from typing import Any, Optional
import torch
from torch import nn
from torchvision.ops import MultiScaleRoIAlign
from libs.vision_libs.ops import misc as misc_nn_ops
from libs.vision_libs.transforms._presets import ObjectDetection
from .roi_heads import RoIHeads
from libs.vision_libs.models._api import register_model, Weights, WeightsEnum
from libs.vision_libs.models._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
from libs.vision_libs.models._utils import _ovewrite_value_param, handle_legacy_interface
from libs.vision_libs.models.resnet import resnet50, ResNet50_Weights
from libs.vision_libs.models.detection._utils import overwrite_eps
from libs.vision_libs.models.detection.backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
from libs.vision_libs.models.detection.faster_rcnn import FasterRCNN, TwoMLPHead, FastRCNNPredictor
from models.config.config_tool import read_yaml
import numpy as np
import torch.nn.functional as F
FEATURE_DIM = 8
def non_maximum_suppression(a):
ap = F.max_pool2d(a, 3, stride=1, padding=1)
mask = (a == ap).float().clamp(min=0.0)
return a * mask
class Bottleneck1D(nn.Module):
def __init__(self, inplanes, outplanes):
super(Bottleneck1D, self).__init__()
planes = outplanes // 2
self.op = nn.Sequential(
nn.BatchNorm1d(inplanes),
nn.ReLU(inplace=True),
nn.Conv1d(inplanes, planes, kernel_size=1),
nn.BatchNorm1d(planes),
nn.ReLU(inplace=True),
nn.Conv1d(planes, planes, kernel_size=3, padding=1),
nn.BatchNorm1d(planes),
nn.ReLU(inplace=True),
nn.Conv1d(planes, outplanes, kernel_size=1),
)
def forward(self, x):
return x + self.op(x)
class LineRCNNPredictor(nn.Module):
def __init__(self,n_pts0 = 32,
n_pts1 = 8,
n_stc_posl =300,
dim_loi = 128,
use_conv = 0,
dim_fc = 1024,
n_out_line = 2500,
n_out_junc =250,
n_dyn_junc = 300,
eval_junc_thres = 0.008,
n_dyn_posl =300,
n_dyn_negl =80,
n_dyn_othr = 600,
use_cood = 0,
use_slop = 0,
n_stc_negl = 40,
head_size = [[2], [1], [2]] ,
**kwargs):
super().__init__()
# self.backbone = backbone
# self.cfg = read_yaml(cfg)
# self.cfg = read_yaml(r'./config/wireframe.yaml')
# print(f'linePredictor cfg:{cfg}')
#
# self.cfg = cfg
# self.n_pts0 = self.cfg['n_pts0']
# self.n_pts1 = self.cfg['n_pts1']
# self.n_stc_posl = self.cfg['n_stc_posl']
# self.dim_loi = self.cfg['dim_loi']
# self.use_conv = self.cfg['use_conv']
# self.dim_fc = self.cfg['dim_fc']
# self.n_out_line = self.cfg['n_out_line']
# self.n_out_junc = self.cfg['n_out_junc']
# self.loss_weight = self.cfg['loss_weight']
# self.n_dyn_junc = self.cfg['n_dyn_junc']
# self.eval_junc_thres = self.cfg['eval_junc_thres']
# self.n_dyn_posl = self.cfg['n_dyn_posl']
# self.n_dyn_negl = self.cfg['n_dyn_negl']
# self.n_dyn_othr = self.cfg['n_dyn_othr']
# self.use_cood = self.cfg['use_cood']
# self.use_slop = self.cfg['use_slop']
# self.n_stc_negl = self.cfg['n_stc_negl']
# self.head_size = self.cfg['head_size']
self.n_pts0 = n_pts0
self.n_pts1 = n_pts1
self.n_stc_posl =n_stc_posl
self.dim_loi = dim_loi
self.use_conv = use_conv
self.dim_fc = dim_fc
self.n_out_line = n_out_line
self.n_out_junc =n_out_junc
# self.loss_weight =
self.n_dyn_junc = n_dyn_junc
self.eval_junc_thres = eval_junc_thres
self.n_dyn_posl =n_dyn_posl
self.n_dyn_negl = n_dyn_negl
self.n_dyn_othr = n_dyn_othr
self.use_cood = use_cood
self.use_slop = use_slop
self.n_stc_negl = n_stc_negl
self.head_size = head_size
self.num_class = sum(sum(self.head_size, []))
self.head_off = np.cumsum([sum(h) for h in self.head_size])
lambda_ = torch.linspace(0, 1, self.n_pts0)[:, None]
self.register_buffer("lambda_", lambda_)
self.do_static_sampling = self.n_stc_posl + self.n_stc_negl > 0
self.fc1 = nn.Conv2d(256, self.dim_loi, 1)
scale_factor = self.n_pts0 // self.n_pts1
if self.use_conv:
self.pooling = nn.Sequential(
nn.MaxPool1d(scale_factor, scale_factor),
Bottleneck1D(self.dim_loi, self.dim_loi),
)
self.fc2 = nn.Sequential(
nn.ReLU(inplace=True), nn.Linear(self.dim_loi * self.n_pts1 + FEATURE_DIM, 1)
)
else:
self.pooling = nn.MaxPool1d(scale_factor, scale_factor)
self.fc2 = nn.Sequential(
nn.Linear(self.dim_loi * self.n_pts1 + FEATURE_DIM, self.dim_fc),
nn.ReLU(inplace=True),
nn.Linear(self.dim_fc, self.dim_fc),
nn.ReLU(inplace=True),
nn.Linear(self.dim_fc, 1),
)
self.loss = nn.BCEWithLogitsLoss(reduction="none")
def forward(self, inputs, features, targets=None):
# outputs, features = input
# for out in outputs:
# print(f'out:{out.shape}')
# outputs=merge_features(outputs,100)
batch, channel, row, col = inputs.shape
# print(f'outputs:{inputs.shape}')
# print(f'batch:{batch}, channel:{channel}, row:{row}, col:{col}')
if targets is not None:
self.training = True
# print(f'target:{targets}')
wires_targets = [t["wires"] for t in targets]
# print(f'wires_target:{wires_targets}')
# 提取所有 'junc_map', 'junc_offset', 'line_map' 的张量
junc_maps = [d["junc_map"] for d in wires_targets]
junc_offsets = [d["junc_offset"] for d in wires_targets]
line_maps = [d["line_map"] for d in wires_targets]
junc_map_tensor = torch.stack(junc_maps, dim=0)
junc_offset_tensor = torch.stack(junc_offsets, dim=0)
line_map_tensor = torch.stack(line_maps, dim=0)
wires_meta = {
"junc_map": junc_map_tensor,
"junc_offset": junc_offset_tensor,
# "line_map": line_map_tensor,
}
else:
self.training = False
t = {
"junc_coords": torch.zeros(1, 2),
"jtyp": torch.zeros(1, dtype=torch.uint8),
"line_pos_idx": torch.zeros(2, 2, dtype=torch.uint8),
"line_neg_idx": torch.zeros(2, 2, dtype=torch.uint8),
"junc_map": torch.zeros([1, 1, 128, 128]),
"junc_offset": torch.zeros([1, 1, 2, 128, 128]),
}
wires_targets = [t for b in range(inputs.size(0))]
wires_meta = {
"junc_map": torch.zeros([1, 1, 128, 128]),
"junc_offset": torch.zeros([1, 1, 2, 128, 128]),
}
T = wires_meta.copy()
n_jtyp = T["junc_map"].shape[1]
offset = self.head_off
result = {}
for stack, output in enumerate([inputs]):
output = output.transpose(0, 1).reshape([-1, batch, row, col]).contiguous()
# print(f"Stack {stack} output shape: {output.shape}") # 打印每层的输出形状
jmap = output[0: offset[0]].reshape(n_jtyp, 2, batch, row, col)
lmap = output[offset[0]: offset[1]].squeeze(0)
joff = output[offset[1]: offset[2]].reshape(n_jtyp, 2, batch, row, col)
if stack == 0:
result["preds"] = {
"jmap": jmap.permute(2, 0, 1, 3, 4).softmax(2)[:, :, 1],
"lmap": lmap.sigmoid(),
"joff": joff.permute(2, 0, 1, 3, 4).sigmoid() - 0.5,
}
# visualize_feature_map(jmap[0, 0], title=f"jmap - Stack {stack}")
# visualize_feature_map(lmap, title=f"lmap - Stack {stack}")
# visualize_feature_map(joff[0, 0], title=f"joff - Stack {stack}")
h = result["preds"]
# print(f'features shape:{features.shape}')
x = self.fc1(features)
# print(f'x:{x.shape}')
n_batch, n_channel, row, col = x.shape
# print(f'n_batch:{n_batch}, n_channel:{n_channel}, row:{row}, col:{col}')
xs, ys, fs, ps, idx, jcs = [], [], [], [], [0], []
for i, meta in enumerate(wires_targets):
p, label, feat, jc = self.sample_lines(
meta, h["jmap"][i], h["joff"][i],
)
# print(f"p.shape:{p.shape},label:{label.shape},feat:{feat.shape},jc:{len(jc)}")
ys.append(label)
if self.training and self.do_static_sampling:
p = torch.cat([p, meta["lpre"]])
feat = torch.cat([feat, meta["lpre_feat"]])
ys.append(meta["lpre_label"])
del jc
else:
jcs.append(jc)
ps.append(p)
fs.append(feat)
p = p[:, 0:1, :] * self.lambda_ + p[:, 1:2, :] * (1 - self.lambda_) - 0.5
p = p.reshape(-1, 2) # [N_LINE x N_POINT, 2_XY]
px, py = p[:, 0].contiguous(), p[:, 1].contiguous()
px0 = px.floor().clamp(min=0, max=127)
py0 = py.floor().clamp(min=0, max=127)
