Help Center> ModelArts> User Guide (Senior AI Engineers)> Model Management> Model Evaluation and Diagnosis> Sample Code for Model Evaluation
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Sample Code for Model Evaluation

Sample code is provided for common scenarios such as image classification, image semantic segmentation, and object detection. You can compile your evaluation code based on the sample code.

Sample Code for Models of the Image Classification Type

The training model corresponding to the following sample code is the built-in algorithm ResNet_v1_50 (TensorFlow engine).

  • model_url: model directory. After a model version is selected on the GUI, this parameter is automatically added in the background.
  • data_url: dataset directory. After a dataset version is selected on the GUI, this parameter is automatically added in the background.
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import json
import logging
import os
import sys
import tempfile

import h5py
import numpy as np
from PIL import Image

import moxing as mox
import tensorflow as tf
from deep_moxing.framework.manifest_api.manifest_api import get_sample_list
from deep_moxing.model_analysis.api import analyse, tmp_save
from deep_moxing.model_analysis.common.constant import TMP_FILE_NAME

logging.basicConfig(level=logging.DEBUG)

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('model_url', '', 'path to saved model')
tf.app.flags.DEFINE_string('data_url', '', 'path to output files')
tf.app.flags.DEFINE_string('adv_param_json',
                           '{"attack_method":"i-FGSM","eps":30, "iter_times":4}',
                           'params for attacks')
FLAGS(sys.argv, known_only=True)


def _preprocess(data_path):
    img = Image.open(data_path)
    img = img.convert('RGB')
    img = np.asarray(img, dtype=np.float32)
    img = img[np.newaxis, :, :, :]
    return img


def softmax(x):
    x = np.array(x)
    orig_shape = x.shape
    if len(x.shape) > 1:
        # Matrix
        x = np.apply_along_axis(lambda x: np.exp(x - np.max(x)), 1, x)
        denominator = np.apply_along_axis(lambda x: 1.0 / np.sum(x), 1, x)
        if len(denominator.shape) == 1:
            denominator = denominator.reshape((denominator.shape[0], 1))
        x = x * denominator
    else:
        # Vector
        x_max = np.max(x)
        x = x - x_max
        numerator = np.exp(x)
        denominator = 1.0 / np.sum(numerator)
        x = numerator.dot(denominator)
    assert x.shape == orig_shape
    return x


def get_dataset(data_path, label_map_dict):
    label_list = []
    img_name_list = []
    if 'manifest' in data_path:
        manifest, _ = get_sample_list(
            manifest_path=data_path, task_type='image_classification')
        for item in manifest:
            if len(item[1]) != 0:
                label_list.append(label_map_dict.get(item[1][0]))
                img_name_list.append(item[0])
            else:
                continue
    else:
        label_name_list = os.listdir(data_path)
        label_dict = {}
        for idx, item in enumerate(label_name_list):
            label_dict[str(idx)] = item
            sub_img_list = os.listdir(os.path.join(data_path, item))
            img_name_list += [
                os.path.join(data_path, item, img_name) for img_name in sub_img_list
            ]
            label_list += [label_map_dict.get(item)] * len(sub_img_list)
    return img_name_list, label_list


def deal_ckpt_and_data_with_obs():
    pb_dir = FLAGS.model_url
    data_path = FLAGS.data_url

    if pb_dir.startswith('obs://'):
        mox.file.copy_parallel(pb_dir, '/cache/ckpt/')
        pb_dir = '/cache/ckpt'
        print('------------- download success ------------')
    if data_path.startswith('obs://'):
        mox.file.copy_parallel(data_path, '/cache/data/')
        data_path = '/cache/data/'
        print('------------- download dataset success ------------')
    assert os.path.isdir(pb_dir), 'Error, pb_dir must be a directory'
    return pb_dir, data_path


