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MetaMOT
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have CUDA device-side error

master
Mahdi Abdollah Pour 2 years ago
parent
6026017485
commit
0b60bffeea
13 changed files with 567 additions and 357 deletions
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experiments/meta_gpu.sh View File

#PBS -N meta_bytetrack
#PBS -m abe
#PBS -M [email protected]
#PBS -l nodes=1:ppn=1:gpus=1
#PBS -q cuda9


export LD_LIBRARY_PATH=/share/apps/cuda/cuda-10.1/lib64:$LD_LIBRARY_PATH
export PATH=/share/apps/cuda/cuda-10.1/bin/:$PATH

source /share/apps/Anaconda/anaconda3.6/bin/activate abdollahpour.ce.sharif
conda activate abd_env


cd /home/abdollahpour.ce.sharif/ByteTrack



python tools/train.py -t metamot -f exps/example/metamot/yolox_x_mot17_on_mot20.py -d 1 -b 1 --fp16 -o -c /home/abdollahpour.ce.sharif/yolox_x.pth --local_rank 0 -expn train_17_on_20

+ 19
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experiments/meta_gpu_resume.sh View File

#PBS -N meta_bytetrack_resume
#PBS -m abe
#PBS -M [email protected]
#PBS -l nodes=1:ppn=1:gpus=1
#PBS -q DP
#PBS -l mem=15GB

export LD_LIBRARY_PATH=/share/apps/cuda/cuda-10.1/lib64:$LD_LIBRARY_PATH
export PATH=/share/apps/cuda/cuda-10.1/bin/:$PATH

source /share/apps/Anaconda/anaconda3.6/bin/activate abdollahpour.ce.sharif
conda activate abd_env


cd /home/abdollahpour.ce.sharif/ByteTrack



python tools/train.py -t metamot -f exps/example/metamot/yolox_x_mot17_on_mot20.py -d 1 -b 1 --fp16 -o --resume --start_epoch 2 -c /home/abdollahpour.ce.sharif/ByteTrack/meta_experiments/train_17_on_20/latest_ckpt.pth.tar --local_rank 0 -expn train_17_on_20_resume

+ 4
- 4
exps/example/metamot/yolox_x_mot17_on_mot20.py View File

self.depth = 1.33 self.depth = 1.33
self.width = 1.25 self.width = 1.25
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0] self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
self.train_dir = '/media/external_10TB/10TB/vision/ByteTrackData/MOT17/annotations'
self.train_dir = '/home/abdollahpour.ce.sharif/ByteTrackData/MOT17/annotations'
onlyfiles = [f for f in listdir(self.train_dir) if isfile(join(self.train_dir, f))] onlyfiles = [f for f in listdir(self.train_dir) if isfile(join(self.train_dir, f))]
self.train_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__('FRCNN')] self.train_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__('FRCNN')]
# # TODO: remove # # TODO: remove
# self.train_anns = self.train_anns[:1]
# self.train_anns = self.train_anns[3:]


self.val_dir = '/media/external_10TB/10TB/vision/ByteTrackData/MOT20/annotations'
self.val_dir = '/home/abdollahpour.ce.sharif/ByteTrackData/MOT20/annotations'
onlyfiles = [f for f in listdir(self.val_dir) if isfile(join(self.val_dir, f))] onlyfiles = [f for f in listdir(self.val_dir) if isfile(join(self.val_dir, f))]
self.val_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__( self.val_anns = [file for file in onlyfiles if file.__contains__('train') and file.__contains__(
'MOT20')] 'MOT20')]
# self.test_size = (736, 1920) # self.test_size = (736, 1920)
self.random_size = (20, 36) self.random_size = (20, 36)
self.max_epoch = 80 self.max_epoch = 80
self.print_interval = 20
self.print_interval = 100
self.eval_interval = 5 self.eval_interval = 5
self.test_conf = 0.001 self.test_conf = 0.001
self.nmsthre = 0.7 self.nmsthre = 0.7

