def __init__(
self,
cfg,
confidence_thresholds_for_classes,
show_mask_heatmaps=False,
masks_per_dim=2,
min_image_size=224,
):
self.cfg = cfg.clone()
self.model = build_detection_model(cfg)
self.model.eval()
self.device = torch.device(cfg.MODEL.DEVICE)
self.model.to(self.device)
self.min_image_size = min_image_size
save_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg,
self.model,
save_dir=save_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
self.transforms = self.build_transform()
mask_threshold = -1 if show_mask_heatmaps else 0.5
self.masker = Masker(threshold=mask_threshold, padding=1)
# used to make colors for each class
self.palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1])
self.cpu_device = torch.device("cpu")
self.confidence_thresholds_for_classes = torch.tensor(
confidence_thresholds_for_classes)
self.show_mask_heatmaps = show_mask_heatmaps
self.masks_per_dim = masks_per_dim
def __init__(
self,
model_name="fcos_R_50_FPN_1x",
nms_thresh=0.6,
cpu_only=False
):
root_dir = os.path.dirname(os.path.abspath(__file__))
self.config_files_dir = os.path.join(root_dir, "configs")
self.cfg_name = model_name + ".yaml"
cfg = base_cfg.clone()
cfg.merge_from_file(os.path.join(self.config_files_dir, self.cfg_name))
cfg.MODEL.WEIGHT = _MODEL_NAMES_TO_INFO_[model_name]["url"]
cfg.MODEL.FCOS.NMS_TH = nms_thresh
if cpu_only:
cfg.MODEL.DEVICE = "cpu"
else:
cfg.MODEL.DEVICE = "cuda"
cfg.freeze()
self.cfg = cfg
self.model = build_detection_model(cfg)
self.model.eval()
self.device = torch.device(cfg.MODEL.DEVICE)
self.model.to(self.device)
checkpointer = DetectronCheckpointer(cfg, self.model)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
self.palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
self.transforms = self.build_transform()
self.cpu_device = torch.device("cpu")
self.model_name = model_name
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
#断点定义，之后可以加载使用
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
#加载数据集
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed): #cfg 0 False
model = build_detection_model(cfg) #实例化模型
device = torch.device(cfg.MODEL.DEVICE) #cfg.MODEL.DEVICE="cuda" 将torch.tensor分配到cuda 即GPU上
model.to(device) #将模型放在gpu上运行
if cfg.MODEL.USE_SYNCBN: #False
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model) # 定义网络训练优化器
scheduler = make_lr_scheduler(cfg, optimizer) #设置学习率
if distributed: #False
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR #"."
save_to_disk = get_rank() == 0 #True
#checkpoint为网络的预训练模型
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data) #将extra_checkpoint_data字典里的数值加入到arguments字典中
#make_data_loader
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed, #False
start_iter=arguments["iteration"], # 0
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD #SOLVER.CHECKPOINT_PERIOD = 2500
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def __init__(self, cfg, local_rank, distributed):
self.writer = SummaryWriter(log_dir=cfg.OUTPUT_DIR)
self.start_epoch = 0
# self.epochs = cfg.MAX_ITER / len()
self.epochs = 5
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
# 核心修改在于dataset，dataloader都是torch.utils.data.data_loader
# import pdb; pdb.set_trace()
# train_loader = build_single_data_loader(cfg)
self.train_loader = make_train_loader(
cfg, start_iter=arguments["iteration"])
# self.val_loader = make_val_loader(cfg)
# train_data_loader = make_data_loader(
# cfg,
# is_train=True,
# is_distributed=distributed,
# start_iter=arguments["iteration"],
# )
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.checkpointer = checkpointer
self.scheduler = scheduler
self.device = device
self.checkpoint_period = checkpoint_period
self.arguments = arguments
self.distributed = distributed
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
# import matplotlib.pyplot as plt
# import numpy as np
#
# def imshow(img):
# #img = img / 2 + 0.5 # unnormalize
# img = img + 115
# img = img[[2, 1, 0]]
# npimg = img.numpy().astype(np.int)
# plt.imshow(np.transpose(npimg, (1, 2, 0)))
# plt.show()
#
# import torchvision
# dataiter = iter(data_loader)
# images, target, _ = dataiter.next() #chwangteg target and pixel is hundreds
#
# imshow(torchvision.utils.make_grid(images.tensors))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Inference")
parser.add_argument(
"--config-file",
default=
"/private/home/fmassa/github/detectron.pytorch_v2/configs/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
