# Tencent is pleased to support the open source community by making GNES available.
#
# Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
import numpy as np
from ..base import BaseImageEncoder
from ...helper import batching
[docs]class BaseONNXImageEncoder(BaseImageEncoder):
batch_size = 64
def __init__(self, model_name: str,
model_dir: str,
*args, **kwargs):
super().__init__(*args, **kwargs)
self.model_dir = model_dir
self.model_name = model_name
[docs] def post_init(self):
import onnxruntime as ort
self.sess = ort.InferenceSession(self.model_dir + '/' + self.model_name)
inputs_info = self.sess.get_inputs()
if len(inputs_info) != 1:
raise ValueError('Now only support encoder with one input')
else:
self.input_name = inputs_info[0].name
self.input_shape = inputs_info[0].shape
self.input_type = inputs_info[0].type
self.batch_size = self.input_shape[0]
[docs] @batching
def encode(self, img: List['np.ndarray'], *args, **kwargs) -> np.ndarray:
pad_batch = 0
if len(img) != self.input_shape[0]:
pad_batch = self.input_shape[0] - len(img)
for _ in range(pad_batch):
img.append(np.zeros_like(img[0]))
img_ = np.array(img, dtype=np.float32).transpose(0, 3, 1, 2)
if list(img_.shape) != self.input_shape:
raise ValueError('Map size not match net, expect', self.input_shape, ',got', img_.shape)
result_npy = self.sess.run(None, {self.input_name: img_})
if pad_batch != 0:
return result_npy[0][0:len(img)]
else:
return result_npy[0]