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streamnet_l.py
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streamnet_l.py
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# encoding: utf-8
import os
import sys
import torch
import torch.nn as nn
import torch.distributed as dist
from yolox.exp import Exp as MyExp
class Exp(MyExp):
def __init__(self):
super(Exp, self).__init__()
self.depth_t = 1
self.width_t = 1
self.depth = 1
self.width = 1
self.data_num_workers = 6
self.num_classes = 8
self.input_size = (600, 960) # (h,w)
self.random_size = (50, 70)
self.test_size = (600, 960)
#
self.basic_lr_per_img = 0.001 / 64.0
self.warmup_epochs = 1
self.max_epoch = 8
self.no_aug_epochs = self.max_epoch
self.eval_interval = 1
self.train_ann = 'train.json'
self.val_ann = 'val.json'
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
self.output_dir = './data/output/stream_yolo/longshort_new_archi_oddil/l3-s1_logits'
self.short_cfg = dict(
frame_num=1,
delta=1,
with_short_cut=False,
out_channels=[((128, 256, 512), 1), ],
)
self.long_cfg = dict(
frame_num=3,
delta=1,
with_short_cut=False,
include_current_frame=False,
# out_channels=[((21, 42, 85), 3), ],
out_channels=[((42, 85, 170), 2), ((44, 86, 172), 1)],
)
self.yolox_cfg = dict(
merge_form="long_fusion",
with_short_cut=True,
)
self.neck_cfg = {
'depth': 1.0,
'hidden_ratio': 1.0,
'in_channels': [256, 512, 1024],
'out_channels': [256, 512, 1024],
'act': 'silu',
'spp': False,
'block_name': 'BasicBlock_3x3_Reverse',
'dcn': True,
}
def get_model(self):
from exps.model.dfp_pafpn import DFPPAFPN
from exps.model.yolox_longshort_v3_oddil import YOLOXLONGSHORTV3ODDIL
from exps.model.dfp_pafpn_long_v3 import DFPPAFPNLONGV3
from exps.model.dfp_pafpn_short_v3 import DFPPAFPNSHORTV3
from exps.model.longshort_backbone_neck_v2 import BACKBONENECKV2
from exps.model.tal_head import TALHead
from exps.model.tal_head_dil import TALHeadDil
from exps.model.tal_head_oddil import TALHeadODDil
import torch.nn as nn
def init_yolo(M):
for m in M.modules():
if isinstance(m, nn.BatchNorm2d):
m.eps = 1e-3
m.momentum = 0.03
if getattr(self, "model", None) is None:
in_channels = [256, 512, 1024]
long_backbone_s = (DFPPAFPNLONGV3(self.depth,
self.width,
in_channels=in_channels,
frame_num=self.long_cfg["frame_num"],
with_short_cut=self.long_cfg["with_short_cut"],
out_channels=self.long_cfg["out_channels"])
if self.long_cfg["frame_num"] != 0 else None)
short_backbone_s = DFPPAFPNSHORTV3(self.depth,
self.width,
in_channels=in_channels,
frame_num=self.short_cfg["frame_num"],
with_short_cut=self.short_cfg["with_short_cut"],
out_channels=self.short_cfg["out_channels"])
backbone_neck_s = BACKBONENECKV2(self.depth,
self.width,
in_channels=in_channels,
neck_cfg=self.neck_cfg)
# head_s = TALHead(self.num_classes, self.width, in_channels=in_channels, gamma=1.0,
# ignore_thr=0.5, ignore_value=1.5)
one_factor = 0.1
head_s = TALHeadODDil(
self.num_classes, self.width, in_channels=in_channels, gamma=1.0,
ignore_thr=0.5, ignore_value=1.5,
hint_cls_factor=one_factor, hint_obj_factor=one_factor, hint_iou_factor=one_factor,
hint_cls_dil_enable=True, hint_obj_dil_enable=True, hint_iou_dil_enable=True,
)
# teacher model
backbone_t = DFPPAFPN(self.depth_t, self.width_t, in_channels=in_channels)
