=torch.cat(obj_labels).view(-1),
rm_box_priors=torch.cat(rm_box_priors),
rel_labels=None,
im_inds=im_inds.long())
result.rm_box_priors_org = torch.cat(rm_box_priors_org)
if len(result.rm_box_priors) <= 1:
raise ValueError(
'at least two objects must be detected to build relationships'
)
result.im_sizes_org = original_image_sizes
result.im_sizes = images.image_sizes
result.fmap = fmap_multiscale[list(
fmap_multiscale.keys())[-1]] # last scale for global feature maps
result.rois = torch.cat(
(im_inds.float()[:, None], result.rm_box_priors), 1)
return result
def forward(self,
x,
im_sizes,
image_offset,
gt_boxes=None,
gt_classes=None,
gt_rels=None,
*args):
"""
Forward pass for detection
:param x: Images@[batch_size, 3, IM_SIZE, IM_SIZE]
:param im_sizes: A numpy array of (h, w, scale) for each image.
:param image_offset: Offset onto what image we're on for MGPU training (if single GPU this is 0)
:param gt_boxes:
Training parameters:
:param gt_boxes: [num_gt, 4] GT boxes over the batch.
:param gt_classes: [num_gt, 2] gt boxes where each one is (img_id, class)
:param train_anchor_inds: a [num_train, 2] array of indices for the anchors that will
be used to compute the training loss. Each (img_ind, fpn_idx)
:return: If train:
scores, boxdeltas, labels, boxes, boxtargets, rpnscores, rpnboxes, rellabels
if test:
prob dists, boxes, img inds, maxscores, classes
"""
with torch.no_grad(): # do not update anything in the detector
targets, x_lst, original_image_sizes = [], [], []
device = self.rel_fc.weight.get_device(
) if self.rel_fc.weight.is_cuda else 'cpu'
gt_boxes = gt_boxes.to(device)
gt_classes = gt_classes.to(device)
gt_rels = gt_rels.to(device)
for i, s, e in enumerate_by_image(gt_classes[:, 0].long().data):
targets.append({
'boxes': copy.deepcopy(gt_boxes[s:e]),
'labels': gt_classes[s:e, 1].long().to(device)
})
x_lst.append(x[i].to(device).squeeze())
original_image_sizes.append(x[i].shape[-2:])
images, targets = self.detector.transform(x_lst, targets)
fmap_multiscale = self.detector.backbone(images.tensors)
if self.mode != 'sgdet':
rois, obj_labels, bbox_targets, rpn_scores, rpn_box_deltas, rel_labels = \
self.gt_boxes(None, im_sizes, image_offset, self.RELS_PER_IMG, gt_boxes,
gt_classes, gt_rels, None, proposals=None,
sample_factor=-1)
rm_box_priors, rm_box_priors_org = [], []
for i, s, e in enumerate_by_image(gt_classes[:,
0].long().data):
rm_box_priors.append(targets[i]['boxes'])
rm_box_priors_org.append(gt_boxes[s:e])
result = Result(od_box_targets=bbox_targets,
rm_box_targets=bbox_targets,
od_obj_labels=obj_labels,
rm_box_priors=torch.cat(rm_box_priors),
rm_obj_labels=obj_labels,
rpn_scores=rpn_scores,
rpn_box_deltas=rpn_box_deltas,
rel_labels=rel_labels,
im_inds=rois[:, 0].long().contiguous() +
image_offset)
result.rm_box_priors_org = torch.cat(rm_box_priors_org)
else:
if isinstance(fmap_multiscale, torch.Tensor):
fmap_multiscale = OrderedDict([(0, fmap_multiscale)])
proposals, _ = self.detector.rpn(images, fmap_multiscale,
targets)
detections, _ = self.detector.roi_heads(
fmap_multiscale, proposals, images.image_sizes, targets)
boxes = copy.deepcopy(detections)
boxes_all_dict = self.detector.transform.postprocess(
detections, images.image_sizes, original_image_sizes)
rm_box_priors, rm_box_priors_org, im_inds, obj_labels = [], [], [], []
for i in range(len(proposals)):
rm_box_priors.append(boxes[i]['boxes'])
rm_box_priors_org.append(boxes_all_dict[i]['boxes'])
obj_labels.append(boxes_all_dict[i]['labels'])