+ 1
- 0
requirements.txt View File

motmetrics motmetrics
filterpy filterpy
h5py h5py
learn2learn


# verified versions # verified versions
onnx==1.8.1 onnx==1.8.1

+ 39
- 20
yolox/core/meta_trainer.py View File

self.train_in_task() self.train_in_task()
self.after_epoch() self.after_epoch()


def train_in_iter(self, task):
for self.iter in range(len(task)):
def train_in_iter(self):
for self.iter in range(len(self.train_loader)):
self.before_iter() self.before_iter()
self.train_one_iter() self.train_one_iter()
self.after_iter() self.after_iter()


def train_in_task(self): def train_in_task(self):
for task in self.train_loaders:
self.before_task(task)
self.train_in_iter(task)
self.after_task()

def before_task(self, train_loader):
logger.info("init prefetcher, this might take one minute or less...")
self.train_loader = train_loader
self.prefetcher = DataPrefetcher(train_loader)
for i in range(len(self.train_loaders)):
self.before_task(i)
self.train_in_iter()

self.after_task(i)

def before_task(self, i):
# logger.info("init prefetcher, this might take one minute or less...")
self.train_loader = self.train_loaders[i]
self.prefetcher = self.prefetchers[i]
self.learner = self.model.clone() self.learner = self.model.clone()
logger.info("model clone created. dataloader:{}".format(i))


def after_task(self):
pass
def after_task(self,i):
self.epoch_progress += len(self.train_loaders[i])


def adapt(self, inps, targets): def adapt(self, inps, targets):
# adapt_inps =inps[:1, ...] # adapt_inps =inps[:1, ...]
outputs = self.learner(inps, targets) outputs = self.learner(inps, targets)
loss = outputs["total_loss"] loss = outputs["total_loss"]
self.learner.adapt(loss) self.learner.adapt(loss)
del outputs, loss


def train_one_iter(self): def train_one_iter(self):
iter_start_time = time.time() iter_start_time = time.time()
targets = targets.to(self.data_type) targets = targets.to(self.data_type)
targets.requires_grad = False targets.requires_grad = False
data_end_time = time.time() data_end_time = time.time()
# logger.info("in train iter")
with torch.cuda.amp.autocast(enabled=self.amp_training): with torch.cuda.amp.autocast(enabled=self.amp_training):
if self.iter % self.adaptation_period == 0: if self.iter % self.adaptation_period == 0:
self.adapt(inps, targets) self.adapt(inps, targets)
lr=lr, lr=lr,
**outputs, **outputs,
) )
del loss, outputs


def before_train(self): def before_train(self):
logger.info("args: {}".format(self.args)) logger.info("args: {}".format(self.args))
# from torchsummary import summary # from torchsummary import summary
# summary(model, input_size=(3, 300, 300), device='cuda') # summary(model, input_size=(3, 300, 300), device='cuda')
# value of epoch will be set in `resume_train` # value of epoch will be set in `resume_train`
model = self.resume_train(model)


self.model = l2l.algorithms.MAML(model, lr=self.exp.inner_lr, first_order=self.exp.first_order) self.model = l2l.algorithms.MAML(model, lr=self.exp.inner_lr, first_order=self.exp.first_order)


# solver related init # solver related init
self.optimizer = self.exp.get_optimizer(self.args.batch_size) self.optimizer = self.exp.get_optimizer(self.args.batch_size)


self.model = self.resume_train(self.model)

# data related init # data related init
self.no_aug = self.start_epoch >= self.max_epoch - self.exp.no_aug_epochs self.no_aug = self.start_epoch >= self.max_epoch - self.exp.no_aug_epochs


print('Getting data loaders')
logger.info('Getting data loaders')
self.train_loaders = self.exp.get_data_loaders( self.train_loaders = self.exp.get_data_loaders(
batch_size=self.args.batch_size, batch_size=self.args.batch_size,
is_distributed=self.is_distributed, is_distributed=self.is_distributed,


