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train.py
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train.py
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# This is the main training file we are using
import os
import argparse
import random
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from PIL import Image
import numpy as np
import cv2
from datasets import Dataset4YoloAngle
from utils import MWtools, timer, visualization
import api
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='rapid_pL1')
parser.add_argument('--backbone', type=str, default='dark53')
parser.add_argument('--dataset', type=str, default='COCO')
parser.add_argument('--batch_size', type=int, default=1)
parser.add_argument('--high_resolution', action='store_true')
parser.add_argument('--checkpoint', type=str, default='')
parser.add_argument('--eval_interval', type=int, default=1000)
parser.add_argument('--img_interval', type=int, default=500)
parser.add_argument('--print_interval', type=int, default=1)
parser.add_argument('--checkpoint_interval', type=int, default=2000)
parser.add_argument('--debug', action='store_true') # default=True)
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
assert torch.cuda.is_available() # Currently do not support CPU training
# -------------------------- settings ---------------------------
target_size = 1024 if args.high_resolution else 608
initial_size = 1088 if args.high_resolution else 672
job_name = f'{args.model}_{args.backbone}_{args.dataset}{target_size}'
# dataloader setting
batch_size = args.batch_size
num_cpu = 0 if batch_size == 1 else 4
subdivision = 128 // batch_size
enable_aug = True
multiscale = True
multiscale_interval = 10
# SGD optimizer
decay_SGD = 0.0005 * batch_size * subdivision
print(f'effective batch size = {batch_size} * {subdivision}')
# dataset setting
print('initialing dataloader...')
if args.dataset == 'COCO':
train_img_dir = '../Datasets/COCO/train2017'
assert 'COCO' in train_img_dir # issue #11
train_json = '../Datasets/COCO/annotations/instances_train2017.json'
val_img_dir = './images/tiny_val/one'
val_json = './images/tiny_val/one.json'
lr_SGD = 0.001 / batch_size / subdivision
# Learning rate setup
def burnin_schedule(i):
burn_in = 500
if i < burn_in:
factor = (i / burn_in) ** 2
elif i < 30000:
factor = 1.0
elif i < 40000:
factor = 0.5
elif i < 100000:
factor = 0.2
elif i < 300000:
factor = 0.1
else:
factor = 0.01
return factor
elif args.dataset == 'MW':
train_img_dir = '../../../MW18Mar/whole'
train_json = '../../../MW18Mar/annotations/no19_nosmall.json'
val_img_dir = './images/tiny_val/one'
val_json = './images/tiny_val/one.json'
lr_SGD = 0.0001 / batch_size / subdivision
# Learning rate setup
def burnin_schedule(i):
burn_in = 500
if i < burn_in:
factor = (i / burn_in) ** 2
elif i < 10000:
factor = 1.0
elif i < 20000:
factor = 0.3
else:
factor = 0.1
return factor
elif args.dataset == 'HBCP':
videos = ['Meeting1', 'Meeting2', 'Lab2',
'Lunch1', 'Lunch2', 'Lunch3', 'Edge_cases', 'IRill', 'Activity']
# if args.high_resolution:
# videos += ['All_off', 'IRfilter', 'IRill']
train_img_dir = [f'../../../COSSY/{s}/' for s in videos]
train_json = [f'../../../COSSY/annotations/{s}.json' for s in videos]
val_img_dir = '../../../COSSY/Lab1/'
val_json = '../../../COSSY/annotations/Lab1.json'
lr_SGD = 0.0001 / batch_size / subdivision
# Learning rate setup
def burnin_schedule(i):
burn_in = 500
if i < burn_in:
factor = (i / burn_in) ** 2
elif i < 10000:
factor = 1.0
elif i < 20000:
factor = 0.3
else:
factor = 0.1
return factor
elif args.dataset == 'HBMW':
train_img_dir = [
'../Datasets/fisheye/HABBOF/Meeting1',
'../Datasets/fisheye/HABBOF/Meeting2',
'../Datasets/fisheye/HABBOF/Lab2',
'../Datasets/fisheye/MW-R'
]
train_json = [
'../Datasets/fisheye/annotations/Meeting1.json',
'../Datasets/fisheye/annotations/Meeting2.json',
'../Datasets/fisheye/annotations/Lab2.json',
'../Datasets/fisheye/annotations/MW-R.json'
]
val_img_dir = '../Datasets/fisheye/HABBOF/Lab1/'
val_json = '../Datasets/fisheye/annotations/Lab1.json'
lr_SGD = 0.0001 / batch_size / subdivision
# Learning rate setup
def burnin_schedule(i):
burn_in = 500
if i < burn_in:
factor = (i / burn_in) ** 2
elif i < 10000:
factor = 1.0
elif i < 20000:
factor = 0.3
else:
factor = 0.1
return factor
elif args.dataset == 'CPMW':
videos = ['Lunch1', 'Lunch2', 'Edge_cases', 'IRill', 'Activity',
'MW']
# if args.high_resolution:
# videos += ['All_off', 'IRfilter']
train_img_dir = [f'../../../COSSY/{s}/' for s in videos]
train_json = [f'../../../COSSY/annotations/{s}.json' for s in videos]
val_img_dir = '../../../COSSY/Lunch3/'
val_json = '../../../COSSY/annotations/Lunch3.json'
lr_SGD = 0.0001 / batch_size / subdivision
# Learning rate setup
def burnin_schedule(i):
burn_in = 500
if i < burn_in:
factor = (i / burn_in) ** 2
elif i < 10000:
factor = 1.0
elif i < 20000:
factor = 0.3
else:
factor = 0.1
return factor