im_inds.append(
torch.zeros(len(detections[i]['boxes']),
device=device).float() + i)
im_inds = torch.cat(im_inds).view(-1, 1)
result = Result(rm_obj_labels=torch.cat(obj_labels).view(-1),
rm_box_priors=torch.cat(rm_box_priors),
rel_labels=None,
im_inds=im_inds.view(-1).long().contiguous() +
image_offset)
result.rm_box_priors_org = torch.cat(rm_box_priors_org)
if len(result.rm_box_priors) <= 1:
raise ValueError(
'at least two objects must be detected to build relationships'
)
if result.is_none():
return ValueError("heck")
if self.detector_model == 'baseline':
if self.slim > 0:
result.fmap = self.fmap_reduce(result.fmap.detach())
else:
result.fmap = result.fmap.detach()
im_inds = result.im_inds - image_offset
boxes = result.rm_box_priors
if not hasattr(result, 'rel_labels'):
result.rel_labels = None
if self.training and result.rel_labels is None:
assert self.mode == 'sgdet'
result.rel_labels = rel_assignments(im_inds.data,
boxes.data,
result.rm_obj_labels.data,
gt_boxes.data,
gt_classes.data,
gt_rels.data,
image_offset,
filter_non_overlap=True,
num_sample_per_gt=1)
rel_inds = self.get_rel_inds(
result.rel_labels if self.training else None, im_inds, boxes)
rois = torch.cat((im_inds[:, None].float(), boxes), 1)
union_rois = torch.cat((
rois[:, 0][rel_inds[:, 1]][:, None],
torch.min(rois[:, 1:3][rel_inds[:, 1]], rois[:, 1:3][rel_inds[:,
2]]),
torch.max(rois[:, 3:5][rel_inds[:, 1]], rois[:, 3:5][rel_inds[:,
2]]),
), 1)
node_feat = self.multiscale_roi_pool(fmap_multiscale, rm_box_priors,
images.image_sizes)
edge_feat = self.multiscale_roi_pool(fmap_multiscale,
convert_roi_to_list(union_rois),
images.image_sizes)
result.rm_obj_dists, result.rel_dists = self.predict(
node_feat, edge_feat, rel_inds, rois, images.image_sizes)
if self.use_bias:
scores_nz = F.softmax(result.rm_obj_dists, dim=1).data
scores_nz[:, 0] = 0.0
_, score_ord = scores_nz[:, 1:].sort(dim=1, descending=True)
result.obj_preds = score_ord[:, 0] + 1
if self.mode == 'predcls':
result.obj_preds = gt_classes.data[:, 1]
freq_pred = self.freq_bias.index_with_labels(
torch.stack((
result.obj_preds[rel_inds[:, 1]],
result.obj_preds[rel_inds[:, 2]],
), 1))
# tune the weight for freq_bias
if self.test_bias:
result.rel_dists = freq_pred
else:
result.rel_dists = result.rel_dists + freq_pred
if self.training:
return result
if self.mode == 'predcls':
result.obj_scores = result.rm_obj_dists.data.new(
gt_classes.size(0)).fill_(1)
result.obj_preds = gt_classes.data[:, 1]
elif self.mode in ['sgcls', 'sgdet']:
scores_nz = F.softmax(result.rm_obj_dists, dim=1).data
scores_nz[:, 0] = 0.0 # does not change actually anything
result.obj_scores, score_ord = scores_nz[:,
1:].sort(dim=1,
descending=True)
result.obj_preds = score_ord[:, 0] + 1
result.obj_scores = result.obj_scores[:, 0]
else:
raise NotImplementedError(self.mode)
result.obj_preds = Variable(result.obj_preds)
result.obj_scores = Variable(result.obj_scores)
# Boxes will get fixed by filter_dets function.
if self.detector_model == 'mrcnn':
bboxes = result.rm_box_priors_org
else:
bboxes = result.rm_box_priors
rel_rep = F.softmax(result.rel_dists, dim=1)
return filter_dets(bboxes, result.obj_scores, result.obj_preds,
rel_inds[:, 1:], rel_rep)
def proposal_assignments_gtbox(rois,
gt_boxes,
gt_classes,
gt_rels,
image_offset,
fg_thresh=0.5):
"""
Assign object detection proposals to ground-truth targets. Produces proposal
classification labels and bounding-box regression targets.
