Add contrib HumanSeg (#229)

* update solver.py and model_builder.py

* update solver.py

* update infer.py

* update model_builder.py for fitting different aug_method

* update model_builder.py

* update model_builder.py

* update export process

* save best model

* update train.py

* update train.py

* update train.py

* eval for last epoch model

* check model path

* check TEST.TEST_MODEL

* reconsitution humanseg

* first refactor humanseg

* update humanseg.py and add main.py

* frist add

* add shufflenet

* first add

* add hrnet

* add tutorial

* add humanseg infer

* add realtime humanseg

* update hrnet.py

* update humanseg.py

* rm cpp deploy

* add infer of resize_bylong

* update

* update infer.py

* update some

* update humanseg.py

* image_segment and video_segment add to class HumanSeg

* add quant

* resolve with_data_parallel problem

* add coeff params

* add demo

* update

* update __init__.py

* add setup.py

contrib/HumanSeg/README.md

+# HumanSeg
+
+## 环境
+将contrib目录加入环境变量PYTHONPATH
+
+## 训练、验证、预测、模型导出参见[turtorial](./turtorial)

contrib/HumanSeg/__init__.py

@@ -17,7 +17,7 @@ from . import nets
 from . import models
 from . import datasets
 from . import transforms
-from .utils.utils import get_environ_info
+from .utils import get_environ_info
 
 env_info = get_environ_info()
 

contrib/HumanSeg/datasets/__init__.py

@@ -11,3 +11,5 @@
 # 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 .dataset import Dataset

contrib/HumanSeg/deploy/README.md

+# 实时人像分割预测部署
+
+本模型基于飞浆开源的人像分割模型，并做了大量的针对视频的光流追踪优化，提供了完整的支持视频流的实时人像分割解决方案，并提供了高性能的`Python`集成部署方案。
+
+
+## 模型下载
+
+支持的模型文件如下，请根据应用场景选择合适的模型：
+
+|模型文件 | 说明 |
+| --- | --- |
+|[shv75_deeplab_0303_quant](https://paddleseg.bj.bcebos.com/deploy/models/shv75_0303_quant.zip)  | 小模型, 适合轻量级计算环境 |
+|[shv75_deeplab_0303](https://paddleseg.bj.bcebos.com/deploy/models/shv75_deeplab_0303.zip)| 小模型，适合轻量级计算环境 |
+|[deeplabv3_xception_humanseg](https://paddleseg.bj.bcebos.com/deploy/models/deeplabv3_xception_humanseg.zip) | 服务端GPU环境 |
+
+**注意：下载后解压到合适的路径，后续该路径将做为预测参数用于加载模型。**
+
+
+## 预测部署
+- [Python预测部署](./python)
+
+## 效果预览
+
+<figure class="half">
+    <img src="https://paddleseg.bj.bcebos.com/deploy/data/input.gif">
+    <img src="https://paddleseg.bj.bcebos.com/deploy/data/output.gif">
+</figure>

contrib/HumanSeg/deploy/python/README.md

+# 实时人像分割Python预测部署方案
+
+本方案基于Python实现，最小化依赖并把所有模型加载、数据预处理、预测、光流处理等后处理都封装在文件`infer.py`中，用户可以直接使用或集成到自己项目中。
+
+
+## 前置依赖
+- Windows(7,8,10) / Linux (Ubuntu 16.04) or MacOS 10.1+
+- Paddle 1.7+
+- Python 3.6+
+
+注意：
+1. 仅支持Paddle 1.7以上版本
+2. MacOS上不支持GPU预测
+
+其它未涉及情形，能正常安装`Paddle` 和`OpenCV`通常都能正常使用。
+
+
+## 安装依赖
+
+执行如下命令
+
+```shell
+pip install -r requirements.txt
+```
+
+## 运行
+
+
+1. 输入图片进行分割
+```
+python infer.py --model_dir /PATH/TO/INFERENCE/MODEL --img_path /PATH/TO/INPUT/IMAGE
+```
+
+预测结果会保存为`result.jpeg`。
+2. 输入视频进行分割
+```shell
+python infer.py --model_dir /PATH/TO/INFERENCE/MODEL --video_path /PATH/TO/INPUT/VIDEO
+```
+
+预测结果会保存在`result.avi`。
+
+3. 使用摄像头视频流
+```shell
+python infer.py --model_dir /PATH/TO/INFERENCE/MODEL --use_camera True
+```
+预测结果会通过可视化窗口实时显示。
+
+**注意：**
+
+
+`GPU`默认关闭, 如果要使用`GPU`进行加速，则先运行
+```
+export CUDA_VISIBLE_DEVICES=0
+```
+然后在前面的预测命令中增加参数`--use_gpu True`即可。