distributed = num_gpus > 1
if distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
synchronize()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
save_dir = ""
logger = setup_logger("fcos_core", save_dir, get_rank())
logger.info("Using {} GPUs".format(num_gpus))
logger.info(cfg)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
iou_types = ("bbox", ) + ("segm", )
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm", )
if cfg.MODEL.KEYPOINT_ON:
iou_types = iou_types + ("keypoints", )
output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
for idx, dataset_name in enumerate(dataset_names):
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference",
dataset_name)
mkdir(output_folder)
output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed)
for output_folder, dataset_name, data_loader_val in zip(
output_folders, dataset_names, data_loaders_val):
inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=False if cfg.MODEL.FCOS_ON or cfg.MODEL.SIPMASK_ON
or cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
)
synchronize()
def create_model(cfg, device):
cfg = copy.deepcopy(cfg)
cfg.freeze()
model = build_detection_model(cfg)
model = model.to(device)
return model
def main():
parser = argparse.ArgumentParser(
description="PyTorch Object Detection Training")
parser.add_argument(
"--run-dir",
default="run/fcos_imprv_R_50_FPN_1x/Baseline_lr1en4_191209",
metavar="FILE",
help="path to config file",
type=str,
)
args = parser.parse_args()
# import pdb; pdb.set_trace()
target_dir = args.run_dir
dir_files = sorted(glob.glob(target_dir + '/*'))
assert (
target_dir + '/new_config.yml'
) in dir_files, "Error! No cfg file found! check if the dir is right."
cfg_file = target_dir + '/new_config.yml' if (
target_dir + '/new_config.yml') in dir_files else None
model_files = [
f for f in dir_files if f.endswith('00.pth') and 'model_' in f
]
tidyed_before = (target_dir + '/run_res_tidy') in dir_files
if tidyed_before:
import pdb
pdb.set_trace()
pass
else:
os.makedirs(target_dir + '/run_res_tidy')
cfg.merge_from_file(cfg_file)
cfg.freeze()
logger = setup_logger("fcos_core",
target_dir + '/run_res_tidy',
0,
filename="test_log.txt")
logger.info(cfg)
# test_str = ''
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
checkpointer = DetectronCheckpointer(cfg,
model,
save_dir=target_dir +
'/run_res_tidy/')
iou_types = ("bbox", )
if cfg.MODEL.MASK_ON:
iou_types = iou_types + ("segm", )
if cfg.MODEL.KEYPOINT_ON:
iou_types = iou_types + ("keypoints", )
# output_folders = [None] * len(cfg.DATASETS.TEST)
dataset_names = cfg.DATASETS.TEST
# if cfg.OUTPUT_DIR:
# for idx, dataset_name in enumerate(dataset_names):
# output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
# mkdir(output_folder)
# output_folders[idx] = output_folder
data_loaders_val = make_data_loader(cfg,
is_train=False,
is_distributed=False)
dataset_name = dataset_names[0]
data_loader_val = data_loaders_val[0]
for i, model_f in enumerate(model_files):
# import pdb; pdb.set_trace()
_ = checkpointer.load(model_f)
output_folder = target_dir + '/run_res_tidy/' + dataset_name + '_' + (
model_f.split('/')[-1][:-4])
os.makedirs(output_folder)
logger.info('Processing {}/{}: {}'.format(i, len(model_f),
output_folder))
# print('Processing {}/{}: {}'.format(i, len(model_f), output_folder))
inference_result = inference(
model,
data_loader_val,
dataset_name=dataset_name,
iou_types=iou_types,
box_only=False if cfg.MODEL.FCOS_ON or cfg.MODEL.RETINANET_ON else
cfg.MODEL.RPN_ONLY,
device=cfg.MODEL.DEVICE,
expected_results=cfg.TEST.EXPECTED_RESULTS,
expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
output_folder=output_folder,
)
summaryStrs = get_neat_inference_result(inference_result[2][0])
# test_str += '\n'+ output_folder.split('/')[-1]+ \
# '\n'.join(summaryStrs)
logger.info(output_folder.split('/')[-1])
logger.info('\n'.join(summaryStrs))
def main():
parser = argparse.ArgumentParser(
description="Export model to the onnx format")
parser.add_argument(
"--config-file",
default="configs/fcos/fcos_imprv_R_50_FPN_1x.yaml",
metavar="FILE",
help="path to config file",
)
parser.add_argument(
"--output",
default="fcos.onnx",
metavar="FILE",
help="path to the output onnx file",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
assert cfg.MODEL.FCOS_ON, "This script is only tested for the detector FCOS."
