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eval_coco.py
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eval_coco.py
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import utils.gpu as gpu
from model.build_model import Build_Model
from eval.evaluator import Evaluator
import argparse
import time
import logging
import config.yolov4_config as cfg
from utils.visualize import *
from utils.torch_utils import *
from utils.log import Logger
import cv2
from eval.cocoapi_evaluator import COCOAPIEvaluator
class Evaluation(object):
def __init__(self, gpu_id=0, weight_path=None, visiual=None, heatmap=False):
self.__num_class = cfg.COCO_DATA["NUM"]
self.__conf_threshold = cfg.VAL["CONF_THRESH"]
self.__nms_threshold = cfg.VAL["NMS_THRESH"]
self.__device = gpu.select_device(gpu_id)
self.__multi_scale_val = cfg.VAL["MULTI_SCALE_VAL"]
self.__flip_val = cfg.VAL["FLIP_VAL"]
self.__visiual = visiual
self.__eval = eval
self.__classes = cfg.COCO_DATA["CLASSES"]
self.__model = Build_Model().to(self.__device)
self.__load_model_weights(weight_path)
self.__evalter = Evaluator(self.__model, showatt=heatmap)
def __load_model_weights(self, weight_path):
print("loading weight file from : {}".format(weight_path))
weight = os.path.join(weight_path)
chkpt = torch.load(weight, map_location=self.__device)
self.__model.load_state_dict(chkpt)
print("loading weight file is done")
del chkpt
def reset(self):
path1 = os.path.join(cfg.DETECTION_PATH, "detection_result/")
path2 = os.path.join(cfg.DETECTION_PATH, "ShowAtt/")
for i in os.listdir(path1):
path_file = os.path.join(path1, i)
if os.path.isfile(path_file):
os.remove(path_file)
else:
for f in os.listdir(path_file):
path_file2 = os.path.join(path_file, f)
if os.path.isfile(path_file2):
os.remove(path_file2)
for i in os.listdir(path2):
path_file = os.path.join(path2, i)
if os.path.isfile(path_file):
os.remove(path_file)
else:
for f in os.listdir(path_file):
path_file2 = os.path.join(path_file, f)
if os.path.isfile(path_file2):
os.remove(path_file2)
def study(self):
# Parameter study
y = []
for i in [0.08, 0.07, 0.06]:
t = time.time()
evaluator = COCOAPIEvaluator(
model_type="YOLOv3",
data_dir=cfg.DATA_PATH,
img_size=cfg.VAL["TEST_IMG_SIZE"],
confthre=i,
nmsthre=cfg.VAL["NMS_THRESH"],
)
_, r = evaluator.evaluate(self.__model)
y.append(
str(i)
+ str(" ")
+ str(r)
+ str(" ")
+ str(
time.time() - t,
)
)
np.savetxt("study.txt", y, fmt="%s") # y = np.loadtxt('study.txt')
def val(self):
global logger
logger.info("***********Start Evaluation****************")
start = time.time()
evaluator = COCOAPIEvaluator(
model_type="YOLOv4",
data_dir=cfg.DATA_PATH,
img_size=cfg.VAL["TEST_IMG_SIZE"],
confthre=cfg.VAL["CONF_THRESH"],
nmsthre=cfg.VAL["NMS_THRESH"],
)
ap50_95, ap50 = evaluator.evaluate(self.__model)
logger.info("ap50_95:{}|ap50:{}".format(ap50_95, ap50))
end = time.time()
logger.info(" ===val cost time:{:.4f}s".format(end - start))
def Inference(self):
global logger
# clear cache
self.reset()
logger.info("***********Start Inference****************")
imgs = os.listdir(self.__visiual)
logger.info("images path: {}".format(self.__visiual))
path = os.path.join(cfg.DETECTION_PATH, "detection_result")
logger.info("saved images at: {}".format(path))
inference_times = []
for v in imgs:
start_time = time.time()
path = os.path.join(self.__visiual, v)
img = cv2.imread(path)
assert img is not None
bboxes_prd = self.__evalter.get_bbox(img, v)
if bboxes_prd.shape[0] != 0:
boxes = bboxes_prd[..., :4]
class_inds = bboxes_prd[..., 5].astype(np.int32)
scores = bboxes_prd[..., 4]
visualize_boxes(
image=img,
boxes=boxes,
labels=class_inds,
probs=scores,
class_labels=self.__classes,
)
path = os.path.join(
cfg.DETECTION_PATH, "detection_result/{}".format(v)
)
cv2.imwrite(path, img)
end_time = time.time()
inference_times.append(end_time - start_time)
inference_time = sum(inference_times) / len(inference_times)
fps = 1.0 / inference_time
logging.info(
"Inference_Time: {:.5f} s/image, FPS: {}".format(
inference_time, fps
)
)
if __name__ == "__main__":
global logger
parser = argparse.ArgumentParser()
parser.add_argument(
"--weight_path",
type=str,
default="weight/best.pt",
help="weight file path",
)
parser.add_argument(
"--log_val_path", type=str, default="log_val", help="weight file path"
)
parser.add_argument(
"--gpu_id",
type=int,
default=-1,
help="whither use GPU(eg:0,1,2,3,4,5,6,7,8) or CPU(-1)",
)
parser.add_argument(
"--visiual", type=str, default="test_pic", help="val data path or None"
)
parser.add_argument(
"--mode", type=str, default="val", help="val or det or study"
)
parser.add_argument(
"--heatmap", type=str, default=False, help="whither show attention map"
)
opt = parser.parse_args()
logger = Logger(
log_file_name=opt.log_val_path + "/log_coco_val.txt",
log_level=logging.DEBUG,
logger_name="YOLOv4",
).get_log()
if opt.mode == "val":
Evaluation(
gpu_id=opt.gpu_id,
weight_path=opt.weight_path,
visiual=opt.visiual,
heatmap=opt.heatmap,
).val()
if opt.mode == "det":
Evaluation(
gpu_id=opt.gpu_id,
weight_path=opt.weight_path,
visiual=opt.visiual,
heatmap=opt.heatmap,
).Inference()
else:
Evaluation(
gpu_id=opt.gpu_id,
weight_path=opt.weight_path,
visiual=opt.visiual,
heatmap=opt.heatmap,
).study()