def eval(args):
# parse config
config = parse_config(args.config)
val_config = merge_configs(config, 'valid', vars(args))
print_configs(val_config, "Valid")
with fluid.dygraph.guard():
val_model = ResNet3D.Resnet('ResNet3D',[2,2,2,2], ResNet3D.get_inplanes())
label_dic = np.load('label_dir.npy', allow_pickle=True).item()
label_dic = {v: k for k, v in label_dic.items()}
# get infer reader
val_reader = KineticsReader(args.model_name.upper(), 'valid', val_config).create_reader()
# if no weight files specified, exit()
if args.weights:
weights = args.weights
else:
print("model path must be specified")
exit()
para_state_dict, _ = fluid.load_dygraph(weights)
val_model.load_dict(para_state_dict)
val_model.eval()
acc_list = []
for batch_id, data in enumerate(val_reader()):
dy_x_data = np.array([x[0] for x in data]).astype('float32')
y_data = np.array([[x[1]] for x in data]).astype('int64')
img = fluid.dygraph.to_variable(dy_x_data)
label = fluid.dygraph.to_variable(y_data)
label.stop_gradient = True
out, acc = val_model(img, label)
acc_list.append(acc.numpy()[0])
print("验证集准确率为:{}".format(np.mean(acc_list)))
def infer(args):
# parse config
config = parse_config(args.config)
infer_config = merge_configs(config, 'infer', vars(args))
print_configs(infer_config, "Infer")
with fluid.dygraph.guard():
infer_model = ResNet3D.Resnet('ResNet3D', [2, 2, 2, 2],
ResNet3D.get_inplanes())
label_dic = np.load('label_dir.npy', allow_pickle=True).item()
label_dic = {v: k for k, v in label_dic.items()}
# get infer reader
infer_reader = KineticsReader(args.model_name.upper(), 'infer',
infer_config).create_reader()
# if no weight files specified, exit()
if args.weights:
weights = args.weights
else:
print("model path must be specified")
exit()
para_state_dict, _ = fluid.load_dygraph(weights)
infer_model.load_dict(para_state_dict)
infer_model.eval()
for batch_id, data in enumerate(infer_reader()):
dy_x_data = np.array([x[0] for x in data]).astype('float32')
y_data = [x[1] for x in data]
img = fluid.dygraph.to_variable(dy_x_data)
out = infer_model(img).numpy()[0]
label_id = np.where(out == np.max(out))
print("实际标签{}, 预测结果{}".format(y_data, label_dic[label_id[0][0]]))