contrib/HumanSeg/deploy/python/infer.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.
+# ==============================================================================
+"""实时人像分割Python预测部署"""
+
+import os
+import argparse
+import numpy as np
+import cv2
+
+import paddle.fluid as fluid
+
+
+def humanseg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
+    """计算光流跟踪匹配点和光流图
+    输入参数:
+        pre_gray: 上一帧灰度图
+        cur_gray: 当前帧灰度图
+        prev_cfd: 上一帧光流图
+        dl_weights: 融合权重图
+        disflow: 光流数据结构
+    返回值:
+        is_track: 光流点跟踪二值图，即是否具有光流点匹配
+        track_cfd: 光流跟踪图
+    """
+    check_thres = 8
+    hgt, wdh = pre_gray.shape[:2]
+    track_cfd = np.zeros_like(prev_cfd)
+    is_track = np.zeros_like(pre_gray)
+    # 计算前向光流
+    flow_fw = disflow.calc(pre_gray, cur_gray, None)
+    # 计算后向光流
+    flow_bw = disflow.calc(cur_gray, pre_gray, None)
+    get_round = lambda data: (int)(data + 0.5) if data >= 0 else (int)(data -
+                                                                       0.5)
+    for row in range(hgt):
+        for col in range(wdh):
+            # 计算光流处理后对应点坐标
+            # (row, col) -> (cur_x, cur_y)
+            fxy_fw = flow_fw[row, col]
+            dx_fw = get_round(fxy_fw[0])
+            cur_x = dx_fw + col
+            dy_fw = get_round(fxy_fw[1])
+            cur_y = dy_fw + row
+            if cur_x < 0 or cur_x >= wdh or cur_y < 0 or cur_y >= hgt:
+                continue
+            fxy_bw = flow_bw[cur_y, cur_x]
+            dx_bw = get_round(fxy_bw[0])
+            dy_bw = get_round(fxy_bw[1])
+            # 光流移动小于阈值
+            lmt = ((dy_fw + dy_bw) * (dy_fw + dy_bw) +
+                   (dx_fw + dx_bw) * (dx_fw + dx_bw))
+            if lmt >= check_thres:
+                continue
+            # 静止点降权
+            if abs(dy_fw) <= 0 and abs(dx_fw) <= 0 and abs(dy_bw) <= 0 and abs(
+                    dx_bw) <= 0:
+                dl_weights[cur_y, cur_x] = 0.05
+            is_track[cur_y, cur_x] = 1
+            track_cfd[cur_y, cur_x] = prev_cfd[row, col]
+    return track_cfd, is_track, dl_weights
+
+
+def humanseg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
+    """光流追踪图和人像分割结构融合
+    输入参数:
+        track_cfd: 光流追踪图
+        dl_cfd: 当前帧分割结果
+        dl_weights: 融合权重图
+        is_track: 光流点匹配二值图
+    返回值:
+        cur_cfd: 光流跟踪图和人像分割结果融合图
+    """
+    cur_cfd = dl_cfd.copy()
+    idxs = np.where(is_track > 0)
+    for i in range(len(idxs)):
+        x, y = idxs[0][i], idxs[1][i]
+        dl_score = dl_cfd[y, x]
+        track_score = track_cfd[y, x]
+        if dl_score > 0.9 or dl_score < 0.1:
+            if dl_weights[x, y] < 0.1:
+                cur_cfd[x, y] = 0.3 * dl_score + 0.7 * track_score
+            else:
+                cur_cfd[x, y] = 0.4 * dl_score + 0.6 * track_score
+        else:
+            cur_cfd[x, y] = dl_weights[x, y] * dl_score + (
+                1 - dl_weights[x, y]) * track_score
+    return cur_cfd
+
+
+def threshold_mask(img, thresh_bg, thresh_fg):
+    """设置背景和前景阈值mask
+    输入参数:
+        img : 原始图像, np.uint8 类型.
+        thresh_bg : 背景阈值百分比，低于该值置为0.
+        thresh_fg : 前景阈值百分比，超过该值置为1.
+    返回值:
+        dst : 原始图像设置完前景背景阈值mask结果, np.float32 类型.
+    """
+    dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
+    dst[np.where(dst > 1)] = 1
+    dst[np.where(dst < 0)] = 0
+    return dst.astype(np.float32)
+
+
+def optflow_handle(cur_gray, scoremap, is_init):
+    """光流优化
+    Args:
+        cur_gray : 当前帧灰度图
+        scoremap : 当前帧分割结果
+        is_init : 是否第一帧
+    Returns:
+        dst : 光流追踪图和预测结果融合图, 类型为 np.float32
+    """
+    width, height = scoremap.shape[0], scoremap.shape[1]
+    disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
+    prev_gray = np.zeros((height, width), np.uint8)
+    prev_cfd = np.zeros((height, width), np.float32)
+    cur_cfd = scoremap.copy()
+    if is_init:
+        is_init = False
+        if height <= 64 or width <= 64:
+            disflow.setFinestScale(1)
+        elif height <= 160 or width <= 160:
+            disflow.setFinestScale(2)
+        else:
+            disflow.setFinestScale(3)
+        fusion_cfd = cur_cfd
+    else:
+        weights = np.ones((width, height), np.float32) * 0.3
+        track_cfd, is_track, weights = humanseg_tracking(
+            prev_gray, cur_gray, prev_cfd, weights, disflow)
+        fusion_cfd = humanseg_track_fuse(track_cfd, cur_cfd, weights, is_track)
+    fusion_cfd = cv2.GaussianBlur(fusion_cfd, (3, 3), 0)
+    return fusion_cfd
+
+
+class HumanSeg:
+    """人像分割类
+    封装了人像分割模型的加载，数据预处理，预测，后处理等
+    """
+
+    def __init__(self, model_dir, mean, scale, eval_size, use_gpu=False):
+
+        self.mean = np.array(mean).reshape((3, 1, 1))
+        self.scale = np.array(scale).reshape((3, 1, 1))
+        self.eval_size = eval_size
+        self.load_model(model_dir, use_gpu)
+
+    def load_model(self, model_dir, use_gpu):
+        """加载模型并创建predictor
+        Args:
+            model_dir: 预测模型路径, 包含 `__model__` 和 `__params__`
+            use_gpu: 是否使用GPU加速
+        """
+        prog_file = os.path.join(model_dir, '__model__')
+        params_file = os.path.join(model_dir, '__params__')
+        config = fluid.core.AnalysisConfig(prog_file, params_file)
+        if use_gpu:
+            config.enable_use_gpu(100, 0)
+            config.switch_ir_optim(True)
+        else:
+            config.disable_gpu()
+        config.disable_glog_info()
+        config.switch_specify_input_names(True)
+        config.enable_memory_optim()
+        self.predictor = fluid.core.create_paddle_predictor(config)
+
+    def preprocess(self, image):
+        """图像预处理
+        hwc_rgb 转换为 chw_bgr，并进行归一化
+        输入参数:
+            image: 原始图像
+        返回值:
+            经过预处理后的图片结果
+        """
+        img_mat = cv2.resize(
+            image, self.eval_size, interpolation=cv2.INTER_LINEAR)
+        # HWC -> CHW
+        img_mat = img_mat.swapaxes(1, 2)
+        img_mat = img_mat.swapaxes(0, 1)
+        # Convert to float
+        img_mat = img_mat[:, :, :].astype('float32')
+        img_mat = (img_mat / 255. - self.mean) / self.scale
+        img_mat = img_mat[np.newaxis, :, :, :]
+        return img_mat
+
+    def postprocess(self, image, output_data):
+        """对预测结果进行后处理
+        Args:
+             image: 原始图，opencv 图片对象
+             output_data: Paddle预测结果原始数据
+        Returns:
+             原图和预测结果融合并做了光流优化的结果图
+        """
+        scoremap = output_data[0, 1, :, :]
+        scoremap = (scoremap * 255).astype(np.uint8)
+        ori_h, ori_w = image.shape[0], image.shape[1]
+        evl_h, evl_w = self.eval_size[0], self.eval_size[1]
+        # 光流处理
+        cur_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        cur_gray = cv2.resize(cur_gray, (evl_w, evl_h))
+        optflow_map = optflow_handle(cur_gray, scoremap, False)
+        optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
+        optflow_map = threshold_mask(optflow_map, thresh_bg=0.2, thresh_fg=0.8)
+        optflow_map = cv2.resize(optflow_map, (ori_w, ori_h))
+        optflow_map = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2)
+        bg_im = np.ones_like(optflow_map) * 255
