add tf mbv1 demo (#1205)

demo/auto_compression/detection/keypoint_utils.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. 
+# Copyright (c) 2022 PaddlePaddle Authors. 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.  

demo/auto_compression/image_classification/README.md

@@ -50,20 +50,6 @@
   - 软件：CUDA 11.2, cuDNN 8.0, TensorRT 8.4
   - 测试配置：batch_size: 1, image size: 224
 
-
-### TensorFlow MobileNetV1模型
-
-| 模型 | 策略 | Top-1 Acc | 耗时(ms) threads=1 | Inference模型 |
-|:------:|:------:|:------:|:------:|:------:|
-| MobileNetV1 | Base模型 | 71.0 | 30.45 | [Model](https://paddle-slim-models.bj.bcebos.com/act/mobilenetv1_inference_model_tf2paddle.tar) |
-| MobileNetV1 | 量化+蒸馏 | 70.22 | 15.86 | [Model](https://paddle-slim-models.bj.bcebos.com/act/mobilenetv1_quant.tar) |
-
-- 测试环境：`骁龙865 4*A77 4*A55`
-
-说明：
-- MobileNetV1模型源自[tensorflow/models](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)
-
-
 ## 3. 自动压缩流程
 
 #### 3.1 准备环境

demo/auto_compression/image_classification/eval.py

+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+
 import os
 import sys
 sys.path[0] = os.path.join(
@@ -12,7 +26,6 @@ import paddle.nn as nn
 from paddle.io import Dataset, BatchSampler, DataLoader
 import imagenet_reader as reader
 from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
-from paddleslim.auto_compression import AutoCompression
 
 
 def argsparser():
@@ -23,22 +36,6 @@ def argsparser():
         default=None,
         help="path of compression strategy config.",
         required=True)
-    parser.add_argument(
-        '--save_dir',
-        type=str,
-        default='output',
-        help="directory to save compressed model.")
-    return parser
-
-
-# yapf: enable
-def reader_wrapper(reader, input_name):
-    def gen():
-        for i, data in enumerate(reader()):
-            imgs = np.float32([item[0] for item in data])
-            yield {input_name: imgs}
-
-    return gen
 
 
 def eval_reader(data_dir, batch_size):

demo/auto_compression/image_classification/infer.py

+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+
 import os
 import sys
 import cv2

demo/auto_compression/image_classification/preprocess.py

-"""
 # Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -12,7 +11,6 @@
 # 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

demo/auto_compression/image_classification/run.py

+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+
 import os
 import sys
 sys.path[0] = os.path.join(
@@ -14,7 +28,6 @@ from paddle.io import Dataset, BatchSampler, DataLoader
 import imagenet_reader as reader
 from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
 from paddleslim.auto_compression import AutoCompression
-from utility import add_arguments, print_arguments
 
 
 def argsparser():

demo/auto_compression/image_classification/run_tf.sh

-# 单卡启动
-export CUDA_VISIBLE_DEVICES=0
-python run.py \
-    --model_dir='inference_model_usex2paddle' \
-    --model_filename='model.pdmodel' \
-    --params_filename='model.pdiparams' \
-    --save_dir='./save_quant_mobilev1/' \
-    --batch_size=128 \
-    --config_path='./configs/mobilenetv1_qat_dis.yaml'\
-    --input_shape 224 224 3 \
-    --image_reader_type='tensorflow' \
-    --input_name "input" \
-    --data_dir='ILSVRC2012'
-    

demo/auto_compression/pytorch_huggingface/infer.py

-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2022 PaddlePaddle Authors. 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.

