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Commit 41b18fd8 authored by zhe chen's avatar zhe chen
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Use pre-commit to reformat code 


Use pre-commit to reformat code
parent ff20ea39
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Showing with 362 additions and 421 deletions
autonomous_driving/Online-HD-Map-Construction/src/models/heads/polyline_generator.py
View file @ 41b18fd8
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import force_fp32
from mmdet.models import HEADS
from torch.distributions.categorical import Categorical
from mmdet.models import HEADS
from .detgen_utils.causal_trans import (CausalTransformerDecoder,
CausalTransformerDecoderLayer)
from .detgen_utils.utils import (dequantize_verts, generate_square_subsequent_mask,
quantize_verts, top_k_logits, top_p_logits)
from mmcv.runner import force_fp32, auto_fp16
CausalTransformerDecoderLayer)
from .detgen_utils.utils import (generate_square_subsequent_mask, top_k_logits,
top_p_logits)
@HEADS.register_module(force=True)
class PolylineGenerator(nn.Module):
......@@ -63,7 +63,7 @@ class PolylineGenerator(nn.Module):
self.fp16_enabled = False
self.coord_dim = coord_dim # if we use xyz else 2 when we use xy
self.kp_coord_dim = coord_dim if coord_dim==2 else 2 # XXX
self.kp_coord_dim = coord_dim if coord_dim == 2 else 2 # XXX
self.register_buffer('canvas_size', torch.tensor(canvas_size))
# initialize the model
......@@ -126,7 +126,7 @@ class PolylineGenerator(nn.Module):
# Discrete vertex value embeddings
vert_embeddings = self.vertex_embed(bbox)
return vert_embeddings + (bbox_embedding+coord_embeddings)[None]
return vert_embeddings + (bbox_embedding + coord_embeddings)[None]
def _prepare_context(self, batch, context):
"""Prepare class label and vertex context."""
......@@ -169,7 +169,7 @@ class PolylineGenerator(nn.Module):
def _embed_inputs(self, seqs, condition_embedding=None):
"""Embeds face sequences and adds within and between face positions.
Args:
seq: B, seqlen=vlen*3,
seq: B, seqlen=vlen*3,
condition_embedding: B, [c,xs,ys,xe,ye](5), h
Returns:
embeddings: B, seqlen, h
......@@ -189,7 +189,7 @@ class PolylineGenerator(nn.Module):
# Aggregate embeddings
embeddings = vert_embeddings + \
(coord_embeddings+pos_embeddings)[None]
(coord_embeddings + pos_embeddings)[None]
embeddings = torch.cat([condition_embedding, embeddings], dim=1)
return embeddings
......@@ -210,7 +210,7 @@ class PolylineGenerator(nn.Module):
return self.forward_train(batch, **kwargs)
else:
return self.inference(batch, **kwargs)
def sperate_forward(self, batch, context, **kwargs):
polyline_length = batch['polyline_masks'].sum(-1)
......@@ -218,24 +218,22 @@ class PolylineGenerator(nn.Module):
sizes = [size, polyline_length.max()]
polyline_logits = []
for c_idx, size in zip([c1,c2], sizes):
new_batch = assign_batch(batch,c_idx, size)
_poly_logits = self._forward_train(new_batch,context,**kwargs)
for c_idx, size in zip([c1, c2], sizes):
new_batch = assign_batch(batch, c_idx, size)
_poly_logits = self._forward_train(new_batch, context, **kwargs)
polyline_logits.append(_poly_logits)
# maybe imporve the speed
for i, (_poly_logits, size) in enumerate(zip(polyline_logits, sizes)):
# maybe imporve the speed
for i, (_poly_logits, size) in enumerate(zip(polyline_logits, sizes)):
if size < sizes[1]:
_poly_logits = F.pad(_poly_logits, (0,0,0,sizes[1]-size), "constant", 0)
_poly_logits = F.pad(_poly_logits, (0, 0, 0, sizes[1] - size), 'constant', 0)
polyline_logits[i] = _poly_logits
polyline_logits = torch.cat(polyline_logits,0)
polyline_logits = torch.cat(polyline_logits, 0)
polyline_logits = polyline_logits[revert_idx]
cat_dist = Categorical(logits=polyline_logits)
return {'polylines':cat_dist}
return {'polylines': cat_dist}
def forward_train(self, batch: dict, context: dict, **kwargs):
"""
......@@ -247,7 +245,7 @@ class PolylineGenerator(nn.Module):
if False:
polyline_logits = self._forward_train(batch, context, **kwargs)
cat_dist = Categorical(logits=polyline_logits)
return {'polylines':cat_dist}
return {'polylines': cat_dist}
else:
return self.sperate_forward(batch, context, **kwargs)
......@@ -260,7 +258,7 @@ class PolylineGenerator(nn.Module):
# we use the gt vertices
global_context, seq_context = self._prepare_context(
batch, context)
logits = self.body(
# Last element not used for preds
batch['polylines'][:, :-1],
......@@ -271,7 +269,7 @@ class PolylineGenerator(nn.Module):
return logits
@force_fp32(apply_to=('global_context_embedding','sequential_context_embeddings','cache'))
@force_fp32(apply_to=('global_context_embedding', 'sequential_context_embeddings', 'cache'))
def body(self,
seqs,
global_context_embedding=None,
......@@ -303,7 +301,7 @@ class PolylineGenerator(nn.Module):
if is_training:
causal_msk = generate_square_subsequent_mask(
decoder_inputs.shape[0], condition_len=condition_len, device=decoder_inputs.device)
decoder_outputs, cache = self.decoder(
tgt=decoder_inputs,
cache=cache,
......@@ -314,28 +312,27 @@ class PolylineGenerator(nn.Module):
decoder_outputs = decoder_outputs.transpose(0, 1)
# since we only need the predict seq
decoder_outputs = decoder_outputs[:, condition_len-1:]
decoder_outputs = decoder_outputs[:, condition_len - 1:]
# Get logits and optionally process for sampling
logits = self._project_to_logits(decoder_outputs)
# y mask
_vert_mask = torch.arange(logits.shape[-1], device=logits.device)
vertices_mask_y = (_vert_mask < self.canvas_size[1]+1)
vertices_mask_y[0] = False # y position doesn't have stop sign
