def forward(self, features, all_phrase_ids, targets, precomp_boxes, precomp_score,
precomp_det_label, image_scale, all_sent_sgs, all_sentences, image_unique_id, det_label_embedding):
"""
:param obj_proposals: proposal from each images
:param features: features maps from the backbone
:param target: gt relation labels
:param object_vocab, object_vocab_len [[xxx,xxx],[xxx],[xxx]], [2,1,1]
:param sent_sg: sentence scene graph
:return: prediction, loss
note that first dimension is images
"""
img_num_per_gpu = len(features)
batch_decode_logits = []
batch_topk_decoder_logits = []
batch_pred_similarity = []
batch_precomp_boxes = []
batch_topk_precomp_boxes=[]
batch_pred_boxes = []
batch_topk_pred_boxes = []
batch_topk_fusion_pred_boxes = []
batch_topk_pred_similarity = []
batch_topk_fusion_similarity = []
batch_boxes_targets = []
batch_ctx_embed = []
batch_ctx_s1_embed = []
batch_pred_targets = []
batch_topk_pred_targets = []
""" Language Embedding"""
batch_phrase_ids, batch_phrase_types, batch_phrase_embed, batch_phrase_len, \
batch_phrase_dec_ids, batch_phrase_mask, batch_decoder_word_embed, batch_phrase_glove_embed, batch_rel_phrase_embed, batch_relation_conn, batch_sent_embed,\
batch_decoder_rel_word_embed, batch_rel_mask, batch_rel_dec_idx = self.phrase_embed(all_sentences, all_phrase_ids, all_sent_sgs)
h, w = features.shape[-2:]
# self.storage = get_event_storage()
for bid in range(img_num_per_gpu):
""" Visual Embedding """
precomp_boxes_bid = precomp_boxes[bid].to(self.device) ## 100*4
order = []
for phr_ids in batch_phrase_ids[bid]:
order.append(all_phrase_ids[bid].index(phr_ids))
target_filter = targets[bid][np.array(order)]
batch_boxes_targets.append(target_filter.to(self.device))
batch_precomp_boxes.append(precomp_boxes_bid)
img_feat_bid = features[[bid]]
visual_features_bid = self.rcnn_top(self.det_roi_pooler([img_feat_bid], [precomp_boxes_bid])).mean(dim=[2, 3]).contiguous()
if cfg.MODEL.VG.SPATIAL_FEAT:
spa_feat = meshgrid_generation(h, w)
spa_feat = self.det_roi_pooler([spa_feat], [precomp_boxes_bid]).view(visual_features_bid.shape[0], -1)
spa_feat = self.spatial_trans(spa_feat)
visual_features_bid = torch.cat((visual_features_bid, spa_feat), dim=1)
visual_features_bid = self.visual_embedding(visual_features_bid)
visual_features_bid = self.vis_batchnorm(visual_features_bid)
""" Noun Phrase embedding """
phrase_embed_bid = batch_phrase_embed[bid]
if phrase_embed_bid.shape[0] == 1 and self.training:
phrase_embed_bid = self.phr_batchnorm(phrase_embed_bid.repeat(2,1))[[0]]
else:
phrase_embed_bid = self.phr_batchnorm(phrase_embed_bid)
""" Similarity and attention prediction """
num_box = precomp_boxes_bid.tensor.size(0)
num_phrase = phrase_embed_bid.size(0)
phr_inds, obj_inds = self.make_pair(num_phrase, num_box)
pred_similarity_bid, pred_targets_bid = self.similarity(visual_features_bid, phrase_embed_bid, obj_inds, phr_inds)
pred_similarity_bid = pred_similarity_bid.reshape(num_phrase, num_box)