# max_iter means iters per epoch # max_iter means iters per epoch
self.max_iter = 0 self.max_iter = 0
for train_loader in self.train_loaders:
self.prefetchers = []
for i, train_loader in enumerate(self.train_loaders):
self.max_iter += len(train_loader) self.max_iter += len(train_loader)
self.prefetchers.append(DataPrefetcher(train_loader))


self.lr_scheduler = self.exp.get_lr_scheduler( self.lr_scheduler = self.exp.get_lr_scheduler(
self.exp.basic_lr_per_img * self.args.batch_size, self.max_iter self.exp.basic_lr_per_img * self.args.batch_size, self.max_iter


def before_epoch(self): def before_epoch(self):
logger.info("---> start train epoch{}".format(self.epoch + 1)) logger.info("---> start train epoch{}".format(self.epoch + 1))
self.epoch_progress = 0
if self.epoch + 1 == self.max_epoch - self.exp.no_aug_epochs or self.no_aug: if self.epoch + 1 == self.max_epoch - self.exp.no_aug_epochs or self.no_aug:


logger.info("--->No mosaic aug now!") logger.info("--->No mosaic aug now!")
self.save_ckpt(ckpt_name="last_mosaic_epoch") self.save_ckpt(ckpt_name="last_mosaic_epoch")


def after_epoch(self): def after_epoch(self):
self.epoch_progress = 0
if self.use_model_ema: if self.use_model_ema:
self.ema_model.update_attr(self.model) self.ema_model.update_attr(self.model)


eta_str = "ETA: {}".format(datetime.timedelta(seconds=int(eta_seconds))) eta_str = "ETA: {}".format(datetime.timedelta(seconds=int(eta_seconds)))


progress_str = "epoch: {}/{}, iter: {}/{}".format( progress_str = "epoch: {}/{}, iter: {}/{}".format(
self.epoch + 1, self.max_epoch, self.iter + 1, self.max_iter
self.epoch + 1, self.max_epoch, self.iter + 1 + self.epoch_progress, self.max_iter
) )
loss_meter = self.meter.get_filtered_meter("loss") loss_meter = self.meter.get_filtered_meter("loss")
loss_str = ", ".join( loss_str = ", ".join(


@property @property
def progress_in_iter(self): def progress_in_iter(self):
return self.epoch * self.max_iter + self.iter
return self.epoch * self.max_iter + self.iter + self.epoch_progress


def resume_train(self, model): def resume_train(self, model):
if self.args.resume: if self.args.resume:
ckpt_file = self.args.ckpt ckpt_file = self.args.ckpt


ckpt = torch.load(ckpt_file, map_location=self.device) ckpt = torch.load(ckpt_file, map_location=self.device)

# handling meta models
# new_dict = {}
# for key in ckpt["model"].keys():
# if key.startswith('module.'):
# new_dict[key[7:]] = ckpt["model"][key]
# else:
# new_dict[key] = ckpt["model"][key]
# del ckpt["model"]
# ckpt["model"] = new_dict

# resume the model/optimizer state dict # resume the model/optimizer state dict
model.load_state_dict(ckpt["model"]) model.load_state_dict(ckpt["model"])
self.optimizer.load_state_dict(ckpt["optimizer"]) self.optimizer.load_state_dict(ckpt["optimizer"])

+ 2
- 2
yolox/data/dataloading.py View File

import torch import torch
from torch.utils.data.dataloader import DataLoader as torchDataLoader from torch.utils.data.dataloader import DataLoader as torchDataLoader
from torch.utils.data.dataloader import default_collate from torch.utils.data.dataloader import default_collate
from yolox import statics
import os import os
import random import random


import yolox import yolox


yolox_path = os.path.dirname(os.path.dirname(yolox.__file__)) yolox_path = os.path.dirname(os.path.dirname(yolox.__file__))
yolox_datadir = os.path.join(yolox_path, "/media/external_10TB/10TB/vision/ByteTrackData")
yolox_datadir = os.path.join(yolox_path, statics.DATA_PATH)
return yolox_datadir return yolox_datadir