vertices_mask_y = (_vert_mask < self.canvas_size[1] + 1)
vertices_mask_y[0] = False # y position doesn't have stop sign
logits[:, 1::self.coord_dim] = logits[:, 1::self.coord_dim] * \
vertices_mask_y - ~vertices_mask_y*1e9
vertices_mask_y - ~vertices_mask_y * 1e9
if self.coord_dim > 2:
# z mask
_vert_mask = torch.arange(logits.shape[-1], device=logits.device)
vertices_mask_z = (_vert_mask < self.canvas_size[2]+1)
vertices_mask_z[0] = False # y position doesn't have stop sign
vertices_mask_z = (_vert_mask < self.canvas_size[2] + 1)
vertices_mask_z[0] = False # y position doesn't have stop sign
logits[:, 2::self.coord_dim] = logits[:, 2::self.coord_dim] * \
vertices_mask_z - ~vertices_mask_z*1e9
vertices_mask_z - ~vertices_mask_z * 1e9
logits = logits/temperature
logits = logits / temperature
logits = top_k_logits(logits, top_k)
logits = top_p_logits(logits, top_p)
if return_logits:
......@@ -350,9 +347,9 @@ class PolylineGenerator(nn.Module):
weight = gt['polyline_weights']
mask = gt['polyline_masks']
loss = -torch.sum(
pred['polylines'].log_prob(gt['polylines']) * mask * weight)/weight.sum()
pred['polylines'].log_prob(gt['polylines']) * mask * weight) / weight.sum()
return {'seq': loss}
......@@ -395,16 +392,15 @@ class PolylineGenerator(nn.Module):
samples = torch.empty(
[batch_size, 0], dtype=torch.int32, device=device)
max_sample_length = max_sample_length or self.max_seq_length
seq_len = max_sample_length*self.coord_dim+1
seq_len = max_sample_length * self.coord_dim + 1
cache = None
decoded_tokens = \
torch.zeros((batch_size,seq_len),
device=device,dtype=torch.long)
torch.zeros((batch_size, seq_len),
device=device, dtype=torch.long)
remain_idx = torch.arange(batch_size, device=device)
for i in range(seq_len):
# While-loop body for autoregression calculation.
pred_dist, cache = self.body(
samples,
......@@ -417,22 +413,22 @@ class PolylineGenerator(nn.Module):
is_training=False)
samples = pred_dist.sample()
decoded_tokens[remain_idx,i] = samples[:,-1]
decoded_tokens[remain_idx, i] = samples[:, -1]
# Stopping conditions for autoregressive calculation.
if not (decoded_tokens[:,:i+1] != 0).all(-1).any():
if not (decoded_tokens[:, :i + 1] != 0).all(-1).any():
break
# update state, check the new position is zero.
valid_idx = (samples[:,-1] != 0).nonzero(as_tuple=True)[0]
valid_idx = (samples[:, -1] != 0).nonzero(as_tuple=True)[0]
remain_idx = remain_idx[valid_idx]
cache = cache[:,:,valid_idx]
cache = cache[:, :, valid_idx]
global_context = global_context[valid_idx]
seq_context = seq_context[valid_idx]
samples = samples[valid_idx]
# decoded_tokens = torch.cat(decoded_tokens,dim=1)
decoded_tokens = decoded_tokens[:,:i+1]
decoded_tokens = decoded_tokens[:, :i + 1]
outputs = self.post_process(decoded_tokens, seq_len,
device, only_return_complete)
......@@ -455,7 +451,7 @@ class PolylineGenerator(nn.Module):
_polyline_mask = torch.arange(sample_seq_length)[None].to(device)
# Get largest stopping point for incomplete samples.
valid_polyline_len = torch.full_like(polyline[:,0], sample_seq_length)
valid_polyline_len = torch.full_like(polyline[:, 0], sample_seq_length)
zero_inds = (polyline == 0).type(torch.int32).argmax(-1)
# Real length
......@@ -463,7 +459,7 @@ class PolylineGenerator(nn.Module):
polyline_mask = _polyline_mask < valid_polyline_len[:, None]
# Mask faces beyond stopping token with zeros
polyline = polyline*polyline_mask
polyline = polyline * polyline_mask
# Pad to maximum size with zeros
pad_size = max_seq_len - sample_seq_length
......@@ -486,27 +482,27 @@ class PolylineGenerator(nn.Module):
}
return outputs
def find_best_sperate_plan(idx,array):
def find_best_sperate_plan(idx, array):
h = array[-1] - array[idx]
w = idx
cost = h*w
cost = h * w
return cost
def get_chunk_idx(polyline_length):
_polyline_length, polyline_length_idx = torch.sort(polyline_length)
costs = []
for i in range(len(_polyline_length)):
cost = find_best_sperate_plan(i,_polyline_length)
cost = find_best_sperate_plan(i, _polyline_length)
costs.append(cost)
seperate_point = torch.stack(costs).argmax()
chunk1 = polyline_length_idx[:seperate_point+1]
chunk2 = polyline_length_idx[seperate_point+1:]
chunk1 = polyline_length_idx[:seperate_point + 1]
chunk2 = polyline_length_idx[seperate_point + 1:]
revert_idx = torch.argsort(polyline_length_idx)
revert_idx = torch.argsort(polyline_length_idx)
return chunk1, chunk2, revert_idx, _polyline_length[seperate_point]
......@@ -517,9 +513,9 @@ def assign_bev(feat, idx):
def assign_batch(batch, idx, size):
new_batch = {}
for k,v in batch.items():
for k, v in batch.items():
new_batch[k] = v[idx]
if new_batch[k].ndim > 1:
new_batch[k] = new_batch[k][:,:size]
new_batch[k] = new_batch[k][:, :size]
return new_batch
autonomous_driving/Online-HD-Map-Construction/src/models/losses/detr_loss.py
View file @ 41b18fd8
import mmcv
import torch
from mmdet.models.builder import LOSSES
from mmdet.models.losses.utils import weighted_loss
from torch import nn as nn
from torch.nn import functional as F
from mmdet.models.losses import l1_loss
from mmdet.models.losses.utils import weighted_loss
import mmcv
from mmdet.models.builder import LOSSES
@mmcv.jit(derivate=True, coderize=True)
......@@ -62,7 +60,7 @@ class LinesLoss(nn.Module):
target (torch.Tensor): The learning target of the prediction.
shape: [bs, ...]
weight (torch.Tensor, optional): The weight of loss for each
prediction. Defaults to None.
prediction. Defaults to None.
it's useful when the predictions are not all valid.
avg_factor (int, optional): Average factor that is used to average
the loss. Defaults to None.