+        comb = (optflow_map * image + (1 - optflow_map) * bg_im).astype(
+            np.uint8)
+        return comb
+
+    def run_predict(self, image):
+        """运行预测并返回可视化结果图
+        输入参数:
+            image: 需要预测的原始图, opencv图片对象
+        返回值:
+            可视化的预测结果图
+        """
+        im_mat = self.preprocess(image)
+        im_tensor = fluid.core.PaddleTensor(im_mat.copy().astype('float32'))
+        output_data = self.predictor.run([im_tensor])[1]
+        output_data = output_data.as_ndarray()
+        return self.postprocess(image, output_data)
+
+    def image_segment(self, path):
+        """对图片文件进行分割
+            结果保存到`result.jpeg`文件中
+            """
+        img_mat = cv2.imread(path)
+        img_mat = self.run_predict(img_mat)
+        cv2.imwrite('result.jpeg', img_mat)
+
+    def video_segment(self, path=None):
+        """
+        对视屏流进行分割，
+        path为None时默认打开摄像头。
+        """
+        if path is None:
+            cap = cv2.VideoCapture(0)
+        else:
+            cap = cv2.VideoCapture(path)
+        if not cap.isOpened():
+            raise IOError("Error opening video stream or file")
+            return
+
+        if path is not None:
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fps = cap.get(cv2.CAP_PROP_FPS)
+            # 用于保存预测结果视频
+            out = cv2.VideoWriter('result.avi',
+                                  cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
+                                  fps, (width, height))
+            # 开始获取视频帧
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if ret:
+                    img_mat = self.run_predict(frame)
+                    out.write(img_mat)
+                else:
+                    break
+            cap.release()
+            out.release()
+
+        else:
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if ret:
+                    img_mat = self.run_predict(frame)
+                    cv2.imshow('HumanSegmentation', img_mat)
+                    if cv2.waitKey(1) & 0xFF == ord('q'):
+                        break
+                else:
+                    break
+            cap.release()
+
+
+def main(args):
+    """预测程序入口
+    完成模型加载, 对视频、摄像头、图片文件等预测过程
+    """
+    model_dir = args.model_dir
+    use_gpu = args.use_gpu
+
+    # 加载模型
+    mean = [0.5, 0.5, 0.5]
+    scale = [0.5, 0.5, 0.5]
+    eval_size = (192, 192)
+    model = HumanSeg(model_dir, mean, scale, eval_size, use_gpu)
+    if args.use_camera:
+        # 开启摄像头
+        model.video_segment()
+    elif args.video_path:
+        # 使用视频文件作为输入
+        model.video_segment(args.video_path)
+    elif args.img_path:
+        # 使用图片文件作为输入
+        model.image_segment(args.img_path)
+    else:
+        raise ValueError(
+            'One of (--model_dir, --video_path, --use_camera) should be given.')
+
+
+def parse_args():
+    """解析命令行参数
+    """
+    parser = argparse.ArgumentParser('Realtime Human Segmentation')
+    parser.add_argument(
+        '--model_dir',
+        type=str,
+        default='',
+        help='path of human segmentation model')
+    parser.add_argument(
+        '--img_path', type=str, default='', help='path of input image')
+    parser.add_argument(
+        '--video_path', type=str, default='', help='path of input video')
+    parser.add_argument(
+        '--use_camera',
+        type=bool,
+        default=False,
+        help='input video stream from camera')
+    parser.add_argument(
+        '--use_gpu', type=bool, default=False, help='enable gpu')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

