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train_bidet_ssd.py
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train_bidet_ssd.py
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import random
import numpy as np
import torch
SEED = 1
def set_seed(seed=1):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
set_seed(SEED)
from data import *
from utils.augmentations import SSDAugmentation
from layers.modules import MultiBoxLoss
from bidet_ssd import build_bidet_ssd
import os
import time
import math
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torch.utils.data as data
import argparse
import wandb
from utils.bop import MomentumWithThresholdBinaryOptimizer
os.environ["CUDA_VISIBLE_DEVICES"] = '2'
REGULARIZATION_LOSS_WEIGHT = 1.
PRIOR_LOSS_WEIGHT = 1.
NMS_CONF_THRE = 0.03
SQRT_2_PI = math.sqrt(2. * math.pi)
GRADIENT_CLIP_NORM = 5.
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
parser = argparse.ArgumentParser(
description='Single Shot MultiBox Detector Training With Pytorch')
train_set = parser.add_mutually_exclusive_group()
parser.add_argument('--dataset', default='VOC', choices=['VOC', 'COCO'],
type=str, help='VOC or COCO')
parser.add_argument('--data_root', default="/path/to/dataset/",
help='Dataset root directory path')
parser.add_argument('--basenet', default='./pretrain/vgg16.pth', type=str,
help='Pretrained base model')
parser.add_argument('--batch_size', default=32, type=int,
help='Batch size for training')
parser.add_argument('--resume', default=False, type=str2bool,
help='Whether to resume training from pretrained weights')
parser.add_argument('--weight_path', default=None, type=str,
help='Checkpoint state_dict file to resume training from')
parser.add_argument('--start_iter', default=0, type=int,
help='Resume training at this iter')
parser.add_argument('--num_workers', default=16, type=int,
help='Number of workers used in dataloading')
parser.add_argument('--cuda', default=True, type=str2bool,
help='Use CUDA to train model')
parser.add_argument('--lr', default=1e-3, type=float,
help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float,
help='Momentum value for optim')
parser.add_argument('--weight_decay', default=0., type=float,
help='Weight decay for SGD')
parser.add_argument('--gamma', default=0.1, type=float,
help='Gamma update for SGD')
parser.add_argument('--reg_weight', default=0., type=float,
help='regularization loss weight for feature maps')
parser.add_argument('--prior_weight', default=0., type=float,
help='loss weight for N(0, 1) prior')
parser.add_argument('--sigma', default=0., type=float,
help='scale factor controlling the sample procedure')
parser.add_argument('--nms_conf_threshold', default=0.03, type=float,
help='confidence threshold for nms')
parser.add_argument('--opt', default='Adam', type=str,
help='Optimizer for training the network')
parser.add_argument('--clip_grad', default=False, type=str2bool,
help='whether to clip gradient when training')
parser.add_argument('--is_rsign', default=False, type=str2bool,
help='whether to clip gradient when training')
parser.add_argument('--add_name_wandb', default="", type=str,
help='add name to wandb project')
args = parser.parse_args()
if torch.cuda.is_available():
if args.cuda:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
if not args.cuda:
print("WARNING: It looks like you have a CUDA device, but aren't " +
"using CUDA.\nRun with --cuda for optimal training speed.")
