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test.py
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import os
import numpy as np
import argparse
import logging as log
import torch
from torchvision import transforms as tf
from pprint import pformat
import sys
sys.path.insert(0, '.')
from utils.envs import initEnv
import data as mydata
import models
from utils.test import voc_wrapper
class HyperParams(object):
def __init__(self, config, train_flag=1):
self.cuda = True
self.labels = config['labels']
self.classes = len(self.labels)
self.data_root = config['data_root_dir']
self.model_name = config['model_name']
# cuda check
if self.cuda:
if not torch.cuda.is_available():
log.debug('CUDA not available')
self.cuda = False
else:
log.debug('CUDA enabled')
if train_flag == 1:
cur_cfg = config
self.nworkers = cur_cfg['nworkers']
self.pin_mem = cur_cfg['pin_mem']
dataset = cur_cfg['dataset']
self.trainfile = f'{self.data_root}/{dataset}.pkl'
self.network_size = cur_cfg['input_shape']
self.batch = cur_cfg['batch_size']
self.mini_batch = cur_cfg['mini_batch_size']
self.max_batches = cur_cfg['max_batches']
self.jitter = 0.3
self.flip = 0.5
self.hue = 0.1
self.sat = 1.5
self.val = 1.5
self.learning_rate = cur_cfg['warmup_lr']
self.momentum = cur_cfg['momentum']
self.decay = cur_cfg['decay']
self.lr_steps = cur_cfg['lr_steps']
self.lr_rates = cur_cfg['lr_rates']
self.backup = cur_cfg['backup_interval']
self.bp_steps = cur_cfg['backup_steps']
self.bp_rates = cur_cfg['backup_rates']
self.backup_dir = cur_cfg['backup_dir']
self.resize = cur_cfg['resize_interval']
self.rs_steps = []
self.rs_rates = []
self.weights = cur_cfg['weights']
self.clear = cur_cfg['clear']
elif train_flag == 2:
cur_cfg = config
dataset = cur_cfg['dataset']
self.testfile = f'{self.data_root}/{dataset}.pkl'
self.nworkers = cur_cfg['nworkers']
self.pin_mem = cur_cfg['pin_mem']
self.network_size = cur_cfg['input_shape']
self.batch = cur_cfg['batch_size']
self.weights = cur_cfg['weights']
self.conf_thresh = cur_cfg['conf_thresh']
self.nms_thresh = cur_cfg['nms_thresh']
self.results = cur_cfg['results']
else:
cur_cfg = config
self.network_size = cur_cfg['input_shape']
self.batch = cur_cfg['batch_size']
self.max_iters = cur_cfg['max_iters']
class CustomDataset(mydata.BramboxDataset):
def __init__(self, hyper_params):
anno = hyper_params.testfile
root = hyper_params.data_root
network_size = hyper_params.network_size
labels = hyper_params.labels
lb = mydata.transform.Letterbox(network_size)
it = tf.ToTensor()
img_tf = mydata.transform.Compose([lb, it])
anno_tf = mydata.transform.Compose([lb])
def identify(img_id):
return f'{img_id}'
super(CustomDataset, self).__init__('anno_pickle', anno, network_size, labels, identify, img_tf, anno_tf)
def __getitem__(self, index):
img, anno = super(CustomDataset, self).__getitem__(index)
for a in anno:
a.ignore = a.difficult # Mark difficult annotations as ignore for pr metric
return img, anno
def VOCTest(hyper_params):
log.debug('Creating network')
model_name = hyper_params.model_name
batch = hyper_params.batch
use_cuda = hyper_params.cuda
weights = hyper_params.weights
conf_thresh = hyper_params.conf_thresh
network_size = hyper_params.network_size
labels = hyper_params.labels
nworkers = hyper_params.nworkers
pin_mem = hyper_params.pin_mem
nms_thresh = hyper_params.nms_thresh
# prefix = hyper_params.prefix
results = hyper_params.results
test_args = {'conf_thresh': conf_thresh, 'network_size': network_size, 'labels': labels}
net = models.__dict__[model_name](hyper_params.classes, weights, train_flag=2, test_args=test_args)
net.eval()
log.info('Net structure\n%s' % net)
# import pdb
# pdb.set_trace()
if use_cuda:
net.cuda()
log.debug('Creating dataset')
loader = torch.utils.data.DataLoader(
CustomDataset(hyper_params),
batch_size=batch,
shuffle=False,
drop_last=False,
num_workers=nworkers if use_cuda else 0,
pin_memory=pin_mem if use_cuda else False,
collate_fn=mydata.list_collate,
)
log.debug('Running network')
tot_loss = []
coord_loss = []
conf_loss = []
cls_loss = []
anno, det = {}, {}
num_det = 0
for idx, (data, box) in enumerate(loader):
print("sssssssssssssize {}".format(np.shape(data)))
if (idx + 1) % 20 == 0:
log.info('%d/%d' % (idx + 1, len(loader)))
if use_cuda:
data = data.cuda()
with torch.no_grad():
output, loss = net(data, box)
print("output:::::::::::::::::::::::::::",output)
key_val = len(anno)
anno.update({loader.dataset.keys[key_val + k]: v for k, v in enumerate(box)})
det.update({loader.dataset.keys[key_val + k]: v for k, v in enumerate(output)})
# print("++++++++++++++++++++++++++")
print("predict img:", idx + 1)
# print(box)
# print(det)
netw, neth = network_size
reorg_dets = voc_wrapper.reorgDetection(det, netw, neth) # , prefix)
result = voc_wrapper.genResults(reorg_dets, results, nms_thresh)
print("------------------>>> detect result:", result)
det = {}
return result
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='OneDet: an one stage framework based on PyTorch')
parser.add_argument('model_name', help='model name: TinyYolov3, Yolov3', default="TinyYolov3")
args = parser.parse_args()
train_flag = 2
config = initEnv(train_flag=train_flag, model_name=args.model_name)
log.info('Config\n\n%s\n' % pformat(config))
# init env
hyper_params = HyperParams(config, train_flag=train_flag)
# init and run eng
result = VOCTest(hyper_params)