......@@ -77,7 +75,7 @@ class LinesLoss(nn.Module):
loss = smooth_l1_loss(
pred, target, weight, reduction=reduction, avg_factor=avg_factor, beta=self.beta)
return loss*self.loss_weight
return loss * self.loss_weight
@mmcv.jit(derivate=True, coderize=True)
......@@ -123,7 +121,8 @@ class MasksLoss(nn.Module):
loss = bce(pred, target, weight, reduction=reduction,
avg_factor=avg_factor)
return loss*self.loss_weight
return loss * self.loss_weight
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
......@@ -165,6 +164,6 @@ class LenLoss(nn.Module):
reduction_override if reduction_override else self.reduction)
loss = ce(pred, target, weight, reduction=reduction,
avg_factor=avg_factor)
avg_factor=avg_factor)
return loss*self.loss_weight
\ No newline at end of file
return loss * self.loss_weight
autonomous_driving/Online-HD-Map-Construction/src/models/mapers/base_mapper.py
View file @ 41b18fd8
from abc import ABCMeta, abstractmethod
from abc import ABCMeta
import torch.nn as nn
from mmcv.runner import auto_fp16
from mmcv.utils import print_log
from mmdet.utils import get_root_logger
from mmdet3d.models.builder import DETECTORS
from mmdet.utils import get_root_logger
MAPPERS = DETECTORS
class BaseMapper(nn.Module, metaclass=ABCMeta):
"""Base class for mappers."""
......@@ -40,7 +39,7 @@ class BaseMapper(nn.Module, metaclass=ABCMeta):
return ((hasattr(self, 'roi_head') and self.roi_head.with_mask)
or (hasattr(self, 'mask_head') and self.mask_head is not None))
#@abstractmethod
# @abstractmethod
def extract_feat(self, imgs):
"""Extract features from images."""
pass
......@@ -48,11 +47,11 @@ class BaseMapper(nn.Module, metaclass=ABCMeta):
def forward_train(self, *args, **kwargs):
pass
#@abstractmethod
# @abstractmethod
def simple_test(self, img, img_metas, **kwargs):
pass
#@abstractmethod
# @abstractmethod
def aug_test(self, imgs, img_metas, **kwargs):
"""Test function with test time augmentation."""
pass
......@@ -88,7 +87,7 @@ class BaseMapper(nn.Module, metaclass=ABCMeta):
should be double nested (i.e. List[Tensor], List[List[dict]]), with
the outer list indicating test time augmentations.
"""
if return_loss:
return self.forward_train(*args, **kwargs)
else:
......@@ -123,7 +122,7 @@ class BaseMapper(nn.Module, metaclass=ABCMeta):
averaging the logs.
"""
loss, log_vars, num_samples = self(**data_dict)
outputs = dict(
loss=loss, log_vars=log_vars, num_samples=num_samples)
......@@ -137,7 +136,7 @@ class BaseMapper(nn.Module, metaclass=ABCMeta):
not implemented with this method, but an evaluation hook.
"""
loss, log_vars, num_samples = self(**data)
outputs = dict(
loss=loss, log_vars=log_vars, num_samples=num_samples)
......@@ -146,4 +145,4 @@ class BaseMapper(nn.Module, metaclass=ABCMeta):
def show_result(self,
**kwargs):
img = None
return img
\ No newline at end of file
return img
autonomous_driving/Online-HD-Map-Construction/src/models/mapers/vectormapnet.py
View file @ 41b18fd8
import mmcv
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet3d.models.builder import build_backbone, build_head, build_neck
from torch.nn.utils.rnn import pad_sequence
from torchvision.models.resnet import resnet18, resnet50
from torchvision.models.resnet import resnet18
from mmdet3d.models.builder import (build_backbone, build_head,
build_neck)
from .base_mapper import BaseMapper, MAPPERS
from .base_mapper import MAPPERS, BaseMapper
@MAPPERS.register_module()
......@@ -31,12 +27,11 @@ class VectorMapNet(BaseMapper):
model_name=None, **kwargs):
super(VectorMapNet, self).__init__()
#Attribute
# Attribute
self.model_name = model_name
self.last_epoch = None
self.only_det = only_det
self.backbone = build_backbone(backbone_cfg)
if neck_cfg is not None:
......@@ -53,17 +48,16 @@ class VectorMapNet(BaseMapper):
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1,
dilation=1, ceil_mode=False),
dilation=1, ceil_mode=False),
trunk.layer1,
nn.Conv2d(64, 128, kernel_size=1, bias=False),
)
# BEV
if hasattr(self.backbone,'bev_w'):
# BEV
if hasattr(self.backbone, 'bev_w'):
self.bev_w = self.backbone.bev_w
self.bev_h = self.backbone.bev_h
self.head = build_head(head_cfg)
def multiscale_neck(self, bev_embedding):
......@@ -79,12 +73,12 @@ class VectorMapNet(BaseMapper):
img: torch.Tensor of shape [B, N, 3, H, W]
N: number of cams
vectors: list[list[Tuple(lines, length, label)]]
- lines: np.array of shape [num_points, 2].
- lines: np.array of shape [num_points, 2].
