427 lines
19 KiB
Python
427 lines
19 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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from torch.utils.data import DataLoader
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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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from utils.adabound import *
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# 320 --epochs 1
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# 0.109 0.297 0.15 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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# 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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# Training hyperparameters d
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hyp = {'giou': 1.153, # giou loss gain
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'xy': 4.062, # xy loss gain
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'wh': 0.1845, # wh loss gain
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'cls': 24.28, # cls loss gain
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'cls_pw': 3.05, # cls BCELoss positive_weight
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'obj': 20.93, # obj loss gain
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'obj_pw': 2.842, # obj BCELoss positive_weight
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'iou_t': 0.2759, # iou training threshold
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'lr0': 0.001357, # initial learning rate
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'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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'momentum': 0.916, # SGD momentum
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'weight_decay': 0.0000572, # optimizer weight decay
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'hsv_s': 0.5, # image HSV-Saturation augmentation (fraction)
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'hsv_v': 0.5, # image HSV-Value augmentation (fraction)
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'degrees': 5, # image rotation (+/- deg)
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'translate': 0.1, # image translation (+/- fraction)
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'scale': 0.1, # image scale (+/- gain)
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'shear': 2} # image shear (+/- deg)
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# # Training hyperparameters e
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# hyp = {'giou': 1.607, # giou loss gain
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# 'xy': 4.062, # xy loss gain
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# 'wh': 0.1845, # wh loss gain
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# 'cls': 39.27, # cls loss gain
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# 'cls_pw': 3.726, # cls BCELoss positive_weight
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# 'obj': 31.26, # obj loss gain
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# 'obj_pw': 2.634, # obj BCELoss positive_weight
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# 'iou_t': 0.273, # iou target-anchor training threshold
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# 'lr0': 0.001542, # initial learning rate
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# 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
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# 'momentum': 0.8364, # SGD momentum
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# 'weight_decay': 0.0008393} # optimizer weight decay
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def train(cfg,
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data,
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img_size=416,
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epochs=100, # 500200 batches at bs 16, 117263 images = 273 epochs
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batch_size=16,
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accumulate=4): # effective bs = batch_size * accumulate = 8 * 8 = 64
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# Initialize
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init_seeds()
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weights = 'weights' + os.sep
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last = weights + 'last.pt'
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best = weights + 'best.pt'
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device = torch_utils.select_device()
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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)
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img_sz_max = round(img_size / 32 * 1.5)
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img_size = img_sz_max * 32 # initiate with maximum multi_scale 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.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'], nesterov=True)
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# optimizer = AdaBound(model.parameters(), lr=hyp['lr0'], final_lr=0.1)
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cutoff = -1 # backbone reaches to cutoff layer
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start_epoch = 0
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best_fitness = 0.0
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if opt.resume or opt.transfer: # Load previously saved model
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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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chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
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model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
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strict=False)
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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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else: # resume from last.pt
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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(last, map_location=device) # load checkpoint
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model.load_state_dict(chkpt['model'])
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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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if chkpt['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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else: # Initialize model with backbone (optional)
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if '-tiny.cfg' in cfg:
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cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
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else:
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cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
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# Remove old 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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# 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]], 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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# 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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# 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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# sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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# Dataloader
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dataloader = DataLoader(dataset,
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batch_size=batch_size,
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num_workers=opt.num_workers,
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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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# Mixed precision training https://github.com/NVIDIA/apex
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mixed_precision = True
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if mixed_precision:
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try:
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from apex import amp
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model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
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except: # not installed: install help: https://github.com/NVIDIA/apex/issues/259
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mixed_precision = False
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# Start training
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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) # P, R, mAP, F1, test_loss
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# n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
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t0 = time.time()
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for epoch in range(start_epoch, epochs):
