Fix quant model export issues(#198)

* save inference model

* fix details

* add export model in quantization

* remove useless code

* remove useless code

* change fluid.layers.data to fluid.data

pdseg/models/model_builder.py

@@ -175,12 +175,16 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
             # 在导出模型的时候，增加图像标准化预处理,减小预测部署时图像的处理流程
             # 预测部署时只须对输入图像增加batch_size维度即可
             if ModelPhase.is_predict(phase):
-                origin_image = fluid.data(
-                    name='image',
-                    shape=[-1, -1, -1, cfg.DATASET.DATA_DIM],
-                    dtype='float32')
-                image, valid_shape, origin_shape = export_preprocess(
-                    origin_image)
+                if cfg.SLIM.PREPROCESS:
+                    image = fluid.data(
+                        name='image', shape=image_shape, dtype='float32')
+                else:
+                    origin_image = fluid.data(
+                        name='image',
+                        shape=[-1, -1, -1, cfg.DATASET.DATA_DIM],
+                        dtype='float32')
+                    image, valid_shape, origin_shape = export_preprocess(
+                        origin_image)
 
             else:
                 image = fluid.data(
@@ -271,15 +275,19 @@ def build_model(main_prog, start_prog, phase=ModelPhase.TRAIN):
                     logit = softmax(logit)
 
                 # 获取有效部分
-                logit = fluid.layers.slice(
-                    logit, axes=[2, 3], starts=[0, 0], ends=valid_shape)
-
-                logit = fluid.layers.resize_bilinear(
-                    logit,
-                    out_shape=origin_shape,
-                    align_corners=False,
-                    align_mode=0)
-                logit = fluid.layers.argmax(logit, axis=1)
+                if cfg.SLIM.PREPROCESS:
+                    return image, logit
+
+                else:
+                    logit = fluid.layers.slice(
+                        logit, axes=[2, 3], starts=[0, 0], ends=valid_shape)
+
+                    logit = fluid.layers.resize_bilinear(
+                        logit,
+                        out_shape=origin_shape,
+                        align_corners=False,
+                        align_mode=0)
+                    logit = fluid.layers.argmax(logit, axis=1)
                 return origin_image, logit
 
             if class_num == 1:

pdseg/utils/config.py

@@ -155,10 +155,10 @@ cfg.SOLVER.BEGIN_EPOCH = 1
 cfg.SOLVER.NUM_EPOCHS = 30
 # loss的选择，支持softmax_loss, bce_loss, dice_loss
 cfg.SOLVER.LOSS = ["softmax_loss"]
-# 是否开启warmup学习策略 
-cfg.SOLVER.LR_WARMUP = False 
+# 是否开启warmup学习策略
+cfg.SOLVER.LR_WARMUP = False
 # warmup的迭代次数
-cfg.SOLVER.LR_WARMUP_STEPS = 2000 
+cfg.SOLVER.LR_WARMUP_STEPS = 2000
 # cross entropy weight, 默认为None，如果设置为'dynamic'，会根据每个batch中各个类别的数目，
 # 动态调整类别权重。
 # 也可以设置一个静态权重(list的方式)，比如有3类，每个类别权重可以设置为[0.1, 2.0, 0.9]
@@ -228,7 +228,6 @@ cfg.MODEL.HRNET.STAGE3.NUM_CHANNELS = [40, 80, 160]
 cfg.MODEL.HRNET.STAGE4.NUM_MODULES = 3
 cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS = [40, 80, 160, 320]
 
-
 ########################## 预测部署模型配置 ###################################
 # 预测保存的模型名称
 cfg.FREEZE.MODEL_FILENAME = '__model__'
@@ -251,4 +250,4 @@ cfg.SLIM.NAS_SPACE_NAME = ""
 
 cfg.SLIM.PRUNE_PARAMS = ''
 cfg.SLIM.PRUNE_RATIOS = []
-
+cfg.SLIM.PREPROCESS = False

