all_classes = get_classes(classes_path)
num_classes = len(all_classes)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
# 多尺度训练
inputs = P.data(name='input_1',
shape=[-1, 3, -1, -1],
append_batch_size=False,
dtype='float32')
output_l, output_m, output_s = YOLOv4(inputs,
num_classes,
num_anchors,
is_test=False,
trainable=True)
eval_fetch_list = [output_l, output_m, output_s]
eval_prog = eval_prog.clone(for_test=True)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
fluid.load(eval_prog, model_path, executor=exe)
_decode = Decode(conf_thresh, nms_thresh, input_shape, exe, eval_prog,
all_classes)
test_dev(_decode, eval_fetch_list, images, test_pre_path, test_batch_size,
draw_image)
# input_shape = (320, 320)
# input_shape = (416, 416)
input_shape = (608, 608)
# 测试时的分数阈值和nms_iou阈值
conf_thresh = 0.001
nms_thresh = 0.45
# 是否画出test集图片
draw_image = False
# 测试时的批大小
test_batch_size = 4
# test集图片的相对路径
test_pre_path = '../COCO/test2017/'
anno_file = '../COCO/annotations/image_info_test-dev2017.json'
with open(anno_file, 'r', encoding='utf-8') as f2:
for line in f2:
line = line.strip()
dataset = json.loads(line)
images = dataset['images']
num_anchors = 3
all_classes = get_classes(classes_path)
num_classes = len(all_classes)
inputs = layers.Input(shape=(None, None, 3))
yolo = YOLOv4(inputs, num_classes, num_anchors)
yolo.load_weights(model_path, by_name=True)
_decode = Decode(conf_thresh, nms_thresh, input_shape, yolo, all_classes)
test_dev(_decode, images, test_pre_path, test_batch_size, draw_image)