updates
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glenn-jocher
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train.py
49
train.py
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@ -11,27 +11,30 @@ from models import *
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from utils.datasets import *
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from utils.utils import *
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# Hyperparameters: train.py --data data/coco.data --img-size 320 --single-scale --batch-size 64 --accumulate 1 --epochs 1 --evolve 0.087 0.281 0.109 0.121
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hyp = {'giou': .035, # giou loss gain
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'xy': 0.20, # xy loss gain
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'wh': 0.10, # wh loss gain
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'cls': 0.035, # cls loss gain
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'cls_pw': 79.0, # cls BCELoss positive_weight
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'conf': 1.61, # conf loss gain
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'conf_pw': 3.53, # conf BCELoss positive_weight
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'iou_t': 0.29, # iou target-anchor training threshold
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# 0.149 0.241 0.126 0.156 6.85 1.008 1.421 0.07989 16.94 6.215 10.61 4.272 0.251 0.001 -4 0.9 0.0005 320 64-1 giou
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hyp = {'giou': 1.008, # giou loss gain
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'xy': 1.421, # xy loss gain
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'wh': 0.07989, # wh loss gain
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'cls': 16.94, # cls loss gain
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'cls_pw': 6.215, # cls BCELoss positive_weight
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'conf': 10.61, # conf loss gain
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'conf_pw': 4.272, # conf BCELoss positive_weight
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'iou_t': 0.251, # iou target-anchor training threshold
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'lr0': 0.001, # initial learning rate
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'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
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'momentum': 0.90, # SGD momentum
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'weight_decay': 0.0005} # optimizer weight decay
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# hyp = {'giou': 1.0, # giou loss gain
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# 'xy': 1.0, # xy loss gain
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# 'wh': 1.0, # wh loss gain
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# 'cls': 1.0, # cls loss gain
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# 0.0945 0.279 0.114 0.131 25 0.035 0.2 0.1 0.035 79 1.61 3.53 0.29 0.001 -4 0.9 0.0005 320 64-1
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# 0.112 0.265 0.111 0.144 12.6 0.035 0.2 0.1 0.035 79 1.61 3.53 0.29 0.001 -4 0.9 0.0005 320 32-2
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# hyp = {'giou': .035, # giou loss gain
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# 'xy': 0.20, # xy loss gain
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# 'wh': 0.10, # wh loss gain
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# 'cls': 0.035, # cls loss gain
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# 'cls_pw': 79.0, # cls BCELoss positive_weight
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# 'conf': 1.0, # conf loss gain
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# 'conf_pw': 6.0, # conf BCELoss positive_weight
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# 'conf': 1.61, # conf loss gain
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# 'conf_pw': 3.53, # conf BCELoss positive_weight
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# 'iou_t': 0.29, # iou target-anchor training threshold
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# 'lr0': 0.001, # initial learning rate
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# 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
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@ -167,7 +170,8 @@ def train(
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t, t0 = time.time(), 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%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'time'))
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print(('\n%8s%12s' + '%10s' * 7) %
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('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'img_size'))
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# Update scheduler
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scheduler.step()
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@ -184,15 +188,16 @@ def train(
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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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for i, (imgs, targets, _, _) in enumerate(dataloader):
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pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
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for i, (imgs, targets, _, _) 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
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if multi_scale:
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if (i + 1 + nb * epoch) / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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if (i + nb * epoch) / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
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img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32
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print('img_size = %g' % img_size)
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# print('img_size = %g' % img_size)
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scale_factor = img_size / max(imgs.shape[-2:])
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imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False)
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@ -229,11 +234,11 @@ def train(
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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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# s = ('%8s%12s' + '%10.3g' * 7) % ('%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
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s = ('%8s%12s' + '%10.3g' * 7) % (
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'%g/%g' % (epoch, epochs - 1),
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'%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
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'%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), img_size)
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t = time.time()
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print(s)
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pbar.set_description(s) # print(s)
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# Report time
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dt = (time.time() - t0) / 3600
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@ -284,7 +284,7 @@ def compute_loss(p, targets, model, giou_loss=False): # predictions, targets, m
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# Compute losses
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bs = p[0].shape[0] # batch size
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k = bs # loss gain
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k = bs / 64 # loss gain
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for i, pi0 in enumerate(p): # layer i predictions, i
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b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
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tconf = torch.zeros_like(pi0[..., 0]) # conf
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@ -303,12 +303,12 @@ def compute_loss(p, targets, model, giou_loss=False): # predictions, targets, m
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lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy loss
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lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss
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# tclsm = torch.zeros_like(pi[..., 5:])
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# tclsm[range(len(b)), tcls[i]] = 1.0
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# lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # class_conf loss
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lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # class_conf loss
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tclsm = torch.zeros_like(pi[..., 5:])
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tclsm[range(len(b)), tcls[i]] = 1.0
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lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE)
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# lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # cls loss (CE)
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# # Append to text file
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# Append targets to text file
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# with open('targets.txt', 'a') as file:
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# [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
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