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train.py
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train.py
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# -*- coding: utf-8 -*-
import datetime
import os
from functools import partial
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from nets.yolo import YoloBody
from nets.yolo_training import (ModelEMA, YOLOLoss, get_lr_scheduler,
set_optimizer_lr, weights_init)
from utils.callbacks import EvalCallback, LossHistory
from utils.dataloader import YoloDataset, yolo_dataset_collate
from utils.utils import (get_classes, seed_everything, show_config,
worker_init_fn)
from utils.utils_fit import fit_one_epoch
if __name__ == "__main__":
Cuda = True
seed = 11
distributed = False
sync_bn = False
fp16 = False
classes_path = 'model_data/sfew_classes.txt'
model_path = 'model_data/yolox_s.pth'
input_shape = [320, 320]
phi = 's'
mosaic = True
mosaic_prob = 0.5
mixup = True
mixup_prob = 0.5
special_aug_ratio = 0.7
Init_Epoch = 0
Freeze_Epoch = 100
Freeze_batch_size = 32
UnFreeze_Epoch = 300
Unfreeze_batch_size = 16
Freeze_Train = True
Init_lr = 1e-2
Min_lr = Init_lr * 0.01
optimizer_type = "sgd"
momentum = 0.937
weight_decay = 5e-4
lr_decay_type = "cos"
save_period = 10
save_dir = 'logs'
eval_flag = True
eval_period = 10
num_workers = 0
train_annotation_path = '2007_train.txt'
val_annotation_path = '2007_val.txt'
seed_everything(seed)
ngpus_per_node = torch.cuda.device_count()
if distributed:
dist.init_process_group(backend="nccl")
local_rank = int(os.environ["LOCAL_RANK"])
rank = int(os.environ["RANK"])
device = torch.device("cuda", local_rank)
if local_rank == 0:
print(f"[{os.getpid()}] (rank = {rank}, local_rank = {local_rank}) training...")
print("Gpu Device Count : ", ngpus_per_node)
else:
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
local_rank = 0
rank = 0
class_names, num_classes = get_classes(classes_path)
model = YoloBody(num_classes, phi)
weights_init(model)
if model_path != '':
if local_rank == 0:
print('Load weights {}.'.format(model_path))
model_dict = model.state_dict()
pretrained_dict = torch.load(model_path, map_location = device)
load_key, no_load_key, temp_dict = [], [], {}
for k, v in pretrained_dict.items():
if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):
temp_dict[k] = v
load_key.append(k)
else:
no_load_key.append(k)
model_dict.update(temp_dict)
model.load_state_dict(model_dict)
if local_rank == 0:
print("\nSuccessful Load Key:", str(load_key)[:500], "……\nSuccessful Load Key Num:", len(load_key))
print("\nFail To Load Key:", str(no_load_key)[:500], "……\nFail To Load Key num:", len(no_load_key))
yolo_loss = YOLOLoss(num_classes, fp16)
if local_rank == 0:
time_str = datetime.datetime.strftime(datetime.datetime.now(),'%Y_%m_%d_%H_%M_%S')
log_dir = os.path.join(save_dir, "loss_" + str(time_str))
loss_history = LossHistory(log_dir, model, input_shape=input_shape)
else:
loss_history = None
if fp16:
from torch.cuda.amp import GradScaler as GradScaler
scaler = GradScaler()
else:
scaler = None
model_train = model.train()
if sync_bn and ngpus_per_node > 1 and distributed:
model_train = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model_train)
elif sync_bn:
print("Sync_bn is not support in one gpu or not distributed.")