def evalution():
    pb_dir, data_path = deal_ckpt_and_data_with_obs()
    adv_param_json = FLAGS.adv_param_json
    index_file = os.path.join(pb_dir, 'index')
    try:
        label_file = h5py.File(index_file, 'r')
        label_array = label_file['labels_list'][:].tolist()
        label_array = [item.decode('utf-8') for item in label_array]
    except Exception as e:
        logging.warning(e)
        logging.warning('index file is not a h5 file, try json.')
        with open(index_file, 'r') as load_f:
            label_file = json.load(load_f)
        label_array = label_file['labels_list'][:]
    label_map_dict = {}
    label_dict = {}
    for idx, item in enumerate(label_array):
        label_map_dict[item] = idx
        label_dict[idx] = item
    print(label_map_dict)
    print(label_dict)

    data_file_list, label_list = get_dataset(data_path, label_map_dict)

    assert len(label_list) > 0, 'missing valid data'
    assert None not in label_list, 'dataset and model not match'

    pred_list = []
    file_name_list = []
    img_list = []
    for img_path in data_file_list:
        img = _preprocess(img_path)
        img_list.append(img)
        file_name_list.append(img_path)
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.gpu_options.visible_device_list = '0'
    with tf.Session(graph=tf.Graph(), config=config) as sess:
        meta_graph_def = tf.saved_model.loader.load(
            sess, [tf.saved_model.tag_constants.SERVING], pb_dir)
        signature = meta_graph_def.signature_def
        signature_key = 'predict_object'
        input_key = 'images'
        output_key = 'logits'
        x_tensor_name = signature[signature_key].inputs[input_key].name
        y_tensor_name = signature[signature_key].outputs[output_key].name
        x = sess.graph.get_tensor_by_name(x_tensor_name)
        y = sess.graph.get_tensor_by_name(y_tensor_name)
        for img in img_list:
            pred_output = sess.run([y], {x: img})
            pred_output = softmax(pred_output[0])
            pred_list.append(pred_output[0].tolist())

    label_dict = json.dumps(label_dict)
    task_type = 'image_classification'

    # analyse
    res = analyse(
        task_type=task_type,
        pred_list=pred_list,
        label_list=label_list,
        name_list=file_name_list,
        label_map_dict=label_dict)


if __name__ == "__main__":
    evalution()

Sample Code for Models of the Image Semantic Segmentation Type

The following sample code corresponds to the D-LinkNet road segmentation model (using the TensorFlow engine).

  • model_url: model directory. After a model version is selected on the GUI, this parameter is automatically added in the background.
  • data_url: dataset directory. After a dataset version is selected on the GUI, this parameter is automatically added in the background.
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import glob
import json
import logging
import os
import sys

import numpy as np
from PIL import Image

import moxing as mox
import tensorflow as tf
from deep_moxing.model_analysis.api import analyse

logging.basicConfig(level=logging.DEBUG)

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('model_url', '', 'path to saved model')
tf.app.flags.DEFINE_string('data_url', '', 'path to data files')
FLAGS(sys.argv, known_only=True)


def _norm(img):
    mean = np.mean(img, axis=(0, 1), keepdims=True)
    std = np.std(img, axis=(0, 1), keepdims=True)
    img = (img - mean) / std
    return img


def _preprocess(data_path):
    img = Image.open(data_path)
    img = img.convert('RGB')
    img = np.asarray(img, dtype=np.float32)
    img = _norm(img)
    img = img[np.newaxis, :, :, :]
    return img


def evalution():
    pb_dir = FLAGS.model_url
    data_path = FLAGS.data_url

    if data_path.startswith('obs://'):
        mox.file.copy_parallel(data_path, '/cache/dataset')
        image_data_path = '/cache/dataset/eval_uint8'
        label_path = '/cache/dataset/eval_label'
    else:
        image_data_path = os.path.join(data_path, 'eval_uint8')
        label_path = os.path.join(data_path, 'eval_label')
    if pb_dir.startswith('obs://'):
        mox.file.copy_parallel(pb_dir, '/cache/model')
        pb_dir = '/cache/model'

    label_dict = {'0': 'background', '1': 'road'}

    pred_list = []
    file_name_list = []
    img_list = []

    label_list = []
    label_file_list = glob.glob(label_path + '/*.' + 'png')
    label_file_list = sorted(label_file_list)
    for img_path in label_file_list:
        label_img = Image.open(img_path)
        label_img = np.asarray(label_img, dtype=np.uint8)
        label_img = (label_img > 128).astype(np.int8)
        label_list.append(label_img)

    data_file_list = glob.glob(image_data_path + '/*.' + 'jpg')
    data_file_list = sorted(data_file_list)
    for img_path in data_file_list:
        img = _preprocess(img_path)
        img_list.append(img)
        file_name_list.append(img_path)