torch.set_default_tensor_type('torch.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
# start_datetime = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
start_datetime = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
# logs_dir = os.path.join('logs', args.dataset, str(start_datetime))
logs_dir = os.path.join('logs', args.dataset + args.add_name_wandb)
if not os.path.exists(logs_dir):
os.makedirs(logs_dir)
def train():
is_wandb = False
if is_wandb:
wandb.init(project="bidet_superpod-" + args.dataset + args.add_name_wandb)
wandb.init(config=args)
global REGULARIZATION_LOSS_WEIGHT, PRIOR_LOSS_WEIGHT, NMS_CONF_THRE
if args.dataset == 'COCO':
cfg = coco
dataset = COCODetection(root=args.data_root,image_set='train2014',
transform=SSDAugmentation(cfg['min_dim'], MEANS))
elif args.dataset == 'VOC':
cfg = voc
dataset = VOCDetection(root=args.data_root,
transform=SSDAugmentation(cfg['min_dim'], MEANS))
ssd_net = build_bidet_ssd('train', cfg['min_dim'], cfg['num_classes'],
nms_conf_thre=NMS_CONF_THRE,is_rsign=args.is_rsign)
net = ssd_net
if args.cuda:
cudnn.benchmark = True
opt_state_dict = None
if args.resume:
print('Resuming training, loading {}...'.format(args.weight_path))
try:
ssd_net.load_state_dict(torch.load(args.weight_path))
except: # checkpoint
print('Extracting from checkpoint')
ckp = torch.load(args.weight_path)
ssd_net.load_state_dict(ckp['weight'])
opt_state_dict = ckp['opt']
else:
if args.basenet.lower() != 'none':
vgg_weights = torch.load(args.basenet)
print('Loading base network...')
try:
ssd_net.vgg.layers.load_state_dict(vgg_weights, strict=True)
except: # ignore missing key
print("Load base network failed")
print("Ignore missing key")
ssd_net.vgg.layers.load_state_dict(vgg_weights, strict=False)
if args.cuda:
# net = nn.DataParallel(ssd_net).cuda()
net = ssd_net.cuda()
if args.opt.lower() == 'SGD'.lower():
optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr,
momentum=args.momentum, weight_decay=args.weight_decay)
elif args.opt.lower() == 'Adam'.lower():
optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=args.lr,
weight_decay=args.weight_decay)
elif args.opt.lower() == 'bop'.lower():
parameters = list(net.named_parameters())
binary_parameters = []
non_binary_parameters = []
for name, parameter in parameters:
if parameter.requires_grad:
if "binary" in name:
binary_parameters.append(parameter)
else:
non_binary_parameters.append(parameter)
print(binary_parameters)
optimizer = MomentumWithThresholdBinaryOptimizer(binary_parameters,non_binary_parameters, lr=args.lr)
else:
exit(-1)
if opt_state_dict is not None:
print('Load optimizer state dict!')
optimizer.load_state_dict(opt_state_dict)
if get_lr(optimizer) != args.lr:
adjust_learning_rate(optimizer, args.lr)
optimizer.zero_grad()
criterion = MultiBoxLoss(cfg['num_classes'], 0.5, True, 0, True, 3, 0.5, False, args.cuda)
net.train()
# loss counters
loss_count = 0. # for prior loss
loc_loss_save = 0.
conf_loss_save = 0.
reg_loss_save = 0.
prior_loss_save = 0.
loss_l = 0.
loss_c = 0.
loss_r = 0.
loss_p = 0.
epoch = 0
print('Loading the dataset...')
epoch_size = len(dataset) // args.batch_size
print('Training SSD on:', dataset.name)
print('Using the specified args:')
print(args)
step_index = 0
data_loader = data.DataLoader(dataset, args.batch_size,
num_workers=args.num_workers,
shuffle=True, collate_fn=detection_collate,
pin_memory=True, drop_last=True,
generator=torch.Generator(device='cuda')
)
# create batch iterator
if is_wandb:
wandb.watch(net, log_freq=1000)
batch_iterator = iter(data_loader)
for iteration in range(args.start_iter, cfg['max_iter']):
t0 = time.time()
lr = get_lr(optimizer)
if iteration % epoch_size == 0 and iteration != 0:
# reset epoch loss counters
epoch += 1
if iteration in cfg['lr_steps']:
# add our BiDet loss in the after the first lr decay
if step_index == 0:
args.reg_weight = 0.1
args.prior_weight = 0.2
REGULARIZATION_LOSS_WEIGHT = args.reg_weight
PRIOR_LOSS_WEIGHT = args.prior_weight
step_index += 1
new_lr = get_lr(optimizer) * args.gamma
adjust_learning_rate(optimizer, new_lr)
print("decay lr")
# load train data
try:
images, targets = next(batch_iterator)
except StopIteration:
batch_iterator = iter(data_loader)
images, targets = next(batch_iterator)
if args.cuda:
with torch.no_grad():
images = Variable(images.float().cuda())
targets = [Variable(ann.cuda()) for ann in targets]
else:
with torch.no_grad():
images = Variable(images.float())
targets = [Variable(ann) for ann in targets]
batch_size = images.size(0)
if PRIOR_LOSS_WEIGHT != 0.:
gt_class = [targets[batch_idx][:, -1] for batch_idx in range(batch_size)]
# forward
out = net(images)
loc_data, conf_data, priors, feature_map = out
# sample loc data from predicted miu and sigma
if args.cuda:
normal_dist = torch.randn(loc_data.size(0), loc_data.size(1), 4).float().cuda()
else:
normal_dist = torch.randn(loc_data.size(0), loc_data.size(1), 4).float()
log_sigma_2 = loc_data[:, :, :4]
miu = loc_data[:, :, 4:]
sigma = torch.exp(log_sigma_2 / 2.)
