def __init__(self, scale, config=ConfigYOLOV4CspDarkNet53()):
super(YoloLossBlock, self).__init__()
self.config = config
if scale == 's':
# anchor mask
idx = (0, 1, 2)
elif scale == 'm':
idx = (3, 4, 5)
elif scale == 'l':
idx = (6, 7, 8)
else:
raise KeyError("Invalid scale value for DetectionBlock")
self.anchors = Tensor([self.config.anchor_scales[i] for i in idx],
ms.float32)
self.ignore_threshold = Tensor(self.config.ignore_threshold,
ms.float32)
self.concat = P.Concat(axis=-1)
self.iou = Iou()
self.reduce_max = P.ReduceMax(keep_dims=False)
self.xy_loss = XYLoss()
self.wh_loss = WHLoss()
self.confidence_loss = ConfidenceLoss()
self.class_loss = ClassLoss()
self.reduce_sum = P.ReduceSum()
self.giou = Giou()
def __init__(self, network):
super(YoloWithLossCell, self).__init__()
self.yolo_network = network
self.config = ConfigYOLOV4CspDarkNet53()
self.loss_big = YoloLossBlock('l', self.config)
self.loss_me = YoloLossBlock('m', self.config)
self.loss_small = YoloLossBlock('s', self.config)
def __init__(self, scale, config=ConfigYOLOV4CspDarkNet53(), is_training=True):
super(DetectionBlock, self).__init__()
self.config = config
if scale == 's':
idx = (0, 1, 2)
self.scale_x_y = 1.2
self.offset_x_y = 0.1
elif scale == 'm':
idx = (3, 4, 5)
self.scale_x_y = 1.1
self.offset_x_y = 0.05
elif scale == 'l':
idx = (6, 7, 8)
self.scale_x_y = 1.05
self.offset_x_y = 0.025
else:
raise KeyError("Invalid scale value for DetectionBlock")
self.anchors = Tensor([self.config.anchor_scales[i] for i in idx], ms.float32)
self.num_anchors_per_scale = 3
self.num_attrib = 4+1+self.config.num_classes
self.lambda_coord = 1
self.sigmoid = nn.Sigmoid()
self.reshape = P.Reshape()
self.tile = P.Tile()
self.concat = P.Concat(axis=-1)
self.conf_training = is_training
def __init__(self, is_training):
super(YOLOV4CspDarkNet53, self).__init__()
self.config = ConfigYOLOV4CspDarkNet53()
# YOLOv4 network
self.feature_map = YOLOv4(backbone=CspDarkNet53(ResidualBlock, detect=True),
backbone_shape=self.config.backbone_shape,
out_channel=self.config.out_channel)
# prediction on the default anchor boxes
self.detect_1 = DetectionBlock('l', is_training=is_training)
self.detect_2 = DetectionBlock('m', is_training=is_training)
self.detect_3 = DetectionBlock('s', is_training=is_training)
def test():
"""test method"""
# init distributed
if args.is_distributed:
init()
args.rank = get_rank()
args.group_size = get_group_size()
# logger
args.outputs_dir = os.path.join(args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
args.logger = get_logger(args.outputs_dir, args.rank)
context.reset_auto_parallel_context()
if args.is_distributed:
parallel_mode = ParallelMode.DATA_PARALLEL
else:
parallel_mode = ParallelMode.STAND_ALONE
context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=1)
args.logger.info('Creating Network....')
network = YOLOV4CspDarkNet53(is_training=False)
args.logger.info(args.pretrained)
if os.path.isfile(args.pretrained):
param_dict = load_checkpoint(args.pretrained)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('yolo_network.'):
param_dict_new[key[13:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load_model {} success'.format(args.pretrained))
else:
args.logger.info('{} not exists or not a pre-trained file'.format(args.pretrained))
assert FileNotFoundError('{} not exists or not a pre-trained file'.format(args.pretrained))
exit(1)
data_root = args.data_root
# annFile = args.annFile
config = ConfigYOLOV4CspDarkNet53()
if args.testing_shape:
config.test_img_shape = conver_testing_shape(args)
data_txt = os.path.join(args.data_dir, 'testdev2017.txt')
ds, data_size = create_yolo_datasetv2(data_root, data_txt=data_txt, batch_size=args.per_batch_size,
max_epoch=1, device_num=args.group_size, rank=args.rank, shuffle=False,
config=config)
args.logger.info('testing shape : {}'.format(config.test_img_shape))
args.logger.info('totol {} images to eval'.format(data_size))
network.set_train(False)
# init detection engine
detection = DetectionEngine(args)
input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
args.logger.info('Start inference....')
