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# ------------------------------------------------------------------------
# ED-Pose
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------
import os
import copy
import torch
import torch.nn.functional as F
from torch import nn
from typing import List
from util.keypoint_ops import keypoint_xyzxyz_to_xyxyzz
from util.misc import NestedTensor, nested_tensor_from_tensor_list,inverse_sigmoid
from .utils import MLP
from .backbone import build_backbone
from ..registry import MODULE_BUILD_FUNCS
from .mask_generate import prepare_for_mask, post_process
from .deformable_transformer import build_deformable_transformer
class UniPose(nn.Module):
""" This is the Cross-Attention Detector module that performs object detection """
def __init__(self, backbone, transformer, num_classes, num_queries,
aux_loss=False, iter_update=False,
query_dim=2,
random_refpoints_xy=False,
fix_refpoints_hw=-1,
num_feature_levels=1,
nheads=8,
# two stage
two_stage_type='no', # ['no', 'standard']
two_stage_add_query_num=0,
dec_pred_class_embed_share=True,
dec_pred_bbox_embed_share=True,
two_stage_class_embed_share=True,
two_stage_bbox_embed_share=True,
decoder_sa_type='sa',
num_patterns=0,
dn_number=100,
dn_box_noise_scale=0.4,
dn_label_noise_ratio=0.5,
dn_labelbook_size=100,
use_label_enc=True,
text_encoder_type='bert-base-uncased',
binary_query_selection=False,
use_cdn=True,
sub_sentence_present=True,
num_body_points=68,
num_box_decoder_layers=2,
):
""" Initializes the model.
Parameters:
backbone: torch module of the backbone to be used. See backbone.py
transformer: torch module of the transformer architecture. See transformer.py
num_classes: number of object classes
num_queries: number of object queries, ie detection slot. This is the maximal number of objects
Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
fix_refpoints_hw: -1(default): learn w and h for each box seperately
>0 : given fixed number
-2 : learn a shared w and h
"""
super().__init__()
self.num_queries = num_queries
self.transformer = transformer
self.num_classes = num_classes
self.hidden_dim = hidden_dim = transformer.d_model
self.num_feature_levels = num_feature_levels
self.nheads = nheads
self.use_label_enc = use_label_enc
if use_label_enc:
self.label_enc = nn.Embedding(dn_labelbook_size + 1, hidden_dim)
else:
raise NotImplementedError
self.label_enc = None
self.max_text_len = 256
self.binary_query_selection = binary_query_selection
self.sub_sentence_present = sub_sentence_present
# setting query dim
self.query_dim = query_dim
assert query_dim == 4
self.random_refpoints_xy = random_refpoints_xy
self.fix_refpoints_hw = fix_refpoints_hw
# for dn training
self.num_patterns = num_patterns
self.dn_number = dn_number
self.dn_box_noise_scale = dn_box_noise_scale
self.dn_label_noise_ratio = dn_label_noise_ratio
self.dn_labelbook_size = dn_labelbook_size
self.use_cdn = use_cdn
self.projection = MLP(512, hidden_dim, hidden_dim, 3)
self.projection_kpt = MLP(512, hidden_dim, hidden_dim, 3)
device = "cuda" if torch.cuda.is_available() else "cpu"
# model, _ = clip.load("ViT-B/32", device=device)
# self.clip_model = model
# visual_parameters = list(self.clip_model.visual.parameters())
# #
# for param in visual_parameters:
# param.requires_grad = False
self.pos_proj = nn.Linear(hidden_dim, 768)
self.padding = nn.Embedding(1, 768)
# prepare input projection layers
if num_feature_levels > 1:
num_backbone_outs = len(backbone.num_channels)
input_proj_list = []
for _ in range(num_backbone_outs):
in_channels = backbone.num_channels[_]
input_proj_list.append(nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
))
for _ in range(num_feature_levels - num_backbone_outs):
input_proj_list.append(nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
nn.GroupNorm(32, hidden_dim),
))
in_channels = hidden_dim
self.input_proj = nn.ModuleList(input_proj_list)
else:
assert two_stage_type == 'no', "two_stage_type should be no if num_feature_levels=1 !!!"
self.input_proj = nn.ModuleList([
nn.Sequential(
nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
nn.GroupNorm(32, hidden_dim),
)])
self.backbone = backbone
self.aux_loss = aux_loss
self.box_pred_damping = box_pred_damping = None
self.iter_update = iter_update
assert iter_update, "Why not iter_update?"
