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import torch
from torchvision.ops.boxes import batched_nms
from util.box_ops import box_cxcywh_to_xyxy
from .deformable_detr.deformable_transformer import DeformableTransformer
class OVTransformer(DeformableTransformer):
def __init__(self, d_model=256, nhead=8,
num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=1024, dropout=0.1,
activation="relu", return_intermediate_dec=False,
num_feature_levels=4, dec_n_points=4, enc_n_points=4,
two_stage=False, two_stage_num_proposals=300,
assign_first_stage=False):
super().__init__(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, dropout,
activation, return_intermediate_dec, num_feature_levels, dec_n_points, enc_n_points,
two_stage, two_stage_num_proposals, assign_first_stage)
def forward(self, srcs, masks, pos_embeds, query_embed=None, llm_feat=None, num_patch=1):
assert self.two_stage or query_embed is not None
# prepare input for encoder
src_flatten = []
mask_flatten = []
lvl_pos_embed_flatten = []
spatial_shapes = []
for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
bs, c, h, w = src.shape
spatial_shape = (h, w)
spatial_shapes.append(spatial_shape)
src = src.flatten(2).transpose(1, 2)
mask = mask.flatten(1)
pos_embed = pos_embed.flatten(2).transpose(1, 2)
lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
lvl_pos_embed_flatten.append(lvl_pos_embed)
src_flatten.append(src)
mask_flatten.append(mask)
src_flatten = torch.cat(src_flatten, 1)
mask_flatten = torch.cat(mask_flatten, 1)
lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
# encoder
memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios,
lvl_pos_embed_flatten, mask_flatten)
# prepare input for decoder
bs, _, c = memory.shape
if self.two_stage:
output_memory, output_proposals, level_ids = \
self.gen_encoder_output_proposals(memory, mask_flatten, spatial_shapes)
# hack implementation for two-stage Deformable DETR
enc_outputs_class = self.decoder.class_embed[self.decoder.num_layers](output_memory)
enc_outputs_coord_unact = self.decoder.bbox_embed[self.decoder.num_layers](output_memory) + output_proposals
topk = self.two_stage_num_proposals
proposal_logit = enc_outputs_class[..., 0]
if self.assign_first_stage:
proposal_boxes = box_cxcywh_to_xyxy(enc_outputs_coord_unact.sigmoid().float()).clamp(0, 1)
topk_proposals = []
for b in range(bs):
prop_boxes_b = proposal_boxes[b]
prop_logits_b = proposal_logit[b]
# pre-nms per-level topk
pre_nms_topk = 1000
pre_nms_inds = []
for lvl in range(len(spatial_shapes)):
lvl_mask = level_ids == lvl
pre_nms_inds.append(torch.topk(prop_logits_b.sigmoid() * lvl_mask, pre_nms_topk)[1])
pre_nms_inds = torch.cat(pre_nms_inds)
# nms on topk indices
post_nms_inds = batched_nms(prop_boxes_b[pre_nms_inds],
prop_logits_b[pre_nms_inds],
level_ids[pre_nms_inds], 0.9)
keep_inds = pre_nms_inds[post_nms_inds]
if len(keep_inds) < self.two_stage_num_proposals:
print(f'[WARNING] nms proposals ({len(keep_inds)}) < {self.two_stage_num_proposals}')
keep_inds = torch.topk(proposal_logit[b], topk)[1]
# keep top Q/L indices for L levels
q_per_l = topk // len(spatial_shapes)
level_shapes = torch.arange(len(spatial_shapes), device=level_ids.device)[:, None]
is_level_ordered = level_ids[keep_inds][None] == level_shapes
keep_inds_mask = is_level_ordered & (is_level_ordered.cumsum(1) <= q_per_l) # LS
keep_inds_mask = keep_inds_mask.any(0) # S
# pad to Q indices (might let ones filtered from pre-nms sneak by...
# unlikely because we pick high conf anyways)
if keep_inds_mask.sum() < topk:
num_to_add = topk - keep_inds_mask.sum()
pad_inds = (~keep_inds_mask).nonzero()[:num_to_add]
keep_inds_mask[pad_inds] = True
# index
keep_inds_topk = keep_inds[keep_inds_mask]
topk_proposals.append(keep_inds_topk)
topk_proposals = torch.stack(topk_proposals)
else:
topk_proposals = torch.topk(proposal_logit, topk, dim=1)[1]
topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
topk_coords_unact = topk_coords_unact.detach()
reference_points = topk_coords_unact.sigmoid()
init_reference_out = reference_points
pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
query_embed, tgt = torch.split(pos_trans_out, c, dim=2)
num_queries = query_embed.shape[1]
query_embed = query_embed.repeat(1, num_patch, 1)
tgt = tgt.repeat(1, num_patch, 1)
topk_feats = torch.stack([output_memory[b][topk_proposals[b]] for b in range(bs)]).detach()
topk_feats = topk_feats.repeat(1, num_patch, 1)
tgt = tgt + self.pix_trans_norm(self.pix_trans(topk_feats))
reference_points = reference_points.repeat(1, num_patch, 1)
init_reference_out = init_reference_out.repeat(1, num_patch, 1)
llm_feat = llm_feat.repeat_interleave(num_queries, 1)
tgt = tgt + llm_feat
else:
raise NotImplementedError
query_embed, tgt = torch.split(query_embed, c, dim=1)
query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)
tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
reference_points = self.reference_points(query_embed).sigmoid()
init_reference_out = reference_points
# decoder mask
decoder_mask = (
torch.ones(
num_queries * num_patch,
num_queries * num_patch,
device=query_embed.device,
) * float("-inf")
)
for i in range(num_patch):
decoder_mask[
i * num_queries : (i + 1) * num_queries,
i * num_queries : (i + 1) * num_queries,
] = 0
# decoder
hs, inter_references = self.decoder(tgt, reference_points, memory,
spatial_shapes, level_start_index, valid_ratios,
query_embed, mask_flatten, tgt_mask=decoder_mask)
inter_references_out = inter_references
if self.two_stage:
return (hs,
init_reference_out,
inter_references_out,
enc_outputs_class,
enc_outputs_coord_unact,
output_proposals.sigmoid())
return hs, init_reference_out, inter_references_out, None, None, None
def build_ov_transformer(args):
return OVTransformer(
d_model=args.hidden_dim,
nhead=args.nheads,
num_encoder_layers=args.enc_layers,
num_decoder_layers=args.dec_layers,
dim_feedforward=args.dim_feedforward,
dropout=args.dropout,
activation="relu",
return_intermediate_dec=True,
num_feature_levels=args.num_feature_levels,
dec_n_points=args.dec_n_points,
enc_n_points=args.enc_n_points,
two_stage=args.two_stage,
two_stage_num_proposals=args.num_queries,
assign_first_stage=args.assign_first_stage)
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