+ 2
- 1
yolox/exp/base_meta_exp.py View File



def __init__(self): def __init__(self):
self.seed = None self.seed = None
self.output_dir = "./YOLOX_outputs"
# self.output_dir = "./YOLOX_outputs"
self.output_dir = "./meta_experiments"
self.print_interval = 100 self.print_interval = 100
self.eval_interval = 10 self.eval_interval = 10



+ 84
- 80
yolox/models/yolo_head.py View File



class YOLOXHead(nn.Module): class YOLOXHead(nn.Module):
def __init__( def __init__(
self,
num_classes,
width=1.0,
strides=[8, 16, 32],
in_channels=[256, 512, 1024],
act="silu",
depthwise=False,
self,
num_classes,
width=1.0,
strides=[8, 16, 32],
in_channels=[256, 512, 1024],
act="silu",
depthwise=False,
): ):
""" """
Args: Args:
expanded_strides = [] expanded_strides = []


for k, (cls_conv, reg_conv, stride_this_level, x) in enumerate( for k, (cls_conv, reg_conv, stride_this_level, x) in enumerate(
zip(self.cls_convs, self.reg_convs, self.strides, xin)
zip(self.cls_convs, self.reg_convs, self.strides, xin)
): ):
x = self.stems[k](x) x = self.stems[k](x)
cls_x = x cls_x = x
y_shifts.append(grid[:, :, 1]) y_shifts.append(grid[:, :, 1])
expanded_strides.append( expanded_strides.append(
torch.zeros(1, grid.shape[1]) torch.zeros(1, grid.shape[1])
.fill_(stride_this_level)
.type_as(xin[0])
.fill_(stride_this_level)
.type_as(xin[0])
) )
if self.use_l1: if self.use_l1:
batch_size = reg_output.shape[0] batch_size = reg_output.shape[0]
outputs.append(output) outputs.append(output)


if self.training: if self.training:
# logger.info("labels.shape:{}".format(labels.shape))
# logger.info("torch.cat(outputs, 1).shape:{}".format(torch.cat(outputs, 1).shape))
return self.get_losses( return self.get_losses(
imgs, imgs,
x_shifts, x_shifts,
return outputs return outputs


def get_losses( def get_losses(
self,
imgs,
x_shifts,
y_shifts,
expanded_strides,
labels,
outputs,
origin_preds,
dtype,
self,
imgs,
x_shifts,
y_shifts,
expanded_strides,
labels,
outputs,
origin_preds,
dtype,
): ):
bbox_preds = outputs[:, :, :4] # [batch, n_anchors_all, 4] bbox_preds = outputs[:, :, :4] # [batch, n_anchors_all, 4]
obj_preds = outputs[:, :, 4].unsqueeze(-1) # [batch, n_anchors_all, 1] obj_preds = outputs[:, :, 4].unsqueeze(-1) # [batch, n_anchors_all, 1]
gt_bboxes_per_image = labels[batch_idx, :num_gt, 1:5] gt_bboxes_per_image = labels[batch_idx, :num_gt, 1:5]
gt_classes = labels[batch_idx, :num_gt, 0] gt_classes = labels[batch_idx, :num_gt, 0]
bboxes_preds_per_image = bbox_preds[batch_idx] bboxes_preds_per_image = bbox_preds[batch_idx]
try: try:
( (
gt_matched_classes, gt_matched_classes,
labels, labels,
imgs, imgs,
) )
except RuntimeError:
except RuntimeError as e:
logger.info( logger.info(
"OOM RuntimeError is raised due to the huge memory cost during label assignment. \ "OOM RuntimeError is raised due to the huge memory cost during label assignment. \
CPU mode is applied in this batch. If you want to avoid this issue, \ CPU mode is applied in this batch. If you want to avoid this issue, \
try to reduce the batch size or image size."
try to reduce the batch size or image size. " + str(e)
) )
print("OOM RuntimeError is raised due to the huge memory cost during label assignment. \ print("OOM RuntimeError is raised due to the huge memory cost during label assignment. \
CPU mode is applied in this batch. If you want to avoid this issue, \ CPU mode is applied in this batch. If you want to avoid this issue, \
try to reduce the batch size or image size.")
try to reduce the batch size or image size. " + str(e))
torch.cuda.empty_cache() torch.cuda.empty_cache()
( (
gt_matched_classes, gt_matched_classes,
imgs, imgs,
"cpu", "cpu",
) )