demo/auto_compression/semantic_segmentation/run.py

+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+
 import os
 import argparse
 import random

demo/auto_compression/tensorflow_mobilenet/README.md

+# TensorFlow图像分类模型自动压缩示例
+
+目录：
+- [1. 简介](#1简介)
+- [2. Benchmark](#2Benchmark)
+- [3. 自动压缩流程](#自动压缩流程)
+  - [3.1 准备环境](#31-准备准备)
+  - [3.2 准备数据集](#32-准备数据集)
+  - [3.3 X2Paddle转换模型流程](#33-X2Paddle转换模型流程)
+  - [3.4 自动压缩并产出模型](#34-自动压缩并产出模型)
+- [4. 预测部署](#4预测部署)
+- [5. FAQ](5FAQ)
+
+
+## 1. 简介
+飞桨模型转换工具[X2Paddle](https://github.com/PaddlePaddle/X2Paddle)支持将```Caffe/TensorFlow/ONNX/PyTorch```的模型一键转为飞桨（PaddlePaddle）的预测模型。借助X2Paddle的能力，PaddleSlim的自动压缩功能可方便地用于各种框架的推理模型。
+
+本示例将以[TensorFlow](https://github.com/tensorflow/tensorflow)框架的MobileNetV1模型为例，介绍如何自动压缩其他框架中的图像分类模型。本示例会利用[TensorFlow](https://github.com/tensorflow/models)开源models库，将TensorFlow框架模型转换为Paddle框架模型，再使用ACT自动压缩功能进行自动压缩。本示例使用的自动压缩策略为量化训练。
+
+## 2. Benchmark
+| 模型 | 策略 | Top-1 Acc | 耗时(ms) threads=1 | Inference模型 |
+|:------:|:------:|:------:|:------:|:------:|
+| MobileNetV1 | Base模型 | 71.0 | 30.45 | [Model](https://paddle-slim-models.bj.bcebos.com/act/mobilenetv1_inference_model_tf2paddle.tar) |
+| MobileNetV1 | 量化+蒸馏 | 70.22 | 15.86 | [Model](https://paddle-slim-models.bj.bcebos.com/act/mobilenetv1_quant.tar) |
+
+- 测试环境：`骁龙865 4*A77 4*A55`
+
+说明：
+- MobileNetV1模型源自[tensorflow/models](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz)
+
+## 3. 自动压缩流程
+
+#### 3.1 准备环境
+- PaddlePaddle >= 2.3 （可从[Paddle官网](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html)下载安装）
+- PaddleSlim develop版本
+- [X2Paddle](https://github.com/PaddlePaddle/X2Paddle) >= 1.3.6
+- opencv-python
+
+（1）安装paddlepaddle：
+```shell
+# CPU
+pip install paddlepaddle
+# GPU
+pip install paddlepaddle-gpu
+```
+
+（2）安装paddleslim：
+```shell
+https://github.com/PaddlePaddle/PaddleSlim.git
+python setup.py install
+```
+
+（3）安装TensorFlow:
+```shell
+pip install tensorflow == 1.14
+```
+
+（3）安装X2Paddle的1.3.6以上版本：
+```shell
+pip install x2paddle
+```
+
+#### 3.2 准备数据集
+本案例默认以ImageNet1k数据进行自动压缩实验。
+
+#### 3.3 准备预测模型
+
+（1）转换模型
+
+```
+x2paddle --framework=tensorflow --model=tf_model.pb --save_dir=pd_model
+```
+即可得到MobileNetV1模型的预测模型（`model.pdmodel` 和 `model.pdiparams`）。如想快速体验，可直接下载上方表格中MobileNetV1的[Base模型](https://paddle-slim-models.bj.bcebos.com/act/mobilenetv1_inference_model_tf2paddle.tar)。
+
+预测模型的格式为：`model.pdmodel` 和 `model.pdiparams`两个，带`pdmodel`的是模型文件，带`pdiparams`后缀的是权重文件。
+
+### 3.4 自动压缩并产出模型
+
+蒸馏量化自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中模型路径、蒸馏、量化、和训练等部分的参数，配置完成后便可对模型进行量化和蒸馏。具体运行命令为：
+```
+# 单卡
+export CUDA_VISIBLE_DEVICES=0
+python run.py --config_path=./configs/mbv1_qat_dis.yaml --save_dir='./output/'
+```
+
+#### 3.5 测试模型精度
+
+使用eval.py脚本得到模型的mAP：
+```
+export CUDA_VISIBLE_DEVICES=0
+python eval.py --config_path=./configs/mbv1_qat_dis.yaml
+```
+
+## 4.预测部署
+
+#### 4.1 PaddleLite端侧部署
+PaddleLite端侧部署可参考：
+- [Paddle Lite部署](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/inference_deployment/paddle_lite_deploy.md)
+
+## 5.FAQ

