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eval.py
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eval.py
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import argparse
import os
import os.path as osp
import pickle
import socket
import time
import warnings
from datetime import datetime
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
import wandb
from matplotlib import pyplot as plt
try:
from mmcv import Config
except:
from mmengine.config import Config
try:
from mmseg.utils import get_root_logger
except:
from mmengine.logging import MMLogger
from torch.nn import MSELoss
from builder import loss_builder
from dataloader.dataset import get_nuScenes_label_name
from train import IGNORE_LABEL_SEMANTIC
from utils.load_save_util import revise_ckpt, revise_ckpt_2, revise_ckpt_linear_probe
from utils.metric_util import MeanIoU
from visualization.training import show3d
VIS = True # False
EMPTY_SEMANTIC_LABEL = 17
IGNORE_LABEL = 255
OCCUPIED_AGNOSTIC_LABEL = 1
warnings.filterwarnings("ignore")
NCLS2CONFIG = {
7: 'config/label_mapping/nuscenes-7cls.yaml',
29: 'config/label_mapping/nuscenes-29cls.yaml',
}
ROAD_SEM_IDX = 11
def pass_print(*args, **kwargs):
pass
def assign_labels_clip(predicted_features, text_features, class_offset, maskclip=False, class_mapping_clip=None,
ignore_label=None, normalized_cosine=False):
if maskclip:
if ignore_label is not None and class_mapping_clip is not None:
nonignored_indices = class_mapping_clip != ignore_label
text_features = text_features[nonignored_indices]
class_mapping_clip_nonignore = class_mapping_clip[nonignored_indices]
else:
class_mapping_clip_nonignore = class_mapping_clip
if normalized_cosine:
predicted_features_norm = F.normalize(predicted_features, dim=-1)
text_features_norm = F.normalize(text_features, dim=-1)[None]
logits = torch.einsum('bnc,bkc->bkn', predicted_features_norm, text_features_norm)
else:
logits = F.conv1d(predicted_features.permute(0, 2, 1), text_features[:, :, None])
class_preds = logits.argmax(1)
if class_mapping_clip is None:
class_preds += class_offset
else:
class_preds = class_mapping_clip_nonignore[class_preds]
logits = max_logits_per_class(logits, class_mapping_clip_nonignore)
else:
# L2-normalize predicted features
predicted_features /= predicted_features.norm(dim=-1, keepdim=True)
# compute cosine similarities
logits = predicted_features @ text_features.T
class_preds = (logits).argmax(-1) + class_offset
return class_preds, logits
def max_logits_per_class(logits, class_mapping):
unique, counts = class_mapping.unique(return_counts=True)
if unique.min() != 0:
unique -= unique.min()
max_labels = counts.max()
n_samples = logits.shape[-1]
n_subclasses = logits.shape[1]
mapping = []
for unq, cnt in zip(unique, counts):
for c in range(cnt):
mapping.append([unq, c])
mapping = torch.tensor(mapping)
logits_dense = -torch.ones((n_samples, unique.max() + 1, max_labels), device=logits.device) * torch.inf
for subclass_idx in range(n_subclasses):
subclass_logits = logits[0, subclass_idx]
subclass_mapping_cls, subclass_mapping_idx = mapping[subclass_idx]
logits_dense[:, subclass_mapping_cls, subclass_mapping_idx] = subclass_logits
max_logits = logits_dense.max(-1)[0].T.unsqueeze(0)
return max_logits
def next_free_port(port, max_port=65535):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
while port <= max_port:
try:
sock.bind(('', port))
sock.close()
return str(port)
except OSError:
print(f'Port {port} is occupied.')
