class Test(object):
def __init__(self, args, work_dir):
self.args = args
self.cfg = cfg
self.time = Timer()
self.input_size = cfg.input_size
self.num_classes = 21
self.class_name = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
# Define Network
# initilize the network here.
if args.net == 'resnet':
model = DSSD(args=args,
cfg=cfg,
net=args.net,
output_stride=32,
num_classes=self.num_classes,
img_size=self.input_size,
pretrained=True)
else:
NotImplementedError
checkpoint = torch.load(work_dir)
model.load_state_dict(checkpoint['state_dict'])
self.model = model.to(self.args.device)
def test(self, img_path):
self.time.batch()
self.model.eval()
image = cv2.imread(img_path)
img, ratio, left, top = self.letterbox(image, self.input_size)
img2 = img
# Normalize
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB
img = np.ascontiguousarray(img, dtype=np.float32)
img = self.normalize(img)
# image transform to input form of network
img = torch.from_numpy(img).unsqueeze(0)
input = img.to(self.args.device)
output = self.model(input)
output = output.squeeze(0).cpu()
output = output[output[:, 4].gt(0)]
output[:, :4] *= self.input_size
for ii, name in enumerate(self.class_name):
print(ii, ':', name, end='. ')
print(output)
# output[:, [0, 2]] = output[:, [0, 2]] / ratio - left
# output[:, [1, 3]] = output[:, [1, 3]] / ratio - top
# self.show_image(image, output)
self.show_image(img2, output)
print("Time cost: %7.3gs" % self.time.batch())
def letterbox(self, img, height=512, color=(127.5, 127.5, 127.5)):
"""resize a rectangular image to a padded square
"""
shape = img.shape[:2] # shape = [height, width]
ratio = float(height) / max(shape) # ratio = old / new
dw = (max(shape) - shape[1]) / 2 # width padding
dh = (max(shape) - shape[0]) / 2 # height padding
left, right = round(dw - 0.1), round(dw + 0.1)
top, bottom = round(dh - 0.1), round(dh + 0.1)
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # padded square
interp = np.random.randint(0, 5)
img = cv2.resize(img, (height, height), interpolation=interp) # resized, no border
return img, ratio, left, top
def normalize(self, img, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
"""norm = (x - mean) / std
"""
img = img / 255.0
mean = np.array(mean)
std = np.array(std)
img = (img - mean[:, np.newaxis, np.newaxis]) / std[:, np.newaxis, np.newaxis]
return img.astype(np.float32)
def show_image(self, img, labels):
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
plt.subplot(1, 1, 1).imshow(img[:, :, ::-1])
plt.plot(labels[:, [0, 2, 2, 0, 0]].T, labels[:, [1, 1, 3, 3, 1]].T, '-')
plt.show()
pass
class Trainer(object):
def __init__(self, args):
self.args = args
self.cfg = cfg
self.time = Timer()
# Define Saver
self.saver = Saver(args, cfg)
self.saver.save_experiment_config()
# Define Dataloader
train_dataset = Detection_Dataset(args, cfg, cfg.train_split, 'train')
self.num_classes = train_dataset.num_classes
self.input_size = train_dataset.input_size
self.train_loader = data.DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=self.args.workers,
shuffle=True,
pin_memory=True,
drop_last=True,
collate_fn=train_dataset.collate_fn)
self.num_batch = len(self.train_loader)