sample_loc_data = normal_dist * sigma * args.sigma + miu
loc_data = sample_loc_data
out = (
loc_data,
conf_data,
priors
)
# BP
loss_l, loss_c = criterion(out, targets)
loss_temp = loss_l + loss_c
# COCO dataset bug, maybe due to wrong annotations?
if loss_temp.item() == float("Inf"):
print('inf loss error!')
# the following code is to clear GPU memory for feature_map
# I don't know other better ways to do so except for BP the loss
loss_temp.backward()
net.zero_grad()
optimizer.zero_grad()
torch.cuda.empty_cache()
continue
if PRIOR_LOSS_WEIGHT != 0.:
loss_count = 0.
# print(priors)
print(loc_data)
print(conf_data)
# print(priors.shape)
print(loc_data.shape)
print(conf_data.shape)
detect_result = net.detect_prior.forward(
loc_data, # localization preds
net.softmax(conf_data), # confidence preds
priors, # default boxes
gt_class
) # [batch, classes, top_k, 5 (score, (y1, x1, y2, x2))]
print(detect_result)
print(detect_result.shape)
num_classes = detect_result.size(1)
print(num_classes)
# skip j = 0, because it's the background class
for j in range(1, num_classes):
all_dets = detect_result[:, j, :, :] # [batch, top_k, 5]
all_mask = all_dets[:, :, :1].gt(0.).expand_as(all_dets) # [batch, top_k, 5]
print(all_dets)
print(all_mask)
exit(0)
for batch_idx in range(batch_size):
# skip non-existed class
if not (gt_class[batch_idx] == j - 1).any():
continue
dets = torch.masked_select(all_dets[batch_idx], all_mask[batch_idx]).view(-1, 5) # [num, 5]
if dets.size(0) == 0:
continue
# if pred num == gt num, skip
if dets.size(0) <= ((gt_class[batch_idx] == j - 1).sum().detach().cpu().item()):
continue
scores = dets[:, 0] # [num]
scores_sum = scores.sum().item() # no grad
scores = scores / scores_sum # normalization
log_scores = log_func(scores)
gt_num = (gt_class[batch_idx] == j - 1).sum().detach().cpu().item()
loss_p += (-1. * log_scores.sum() / float(gt_num))
loss_count += 1.
loss_p /= (loss_count + 1e-6)
loss_p *= PRIOR_LOSS_WEIGHT
# Calculate regularization loss on feature maps
# directly use L2 loss here
if REGULARIZATION_LOSS_WEIGHT != 0.:
f_num = len(feature_map)
loss_r = 0.
for f_m in feature_map:
loss_r += (f_m ** 2).mean()
loss_r *= REGULARIZATION_LOSS_WEIGHT
loss_r /= float(f_num)
loss = loss_l + loss_c + loss_r + loss_p
# COCO dataset bug, maybe due to wrong annotations?
if loss.item() == float("Inf"):
print('inf loss error!')