torch.cuda.empty_cache() torch.cuda.empty_cache()
num_fg += num_fg_img num_fg += num_fg_img


if self.use_l1: if self.use_l1:
l1_targets = torch.cat(l1_targets, 0) l1_targets = torch.cat(l1_targets, 0)



# TODO: check loss parts shapes

num_fg = max(num_fg, 1) num_fg = max(num_fg, 1)
loss_iou = ( loss_iou = (
self.iou_loss(bbox_preds.view(-1, 4)[fg_masks], reg_targets)
).sum() / num_fg
self.iou_loss(bbox_preds.view(-1, 4)[fg_masks], reg_targets)
).sum() / num_fg
loss_obj = ( loss_obj = (
self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets)
).sum() / num_fg
self.bcewithlog_loss(obj_preds.view(-1, 1), obj_targets)
).sum() / num_fg
loss_cls = ( loss_cls = (
self.bcewithlog_loss(
cls_preds.view(-1, self.num_classes)[fg_masks], cls_targets
)
).sum() / num_fg
self.bcewithlog_loss(
cls_preds.view(-1, self.num_classes)[fg_masks], cls_targets
)
).sum() / num_fg
if self.use_l1: if self.use_l1:
loss_l1 = ( loss_l1 = (
self.l1_loss(origin_preds.view(-1, 4)[fg_masks], l1_targets)
).sum() / num_fg
self.l1_loss(origin_preds.view(-1, 4)[fg_masks], l1_targets)
).sum() / num_fg
else: else:
loss_l1 = 0.0 loss_l1 = 0.0




@torch.no_grad() @torch.no_grad()
def get_assignments( def get_assignments(
self,
batch_idx,
num_gt,
total_num_anchors,
gt_bboxes_per_image,
gt_classes,
bboxes_preds_per_image,
expanded_strides,
x_shifts,
y_shifts,
cls_preds,
bbox_preds,
obj_preds,
labels,
imgs,
mode="gpu",
self,
batch_idx,
num_gt,
total_num_anchors,
gt_bboxes_per_image,
gt_classes,
bboxes_preds_per_image,
expanded_strides,
x_shifts,
y_shifts,
cls_preds,
bbox_preds,
obj_preds,
labels,
imgs,
mode="gpu",
): ):


if mode == "cpu": if mode == "cpu":


gt_cls_per_image = ( gt_cls_per_image = (
F.one_hot(gt_classes.to(torch.int64), self.num_classes) F.one_hot(gt_classes.to(torch.int64), self.num_classes)
.float()
.unsqueeze(1)
.repeat(1, num_in_boxes_anchor, 1)
.float()
.unsqueeze(1)
.repeat(1, num_in_boxes_anchor, 1)
) )
pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8) pair_wise_ious_loss = -torch.log(pair_wise_ious + 1e-8)




with torch.cuda.amp.autocast(enabled=False): with torch.cuda.amp.autocast(enabled=False):
cls_preds_ = ( cls_preds_ = (
cls_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
* obj_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
cls_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
* obj_preds_.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
) )
pair_wise_cls_loss = F.binary_cross_entropy( pair_wise_cls_loss = F.binary_cross_entropy(
cls_preds_.sqrt_(), gt_cls_per_image, reduction="none" cls_preds_.sqrt_(), gt_cls_per_image, reduction="none"
del cls_preds_ del cls_preds_


cost = ( cost = (
pair_wise_cls_loss
+ 3.0 * pair_wise_ious_loss
+ 100000.0 * (~is_in_boxes_and_center)
pair_wise_cls_loss
+ 3.0 * pair_wise_ious_loss
+ 100000.0 * (~is_in_boxes_and_center)
) )