# Define Network
# initilize the network here.
if args.net == 'resnet':
model = DSSD(args=args,
cfg=cfg,
net=args.net,
output_stride=32,
num_classes=self.num_classes,
img_size=self.input_size,
pretrained=True)
else:
NotImplementedError
train_params = [{
'params': model.get_1x_lr_params(),
'lr': cfg.lr
}, {
'params': model.get_10x_lr_params(),
'lr': cfg.lr * 10
}]
# Define Optimizer
optimizer = torch.optim.SGD(train_params,
momentum=cfg.lr,
weight_decay=cfg.weight_decay,
nesterov=False)
# Define Criterion
# Whether to use class balanced weights
if args.use_balanced_weights:
classes_weights_path = os.path.join(Path.db_root_dir(args.dataset))
if osp.isfile(classes_weights_path):
weight = np.load(classes_weights_path)
else:
weight = calculate_weigths_labels(args.dataset,
self.train_loader,
cfg.num_classes)
weight = torch.from_numpy(weight.astype(np.float32))
else:
weight = None
self.criterion = MultiBoxLoss(args, cfg, self.num_classes, weight)
self.model, self.optimizer = model, optimizer
# Define lr scherduler
self.scheduler = LR_Scheduler(args.lr_scheduler, cfg.lr, args.epochs,
len(self.train_loader))
# Resuming Checkpoint
self.best_pred = 0.0
if args.resume is not None:
if not os.path.isfile(args.resume):
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
self.model.load_state_dict(checkpoint['state_dict'])
if not args.ft:
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.best_pred = checkpoint['best_pred']
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
# Using cuda
# self.optimizer = self.model.to(self.args.device)
self.model = self.model.to(self.args.device)
if args.ng > 1 and args.use_multi_gpu:
print("Using multiple gpu")
self.model = torch.nn.DataParallel(self.model,
device_ids=args.gpu_ids)
# Clear start epoch if fine-tuning
if args.ft:
self.start_epoch = 0
else:
self.start_epoch = args.start_epoch
# Visdom
if args.visdom:
vis = visdom.Visdom()
vis_legend = ['Loss_local', 'Loss_confidence', 'mAP', 'mF1']
self.epoch_plot = create_vis_plot(vis, 'Epoch', 'Loss',
'train loss', vis_legend[0:2])
self.batch_plot = create_vis_plot(vis, 'Batch', 'Loss',
'batch loss', vis_legend[0:2])
self.val_plot = create_vis_plot(vis, 'Epoch', 'result', 'val loss',
vis_legend[2:4])
self.vis = vis
self.vis_legend = vis_legend
model_info(self.model)
def training(self, epoch):
self.time.epoch()
self.model.train()
ave_loss_l = 0.
ave_loss_c = 0.
for ii, (images, targets, _, _) in enumerate(self.train_loader):
num_target = [len(ann) for ann in targets]
# continue if exist image no target.
if 0 in num_target:
continue
self.time.batch()
images = images.to(self.args.device)
targets = [ann.to(self.args.device) for ann in targets]
self.scheduler(self.optimizer, ii, epoch, self.best_pred)
self.optimizer.zero_grad()
output = self.model(images)
loss_l, loss_c = self.criterion(output, targets)
loss = loss_l + loss_c
ave_loss_c += (loss_c - ave_loss_c) / (ii + 1)
ave_loss_l += (loss_l - ave_loss_l) / (ii + 1)
assert not torch.isnan(
loss), 'WARNING: nan loss detected, ending training'
loss.backward()
self.optimizer.step()
# visdom
if self.args.visdom:
update_vis_plot(self.vis, ii, [loss_l, loss_c],
self.batch_plot, 'append')
show_info = '[mode: train' +\
'Epoch: [%d][%d/%d], ' % (epoch, ii, self.num_batch) +\
'lr: %5.4g, ' % self.optimizer.param_groups[0]['lr'] +\
'loc_loss: %5.3g, conf_loss: %5.3g, time: %5.2gs]' %\
(loss_l, loss_c, self.time.batch())
if (ii + 1) % 50 == 0:
print(show_info)
# Save log info
self.saver.save_log(show_info)
epoch_show_info = '[mode: train, ' +\
'Epoch: [%d], ' % epoch +\
'lr: %5.4g, ' % self.optimizer.param_groups[0]['lr'] +\
'average_loc_loss: %5.3g, ' % ave_loss_l +\
'average_conf_loss: %5.3g, ' % ave_loss_c +\
'time: %5.2gm]' % self.time.epoch()
print(epoch_show_info)
# Save log info
self.saver.save_log(epoch_show_info)
# visdom
if self.args.visdom:
update_vis_plot(self.vis, epoch, [ave_loss_l, ave_loss_c],
self.epoch_plot, 'append')