port += 1
raise IOError('no free ports')
def semantic2agnostic(val_pt_labs, mapping):
val_pt_labs_agnostic = torch.zeros_like(val_pt_labs)
val_pt_labs_agnostic[val_pt_labs < EMPTY_SEMANTIC_LABEL] = 1
return val_pt_labs_agnostic
@torch.no_grad()
def main(local_rank, args):
# global settings
torch.backends.cudnn.benchmark = True
print(f'socket.gethostname(): {socket.gethostname()}')
fill_label_gt = None if args.num_classes is None else args.num_classes + 1
if fill_label_gt is not None:
EMPTY_SEMANTIC_LABEL = fill_label_gt
else:
EMPTY_SEMANTIC_LABEL = 17
# load config
cfg = Config.fromfile(args.py_config)
cfg.work_dir = args.work_dir
# modify the config with passed arguments
if args.dec_layers_occupancy is not None:
cfg.model['tpv_aggregator']['dec_layers_occupancy'] = args.dec_layers_occupancy
if args.hidden_dims is not None:
cfg.model['tpv_aggregator']['hidden_dims'] = args.hidden_dims
if args.dec_layers_features is not None:
cfg.model['tpv_aggregator']['dec_layers_features'] = args.dec_layers_features
if args.hidden_dims_ft is not None:
cfg.model['tpv_aggregator']['hidden_dims_ft'] = args.hidden_dims_ft
free_label = cfg.dataset_params.fill_label
agnostic = False
if args.class_weights_path is None:
try:
args.class_weights_path = cfg.class_weights_path
except:
pass
dataset_config = cfg.dataset_params
ignore_label = dataset_config['ignore_label']
version = dataset_config['version']
train_dataloader_config = cfg.train_data_loader
val_dataloader_config = cfg.val_data_loader
test_dataloader_config = cfg.test_data_loader
max_num_epochs = cfg.max_epochs
grid_size = cfg.grid_size
# init DDP
if local_rank == -1:
distributed = False
global_rank = 0
else:
distributed = True
ip = os.environ.get("MASTER_ADDR", "127.0.0.1")
port = os.environ.get("MASTER_PORT", "20506")
# port = next_free_port(int(port))
print(f'Initial port: {port}')
hosts = int(os.environ.get("WORLD_SIZE", 1)) # number of nodes
rank = int(os.environ.get("RANK", 0)) # node id
gpus = torch.cuda.device_count() # gpus per node
global_rank = rank * gpus + local_rank
print(f"tcp://{ip}:{port}")
dist.init_process_group(
backend="nccl", init_method=f"tcp://{ip}:{port}",
world_size=hosts * gpus, rank=global_rank
)
world_size = dist.get_world_size()
cfg.gpu_ids = range(world_size)
torch.cuda.set_device(local_rank)
if global_rank == 0 and not args.no_wandb:
config = vars(args)
# start a new wandb run to track this script
wandb.init(
# set the wandb project where this run will be logged
project="TPVFormer-Open",
# set the wandb run name
name=args.name,
# track hyperparameters and run metadata
config=config
)
# configure logger
if not distributed or dist.get_rank() == 0:
os.makedirs(args.work_dir, exist_ok=True)
cfg.dump(osp.join(args.work_dir, osp.basename(args.py_config)))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(args.work_dir, f'{timestamp}.log')
try:
logger = get_root_logger(log_file=log_file, log_level='INFO')
except:
logger = MMLogger.get_current_instance()
logger.info(f'Config:\n{cfg.pretty_text}')
# build model
if cfg.get('occupancy', False):
from builder import tpv_occupancy_builder as model_builder
else:
from builder import tpv_lidarseg_builder as model_builder
my_model = model_builder.build(cfg.model)
n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad)
logger.info(f'Number of params: {n_parameters}')
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
ddp_model_module = torch.nn.parallel.DistributedDataParallel
my_model = ddp_model_module(
my_model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
my_model = my_model.cuda()
print(f'done ddp model (local_rank {local_rank}), torch.cuda.current_device(): {torch.cuda.current_device()}')
# generate datasets
SemKITTI_label_name = get_nuScenes_label_name(dataset_config["label_mapping"])
unique_label = np.asarray(cfg.unique_label)
unique_label_str = [SemKITTI_label_name[x] for x in unique_label] if len(unique_label) > 2 else ['empty',
'occupied']
from builder import data_builder
train_dataset_loader, val_dataset_loader = \
data_builder.build(
dataset_config,
train_dataloader_config,
val_dataloader_config,
test_dataloader_config,
grid_size=grid_size,
version=version,
dist=distributed,
scale_rate=cfg.get('scale_rate', 1),
# eval_mode=True,
num_workers=0 if (args.show or args.debug) else 1,
class_agnostic=False,
fill_label_gt=fill_label_gt,
)
# setup configuration and loss for feature learning
# if the feature learning is ON
try:
feature_learning = cfg.feature_learning
except:
feature_learning = False
if feature_learning:
clip_features = dataset_config['features_type'] == 'clip'
else:
clip_features = False
if clip_features:
class_mapping_clip = None
if args.num_classes is None:
unique_label_clip = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
SemKITTI_label_name_clip = get_nuScenes_label_name("./config/label_mapping/nuscenes.yaml")
unique_label_str_clip = [SemKITTI_label_name_clip[x] for x in unique_label_clip]
else:
unique_label_clip = [i for i in range(1, args.num_classes + 1)]
SemKITTI_label_name_clip = get_nuScenes_label_name(NCLS2CONFIG[args.num_classes])
unique_label_str_clip = [SemKITTI_label_name_clip[x] for x in unique_label_clip]
if args.text_embeddings_path is not None and os.path.exists(args.text_embeddings_path):
text_features = torch.load(args.text_embeddings_path, map_location='cpu')
if type(text_features) in [tuple, list]:
text_features, class_mapping_clip = text_features
learning_map_gt = train_dataset_loader.dataset.imagepoint_dataset.learning_map_gt
class_mapping_clip = torch.tensor([learning_map_gt[c] for c in class_mapping_clip]).cuda()
if 'odise' in args.text_embeddings_path.lower():
embedding_dim = 256
else:
embedding_dim = 512
if text_features.shape[0] == embedding_dim:
text_features = text_features.T
text_features = text_features.float().cuda()
voxel_feature_loss = None
if feature_learning:
try:
voxel_feature_loss_name = cfg.voxel_feature_loss.lower()
except:
voxel_feature_loss_name = None
if voxel_feature_loss_name is not None:
if voxel_feature_loss_name in ['l2', 'mse']:
voxel_feature_loss = MSELoss(reduction='mean')
class_weights = None
if args.class_weights_path is not None and os.path.exists(args.class_weights_path):
with open(args.class_weights_path, 'rb') as f:
class_weights = pickle.load(f)
if class_weights is not None:
class_weights = class_weights.float().cuda()
# get optimizer, loss, scheduler
loss_func, lovasz_softmax = loss_builder.build(ignore_label=ignore_label, weight=class_weights)
loss_func_noW, _ = loss_builder.build(ignore_label=-100)
CalMeanIou_vox_occupied_agnostic_unique = MeanIoU([0, 1], IGNORE_LABEL, ['empty', 'occupied'], 'vox_occ_agn_unq')
CalMeanIou_pts = MeanIoU(unique_label, ignore_label, unique_label_str, 'pts')
if clip_features:
CalMeanIou_pts_clip = MeanIoU(unique_label_clip, ignore_label, unique_label_str_clip, 'pts_clip')
CalMeanIou_pts_clip_ignore0 = MeanIoU(unique_label_clip, 0, unique_label_str_clip, 'pts_clip')
CalMeanIou_pts_clip_wRoad = MeanIoU(unique_label_clip, 0, unique_label_str_clip, 'pts_clip_wRoad')
CalMeanIou_pts_clip_visible = MeanIoU(unique_label_clip, IGNORE_LABEL_SEMANTIC, unique_label_str_clip,
'pts_clip_visible')
CalMeanIou_pts_clip_visible_gt = MeanIoU(unique_label_clip, ignore_label, unique_label_str_clip,
'pts_clip_visible_gt')
CalMeanIou_vox_clip_all = MeanIoU(unique_label_clip + [EMPTY_SEMANTIC_LABEL], IGNORE_LABEL,
unique_label_str_clip + ['empty'],
'vox_clip_all', extra_classes=0)
CalMeanIou_vox_clip_occupied = MeanIoU(unique_label_clip, ignore_label,
unique_label_str_clip, 'vox_clip_occupied', extra_classes=0,
extra_classes_pred=1)
CalMeanIou_pts_clip_gt = MeanIoU(unique_label_clip, ignore_label, unique_label_str_clip, 'vox_clip_occupied')
CalMeanIou_vox_all_agnostic = MeanIoU([0, 1], ignore_label=IGNORE_LABEL, label_str=['empty', 'occupied'],
name='vox_agn_all')
CalMeanIou_pts_agnostic = MeanIoU([0, 1], ignore_label=IGNORE_LABEL, label_str=['empty', 'occupied'],
name='pts_agn')
# resume and load
epoch = 0
best_val_miou_pts, best_val_miou_vox = 0, 0
global_iter = 0
cfg.resume_from = ''
if osp.exists(osp.join(args.work_dir, 'latest.pth')):
cfg.resume_from = osp.join(args.work_dir, 'latest.pth')
if args.resume_from:
cfg.resume_from = args.resume_from
print('work dir: ', args.work_dir)
assert args.debug or (cfg.resume_from and osp.isfile(cfg.resume_from))
if cfg.resume_from and osp.isfile(cfg.resume_from):
print('resume from: ', cfg.resume_from, f'(local_rank {local_rank})')
map_location = 'cpu'
ckpt = torch.load(cfg.resume_from, map_location=map_location)
if not distributed or args.no_dist:
revise_fnc = revise_ckpt_linear_probe
else:
revise_fnc = revise_ckpt
print(my_model.load_state_dict(revise_fnc(ckpt['state_dict'], ddp=distributed and not args.no_dist),
strict=True),f'(local_rank {local_rank})')
epoch = ckpt['epoch']
if 'best_val_miou_pts' in ckpt:
best_val_miou_pts = ckpt['best_val_miou_pts']
if 'best_val_miou_vox' in ckpt:
best_val_miou_vox = ckpt['best_val_miou_vox']
global_iter = ckpt['global_iter']
print(f'successfully resumed from epoch {epoch} (local_rank {local_rank})')
elif cfg.resume_from and not osp.isfile(cfg.resume_from):
print(f'Given path {cfg.resume_from} but it does not exist!')