# prepare pred layers
self.dec_pred_class_embed_share = dec_pred_class_embed_share
self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
# prepare class & box embed
_class_embed = ContrastiveAssign()
_bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
_pose_embed = MLP(hidden_dim, hidden_dim, 2, 3)
_pose_hw_embed = MLP(hidden_dim, hidden_dim, 2, 3)
nn.init.constant_(_pose_embed.layers[-1].weight.data, 0)
nn.init.constant_(_pose_embed.layers[-1].bias.data, 0)
if dec_pred_bbox_embed_share:
box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
else:
box_embed_layerlist = [copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)]
if dec_pred_class_embed_share:
class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
else:
class_embed_layerlist = [copy.deepcopy(_class_embed) for i in range(transformer.num_decoder_layers)]
if dec_pred_bbox_embed_share:
pose_embed_layerlist = [_pose_embed for i in
range(transformer.num_decoder_layers - num_box_decoder_layers + 1)]
else:
pose_embed_layerlist = [copy.deepcopy(_pose_embed) for i in
range(transformer.num_decoder_layers - num_box_decoder_layers + 1)]
pose_hw_embed_layerlist = [_pose_hw_embed for i in
range(transformer.num_decoder_layers - num_box_decoder_layers)]
self.num_box_decoder_layers = num_box_decoder_layers
self.bbox_embed = nn.ModuleList(box_embed_layerlist)
self.class_embed = nn.ModuleList(class_embed_layerlist)
self.num_body_points = num_body_points
self.pose_embed = nn.ModuleList(pose_embed_layerlist)
self.pose_hw_embed = nn.ModuleList(pose_hw_embed_layerlist)
self.transformer.decoder.bbox_embed = self.bbox_embed
self.transformer.decoder.class_embed = self.class_embed
self.transformer.decoder.pose_embed = self.pose_embed
self.transformer.decoder.pose_hw_embed = self.pose_hw_embed
self.transformer.decoder.num_body_points = num_body_points
# two stage
self.two_stage_type = two_stage_type
self.two_stage_add_query_num = two_stage_add_query_num
assert two_stage_type in ['no', 'standard'], "unknown param {} of two_stage_type".format(two_stage_type)
if two_stage_type != 'no':
if two_stage_bbox_embed_share:
assert dec_pred_class_embed_share and dec_pred_bbox_embed_share
self.transformer.enc_out_bbox_embed = _bbox_embed
else:
self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
if two_stage_class_embed_share:
assert dec_pred_class_embed_share and dec_pred_bbox_embed_share
self.transformer.enc_out_class_embed = _class_embed
else:
self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
self.refpoint_embed = None
if self.two_stage_add_query_num > 0:
self.init_ref_points(two_stage_add_query_num)
self.decoder_sa_type = decoder_sa_type
assert decoder_sa_type in ['sa', 'ca_label', 'ca_content']
# self.replace_sa_with_double_ca = replace_sa_with_double_ca
if decoder_sa_type == 'ca_label':
self.label_embedding = nn.Embedding(num_classes, hidden_dim)
for layer in self.transformer.decoder.layers:
layer.label_embedding = self.label_embedding
else:
for layer in self.transformer.decoder.layers:
layer.label_embedding = None
self.label_embedding = None
self._reset_parameters()
def open_set_transfer_init(self):
for name, param in self.named_parameters():
if 'fusion_layers' in name:
continue
if 'ca_text' in name:
continue
if 'catext_norm' in name:
continue
if 'catext_dropout' in name:
continue
if "text_layers" in name:
continue
if 'bert' in name:
continue
if 'bbox_embed' in name:
continue
if 'label_enc.weight' in name:
continue
if 'feat_map' in name:
continue
if 'enc_output' in name:
continue
param.requires_grad_(False)
# import ipdb; ipdb.set_trace()
def _reset_parameters(self):
# init input_proj
for proj in self.input_proj:
nn.init.xavier_uniform_(proj[0].weight, gain=1)
nn.init.constant_(proj[0].bias, 0)
def init_ref_points(self, use_num_queries):
self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
if self.random_refpoints_xy:
# import ipdb; ipdb.set_trace()
self.refpoint_embed.weight.data[:, :2].uniform_(0, 1)
self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2])
self.refpoint_embed.weight.data[:, :2].requires_grad = False
if self.fix_refpoints_hw > 0:
print("fix_refpoints_hw: {}".format(self.fix_refpoints_hw))
assert self.random_refpoints_xy
self.refpoint_embed.weight.data[:, 2:] = self.fix_refpoints_hw
self.refpoint_embed.weight.data[:, 2:] = inverse_sigmoid(self.refpoint_embed.weight.data[:, 2:])
self.refpoint_embed.weight.data[:, 2:].requires_grad = False
elif int(self.fix_refpoints_hw) == -1:
pass
elif int(self.fix_refpoints_hw) == -2:
print('learn a shared h and w')
assert self.random_refpoints_xy
self.refpoint_embed = nn.Embedding(use_num_queries, 2)
self.refpoint_embed.weight.data[:, :2].uniform_(0, 1)
self.refpoint_embed.weight.data[:, :2] = inverse_sigmoid(self.refpoint_embed.weight.data[:, :2])
self.refpoint_embed.weight.data[:, :2].requires_grad = False
self.hw_embed = nn.Embedding(1, 1)
else:
raise NotImplementedError('Unknown fix_refpoints_hw {}'.format(self.fix_refpoints_hw))
def forward(self, samples: NestedTensor, targets: List = None, **kw):
""" The forward expects a NestedTensor, which consists of:
- samples.tensor: batched images, of shape [batch_size x 3 x H x W]
- samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
It returns a dict with the following elements:
- "pred_logits": the classification logits (including no-object) for all queries.
