Bienqq
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car-detection-bayes
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updates

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glenn-jocher 2019-06-30 17:34:29 +02:00
parent 5927d12aa7
commit db2674aa31
2 changed files with 33 additions and 28 deletions
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49
train.py
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@ -11,27 +11,30 @@ from models import *
from utils.datasets import * from utils.datasets import *
from utils.utils 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 # 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': .035, # giou loss gain hyp = {'giou': 1.008, # giou loss gain
'xy': 0.20, # xy loss gain 'xy': 1.421, # xy loss gain
'wh': 0.10, # wh loss gain 'wh': 0.07989, # wh loss gain
'cls': 0.035, # cls loss gain 'cls': 16.94, # cls loss gain
'cls_pw': 79.0, # cls BCELoss positive_weight 'cls_pw': 6.215, # cls BCELoss positive_weight
'conf': 1.61, # conf loss gain 'conf': 10.61, # conf loss gain
'conf_pw': 3.53, # conf BCELoss positive_weight 'conf_pw': 4.272, # conf BCELoss positive_weight
'iou_t': 0.29, # iou target-anchor training threshold 'iou_t': 0.251, # iou target-anchor training threshold
'lr0': 0.001, # initial learning rate 'lr0': 0.001, # initial learning rate
'lrf': -4., # final learning rate = lr0 * (10 ** lrf) 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.90, # SGD momentum 'momentum': 0.90, # SGD momentum
'weight_decay': 0.0005} # optimizer weight decay 'weight_decay': 0.0005} # optimizer weight decay
# hyp = {'giou': 1.0, # giou loss gain
# 'xy': 1.0, # xy 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
# 'wh': 1.0, # wh loss gain # 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
# 'cls': 1.0, # cls loss gain # 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 # 'cls_pw': 79.0, # cls BCELoss positive_weight
# 'conf': 1.0, # conf loss gain # 'conf': 1.61, # conf loss gain
# 'conf_pw': 6.0, # conf BCELoss positive_weight # 'conf_pw': 3.53, # conf BCELoss positive_weight
# 'iou_t': 0.29, # iou target-anchor training threshold # 'iou_t': 0.29, # iou target-anchor training threshold
# 'lr0': 0.001, # initial learning rate # 'lr0': 0.001, # initial learning rate
# 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) # 'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
@ -167,7 +170,8 @@ def train(
t, t0 = time.time(), time.time() t, t0 = time.time(), time.time()
for epoch in range(start_epoch, epochs): for epoch in range(start_epoch, epochs):
model.train() 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 # Update scheduler
scheduler.step() scheduler.step()
@ -184,15 +188,16 @@ def train(
# dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index # dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index
mloss = torch.zeros(5).to(device) # mean losses 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) imgs = imgs.to(device)
targets = targets.to(device) targets = targets.to(device)
# Multi-Scale training # Multi-Scale training
if multi_scale: 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 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:]) scale_factor = img_size / max(imgs.shape[-2:])
imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False) imgs = F.interpolate(imgs, scale_factor=scale_factor, mode='bilinear', align_corners=False)
@ -229,11 +234,11 @@ def train(
# Print batch results # Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses 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) % ( s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1), '%g/%g' % (epoch, epochs - 1), '%g/%g' % (i, nb - 1), *mloss, len(targets), img_size)
'%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
t = time.time() t = time.time()
print(s) pbar.set_description(s) # print(s)
# Report time # Report time
dt = (time.time() - t0) / 3600 dt = (time.time() - t0) / 3600

12
utils/utils.py
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@ -284,7 +284,7 @@ def compute_loss(p, targets, model, giou_loss=False): # predictions, targets, m
# Compute losses # Compute losses
bs = p[0].shape[0] # batch size 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 for i, pi0 in enumerate(p): # layer i predictions, i
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tconf = torch.zeros_like(pi0[..., 0]) # conf 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 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 lwh += (k * h['wh']) * MSE(pi[..., 2:4], twh[i]) # wh yolo loss
# tclsm = torch.zeros_like(pi[..., 5:]) tclsm = torch.zeros_like(pi[..., 5:])
# tclsm[range(len(b)), tcls[i]] = 1.0 tclsm[range(len(b)), tcls[i]] = 1.0
# lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # class_conf loss lcls += (k * h['cls']) * BCEcls(pi[..., 5:], tclsm) # cls loss (BCE)
lcls += (k * h['cls']) * CE(pi[..., 5:], tcls[i]) # class_conf loss # 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: # 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)] # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]