( (
) )


def get_in_boxes_info( def get_in_boxes_info(
self,
gt_bboxes_per_image,
expanded_strides,
x_shifts,
y_shifts,
total_num_anchors,
num_gt,
img_size
self,
gt_bboxes_per_image,
expanded_strides,
x_shifts,
y_shifts,
total_num_anchors,
num_gt,
img_size
): ):
expanded_strides_per_image = expanded_strides[0] expanded_strides_per_image = expanded_strides[0]
x_shifts_per_image = x_shifts[0] * expanded_strides_per_image x_shifts_per_image = x_shifts[0] * expanded_strides_per_image
y_shifts_per_image = y_shifts[0] * expanded_strides_per_image y_shifts_per_image = y_shifts[0] * expanded_strides_per_image
x_centers_per_image = ( x_centers_per_image = (
(x_shifts_per_image + 0.5 * expanded_strides_per_image) (x_shifts_per_image + 0.5 * expanded_strides_per_image)
.unsqueeze(0)
.repeat(num_gt, 1)
.unsqueeze(0)
.repeat(num_gt, 1)
) # [n_anchor] -> [n_gt, n_anchor] ) # [n_anchor] -> [n_gt, n_anchor]
y_centers_per_image = ( y_centers_per_image = (
(y_shifts_per_image + 0.5 * expanded_strides_per_image) (y_shifts_per_image + 0.5 * expanded_strides_per_image)
.unsqueeze(0)
.repeat(num_gt, 1)
.unsqueeze(0)
.repeat(num_gt, 1)
) )


gt_bboxes_per_image_l = ( gt_bboxes_per_image_l = (
(gt_bboxes_per_image[:, 0] - 0.5 * gt_bboxes_per_image[:, 2]) (gt_bboxes_per_image[:, 0] - 0.5 * gt_bboxes_per_image[:, 2])
.unsqueeze(1)
.repeat(1, total_num_anchors)
.unsqueeze(1)
.repeat(1, total_num_anchors)
) )
gt_bboxes_per_image_r = ( gt_bboxes_per_image_r = (
(gt_bboxes_per_image[:, 0] + 0.5 * gt_bboxes_per_image[:, 2]) (gt_bboxes_per_image[:, 0] + 0.5 * gt_bboxes_per_image[:, 2])
.unsqueeze(1)
.repeat(1, total_num_anchors)
.unsqueeze(1)
.repeat(1, total_num_anchors)
) )
gt_bboxes_per_image_t = ( gt_bboxes_per_image_t = (
(gt_bboxes_per_image[:, 1] - 0.5 * gt_bboxes_per_image[:, 3]) (gt_bboxes_per_image[:, 1] - 0.5 * gt_bboxes_per_image[:, 3])
.unsqueeze(1)
.repeat(1, total_num_anchors)
.unsqueeze(1)
.repeat(1, total_num_anchors)
) )
gt_bboxes_per_image_b = ( gt_bboxes_per_image_b = (
(gt_bboxes_per_image[:, 1] + 0.5 * gt_bboxes_per_image[:, 3]) (gt_bboxes_per_image[:, 1] + 0.5 * gt_bboxes_per_image[:, 3])
.unsqueeze(1)
.repeat(1, total_num_anchors)
.unsqueeze(1)
.repeat(1, total_num_anchors)
) )


b_l = x_centers_per_image - gt_bboxes_per_image_l b_l = x_centers_per_image - gt_bboxes_per_image_l
is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all is_in_boxes_anchor = is_in_boxes_all | is_in_centers_all


is_in_boxes_and_center = ( is_in_boxes_and_center = (
is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor]
is_in_boxes[:, is_in_boxes_anchor] & is_in_centers[:, is_in_boxes_anchor]
) )
del gt_bboxes_per_image_clip del gt_bboxes_per_image_clip
return is_in_boxes_anchor, is_in_boxes_and_center return is_in_boxes_anchor, is_in_boxes_and_center

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