本文整理汇总了Python中core.loader.TestDataLoader方法的典型用法代码示例。如果您正苦于以下问题：Python loader.TestDataLoader方法的具体用法？Python loader.TestDataLoader怎么用？Python loader.TestDataLoader使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类core.loader的用法示例。
在下文中一共展示了loader.TestDataLoader方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_deeplab
# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestDataLoader [as 别名]
def test_deeplab(network, dataset, image_set, root_path, dataset_path,
ctx, prefix, epoch,
vis, logger=None, output_path=None):
if not logger:
assert False, 'require a logger'
# print config
pprint.pprint(config)
logger.info('testing config:{}\n'.format(pprint.pformat(config)))
# load symbol and testing data
sym = eval('get_' + network + '_test')(num_classes=config.dataset.NUM_CLASSES)
imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=output_path)
segdb = imdb.gt_segdb()
# get test data iter
test_data = TestDataLoader(segdb, batch_size=len(ctx))
# load model
# arg_params, aux_params = load_param(prefix, epoch, convert=True, ctx=ctx, process=True)
arg_params, aux_params = load_param(prefix, epoch, process=True)
# infer shape
data_shape_dict = dict(test_data.provide_data_single)
arg_shape, _, aux_shape = sym.infer_shape(**data_shape_dict)
arg_shape_dict = dict(zip(sym.list_arguments(), arg_shape))
aux_shape_dict = dict(zip(sym.list_auxiliary_states(), aux_shape))
# check parameters
for k in sym.list_arguments():
if k in data_shape_dict or k in ['softmax_label']:
continue
assert k in arg_params, k + ' not initialized'
assert arg_params[k].shape == arg_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(arg_shape_dict[k]) + ' provided ' + str(arg_params[k].shape)
for k in sym.list_auxiliary_states():
assert k in aux_params, k + ' not initialized'
assert aux_params[k].shape == aux_shape_dict[k], \
'shape inconsistent for ' + k + ' inferred ' + str(aux_shape_dict[k]) + ' provided ' + str(aux_params[k].shape)
# decide maximum shape
data_names = [k[0] for k in test_data.provide_data_single]
label_names = ['softmax_label']
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
# create predictor
predictor = Predictor(sym, data_names, label_names,
context=ctx, max_data_shapes=max_data_shape,
provide_data=test_data.provide_data, provide_label=test_data.provide_label,
arg_params=arg_params, aux_params=aux_params)
# start detection
pred_eval(predictor, test_data, imdb, vis=vis, logger=logger) 示例2: test_deeplab
# 需要导入模块: from core import loader [as 别名]
# 或者: from core.loader import TestDataLoader [as 别名]
def test_deeplab():
epoch = config.TEST.test_epoch
ctx = [mx.gpu(int(i)) for i in config.gpus.split(',')]
image_set = config.dataset.test_image_set
root_path = config.dataset.root_path
dataset = config.dataset.dataset
dataset_path = config.dataset.dataset_path
logger, final_output_path = create_logger(config.output_path, args.cfg, image_set)
prefix = os.path.join(final_output_path, '..', '_'.join([iset for iset in config.dataset.image_set.split('+')]), config.TRAIN.model_prefix)
# print config
pprint.pprint(config)
logger.info('testing config:{}\n'.format(pprint.pformat(config)))
# load symbol and testing data
sym_instance = eval(config.symbol + '.' + config.symbol)()
sym = sym_instance.get_symbol(config, is_train=False)
imdb = eval(dataset)(image_set, root_path, dataset_path, result_path=final_output_path)
segdb = imdb.gt_segdb()
# get test data iter
test_data = TestDataLoader(segdb, config=config, batch_size=len(ctx))
# infer shape
data_shape_dict = dict(test_data.provide_data_single)
sym_instance.infer_shape(data_shape_dict)
# load model and check parameters
arg_params, aux_params = load_param(prefix, epoch, process=True)
sym_instance.check_parameter_shapes(arg_params, aux_params, data_shape_dict, is_train=False)
# decide maximum shape
data_names = [k[0] for k in test_data.provide_data_single]
label_names = ['softmax_label']
max_data_shape = [[('data', (1, 3, max([v[0] for v in config.SCALES]), max([v[1] for v in config.SCALES])))]]
# create predictor
predictor = Predictor(sym, data_names, label_names,
context=ctx, max_data_shapes=max_data_shape,
provide_data=test_data.provide_data, provide_label=test_data.provide_label,
arg_params=arg_params, aux_params=aux_params)
# start detection
pred_eval(predictor, test_data, imdb, vis=args.vis, ignore_cache=args.ignore_cache, logger=logger)