pred_targets_bid = pred_targets_bid.reshape(num_phrase, num_box, 4)
batch_pred_targets.append(pred_targets_bid)
if cfg.MODEL.VG.USING_DET_KNOWLEDGE :
det_label_embedding_bid = det_label_embedding[bid].to(self.device)
sim = self.cal_det_label_sim_max(det_label_embedding_bid, batch_phrase_glove_embed[bid])
pred_similarity_bid = pred_similarity_bid * sim
sim_mask = (sim > 0).float()
atten_bid = numerical_stability_masked_softmax(pred_similarity_bid, sim_mask, dim=1)
else:
atten_bid = F.softmax(pred_similarity_bid, dim=1)
## reconstruction visual features
visual_reconst_bid = torch.mm(atten_bid, visual_features_bid)
decode_phr_logits = self.phrase_decoder(visual_reconst_bid, batch_decoder_word_embed[bid])
batch_decode_logits.append(decode_phr_logits)
atten_score_topk, atten_ranking_topk = torch.topk(atten_bid, dim=1, k=self.s2_topk) ## (N, 10)
ind_phr_topk = np.arange(num_phrase).repeat(self.s2_topk)
## -----------------------------------------------------##
## crop 2st features
## -----------------------------------------------------##
if self.storage.iter <= cfg.SOLVER.REG_START_ITER:
visual_features_topk_bid = visual_features_bid[atten_ranking_topk.reshape(-1)]
precomp_boxes_topk_bid = precomp_boxes_bid[atten_ranking_topk.reshape(-1)]
batch_topk_precomp_boxes.append(precomp_boxes_topk_bid)
else:
topk_box_ids = atten_ranking_topk.reshape(-1) + torch.as_tensor(ind_phr_topk, dtype=torch.long).to(self.device)*num_box
precomp_boxes_tensor, box_size = precomp_boxes_bid.tensor, precomp_boxes_bid.size
precomp_boxes_topk_tensor = precomp_boxes_tensor[atten_ranking_topk.reshape(-1)] ## (N*10, 4)
pred_targets_s0 = pred_targets_bid.view(-1, 4)[topk_box_ids]
precomp_boxes_topk_bid = self.box2box_translation.apply_deltas(pred_targets_s0, precomp_boxes_topk_tensor)
precomp_boxes_topk_bid = Boxes(precomp_boxes_topk_bid, box_size)
precomp_boxes_topk_bid.clip()
batch_topk_precomp_boxes.append(precomp_boxes_topk_bid)
visual_features_topk_bid = self.rcnn_top(self.det_roi_pooler([img_feat_bid], [precomp_boxes_topk_bid])).mean(dim=[2, 3]).contiguous()
if cfg.MODEL.VG.SPATIAL_FEAT:
spa_feat = meshgrid_generation(h, w)
spa_feat = self.det_roi_pooler([spa_feat], [precomp_boxes_topk_bid]).view(visual_features_topk_bid.shape[0], -1)
spa_feat = self.spatial_trans(spa_feat)
visual_features_topk_bid = torch.cat((visual_features_topk_bid, spa_feat), dim=1)
visual_features_topk_bid = self.visual_embedding(visual_features_topk_bid)## (N*10, 1024)
visual_features_topk_bid = self.vis_batchnorm(visual_features_topk_bid)
pred_similarity_topk_bid, pred_targets_topk_bid = self.similarity_topk(visual_features_topk_bid, phrase_embed_bid, ind_phr_topk)
pred_similarity_topk_bid = pred_similarity_topk_bid.reshape(num_phrase, self.s2_topk)
pred_targets_topk_bid = pred_targets_topk_bid.reshape(num_phrase, self.s2_topk, 4)
batch_topk_pred_targets.append(pred_targets_topk_bid)
if cfg.MODEL.VG.USING_DET_KNOWLEDGE:
sim_topk = torch.gather(sim, dim=1, index=atten_ranking_topk.long())
sim_mask = (sim_topk>0).float()
