Dubhe/dubhe_data_process/algorithm/image-classification/of_cnn_resnet.py

# !/usr/bin/env python
# -*- coding:utf-8 -*-

"""
Copyright 2020 Tianshu AI Platform. 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 __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import codecs
import os
import numpy as np
from PIL import Image
import oneflow as flow
from resnet_model import resnet50

sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach())
current_dir = os.path.dirname(os.path.abspath(__file__))

def init_resnet():
    """Initialize ResNet with pretrained weights"""
    model_load_dir = current_dir + os.sep + "model" + os.sep + 'resnet_v15_of_best_model_val_top1_773/'
    assert os.path.isdir(model_load_dir)
    check_point = flow.train.CheckPoint()
    check_point.load(model_load_dir)


def load_image(image_path):
    """Load and preprocess the image"""
    rgb_mean = [123.68, 116.779, 103.939]
    rgb_std = [58.393, 57.12, 57.375]
    im = Image.open(image_path).convert('RGB')
    im = im.resize((224, 224))
    im = np.array(im).astype('float32')
    im = (im - rgb_mean) / rgb_std
    im = np.transpose(im, (2, 0, 1))
    im = np.expand_dims(im, axis=0)
    return np.ascontiguousarray(im, 'float32')


@flow.global_function(flow.function_config())
def InferenceNet(images=flow.FixedTensorDef(
        (1, 3, 224, 224), dtype=flow.float)):
    """Run the inference of ResNet"""
    logits = resnet50(images, training=False)
    predictions = flow.nn.softmax(logits)
    return predictions


def resnet_inf(image_path):
    """The whole procedure of inference of ResNet and return the category_id and the corresponding score"""
    image = load_image(image_path.encode('utf-8'))
    predictions = InferenceNet(image).get()
    clsidx = predictions.ndarray().argmax()
    return predictions.ndarray().max(), clsidx