forked from liucheng/DeepBurning-MixQ
346 lines
16 KiB
Python
346 lines
16 KiB
Python
import argparse
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import torch.distributed as dist
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import torch.optim as optim
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import torch.optim.lr_scheduler as lr_scheduler
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import time
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import sys
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sys.path.append('..')
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import localconfig
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import test
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from datasets import *
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from yolo_utils import *
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from mymodel import *
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import mymodel
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wdir = 'weights' + os.sep # weights dir
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# Hyperparameters (results68: 59.9 mAP@0.5 yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310
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hyp = {'giou': 3.54, # giou loss gain
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'cls': 37.4, # cls loss gain
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'cls_pw': 1.0, # cls BCELoss positive_weight
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'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
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'obj_pw': 1.0, # obj BCELoss positive_weight
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'iou_t': 0.225, # iou training threshold
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'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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'momentum': 0.937, # SGD momentum
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'weight_decay': 0.000484, # optimizer weight decay
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'fl_gamma': 0.5, # focal loss gamma
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'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
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'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
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'degrees': 1.98, # image rotation (+/- deg)
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'translate': 0.05, # image translation (+/- fraction)
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'scale': 0.05, # image scale (+/- gain)
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'shear': 0.641} # image shear (+/- deg)
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# Overwrite hyp with hyp*.txt (optional)
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f = glob.glob('hyp*.txt')
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if f:
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print('Using %s' % f[0])
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for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
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hyp[k] = v
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def train():
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img_size, img_size_test = opt.img_size if len(opt.img_size) == 2 else opt.img_size * 2 # train, test sizes
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epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
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batch_size = opt.batch_size
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accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
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weights = opt.weights # initial training weights
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# Initialize
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init_seeds()
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# Configure run
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train_path = localconfig.train_path
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test_path = localconfig.test_path
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nc = 1
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results_file = 'results/%s.txt'%opt.name
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# Initialize model
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if opt.model != '':
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model = getattr(mymodel, opt.model)(opt.bitw, opt.bita).to(device)
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else:
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if opt.bypass:
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model = UltraNetBypass_FixQ(opt.bitw, opt.bita).to(device)
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else:
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model = UltraNet_FixQ(opt.bitw, opt.bita).to(device)
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# Optimizer
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pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
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for k, v in dict(model.named_parameters()).items():
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if '.bias' in k:
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pg2 += [v] # biases
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elif 'Conv2d.weight' in k:
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pg1 += [v] # apply weight_decay
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else:
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pg0 += [v] # all else
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if opt.adam:
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# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
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optimizer = optim.Adam(pg0, lr=hyp['lr0'])
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else:
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optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
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optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
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optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
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optimizer.param_groups[2]['lr'] *= 2.0 # bias lr
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del pg0, pg1, pg2
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start_epoch = 0
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test_best_iou = 0.0
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# load weights
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if weights.endswith('.pt'): # pytorch format
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# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
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chkpt = torch.load(weights, map_location=device)
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# load model
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try:
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chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
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model.load_state_dict(chkpt['model'], strict=False)
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except KeyError as e:
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s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " % (opt.weights, opt.cfg, opt.weights)
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raise KeyError(s) from e
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if opt.resume:
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# load optimizer
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if chkpt['optimizer'] is not None:
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optimizer.load_state_dict(chkpt['optimizer'])
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best_fitness = chkpt['best_fitness']
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# load results
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if chkpt.get('training_results') is not None:
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with open(results_file, 'w') as file:
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file.write(chkpt['training_results']) # write results.txt
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start_epoch = chkpt['epoch'] + 1
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del chkpt
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# Scheduler https://github.com/ultralytics/yolov3/issues/238
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lf = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.999 + 0.001 # cosine https://arxiv.org/pdf/1812.01187.pdf
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scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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scheduler.last_epoch = start_epoch
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# Initialize distributed training
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if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
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dist.init_process_group(backend='nccl', # 'distributed backend'
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init_method='tcp://127.0.0.1:5000', # distributed training init method
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world_size=1, # number of nodes for distributed training
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rank=0) # distributed training node rank
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model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
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model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
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# Dataloader
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#batch_size = min(batch_size, len(dataset))
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nw = min([os.cpu_count()//4, batch_size//4 if batch_size > 1 else 0, 8]) # number of workers
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# Testloader
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testset = LoadImagesAndLabels(test_path, img_size_test, batch_size,
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hyp=hyp,
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rect=False,
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cache_images=opt.cache_images,
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single_cls=opt.single_cls)
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testloader = torch.utils.data.DataLoader(testset,
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batch_size=batch_size,
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num_workers=0,
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pin_memory=True,
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collate_fn=testset.collate_fn)
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test.test(batch_size=batch_size,
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img_size=img_size_test,
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model=model,
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dataloader=testloader) # make forward
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bops, bita, bitw, dsps, brams = model.fetch_arch_info()
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print('model with bops: {:.3f}M, bita: {:.3f}K, bitw: {:.3f}M, dsps: {:.3f}M, bram: {:.3f}K'.format(bops, bita, bitw, dsps, brams))
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# Dataset
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dataset = LoadImagesAndLabels(train_path, img_size, batch_size,
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augment=True,
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hyp=hyp, # augmentation hyperparameters
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rect=opt.rect, # rectangular training
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cache_images=opt.cache_images,
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single_cls=opt.single_cls)
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dataloader = torch.utils.data.DataLoader(dataset,
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batch_size=batch_size,
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num_workers=nw,
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shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
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pin_memory=True,
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collate_fn=dataset.collate_fn)