elif cfg.load_from:
ckpt = torch.load(cfg.load_from, map_location='cpu')
if 'state_dict' in ckpt:
state_dict = ckpt['state_dict']
else:
state_dict = ckpt
if args.no_dist:
print(f'Loading state dict from {cfg.load_from}')
print(my_model.img_backbone.load_state_dict(state_dict, strict=False))
else:
state_dict = revise_ckpt(state_dict, add_image_bbn_name='small' in args.py_config)
print(f'Loading state dict from {cfg.load_from}')
try:
print(my_model.load_state_dict(state_dict, strict=False))
except:
state_dict = revise_ckpt_2(state_dict)
print(my_model.load_state_dict(state_dict, strict=False))
# printing frequency
print_freq = cfg.print_freq
try:
print_freq_wandb_train = cfg.print_freq_wandb_train
except:
print_freq_wandb_train = cfg.print_freq
try:
print_freq_wandb_val = cfg.print_freq_wandb_val
except:
print_freq_wandb_val = cfg.print_freq
print(f'Start training! (local_rank {local_rank})')
val_vis_iter = 0
# eval
my_model.eval()
val_loss_list = []
val_loss_dict = {}
CalMeanIou_pts.reset()
CalMeanIou_vox_occupied_agnostic_unique.reset()
CalMeanIou_pts_agnostic.reset()
CalMeanIou_vox_all_agnostic.reset()
if clip_features:
CalMeanIou_pts_clip.reset()
CalMeanIou_pts_clip_ignore0.reset()
CalMeanIou_pts_clip_visible.reset()
CalMeanIou_pts_clip_visible_gt.reset()
CalMeanIou_pts_clip_gt.reset()
CalMeanIou_vox_clip_all.reset()
CalMeanIou_vox_clip_occupied.reset()
CalMeanIou_pts_clip_wRoad.reset()
data_time_s = time.time()
time_val_ep_start = time.time()
sem2agn_mapping = {i: 1 for i in range(EMPTY_SEMANTIC_LABEL)}
sem2agn_mapping[EMPTY_SEMANTIC_LABEL] = 0
if args.plot_dir is not None:
if not os.path.exists(args.plot_dir):
os.makedirs(args.plot_dir)
used_dataloader = train_dataset_loader if args.val_on_train else val_dataset_loader
print(f'len(used_dataloader): {len(used_dataloader)}')
with torch.no_grad():
for i_iter_val, loaded_data in enumerate(used_dataloader):
time_it_s = time.time()
if args.max_iter is not None and i_iter_val >= args.max_iter:
break
data_time_e = time.time()
time_s = time.time()
imgs, img_metas, val_vox_label_semantic, val_grid, val_pt_labs, val_vox_label_cls, val_grid_fts, val_pt_fts, val_vox_label_cls_val, matching_points, *_ = loaded_data
imgs = imgs.cuda()
val_grid_float = val_grid.to(torch.float32).cuda()
val_grid_int = val_grid.to(torch.long).cuda()
val_vox_label_cls_val = val_vox_label_cls_val.cuda()
val_pt_labs = val_pt_labs.cuda()
val_pt_labs_agnostic = semantic2agnostic(val_pt_labs, sem2agn_mapping)
val_vox_label_agnostic = semantic2agnostic(val_vox_label_semantic, sem2agn_mapping).cuda()
val_grid_fts = val_grid_fts.cuda()
if voxel_feature_loss is not None:
val_pt_fts = val_pt_fts.cuda()
predict_labels_vox_agn, predict_labels_pts_agn, predict_fts_vox, predicted_features_pts, *_ = my_model \
(img=imgs, img_metas=img_metas, points=val_grid_float, features=val_grid_float.clone())
# get the occupied voxels; for these voxels, get the feature predictions
occupied_voxels_loc = torch.stack(torch.where(predict_labels_vox_agn.argmax(1) == 1))
n_occ = occupied_voxels_loc.shape[1]
# predict features at those positions
outputs_vox_clip_all = torch.ones_like(val_vox_label_cls_val,
device=imgs.device) * EMPTY_SEMANTIC_LABEL
if n_occ > 0:
predicted_features_occupied_vox = predict_fts_vox[occupied_voxels_loc[0], :,
occupied_voxels_loc[1], occupied_voxels_loc[2],
occupied_voxels_loc[3]].unsqueeze(0)