demo/auto_compression/tensorflow_mobilenet/configs/mbv1_qat_dis.yaml

+Global:
+  input_name: input
+  model_dir: inference_model_usex2paddle
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+  batch_size: 32
+  data_dir: ./ILSVRC2012
+Distillation:
+  alpha: 1.0
+  loss: l2
+  node:
+  - batch_norm_0.tmp_3
+  - batch_norm_1.tmp_3
+  - batch_norm_2.tmp_3
+  - batch_norm_3.tmp_3
+  - batch_norm_4.tmp_3
+  - batch_norm_5.tmp_3
+  - batch_norm_6.tmp_3
+  - batch_norm_7.tmp_3
+  - batch_norm_8.tmp_3
+  - batch_norm_9.tmp_3
+  - batch_norm_10.tmp_3
+  - batch_norm_11.tmp_3
+  - batch_norm_12.tmp_3
+  - batch_norm_13.tmp_3
+  - batch_norm_14.tmp_3
+  - batch_norm_15.tmp_3
+  - batch_norm_16.tmp_3
+  - batch_norm_17.tmp_3
+  - batch_norm_18.tmp_3
+  - batch_norm_19.tmp_3
+  - batch_norm_20.tmp_3
+  - batch_norm_21.tmp_3
+  - batch_norm_22.tmp_3
+  - batch_norm_23.tmp_3
+  - batch_norm_24.tmp_3
+  - batch_norm_25.tmp_3
+  - batch_norm_26.tmp_3
+  - conv2d_42.tmp_1
+
+Quantization:
+  use_pact: true
+  activation_bits: 8
+  is_full_quantize: false
+  not_quant_pattern:
+  - skip_quant
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+  weight_bits: 8
+  activation_quantize_type: moving_average_abs_max
+  weight_quantize_type: channel_wise_abs_max
+
+TrainConfig:
+  epochs: 1000
+  eval_iter: 1000
+  learning_rate: 0.00001
+  optimizer_builder: 
+    optimizer:
+      type: SGD
+    weight_decay: 4.0e-05
+  origin_metric: 0.71028

demo/auto_compression/tensorflow_mobilenet/eval.py

+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+
+import os
+import sys
+import argparse
+import functools
+from functools import partial
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+from paddle.io import DataLoader
+from imagenet_reader import ImageNetDataset
+from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
+
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.",
+        required=True)
+    return parser
+
+
+def eval_reader(data_dir, batch_size):
+    val_reader = ImageNetDataset(mode='val', data_dir=data_dir)
+    val_loader = DataLoader(
+        val_reader,
+        batch_size=global_config['batch_size'],
+        shuffle=False,
+        drop_last=False,
+        num_workers=0)
+    return val_loader
+
+
+def eval():
+    devices = paddle.device.get_device().split(':')[0]
+    places = paddle.device._convert_to_place(devices)
+    exe = paddle.static.Executor(places)
+    val_program, feed_target_names, fetch_targets = paddle.static.load_inference_model(
+        global_config["model_dir"],
+        exe,
+        model_filename=global_config["model_filename"],
+        params_filename=global_config["params_filename"])
+    print('Loaded model from: {}'.format(global_config["model_dir"]))
+
+    val_reader = eval_reader(data_dir, batch_size=global_config['batch_size'])
+    image = paddle.static.data(
+        name=global_config['input_name'],
+        shape=[None, 224, 224, 3],
+        dtype='float32')
+    label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
+    results = []
+    print('Evaluating... It will take a while. Please wait...')
+    for batch_id, (image, label) in enumerate(val_reader):
+        # top1_acc, top5_acc
+        image = np.array(image)
+        label = np.array(label).astype('int64')
+        pred = exe.run(val_program,
+                       feed={feed_target_names[0]: image},
+                       fetch_list=fetch_targets)
+        pred = np.array(pred[0])
+        label = np.array(label)
+        sort_array = pred.argsort(axis=1)
+        top_1_pred = sort_array[:, -1:][:, ::-1]
+        top_1 = np.mean(label == top_1_pred)
+        top_5_pred = sort_array[:, -5:][:, ::-1]
+        acc_num = 0
+        for i in range(len(label)):
+            if label[i][0] in top_5_pred[i]:
+                acc_num += 1
+        top_5 = float(acc_num) / len(label)
+        results.append([top_1, top_5])
+    result = np.mean(np.array(results), axis=0)
+    return result[0]
+
+
+def main():
+    global global_config
+    all_config = load_slim_config(args.config_path)
+    assert "Global" in all_config, f"Key 'Global' not found in config file. \n{all_config}"
+    global_config = all_config["Global"]
+    global data_dir
+    data_dir = global_config['data_dir']
+    result = eval()
+    print('Eval Top1:', result)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    args = parser.parse_args()
+    main()