# head_t = PIPEHeadDil(self.num_classes, self.width_t, in_channels=in_channels)
head_t = TALHeadDil(self.num_classes, self.width_t, in_channels=in_channels, gamma=1.0,
ignore_thr=0.5, ignore_value=1.5, eval_decode=False)
self.model = YOLOXLONGSHORTV3ODDIL(long_backbone_s,
short_backbone_s,
backbone_neck_s,
head_s,
backbone_t=backbone_t,
head_t=head_t,
merge_form=self.yolox_cfg["merge_form"],
in_channels=in_channels,
width=self.width,
with_short_cut=self.yolox_cfg["with_short_cut"],
long_cfg=self.long_cfg)
# self.model = YOLOXODDIL(backbone_s, head_s, backbone_t, head_t,
# coef_cfg=self.coef_cfg, dil_neck_weight=0.5, still_teacher=False)
self.model.apply(init_yolo)
self.model.head.initialize_biases(1e-2)
return self.model
def get_data_loader(self, batch_size, is_distributed, no_aug=False, local_rank=0, cache_img=False):
# from exps.dataset.longshort.tal_flip_long_short_argoversedataset import LONGSHORT_ARGOVERSEDataset
from exps.dataset.distillation.longshort.tal_flip_long_short_argoversedataset_dil import LONGSHORT_Dil_ARGOVERSEDataset
from exps.data.tal_flip_mosaicdetection import LongShortMosaicDetectionDil
from exps.data.data_augment_flip import LongShortTrainTransformDil
from yolox.data import (
YoloBatchSampler,
DataLoader,
InfiniteSampler,
worker_init_reset_seed,
)
dataset = LONGSHORT_Dil_ARGOVERSEDataset(
data_dir='./data',
json_file=self.train_ann,
name='train',
img_size=self.input_size,
preproc=LongShortTrainTransformDil(max_labels=50,
hsv=False,
flip=True,
short_frame_num=self.short_cfg["frame_num"],
long_frame_num=self.long_cfg["frame_num"]),
cache=cache_img,
short_cfg=self.short_cfg,
long_cfg=self.long_cfg,
)
dataset = LongShortMosaicDetectionDil(dataset,
mosaic=not no_aug,
img_size=self.input_size,
preproc=LongShortTrainTransformDil(max_labels=120,
hsv=False,
flip=True,
short_frame_num=self.short_cfg["frame_num"],
long_frame_num=self.long_cfg["frame_num"]),
degrees=self.degrees,
translate=self.translate,
scale=self.mosaic_scale,
shear=self.shear,
perspective=0.0,
enable_mixup=self.enable_mixup,
mosaic_prob=self.mosaic_prob,
mixup_prob=self.mixup_prob,
)
self.dataset = dataset
if is_distributed:
batch_size = batch_size // dist.get_world_size()
sampler = InfiniteSampler(len(self.dataset), seed=self.seed if self.seed else 0)
batch_sampler = YoloBatchSampler(
sampler=sampler,
batch_size=batch_size,
drop_last=False,
mosaic=not no_aug)
dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True}
dataloader_kwargs["batch_sampler"] = batch_sampler
# Make sure each process has different random seed, especially for 'fork' method
dataloader_kwargs["worker_init_fn"] = worker_init_reset_seed
train_loader = DataLoader(self.dataset, **dataloader_kwargs)
return train_loader
def get_eval_loader(self, batch_size, is_distributed, testdev=False):
# from exps.dataset.tal_flip_one_future_argoversedataset import ONE_ARGOVERSEDataset
# from exps.data.data_augment_flip import DoubleValTransform
from exps.dataset.longshort.tal_flip_long_short_argoversedataset import LONGSHORT_ARGOVERSEDataset
from exps.data.data_augment_flip import LongShortValTransform
valdataset = LONGSHORT_ARGOVERSEDataset(
data_dir='./data',
json_file='val.json',
name='val',
img_size=self.test_size,
preproc=LongShortValTransform(short_frame_num=self.short_cfg["frame_num"],
long_frame_num=self.long_cfg["frame_num"]),
short_cfg=self.short_cfg,
long_cfg=self.long_cfg,