# get the occupied voxels; for these voxels, get the feature predictions
occupied_voxels_loc = torch.stack(torch.where(predict_labels_vox_agn.argmax(1) == 1))
# assign labels
_outputs_vox_clip_predOcc = assign_clip_labels(
args, class_mapping_clip, None, None, predicted_features_occupied_vox,
text_features, None, None, assignment_only=True)
outputs_vox_clip_all[occupied_voxels_loc[0], occupied_voxels_loc[1], occupied_voxels_loc[2],
occupied_voxels_loc[3]] = _outputs_vox_clip_predOcc
# evaluate voxels
if args.show or args.plot_dir is not None:
xyz_pred = torch.stack(torch.where(outputs_vox_clip_all.cpu() != EMPTY_SEMANTIC_LABEL))[1:]
xyz_tgt = torch.stack(
torch.where(
torch.bitwise_and(
val_vox_label_cls_val != IGNORE_LABEL,
val_vox_label_cls_val != EMPTY_SEMANTIC_LABEL
)
)
)[1:]
fig = plt.figure()
show3d(xyz_pred.detach().cpu(), fig, 1, 2, 1)
show3d(xyz_tgt.detach().cpu(), fig, 1, 2, 2)
if args.plot_dir is not None:
save_path = os.path.join(args.plot_dir, f'{i_iter_val}pred.jpg')
plt.savefig(save_path, bbox_inches='tight')
print(f'Saved to {save_path}')
plt.show()
# a) all voxels
CalMeanIou_vox_clip_all._after_step(outputs_vox_clip_all, val_vox_label_cls_val)
# b) GT-occupied voxels only
_occupied_idx = torch.bitwise_and(val_vox_label_cls[0] != EMPTY_SEMANTIC_LABEL,
val_vox_label_cls[0] != IGNORE_LABEL_SEMANTIC)
targets = val_vox_label_semantic[0, _occupied_idx]
predictions = outputs_vox_clip_all[0, _occupied_idx]
CalMeanIou_vox_clip_occupied._after_step(predictions, targets.cuda())
# assign labels at occupied points
_outputs_pts_clip_gtOcc = assign_clip_labels(
args, class_mapping_clip, None, None, predicted_features_pts,
text_features, None, None, assignment_only=True)
CalMeanIou_pts_clip._after_step(_outputs_pts_clip_gtOcc.squeeze(), val_pt_labs.squeeze())
CalMeanIou_pts_clip_ignore0._after_step(_outputs_pts_clip_gtOcc.squeeze(),
val_pt_labs.squeeze())
CalMeanIou_pts_clip_visible._after_step(_outputs_pts_clip_gtOcc[:, matching_points[0]].squeeze(),
val_pt_labs[:, matching_points[0]].squeeze())
# at invisible points, predict drivable surface
_outputs_pts_clip_gtOcc_wRoad = torch.ones_like(_outputs_pts_clip_gtOcc) * ROAD_SEM_IDX
_outputs_pts_clip_gtOcc_wRoad[:, matching_points[0]] = _outputs_pts_clip_gtOcc[:, matching_points[0]]
if i_iter_val == 0:
print(f'_outputs_pts_clip_gtOcc_wRoad.unique(): {_outputs_pts_clip_gtOcc_wRoad.unique()}, '
f'val_pt_labs.unique(): {val_pt_labs.unique()}')
CalMeanIou_pts_clip_wRoad._after_step(_outputs_pts_clip_gtOcc_wRoad.squeeze(),
val_pt_labs.squeeze())
######################
# PROJECTED FEATURES #
######################
_outputs_pts_clip_projFt = assign_clip_labels(
args, class_mapping_clip, None, None, predicted_features_pts,
text_features, None, None, assignment_only=True)
CalMeanIou_pts_clip_gt._after_step(_outputs_pts_clip_projFt.squeeze(), val_pt_labs.squeeze())
CalMeanIou_pts_clip_visible_gt._after_step(_outputs_pts_clip_projFt[:, matching_points[0]].squeeze(),
val_pt_labs[:, matching_points[0]].squeeze())
predict_labels_pts_agn = predict_labels_pts_agn.squeeze(-1).squeeze(-1)
predict_labels_pts_agn = torch.argmax(predict_labels_pts_agn, dim=1) # bs, n
predict_labels_pts_agn = predict_labels_pts_agn.detach()
val_pt_labs = val_pt_labs.squeeze(-1)
predict_labels_vox_agn = torch.argmax(predict_labels_vox_agn, dim=1)
predict_labels_vox_agn = predict_labels_vox_agn.detach()
gt_labels_vox_agnostic = val_vox_label_agnostic
predict_labels_vox_agnostic = predict_labels_vox_agn
gt_labels_pts_agnostic = val_pt_labs_agnostic