- length: int
- label: int
len(vectors) = batch_size
len(vectors[_b]) = num of lines in sample _b
img_metas:
img_metas:
img_metas['lidar2img']: [B, N, 4, 4]
Out:
loss, log_vars, num_sample
......@@ -92,12 +86,12 @@ class VectorMapNet(BaseMapper):
# prepare labels and images
batch, img, img_metas, valid_idx, points = self.batch_data(
polys, img, img_metas, img.device, points)
# corner cases use hard code to prevent code fail
if self.last_epoch is None:
self.last_epoch = [batch, img, img_metas, valid_idx, points]
if len(valid_idx)==0:
if len(valid_idx) == 0:
batch, img, img_metas, valid_idx, points = self.last_epoch
else:
del self.last_epoch
......@@ -110,15 +104,15 @@ class VectorMapNet(BaseMapper):
# Neck
bev_feats = self.neck(_bev_feats)
preds_dict, losses_dict = \
self.head(batch,
self.head(batch,
context={
'bev_embeddings': bev_feats,
'batch_input_shape': _bev_feats.shape[2:],
'img_shape': img_shape,
'raw_bev_embeddings': _bev_feats},
only_det=self.only_det)
'bev_embeddings': bev_feats,
'batch_input_shape': _bev_feats.shape[2:],
'img_shape': img_shape,
'raw_bev_embeddings': _bev_feats},
only_det=self.only_det)
# format outputs
loss = 0
......@@ -150,13 +144,13 @@ class VectorMapNet(BaseMapper):
bev_feats = self.neck(_bev_feats)
context = {'bev_embeddings': bev_feats,
'batch_input_shape': _bev_feats.shape[2:],
'img_shape': img_shape, # XXX
'batch_input_shape': _bev_feats.shape[2:],
'img_shape': img_shape, # XXX
'raw_bev_embeddings': _bev_feats}
preds_dict = self.head(batch={},
context=context,
condition_on_det=True,
condition_on_det=True,
gt_condition=False,
only_det=self.only_det)
......@@ -173,7 +167,7 @@ class VectorMapNet(BaseMapper):
valid_idx = [i for i in range(len(polys)) if len(polys[i])]
imgs = imgs[valid_idx]
img_metas = [img_metas[i] for i in valid_idx]
polys = [polys[i] for i in valid_idx]
if points is not None:
......@@ -184,16 +178,16 @@ class VectorMapNet(BaseMapper):
return None, None, None, valid_idx, None
batch = {}
batch['det'] = format_det(polys,device)
batch['gen'] = format_gen(polys,device)
batch['det'] = format_det(polys, device)
batch['gen'] = format_gen(polys, device)
return batch, imgs, img_metas, valid_idx, points
def batch_points(self, points):
pad_points = pad_sequence(points, batch_first=True)
points_mask = torch.zeros_like(pad_points[:,:,0]).bool()
points_mask = torch.zeros_like(pad_points[:, :, 0]).bool()
for i in range(len(points)):
valid_num = points[i].shape[0]
points_mask[i][:valid_num] = True
......@@ -202,41 +196,38 @@ class VectorMapNet(BaseMapper):
def format_det(polys, device):
batch = {
'class_label':[],
'batch_idx':[],
'class_label': [],
'batch_idx': [],
'bbox': [],
}
for batch_idx, poly in enumerate(polys):
keypoint_label = torch.from_numpy(poly['det_label']).to(device)
keypoint = torch.from_numpy(poly['keypoint']).to(device)
batch['class_label'].append(keypoint_label)
batch['bbox'].append(keypoint)
return batch
def format_gen(polys,device):
def format_gen(polys, device):
line_cls = []
polylines, polyline_masks, polyline_weights = [], [], []
bbox, line_cls, line_bs_idx = [], [], []
for batch_idx, poly in enumerate(polys):
# convert to cuda tensor
for k in poly.keys():
if isinstance(poly[k],np.ndarray):
if isinstance(poly[k], np.ndarray):
poly[k] = torch.from_numpy(poly[k]).to(device)
else:
poly[k] = [torch.from_numpy(v).to(device) for v in poly[k]]
line_cls += poly['gen_label']
line_bs_idx += [batch_idx]*len(poly['gen_label'])
line_bs_idx += [batch_idx] * len(poly['gen_label'])
# condition
bbox += poly['qkeypoint']
......@@ -257,5 +248,5 @@ def format_gen(polys,device):
batch['polylines'] = pad_sequence(polylines, batch_first=True)
batch['polyline_masks'] = pad_sequence(polyline_masks, batch_first=True)
batch['polyline_weights'] = pad_sequence(polyline_weights, batch_first=True)
return batch
\ No newline at end of file
return batch
autonomous_driving/Online-HD-Map-Construction/src/models/transformer_utils/__init__.py
View file @ 41b18fd8
from .deformable_transformer import DeformableDetrTransformer_, DeformableDetrTransformerDecoder_
from .base_transformer import PlaceHolderEncoder
\ No newline at end of file
autonomous_driving/Online-HD-Map-Construction/src/models/transformer_utils/base_transformer.py
View file @ 41b18fd8
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.registry import TRANSFORMER_LAYER_SEQUENCE
from mmcv.cnn import xavier_init, constant_init
from mmcv.cnn.bricks.registry import (ATTENTION,
TRANSFORMER_LAYER_SEQUENCE)
from mmcv.cnn.bricks.transformer import (MultiScaleDeformableAttention,
TransformerLayerSequence,
build_transformer_layer_sequence)
from mmcv.runner.base_module import BaseModule
from mmdet.models.utils.builder import TRANSFORMER
@TRANSFORMER_LAYER_SEQUENCE.register_module()
class PlaceHolderEncoder(nn.Module):
......@@ -21,5 +10,4 @@ class PlaceHolderEncoder(nn.Module):
self.embed_dims = embed_dims
def forward(self, *args, query=None, **kwargs):
return query
\ No newline at end of file
return query
autonomous_driving/Online-HD-Map-Construction/src/models/transformer_utils/deformable_transformer.py
View file @ 41b18fd8
......@@ -4,18 +4,12 @@ import warnings
import torch
import torch.nn as nn
from mmcv.cnn import build_activation_layer, build_norm_layer, xavier_init
from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER,
TRANSFORMER_LAYER_SEQUENCE)
from mmcv.cnn.bricks.transformer import (BaseTransformerLayer,
TransformerLayerSequence,
build_transformer_layer_sequence)
from mmcv.runner.base_module import BaseModule
from torch.nn.init import normal_
from mmcv.cnn import xavier_init
from mmcv.cnn.bricks.registry import TRANSFORMER_LAYER_SEQUENCE
from mmcv.cnn.bricks.transformer import TransformerLayerSequence
from mmdet.models.utils.builder import TRANSFORMER
from mmdet.models.utils.transformer import Transformer
from torch.nn.init import normal_
try:
from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
......@@ -27,6 +21,7 @@ except ImportError:
from .fp16_dattn import MultiScaleDeformableAttentionFp16
def inverse_sigmoid(x, eps=1e-5):
"""Inverse function of sigmoid.
Args:
......@@ -44,6 +39,7 @@ def inverse_sigmoid(x, eps=1e-5):
x2 = (1 - x).clamp(min=eps)
return torch.log(x1 / x2)
@TRANSFORMER_LAYER_SEQUENCE.register_module()
class DeformableDetrTransformerDecoder_(TransformerLayerSequence):
"""Implements the decoder in DETR transformer.