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# Start training
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nb = len(dataloader)
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prebias = start_epoch == 0
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model.nc = nc # attach number of classes to model
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model.arc = opt.arc # attach yolo architecture
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model.hyp = hyp # attach hyperparameters to model
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model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
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# torch.autograd.set_detect_anomaly(True)
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results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
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t0 = time.time()
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torch_utils.model_info(model, report='summary') # 'full' or 'summary'
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print('Using %g dataloader workers' % nw)
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print('Starting training for %g epochs...' % epochs)
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for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
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model.train()
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model.gr = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2 # GIoU <-> 1.0 loss ratio
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# Prebias
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if prebias:
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ne = max(round(30 / nb), 3) # number of prebias epochs
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ps = np.interp(epoch, [0, ne], [0.1, hyp['lr0'] * 2]), \
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np.interp(epoch, [0, ne], [0.9, hyp['momentum']]) # prebias settings (lr=0.1, momentum=0.9)
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if epoch == ne:
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# print_model_biases(model)
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prebias = False
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# Bias optimizer settings
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optimizer.param_groups[2]['lr'] = ps[0]
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if optimizer.param_groups[2].get('momentum') is not None: # for SGD but not Adam
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optimizer.param_groups[2]['momentum'] = ps[1]
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mloss = torch.zeros(4).to(device) # mean losses
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print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'iouloss', 'objloss', 'triou', 'mloss', 'targets', 'img_size'))
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pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
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for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
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ni = i + nb * epoch # number integrated batches (since train start)
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imgs = imgs.to(device).float() / 256.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
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targets = targets.to(device)
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# Run model
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pred = model(imgs)
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# Compute loss
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loss, loss_items = compute_loss(pred, targets, model)
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if not torch.isfinite(loss):
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print('WARNING: non-finite loss, ending training ', loss_items)
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return results
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# Scale loss by nominal batch_size of 64
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loss *= batch_size / 64
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loss.backward()
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# Optimize accumulated gradient
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if ni % accumulate == 0:
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optimizer.step()
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optimizer.zero_grad()
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# Print batch results
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mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
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mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB)
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s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
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pbar.set_description(s)
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# end batch ------------------------------------------------------------------------------------------------
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# Update scheduler
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scheduler.step()
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train_iou = mloss[2]
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# Process epoch results
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final_epoch = epoch + 1 == epochs
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if not opt.notest or final_epoch: # Calculate mAP
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results = test.test(batch_size=batch_size,
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img_size=img_size_test,
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model=model,
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dataloader=testloader)
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# Write epoch results
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with open(results_file, 'a') as f:
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f.write(s + '%10.3g' * len(results) % results + '\n') # test_losses=(iou, loss_sum, lobj, lcls)
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# Update best mAP
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results = torch.tensor(results, device = 'cpu')
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test_iou = results[0]
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if test_iou > test_best_iou:
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test_best_iou = test_iou
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# Save training results
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save = (not opt.nosave) or (final_epoch)
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if save:
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with open(results_file, 'r') as f:
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# Create checkpoint
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chkpt = {'epoch': epoch,
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'training_results': f.read(),
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'model': model.module.state_dict() if type(
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model) is nn.parallel.DistributedDataParallel else model.state_dict(),
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'optimizer': None if final_epoch else optimizer.state_dict(),
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'model_params':model.model_params, # arch param
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'extra': {'time': time.ctime(), 'name': opt.name}}
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# Save last checkpoint
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torch.save(chkpt, wdir + '%s_last.pt'%opt.name)
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if test_iou == test_best_iou:
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torch.save(chkpt, wdir + '%s_best.pt'%opt.name)
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# Delete checkpoint
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del chkpt
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# end epoch ----------------------------------------------------------------------------------------------------
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# end training
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n = opt.name
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print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
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dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
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torch.cuda.empty_cache()
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with open('results.csv', 'a') as f:
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print("fixed,%s,%d/%d, , ,%s,%s,%.1f,%.1f, , , ,%d, ,%.3f, "%
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(opt.name,epochs-1,epochs,opt.bitw,opt.bita,train_iou*100,(test_iou+test_best_iou)*50,
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int(round(bops)), dsps), file=f)
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return results
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--bypass', action='store_true', help='use bypass model')
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parser.add_argument('--epochs', type=int, default=200) # 500200 batches at bs 16, 117263 COCO images = 273 epochs
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parser.add_argument('--batch-size', type=int, default=64) # effective bs = batch_size * accumulate = 16 * 4 = 64
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parser.add_argument('--accumulate', type=int, default=1, help='batches to accumulate before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-tiny-1cls_1.cfg', help='*.cfg path')
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parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[320], help='train and test image-sizes')
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parser.add_argument('--rect', action='store_true', help='rectangular training')
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parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
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parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
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parser.add_argument('--notest', action='store_true', help='only test final epoch')
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
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parser.add_argument('--weights', type=str, default='', help='initial weights path')
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parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # default, uCE, uBCE
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parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
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parser.add_argument('--adam', action='store_true', help='use adam optimizer')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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parser.add_argument('--mixm', type=str)
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parser.add_argument('--bitw', type=str, default='')
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parser.add_argument('--bita', type=str, default='')
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parser.add_argument('--var', type=float, help='debug variable')
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parser.add_argument('--model', type=str, default='', help='use specific model')
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opt = parser.parse_args()
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if opt.mixm is not None:
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wmix = torch.load('weights/%s.pt'%opt.mixm)
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opt.bitw = wmix['extra']['bestw']
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opt.bita = wmix['extra']['besta']
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del wmix
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last = wdir + 'last_%s.pt'%opt.name
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opt.weights = last if opt.resume else opt.weights
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print(opt)
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device = torch_utils.select_device(opt.device, batch_size=opt.batch_size)
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train() # train normally
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