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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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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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# w = model.class_weights.cpu().numpy() * (1 - maps) # 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) # random weighted index
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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:
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imgs = imgs.to(device)
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targets = targets.to(device)
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# Multi-Scale training TODO: short-side to 32-multiple https://github.com/ultralytics/yolov3/issues/358
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if multi_scale:
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if (i + nb * epoch) / 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
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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 epoch == 0 and i == 0:
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plot_images(imgs=imgs, targets=targets, paths=paths, fname='train_batch%g.jpg' % i)
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# SGD burn-in
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# if epoch == 0 and i <= n_burnin:
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# g = (i / n_burnin) ** 4 # gain
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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, giou_loss=not opt.xywh)
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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 (i + 1) % accumulate == 0 or (i + 1) == nb:
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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) # print(s)
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# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
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if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
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with torch.no_grad():
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results, maps = test.test(cfg, data, batch_size=batch_size, img_size=opt.img_size, model=model,
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conf_thres=0.1)
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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' * 5 % results + '\n') # P, R, mAP, F1, test_loss
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# Update best map
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fitness = results[2]
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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 (epoch == epochs - 1))
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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': 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, weights + '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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print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
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del model, optimizer
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return results
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def print_mutation(hyp, results):
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# Write mutation results
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a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
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b = '%11.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values
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c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
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print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
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if opt.bucket:
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os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt
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with open('evolve.txt', 'a') as f: # append result
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f.write(c + b + '\n')
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x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows
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np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%11.3g') # save sort by fitness
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os.system('gsutil cp evolve.txt gs://%s' % opt.bucket) # upload evolve.txt
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else:
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with open('evolve.txt', 'a') as f:
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f.write(c + b + '\n')
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def fitness(x): # returns fitness of hyp evolution vectors
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return x[:, 2] * 0.5 + x[:, 3] * 0.5 # fitness = weighted combination of mAP and F1
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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=100, help='number of epochs')
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parser.add_argument('--batch-size', type=int, default=16, help='batch size')
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parser.add_argument('--accumulate', type=int, default=4, help='number of 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_64img.data', help='coco.data file path')
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parser.add_argument('--multi-scale', action='store_true', help='train at (1/1.5)x - 1.5x sizes')
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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 flag')
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parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
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parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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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('--xywh', action='store_true', help='use xywh loss instead of GIoU loss')
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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('--var', default=0, type=int, help='debug variable')
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opt = parser.parse_args()
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print(opt)
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if opt.evolve:
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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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# Train
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results = train(opt.cfg,
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opt.data,
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img_size=opt.img_size,
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epochs=opt.epochs,
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batch_size=opt.batch_size,
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accumulate=opt.accumulate)
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# Evolve hyperparameters (optional)
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if opt.evolve:
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print_mutation(hyp, results) # Write mutation results
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for _ in range(1000): # generations to evolve
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# Get best hyperparameters
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x = np.loadtxt('evolve.txt', ndmin=2)
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x = x[fitness(x).argmax()] # select best fitness hyps
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for i, k in enumerate(hyp.keys()):
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hyp[k] = x[i + 5]
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# Mutate
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init_seeds(seed=int(time.time()))
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s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .05, .20, .20, .20, .20, .20, .20, .20] # sigmas
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for i, k in enumerate(hyp.keys()):
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x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
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hyp[k] *= float(x) # vary by sigmas
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# Clip to limits
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keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale']
|
||
limits = [(1e-4, 1e-2), (0.00, 0.70), (0.60, 0.97), (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(opt.cfg,
|
||
opt.data,
|
||
img_size=opt.img_size,
|
||
epochs=opt.epochs,
|
||
batch_size=opt.batch_size,
|
||
accumulate=opt.accumulate)
|
||
|
||
# Write mutation results
|
||
print_mutation(hyp, results)
|
||
|
||
# # Plot results
|
||
# import numpy as np
|
||
# import matplotlib.pyplot as plt
|
||
# a = np.loadtxt('evolve.txt')
|
||
# x = fitness(a)
|
||
# weights = (x - x.min()) ** 2
|
||
# fig = plt.figure(figsize=(10, 10))
|
||
# for i in range(len(hyp)):
|
||
# y = a[:, i + 5]
|
||
# mu = (y * weights).sum() / weights.sum()
|
||
# plt.subplot(4, 5, i + 1)
|
||
# plt.plot(x.max(), mu, 'o')
|
||
# plt.plot(x, y, '.')
|
||
# print(list(hyp.keys())[i], '%.4g' % mu)
|