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.gpu_options.visible_device_list = '0'
    with tf.Session(graph=tf.Graph(), config=config) as sess:
        meta_graph_def = tf.saved_model.loader.load(
            sess, [tf.saved_model.tag_constants.SERVING], pb_dir)
        signature = meta_graph_def.signature_def
        signature_key = 'segmentation'
        input_key = 'images'
        output_key = 'logists'
        x_tensor_name = signature[signature_key].inputs[input_key].name
        y_tensor_name = signature[signature_key].outputs[output_key].name
        x = sess.graph.get_tensor_by_name(x_tensor_name)
        y = sess.graph.get_tensor_by_name(y_tensor_name)
        for idx, img in enumerate(img_list):
            pred_output, = sess.run([y], {x: img})
            pred_output = np.squeeze(pred_output)
            pred_list.append(pred_output.tolist())
            logging.info(file_name_list[idx])

    label_dict = json.dumps(label_dict)
    task_type = 'image_segmentation'

    # analyse
    res = analyse(
        task_type=task_type,
        pred_list=pred_list,
        label_list=label_list,
        name_list=file_name_list,
        label_map_dict=label_dict,)


if __name__ == "__main__":
    evalution()

Sample Code for Models of the Object Detection Type

The training model corresponding to the following example code is the built-in algorithm Faster_RCNN_ResNet_v1_50 (TensorFlow engine).

  • model_url: model directory. After a model version is selected on the GUI, this parameter is automatically added in the background.
  • data_url: dataset directory. After a dataset version is selected on the GUI, this parameter is automatically added in the background.
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import moxing as mox
from deep_moxing.model_analysis.api import analyse
from deep_moxing.framework.manifest_api.manifest_api import get_list
import tensorflow as tf
from PIL import Image
import numpy as np
import xml.etree.ElementTree as ET
import h5py
import os
import json
import logging
import time
import sys

logging.basicConfig(level=logging.DEBUG)

FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('model_url', '', 'path to saved model')
tf.app.flags.DEFINE_string('data_url', '', 'path to output files')
FLAGS(sys.argv, known_only=True)


def _get_label(label_path, label_map_dict):
    root = ET.parse(label_path).getroot()
    bbox_list = []
    label_list = []
    for obj in root.iter('object'):
        xml_box = obj.find('bndbox')
        xmin = int(float(xml_box.find('xmin').text))
        ymin = int(float(xml_box.find('ymin').text))
        xmax = int(float(xml_box.find('xmax').text))
        ymax = int(float(xml_box.find('ymax').text))
        label_name = obj.find('name').text
        bbox_list.append([ymin, xmin, ymax, xmax])
        label_list.append(label_map_dict.get(label_name))
    assert None not in label_list, 'dataset and model not match'
    return [bbox_list, label_list]


def _preprocess(data_path):
    img = Image.open(data_path)
    img = img.convert('RGB')
    img = np.asarray(img, dtype=np.float32)
    img = img[np.newaxis, :, :, :]
    return img


def get_data_ckpt_local():
    pb_dir = FLAGS.model_url
    data_path = FLAGS.data_url
    data_file_list = []
    label_file_list = []
    if 'manifest' in data_path:
        data_file_list, label_file_list = get_list(manifest_path=data_path)
        print('------------- download ------------')
        mox.file.copy_parallel(pb_dir, '/cache/ckpt/')
        pb_dir = '/cache/ckpt'
        print('------------- download success ------------')
    elif data_path.startswith('obs://'):
        print('------------- download ------------')
        mox.file.copy_parallel(pb_dir, '/cache/ckpt/')
        mox.file.copy_parallel(data_path, '/cache/data/')
        pb_dir = '/cache/ckpt'
        data_path = '/cache/data/'
        print('------------- download success ------------')

