def main(batch_size, data_shape): # parser = argparse.ArgumentParser(description='predict an image on imagenet') # parser.add_argument('--batch-size', type=int, default=1, # help='the batch size') # parser.add_argument('--data-shape', type=int, default=299, # help='set image\'s shape') # args = parser.parse_args() # data_shape = (3, args.data_shape, args.data_shape) img_data_shape = (3, data_shape, data_shape) #图片格式转换 if not os.path.exists(test_prefix_path + '.rec'): call_im2rec.convert(test_prefix_path, test_img_root_path) test = mx.io.ImageRecordIter( path_imgrec=test_prefix_path + '.rec', rand_crop=False, rand_mirror=False, data_shape=img_data_shape, batch_size=batch_size, ) # predict pro, data, label = model_load.predict(test, return_data=True) #result process pro_list = pro.tolist() right_label = [int(x) for x in label.tolist()] label_dict = get_label(train_prefix_path) right_label_name = [] for index in right_label: right_label_name.append(label_dict[index]) predict_label = [each_pro.index(max(each_pro)) for each_pro in pro_list] predict_label_name = [] for index in predict_label: predict_label_name.append(label_dict[index]) return pro_list, right_label, right_label_name, predict_label, predict_label_name, label_dict
def main(batch_size, data_shape): # parser = argparse.ArgumentParser(description='predict an image on imagenet') # parser.add_argument('--batch-size', type=int, default=1, # help='the batch size') # parser.add_argument('--data-shape', type=int, default=299, # help='set image\'s shape') # args = parser.parse_args() # data_shape = (3, args.data_shape, args.data_shape) img_data_shape = (3, data_shape, data_shape) #图片格式转换 if not os.path.exists(test_prefix_path + '.rec'): call_im2rec.convert(test_prefix_path, test_img_root_path) test = mx.io.ImageRecordIter( path_imgrec = test_prefix_path + '.rec', rand_crop = False, rand_mirror = False, data_shape = img_data_shape, batch_size = batch_size, ) # predict pro, data, label = model_load.predict(test, return_data=True) #result process pro_list = pro.tolist() right_label = [int(x) for x in label.tolist()] label_dict = get_label(train_prefix_path) right_label_name = [] for index in right_label: right_label_name.append(label_dict[index]) predict_label = [each_pro.index(max(each_pro)) for each_pro in pro_list] predict_label_name = [] for index in predict_label: predict_label_name.append(label_dict[index]) return pro_list, right_label, right_label_name, predict_label, predict_label_name, label_dict
help='directory of the log file') parser.add_argument('--train-dataset', type=str, default="train.rec", help='train dataset name') parser.add_argument('--val-dataset', type=str, default="val.rec", help="validation dataset name") parser.add_argument('--data-shape', type=int, default=299, help='set image\'s shape') args = parser.parse_args() # network import importlib net = importlib.import_module('symbol_' + args.network).get_symbol(args.num_classes) # data if not os.path.exists(train_prefix_path + '.rec'): call_im2rec.convert(train_prefix_path, train_img_root_path) if not os.path.exists(val_prefix_path + '.rec'): call_im2rec.convert(val_prefix_path, val_img_root_path) def get_iterator(args, kv): data_shape = (3, args.data_shape, args.data_shape) train = mx.io.ImageRecordIter( path_imgrec = train_prefix_path + '.rec', data_shape = data_shape, batch_size = args.batch_size, rand_crop = True, rand_mirror = True, ) val = mx.io.ImageRecordIter(
default="val.rec", help="validation dataset name") parser.add_argument('--data-shape', type=int, default=299, help='set image\'s shape') args = parser.parse_args() # network import importlib net = importlib.import_module('symbol_' + args.network).get_symbol( args.num_classes) # data if not os.path.exists(train_prefix_path + '.rec'): call_im2rec.convert(train_prefix_path, train_img_root_path) if not os.path.exists(val_prefix_path + '.rec'): call_im2rec.convert(val_prefix_path, val_img_root_path) def get_iterator(args, kv): data_shape = (3, args.data_shape, args.data_shape) train = mx.io.ImageRecordIter( path_imgrec=train_prefix_path + '.rec', data_shape=data_shape, batch_size=args.batch_size, rand_crop=True, rand_mirror=True, ) val = mx.io.ImageRecordIter(