)
if is_distributed:
batch_size = batch_size // dist.get_world_size()
sampler = torch.utils.data.distributed.DistributedSampler(valdataset, shuffle=False)
else:
sampler = torch.utils.data.SequentialSampler(valdataset)
dataloader_kwargs = {"num_workers": self.data_num_workers, "pin_memory": True, "sampler": sampler}
dataloader_kwargs["batch_size"] = batch_size
val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)
return val_loader
def random_resize(self, data_loader, epoch, rank, is_distributed):
import random
tensor = torch.LongTensor(2).cuda()
if rank == 0:
if epoch >= self.max_epoch - 1:
size = self.input_size
else:
size_factor = self.input_size[0] * 1.0 / self.input_size[1]
size = random.randint(*self.random_size)
size = (16 * int(size * size_factor), int(16 * size))
tensor[0] = size[0]
tensor[1] = size[1]
if is_distributed:
dist.barrier()
dist.broadcast(tensor, 0)
input_size = (tensor[0].item(), tensor[1].item())
return input_size
def preprocess(self, inputs, targets, tsize):
scale_y = tsize[0] / self.input_size[0]
scale_x = tsize[1] / self.input_size[1]
if scale_x != 1 or scale_y != 1:
inputs[0] = nn.functional.interpolate(
inputs[0], size=tsize, mode="bilinear", align_corners=False
)
inputs[1] = nn.functional.interpolate(
inputs[1], size=tsize, mode="bilinear", align_corners=False
) if inputs[1].ndim == 4 else inputs[1] # inputs[1].ndim != 4 为不使用long支路的情况
targets[0][..., 1::2] = targets[0][..., 1::2] * scale_x
targets[0][..., 2::2] = targets[0][..., 2::2] * scale_y
targets[1][..., 1::2] = targets[1][..., 1::2] * scale_x
targets[1][..., 2::2] = targets[1][..., 2::2] * scale_y
return inputs, targets
def get_evaluator(self, batch_size, is_distributed, testdev=False):
# from exps.evaluators.onex_stream_evaluator import ONEX_COCOEvaluator
from exps.evaluators.longshort_onex_stream_evaluator import LONGSHORT_ONEX_COCOEvaluator
val_loader = self.get_eval_loader(batch_size, is_distributed, testdev)
evaluator = LONGSHORT_ONEX_COCOEvaluator(
dataloader=val_loader,
img_size=self.test_size,
confthre=self.test_conf,
nmsthre=self.nmsthre,
num_classes=self.num_classes,
testdev=testdev,
)
return evaluator
def get_trainer(self, args):
from exps.train_utils.longshort_dil_trainer import Trainer
trainer = Trainer(self, args)
# NOTE: trainer shouldn't be an attribute of exp object
return trainer
def eval(self, model, evaluator, is_distributed, half=False):
return evaluator.evaluate(model, is_distributed, half)
def get_optimizer(self, batch_size, ignore_keys=None):
if "optimizer" not in self.__dict__:
ignore_keys = ignore_keys if ignore_keys is not None else []
if self.warmup_epochs > 0:
lr = self.warmup_lr
else:
lr = self.basic_lr_per_img * batch_size
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in self.model.named_modules():
ig_flag = False
for ig_k in ignore_keys:
if ig_k in k:
ig_flag = True
break
if ig_flag:
continue
if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d) or "bn" in k:
pg0.append(v.weight) # no decay
elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
optimizer = torch.optim.SGD(
pg0, lr=lr, momentum=self.momentum, nesterov=True
)
optimizer.add_param_group(
{"params": pg1, "weight_decay": self.weight_decay}
) # add pg1 with weight_decay
optimizer.add_param_group({"params": pg2})
self.optimizer = optimizer
return self.optimizer