    if pb_dir:
        assert os.path.isdir(pb_dir), 'Error, pb_dir must be a directory'

    index_file = os.path.join(pb_dir, 'index')
    label_list = []
    file_name_list = []
    img_list = []
    try:
        label_file = h5py.File(index_file, 'r')
        label_array = label_file['labels_list'][:].tolist()
        label_array = [item.decode('utf-8') for item in label_array]
    except Exception as e:
        logging.warning(e)
        logging.warning('index file is not a h5 file, try json.')
        with open(index_file, 'r') as load_f:
            label_file = json.load(load_f)
        label_array = label_file['labels_list'][:]
    label_map_dict = {}
    label_dict = {}
    for idx, item in enumerate(label_array):
        label_map_dict[item] = idx
        label_dict[idx] = item
    if 'manifest' in data_path:
        for img_path, xml_path in zip(data_file_list, label_file_list):
            label = _get_label(xml_path, label_map_dict)
            img = _preprocess(img_path)
            label_list.append(label)
            img_list.append(img)
            file_name_list.append(img_path)
    else:
        file_list = os.listdir(data_path)
        for item in file_list:
            if ('jpg' in item) or ('bmp' in item) or ('png' in item):
                xml_path = os.path.join(data_path, item.split('.')[0] + '.xml')
                img_path = os.path.join(data_path, item)
                label = _get_label(xml_path, label_map_dict)
                img = _preprocess(img_path)
                label_list.append(label)
                img_list.append(img)
                file_name_list.append(img_path)
            else:
                continue
    assert len(label_list) > 0, 'missing valid data'
    return pb_dir, label_list, label_dict, file_name_list, img_list


def evalution():
    pred_list = []
    pb_dir, label_list, label_dict, file_name_list, img_list = get_data_ckpt_local()
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    config.gpu_options.visible_device_list = '0'
    with tf.Session(graph=tf.Graph(), config=config) as sess:
        meta_graph_def = tf.saved_model.loader.load(
            sess, [tf.saved_model.tag_constants.SERVING], pb_dir)
        signature = meta_graph_def.signature_def
        signature_key = 'predict_object'
        input_key = 'images'
        output_key0 = 'detection_boxes'
        output_key1 = 'detection_classes'
        output_key2 = 'detection_scores'
        x_tensor_name = signature[signature_key].inputs[input_key].name
        y_tensor_name0 = signature[signature_key].outputs[output_key0].name
        y_tensor_name1 = signature[signature_key].outputs[output_key1].name
        y_tensor_name2 = signature[signature_key].outputs[output_key2].name
        x = sess.graph.get_tensor_by_name(x_tensor_name)
        y0 = sess.graph.get_tensor_by_name(y_tensor_name0)
        y1 = sess.graph.get_tensor_by_name(y_tensor_name1)
        y2 = sess.graph.get_tensor_by_name(y_tensor_name2)
        start = time.time()
        for img in img_list:
            pred_detection_boxes, pred_detection_classes, \
                pred_detection_scores = sess.run([y0, y1, y2], {x: img})
            if pred_detection_boxes.ndim == 3:
                pred_detection_boxes = pred_detection_boxes[0]
                pred_detection_classes = pred_detection_classes[0]
                pred_detection_scores = pred_detection_scores[0]
            pred_list.append([
                pred_detection_boxes.tolist(),
                (pred_detection_classes - 1).tolist(),
                pred_detection_scores.tolist()
            ])
        end = time.time()
        fps = len(img_list) / (end - start)

    diy_metric = {'fps': {'value': {'fps': fps}}}
    label_dict = json.dumps(label_dict)
    task_type = 'image_object_detection'

    # analyse
    res = analyse(
        task_type=task_type,
        pred_list=pred_list,
        label_list=label_list,
        name_list=file_name_list,
        custom_metric=diy_metric,
        label_map_dict=label_dict)

if __name__ == "__main__":
    evalution()