:param rpn_rois: [img_ind, x1, y1, x2, y2]
:param gt_boxes: [num_boxes, 4] array of x0, y0, x1, y1]. Not needed it seems
:param gt_classes: [num_boxes, 2.0] array of [img_ind, class]
Note, the img_inds here start at image_offset
:param gt_rels [num_boxes, 4] array of [img_ind, box_0, box_1, rel type].
Note, the img_inds here start at image_offset
:param Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH)
:return:
rois: [num_rois, 5]
labels: [num_rois] array of labels
bbox_targets [num_rois, 4] array of targets for the labels.
rel_labels: [num_rels, 4] (img ind, box0 ind, box1ind, rel type)
"""
im_inds = rois[:, 0].long()
num_im = im_inds[-1] + 1
# Offset the image indices in fg_rels to refer to absolute indices (not just within img i)
fg_rels = gt_rels.clone()
fg_rels[:, 0] -= image_offset
offset = {}
for i, s, e in enumerate_by_image(im_inds):
offset[i] = s
for i, s, e in enumerate_by_image(fg_rels[:, 0]):
fg_rels[s:e, 1:3] += offset[i]
#----------------------------------------------------------------------------#
fg_rel_list = []
for i in range(num_im):
fg_rel_list.append(sum(fg_rels[:, 0] == i).item())
longest_len = max(fg_rel_list)
bg_rel_length = [longest_len - i for i in fg_rel_list]
#----------------------------------------------------------------------------#
# Try ALL things, not just intersections.
is_cand = (im_inds[:, None] == im_inds[None])
is_cand.view(-1)[diagonal_inds(is_cand)] = 0
# # Compute salience
# gt_inds = fg_rels[:, ĺeftright:3].contiguous().view(-ĺeftright)
# labels_arange = labels.data.new(labels.size(0))
# torch.arange(0, labels.size(0), out=labels_arange)
# salience_labels = ((gt_inds[:, None] == labels_arange[None]).long().sum(0) > 0).long()
# labels = torch.stack((labels, salience_labels), ĺeftright)
# Add in some BG labels
# NOW WE HAVE TO EXCLUDE THE FGs.
# TODO: check if this causes an error if many duplicate GTs havent been filtered out
is_cand.view(-1)[fg_rels[:, 1] * im_inds.size(0) + fg_rels[:, 2]] = 0
is_bgcand = is_cand.nonzero()
# TODO: make this sample on a per image case
# If too many then sample
num_fg = min(fg_rels.size(0), int(RELS_PER_IMG * REL_FG_FRACTION * num_im))
if num_fg < fg_rels.size(0):
fg_rels = random_choose(fg_rels, num_fg)
# If too many then sample
num_bg = min(
is_bgcand.size(0) if is_bgcand.dim() > 0 else 0, int(num_fg / 2))
bg_rels = torch.cat((
im_inds[is_bgcand[:, 0]][:, None],
is_bgcand,
(is_bgcand[:, 0, None] < -10).long(),
), 1)
rel_labels = fg_rels
for i, j in enumerate(bg_rel_length):
if bg_rels[bg_rels[:, 0] == i, :].shape[0] >= j:
bg_rel_per_image = random_choose(bg_rels[bg_rels[:, 0] == i, :], j)
else:
bg_rel_per_image = torch.cat(
(bg_rels[bg_rels[:, 0] == i, :],
random_choose(bg_rels[bg_rels[:, 0] == i, :],
j - bg_rels[bg_rels[:, 0] == i, :].shape[0])),
0)
rel_labels = torch.cat((rel_labels, bg_rel_per_image), 0)
# last sort by rel.
_, perm = torch.sort(rel_labels[:, 0] * (gt_boxes.size(0)**2) +
rel_labels[:, 1] * gt_boxes.size(0) +
rel_labels[:, 2])
rel_labels = rel_labels[perm].contiguous()
labels = gt_classes[:, 1].contiguous()
return rois, labels, rel_labels