......@@ -53,8 +49,8 @@ class DeformableDetrTransformerDecoder_(TransformerLayerSequence):
`LN`.
"""
def __init__(self, *args,
return_intermediate=False, coord_dim=2, kp_coord_dim=2, **kwargs):
def __init__(self, *args,
return_intermediate=False, coord_dim=2, kp_coord_dim=2, **kwargs):
super(DeformableDetrTransformerDecoder_, self).__init__(*args, **kwargs)
self.return_intermediate = return_intermediate
......@@ -94,25 +90,26 @@ class DeformableDetrTransformerDecoder_(TransformerLayerSequence):
for lid, layer in enumerate(self.layers):
reference_points_input = \
reference_points[:, :, None,:self.kp_coord_dim] * \
valid_ratios[:, None,:,:self.kp_coord_dim]
reference_points[:, :, None, :self.kp_coord_dim] * \
valid_ratios[:, None, :, :self.kp_coord_dim]
# if reference_points.shape[-1] == 3 and self.kp_coord_dim==2:
output = layer(
output,
*args,
reference_points=reference_points_input[...,:self.kp_coord_dim],
reference_points=reference_points_input[..., :self.kp_coord_dim],
**kwargs)
output = output.permute(1, 0, 2)
if reg_branches is not None:
tmp = reg_branches[lid](output)
tmp = reg_branches[lid](output)
new_reference_points = tmp
new_reference_points[..., :self.kp_coord_dim] = tmp[
..., :self.kp_coord_dim] + inverse_sigmoid(reference_points)
..., :self.kp_coord_dim] + inverse_sigmoid(
reference_points)
new_reference_points = new_reference_points.sigmoid()
if reference_points.shape[-1] == 3 and self.kp_coord_dim==2:
reference_points[...,-1] = tmp[...,-1].sigmoid().detach()
reference_points[...,:self.coord_dim] = new_reference_points.detach()
if reference_points.shape[-1] == 3 and self.kp_coord_dim == 2:
reference_points[..., -1] = tmp[..., -1].sigmoid().detach()
reference_points[..., :self.coord_dim] = new_reference_points.detach()
output = output.permute(1, 0, 2)
if self.return_intermediate:
......@@ -174,7 +171,7 @@ class DeformableDetrTransformer_(Transformer):
for m in self.modules():
if isinstance(m, MultiScaleDeformableAttention):
m.init_weights()
elif isinstance(m,MultiScaleDeformableAttentionFp16):
elif isinstance(m, MultiScaleDeformableAttentionFp16):
m.init_weights()
if not self.as_two_stage:
xavier_init(self.reference_points_embed, distribution='uniform', bias=0.)
......@@ -204,9 +201,9 @@ class DeformableDetrTransformer_(Transformer):
torch.linspace(
0.5, W - 0.5, W, dtype=torch.float32, device=device))
ref_y = ref_y.reshape(-1)[None] / (
valid_ratios[:, None, lvl, 1] * H)
valid_ratios[:, None, lvl, 1] * H)
ref_x = ref_x.reshape(-1)[None] / (
valid_ratios[:, None, lvl, 0] * W)
valid_ratios[:, None, lvl, 0] * W)
ref = torch.stack((ref_x, ref_y), -1)
reference_points_list.append(ref)
reference_points = torch.cat(reference_points_list, 1)
......@@ -231,7 +228,7 @@ class DeformableDetrTransformer_(Transformer):
scale = 2 * math.pi
dim_t = torch.arange(
num_pos_feats, dtype=torch.float32, device=proposals.device)
dim_t = temperature**(2 * (dim_t // 2) / num_pos_feats)
dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
# N, L, 4
proposals = proposals.sigmoid() * scale
# N, L, 4, 128
......@@ -317,7 +314,7 @@ class DeformableDetrTransformer_(Transformer):
spatial_shapes = torch.as_tensor(
spatial_shapes, dtype=torch.long, device=feat_flatten.device)
level_start_index = torch.cat((spatial_shapes.new_zeros(
(1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
(1,)), spatial_shapes.prod(1).cumsum(0)[:-1]))
valid_ratios = torch.stack(
[self.get_valid_ratio(m) for m in mlvl_masks], 1)
......@@ -343,7 +340,7 @@ class DeformableDetrTransformer_(Transformer):
memory = feat_flatten.permute(1, 0, 2)
bs, _, c = memory.shape
query_pos, query = torch.split(query_embed, c, dim=-1)
reference_points = self.reference_points_embed(query_pos).sigmoid()
init_reference_out = reference_points
......@@ -366,4 +363,4 @@ class DeformableDetrTransformer_(Transformer):
**kwargs)
inter_references_out = inter_references
return inter_states, init_reference_out, inter_references_out
\ No newline at end of file
return inter_states, init_reference_out, inter_references_out
autonomous_driving/Online-HD-Map-Construction/src/models/transformer_utils/fp16_dattn.py
View file @ 41b18fd8
from turtle import forward
import warnings
try:
from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
except ImportError:
......@@ -7,12 +7,6 @@ except ImportError:
'`MultiScaleDeformableAttention` in MMCV has been moved to '
'`mmcv.ops.multi_scale_deform_attn`, please update your MMCV')
from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention
from mmcv.runner import force_fp32, auto_fp16
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.runner.base_module import BaseModule, ModuleList, Sequential
from mmcv.cnn.bricks.transformer import build_attention
import math
import warnings
......@@ -20,13 +14,15 @@ import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd.function import Function, once_differentiable
from mmcv import deprecated_api_warning
from mmcv.cnn import constant_init, xavier_init
from mmcv.cnn.bricks.registry import ATTENTION
from mmcv.runner import BaseModule
from mmcv.cnn.bricks.transformer import build_attention
from mmcv.runner import force_fp32
from mmcv.runner.base_module import BaseModule
from mmcv.utils import ext_loader
from torch.autograd.function import Function, once_differentiable
ext_module = ext_loader.load_ext(
'_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward'])
from torch.cuda.amp import custom_bwd, custom_fwd
......@@ -35,16 +31,15 @@ from torch.cuda.amp import custom_bwd, custom_fwd
@ATTENTION.register_module()
class MultiScaleDeformableAttentionFp16(BaseModule):
def __init__(self, attn_cfg=None,init_cfg=None,**kwarg):
super(MultiScaleDeformableAttentionFp16,self).__init__(init_cfg)
def __init__(self, attn_cfg=None, init_cfg=None, **kwarg):
super(MultiScaleDeformableAttentionFp16, self).__init__(init_cfg)
# import ipdb; ipdb.set_trace()
self.deformable_attention = build_attention(attn_cfg)
self.deformable_attention.init_weights()
self.fp16_enabled = False
@force_fp32(apply_to=('query', 'key', 'value', 'query_pos', 'reference_points','identity'))
@force_fp32(apply_to=('query', 'key', 'value', 'query_pos', 'reference_points', 'identity'))
def forward(self, query,
key=None,
value=None,
......@@ -57,15 +52,14 @@ class MultiScaleDeformableAttentionFp16(BaseModule):
**kwargs):
# import ipdb; ipdb.set_trace()
return self.deformable_attention(query,
key=key,
value=value,
identity=identity,
query_pos=query_pos,
key_padding_mask=key_padding_mask,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index,**kwargs)
key=key,
value=value,
identity=identity,
query_pos=query_pos,
key_padding_mask=key_padding_mask,
reference_points=reference_points,
spatial_shapes=spatial_shapes,
level_start_index=level_start_index, **kwargs)
class MultiScaleDeformableAttnFunctionFp32(Function):
......@@ -118,8 +112,8 @@ class MultiScaleDeformableAttnFunctionFp32(Function):
Tuple[Tensor]: Gradient
of input tensors in forward.