save_dir = ""
logger = setup_logger("fcos_core", save_dir, get_rank())
logger.info(cfg)
logger.info("Collecting env info (might take some time)")
logger.info("\n" + collect_env_info())
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
output_dir = cfg.OUTPUT_DIR
checkpointer = DetectronCheckpointer(cfg, model, save_dir=output_dir)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
onnx_model = torch.nn.Sequential(
OrderedDict([
('backbone', model.backbone),
('heads', model.rpn.head),
]))
input_names = ["input_image"]
dummy_input = torch.zeros((1, 3, 800, 1216)).to(cfg.MODEL.DEVICE)
output_names = []
for l in range(len(cfg.MODEL.FCOS.FPN_STRIDES)):
fpn_name = "P{}/".format(3 + l)
output_names.extend([
fpn_name + "logits", fpn_name + "bbox_reg", fpn_name + "centerness"
])
torch.onnx.export(onnx_model,
dummy_input,
args.output,
verbose=True,
input_names=input_names,
output_names=output_names,
keep_initializers_as_inputs=True)
logger.info("Done. The onnx model is saved into {}.".format(args.output))
def run_accCal(model_path,
test_base_path,
save_base_path,
labels_dict,
config_file,
input_size=640,
confidence_thresholds=(0.3, )):
save_res_path = os.path.join(save_base_path, 'all')
if os.path.exists(save_res_path):
shutil.rmtree(save_res_path)
os.mkdir(save_res_path)
save_recall_path = os.path.join(save_base_path, 'recall')
if os.path.exists(save_recall_path):
shutil.rmtree(save_recall_path)
os.mkdir(save_recall_path)
save_ero_path = os.path.join(save_base_path, 'ero')
if os.path.exists(save_ero_path):
shutil.rmtree(save_ero_path)
os.mkdir(save_ero_path)
save_ori_path = os.path.join(save_base_path, 'ori')
if os.path.exists(save_ori_path):
shutil.rmtree(save_ori_path)
os.mkdir(save_ori_path)
test_img_path = os.path.join(test_base_path, 'VOC2007/JPEGImages')
test_ano_path = os.path.join(test_base_path, 'VOC2007/Annotations')
img_list = glob.glob(test_img_path + '/*.jpg')
cfg.merge_from_file(config_file)
cfg.MODEL.WEIGHT = model_path
cfg.TEST.IMS_PER_BATCH = 1 # only test single image
cfg.freeze()
dbg_cfg = cfg
model = build_detection_model(cfg)
model.to(cfg.MODEL.DEVICE)
checkpointer = DetectronCheckpointer(cfg, model, save_dir=cfg.OUTPUT_DIR)
checkpointer.load(cfg.MODEL.WEIGHT)
model.eval()
normalize_transform = T.Normalize(
mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD
)
transform = T.Compose(
[
T.ToPILImage(),
T.Resize(input_size),
T.ToTensor(),
T.Lambda(lambda x: x * 255),
normalize_transform,
]
)
sad_accuracy = [0] * len(confidence_thresholds)
sad_precision = [0] * len(confidence_thresholds)
sad_recall = [0] * len(confidence_thresholds)
spend_time = []
for idx, img_name in enumerate(img_list):
progress(int(idx/len(img_list) * 100))
base_img_name = os.path.split(img_name)[-1]
frame = cv2.imread(img_name)
ori_frame = copy.deepcopy(frame)
h, w = frame.shape[:2]
image = transform(frame)
image_list = to_image_list(image, cfg.DATALOADER.SIZE_DIVISIBILITY)
image_list = image_list.to(cfg.MODEL.DEVICE)
start_time = time.time()
with torch.no_grad():
predictions = model(image_list)
prediction = predictions[0].to("cpu")
end_time = time.time()
spend_time.append(end_time - start_time)