demo/auto_compression/image_classification/tf_imagenet_reader.py → demo/auto_compression/tensorflow_mobilenet/imagenet_reader.py

@@ -128,54 +128,51 @@ def process_image(sample, mode, color_jitter, rotate):
         return [img]
 
 
-def _reader_creator(file_list,
-                    mode,
-                    shuffle=False,
-                    color_jitter=False,
-                    rotate=False,
-                    data_dir=DATA_DIR,
-                    batch_size=1):
-    def reader():
-        try:
-            with open(file_list) as flist:
+class ImageNetDataset(Dataset):
+    def __init__(self, data_dir=DATA_DIR, mode='train'):
+        super(ImageNetDataset, self).__init__()
+        self.data_dir = data_dir
+        train_file_list = os.path.join(data_dir, 'train_list.txt')
+        val_file_list = os.path.join(data_dir, 'val_list.txt')
+        test_file_list = os.path.join(data_dir, 'test_list.txt')
+        self.mode = mode
+        if mode == 'train':
+            with open(train_file_list) as flist:
                 full_lines = [line.strip() for line in flist]
-                if shuffle:
-                    np.random.shuffle(full_lines)
+                np.random.shuffle(full_lines)
                 lines = full_lines
-                for line in lines:
-                    if mode == 'train' or mode == 'val':
-                        img_path, label = line.split()
-                        img_path = os.path.join(data_dir, img_path)
-                        yield img_path, int(label) + 1
-                    elif mode == 'test':
-                        img_path = os.path.join(data_dir, line)
-                        yield [img_path]
-        except Exception as e:
-            print("Reader failed!\n{}".format(str(e)))
-            os._exit(1)
-
-    mapper = functools.partial(
-        process_image, mode=mode, color_jitter=color_jitter, rotate=rotate)
-
-    return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
-
-
-def train(data_dir=DATA_DIR):
-    file_list = os.path.join(data_dir, 'train_list.txt')
-    return _reader_creator(
-        file_list,
-        'train',
-        shuffle=True,
-        color_jitter=False,
-        rotate=False,
-        data_dir=data_dir)
-
-
-def val(data_dir=DATA_DIR):
-    file_list = os.path.join(data_dir, 'val_list.txt')
-    return _reader_creator(file_list, 'val', shuffle=False, data_dir=data_dir)
-
-
-def test(data_dir=DATA_DIR):
-    file_list = os.path.join(data_dir, 'test_list.txt')
-    return _reader_creator(file_list, 'test', shuffle=False, data_dir=data_dir)
+            self.data = [line.split() for line in lines]
+        else:
+            with open(val_file_list) as flist:
+                lines = [line.strip() for line in flist]
+                self.data = [line.split() for line in lines]
+
+    def __getitem__(self, index):
+        sample = self.data[index]
+        data_path = os.path.join(self.data_dir, sample[0])
+        if self.mode == 'train':
+            data, label = process_image(
+                [data_path, sample[1]],
+                mode='train',
+                color_jitter=False,
+                rotate=False)
+            return np.array(data).astype('float32'), (
+                np.array([label]).astype('int64') + 1)
+        elif self.mode == 'val':
+            data, label = process_image(
+                [data_path, sample[1]],
+                mode='val',
+                color_jitter=False,
+                rotate=False)
+            return np.array(data).astype('float32'), (
+                np.array([label]).astype('int64') + 1)
+        elif self.mode == 'test':
+            data = process_image(
+                [data_path, sample[1]],
+                mode='test',
+                color_jitter=False,
+                rotate=False)
+            return np.array(data).astype('float32')
+
+    def __len__(self):
+        return len(self.data)