slim/quantization/README.md

@@ -133,7 +133,20 @@ TRAIN.SYNC_BATCH_NORM False \
 BATCH_SIZE 16 \
 ```
 
+## 导出模型
+使用脚本[slim/quantization/export_model.py](./export_model.py)导出模型。
 
+导出命令：
+
+分割库根目录下运行
+```
+python -u ./slim/quantization/export_model.py --not_quant_pattern last_conv  --cfg configs/deeplabv3p_mobilenetv2_cityscapes.yaml  \
+TEST.TEST_MODEL "./snapshots/mobilenetv2_quant/best_model" \
+MODEL.DEEPLAB.ENCODER_WITH_ASPP False \
+MODEL.DEEPLAB.ENABLE_DECODER False \
+TRAIN.SYNC_BATCH_NORM False \
+SLIM.PREPROCESS True \
+```
 
 ## 量化结果
 

slim/quantization/export_model.py

+# coding: utf8
+# copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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 os
+import sys
+import time
+import pprint
+import cv2
+import argparse
+import numpy as np
+import paddle.fluid as fluid
+
+from utils.config import cfg
+from models.model_builder import build_model
+from models.model_builder import ModelPhase
+from paddleslim.quant import quant_aware, convert
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='PaddleSeg Inference Model Exporter')
+    parser.add_argument(
+        '--cfg',
+        dest='cfg_file',
+        help='Config file for training (and optionally testing)',
+        default=None,
+        type=str)
+    parser.add_argument(
+        "--not_quant_pattern",
+        nargs='+',
+        type=str,
+        help=
+        "Layers which name_scope contains string in not_quant_pattern will not be quantized"
+    )
+    parser.add_argument(
+        'opts',
+        help='See utils/config.py for all options',
+        default=None,
+        nargs=argparse.REMAINDER)
+    if len(sys.argv) == 1:
+        parser.print_help()
+        sys.exit(1)
+    return parser.parse_args()
+
+
+def export_inference_config():
+    deploy_cfg = '''DEPLOY:
+        USE_GPU : 1
+        MODEL_PATH : "%s"
+        MODEL_FILENAME : "%s"
+        PARAMS_FILENAME : "%s"
+        EVAL_CROP_SIZE : %s
+        MEAN : %s
+        STD : %s
+        IMAGE_TYPE : "%s"
+        NUM_CLASSES : %d
+        CHANNELS : %d
+        PRE_PROCESSOR : "SegPreProcessor"
+        PREDICTOR_MODE : "ANALYSIS"
+        BATCH_SIZE : 1
+    ''' % (cfg.FREEZE.SAVE_DIR, cfg.FREEZE.MODEL_FILENAME,
+           cfg.FREEZE.PARAMS_FILENAME, cfg.EVAL_CROP_SIZE, cfg.MEAN, cfg.STD,
+           cfg.DATASET.IMAGE_TYPE, cfg.DATASET.NUM_CLASSES, len(cfg.STD))
+    if not os.path.exists(cfg.FREEZE.SAVE_DIR):
+        os.mkdir(cfg.FREEZE.SAVE_DIR)
+    yaml_path = os.path.join(cfg.FREEZE.SAVE_DIR, 'deploy.yaml')
+    with open(yaml_path, "w") as fp:
+        fp.write(deploy_cfg)
+    return yaml_path
+
+
+def export_inference_model(args):
+    """
+    Export PaddlePaddle inference model for prediction depolyment and serving.
+    """
+    print("Exporting inference model...")
+    startup_prog = fluid.Program()
+    infer_prog = fluid.Program()
+    image, logit_out = build_model(
+        infer_prog, startup_prog, phase=ModelPhase.PREDICT)
+
+    # Use CPU for exporting inference model instead of GPU
+    place = fluid.CPUPlace()
+    exe = fluid.Executor(place)
+    exe.run(startup_prog)
+    infer_prog = infer_prog.clone(for_test=True)
+    not_quant_pattern_list = []
+    if args.not_quant_pattern is not None:
+        not_quant_pattern_list = args.not_quant_pattern
+
+    config = {
+        'weight_quantize_type': 'channel_wise_abs_max',
+        'activation_quantize_type': 'moving_average_abs_max',
+        'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
+        'not_quant_pattern': not_quant_pattern_list
+    }
+
+    infer_prog = quant_aware(infer_prog, place, config, for_test=True)
+    if os.path.exists(cfg.TEST.TEST_MODEL):
+        fluid.io.load_persistables(
+            exe, cfg.TEST.TEST_MODEL, main_program=infer_prog)
+    else:
+        print("TEST.TEST_MODEL diretory is empty!")
+        exit(-1)
+
+    infer_prog = convert(infer_prog, place, config)
+
+    fluid.io.save_inference_model(
+        cfg.FREEZE.SAVE_DIR,
+        feeded_var_names=[image.name],
+        target_vars=[logit_out],
+        executor=exe,
+        main_program=infer_prog,
+        model_filename=cfg.FREEZE.MODEL_FILENAME,
+        params_filename=cfg.FREEZE.PARAMS_FILENAME)
+    print("Inference model exported!")
+    print("Exporting inference model config...")
+    deploy_cfg_path = export_inference_config()
+    print("Inference model saved : [%s]" % (deploy_cfg_path))
+
+
+def main():
+    args = parse_args()
+    if args.cfg_file is not None:
+        cfg.update_from_file(args.cfg_file)
+    if args.opts:
+        cfg.update_from_list(args.opts)
+    cfg.check_and_infer()
+    print(pprint.pformat(cfg))
+    export_inference_model(args)
+
+
+if __name__ == '__main__':
+    main()