"""
value, value_spatial_shapes, value_level_start_index,\
sampling_locations, attention_weights = ctx.saved_tensors
value, value_spatial_shapes, value_level_start_index, \
sampling_locations, attention_weights = ctx.saved_tensors
grad_value = torch.zeros_like(value)
grad_sampling_loc = torch.zeros_like(sampling_locations)
grad_attn_weight = torch.zeros_like(attention_weights)
......@@ -137,7 +131,7 @@ class MultiScaleDeformableAttnFunctionFp32(Function):
im2col_step=ctx.im2col_step)
return grad_value, None, None, \
grad_sampling_loc, grad_attn_weight, None
grad_sampling_loc, grad_attn_weight, None
def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
......@@ -161,7 +155,7 @@ def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
"""
bs, _, num_heads, embed_dims = value.shape
_, num_queries, num_heads, num_levels, num_points, _ =\
_, num_queries, num_heads, num_levels, num_points, _ = \
sampling_locations.shape
value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes],
dim=1)
......@@ -178,7 +172,7 @@ def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes,
# bs, num_heads, num_queries, num_points, 2 ->
# bs*num_heads, num_queries, num_points, 2
sampling_grid_l_ = sampling_grids[:, :, :,
level].transpose(1, 2).flatten(0, 1)
level].transpose(1, 2).flatten(0, 1)
# bs*num_heads, embed_dims, num_queries, num_points
sampling_value_l_ = F.grid_sample(
value_l_,
......@@ -281,8 +275,8 @@ class MultiScaleDeformableAttentionFP32(BaseModule):
grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
grid_init = (grid_init /
grid_init.abs().max(-1, keepdim=True)[0]).view(
self.num_heads, 1, 1,
2).repeat(1, self.num_levels, self.num_points, 1)
self.num_heads, 1, 1,
2).repeat(1, self.num_levels, self.num_points, 1)
for i in range(self.num_points):
grid_init[:, :, i, :] *= i + 1
......@@ -373,13 +367,13 @@ class MultiScaleDeformableAttentionFP32(BaseModule):
offset_normalizer = torch.stack(
[spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
sampling_locations = reference_points[:, :, None, :, None, :] \
+ sampling_offsets \
/ offset_normalizer[None, None, None, :, None, :]
+ sampling_offsets \
/ offset_normalizer[None, None, None, :, None, :]
elif reference_points.shape[-1] == 4:
sampling_locations = reference_points[:, :, None, :, None, :2] \
+ sampling_offsets / self.num_points \
* reference_points[:, :, None, :, None, 2:] \
* 0.5
+ sampling_offsets / self.num_points \
* reference_points[:, :, None, :, None, 2:] \
* 0.5
else:
raise ValueError(
f'Last dim of reference_points must be'
......@@ -399,4 +393,4 @@ class MultiScaleDeformableAttentionFP32(BaseModule):
# (num_query, bs ,embed_dims)
output = output.permute(1, 0, 2)
return self.dropout(output) + identity
\ No newline at end of file
return self.dropout(output) + identity
autonomous_driving/Online-HD-Map-Construction/tools/evaluate_submission.py
View file @ 41b18fd8
import sys
import os
sys.path.append(os.path.abspath('.'))
from src.datasets.evaluation.vector_eval import VectorEvaluate
import sys
sys.path.append(os.path.abspath('.'))
import argparse
from src.datasets.evaluation.vector_eval import VectorEvaluate
def parse_args():
parser = argparse.ArgumentParser(
description='Evaluate a submission file')
parser.add_argument('submission',
help='submission file in pickle or json format to be evaluated')
parser.add_argument('gt',
help='gt annotation file')
parser.add_argument('submission',
help='submission file in pickle or json format to be evaluated')
parser.add_argument('gt',
help='gt annotation file')
args = parser.parse_args()
return args
def main(args):
evaluator = VectorEvaluate(args.gt, n_workers=0)
results = evaluator.evaluate(args.submission)
print(results)
if __name__ == '__main__':
args = parse_args()
main(args)
autonomous_driving/Online-HD-Map-Construction/tools/mmdet_test.py
View file @ 41b18fd8
......@@ -9,7 +9,6 @@ import torch
import torch.distributed as dist
from mmcv.image import tensor2imgs
from mmcv.runner import get_dist_info
from mmdet.core import encode_mask_results
......@@ -120,7 +119,7 @@ def collect_results_cpu(result_part, size, tmpdir=None):
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
dir_tensor = torch.full((MAX_LEN,),
32,
dtype=torch.uint8,
device='cuda')
......