for i, data in enumerate(ds.create_dict_iterator()):
image = Tensor(data["image"])
image_shape = Tensor(data["image_shape"])
image_id = Tensor(data["img_id"])
prediction = network(image, input_shape)
output_big, output_me, output_small = prediction
output_big = output_big.asnumpy()
output_me = output_me.asnumpy()
output_small = output_small.asnumpy()
image_id = image_id.asnumpy()
image_shape = image_shape.asnumpy()
detection.detect([output_small, output_me, output_big], args.per_batch_size, image_shape, image_id)
if i % 1000 == 0:
args.logger.info('Processing... {:.2f}% '.format(i * args.per_batch_size / data_size * 100))
args.logger.info('Calculating mAP...')
detection.do_nms_for_results()
result_file_path = detection.write_result()
args.logger.info('result file path: {}'.format(result_file_path))
if __name__ == "__main__":
loss_meter = AverageMeter('loss')
context.reset_auto_parallel_context()
parallel_mode = ParallelMode.STAND_ALONE
degree = 1
if args.is_distributed:
parallel_mode = ParallelMode.DATA_PARALLEL
degree = get_group_size()
context.set_auto_parallel_context(parallel_mode=parallel_mode,
gradients_mean=True,
device_num=degree)
network = YOLOV4CspDarkNet53(is_training=True)
# default is kaiming-normal
config = ConfigYOLOV4CspDarkNet53()
args.checkpoint_filter_list = config.checkpoint_filter_list
default_recurisive_init(network)
load_yolov4_params(args, network)
network = YoloWithLossCell(network)
args.logger.info('finish get network')
config.label_smooth = args.label_smooth
config.label_smooth_factor = args.label_smooth_factor
if args.training_shape:
config.multi_scale = [convert_training_shape(args.training_shape)]
if args.resize_rate:
config.resize_rate = args.resize_rate
def train():
"""Train function."""
args = parse_args()
devid = int(os.getenv('DEVICE_ID', '0'))
context.set_context(mode=context.GRAPH_MODE,
enable_auto_mixed_precision=True,
device_target=args.device_target,
save_graphs=False,
device_id=devid)
loss_meter = AverageMeter('loss')
network = YOLOV4CspDarkNet53(is_training=True)
# default is kaiming-normal
default_recursive_init(network)
if args.pretrained_backbone:
pretrained_backbone_slice = args.pretrained_backbone.split('/')
backbone_ckpt_file = pretrained_backbone_slice[
len(pretrained_backbone_slice) - 1]
local_backbone_ckpt_path = '/cache/' + backbone_ckpt_file
# download backbone checkpoint
mox.file.copy_parallel(src_url=args.pretrained_backbone,
dst_url=local_backbone_ckpt_path)
args.pretrained_backbone = local_backbone_ckpt_path
load_yolov4_params(args, network)
network = YoloWithLossCell(network)
args.logger.info('finish get network')
config = ConfigYOLOV4CspDarkNet53()
config.label_smooth = args.label_smooth
config.label_smooth_factor = args.label_smooth_factor
if args.training_shape:
config.multi_scale = [convert_training_shape(args)]
if args.resize_rate:
config.resize_rate = args.resize_rate
# data download
local_data_path = '/cache/data'
local_ckpt_path = '/cache/ckpt_file'
print('Download data.')
