def really_commands(mode): modules = utils.get_classes('workflow.{0}'.format(mode)) for module in modules: # get RCE if you can edit general file in workflow folder :) module_name = module[0].strip() module_object = eval('{0}.{1}'.format(mode, module_name)) # parsing commands try: routines = module_object.commands except: continue for routine, commands in routines.items(): for command in commands: item = command item['mode'] = mode item['speed'] = routine item['module'] = module_name item['alias'] = module_name + "__" + routine.lower() + "__" + \ str(item.get('banner')).lower() Commands.objects.create(**item) reports = module_object.reports parse_report(reports, module_name, mode)
# File name : nms_demo.py # Author : YunYang1994 # Created date: 2018-11-27 13:02:17 # Description : # #================================================================ import time import numpy as np import tensorflow as tf from PIL import Image from core import utils SIZE = [416, 416] # SIZE = [608, 608] classes = utils.get_classes('./data/coco.names') num_classes = len(classes) img = Image.open('./data/demo_data/611.jpg') img_resized = np.array(img.resize(size=tuple(SIZE)), dtype=np.float32) img_resized = img_resized / 255. cpu_nms_graph, gpu_nms_graph = tf.Graph(), tf.Graph() # nms on GPU input_tensor, output_tensors = utils.read_pb_return_tensors( gpu_nms_graph, "./checkpoint/yolov3_gpu_nms.pb", ["Placeholder:0", "concat_10:0", "concat_11:0", "concat_12:0"]) with tf.Session(graph=gpu_nms_graph) as sess: for i in range(5): start = time.time() boxes, scores, labels = sess.run( output_tensors,
config.gpu_options.allocator_type = 'BFC' # A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc. config.gpu_options.per_process_gpu_memory_fraction = 0.8 config.gpu_options.allow_growth = True set_session(tf.Session(config=config)) if __name__ == "__main__": # 训练后的模型保存路径 log_dir = os.path.join(cfg.PATH.logs, 'v4') # 权值文件 weights_path = cfg.PATH.weight_path # 输入的shape大小 input_shape = cfg.TRAIN.input_size # 是否对损失进行归一化 normalize = True # 获取classes和anchor class_names = get_classes(cfg.PATH.classes_info) anchors = get_anchors(cfg.PATH.anchors_info) # 一共有多少类和多少先验框 num_classes = len(class_names) num_anchors = len(anchors) #------------------------------------------------------# # Yolov4的tricks应用 # mosaic 马赛克数据增强 True or False # 实际测试时mosaic数据增强并不稳定,所以默认为False # Cosine_scheduler 余弦退火学习率 True or False # label_smoothing 标签平滑 0.01以下一般 如0.01、0.005 #------------------------------------------------------# mosaic = False Cosine_scheduler = False label_smoothing = 0.005