predict_labels_pts_agnostic = predict_labels_pts_agn
for count in range(len(val_grid_int)):
# points
CalMeanIou_pts_agnostic._after_step(predict_labels_pts_agnostic[count],
gt_labels_pts_agnostic[count, :, 0])
# voxels
# a) all voxels
val_vox_label_occupancy_val = val_vox_label_cls_val.clone()
# occluded areas + semantic ignore labels
val_vox_label_occupancy_val[val_vox_label_cls_val != IGNORE_LABEL] = 1
# add back the semantic ignore areas
val_vox_label_occupancy_val[val_vox_label_cls == IGNORE_LABEL_SEMANTIC] = 1
val_vox_label_occupancy_val[val_vox_label_cls_val == EMPTY_SEMANTIC_LABEL] = 0
CalMeanIou_vox_all_agnostic._after_step(predict_labels_vox_agnostic[count].flatten(),
val_vox_label_occupancy_val[count].flatten())
# b) occupied only
_occupied_idx = val_vox_label_agnostic[count] == OCCUPIED_AGNOSTIC_LABEL
targets = val_vox_label_agnostic[count, _occupied_idx]
predictions = predict_labels_vox_agnostic[count, _occupied_idx]
CalMeanIou_vox_occupied_agnostic_unique._after_step(predictions, targets)
if i_iter_val % print_freq == 0 and (args.no_dist or dist.get_rank() == 0):
logger.info('[EVAL] Epoch %d Iter %5d' % (epoch, i_iter_val))
time_e = time.time()
pass_iter_time = time_e - time_s
data_iter_time = data_time_e - data_time_s
if global_rank == 0 and not args.no_wandb:
wandb.log({"time/val_iter": pass_iter_time, "time/val_data_iter": data_iter_time}, commit=False)
data_time_s = time.time()
val_vis_iter += 1
log2wandb = global_rank == 0 and not args.no_wandb
val_miou_pts = CalMeanIou_pts._after_epoch(log_wandb=log2wandb, tag="miou_pts/val",
# step=global_iter
step=None
)
val_miou_vox_occupied_agnostic_unique = CalMeanIou_vox_occupied_agnostic_unique._after_epoch(
log_wandb=log2wandb, tag="miou_vox_occupied_agnostic_unique/val", step=None
)
val_miou_pts_agn = CalMeanIou_pts_agnostic._after_epoch(log_wandb=log2wandb, tag="miou_pts_agn/val", step=None)
val_miou_vox_all = CalMeanIou_vox_all_agnostic._after_epoch(log_wandb=log2wandb, tag="miou_vox_agn/val", step=None)
if clip_features:
val_miou_pts_clip = CalMeanIou_pts_clip._after_epoch(log_wandb=log2wandb, tag="miou_pts_clip/val", step=None)
val_miou_pts_clip_ignore0 = CalMeanIou_pts_clip_ignore0._after_epoch(log_wandb=log2wandb,
tag="miou_pts_clip_ignore0/val", step=None)
val_miou_pts_clip_visible = CalMeanIou_pts_clip_visible._after_epoch(log_wandb=log2wandb,
tag="miou_pts_clip_visible/val", step=None)
val_miou_pts_clip_gt = CalMeanIou_pts_clip_gt._after_epoch(log_wandb=log2wandb, tag="miou_pts_clip_gt/val",
step=None)
val_miou_vox_clip_all = CalMeanIou_vox_clip_all._after_epoch(log_wandb=log2wandb, tag="miou_vox_clip_all/val",
step=None)
val_miou_vox_clip_occupied = CalMeanIou_vox_clip_occupied._after_epoch(
log_wandb=log2wandb, tag="miou_vox_clip_occupied/val", step=None)
val_miou_pts_clip_wRoad = CalMeanIou_pts_clip_wRoad._after_epoch(
log_wandb=log2wandb, tag="miou_pts_clip_wRoad/val", step=None)
#print(f'val_miou_vox_agn (evaluate voxel agnostic segmentation everywhere): {val_miou_vox_all}')
print(f'val_miou_vox_agn: {val_miou_vox_all}')
print(f'val_miou_vox_clip_all: {val_miou_vox_clip_all}')
time_val_ep_elapsed = time.time() - time_val_ep_start
# log metrics to wandb
if global_rank == 0 and not args.no_wandb:
wandb.log({"miou_pts/val": val_miou_pts, "miou_vox_all/val": val_miou_vox_all}, commit=False)
wandb.log({"miou_pts_agn/val": val_miou_pts_agn, "miou_vox_agn/val": val_miou_vox_all}, commit=False)
