From d6edefa8abcb11729431605b2399097762d115a5 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 20 Jul 2019 01:28:29 +0200 Subject: [PATCH] updates --- models.py | 1 + train.py | 56 +++++++++++++++++++++++++++----------------------- utils/utils.py | 5 +++-- 3 files changed, 34 insertions(+), 28 deletions(-) diff --git a/models.py b/models.py index 1c2c8972..98603fd9 100755 --- a/models.py +++ b/models.py @@ -34,6 +34,7 @@ def create_modules(module_defs): if bn: modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters)) if module_def['activation'] == 'leaky': + # modules.add_module('leaky_%d' % i, nn.PReLU(num_parameters=filters, init=0.10)) modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1, inplace=True)) elif module_def['type'] == 'maxpool': diff --git a/train.py b/train.py index 34982bab..182044bb 100644 --- a/train.py +++ b/train.py @@ -11,44 +11,48 @@ from models import * from utils.datasets import * from utils.utils import * +# 320 --epochs 1 # 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 320 giou + best_anchor False # 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 mAP/F1 - 50/50 weighting # 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 # 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 -# 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 +# 0.187 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 + +# 320 --epochs 2 +# 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 # 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 # 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 -# 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 320 --epochs 2 - - -# Training hyperparameters a -hyp = {'giou': 0.8541, # giou loss gain - 'xy': 4.062, # xy loss gain - 'wh': 0.1845, # wh loss gain - 'cls': 21.61, # cls loss gain - 'cls_pw': 1.957, # cls BCELoss positive_weight - 'obj': 22.9, # obj loss gain - 'obj_pw': 2.894, # obj BCELoss positive_weight - 'iou_t': 0.3689, # iou target-anchor training threshold - 'lr0': 0.001844, # initial learning rate - 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) - 'momentum': 0.913, # SGD momentum - 'weight_decay': 0.000467} # optimizer weight decay +# 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 # Training hyperparameters d -# hyp = {'giou': 1.153, # giou loss gain +hyp = {'giou': 1.153, # giou loss gain + 'xy': 4.062, # xy loss gain + 'wh': 0.1845, # wh loss gain + 'cls': 24.28, # cls loss gain + 'cls_pw': 3.05, # cls BCELoss positive_weight + 'obj': 20.93, # obj loss gain + 'obj_pw': 2.842, # obj BCELoss positive_weight + 'iou_t': 0.2759, # iou target-anchor training threshold + 'lr0': 0.001357, # initial learning rate + 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) + 'momentum': 0.916, # SGD momentum + 'weight_decay': 0.000572} # optimizer weight decay + + +# # Training hyperparameters e +# hyp = {'giou': 1.607, # giou loss gain # 'xy': 4.062, # xy loss gain # 'wh': 0.1845, # wh loss gain -# 'cls': 24.28, # cls loss gain -# 'cls_pw': 3.05, # cls BCELoss positive_weight -# 'obj': 20.93, # obj loss gain -# 'obj_pw': 2.842, # obj BCELoss positive_weight -# 'iou_t': 0.2759, # iou target-anchor training threshold -# 'lr0': 0.001357, # initial learning rate +# 'cls': 39.27, # cls loss gain +# 'cls_pw': 3.726, # cls BCELoss positive_weight +# 'obj': 31.26, # obj loss gain +# 'obj_pw': 2.634, # obj BCELoss positive_weight +# 'iou_t': 0.273, # iou target-anchor training threshold +# 'lr0': 0.001542, # initial learning rate # 'lrf': -4., # final learning rate = lr0 * (10 ** lrf) -# 'momentum': 0.916, # SGD momentum -# 'weight_decay': 0.000572} # optimizer weight decay +# 'momentum': 0.8364, # SGD momentum +# 'weight_decay': 0.0008393} # optimizer weight decay def train(cfg, diff --git a/utils/utils.py b/utils/utils.py index d8d4809a..4148c645 100755 --- a/utils/utils.py +++ b/utils/utils.py @@ -534,11 +534,12 @@ def coco_only_people(path='../coco/labels/val2014/'): print(labels.shape[0], file) -def select_best_evolve(path='../../Downloads/evolve*.txt'): # from utils.utils import *; select_best_evolve() +def select_best_evolve(path='evolve*.txt'): # from utils.utils import *; select_best_evolve() # Find best evolved mutation for file in sorted(glob.glob(path)): x = np.loadtxt(file, dtype=np.float32) - print(file, x[x[:, 2].argmax()]) + fitness = x[:, 2] * 0.5 + x[:, 3] * 0.5 # weighted mAP and F1 combination + print(file, x[fitness.argmax()]) def kmeans_targets(path='./data/coco_64img.txt'): # from utils.utils import *; kmeans_targets()