414 lines
20 KiB
Python
414 lines
20 KiB
Python
import argparse
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import time
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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 test # import test.py to get mAP after each epoch
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from models import *
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from utils.datasets import *
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from utils.utils import *
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mixed_precision = True
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try: # Mixed precision training https://github.com/NVIDIA/apex
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from apex import amp
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except:
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mixed_precision = False # not installed
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# 320 --epochs 1
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# 0.109 0.297 0.150 0.126 7.04 1.666 4.062 0.1845 42.6 3.34 12.61 8.338 0.2705 0.001 -4 0.9 0.0005 a 320 giou + best_anchor False
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# 0.223 0.218 0.138 0.189 9.28 1.153 4.376 0.08263 24.28 3.05 20.93 2.842 0.2759 0.001357 -5.036 0.9158 0.0005722 b mAP/F1 - 50/50 weighting
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# 0.231 0.215 0.135 0.191 9.51 1.432 3.007 0.06082 24.87 3.477 24.13 2.802 0.3436 0.001127 -5.036 0.9232 0.0005874 c
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# 0.246 0.194 0.128 0.192 8.12 1.101 3.954 0.0817 22.83 3.967 19.83 1.779 0.3352 0.000895 -5.036 0.9238 0.0007973 d
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# 0.187 0.237 0.144 0.186 14.6 1.607 4.202 0.09439 39.27 3.726 31.26 2.634 0.273 0.001542 -5.036 0.8364 0.0008393 e
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# 0.250 0.217 0.136 0.195 3.3 1.2 2 0.604 15.7 3.67 20 1.36 0.194 0.00128 -4 0.95 0.000201 0.8 0.388 1.2 0.119 0.0589 0.401 f
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# 0.269 0.225 0.149 0.218 6.71 1.13 5.25 0.246 22.4 3.64 17.8 1.31 0.256 0.00146 -4 0.936 0.00042 0.123 0.18 1.81 0.0987 0.0788 0.441 g
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# 0.179 0.274 0.165 0.187 7.95 1.22 7.62 0.224 17 5.71 17.7 3.28 0.295 0.00136 -4 0.875 0.000319 0.131 0.208 2.14 0.14 0.0773 0.228 h
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# 0.296 0.228 0.152 0.220 5.18 1.43 4.27 0.265 11.7 4.81 11.5 1.56 0.281 0.0013 -4 0.944 0.000427 0.0599 0.142 1.03 0.0552 0.0555 0.434 i
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# 320 --epochs 2
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# 0.242 0.296 0.196 0.231 5.67 0.8541 4.286 0.1539 21.61 1.957 22.9 2.894 0.3689 0.001844 -4 0.913 0.000467 # ha 0.417 mAP @ epoch 100
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# 0.298 0.244 0.167 0.247 4.99 0.8896 4.067 0.1694 21.41 2.033 25.61 1.783 0.4115 0.00128 -4 0.950 0.000377 # hb
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# 0.268 0.268 0.178 0.240 4.36 1.104 5.596 0.2087 14.47 2.599 16.27 2.406 0.4114 0.001585 -4 0.950 0.000524 # hc
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# 0.161 0.327 0.190 0.193 7.82 1.153 4.062 0.1845 24.28 3.05 20.93 2.842 0.2759 0.001357 -4 0.916 0.000572 # hd 0.438 mAP @ epoch 100
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# Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310
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# Transfer learning edge layers: 0.1 lr0, 0.9 momentum
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hyp = {'giou': 1.582, # giou loss gain
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'xy': 4.688, # xy loss gain
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'wh': 0.1857, # wh loss gain
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'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20)
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'cls_pw': 1.446, # cls BCELoss positive_weight
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'obj': 21.35, # obj loss gain (*=80 for uBCE with 80 classes)
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'obj_pw': 3.941, # obj BCELoss positive_weight
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'iou_t': 0.2635, # iou training threshold
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'lr0': 0.002324, # initial learning rate
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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'momentum': 0.97, # SGD momentum
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'weight_decay': 0.0004569, # optimizer weight decay
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'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.3174, # image HSV-Value augmentation (fraction)
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'degrees': 1.113, # image rotation (+/- deg)
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'translate': 0.06797, # image translation (+/- fraction)
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'scale': 0.1059, # image scale (+/- gain)
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'shear': 0.5768} # image shear (+/- deg)
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def train():
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cfg = opt.cfg
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data = opt.data
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img_size = opt.img_size
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epochs = opt.epochs # 500200 batches at bs 16, 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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wdir = 'weights' + os.sep # weights dir
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last = wdir + 'last.pt'
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best = wdir + 'best.pt'
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device = torch_utils.select_device(apex=mixed_precision)
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multi_scale = opt.multi_scale
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if multi_scale:
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img_sz_min = round(img_size / 32 / 1.5) + 1
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img_sz_max = round(img_size / 32 * 1.5) - 1
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img_size = img_sz_max * 32 # initiate with maximum multi_scale size
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print('Using multi-scale %g - %g' % (img_sz_min * 32, img_size))
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# Configure run
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data_dict = parse_data_cfg(data)
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train_path = data_dict['train']
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nc = int(data_dict['classes']) # number of classes
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# Initialize model
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model = Darknet(cfg).to(device)
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# Optimizer
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# optimizer = optim.Adam(model.parameters(), lr=hyp['lr0'], weight_decay=hyp['weight_decay'])
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# optimizer = AdaBound(model.parameters(), lr=hyp['lr0'], final_lr=0.1)
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optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'],
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nesterov=True)
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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best_fitness = 0.
