valid_loss, valid_gt, valid_probs = valid_trainer(
model=model,
valid_loader=valid_loader,
criterion=criterion,
)
valid_result = get_pedestrian_metrics(valid_gt, valid_probs)
#print result
print(
f'Evaluation on test set, \n',
'ma: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
valid_result.ma, np.mean(valid_result.label_pos_recall),
np.mean(valid_result.label_neg_recall)),
'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
valid_result.instance_acc, valid_result.instance_prec,
valid_result.instance_recall, valid_result.instance_f1))
for index in range(len(valid_set.attr_name)):
print(f'{valid_set.attr_name[index]}')
print(
f'pos recall: {valid_result.label_pos_recall[index]} neg_recall: {valid_result.label_neg_recall[index]} ma: {valid_result.label_ma[index]}'
)
if __name__ == '__main__':
parser = argument_parser()
args = parser.parse_args()
main(args)
# os.path.abspath()
def __init__(self):
device = torch.device('cpu')
FORCE_TO_CPU = False
parser = argument_parser()
args = parser.parse_args(['PETA', '--model=dpn107'])
visenv_name = 'PETA'
exp_dir = os.path.join('exp_result', visenv_name)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(
model_dir, 'ckpt_max_e0384293_2020-09-17_18-35-21.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
_, predict_tsfm = get_transform(args)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=predict_tsfm)
args.att_list = valid_set.attr_id
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter,
nattr=valid_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available() and not FORCE_TO_CPU:
model = torch.nn.DataParallel(model).cuda()
ckpt = torch.load(save_model_path)
print(f'Model is served with GPU ')
else:
model = torch.nn.DataParallel(model)
ckpt = torch.load(save_model_path,
map_location=torch.device('cpu'))
print(f'Model is served with CPU ')
model.load_state_dict(ckpt['state_dicts'])
model.eval()
# from torchsummary import summary
# summary(model, input_size=(3, 256, 192))
print('Total number of parameters: ',
sum(p.numel() for p in model.parameters() if p.requires_grad))
self.args = args
self.predict_tsfm = predict_tsfm
self.model = model