prediction = prediction.resize((w, h)).convert("xyxy")
# scores = prediction.get_field("scores")
# keep = torch.nonzero(scores > confidence_threshold).squeeze(1)
# prediction = prediction[keep]
scores = prediction.get_field("scores")
_, idx = scores.sort(0, descending=True)
prediction = prediction[idx]
scores = prediction.get_field("scores").numpy()
labels = prediction.get_field("labels").numpy()
bboxes = prediction.bbox.numpy().astype(np.int32)
bboxes_area = (bboxes[:, 2] - bboxes[:, 0]) * (bboxes[:, 3] - bboxes[:, 1])
for ii, confidence_threshold in enumerate(confidence_thresholds):
_keep = np.where((scores > confidence_threshold) & (bboxes_area > 0), True, False)
_scores = scores[_keep].tolist()
_labels = labels[_keep].tolist()
_bboxes = bboxes[_keep].tolist()
_labels, _bboxes, _scores = soft_nms(_labels, _bboxes, _scores, confidence_threshold)
if ii == 0:
for i, b in enumerate(_bboxes):
# save all
frame = cv2.rectangle(frame,
(b[0], b[1]), (b[2], b[3]),
(100, 220, 200), 2)
frame = cv2.putText(frame,
str(_labels[i]) + '-' + str(int(_scores[i] * 100)),
(b[0], b[1]), 1, 1,
(0, 0, 255), 1)
# cv2.imwrite(os.path.join(save_res_path, base_img_name), frame)
boxes_list_tmp = copy.deepcopy(_bboxes)
classes_list_tmp = copy.deepcopy(_labels)
score_list_tmp = copy.deepcopy(_scores)
fg_cnt = 0
recall_flag = False
xml_name = base_img_name[:-4] + '.xml'
anno_path = os.path.join(test_ano_path, xml_name)
tree = ET.parse(anno_path)
root = tree.getroot()
rc_box = []
for siz in root.findall('size'):
width_ = siz.find('width').text
height_ = siz.find('height').text
if not int(width_) or not int(height_):
width_ = w
height_ = h
for obj in root.findall('object'):
name = obj.find('name').text
# class_tmp = get_cls(name, labels_dict)
for bndbox in obj.findall('bndbox'):
xmin = bndbox.find('xmin').text
ymin = bndbox.find('ymin').text
xmax = bndbox.find('xmax').text
ymax = bndbox.find('ymax').text
tmp_bbox = [int(int(xmin) * w / int(width_)),
int(int(ymin) * h / int(height_)),
int(int(xmax) * w / int(width_)),
int(int(ymax) * h / int(height_))]
map_flag = False
for bbox_idx in range(len(boxes_list_tmp)):
min_area, box_s, min_flag, iou_score = \
get_iou(tmp_bbox, boxes_list_tmp[bbox_idx])
if iou_score > 0.3:
map_flag = True
del classes_list_tmp[bbox_idx]
del boxes_list_tmp[bbox_idx]
del score_list_tmp[bbox_idx]
break
# 如果没找到匹配，属于漏检，算到召回率/检出率中
if not map_flag:
recall_flag = True
rc_box.append(tmp_bbox)
fg_cnt = fg_cnt + 1
if recall_flag:
sad_recall[ii] += 1
if ii == 0:
for box_idx in range(len(rc_box)):
x1, y1, x2, y2 = rc_box[box_idx]
rca_frame = cv2.rectangle(frame,
(int(x1), int(y1)), (int(x2), int(y2)),
(255, 0, 0), 4)
cv2.imwrite(os.path.join(save_recall_path, base_img_name), rca_frame)
shutil.copy(img_name, os.path.join(save_ori_path, base_img_name))
shutil.copy(anno_path, os.path.join(save_ori_path, xml_name))
# print("sad_recall: " + str(sad_recall))
# 如果有多出来的，属于误检，ground_truth中没有这个框，算到准确率中
if len(classes_list_tmp) > 0:
sad_precision[ii] += 1
if ii == 0:
for box_idx in range(len(boxes_list_tmp)):
x1, y1, x2, y2 = boxes_list_tmp[box_idx]
ero_frame = cv2.rectangle(frame,
(int(x1), int(y1)), (int(x2), int(y2)),
(0, 0, 255), 4)
