def test_detection_dataset():
from cfgs.config_v2 import add_cfg
dataset_yaml = '/home/cory/yolo2-pytorch/cfgs/config_voc.yaml'
exp_yaml = '/home/cory/yolo2-pytorch/cfgs/exps/voc0712/voc0712_baseline.yaml'
cfg = dict()
add_cfg(cfg, dataset_yaml)
add_cfg(cfg, exp_yaml)
dataset = DetectionDataset(cfg)
num_workers = 4
batch_size = 16
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
t0 = time.time()
for i, data in enumerate(dataloader):
if i > 100:
break
# get the inputs
inputs, labels = data
print(i, inputs.size(), labels.size())
# wrap them in Variable
inputs, labels = Variable(inputs.cuda()), labels
import numpy as np
assert np.sum(inputs.data.cpu().numpy()) > 0
t1 = time.time()
print(t1 - t0)
def voc_ap_main():
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
dataset_yaml = '/home/cory/yolo2-pytorch/cfgs/config_voc.yaml'
exp_yaml = '/home/cory/yolo2-pytorch/cfgs/exps/voc0712/voc0712_baseline_v3_rand.yaml'
cfg = dict()
add_cfg(cfg, dataset_yaml)
add_cfg(cfg, exp_yaml)
epoch = 160
model_dir = cfg['train_output_dir']
model_name = cfg['exp_name']
model = model_dir + '/' + model_name + '_' + str(epoch) + '.h5'
# model = '/home/cory/yolo2-pytorch/models/yolo-voc.weights.h5'
print(model)
voc_ap(model, cfg)
def test_detection_dataset():
from cfgs.config_v2 import add_cfg
dataset_yaml = '/home/cory/yolo2-pytorch/cfgs/config_voc.yaml'
exp_yaml = '/home/cory/yolo2-pytorch/cfgs/exps/voc0712/voc0712_baseline.yaml'
cfg = dict()
add_cfg(cfg, dataset_yaml)
add_cfg(cfg, exp_yaml)
dataset = DetectionDataset(cfg)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=16,
shuffle=True,
num_workers=4)
for i, data in enumerate(dataloader):
# get the inputs
print(i)
inputs, labels = data
print(inputs.size(), labels.size())
# wrap them in Variable
inputs, labels = Variable(inputs), labels
from cfgs.config_v2 import add_cfg
import utils.network as net_utils
from darknet_v3 import Darknet19
from datasets.ImageFileDataset_v2 import ImageFileDataset
from utils.timer import Timer
from train.train_util_v2 import *
# dataset_yaml = '/home/cory/yolo2-pytorch/cfgs/config_kitti.yaml'
# exp_yaml = '/home/cory/yolo2-pytorch/cfgs/exps/kitti_new_2.yaml'
dataset_yaml = '/home/cory/yolo2-pytorch/cfgs/config_voc.yaml'
exp_yaml = '/home/cory/yolo2-pytorch/cfgs/exps/voc0712_template.yaml'
cfg = dict()
# add_cfg(cfg, '/home/cory/yolo2-pytorch/cfgs/config_voc.yaml')
add_cfg(cfg, dataset_yaml)
add_cfg(cfg, exp_yaml)
# data loader
imdb = ImageFileDataset(cfg,
ImageFileDataset.preprocess_train,
processes=4,
shuffle=False,
dst_size=None,
mode='val')
print('imdb load data succeeded')
net = Darknet19(cfg)
# CUDA_VISIBLE_DEVICES=1
# 20 0.68