px1 = (px0 + 1).clamp(min=0, max=127)
py1 = (py0 + 1).clamp(min=0, max=127)
px0l, py0l, px1l, py1l = px0.long(), py0.long(), px1.long(), py1.long()
# xp: [N_LINE, N_CHANNEL, N_POINT]
xp = (
(
x[i, :, px0l, py0l] * (px1 - px) * (py1 - py)
+ x[i, :, px1l, py0l] * (px - px0) * (py1 - py)
+ x[i, :, px0l, py1l] * (px1 - px) * (py - py0)
+ x[i, :, px1l, py1l] * (px - px0) * (py - py0)
)
.reshape(n_channel, -1, self.n_pts0)
.permute(1, 0, 2)
)
xp = self.pooling(xp)
# print(f'xp.shape:{xp.shape}')
xs.append(xp)
idx.append(idx[-1] + xp.shape[0])
# print(f'idx__:{idx}')
x, y = torch.cat(xs), torch.cat(ys)
f = torch.cat(fs)
x = x.reshape(-1, self.n_pts1 * self.dim_loi)
# print("Weight dtype:", self.fc2.weight.dtype)
x = torch.cat([x, f], 1)
# print("Input dtype:", x.dtype)
x = x.to(dtype=torch.float32)
# print("Input dtype1:", x.dtype)
x = self.fc2(x).flatten()
# return x,idx,jcs,n_batch,ps,self.n_out_line,self.n_out_junc
return x, y, idx, jcs, n_batch, ps, self.n_out_line, self.n_out_junc
# if mode != "training":
# self.inference(x, idx, jcs, n_batch, ps)
# return result
def sample_lines(self, meta, jmap, joff):
device = jmap.device
with torch.no_grad():
junc = meta["junc_coords"].to(device) # [N, 2]
jtyp = meta["jtyp"].to(device) # [N]
Lpos = meta["line_pos_idx"].to(device)
Lneg = meta["line_neg_idx"].to(device)
n_type = jmap.shape[0]
jmap = non_maximum_suppression(jmap).reshape(n_type, -1)
joff = joff.reshape(n_type, 2, -1)
max_K = self.n_dyn_junc // n_type
N = len(junc)
# if mode != "training":
if not self.training:
K = min(int((jmap > self.eval_junc_thres).float().sum().item()), max_K)
else:
K = min(int(N * 2 + 2), max_K)
if K < 2:
K = 2
device = jmap.device
# index: [N_TYPE, K]
score, index = torch.topk(jmap, k=K)
y = (index // 128).float() + torch.gather(joff[:, 0], 1, index) + 0.5
x = (index % 128).float() + torch.gather(joff[:, 1], 1, index) + 0.5
# xy: [N_TYPE, K, 2]
xy = torch.cat([y[..., None], x[..., None]], dim=-1)
xy_ = xy[..., None, :]
del x, y, index
# dist: [N_TYPE, K, N]
dist = torch.sum((xy_ - junc) ** 2, -1)
cost, match = torch.min(dist, -1)
# xy: [N_TYPE * K, 2]
# match: [N_TYPE, K]
for t in range(n_type):
match[t, jtyp[match[t]] != t] = N
match[cost > 1.5 * 1.5] = N
match = match.flatten()
_ = torch.arange(n_type * K, device=device)
u, v = torch.meshgrid(_, _)
u, v = u.flatten(), v.flatten()
up, vp = match[u], match[v]
label = Lpos[up, vp]
# if mode == "training":
if self.training:
c = torch.zeros_like(label, dtype=torch.bool)
# sample positive lines
cdx = label.nonzero().flatten()
if len(cdx) > self.n_dyn_posl:
# print("too many positive lines")
perm = torch.randperm(len(cdx), device=device)[: self.n_dyn_posl]
cdx = cdx[perm]
c[cdx] = 1
# sample negative lines
cdx = Lneg[up, vp].nonzero().flatten()
if len(cdx) > self.n_dyn_negl:
# print("too many negative lines")
perm = torch.randperm(len(cdx), device=device)[: self.n_dyn_negl]
cdx = cdx[perm]
c[cdx] = 1
# sample other (unmatched) lines
cdx = torch.randint(len(c), (self.n_dyn_othr,), device=device)
c[cdx] = 1
else:
c = (u < v).flatten()
# sample lines
u, v, label = u[c], v[c], label[c]
xy = xy.reshape(n_type * K, 2)
xyu, xyv = xy[u], xy[v]
u2v = xyu - xyv
u2v /= torch.sqrt((u2v ** 2).sum(-1, keepdim=True)).clamp(min=1e-6)
feat = torch.cat(
[
xyu / 128 * self.use_cood,
xyv / 128 * self.use_cood,
u2v * self.use_slop,
(u[:, None] > K).float(),
(v[:, None] > K).float(),
],
1,
)
line = torch.cat([xyu[:, None], xyv[:, None]], 1)
xy = xy.reshape(n_type, K, 2)
jcs = [xy[i, score[i] > 0.03] for i in range(n_type)]
return line, label.float(), feat, jcs
_COMMON_META = {
"categories": _COCO_PERSON_CATEGORIES,
"keypoint_names": _COCO_PERSON_KEYPOINT_NAMES,
"min_size": (1, 1),
}