pred_similarity_topk_bid = pred_similarity_topk_bid * sim_topk
atten_topk_bid = numerical_stability_masked_softmax(pred_similarity_topk_bid, sim_mask, dim=1)
else:
atten_topk_bid = F.softmax(pred_similarity_topk_bid, dim=1)
atten_fusion = atten_topk_bid * atten_score_topk ## N*10
visual_features_topk_bid = visual_features_topk_bid.view(num_phrase, self.s2_topk, -1)
visual_reconst_topk_bid = (atten_fusion.unsqueeze(2)*visual_features_topk_bid).sum(1) ## N*1024
decoder_phr_topk_logits = self.phrase_decoder(visual_reconst_topk_bid, batch_decoder_word_embed[bid])
batch_topk_decoder_logits.append(decoder_phr_topk_logits)
## construct the discriminative loss
batch_ctx_s1_embed.append(self.visual_mlp(visual_reconst_bid.mean(0, keepdim=True)))
batch_ctx_embed.append(self.visual_mlp(visual_reconst_topk_bid.mean(0, keepdim=True)))
batch_pred_similarity.append(atten_bid)
batch_topk_pred_similarity.append(atten_topk_bid)
batch_topk_fusion_similarity.append(atten_fusion)
### transform boxes for stage-1
num_phrase_indices = torch.arange(num_phrase).long().to(self.device)
max_box_ind = atten_bid.detach().cpu().numpy().argmax(1)
precomp_boxes_delta_max = pred_targets_bid[num_phrase_indices, max_box_ind] ## numPhrase*4
max_topk_id = torch.topk(atten_topk_bid, dim=1, k=1)[1].long().squeeze(1)
precomp_boxes_delta_max_topk = pred_targets_topk_bid[num_phrase_indices, max_topk_id] ## num_phrase*4
precomp_boxes_topk_bid_tensor = precomp_boxes_topk_bid.tensor.reshape(-1, self.s2_topk, 4)
max_fusion_topk_id = torch.topk(atten_fusion, dim=1, k=1)[1].long().squeeze()
precomp_boxes_delta_max_topk_fusion = pred_targets_topk_bid[num_phrase_indices, max_fusion_topk_id] ## num_phrase*4
phr_index = torch.arange(num_phrase).to(self.device) * self.s2_topk
if self.storage.iter <= cfg.SOLVER.REG_START_ITER:
max_select_boxes = precomp_boxes_bid[max_box_ind]
max_precomp_boxes = precomp_boxes_topk_bid[max_topk_id + phr_index]
max_fusion_precomp_boxes = precomp_boxes_topk_bid[max_fusion_topk_id + phr_index]
else:
max_select_boxes = Boxes(self.box2box_translation.apply_deltas(precomp_boxes_delta_max, precomp_boxes_bid[max_box_ind].tensor), precomp_boxes_bid.size)
max_precomp_boxes = Boxes(self.box2box_translation.apply_deltas(precomp_boxes_delta_max_topk, precomp_boxes_topk_bid_tensor[num_phrase_indices, max_topk_id]), precomp_boxes_bid.size)
max_fusion_precomp_boxes = Boxes(self.box2box_translation.apply_deltas(precomp_boxes_delta_max_topk_fusion, precomp_boxes_topk_bid_tensor[num_phrase_indices, max_fusion_topk_id]), precomp_boxes_bid.size)
batch_pred_boxes.append(max_select_boxes)
batch_topk_pred_boxes.append(max_precomp_boxes)
batch_topk_fusion_pred_boxes.append(max_fusion_precomp_boxes)
batch_ctx_sim, batch_ctx_sim_s1 = self.generate_image_sent_discriminative(batch_sent_embed, batch_ctx_embed, batch_ctx_s1_embed)
noun_reconst_loss, noun_topk_reconst_loss, disc_img_sent_loss_s1, disc_img_sent_loss_s2, reg_loss, \