# the following code is to clear GPU memory for feature_map
# I don't know other better ways to do so except for BP the loss
loss.backward()
net.zero_grad()
optimizer.zero_grad()
torch.cuda.empty_cache()
continue
# compute gradient and do optimizer step
loss.backward()
# clip gradient because binary net training is very unstable
if args.clip_grad:
grad_norm = get_grad_norm(net)
nn.utils.clip_grad_norm_(net.parameters(), GRADIENT_CLIP_NORM)
optimizer.step()
optimizer.zero_grad()
loss_l = loss_l.detach().cpu().item()
loss_c = loss_c.detach().cpu().item()
if REGULARIZATION_LOSS_WEIGHT != 0.:
loss_r = loss_r.detach().cpu().item()
if PRIOR_LOSS_WEIGHT != 0.:
loss_p = loss_p.detach().cpu().item()
loc_loss_save += loss_l
conf_loss_save += loss_c
reg_loss_save += loss_r
prior_loss_save += loss_p
t1 = time.time()
if iteration % 1000 == 0:
print('timer: %.4f sec.' % (t1 - t0))
print('iter:', iteration, 'loss:', round(loss.detach().cpu().item(), 4))
print('conf_loss:', round(loss_c, 4), 'loc_loss:', round(loss_l, 4),
'reg_loss:', round(loss_r, 4), 'prior_loss:', round(loss_p, 4),
'lr:', lr)
if args.clip_grad:
print('gradient norm:', grad_norm)
torch.cuda.empty_cache()
if is_wandb:
wandb.log({'conf_loss:': round(loss_c, 4), 'loc_loss:': round(loss_l, 4),
'reg_loss:': round(loss_r, 4), 'prior_loss:': round(loss_p, 4),
'lr:': lr})
if iteration != 0 and iteration % 5000 == 0:
print('Saving state, iter:', iteration)
loss_save = loc_loss_save + conf_loss_save + reg_loss_save + prior_loss_save
checkpoint = {
'weight': net.state_dict(),
'opt': optimizer.state_dict()
}
torch.save(checkpoint, logs_dir + '/model_' + str(iteration) +
'_loc_' + str(round(loc_loss_save / 5000., 4)) +
'_conf_' + str(round(conf_loss_save / 5000., 4)) +
'_reg_' + str(round(reg_loss_save / 5000., 4)) +
'_prior_' + str(round(prior_loss_save / 5000., 4)) +
'_loss_' + str(round(loss_save / 5000., 4)) +
'_lr_' + str(round(args.lr * (args.gamma ** step_index), 6)) + '.pth')
torch.save(checkpoint, logs_dir + '/model_last.pth')
loc_loss_save = 0.
conf_loss_save = 0.
reg_loss_save = 0.
prior_loss_save = 0.
loss_l = 0.
loss_c = 0.
loss_r = 0.
loss_p = 0.
loss_count = 0.
torch.save(net.state_dict(), logs_dir + '/' + args.dataset + '_final.pth')
def log_func(tensor):
return tensor * torch.log(tensor)
def adjust_learning_rate(optimizer, new_lr):
"""Sets the learning rate of optimizer to new_lr."""
for param_group in optimizer.param_groups:
param_group['lr'] = new_lr
def get_lr(optimizer):
return optimizer.param_groups[0]['lr']
def get_grad_norm(model):
"""Show the max gradient in a step of all the model's parameters."""
total_norm = 0
for p in model.parameters():
if p.requires_grad and p.grad is not None:
module_norm = p.grad.norm()
total_norm += module_norm ** 2
total_norm = torch.sqrt(total_norm).item()
return total_norm
if __name__ == '__main__':
REGULARIZATION_LOSS_WEIGHT = args.reg_weight
PRIOR_LOSS_WEIGHT = args.prior_weight
NMS_CONF_THRE = args.nms_conf_threshold
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
train()