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train.py
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train.py
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from __future__ import print_function
import os
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import argparse
from torch.nn.parallel import DataParallel, DistributedDataParallel
from data import AnnotationTransform, VOCDetection, COCODetection, detection_collate, VOCroot, COCOroot, \
VOC_300, VOC_512, COCO_300, COCO_512, preproc
from models.RFB_Net_vgg import build_net
from layers.modules.multibox_loss_combined import MultiBoxLoss_combined
from layers.functions import PriorBox
from utils.box_utils import match
from utils.solver import build_optimizer, build_lr_scheduler
from utils.checkpointer import DetectionCheckpointer, PeriodicCheckpointer
from utils.logger import setup_logger
from utils.sampler import TrainingSampler
from utils.event import EventStorage, CommonMetricPrinter, TensorboardXWriter
# np.random.seed(100)
parser = argparse.ArgumentParser(
description='Context-Transformer')
# Model and Dataset
parser.add_argument('-s', '--size', default='300',
help='300 or 512 input size.')
parser.add_argument('--basenet', default='./weights/vgg16_reducedfc.pth',
help='Pretrained base model')
parser.add_argument('-d', '--dataset', default='VOC',
help='VOC or COCO dataset.')
parser.add_argument('--split', type=int, default=1,
help='VOC base/novel split, for VOC only.')
# Training Parameters
parser.add_argument('--setting', default='transfer',
help='Training setting: transfer or incre.')
parser.add_argument('-p', '--phase', type=int, default=1,
help='Training phase. 1: source pretraining, 2: target fintuning.')
parser.add_argument('-m', '--method', default='ours',
help='ft(baseline) or ours, for phase 2 only.')
parser.add_argument('--shot', type=int, default=5,
help="Number of shot, for phase 2 only.")
parser.add_argument('--init-iter', type=int, default=50,
help="Number of iterations for OBJ(Target) initialization")
parser.add_argument('-max', '--max-iter', type=int, default=180000,
help='Number of training iterations.')
parser.add_argument('-b', '--batch-size', type=int, default=64,
help='Batch size for training')
parser.add_argument('--lr', '--learning-rate', type=float, default=4e-3,
help='Initial learning rate')
parser.add_argument('--steps', type=int, nargs='+', default=[120000, 150000],
help='Learning rate decrease steps.')
parser.add_argument('--warmup-iter', type=int, default=5000,
help='Number of warmup iterations')
parser.add_argument('--ngpu', type=int, default=4, help='gpus')
parser.add_argument('--num-workers', type=int, default=4,
help='Number of workers used in dataloading')
parser.add_argument('--cuda', type=bool, default=True,
help='Use cuda to train model')
parser.add_argument('--momentum', default=0.9, type=float,
help='Momentum for SGD')
parser.add_argument('--weight-decay', type=float, default=5e-4,
help='Weight decay for SGD')
parser.add_argument('--gamma', type=float, default=0.1,
help='Gamma update for SGD')
parser.add_argument('--load-file', default=None,
help='Model checkpoint for loading.')
parser.add_argument('--resume', action='store_true',
help='Whether resume from the last checkpoint.'
'If True, no need to specify --load-file.')
parser.add_argument('-is', '--instance-shot', action='store_true',
help='If True, instance shot will be applied for transfer setting.')
# TODO
# Mixup
parser.add_argument('--mixup', action='store_true',
help='Whether to enable mixup.')
parser.add_argument('--no-mixup-iter', type=int, default=800,
help='Disable mixup for the last few iterations.')
# Output
parser.add_argument('--save-folder', default='./weights/',
help='Location to save checkpoint models')
parser.add_argument('--checkpoint-period', type=int, default=10000,
help='Checkpoint period.')
args = parser.parse_args()
if not os.path.exists(args.save_folder):
os.makedirs(args.save_folder)
logger = setup_logger(args.save_folder)
if args.dataset == 'VOC':
if args.phase == 2 and (args.setting == 'incre' or args.instance_shot):
train_sets = [('2007', 'trainval')]
else:
train_sets = [('2007', 'trainval'), ('2012', 'trainval')]
cfg = (VOC_300, VOC_512)[args.size == '512']
elif args.dataset == 'COCO':
train_sets = [('2014', 'split_nonvoc_train'), ('2014', 'split_nonvoc_valminusminival')]
cfg = (COCO_300, COCO_512)[args.size == '512']
else:
raise ValueError(f"Unknown dataset: {args.dataset}")
if args.phase == 1:
if args.dataset == 'VOC':
src_cls_dim = 15
num_classes = 16 # include background
elif args.dataset == 'COCO':
src_cls_dim = 60
num_classes = 61 # include background
elif args.phase == 2:
if args.setting == 'transfer':
if args.method == 'ours':
src_cls_dim = 60
num_classes = 21
elif args.method == 'ft':
src_cls_dim = 20
num_classes = 21
else:
raise ValueError(f"Unknown method: {args.method}")
elif args.setting == 'incre':
if args.method == 'ours':
src_cls_dim = 15
num_classes = 21
else:
raise ValueError('We only support our method for incremental setting.')