dataset = Dataset4YoloAngle(train_img_dir, train_json, initial_size, enable_aug,
only_person=True, debug_mode=args.debug)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True,
num_workers=num_cpu, pin_memory=True, drop_last=False)
dataiterator = iter(dataloader)
if args.model == 'rapid_pL1':
from models.rapid import RAPiD
model = RAPiD(backbone=args.backbone, img_norm=False,
loss_angle='period_L1')
elif args.model == 'rapid_pL2':
from models.rapid import RAPiD
model = RAPiD(backbone=args.backbone, img_norm=False,
loss_angle='period_L2')
model = model.cuda()
start_iter = -1
if args.checkpoint:
print("loading ckpt...", args.checkpoint)
weights_path = os.path.join('./weights/', args.checkpoint)
state = torch.load(weights_path)
model.load_state_dict(state['model'])
start_iter = state['iter']
val_set = MWtools.MWeval(val_json, iou_method='rle')
eval_img_names = os.listdir('./images/')
eval_img_paths = [os.path.join('./images/',s) for s in eval_img_names if s.endswith('.jpg')]
logger = SummaryWriter(f'./logs/{job_name}')
# optimizer setup
params = []
# set weight decay only on conv.weight
for key, value in model.named_parameters():
if 'conv.weight' in key:
params += [{'params':value, 'weight_decay':decay_SGD}]
else:
params += [{'params':value, 'weight_decay':0.0}]
if args.dataset == 'COCO':
optimizer = torch.optim.SGD(params, lr=lr_SGD, momentum=0.9, dampening=0,
weight_decay=decay_SGD)
elif args.dataset in {'MW', 'HBCP', 'HBMW', 'CPMW'}:
assert args.checkpoint is not None
optimizer = torch.optim.SGD(params, lr=lr_SGD)
else:
raise NotImplementedError()
if args.dataset not in args.checkpoint:
start_iter = -1
else:
optimizer.load_state_dict(state['optimizer_state_dict'])
print(f'begin from iteration: {start_iter}')
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, burnin_schedule, last_epoch=start_iter)
# start training loop
today = timer.today()
start_time = timer.tic()
for iter_i in range(start_iter, 500000):
# evaluation
if iter_i % args.eval_interval == 0 and (args.dataset != 'COCO' or iter_i > 0):
with timer.contexttimer() as t0:
model.eval()
model_eval = api.Detector(conf_thres=0.005, model=model)
dts = model_eval.detect_imgSeq(val_img_dir, input_size=target_size)
str_0 = val_set.evaluate_dtList(dts, metric='AP')
s = f'\nCurrent time: [ {timer.now()} ], iteration: [ {iter_i} ]\n\n'
s += str_0 + '\n\n'
s += f'Validation elapsed time: [ {t0.time_str} ]'
print(s)
logger.add_text('Validation summary', s, iter_i)
logger.add_scalar('Validation AP[IoU=0.5]', val_set._getAP(0.5), iter_i)
logger.add_scalar('Validation AP[IoU=0.75]', val_set._getAP(0.75), iter_i)
logger.add_scalar('Validation AP[IoU=0.5:0.95]', val_set._getAP(), iter_i)
model.train()
# subdivision loop
optimizer.zero_grad()
for inner_iter_i in range(subdivision):
try:
imgs, targets, cats, _, _ = next(dataiterator) # load a batch
except StopIteration:
dataiterator = iter(dataloader)
imgs, targets, cats, _, _ = next(dataiterator) # load a batch
# visualization.imshow_tensor(imgs)
imgs = imgs.cuda()
loss = model(imgs, targets, labels_cats=cats)
loss.backward()
optimizer.step()
scheduler.step()
# logging
if iter_i % args.print_interval == 0:
sec_used = timer.tic() - start_time
time_used = timer.sec2str(sec_used)
avg_iter = timer.sec2str(sec_used/(iter_i+1-start_iter))
avg_epoch = avg_iter / batch_size / subdivision * 118287
print(f'\nTotal time: {time_used}, iter: {avg_iter}, epoch: {avg_epoch}')
current_lr = scheduler.get_last_lr()[0] * batch_size * subdivision
print(f'[Iteration {iter_i}] [learning rate {current_lr:.3g}]',
f'[Total loss {loss:.2f}] [img size {dataset.img_size}]')
print(model.loss_str)
max_cuda = torch.cuda.max_memory_allocated(0) / 1024 / 1024 / 1024
print(f'Max GPU memory usage: {max_cuda} GigaBytes')
torch.cuda.reset_peak_memory_stats(0)
# random resizing
if multiscale and iter_i > 0 and (iter_i % multiscale_interval == 0):
if args.high_resolution:
imgsize = random.randint(16, 34) * 32
else:
low = 10 if args.dataset == 'COCO' else 16
imgsize = random.randint(low, 21) * 32
dataset.img_size = imgsize
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True,
num_workers=num_cpu, pin_memory=True, drop_last=False)
dataiterator = iter(dataloader)
# save checkpoint
if iter_i > 0 and (iter_i % args.checkpoint_interval == 0):
state_dict = {
'iter': iter_i,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
save_path = os.path.join('./weights', f'{job_name}_{today}_{iter_i}.ckpt')
torch.save(state_dict, save_path)
# save detection
if iter_i > 0 and iter_i % args.img_interval == 0:
for img_path in eval_img_paths:
eval_img = Image.open(img_path)
dts = api.detect_once(model, eval_img, conf_thres=0.1, input_size=target_size)
np_img = np.array(eval_img)
visualization.draw_dt_on_np(np_img, dts)
np_img = cv2.resize(np_img, (416,416))
# cv2.imwrite(f'./results/eval_imgs/{job_name}_{today}_{iter_i}.jpg', np_img)
logger.add_image(img_path, np_img, iter_i, dataformats='HWC')
model.train()