wandb.log({"loss/val_mean_ep": np.mean(val_loss_list)}, commit=False)
if clip_features:
wandb.log({"miou_pts_clip/val": val_miou_pts_clip})
wandb.log({"miou_pts_clip_visible/val": val_miou_pts_clip_visible})
wandb.log({"miou_pts_clip_gt/val": val_miou_pts_clip_gt})
wandb.log({"miou_vox_clip/val": val_miou_vox_clip_all})
for key, vals in val_loss_dict.items():
wandb.log({f"loss/val_{key}": torch.mean(torch.tensor(vals))}, commit=False)
wandb.log({"time/val_epoch": time_val_ep_elapsed})
def assign_clip_labels(args, class_mapping_clip, loss_func_noW, lovasz_softmax, outputs_pts_fts, text_features,
train_grid_fts, train_vox_label_cls, ignore_label=0, compute_loss=True, assignment_only=False,
no_nonignore=False, logits_only=False, cpu=False):
# points
outputs_pts_clip, logits_clip = assign_labels_clip(
outputs_pts_fts.float().to('cpu' if cpu else 'cuda'), text_features, 1, maskclip=True,
class_mapping_clip=class_mapping_clip, ignore_label=ignore_label, normalized_cosine=args.normalized_cosine)
if logits_only:
return logits_clip
if assignment_only:
return outputs_pts_clip
train_grid_fts_int = train_grid_fts.long()
train_pt_labs_fts_list = []
for bi in range(train_grid_fts_int.shape[0]):
train_grid_fts_int_cur = train_grid_fts_int[bi]
train_pt_labs_fts_cur = train_vox_label_cls[
bi, train_grid_fts_int_cur[:, 0], train_grid_fts_int_cur[:, 1], train_grid_fts_int_cur[:, 2]
]
train_pt_labs_fts_list.append(train_pt_labs_fts_cur)
train_pt_labs_fts = torch.cat(train_pt_labs_fts_list).unsqueeze(0).to('cpu' if cpu else 'cuda')
if no_nonignore:
non_ignore = torch.ones_like(train_pt_labs_fts, dtype=bool)
else:
non_ignore = train_pt_labs_fts != ignore_label
targets = (train_pt_labs_fts[non_ignore] - 1).unsqueeze(0)
outputs_pts_clip = outputs_pts_clip[non_ignore].unsqueeze(0).to('cpu' if cpu else 'cuda')
loss_ce_clip, loss_lovasz_clip = None, None
if compute_loss:
logits_clip_non_ignore = logits_clip.permute(0, 2, 1)[non_ignore].unsqueeze(0).permute(0, 2, 1)
loss_ce_clip = loss_func_noW(logits_clip_non_ignore.detach(), targets.detach())
loss_lovasz_clip = lovasz_softmax(
torch.nn.functional.softmax(logits_clip_non_ignore.unsqueeze(-1).unsqueeze(-1), dim=1).detach(),
targets.unsqueeze(-1).detach())
else:
train_grid_fts_int = train_grid_fts_int[non_ignore].unsqueeze(0)
return loss_ce_clip, loss_lovasz_clip, non_ignore, outputs_pts_clip, targets, train_grid_fts_int
def get_agnostic_labels(args, predict_labels_pts, predict_labels_vox, val_vox_labs, val_pt_labs):
if not args.agnostic:
# 1) voxel grid
# A) GT
gt_labels_vox_agnostic = torch.zeros_like(val_vox_labs)
gt_labels_vox_agnostic_occ = torch.where(val_vox_labs < EMPTY_SEMANTIC_LABEL)
gt_labels_vox_agnostic[gt_labels_vox_agnostic_occ] = 1
# B) predictions
predict_labels_vox_agnostic = torch.zeros_like(predict_labels_vox)
predict_labels_vox_agnostic_occ = torch.where(predict_labels_vox < EMPTY_SEMANTIC_LABEL)
predict_labels_vox_agnostic[predict_labels_vox_agnostic_occ] = 1
# 2) points
# A) GT
gt_labels_pts_agnostic = torch.zeros_like(val_pt_labs)
gt_labels_pts_agnostic_occ = torch.where(val_pt_labs < EMPTY_SEMANTIC_LABEL)
gt_labels_pts_agnostic[gt_labels_pts_agnostic_occ] = 1
# B) predictions
predict_labels_pts_agnostic = torch.zeros_like(predict_labels_pts)
predict_labels_pts_agnostic_occ = torch.where(predict_labels_pts < EMPTY_SEMANTIC_LABEL)
predict_labels_pts_agnostic[predict_labels_pts_agnostic_occ] = 1
else:
# in this setup, we do have class-agnostic predictions and class-agnostic targets
# this means that we do not need to do anything about the labels
assert len(val_vox_labs.unique()) <= 2 and len(val_pt_labs.unique()) == 1 and len(
predict_labels_vox.unique()) <= 2 and len(predict_labels_pts) <= 2
# 1) voxels
gt_labels_vox_agnostic = val_vox_labs.detach().cpu()
predict_labels_vox_agnostic = predict_labels_vox.detach().cpu()
# 2) points
gt_labels_pts_agnostic = val_pt_labs.detach().cpu()
predict_labels_pts_agnostic = predict_labels_pts.detach().cpu()
return gt_labels_vox_agnostic, predict_labels_vox_agnostic, gt_labels_pts_agnostic, predict_labels_pts_agnostic
if __name__ == '__main__':
# Training settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('--py-config', default='config/tpv_lidarseg.py')
parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg')
parser.add_argument('--resume-from', type=str, default='')
parser.add_argument('--name', type=str, default=None)
parser.add_argument('--eval-at-start', action='store_true')
parser.add_argument('--no-dist', action='store_true', help='Do not use distributed setup.')
parser.add_argument('--compute-weights', action='store_true')
parser.add_argument('--compute-upperbound-clip', action='store_true')
parser.add_argument('--debug', action='store_true')
parser.add_argument('--agnostic', action='store_true')
parser.add_argument('--class-weights-path',
# default='./class_weights.pkl',
default=None,
type=str)
parser.add_argument('--no-wandb', action='store_true')
parser.add_argument('--ft-loss-weight', type=float, default=1., help='Weight of the feature loss.')
parser.add_argument('--maskclip', action='store_true')
parser.add_argument('--show', action='store_true')
parser.add_argument('--text-embeddings-path', default=None, type=str)
parser.add_argument('--max-iter', default=None, type=int)
parser.add_argument('--projected-features', action='store_true')
parser.add_argument('--plot-dir', default=None, type=str)
# number of layers in the decoders
parser.add_argument('--dec-layers-occupancy', default=None, type=int,
help='Number of layers in the occupancy decoder.')
parser.add_argument('--dec-layers-features', default=None, type=int,
help='Number of layers in the feature decoder.')
# number of channels in the hidden decoder layers
parser.add_argument('--hidden-dims', default=512, type=int,
help='Number of channels in the hidden layers of the occupancy decoder.')
parser.add_argument('--hidden-dims-ft', default=1024, type=int,
help='Number of channels in the hidden layers of the feature decoder.')
parser.add_argument('--num-classes', default=None, type=int, help='Used for evaluation.')
parser.add_argument('--normalized-cosine', action='store_true')
parser.add_argument('--val-on-train', action='store_true')
args = parser.parse_args()
args.agnostic = args.agnostic or 'agnostic' in args.py_config
args.maskclip = args.maskclip or 'maskclip' in args.py_config
if args.name is None:
args.name = args.work_dir.split(os.path.sep)[-1]
if args.ft_loss_weight != 1.:
args.name += f'_{args.ft_loss_weight}ftW'
args.work_dir += f'_{args.ft_loss_weight}ftW'
timestamp = datetime.now().strftime("%d%m%Y_%H%M%S")
args.name += f'_{timestamp}'
args.work_dir += f'_{timestamp}'
if args.debug:
args.name += '_debug'
args.work_dir += '_debug'
ngpus = torch.cuda.device_count()
args.gpus = ngpus
print(args)
if args.show or args.no_dist:
main(-1, args)
else:
torch.multiprocessing.spawn(main, args=(args,), nprocs=args.gpus)