err_rect_name = base_img_name[:-4] + '_' + str(box_idx) + '.jpg'
cv2.imwrite(os.path.join(save_ero_path, err_rect_name),
ori_frame[y1: y2, x1: x2, :])
cv2.imwrite(os.path.join(save_ero_path, base_img_name), ero_frame)
shutil.copy(img_name, os.path.join(save_ori_path, base_img_name))
shutil.copy(anno_path, os.path.join(save_ori_path, xml_name))
if not recall_flag and len(classes_list_tmp) == 0:
sad_accuracy[ii] += 1
# print("cur sad: " + str(sad))
# print("fg_cnt: " + str(fg_cnt))
# print("pred_cnt: " + str(len(classes_list_tmp)))
# 单图所有框都检测正确才正确率，少一个框算漏检，多一个框算误检，不看mAP
print('\nfps is : ', 1 / np.average(spend_time))
for ii, confidence_threshold in enumerate(confidence_thresholds):
print("confidence th is : {}".format(confidence_threshold))
accuracy = float(sad_accuracy[ii] / len(img_list))
print("accuracy is : {}".format(accuracy))
precision = 1 - float(sad_precision[ii] / len(img_list))
print("precision is : {}".format(precision))
recall = 1 - float(sad_recall[ii] / len(img_list))
print("recall is : {}\n".format(recall))
def train(cfg, local_rank, distributed, iter_clear, ignore_head):
model = build_detection_model(cfg)
# model, conversion_count = convert_to_shift_dbg(
# model,
# cfg.DEEPSHIFT_DEPTH,
# cfg.DEEPSHIFT_TYPE,
# convert_weights=True,
# use_kernel=cfg.DEEPSHIFT_USEKERNEL,
# rounding=cfg.DEEPSHIFT_ROUNDING,
# shift_range=cfg.DEEPSHIFT_RANGE)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
if iter_clear:
load_opt = False
load_sch = False
else:
load_opt = True
load_sch = True
if ignore_head:
load_body = True
load_fpn = True
load_head = False
else:
load_body = True
load_fpn = True
load_head = True
# 预加载模型或者是通常的模型，或者是deepshift模型
if cfg.MODEL.WEIGHT:
checkpointer = DetectronCheckpointer(
cfg, model, None, None, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT, load_opt=False, load_sch=False,
load_body=load_body, load_fpn=load_fpn, load_head=load_head)
model, conversion_count = convert_to_shift(
model,
cfg.DEEPSHIFT_DEPTH,
cfg.DEEPSHIFT_TYPE,
convert_weights=True,
use_kernel=cfg.DEEPSHIFT_USEKERNEL,
rounding=cfg.DEEPSHIFT_ROUNDING,
shift_range=cfg.DEEPSHIFT_RANGE)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
else:
model, conversion_count = convert_to_shift(
model,
cfg.DEEPSHIFT_DEPTH,
cfg.DEEPSHIFT_TYPE,
convert_weights=True,
use_kernel=cfg.DEEPSHIFT_USEKERNEL,
rounding=cfg.DEEPSHIFT_ROUNDING,
shift_range=cfg.DEEPSHIFT_RANGE)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT, load_opt=False, load_sch=False,
load_body=load_body, load_fpn=load_fpn, load_head=load_head)
conv2d_layers_count = count_layer_type(model, torch.nn.Conv2d)
linear_layers_count = count_layer_type(model, torch.nn.Linear)
print("###### conversion_count: {}, not convert conv2d layer: {}, linear layer: {}".format(
conversion_count, conv2d_layers_count, linear_layers_count))
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
arguments.update(extra_checkpoint_data)
if iter_clear:
arguments["iteration"] = 0
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
model = round_shift_weights(model)
torch.save({"model": model.state_dict()}, os.path.join(output_dir, "model_final_round.pth"))
return model
