#!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import os
import random
import torch
import torch.distributed as dist
import torch.nn as nn
from .base_exp import BaseExp
class Exp(BaseExp):
def __init__(self):
super().__init__()
# ---------------- model config ---------------- #
self.num_classes = 80
self.depth = 1.00
self.width = 1.00
# ---------------- dataloader config ---------------- #
# set worker to 4 for shorter dataloader init time
self.data_num_workers = 4
self.input_size = (640, 640) # (height, width)
# Actual multiscale ranges: [640-5*32, 640+5*32].
# To disable multiscale training, set the
# self.multiscale_range to 0.
self.multiscale_range = 5
# You can uncomment this line to specify a multiscale range
# self.random_size = (14, 26)
self.data_dir = None
self.train_ann = "instances_train2017.json"
self.val_ann = "instances_val2017.json"
# --------------- transform config ----------------- #
self.mosaic_prob = 1.0
self.mixup_prob = 1.0
self.hsv_prob = 1.0
self.flip_prob = 0.5
self.degrees = 10.0
self.translate = 0.1
self.mosaic_scale = (0.1, 2)
self.mixup_scale = (0.5, 1.5)
self.shear = 2.0
self.perspective = 0.0
self.enable_mixup = True
# -------------- training config --------------------- #
self.warmup_epochs = 5
self.max_epoch = 300
self.warmup_lr = 0
self.basic_lr_per_img = 0.01 / 64.0
self.scheduler = "yoloxwarmcos"
self.no_aug_epochs = 15
self.min_lr_ratio = 0.05
self.ema = True
self.weight_decay = 5e-4
self.momentum = 0.9
self.print_interval = 10
self.eval_interval = 10
self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
# ----------------- testing config ------------------ #
self.test_size = (640, 640)
self.test_conf = 0.01
self.nmsthre = 0.65
def get_model(self):
from yolox.models import YOLOX, YOLOPAFPN, YOLOXHead
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 = YOLOPAFPN(self.depth, self.width, in_channels=in_channels)
head = YOLOXHead(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, cache_img=False
):
from yolox.data import (
COCODataset,
TrainTransform,
YoloBatchSampler,
DataLoader,
InfiniteSampler,
MosaicDetection,
worker_init_reset_seed,
)
from yolox.utils import (
wait_for_the_master,
get_local_rank,
)
local_rank = get_local_rank()
with wait_for_the_master(local_rank):
dataset = COCODataset(
data_dir=self.data_dir,
json_file=self.train_ann,
img_size=self.input_size,
preproc=TrainTransform(
max_labels=50,
flip_prob=self.flip_prob,
hsv_prob=self.hsv_prob),
cache=cache_img,
)
dataset = MosaicDetection(
dataset,
mosaic=not no_aug,
img_size=self.input_size,
preproc=TrainTransform(
max_labels=120,
flip_prob=self.flip_prob,
hsv_prob=self.hsv_prob),
degrees=self.degrees,
translate=self.translate,
mosaic_scale=self.mosaic_scale,
mixup_scale=self.mixup_scale,
shear=self.shear,
perspective=self.perspective,
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.
# Check https://github.com/pytorch/pytorch/issues/63311 for more details.
dataloader_kwargs["worker_init_fn"] = worker_init_reset_seed
train_loader = DataLoader(self.dataset, **dataloader_kwargs)
return train_loader
def random_resize(self, data_loader, epoch, rank, is_distributed):
tensor = torch.LongTensor(2).cuda()
if rank == 0:
size_factor = self.input_size[1] * 1.0 / self.input_size[0]
if not hasattr(self, 'random_size'):
min_size = int(self.input_size[0] / 32) - self.multiscale_range
max_size = int(self.input_size[0] / 32) + self.multiscale_range
self.random_size = (min_size, max_size)
size = random.randint(*self.random_size)
size = (int(32 * size), 32 * int(size * size_factor))
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[..., 1::2] = targets[..., 1::2] * scale_x
targets[..., 2::2] = targets[..., 2::2] * scale_y
return inputs, targets
def get_optimizer(self, batch_size):
if "optimizer" not in self.__dict__:
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():
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
def get_lr_scheduler(self, lr, iters_per_epoch):
from yolox.utils import LRScheduler
scheduler = LRScheduler(
self.scheduler,
lr,
iters_per_epoch,
self.max_epoch,
warmup_epochs=self.warmup_epochs,
warmup_lr_start=self.warmup_lr,
no_aug_epochs=self.no_aug_epochs,
min_lr_ratio=self.min_lr_ratio,
)
return scheduler
def get_eval_loader(self, batch_size, is_distributed, testdev=False, legacy=False):
from yolox.data import COCODataset, ValTransform
valdataset = COCODataset(
data_dir=self.data_dir,
json_file=self.val_ann if not testdev else "image_info_test-dev2017.json",
name="val2017" if not testdev else "test2017",
img_size=self.test_size,
preproc=ValTransform(legacy=legacy),
)
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 get_evaluator(self, batch_size, is_distributed, testdev=False, legacy=False):
from yolox.evaluators import COCOEvaluator
val_loader = self.get_eval_loader(batch_size, is_distributed, testdev, legacy)
evaluator = 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 eval(self, model, evaluator, is_distributed, half=False):
return evaluator.evaluate(model, is_distributed, half)