mox.file.copy_parallel(src_url=args.data_url, dst_url=local_data_path)
ds, data_size = create_yolo_dataset(
image_dir=os.path.join(local_data_path, 'images'),
anno_path=os.path.join(local_data_path, 'annotation.json'),
is_training=True,
batch_size=args.per_batch_size,
max_epoch=args.max_epoch,
device_num=args.group_size,
rank=args.rank,
config=config)
args.logger.info('Finish loading dataset')
args.steps_per_epoch = int(data_size / args.per_batch_size /
args.group_size)
if not args.ckpt_interval:
args.ckpt_interval = args.steps_per_epoch * 10
lr = get_lr(args)
opt = Momentum(params=get_param_groups(network),
learning_rate=Tensor(lr),
momentum=args.momentum,
weight_decay=args.weight_decay,
loss_scale=args.loss_scale)
is_gpu = context.get_context("device_target") == "GPU"
if is_gpu:
loss_scale_value = 1.0
loss_scale = FixedLossScaleManager(loss_scale_value,
drop_overflow_update=False)
network = amp.build_train_network(network,
optimizer=opt,
loss_scale_manager=loss_scale,
level="O2",
keep_batchnorm_fp32=False)
keep_loss_fp32(network)
else:
network = TrainingWrapper(network, opt)
network.set_train()
# checkpoint save
ckpt_max_num = 10
ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
keep_checkpoint_max=ckpt_max_num)
ckpt_cb = ModelCheckpoint(config=ckpt_config,
directory=local_ckpt_path,
prefix='yolov4')
cb_params = _InternalCallbackParam()
cb_params.train_network = network
cb_params.epoch_num = ckpt_max_num
cb_params.cur_epoch_num = 1
run_context = RunContext(cb_params)
ckpt_cb.begin(run_context)
old_progress = -1
t_end = time.time()
data_loader = ds.create_dict_iterator(output_numpy=True, num_epochs=1)
for i, data in enumerate(data_loader):
images = data["image"]
input_shape = images.shape[2:4]
images = Tensor.from_numpy(images)
batch_y_true_0 = Tensor.from_numpy(data['bbox1'])
batch_y_true_1 = Tensor.from_numpy(data['bbox2'])
batch_y_true_2 = Tensor.from_numpy(data['bbox3'])
batch_gt_box0 = Tensor.from_numpy(data['gt_box1'])
batch_gt_box1 = Tensor.from_numpy(data['gt_box2'])
batch_gt_box2 = Tensor.from_numpy(data['gt_box3'])
input_shape = Tensor(tuple(input_shape[::-1]), ms.float32)
loss = network(images, batch_y_true_0, batch_y_true_1, batch_y_true_2,
batch_gt_box0, batch_gt_box1, batch_gt_box2,
input_shape)
loss_meter.update(loss.asnumpy())
# ckpt progress
cb_params.cur_step_num = i + 1 # current step number
cb_params.batch_num = i + 2
ckpt_cb.step_end(run_context)
if i % args.log_interval == 0:
time_used = time.time() - t_end
epoch = int(i / args.steps_per_epoch)
fps = args.per_batch_size * (
i - old_progress) * args.group_size / time_used
if args.rank == 0:
args.logger.info(
'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, lr:{}'.format(
epoch, i, loss_meter, fps, lr[i]))
t_end = time.time()
loss_meter.reset()
old_progress = i
if (i + 1) % args.steps_per_epoch == 0:
cb_params.cur_epoch_num += 1
args.logger.info('==========end training===============')
# upload checkpoint files
print('Upload checkpoint.')
mox.file.copy_parallel(src_url=local_ckpt_path, dst_url=args.train_url)
def test():
"""The function of eval."""
start_time = time.time()
args = parse_args()
devid = int(os.getenv('DEVICE_ID')) if os.getenv('DEVICE_ID') else 0
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target,
save_graphs=False, device_id=devid)
# logger
args.outputs_dir = os.path.join(args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
rank_id = int(os.environ.get('RANK_ID')) if os.environ.get('RANK_ID') else 0
args.logger = get_logger(args.outputs_dir, rank_id)
args.logger.info('Creating Network....')
network = YOLOV4CspDarkNet53(is_training=False)
ckpt_file_slice = args.checkpoint_path.split('/')
ckpt_file = ckpt_file_slice[len(ckpt_file_slice)-1]
local_ckpt_path = '/cache/'+ckpt_file
# download checkpoint
mox.file.copy_parallel(src_url=args.checkpoint_path, dst_url=local_ckpt_path)
args.logger.info(local_ckpt_path)
if os.path.isfile(local_ckpt_path):
param_dict = load_checkpoint(local_ckpt_path)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('yolo_network.'):
param_dict_new[key[13:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load_model {} success'.format(local_ckpt_path))
else:
args.logger.info('{} not exists or not a pre-trained file'.format(local_ckpt_path))
assert FileNotFoundError('{} not exists or not a pre-trained file'.format(local_ckpt_path))
exit(1)
config = ConfigYOLOV4CspDarkNet53()
if args.testing_shape:
config.test_img_shape = convert_testing_shape(args)
local_data_path = '/cache/data'
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args.data_url, dst_url=local_data_path)
args.data_root = os.path.join(local_data_path, 'images')
args.ann_file = os.path.join(local_data_path, 'eval_annotation.json')
ds, data_size = create_yolo_dataset(args.data_root, args.ann_file,
is_training=False, batch_size=args.per_batch_size,
max_epoch=1, device_num=1, rank=rank_id, shuffle=False,
config=config)
args.logger.info('testing shape: {}'.format(config.test_img_shape))
args.logger.info('total {} images to eval'.format(data_size))
network.set_train(False)
# init detection engine
detection = DetectionEngine(args)
input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
args.logger.info('Start inference....')