Shape= [batch_size x num_queries x num_classes]
- "pred_boxes": The normalized boxes coordinates for all queries, represented as
(center_x, center_y, width, height). These values are normalized in [0, 1],
relative to the size of each individual image (disregarding possible padding).
See PostProcess for information on how to retrieve the unnormalized bounding box.
- "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
dictionnaries containing the two above keys for each decoder layer.
"""
captions = [t['instance_text_prompt'] for t in targets]
bs=len(captions)
tensor_list = [tgt["object_embeddings_text"] for tgt in targets]
max_size = 350
padded_tensors = [torch.cat([tensor, torch.zeros(max_size - tensor.size(0), tensor.size(1),device=tensor.device)]) if tensor.size(0) < max_size else tensor for tensor in tensor_list]
object_embeddings_text = torch.stack(padded_tensors)
kpts_embeddings_text = torch.stack([tgt["kpts_embeddings_text"] for tgt in targets])[:, :self.num_body_points]
encoded_text=self.projection(object_embeddings_text) # bs, 81, 101, 256
kpt_embeddings_specific=self.projection_kpt(kpts_embeddings_text) # bs, 81, 101, 256
kpt_vis = torch.stack([tgt["kpt_vis_text"] for tgt in targets])[:, :self.num_body_points]
kpt_mask = torch.cat((torch.ones_like(kpt_vis, device=kpt_vis.device)[..., 0].unsqueeze(-1), kpt_vis), dim=-1)
num_classes = encoded_text.shape[1] # bs, 81, 101, 256
text_self_attention_masks = torch.eye(num_classes).unsqueeze(0).expand(bs, -1, -1).bool().to(samples.device)
text_token_mask = torch.zeros(samples.shape[0],num_classes).to(samples.device)>0
for i in range(bs):
text_token_mask[i,:len(captions[i])]=True
position_ids = torch.zeros(samples.shape[0], num_classes).to(samples.device)
for i in range(bs):
position_ids[i,:len(captions[i])]= 1
text_dict = {
'encoded_text': encoded_text, # bs, 195, d_model
'text_token_mask': text_token_mask, # bs, 195
'position_ids': position_ids, # bs, 195
'text_self_attention_masks': text_self_attention_masks # bs, 195,195
}
# import ipdb; ipdb.set_trace()
if isinstance(samples, (list, torch.Tensor)):
samples = nested_tensor_from_tensor_list(samples)
features, poss = self.backbone(samples)
if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
import ipdb;
ipdb.set_trace()
srcs = []
masks = []
for l, feat in enumerate(features):
src, mask = feat.decompose()
srcs.append(self.input_proj[l](src))
masks.append(mask)
assert mask is not None
if self.num_feature_levels > len(srcs):
_len_srcs = len(srcs)
for l in range(_len_srcs, self.num_feature_levels):
if l == _len_srcs:
src = self.input_proj[l](features[-1].tensors)
else:
src = self.input_proj[l](srcs[-1])
m = samples.mask
mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
srcs.append(src)
masks.append(mask)
poss.append(pos_l)
if self.label_enc is not None:
label_enc = self.label_enc
else:
raise NotImplementedError
label_enc = encoded_text
if self.dn_number > 0 or targets is not None:
input_query_label, input_query_bbox, attn_mask, attn_mask2, dn_meta = \
prepare_for_mask(kpt_mask=kpt_mask)
else:
assert targets is None
input_query_bbox = input_query_label = attn_mask = attn_mask2 = dn_meta = None
hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(srcs, masks, input_query_bbox, poss,
input_query_label, attn_mask, attn_mask2,
text_dict, dn_meta,targets,kpt_embeddings_specific)
# In case num object=0
if self.label_enc is not None:
hs[0] += self.label_enc.weight[0, 0] * 0.0