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if weights.endswith('.pt'): # pytorch format
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# possible weights are 'last.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
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if opt.bucket:
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os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket
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chkpt = torch.load(weights, map_location=device)
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# load model
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if opt.transfer:
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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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# 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.txt', '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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elif weights.endswith('.weights'): # darknet format
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# possible weights are 'yolov3.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
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cutoff = load_darknet_weights(model, weights)
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if opt.transfer: # transfer learning
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nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
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for p in model.parameters():
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p.requires_grad = True if p.shape[0] == nf else False
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# Scheduler https://github.com/ultralytics/yolov3/issues/238
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# lf = lambda x: 1 - x / epochs # linear ramp to zero
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# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
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# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
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# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
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scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in [0.8, 0.9]], gamma=0.1)
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scheduler.last_epoch = start_epoch - 1
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# # Plot lr schedule
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# y = []
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# for _ in range(epochs):
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# scheduler.step()
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# y.append(optimizer.param_groups[0]['lr'])
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# plt.plot(y, label='LambdaLR')
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# plt.xlabel('epoch')
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# plt.ylabel('LR')
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# plt.tight_layout()
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# plt.savefig('LR.png', dpi=300)
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# Mixed precision training https://github.com/NVIDIA/apex
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if mixed_precision:
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
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# Initialize distributed training
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if torch.cuda.device_count() > 1:
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dist.init_process_group(backend='nccl', # 'distributed backend'
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init_method='tcp://127.0.0.1:9999', # 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)
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model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
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# Dataset
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dataset = LoadImagesAndLabels(train_path,
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img_size,
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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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image_weights=opt.img_weights,
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cache_images=opt.cache_images)
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# Dataloader
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dataloader = torch.utils.data.DataLoader(dataset,
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batch_size=batch_size,
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num_workers=min(os.cpu_count(), batch_size),
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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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# Remove previous results
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for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
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os.remove(f)
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# Start training
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model.nc = nc # attach number of classes to model
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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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model_info(model, report='summary') # 'full' or 'summary'
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nb = len(dataloader)
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maps = np.zeros(nc) # mAP per class
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results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
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t0 = time.time()
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for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
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model.train()
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print(('\n' + '%10s' * 9) %
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('Epoch', 'gpu_mem', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size'))
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# Update scheduler
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if epoch > 0:
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scheduler.step()
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# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
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freeze_backbone = False
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if freeze_backbone and epoch < 2:
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for name, p in model.named_parameters():
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if int(name.split('.')[1]) < cutoff: # if layer < 75
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p.requires_grad = False if epoch == 0 else True
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# Update image weights (optional)
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if dataset.image_weights:
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w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
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image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
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dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
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mloss = torch.zeros(5).to(device) # mean losses
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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)
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targets = targets.to(device)
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# Multi-Scale training
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if multi_scale:
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if ni / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
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sf = img_size / max(imgs.shape[2:]) # scale factor
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if sf != 1:
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ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape (stretched to 32-multiple)
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imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
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# Plot images with bounding boxes
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if ni == 0:
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fname = 'train_batch%g.jpg' % i
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plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
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if tb_writer:
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tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
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# Hyperparameter burn-in
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# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
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# if ni <= n_burn:
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# for m in model.named_modules():
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# if m[0].endswith('BatchNorm2d'):
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# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
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# g = (i / n_burn) ** 4 # gain rises from 0 - 1
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# for x in optimizer.param_groups:
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# x['lr'] = hyp['lr0'] * g
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# x['weight_decay'] = hyp['weight_decay'] * g
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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 torch.isnan(loss):
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print('WARNING: nan loss detected, ending training')
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return results
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# Compute gradient
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if mixed_precision:
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with amp.scale_loss(loss, optimizer) as scaled_loss:
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scaled_loss.backward()
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else:
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loss.backward()
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# Accumulate gradient for x batches before optimizing
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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 = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
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s = ('%10s' * 2 + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
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pbar.set_description(s)
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# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
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final_epoch = epoch + 1 == epochs
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if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch:
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with torch.no_grad():
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results, maps = test.test(cfg,
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data,
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batch_size=batch_size,
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img_size=opt.img_size,
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model=model,
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conf_thres=0.001 if final_epoch and epoch > 0 else 0.1, # 0.1 for speed
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save_json=final_epoch and epoch > 0 and 'coco.data' in data)
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# Write epoch results
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with open('results.txt', 'a') as file:
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file.write(s + '%11.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
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# Write Tensorboard results
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if tb_writer:
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x = list(mloss[:5]) + list(results[:7])
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titles = ['GIoU/XY', 'Width/Height', 'Objectness', 'Classification', 'Train loss', 'Precision', 'Recall',
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'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification']
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for xi, title in zip(x, titles):
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tb_writer.add_scalar(title, xi, epoch)
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# Update best map
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fitness = results[2] # mAP
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if fitness > best_fitness:
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best_fitness = fitness
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# Save training results
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save = (not opt.nosave) or ((not opt.evolve) and final_epoch)
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if save:
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with open('results.txt', 'r') as file:
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# Create checkpoint
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chkpt = {'epoch': epoch,
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'best_fitness': best_fitness,
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'training_results': file.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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# Save last checkpoint
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torch.save(chkpt, last)
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if opt.bucket:
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os.system('gsutil cp %s gs://%s' % (last, opt.bucket)) # upload to bucket
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# Save best checkpoint
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if best_fitness == fitness:
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torch.save(chkpt, best)
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# Save backup every 10 epochs (optional)
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if epoch > 0 and epoch % 10 == 0:
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torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
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# Delete checkpoint
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del chkpt
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# Report time
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plot_results() # save as results.png
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print('%g epochs completed in %.3f hours.' % (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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return results
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--epochs', type=int, default=273) # 500200 batches at bs 16, 117263 images = 273 epochs
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parser.add_argument('--batch-size', type=int, default=32) # effective bs = batch_size * accumulate = 16 * 4 = 64
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parser.add_argument('--accumulate', type=int, default=2, help='batches to accumulate before optimizing')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
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parser.add_argument('--data', type=str, default='data/coco.data', help='*.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches')
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parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
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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('--transfer', action='store_true', help='transfer learning')
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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('--evolve', action='store_true', help='evolve hyperparameters')
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parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
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parser.add_argument('--img-weights', action='store_true', help='select training images by weight')
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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') # i.e. weights/darknet.53.conv.74
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opt = parser.parse_args()
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opt.weights = 'weights/last.pt' if opt.resume else opt.weights
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print(opt)
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tb_writer = None
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if not opt.evolve: # Train normally
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try:
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# Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
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from torch.utils.tensorboard import SummaryWriter
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tb_writer = SummaryWriter()
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except:
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pass
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results = train()
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else: # Evolve hyperparameters (optional)
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opt.notest = True # only test final epoch
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opt.nosave = True # only save final checkpoint
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if opt.bucket:
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os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
|
||
|
||
for _ in range(100): # generations to evolve
|
||
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
|
||
# Get best hyperparameters
|
||
x = np.loadtxt('evolve.txt', ndmin=2)
|
||
x = x[fitness(x).argmax()] # select best fitness hyps
|
||
for i, k in enumerate(hyp.keys()):
|
||
hyp[k] = x[i + 7]
|
||
|
||
# Mutate
|
||
init_seeds(seed=int(time.time()))
|
||
s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .02, .20, .20, .20, .20, .20, .20, .20] # sigmas
|
||
for i, k in enumerate(hyp.keys()):
|
||
x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
|
||
hyp[k] *= float(x) # vary by sigmas
|
||
|
||
# Clip to limits
|
||
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale']
|
||
limits = [(1e-4, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9)]
|
||
for k, v in zip(keys, limits):
|
||
hyp[k] = np.clip(hyp[k], v[0], v[1])
|
||
|
||
# Train mutation
|
||
results = train()
|
||
|
||
# Write mutation results
|
||
print_mutation(hyp, results, opt.bucket)
|
||
|
||
# Plot results
|
||
# plot_evolution_results(hyp)
|