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# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-research. All Rights Reserved.
# ------------------------------------------------------------------------
import math
import torch
from torch import nn
from util import box_ops
from models.structures import Boxes, Instances, pairwise_iou
def random_drop_tracks(track_instances: Instances, drop_probability: float) -> Instances:
if drop_probability > 0 and len(track_instances) > 0:
keep_idxes = torch.rand_like(track_instances.scores) > drop_probability
track_instances = track_instances[keep_idxes]
return track_instances
class QueryInteractionBase(nn.Module):
def __init__(self, args, dim_in, hidden_dim, dim_out):
super().__init__()
self.args = args
self._build_layers(args, dim_in, hidden_dim, dim_out)
self._reset_parameters()
def _build_layers(self, args, dim_in, hidden_dim, dim_out):
raise NotImplementedError()
def _reset_parameters(self):
for p in self.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
def _select_active_tracks(self, data: dict) -> Instances:
raise NotImplementedError()
def _update_track_embedding(self, track_instances):
raise NotImplementedError()
class FFN(nn.Module):
def __init__(self, d_model, d_ffn, dropout=0):
super().__init__()
self.linear1 = nn.Linear(d_model, d_ffn)
self.activation = nn.ReLU(True)
self.dropout1 = nn.Dropout(dropout)
self.linear2 = nn.Linear(d_ffn, d_model)
self.dropout2 = nn.Dropout(dropout)
self.norm = nn.LayerNorm(d_model)
def forward(self, tgt):
tgt2 = self.linear2(self.dropout1(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm(tgt)
return tgt
class QueryInteractionModule(QueryInteractionBase):
def __init__(self, args, dim_in, hidden_dim, dim_out):
raise NotImplementedError
class QueryInteractionModulev2(QueryInteractionBase):
def __init__(self, args, dim_in, hidden_dim, dim_out):
super().__init__(args, dim_in, hidden_dim, dim_out)
self.random_drop = args.random_drop
self.fp_ratio = args.fp_ratio
self.update_query_pos = args.update_query_pos
self.score_thr = 0.5
def _build_layers(self, args, dim_in, hidden_dim, dim_out):
dropout = args.merger_dropout
self.self_attn = nn.MultiheadAttention(dim_in, 8, dropout)
self.linear1 = nn.Linear(dim_in, hidden_dim)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(hidden_dim, dim_in)
if args.update_query_pos:
self.linear_pos1 = nn.Linear(dim_in, hidden_dim)
self.linear_pos2 = nn.Linear(hidden_dim, dim_in)
self.dropout_pos1 = nn.Dropout(dropout)
self.dropout_pos2 = nn.Dropout(dropout)
self.norm_pos = nn.LayerNorm(dim_in)
self.linear_feat1 = nn.Linear(dim_in, hidden_dim)
self.linear_feat2 = nn.Linear(hidden_dim, dim_in)
self.dropout_feat1 = nn.Dropout(dropout)
self.dropout_feat2 = nn.Dropout(dropout)
self.norm_feat = nn.LayerNorm(dim_in)
self.norm1 = nn.LayerNorm(dim_in)
self.norm2 = nn.LayerNorm(dim_in)
if args.update_query_pos:
self.norm3 = nn.LayerNorm(dim_in)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
if args.update_query_pos:
self.dropout3 = nn.Dropout(dropout)
self.dropout4 = nn.Dropout(dropout)
self.activation = nn.ReLU(True)
def _random_drop_tracks(self, track_instances: Instances) -> Instances:
return random_drop_tracks(track_instances, self.random_drop)
def _add_fp_tracks(self, track_instances: Instances, active_track_instances: Instances) -> Instances:
inactive_instances = track_instances[track_instances.obj_idxes < 0]
# add fp for each active track in a specific probability.
fp_prob = torch.ones_like(active_track_instances.scores) * self.fp_ratio
selected_active_track_instances = active_track_instances[torch.bernoulli(fp_prob).bool()]
if len(inactive_instances) > 0 and len(selected_active_track_instances) > 0:
num_fp = len(selected_active_track_instances)
if num_fp >= len(inactive_instances):
fp_track_instances = inactive_instances
else:
inactive_boxes = Boxes(box_ops.box_cxcywh_to_xyxy(inactive_instances.pred_boxes))
selected_active_boxes = Boxes(box_ops.box_cxcywh_to_xyxy(selected_active_track_instances.pred_boxes))
ious = pairwise_iou(inactive_boxes, selected_active_boxes)
# select the fp with the largest IoU for each active track.
fp_indexes = ious.max(dim=0).indices
# remove duplicate fp.
fp_indexes = torch.unique(fp_indexes)
fp_track_instances = inactive_instances[fp_indexes]
merged_track_instances = Instances.cat([active_track_instances, fp_track_instances])
return merged_track_instances
return active_track_instances
def _select_active_tracks(self, data: dict) -> Instances:
track_instances: Instances = data['track_instances']
if self.training:
active_idxes = (track_instances.obj_idxes >= 0) | (track_instances.scores > 0.5)
active_track_instances = track_instances[active_idxes]
active_track_instances.obj_idxes[active_track_instances.iou <= 0.5] = -1
else:
active_track_instances = track_instances[track_instances.obj_idxes >= 0]
return active_track_instances
def _update_track_embedding(self, track_instances: Instances) -> Instances:
is_pos = track_instances.scores > self.score_thr
track_instances.ref_pts[is_pos] = track_instances.pred_boxes.detach().clone()[is_pos]
out_embed = track_instances.output_embedding
query_feat = track_instances.query_pos
query_pos = pos2posemb(track_instances.ref_pts)
q = k = query_pos + out_embed
tgt = out_embed
tgt2 = self.self_attn(q[:, None], k[:, None], value=tgt[:, None])[0][:, 0]
tgt = tgt + self.dropout1(tgt2)
tgt = self.norm1(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
if self.update_query_pos:
query_pos2 = self.linear_pos2(self.dropout_pos1(self.activation(self.linear_pos1(tgt))))
query_pos = query_pos + self.dropout_pos2(query_pos2)
query_pos = self.norm_pos(query_pos)
track_instances.query_pos = query_pos
query_feat2 = self.linear_feat2(self.dropout_feat1(self.activation(self.linear_feat1(tgt))))
query_feat = query_feat + self.dropout_feat2(query_feat2)
query_feat = self.norm_feat(query_feat)
track_instances.query_pos[is_pos] = query_feat[is_pos]
return track_instances
def forward(self, data) -> Instances:
active_track_instances = self._select_active_tracks(data)
active_track_instances = self._update_track_embedding(active_track_instances)
return active_track_instances
def pos2posemb(pos, num_pos_feats=64, temperature=10000):
scale = 2 * math.pi
pos = pos * scale
dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos.device)
dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
posemb = pos[..., None] / dim_t
posemb = torch.stack((posemb[..., 0::2].sin(), posemb[..., 1::2].cos()), dim=-1).flatten(-3)
return posemb
def build(args, layer_name, dim_in, hidden_dim, dim_out):
interaction_layers = {
'QIM': QueryInteractionModule,
'QIMv2': QueryInteractionModulev2,
}
assert layer_name in interaction_layers, 'invalid query interaction layer: {}'.format(layer_name)
return interaction_layers[layer_name](args, dim_in, hidden_dim, dim_out)
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