hs[0] += self.pos_proj.weight[0, 0] * 0.0
hs[0] += self.pos_proj.bias[0] * 0.0
hs[0] += self.padding.weight[0, 0] * 0.0
num_group = 50
effective_dn_number = dn_meta['pad_size'] if self.training else 0
outputs_coord_list = []
outputs_class = []
for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_cls_embed, layer_hs) in enumerate(
zip(reference[:-1], self.bbox_embed, self.class_embed, hs)):
if dec_lid < self.num_box_decoder_layers:
layer_delta_unsig = layer_bbox_embed(layer_hs)
layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
layer_outputs_unsig = layer_outputs_unsig.sigmoid()
layer_cls = layer_cls_embed(layer_hs, text_dict)
outputs_coord_list.append(layer_outputs_unsig)
outputs_class.append(layer_cls)
else:
layer_hs_bbox_dn = layer_hs[:, :effective_dn_number, :]
layer_hs_bbox_norm = layer_hs[:, effective_dn_number:, :][:, 0::(self.num_body_points + 1), :]
bs = layer_ref_sig.shape[0]
reference_before_sigmoid_bbox_dn = layer_ref_sig[:, :effective_dn_number, :]
reference_before_sigmoid_bbox_norm = layer_ref_sig[:, effective_dn_number:, :][:,
0::(self.num_body_points + 1), :]
layer_delta_unsig_dn = layer_bbox_embed(layer_hs_bbox_dn)
layer_delta_unsig_norm = layer_bbox_embed(layer_hs_bbox_norm)
layer_outputs_unsig_dn = layer_delta_unsig_dn + inverse_sigmoid(reference_before_sigmoid_bbox_dn)
layer_outputs_unsig_dn = layer_outputs_unsig_dn.sigmoid()
layer_outputs_unsig_norm = layer_delta_unsig_norm + inverse_sigmoid(reference_before_sigmoid_bbox_norm)
layer_outputs_unsig_norm = layer_outputs_unsig_norm.sigmoid()
layer_outputs_unsig = torch.cat((layer_outputs_unsig_dn, layer_outputs_unsig_norm), dim=1)
layer_cls_dn = layer_cls_embed(layer_hs_bbox_dn, text_dict)
layer_cls_norm = layer_cls_embed(layer_hs_bbox_norm, text_dict)
layer_cls = torch.cat((layer_cls_dn, layer_cls_norm), dim=1)
outputs_class.append(layer_cls)
outputs_coord_list.append(layer_outputs_unsig)
# update keypoints
outputs_keypoints_list = []
outputs_keypoints_hw = []
kpt_index = [x for x in range(num_group * (self.num_body_points + 1)) if x % (self.num_body_points + 1) != 0]
for dec_lid, (layer_ref_sig, layer_hs) in enumerate(zip(reference[:-1], hs)):
if dec_lid < self.num_box_decoder_layers:
assert isinstance(layer_hs, torch.Tensor)
bs = layer_hs.shape[0]
layer_res = layer_hs.new_zeros((bs, self.num_queries, self.num_body_points * 3))
outputs_keypoints_list.append(layer_res)
else:
bs = layer_ref_sig.shape[0]
layer_hs_kpt = layer_hs[:, effective_dn_number:, :].index_select(1, torch.tensor(kpt_index,
device=layer_hs.device))
delta_xy_unsig = self.pose_embed[dec_lid - self.num_box_decoder_layers](layer_hs_kpt)
layer_ref_sig_kpt = layer_ref_sig[:, effective_dn_number:, :].index_select(1, torch.tensor(kpt_index,
device=layer_hs.device))
layer_outputs_unsig_keypoints = delta_xy_unsig + inverse_sigmoid(layer_ref_sig_kpt[..., :2])
vis_xy_unsig = torch.ones_like(layer_outputs_unsig_keypoints,
device=layer_outputs_unsig_keypoints.device)
xyv = torch.cat((layer_outputs_unsig_keypoints, vis_xy_unsig[:, :, 0].unsqueeze(-1)), dim=-1)
xyv = xyv.sigmoid()
layer_res = xyv.reshape((bs, num_group, self.num_body_points, 3)).flatten(2, 3)
layer_hw = layer_ref_sig_kpt[..., 2:].reshape(bs, num_group, self.num_body_points, 2).flatten(2, 3)
layer_res = keypoint_xyzxyz_to_xyxyzz(layer_res)
outputs_keypoints_list.append(layer_res)
outputs_keypoints_hw.append(layer_hw)
if self.dn_number > 0 and dn_meta is not None:
outputs_class, outputs_coord_list = \
post_process(outputs_class, outputs_coord_list,
dn_meta, self.aux_loss, self._set_aux_loss)
out = {'pred_logits': outputs_class[-1], 'pred_boxes': outputs_coord_list[-1],
'pred_keypoints': outputs_keypoints_list[-1]}
return out
@MODULE_BUILD_FUNCS.registe_with_name(module_name='UniPose')
def build_unipose(args):
num_classes = args.num_classes
device = torch.device(args.device)
backbone = build_backbone(args)
transformer = build_deformable_transformer(args)
try:
match_unstable_error = args.match_unstable_error
dn_labelbook_size = args.dn_labelbook_size
except:
match_unstable_error = True
dn_labelbook_size = num_classes
try:
dec_pred_class_embed_share = args.dec_pred_class_embed_share
except:
dec_pred_class_embed_share = True
try:
dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
except:
dec_pred_bbox_embed_share = True
binary_query_selection = False
try:
binary_query_selection = args.binary_query_selection
except:
binary_query_selection = False
use_cdn = True
try:
use_cdn = args.use_cdn
except:
use_cdn = True
sub_sentence_present = True
try:
sub_sentence_present = args.sub_sentence_present
except:
sub_sentence_present = True
# print('********* sub_sentence_present', sub_sentence_present)
model = UniPose(
backbone,
transformer,
num_classes=num_classes,
num_queries=args.num_queries,
aux_loss=True,
iter_update=True,
query_dim=4,
random_refpoints_xy=args.random_refpoints_xy,
fix_refpoints_hw=args.fix_refpoints_hw,
num_feature_levels=args.num_feature_levels,
nheads=args.nheads,
dec_pred_class_embed_share=dec_pred_class_embed_share,
dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
# two stage
two_stage_type=args.two_stage_type,
# box_share
two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
two_stage_class_embed_share=args.two_stage_class_embed_share,
decoder_sa_type=args.decoder_sa_type,
num_patterns=args.num_patterns,
dn_number=args.dn_number if args.use_dn else 0,
dn_box_noise_scale=args.dn_box_noise_scale,
dn_label_noise_ratio=args.dn_label_noise_ratio,
dn_labelbook_size=dn_labelbook_size,
use_label_enc=args.use_label_enc,
text_encoder_type=args.text_encoder_type,
binary_query_selection=binary_query_selection,
use_cdn=use_cdn,
sub_sentence_present=sub_sentence_present
)
return model
class ContrastiveAssign(nn.Module):
def __init__(self, project=False, cal_bias=None, max_text_len=256):
"""
:param x: query
:param y: text embed
:param proj:
:return:
"""
super().__init__()
self.project = project
self.cal_bias = cal_bias
self.max_text_len = max_text_len
def forward(self, x, text_dict):
"""_summary_
Args:
x (_type_): _description_
text_dict (_type_): _description_
{
'encoded_text': encoded_text, # bs, 195, d_model
'text_token_mask': text_token_mask, # bs, 195
# True for used tokens. False for padding tokens
}
Returns:
_type_: _description_
"""
assert isinstance(text_dict, dict)
y = text_dict['encoded_text']
max_text_len = y.shape[1]
text_token_mask = text_dict['text_token_mask']
if self.cal_bias is not None:
raise NotImplementedError
return x @ y.transpose(-1, -2) + self.cal_bias.weight.repeat(x.shape[0], x.shape[1], 1)
res = x @ y.transpose(-1, -2)
res.masked_fill_(~text_token_mask[:, None, :], float('-inf'))
# padding to max_text_len
new_res = torch.full((*res.shape[:-1], max_text_len), float('-inf'), device=res.device)
new_res[..., :res.shape[-1]] = res
return new_res
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