reg_loss_s1 = self.VGLoss(batch_phrase_mask, batch_decode_logits, batch_topk_decoder_logits, batch_phrase_dec_ids,
batch_ctx_sim, batch_ctx_sim_s1, batch_pred_similarity, batch_topk_pred_similarity, batch_boxes_targets, batch_precomp_boxes,
batch_pred_targets, batch_topk_pred_targets,
batch_topk_precomp_boxes)
all_loss = dict(noun_reconst_loss=noun_reconst_loss, noun_topk_reconst_loss=noun_topk_reconst_loss, disc_img_sent_loss_s1=disc_img_sent_loss_s1,
disc_img_sent_loss_s2=disc_img_sent_loss_s2, reg_loss_s1=reg_loss, reg_loss_s2=reg_loss_s1)
if self.training:
return all_loss, None
else:
return all_loss, (batch_phrase_ids, batch_phrase_types, move2cpu(batch_pred_boxes), move2cpu(batch_pred_similarity),
move2cpu(batch_boxes_targets), move2cpu(batch_precomp_boxes), image_unique_id, move2cpu(batch_topk_pred_similarity),
move2cpu(batch_topk_fusion_similarity), move2cpu(batch_topk_pred_boxes), move2cpu(batch_topk_fusion_pred_boxes),
move2cpu(batch_topk_precomp_boxes), move2cpu(batch_topk_pred_targets), move2cpu(batch_pred_targets))
def forward(self, features, all_phrase_ids, targets, precomp_boxes, precomp_score,
precomp_det_label, image_scale, all_sent_sgs, all_sentences, image_unique_id, det_label_embedding):
"""
:param obj_proposals: proposal from each images
:param features: features maps from the backbone
:param target: gt relation labels
:param object_vocab, object_vocab_len [[xxx,xxx],[xxx],[xxx]], [2,1,1]
:param sent_sg: sentence scene graph
:return: prediction, loss
note that first dimension is images
"""
img_num_per_gpu = len(features)
batch_decode_logits = []
batch_topk_decoder_logits = []
batch_pred_similarity = []
batch_precomp_boxes = []
batch_pred_boxes = []
batch_topk_pred_boxes = []
batch_topk_fusion_pred_boxes = []
batch_topk_pred_similarity = []
batch_topk_fusion_similarity = []
batch_boxes_targets = []
batch_ctx_embed = []
batch_ctx_s1_embed = []
""" Language Embedding"""
batch_phrase_ids, batch_phrase_types, batch_phrase_embed, batch_phrase_len, \
batch_phrase_dec_ids, batch_phrase_mask, batch_decoder_word_embed, batch_phrase_glove_embed, batch_rel_phrase_embed, batch_relation_conn, batch_sent_embed,\
batch_decoder_rel_word_embed, batch_rel_mask, batch_rel_dec_idx = self.phrase_embed(all_sentences, all_phrase_ids, all_sent_sgs)
h, w = features.shape[-2:]
for bid in range(img_num_per_gpu):
""" Visual Embedding """
precomp_boxes_bid = precomp_boxes[bid].to(self.device) ## 100*4
order = []
for phr_ids in batch_phrase_ids[bid]:
order.append(all_phrase_ids[bid].index(phr_ids))
target_filter = targets[bid][np.array(order)]
batch_boxes_targets.append(target_filter.to(self.device))
batch_precomp_boxes.append(precomp_boxes_bid)
img_feat_bid = features[[bid]]
visual_features_bid = self.rcnn_top(self.det_roi_pooler([img_feat_bid], [precomp_boxes_bid])).mean(dim=[2, 3]).contiguous()
if cfg.MODEL.VG.SPATIAL_FEAT:
spa_feat = meshgrid_generation(h, w)
spa_feat = self.det_roi_pooler([spa_feat], [precomp_boxes_bid]).view(visual_features_bid.shape[0], -1)
