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car-detection-bayes
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align loss to darknet

This commit is contained in:
Glenn Jocher 2018-09-24 21:25:17 +02:00
parent a75119b8f0
commit 396a71001e
2 changed files with 7 additions and 9 deletions
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2
test.py
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@ -7,7 +7,7 @@ parser = argparse.ArgumentParser()
parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch') parser.add_argument('-batch_size', type=int, default=32, help='size of each image batch')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file') parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='path to model config file')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file') parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='path to data config file')
parser.add_argument('-weights_path', type=str, default='checkpoints/yolov3.pt', help='path to weights file') parser.add_argument('-weights_path', type=str, default='checkpoints/latest.pt', help='path to weights file')
parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file') parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file')
parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected') parser.add_argument('-iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold') parser.add_argument('-conf_thres', type=float, default=0.5, help='object confidence threshold')

14
train.py
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@ -6,7 +6,7 @@ from utils.datasets import *
from utils.utils import * from utils.utils import *
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('-epochs', type=int, default=1, help='number of epochs') parser.add_argument('-epochs', type=int, default=160, help='number of epochs')
parser.add_argument('-batch_size', type=int, default=12, help='size of each image batch') parser.add_argument('-batch_size', type=int, default=12, help='size of each image batch')
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path') parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
@ -69,9 +69,9 @@ def main(opt):
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3, optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3,
momentum=.9, weight_decay=5e-4, nesterov=True) momentum=.9, weight_decay=5e-4, nesterov=True)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None: if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer']) optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch'] + 1
best_loss = checkpoint['best_loss'] best_loss = checkpoint['best_loss']
del checkpoint # current, saved del checkpoint # current, saved
@ -115,12 +115,10 @@ def main(opt):
continue continue
# SGD burn-in # SGD burn-in
# if (epoch == 0) & (i <= 1000): if (epoch == 0) & (i <= 1000):
# power = 4 lr = 1e-3 * (i / 1000) ** 4
# lr = 1e-3 * (i / 1000) ** power for g in optimizer.param_groups:
# for g in optimizer.param_groups: g['lr'] = lr
# g['lr'] = lr
# # print('SGD Burn-In LR = %9.5g' % lr, end='')
# Compute loss, compute gradient, update parameters # Compute loss, compute gradient, update parameters
loss = model(imgs.to(device), targets, requestPrecision=True) loss = model(imgs.to(device), targets, requestPrecision=True)