model_train = model_train.cuda()
ema = ModelEMA(model_train)
with open(train_annotation_path, encoding='utf-8') as f:
train_lines = f.readlines()
with open(val_annotation_path, encoding='utf-8') as f:
val_lines = f.readlines()
num_train = len(train_lines)
num_val = len(val_lines)
if local_rank == 0:
show_config(
classes_path = classes_path, model_path = model_path, input_shape = input_shape, \
Init_Epoch = Init_Epoch, Freeze_Epoch = Freeze_Epoch, UnFreeze_Epoch = UnFreeze_Epoch, Freeze_batch_size = Freeze_batch_size, Unfreeze_batch_size = Unfreeze_batch_size, Freeze_Train = Freeze_Train, \
Init_lr = Init_lr, Min_lr = Min_lr, optimizer_type = optimizer_type, momentum = momentum, lr_decay_type = lr_decay_type, \
save_period = save_period, save_dir = save_dir, num_workers = num_workers, num_train = num_train, num_val = num_val
)
wanted_step = 5e4 if optimizer_type == "sgd" else 1.5e4
total_step = num_train // Unfreeze_batch_size * UnFreeze_Epoch
if True:
UnFreeze_flag = False
if Freeze_Train:
for param in model.backbone.parameters():
param.requires_grad = False
batch_size = Freeze_batch_size if Freeze_Train else Unfreeze_batch_size
nbs = 64
lr_limit_max = 1e-3 if optimizer_type == 'adam' else 5e-2
lr_limit_min = 3e-4 if optimizer_type == 'adam' else 5e-4
Init_lr_fit = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)
Min_lr_fit = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
pg0, pg1, pg2 = [], [], []
for k, v in model.named_modules():
if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias)
if isinstance(v, nn.BatchNorm2d) or "bn" in k:
pg0.append(v.weight)
elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight)
optimizer = {
'adam' : optim.Adam(pg0, Init_lr_fit, betas = (momentum, 0.999)),
'sgd' : optim.SGD(pg0, Init_lr_fit, momentum = momentum, nesterov=True)
}[optimizer_type]
optimizer.add_param_group({"params": pg1, "weight_decay": weight_decay})
optimizer.add_param_group({"params": pg2})
lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
epoch_step = num_train // batch_size
epoch_step_val = num_val // batch_size
if ema:
ema.updates = epoch_step * Init_Epoch
train_dataset = YoloDataset(train_lines, input_shape, num_classes, epoch_length = UnFreeze_Epoch, \
mosaic=mosaic, mixup=mixup, mosaic_prob=mosaic_prob, mixup_prob=mixup_prob, train=True, special_aug_ratio=special_aug_ratio)
val_dataset = YoloDataset(val_lines, input_shape, num_classes, epoch_length = UnFreeze_Epoch, \
mosaic=False, mixup=False, mosaic_prob=0, mixup_prob=0, train=False, special_aug_ratio=0)
if distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True,)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False,)
batch_size = batch_size // ngpus_per_node
shuffle = False
else:
train_sampler = None
val_sampler = None
shuffle = True
gen = DataLoader(train_dataset, shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True,
drop_last=True, collate_fn=yolo_dataset_collate, sampler=train_sampler,
worker_init_fn=partial(worker_init_fn, rank=rank, seed=seed))
gen_val = DataLoader(val_dataset , shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True,
drop_last=True, collate_fn=yolo_dataset_collate, sampler=val_sampler,
worker_init_fn=partial(worker_init_fn, rank=rank, seed=seed))
if local_rank == 0:
eval_callback = EvalCallback(model, input_shape, class_names, num_classes, val_lines, log_dir, Cuda, \
eval_flag=eval_flag, period=eval_period)
else:
eval_callback = None
for epoch in range(Init_Epoch, UnFreeze_Epoch):
if epoch >= Freeze_Epoch and not UnFreeze_flag and Freeze_Train:
batch_size = Unfreeze_batch_size
nbs = 64
lr_limit_max = 1e-3 if optimizer_type == 'adam' else 5e-2
lr_limit_min = 3e-4 if optimizer_type == 'adam' else 5e-4
Init_lr_fit = min(max(batch_size / nbs * Init_lr, lr_limit_min), lr_limit_max)
Min_lr_fit = min(max(batch_size / nbs * Min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
lr_scheduler_func = get_lr_scheduler(lr_decay_type, Init_lr_fit, Min_lr_fit, UnFreeze_Epoch)
for param in model.backbone.parameters():
param.requires_grad = True
epoch_step = num_train // batch_size
epoch_step_val = num_val // batch_size
if distributed:
batch_size = batch_size // ngpus_per_node
if ema:
ema.updates = epoch_step * epoch
gen = DataLoader(train_dataset, shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True,
drop_last=True, collate_fn=yolo_dataset_collate, sampler=train_sampler,
worker_init_fn=partial(worker_init_fn, rank=rank, seed=seed))
gen_val = DataLoader(val_dataset , shuffle = shuffle, batch_size = batch_size, num_workers = num_workers, pin_memory=True,
drop_last=True, collate_fn=yolo_dataset_collate, sampler=val_sampler,
worker_init_fn=partial(worker_init_fn, rank=rank, seed=seed))
UnFreeze_flag = True
gen.dataset.epoch_now = epoch
gen_val.dataset.epoch_now = epoch
if distributed:
train_sampler.set_epoch(epoch)
set_optimizer_lr(optimizer, lr_scheduler_func, epoch)
fit_one_epoch(model_train, model, ema, yolo_loss, loss_history, eval_callback, optimizer, epoch, epoch_step, epoch_step_val, gen, gen_val, UnFreeze_Epoch, Cuda, fp16, scaler, save_period, save_dir, local_rank)
if distributed:
dist.barrier()
if local_rank == 0:
loss_history.writer.close()