demo/auto_compression/tensorflow_mobilenet/run.py

+# Copyright (c) 2022 PaddlePaddle Authors. 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.
+
+import os
+import sys
+import argparse
+import functools
+from functools import partial
+
+import numpy as np
+import paddle
+import paddle.nn as nn
+from paddle.io import DataLoader
+from imagenet_reader import ImageNetDataset
+from paddleslim.auto_compression.config_helpers import load_config as load_slim_config
+from paddleslim.auto_compression import AutoCompression
+
+
+def argsparser():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        '--config_path',
+        type=str,
+        default=None,
+        help="path of compression strategy config.",
+        required=True)
+    parser.add_argument(
+        '--save_dir',
+        type=str,
+        default='output',
+        help="directory to save compressed model.")
+    return parser
+
+
+# yapf: enable
+def reader_wrapper(reader, input_name):
+    def gen():
+        for i, (imgs, label) in enumerate(reader()):
+            yield {input_name: imgs}
+
+    return gen
+
+
+def eval_reader(data_dir, batch_size):
+    val_reader = ImageNetDataset(mode='val', data_dir=data_dir)
+    val_loader = DataLoader(
+        val_reader,
+        batch_size=global_config['batch_size'],
+        shuffle=False,
+        drop_last=False,
+        num_workers=0)
+    return val_loader
+
+
+def eval_function(exe, compiled_test_program, test_feed_names, test_fetch_list):
+    val_loader = eval_reader(data_dir, batch_size=global_config['batch_size'])
+
+    results = []
+    for batch_id, (image, label) in enumerate(val_loader):
+        # top1_acc, top5_acc
+        if len(test_feed_names) == 1:
+            image = np.array(image)
+            label = np.array(label).astype('int64')
+            pred = exe.run(compiled_test_program,
+                           feed={test_feed_names[0]: image},
+                           fetch_list=test_fetch_list)
+            pred = np.array(pred[0])
+            label = np.array(label)
+            sort_array = pred.argsort(axis=1)
+            top_1_pred = sort_array[:, -1:][:, ::-1]
+            top_1 = np.mean(label == top_1_pred)
+            top_5_pred = sort_array[:, -5:][:, ::-1]
+            acc_num = 0
+            for i in range(len(label)):
+                if label[i][0] in top_5_pred[i]:
+                    acc_num += 1
+            top_5 = float(acc_num) / len(label)
+            results.append([top_1, top_5])
+        else:
+            # eval "eval model", which inputs are image and label, output is top1 and top5 accuracy
+            image = np.array(image)
+            label = np.array(label).astype('int64')
+            result = exe.run(
+                compiled_test_program,
+                feed={test_feed_names[0]: image,
+                      test_feed_names[1]: label},
+                fetch_list=test_fetch_list)
+            result = [np.mean(r) for r in result]
+            results.append(result)
+        if batch_id % 50 == 0:
+            print('Eval iter: ', batch_id)
+    result = np.mean(np.array(results), axis=0)
+    return result[0]
+
+
+def main():
+    global global_config
+    all_config = load_slim_config(args.config_path)
+    assert "Global" in all_config, f"Key 'Global' not found in config file. \n{all_config}"
+    global_config = all_config["Global"]
+    global data_dir
+    data_dir = global_config['data_dir']
+
+    train_dataset = ImageNetDataset(mode='train', data_dir=data_dir)
+
+    train_loader = DataLoader(
+        train_dataset,
+        batch_size=global_config['batch_size'],
+        shuffle=True,
+        drop_last=True,
+        num_workers=0)
+    train_dataloader = reader_wrapper(train_loader, global_config['input_name'])
+
+    ac = AutoCompression(
+        model_dir=global_config['model_dir'],
+        model_filename=global_config['model_filename'],
+        params_filename=global_config['params_filename'],
+        save_dir=args.save_dir,
+        config=all_config,
+        train_dataloader=train_dataloader,
+        eval_callback=eval_function)
+
+    ac.compress()
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    parser = argsparser()
+    args = parser.parse_args()
+    print_arguments(args)
+    main()