autonomous_driving/Online-HD-Map-Construction/tools/mmdet_train.py
View file @ 41b18fd8
......@@ -8,7 +8,6 @@ from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
Fp16OptimizerHook, OptimizerHook, build_optimizer,
build_runner)
from mmcv.utils import build_from_cfg
from mmdet.core import DistEvalHook, EvalHook
from mmdet.datasets import (build_dataloader, build_dataset,
replace_ImageToTensor)
......
autonomous_driving/Online-HD-Map-Construction/tools/test.py
View file @ 41b18fd8
import argparse
import mmcv
import os
import os.path as osp
import torch
import warnings
from mmcv import Config, DictAction
from mmcv import Config
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
wrap_fp16_model)
from mmdet3d.apis import single_gpu_test
from mmdet3d.datasets import build_dataloader, build_dataset
from mmdet3d.models import build_model
from mmdet.datasets import replace_ImageToTensor
from mmdet_test import multi_gpu_test
from mmdet_train import set_random_seed
from mmdet.datasets import replace_ImageToTensor
def parse_args():
......@@ -29,13 +27,13 @@ def parse_args():
'--fuse-conv-bn',
action='store_true',
help='Whether to fuse conv and bn, this will slightly increase'
'the inference speed')
'the inference speed')
parser.add_argument(
'--format-only',
action='store_true',
help='Format the output results without perform evaluation. It is'
'useful when you want to format the result to a specific format and '
'submit it to the test server')
'useful when you want to format the result to a specific format and '
'submit it to the test server')
parser.add_argument(
'--eval',
action='store_true',
......@@ -47,7 +45,7 @@ def parse_args():
parser.add_argument(
'--tmpdir',
help='tmp directory used for collecting results from multiple '
'workers, available when gpu-collect is not specified')
'workers, available when gpu-collect is not specified')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument(
'--deterministic',
......@@ -74,8 +72,8 @@ def main():
if (args.eval and args.format_only) or (not args.eval and not args.format_only):
raise ValueError('Please specify exactly one operation (eval/format) '
'with the argument "--eval" or "--format-only"')
'with the argument "--eval" or "--format-only"')
if args.eval and args.split == 'test':
raise ValueError('Cannot evaluate on test set')
......@@ -90,7 +88,7 @@ def main():
# import modules from plguin/xx, registry will be updated
import sys
sys.path.append(os.path.abspath('.'))
sys.path.append(os.path.abspath('.'))
if hasattr(cfg, 'plugin'):
if cfg.plugin:
import importlib
......@@ -106,11 +104,11 @@ def main():
plg_lib = importlib.import_module(_module_path)
plugin_dirs = cfg.plugin_dir
if not isinstance(plugin_dirs,list):
plugin_dirs = [plugin_dirs,]
if not isinstance(plugin_dirs, list):
plugin_dirs = [plugin_dirs, ]
for plugin_dir in plugin_dirs:
import_path(plugin_dir)
else:
# import dir is the dirpath for the config file
_module_dir = os.path.dirname(args.config)
......@@ -151,10 +149,10 @@ def main():
elif cfg.get('work_dir', None) is None:
# use config filename as default work_dir if cfg.work_dir is None
cfg.work_dir = osp.join('./work_dirs',
osp.splitext(osp.basename(args.config))[0])
osp.splitext(osp.basename(args.config))[0])
cfg_data_dict.work_dir = cfg.work_dir
print('work_dir: ',cfg.work_dir)
print('work_dir: ', cfg.work_dir)
dataset = build_dataset(cfg_data_dict)
data_loader = build_dataloader(
dataset,
......@@ -181,7 +179,7 @@ def main():
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
outputs = multi_gpu_test(model, data_loader, args.tmpdir,
args.gpu_collect)
args.gpu_collect)
rank, _ = get_dist_info()
if rank == 0:
......
autonomous_driving/Online-HD-Map-Construction/tools/train.py
View file @ 41b18fd8
......@@ -2,26 +2,25 @@ from __future__ import division
import argparse
import copy
import mmcv
import os
import time
import torch
import warnings
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from os import path as osp
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import get_dist_info, init_dist
from mmdet import __version__ as mmdet_version
from mmdet3d import __version__ as mmdet3d_version
from mmdet3d.apis import train_model
from mmdet3d.datasets import build_dataset
# from builder import build_model
from mmdet3d.models import build_model
from mmdet3d.utils import collect_env, get_root_logger
from mmseg import __version__ as mmseg_version
# warper
from mmdet_train import set_random_seed
# from builder import build_model
from mmdet3d.models import build_model
from mmseg import __version__ as mmseg_version
def parse_args():
......@@ -39,13 +38,13 @@ def parse_args():
'--gpus',
type=int,
help='number of gpus to use '
'(only applicable to non-distributed training)')
'(only applicable to non-distributed training)')
group_gpus.add_argument(
'--gpu-ids',
type=int,
nargs='+',
help='ids of gpus to use '
'(only applicable to non-distributed training)')
'(only applicable to non-distributed training)')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument(
'--deterministic',
......@@ -56,18 +55,18 @@ def parse_args():
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file (deprecate), '
'change to --cfg-options instead.')
'in xxx=yyy format will be merged into config file (deprecate), '
'change to --cfg-options instead.')
parser.add_argument(
'--cfg-options',
nargs='+',
action=DictAction,
help='override some settings in the used config, the key-value pair '
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
'in xxx=yyy format will be merged into config file. If the value to '
'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
'Note that the quotation marks are necessary and that no white space '
'is allowed.')
parser.add_argument(
'--launcher',
choices=['none', 'pytorch', 'slurm', 'mpi'],
......@@ -110,7 +109,7 @@ def main():
# import modules, registry will be updated
import sys
sys.path.append(os.path.abspath('.'))
sys.path.append(os.path.abspath('.'))
if hasattr(cfg, 'plugin'):
if cfg.plugin:
import importlib
......@@ -126,11 +125,11 @@ def main():
plg_lib = importlib.import_module(_module_path)
plugin_dirs = cfg.plugin_dir
if not isinstance(plugin_dirs,list):
plugin_dirs = [plugin_dirs,]
if not isinstance(plugin_dirs, list):
plugin_dirs = [plugin_dirs, ]
for plugin_dir in plugin_dirs:
import_path(plugin_dir)
else:
# import dir is the dirpath for the config file
_module_dir = os.path.dirname(args.config)
......
