From db2674aa316a8aac7c6a96d450b1f1e63eef98d5 Mon Sep 17 00:00:00 2001 From: glenn-jocher Date: Sun, 30 Jun 2019 17:34:29 +0200 Subject: [PATCH] updates --- train.py | 49 +++++++++++++++++++++++++++---------------------- utils/utils.py | 12 ++++++------ 2 files changed, 33 insertions(+), 28 deletions(-) diff --git a/train.py b/train.py index 9154e230..790fc20e 100644 --- a/train.py +++ b/train.py @@ -11,27 +11,30 @@ from models import * from utils.datasets import * from utils.utils import * -# 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 -hyp = {'giou': .035, # giou loss gain - 'xy': 0.20, # xy loss gain - 'wh': 0.10, # wh loss gain - 'cls': 0.035, # cls loss gain - 'cls_pw': 79.0, # cls BCELoss positive_weight - 'conf': 1.61, # conf loss gain - 'conf_pw': 3.53, # conf BCELoss positive_weight - 'iou_t': 0.29, # iou target-anchor training threshold +# 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 +hyp = {'giou': 1.008, # giou loss gain + 'xy': 1.421, # xy loss gain + 'wh': 0.07989, # wh loss gain + 'cls': 16.94, # cls loss gain + 'cls_pw': 6.215, # cls BCELoss positive_weight + 'conf': 10.61, # conf loss gain + 'conf_pw': 4.272, # conf BCELoss positive_weight + 'iou_t': 0.251, # iou target-anchor training threshold 'lr0': 0.001, # initial learning rate 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) 'momentum': 0.90, # SGD momentum 'weight_decay': 0.0005} # optimizer weight decay -# hyp = {'giou': 1.0, # giou loss gain -# 'xy': 1.0, # xy loss gain -# 'wh': 1.0, # wh loss gain -# 'cls': 1.0, # cls loss gain + +# 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 +# 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 +# hyp = {'giou': .035, # giou loss gain +# 'xy': 0.20, # xy loss gain +# 'wh': 0.10, # wh loss gain +# 'cls': 0.035, # cls loss gain # 'cls_pw': 79.0, # cls BCELoss positive_weight -# 'conf': 1.0, # conf loss gain -# 'conf_pw': 6.0, # conf BCELoss positive_weight +# 'conf': 1.61, # conf loss gain +# 'conf_pw': 3.53, # conf BCELoss positive_weight # 'iou_t': 0.29, # iou target-anchor training threshold # 'lr0': 0.001, # initial learning rate # 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) @@ -167,7 +170,8 @@ def train( t, t0 = time.time(), time.time() for epoch in range(start_epoch, epochs): model.train() - print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'time')) + print(('\n%8s%12s' + '%10s' * 7) % + ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'img_size')) # Update scheduler scheduler.step() @@ -184,15 +188,16 @@ def train( # dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index mloss = torch.zeros(5).to(device) # mean losses - for i, (imgs, targets, _, _) in enumerate(dataloader): + pbar = tqdm(enumerate(dataloader), total=nb) # progress bar + for i, (imgs, targets, _, _) in pbar: imgs = imgs.to(device) targets = targets.to(device) # Multi-Scale training if multi_scale: - if (i + 1 + nb * epoch) / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches + if (i + nb * epoch) / accumulate % 10 == 0: #  adjust (67% - 150%) every 10 batches img_size = random.choice(range(img_size_min, img_size_max + 1)) * 32 - print('img_size = %g' % img_size) + # print('img_size = %g' % img_size) scale_factor = img_size / max(imgs.shape[-2:]) imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False) @@ -229,11 +234,11 @@ def train( # Print batch results mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + # s = ('%8s%12s' + '%10.3g' * 7) % ('%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t) s = ('%8s%12s' + '%10.3g' * 7) % ( - '%g/%g' % (epoch, epochs - 1), - '%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t) + '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), img_size) t = time.time() - print(s) + pbar.set_description(s) # print(s) # Report time dt = (time.time() - t0) / 3600 diff --git a/utils/utils.py b/utils/utils.py index 4c0b179c..7d756f1d 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -284,7 +284,7 @@ def compute_loss(p, targets, model, giou_loss=False): # predictions, targets, m # Compute losses bs = p[0].shape[0] # batch size - k = bs # loss gain + k = bs / 64 # loss gain for i, pi0 in enumerate(p): # layer i predictions, i b, a, gj, gi = indices[i] # image, anchor, gridy, gridx tconf = torch.zeros_like(pi0[..., 0]) # conf @@ -303,12 +303,12 @@ def compute_loss(p, targets, model, giou_loss=False): # predictions, targets, m lxy += (k * h['xy']) * MSE(torch.sigmoid(pi[..., 0:2]), txy[i]) # xy loss lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss - # tclsm = torch.zeros_like(pi[..., 5:]) - # tclsm[range(len(b)), tcls[i]] = 1.0 - # lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # class_conf loss - lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # class_conf loss + tclsm = torch.zeros_like(pi[..., 5:]) + tclsm[range(len(b)), tcls[i]] = 1.0 + lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE) + # lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # cls loss (CE) - # # Append to text file + # Append targets to text file # with open('targets.txt', 'a') as file: # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]