for i, data in enumerate(ds.create_dict_iterator(num_epochs=1)):
image = data["image"]
image_shape = data["image_shape"]
image_id = data["img_id"]
prediction = network(image, input_shape)
output_big, output_me, output_small = prediction
output_big = output_big.asnumpy()
output_me = output_me.asnumpy()
output_small = output_small.asnumpy()
image_id = image_id.asnumpy()
image_shape = image_shape.asnumpy()
detection.detect([output_small, output_me, output_big], args.per_batch_size,
image_shape, image_id, config)
if i % 1000 == 0:
args.logger.info('Processing... {:.2f}% '.format(i * args.per_batch_size / data_size * 100))
args.logger.info('Calculating mAP...')
detection.do_nms_for_results()
result_file_path = detection.write_result()
args.logger.info('result file path: {}'.format(result_file_path))
eval_result = detection.get_eval_result()
cost_time = time.time() - start_time
args.logger.info('\n=============coco eval result=========\n' + eval_result)
args.logger.info('testing cost time {:.2f}h'.format(cost_time / 3600.))
def predict():
"""The function of predict."""
args = parse_args()
# logger
args.outputs_dir = os.path.join(
args.log_path,
datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
rank_id = int(os.environ.get('RANK_ID'))
args.logger = get_logger(args.outputs_dir, rank_id)
args.logger.info('Creating Network....')
network = YOLOV4CspDarkNet53(is_training=False)
ckpt_file_slice = args.checkpoint_path.split('/')
ckpt_file = ckpt_file_slice[len(ckpt_file_slice) - 1]
local_ckpt_path = '/cache/' + ckpt_file
# download checkpoint
mox.file.copy_parallel(src_url=args.checkpoint_path,
dst_url=local_ckpt_path)
args.logger.info(local_ckpt_path)
if os.path.isfile(local_ckpt_path):
param_dict = load_checkpoint(local_ckpt_path)
param_dict_new = {}
for key, values in param_dict.items():
if key.startswith('moments.'):
continue
elif key.startswith('yolo_network.'):
param_dict_new[key[13:]] = values
else:
param_dict_new[key] = values
load_param_into_net(network, param_dict_new)
args.logger.info('load_model {} success'.format(local_ckpt_path))
else:
args.logger.info(
'{} not exists or not a pre-trained file'.format(local_ckpt_path))
assert FileNotFoundError(
'{} not exists or not a pre-trained file'.format(local_ckpt_path))
exit(1)
config = ConfigYOLOV4CspDarkNet53()
local_data_path = '/cache/data'
# data download
print('Download data.')
mox.file.copy_parallel(src_url=args.data_url, dst_url=local_data_path)
# init detection engine
detection = DetectionEngine(args)
# preprocess the image
images = os.listdir(local_data_path)
image_path = os.path.join(local_data_path, images[0])
image, image_shape = data_preprocess(image_path, config)
args.logger.info('testing shape: {}'.format(config.test_img_shape))
input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
args.logger.info('Start inference....')
network.set_train(False)
prediction = network(Tensor(image.reshape(1, 3, 416, 416), ms.float32),
input_shape)
output_big, output_me, output_small = prediction
output_big = output_big.asnumpy()
output_me = output_me.asnumpy()
output_small = output_small.asnumpy()
detection.detect([output_small, output_me, output_big],
args.per_batch_size, image_shape, config)
detection.do_nms_for_results()
img = detection.draw_boxes_in_image(image_path)
local_output_dir = '/cache/output'
if not os.path.exists(local_output_dir):
os.mkdir(local_output_dir)
cv2.imwrite(os.path.join(local_output_dir, 'output.jpg'), img)
# upload output image files
print('Upload output image.')
mox.file.copy_parallel(src_url=local_output_dir, dst_url=args.train_url)