spa_feat = self.spatial_trans(spa_feat)
visual_features_bid = torch.cat((visual_features_bid, spa_feat), dim=1)
visual_features_bid = self.visual_embedding(visual_features_bid)
visual_features_bid = self.vis_batchnorm(visual_features_bid)
""" Noun Phrase embedding """
phrase_embed_bid = batch_phrase_embed[bid]
if phrase_embed_bid.shape[0] == 1 and self.training:
phrase_embed_bid = self.phr_batchnorm(phrase_embed_bid.repeat(2,1))[[0]]
else:
phrase_embed_bid = self.phr_batchnorm(phrase_embed_bid)
""" Similarity and attention prediction """
num_box = precomp_boxes_bid.tensor.size(0)
num_phrase = phrase_embed_bid.size(0)
phr_inds, obj_inds = self.make_pair(num_phrase, num_box)
pred_similarity_bid = self.similarity(visual_features_bid, phrase_embed_bid, obj_inds, phr_inds)
pred_similarity_bid = pred_similarity_bid.reshape(num_phrase, num_box)
if cfg.MODEL.VG.USING_DET_KNOWLEDGE:
det_label_embedding_bid = det_label_embedding[bid].to(self.device)
sim = self.cal_det_label_sim_max(det_label_embedding_bid, batch_phrase_glove_embed[bid], precomp_score[bid])
pred_similarity_bid = pred_similarity_bid * sim
sim_mask = (sim > 0).float()
atten_bid = numerical_stability_masked_softmax(pred_similarity_bid, sim_mask, dim=1)
else:
atten_bid = F.softmax(pred_similarity_bid, dim=1)
## reconstruction visual features
visual_reconst_bid = torch.mm(atten_bid, visual_features_bid)
decode_phr_logits = self.phrase_decoder(visual_reconst_bid, batch_decoder_word_embed[bid])
batch_decode_logits.append(decode_phr_logits)
atten_score_topk, atten_ranking_topk = torch.topk(atten_bid, dim=1, k=self.s2_topk) ## (N, 10)
ind_phr_topk = np.arange(num_phrase).repeat(self.s2_topk)
visual_features_topk_bid = visual_features_bid[atten_ranking_topk.reshape(-1)] ## (N*10, 1024)
pred_similarity_topk_bid = self.similarity_topk(visual_features_topk_bid, phrase_embed_bid, ind_phr_topk)
pred_similarity_topk_bid = pred_similarity_topk_bid.reshape(num_phrase, self.s2_topk)
if cfg.MODEL.VG.USING_DET_KNOWLEDGE:
sim_topk = torch.gather(sim, dim=1, index=atten_ranking_topk.long())
sim_mask = (sim_topk>0).float()
pred_similarity_topk_bid = pred_similarity_topk_bid * sim_topk
atten_topk_bid = numerical_stability_masked_softmax(pred_similarity_topk_bid, sim_mask, dim=1)
else:
atten_topk_bid = F.softmax(pred_similarity_topk_bid, dim=1)
atten_fusion = atten_topk_bid * atten_score_topk ## N*10
visual_features_topk_bid = visual_features_topk_bid.view(num_phrase, self.s2_topk, -1)
visual_reconst_topk_bid = (atten_fusion.unsqueeze(2)*visual_features_topk_bid).sum(1) # N*1024
decoder_phr_topk_logits = self.phrase_decoder(visual_reconst_topk_bid, batch_decoder_word_embed[bid])
batch_topk_decoder_logits.append(decoder_phr_topk_logits)
batch_ctx_s1_embed.append(self.visual_mlp(visual_reconst_bid.mean(0, keepdim=True)))
batch_ctx_embed.append(self.visual_mlp(visual_reconst_topk_bid.mean(0, keepdim=True)))
batch_pred_similarity.append(atten_bid)
batch_topk_pred_similarity.append(atten_topk_bid)
