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l_s50_still_dfp_flip.py
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l_s50_still_dfp_flip.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 = 1.0
self.width = 1.0
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 = 15
self.no_aug_epochs = 15
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'
def get_model(self):
from exps.model.yolox import YOLOX
from exps.model.dfp_pafpn import DFPPAFPN
from exps.model.pipe_head import PIPEHead
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]
backbone = DFPPAFPN(self.depth, self.width, in_channels=in_channels)
head = PIPEHead(self.num_classes, self.width, in_channels=in_channels)
self.model = YOLOX(backbone, head)
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.still_argoversedataset import STILL_ARGOVERSEDataset
from exps.data.mosaicdetection import MosaicDetection
from exps.data.data_augment_flip import TrainTransform
from yolox.data import (
YoloBatchSampler,
DataLoader,
InfiniteSampler,
worker_init_reset_seed,
)
dataset = STILL_ARGOVERSEDataset(
data_dir='/data',
json_file=self.train_ann,
name='train',
img_size=self.input_size,
preproc=TrainTransform(max_labels=50, hsv=False, flip=True),
cache=cache_img,
)
dataset = MosaicDetection(dataset,
mosaic=not no_aug,
img_size=self.input_size,
preproc=TrainTransform(max_labels=120, hsv=False, flip=True),
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.still_argoversedataset import STILL_ARGOVERSEDataset
from exps.data.data_augment_flip import ValTransform
valdataset = STILL_ARGOVERSEDataset(
data_dir='/data',
json_file='val.json',
name='val',
img_size=self.test_size,
preproc=ValTransform(),
)
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 = nn.functional.interpolate(
inputs, size=tsize, mode="bilinear", align_corners=False
)
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.still_stream_evaluator import STILL_COCOEvaluator
val_loader = self.get_eval_loader(batch_size, is_distributed, testdev)
evaluator = STILL_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.double_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)