autonomous_driving/README.md
View file @ 41b18fd8
<div id="top" align="center">
# InternImage for CVPR 2023 Workshop on End-to-End Autonomous Driving
</div>
</div>
## 1. InternImage-based Baseline for CVPR23 Occupancy Prediction Challenge
We achieve an improvement of 1.44 in MIOU baseline by leveraging the InterImage-based model.
model name|weight| mIoU | others | barrier | bicycle | bus | car | construction_vehicle | motorcycle | pedestrian | traffic_cone | trailer | truck | driveable_surface | other_flat | sidewalk | terrain | manmade | vegetation |
----|:----------:| :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :----------------------: | :---: | :------: | :------: |
bevformer_intern-s_occ|[Google Drive](https://drive.google.com/file/d/1LV9K8hrskKf51xY1wbqTKzK7WZmVXEV_/view?usp=sharing)| 25.11 | 6.93 | 35.57 | 10.40 | 35.97 | 41.23 | 13.72 | 20.30 | 21.10 | 18.34 | 19.18 | 28.64 | 49.82 | 30.74 | 31.00 | 27.44 | 19.29 | 17.29 |
bevformer_base_occ|[Google Drive](https://drive.google.com/file/d/1NyoiosafAmne1qiABeNOPXR-P-y0i7_I/view?usp=share_link)| 23.67 | 5.03 | 38.79 | 9.98 | 34.41 | 41.09 | 13.24 | 16.50 | 18.15 | 17.83 | 18.66 | 27.70 | 48.95 | 27.73 | 29.08 | 25.38 | 15.41 | 14.46 |
| model name | weight | mIoU | others | barrier | bicycle | bus | car | construction_vehicle | motorcycle | pedestrian | traffic_cone | trailer | truck | driveable_surface | other_flat | sidewalk | terrain | manmade | vegetation |
| ---------------------- | :---------------------------------------------------------------------------------------------------: | :---: | :----: | :-----: | :-----: | :---: | :---: | :------------------: | :--------: | :--------: | :----------: | :-----: | :---: | :---------------: | :--------: | :------: | :-----: | :-----: | :--------: |
| bevformer_intern-s_occ | [Google Drive](https://drive.google.com/file/d/1LV9K8hrskKf51xY1wbqTKzK7WZmVXEV_/view?usp=sharing) | 25.11 | 6.93 | 35.57 | 10.40 | 35.97 | 41.23 | 13.72 | 20.30 | 21.10 | 18.34 | 19.18 | 28.64 | 49.82 | 30.74 | 31.00 | 27.44 | 19.29 | 17.29 |
| bevformer_base_occ | [Google Drive](https://drive.google.com/file/d/1NyoiosafAmne1qiABeNOPXR-P-y0i7_I/view?usp=share_link) | 23.67 | 5.03 | 38.79 | 9.98 | 34.41 | 41.09 | 13.24 | 16.50 | 18.15 | 17.83 | 18.66 | 27.70 | 48.95 | 27.73 | 29.08 | 25.38 | 15.41 | 14.46 |
### Get Started
please refer to [README.md](./occupancy_prediction/README.md)
please refer to [README.md](./occupancy_prediction/README.md)
## 2. InternImage-based Baseline for Online HD Map Construction Challenge For Autonomous Driving
By incorporating the InterImage-based model, we observe an enhancement of 6.56 in mAP baseline.
model name|weight|$\mathrm{mAP}$ | $\mathrm{AP}_{pc}$ | $\mathrm{AP}_{div}$ | $\mathrm{AP}_{bound}$ |
----|:----------:| :--: | :--: | :--: | :--: |
vectormapnet_intern|[Checkpoint](https://github.com/OpenGVLab/InternImage/releases/download/track_model/vectormapnet_internimage.pth) | 49.35 | 45.05 | 56.78 | 46.22 |
vectormapnet_base|[Google Drive](https://drive.google.com/file/d/16D1CMinwA8PG1sd9PV9_WtHzcBohvO-D/view) | 42.79 | 37.22 | 50.47 | 40.68 |
| model name | weight | $\\mathrm{mAP}$ | $\\mathrm{AP}\_{pc}$ | $\\mathrm{AP}\_{div}$ | $\\mathrm{AP}\_{bound}$ |
| ------------------- | :---------------------------------------------------------------------------------------------------------------: | :-------------: | :------------------: | :-------------------: | :---------------------: |
| vectormapnet_intern | [Checkpoint](https://github.com/OpenGVLab/InternImage/releases/download/track_model/vectormapnet_internimage.pth) | 49.35 | 45.05 | 56.78 | 46.22 |
| vectormapnet_base | [Google Drive](https://drive.google.com/file/d/16D1CMinwA8PG1sd9PV9_WtHzcBohvO-D/view) | 42.79 | 37.22 | 50.47 | 40.68 |
### Get Started
please refer to [README.md](Online-HD-Map-Construction/README.md)
please refer to [README.md](Online-HD-Map-Construction/README.md)
## 3. InternImage-based Baseline for CVPR23 OpenLane-V2 Challenge
Through the implementation of the InterImage-based model, we achieve an advancement of 0.009 in F-score baseline.
Through the implementation of the InterImage-based model, we achieve an advancement of 0.009 in F-score baseline.
| | OpenLane-V2 Score | DET<sub>l</sub> | DET<sub>t</sub> | TOP<sub>ll</sub> | TOP<sub>lt</sub> | F-Score |
|-------------|-------------------|-----------------|-----------------|------------------|------------------|---------|
| ----------- | ----------------- | --------------- | --------------- | ---------------- | ---------------- | ------- |
| base r50 | 0.292 | 0.183 | 0.457 | 0.022 | 0.143 | 0.215 |
| InternImage | 0.325 | 0.194 | 0.537 | 0.02 | 0.17 | 0.224 |
### Get Started
please refer to [README.md](./openlane-v2/README.md)
please refer to [README.md](./openlane-v2/README.md)
autonomous_driving/occupancy_prediction/CITATION.cff
View file @ 41b18fd8
......@@ -5,4 +5,4 @@ authors:
title: "OpenOccupancy: 3D Occupancy Benchmark for Scene Perception in Autonomous Driving"
date-released: 2023-02-10
url: "https://github.com/CVPR2023-Occupancy-Prediction-Challenge/CVPR2023-Occupancy-Prediction-Challenge"
license: Apache-2.0
\ No newline at end of file
license: Apache-2.0
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