batch_topk_fusion_similarity.append(atten_fusion)
max_box_ind = atten_bid.detach().cpu().numpy().argmax(1)
batch_pred_boxes.append(precomp_boxes_bid[max_box_ind])
max_topk_id = torch.topk(atten_topk_bid, dim=1, k=1)[1].long()
max_topk_box_ind = torch.gather(atten_ranking_topk, dim=1, index=max_topk_id).squeeze(1).cpu().numpy()
batch_topk_pred_boxes.append(precomp_boxes_bid[max_topk_box_ind])
max_fusion_topk_id = torch.topk(atten_fusion, dim=1, k=1)[1].long()
max_fusion_box_ind = torch.gather(atten_ranking_topk, dim=1, index=max_fusion_topk_id).squeeze(1).cpu().numpy()
batch_topk_fusion_pred_boxes.append(precomp_boxes_bid[max_fusion_box_ind])
batch_sent_embed = torch.cat(batch_sent_embed, dim=0) ## b*1024
batch_sent_embed = self.sent_mlp(batch_sent_embed) ## N*512
batch_ctx_embed = torch.cat(batch_ctx_embed, dim=0) ## N*512
batch_ctx_s1_embed = torch.cat(batch_ctx_s1_embed, dim=0) ## N*512
batch_ctx_sim = torch.mm(batch_ctx_embed, batch_sent_embed.permute(1, 0)) / 512 ** 0.5
batch_ctx_sim_s1 = torch.mm(batch_ctx_s1_embed, batch_sent_embed.permute(1, 0)) / 512**0.5
noun_reconst_loss, noun_topk_reconst_loss, disc_img_sent_loss_s1, \
disc_img_sent_loss_s2 = self.VGLoss(batch_phrase_mask, batch_decode_logits, batch_topk_decoder_logits, batch_phrase_dec_ids,
batch_ctx_sim, batch_ctx_sim_s1, batch_pred_similarity, batch_topk_pred_similarity, batch_boxes_targets, batch_precomp_boxes)
all_loss = dict(noun_reconst_loss=noun_reconst_loss, noun_topk_reconst_loss=noun_topk_reconst_loss, disc_img_sent_loss_s1=disc_img_sent_loss_s1, disc_img_sent_loss_s2=disc_img_sent_loss_s2)
if self.training:
return all_loss, None
else:
return all_loss, (batch_phrase_ids, batch_phrase_types, move2cpu(batch_pred_boxes), move2cpu(batch_pred_similarity), move2cpu(batch_boxes_targets),
move2cpu(batch_precomp_boxes), image_unique_id, move2cpu(batch_topk_pred_similarity),
move2cpu(batch_topk_fusion_similarity), move2cpu(batch_topk_pred_boxes), move2cpu(batch_topk_fusion_pred_boxes))
def forward(self, features, all_phrase_ids, targets, precomp_boxes,
precomp_score, precomp_det_label, image_scale, all_sent_sgs,
all_sentences, image_unique_id, det_label_embedding):
"""
:param obj_proposals: proposal from each images
:param features: features maps from the backbone
:param target: gt relation labels
:param object_vocab, object_vocab_len [[xxx,xxx],[xxx],[xxx]], [2,1,1]
:param sent_sg: sentence scene graph
:return: prediction, loss
note that first dimension is images
"""
img_num_per_gpu = len(features)
batch_decode_logits = []
batch_pred_similarity = []
batch_precomp_boxes = []
batch_pred_boxes = []
batch_boxes_targets = []
""" Language Embedding"""
batch_phrase_ids, batch_phrase_types, batch_phrase_embed, batch_phrase_len, \
batch_phrase_dec_ids, batch_phrase_mask, batch_decoder_word_embed, batch_phrase_glove_embed, batch_cst_qid, batch_max_len = \
self.phrase_embed(all_sentences, all_phrase_ids, all_sent_sgs)
h, w = features.shape[-2:]
for bid in range(img_num_per_gpu):