else:
raise ValueError(f"Unknown setting: {args.setting}")
else:
raise ValueError(f"Unknown phase: {args.phase}")
img_dim = (300, 512)[args.size == '512']
rgb_means = (104, 117, 123)
p = 0.6
overlap_threshold = 0.5
priorbox = PriorBox(cfg)
with torch.no_grad():
priors = priorbox.forward()
if args.cuda:
priors = priors.cuda()
num_priors = priors.size(0)
def train(model, resume=False):
model.train()
optimizer = build_optimizer(args, model)
scheduler = build_lr_scheduler(args, optimizer)
checkpointer = DetectionCheckpointer(
model, args, optimizer=optimizer, scheduler=scheduler
)
criterion = MultiBoxLoss_combined(num_classes, overlap_threshold, True, 0, True, 3, 0.5, False)
start_iter = (
checkpointer.resume_or_load(args.basenet if args.phase == 1 else args.load_file,
resume=resume).get("iteration", -1) + 1
)
max_iter = args.max_iter
periodic_checkpointer = PeriodicCheckpointer(
checkpointer, args.checkpoint_period, max_iter=max_iter
)
writers = (
[
CommonMetricPrinter(max_iter),
TensorboardXWriter(args.save_folder),
]
)
if args.dataset == 'VOC':
dataset = VOCDetection(args, VOCroot, train_sets, preproc(
img_dim, rgb_means, p), AnnotationTransform(0 if args.setting == 'transfer' else args.split))
elif args.dataset == 'COCO':
dataset = COCODetection(COCOroot, train_sets, preproc(
img_dim, rgb_means, p))
else:
raise ValueError(f"Unknown dataset: {args.dataset}")
if args.phase == 2 and args.method == 'ours':
sampler = TrainingSampler(len(dataset))
data_loader = torch.utils.data.DataLoader(
dataset,
args.batch_size,
sampler=sampler,
num_workers=args.num_workers,
collate_fn=detection_collate,
)
# initialize the OBJ(Target) parameters
init_reweight(args, model, data_loader)
dataset.set_mixup(np.random.beta, 1.5, 1.5)
logger.info('Fine tuning on ' + str(args.shot) + '-shot task')
sampler = TrainingSampler(len(dataset))
data_loader = iter(torch.utils.data.DataLoader(
dataset,
args.batch_size,
sampler=sampler,
num_workers=args.num_workers,
collate_fn=detection_collate,
))
assert model.training, 'Model.train() must be True during training.'
logger.info("Starting training from iteration {}".format(start_iter))
# scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones, gamma=args.gamma, last_epoch=epoch - 1)
with EventStorage(start_iter) as storage:
for iteration in range(start_iter, max_iter):
iteration = iteration + 1
storage.step()
if args.phase == 2 and args.method == 'ours' and \
iteration == (args.max_iter - args.no_mixup_iter):
dataset.set_mixup(None)
data_loader = iter(torch.utils.data.DataLoader(
dataset,
args.batch_size,
sampler=sampler,
num_workers=args.num_workers,
collate_fn=detection_collate,
))
data, targets = next(data_loader)
# storage.put_image('image', vis_tensorboard(data))
output = model(data)
loss_dict = criterion(output, priors, targets)
losses = sum(loss for loss in loss_dict.values())
# assert torch.isfinite(losses).all(), loss_dict
storage.put_scalars(total_loss=losses, **loss_dict)
optimizer.zero_grad()
losses.backward()
optimizer.step()
if args.phase == 2 and args.method == 'ours':
if isinstance(model, (DistributedDataParallel, DataParallel)):
model.module.normalize()
else:
model.normalize()
storage.put_scalar("lr", optimizer.param_groups[-1]["lr"], smoothing_hint=False)
scheduler.step()
if iteration - start_iter > 5 and (iteration % 20 == 0 or iteration == max_iter):
for writer in writers:
writer.write()
periodic_checkpointer.step(iteration)
def vis_tensorboard(images):
rgb_mean = torch.Tensor(rgb_means).to(images.device)
image = images[0] + rgb_mean[:, None, None]
image = image[[2, 1, 0]].byte()
return image
def init_reweight(args, model, data_loader):
"""
Initialize the OBJ(Target) parameters.
"""
logger.info('Initializing the OBJ(Target) parameters...')
device = 'cuda' if args.cuda and torch.cuda.is_available() else 'cpu'
cls_list = [torch.empty(0).to(device) for _ in range(num_classes-1)]
for (data, targets), iteration in zip(data_loader, range(args.init_iter)):
# vis_picture(images, targets)
num = data.size(0)
targets = [anno.to(device) for anno in targets]
with torch.no_grad():
conf_data = model(data, init=True)
loc_t = torch.Tensor(num, num_priors, 4).to(device)
conf_t = torch.Tensor(num, num_priors, 2).to(device)
obj_t = torch.BoolTensor(num, num_priors).to(device)
# match priors with gt
for idx in range(num): # batch_size
truths = targets[idx][:, :-2].data # [obj_num, 4]
labels = targets[idx][:, -2:].data # [obj_num]
defaults = priors.data # [num_priors,4]
match(overlap_threshold, truths, defaults, [0.1, 0.2], labels, loc_t, conf_t, obj_t, idx)
conf_data_list = [conf_data[conf_t[:, :, 0] == i] for i in range(1, num_classes)]
cls_list = [torch.cat((cls_list[i], conf_data_list[i]), 0) for i in range(num_classes-1)]
cls_list = [(item / item.norm(dim=1, keepdim=True)).mean(0) for item in cls_list]
if args.setting == 'incre':
cls_list = cls_list[15:]
if isinstance(model, (DistributedDataParallel, DataParallel)):
model.module.OBJ_Target.weight.data = torch.stack([item / item.norm() for item in cls_list], 0)
else:
model.OBJ_Target.weight.data = torch.stack([item / item.norm() for item in cls_list], 0)
if __name__ == '__main__':
model = build_net(args, img_dim, src_cls_dim)
logger.info("Model:\n{}".format(model))
if args.cuda and torch.cuda.is_available():
model.device = 'cuda'
model.cuda()
cudnn.benchmark = True
if args.ngpu > 1:
model = torch.nn.DataParallel(model, device_ids=list(range(args.ngpu)))
else:
model.device = 'cpu'
train(model, args.resume)