""" Visual Embedding """
precomp_boxes_bid = precomp_boxes[bid].to(self.device) ## 100*4
order = []
for phr_ids in batch_phrase_ids[bid]:
order.append(all_phrase_ids[bid].index(phr_ids))
target_filter = targets[bid][np.array(order)]
batch_boxes_targets.append(target_filter.to(self.device))
batch_precomp_boxes.append(precomp_boxes_bid)
img_feat_bid = features[[bid]]
visual_features_bid = self.rcnn_top(
self.det_roi_pooler(
tuple([img_feat_bid]),
[precomp_boxes_bid])).mean(dim=[2, 3]).contiguous()
if cfg.MODEL.VG.SPATIAL_FEAT:
spa_feat = meshgrid_generation(h, w)
spa_feat = self.det_roi_pooler(
tuple([spa_feat]),
[precomp_boxes_bid]).view(visual_features_bid.shape[0], -1)
spa_feat = self.spatial_trans(spa_feat)
visual_features_bid = torch.cat(
(visual_features_bid, spa_feat), dim=1)
visual_features_bid = self.visual_embedding(visual_features_bid)
visual_features_bid = self.vis_batchnorm(visual_features_bid)
""" Noun Phrase embedding """
phrase_embed_bid = batch_phrase_embed[bid]
if phrase_embed_bid.shape[0] == 1 and self.training:
phrase_embed_bid = self.phr_batchnorm(
phrase_embed_bid.repeat(2, 1))[[0]]
else:
phrase_embed_bid = self.phr_batchnorm(phrase_embed_bid)
""" Similarity and attention prediction """
# num_box = precomp_boxes_bid.tensor.size(0)
# num_phrase = phrase_embed_bid.size(0)
# phr_inds, obj_inds = self.make_pair(num_phrase, num_box)
# pred_similarity_bid = self.similarity(visual_features_bid, phrase_embed_bid, obj_inds, phr_inds)
pred_similarity_bid = torch.mm(
phrase_embed_bid, visual_features_bid.permute(
1, 0)) / self.phrase_embed_dim**0.5
# pred_similarity_bid = pred_similarity_bid.reshape(num_phrase, num_box)
if cfg.MODEL.VG.USING_DET_KNOWLEDGE:
det_label_embedding_bid = det_label_embedding[bid].to(
self.device)
sim = self.cal_det_label_sim(det_label_embedding_bid,
batch_phrase_glove_embed[bid],
precomp_score[bid])
pred_similarity_bid = pred_similarity_bid * sim
sim_mask = (sim > 0).float()
atten_bid = numerical_stability_masked_softmax(
pred_similarity_bid, sim_mask, dim=1)
else:
atten_bid = F.softmax(pred_similarity_bid, dim=1)
## reconstruction visual features
visual_reconst_bid = torch.mm(atten_bid, visual_features_bid)
decode_phr_logits, decode_phr_logits_cst = self.phrase_decoder(
visual_reconst_bid, batch_decoder_word_embed[bid], cst_phr,
num_cst)
batch_decode_logits.append(decode_phr_logits)
if not self.training:
batch_pred_similarity.append(atten_bid)
max_box_ind = atten_bid.detach().cpu().numpy().argmax(1)
batch_pred_boxes.append(precomp_boxes_bid[max_box_ind])
noun_reconst_loss, noun_cst_loss = self.VGLoss(
batch_phrase_mask, batch_decode_logits, batch_phrase_dec_ids,
batch_cst_mask, batch_cst_decoder_logits, batch_cst_dec_ids)
all_loss = dict(noun_reconst_loss=noun_reconst_loss,
noun_cst_loss=noun_cst_loss)
if self.training:
return all_loss, None
else:
return all_loss, (batch_phrase_ids, batch_phrase_types,
batch_pred_boxes, batch_pred_similarity,
batch_boxes_targets, batch_precomp_boxes,
image_unique_id)