contrib/HumanSeg/deploy/python/infer_resizebylong.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.
+# ==============================================================================
+"""实时人像分割Python预测部署"""
+
+import os
+import argparse
+import numpy as np
+import cv2
+
+import paddle.fluid as fluid
+
+
+def humanseg_tracking(pre_gray, cur_gray, prev_cfd, dl_weights, disflow):
+    """计算光流跟踪匹配点和光流图
+    输入参数:
+        pre_gray: 上一帧灰度图
+        cur_gray: 当前帧灰度图
+        prev_cfd: 上一帧光流图
+        dl_weights: 融合权重图
+        disflow: 光流数据结构
+    返回值:
+        is_track: 光流点跟踪二值图，即是否具有光流点匹配
+        track_cfd: 光流跟踪图
+    """
+    check_thres = 8
+    hgt, wdh = pre_gray.shape[:2]
+    track_cfd = np.zeros_like(prev_cfd)
+    is_track = np.zeros_like(pre_gray)
+    # 计算前向光流
+    flow_fw = disflow.calc(pre_gray, cur_gray, None)
+    # 计算后向光流
+    flow_bw = disflow.calc(cur_gray, pre_gray, None)
+    get_round = lambda data: (int)(data + 0.5) if data >= 0 else (int)(data -
+                                                                       0.5)
+    for row in range(hgt):
+        for col in range(wdh):
+            # 计算光流处理后对应点坐标
+            # (row, col) -> (cur_x, cur_y)
+            fxy_fw = flow_fw[row, col]
+            dx_fw = get_round(fxy_fw[0])
+            cur_x = dx_fw + col
+            dy_fw = get_round(fxy_fw[1])
+            cur_y = dy_fw + row
+            if cur_x < 0 or cur_x >= wdh or cur_y < 0 or cur_y >= hgt:
+                continue
+            fxy_bw = flow_bw[cur_y, cur_x]
+            dx_bw = get_round(fxy_bw[0])
+            dy_bw = get_round(fxy_bw[1])
+            # 光流移动小于阈值
+            lmt = ((dy_fw + dy_bw) * (dy_fw + dy_bw) +
+                   (dx_fw + dx_bw) * (dx_fw + dx_bw))
+            if lmt >= check_thres:
+                continue
+            # 静止点降权
+            if abs(dy_fw) <= 0 and abs(dx_fw) <= 0 and abs(dy_bw) <= 0 and abs(
+                    dx_bw) <= 0:
+                dl_weights[cur_y, cur_x] = 0.05
+            is_track[cur_y, cur_x] = 1
+            track_cfd[cur_y, cur_x] = prev_cfd[row, col]
+    return track_cfd, is_track, dl_weights
+
+
+def humanseg_track_fuse(track_cfd, dl_cfd, dl_weights, is_track):
+    """光流追踪图和人像分割结构融合
+    输入参数:
+        track_cfd: 光流追踪图
+        dl_cfd: 当前帧分割结果
+        dl_weights: 融合权重图
+        is_track: 光流点匹配二值图
+    返回值:
+        cur_cfd: 光流跟踪图和人像分割结果融合图
+    """
+    cur_cfd = dl_cfd.copy()
+    idxs = np.where(is_track > 0)
+    for i in range(len(idxs)):
+        x, y = idxs[0][i], idxs[1][i]
+        dl_score = dl_cfd[y, x]
+        track_score = track_cfd[y, x]
+        if dl_score > 0.9 or dl_score < 0.1:
+            if dl_weights[x, y] < 0.1:
+                cur_cfd[x, y] = 0.3 * dl_score + 0.7 * track_score
+            else:
+                cur_cfd[x, y] = 0.4 * dl_score + 0.6 * track_score
+        else:
+            cur_cfd[x, y] = dl_weights[x, y] * dl_score + (
+                1 - dl_weights[x, y]) * track_score
+    return cur_cfd
+
+
+def threshold_mask(img, thresh_bg, thresh_fg):
+    """设置背景和前景阈值mask
+    输入参数:
+        img : 原始图像, np.uint8 类型.
+        thresh_bg : 背景阈值百分比，低于该值置为0.
+        thresh_fg : 前景阈值百分比，超过该值置为1.
+    返回值:
+        dst : 原始图像设置完前景背景阈值mask结果, np.float32 类型.
+    """
+    dst = (img / 255.0 - thresh_bg) / (thresh_fg - thresh_bg)
+    dst[np.where(dst > 1)] = 1
+    dst[np.where(dst < 0)] = 0
+    return dst.astype(np.float32)
+
+
+def optflow_handle(cur_gray, scoremap, is_init):
+    """光流优化
+    Args:
+        cur_gray : 当前帧灰度图
+        scoremap : 当前帧分割结果
+        is_init : 是否第一帧
+    Returns:
+        dst : 光流追踪图和预测结果融合图, 类型为 np.float32
+    """
+    width, height = scoremap.shape[0], scoremap.shape[1]
+    disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
+    prev_gray = np.zeros((height, width), np.uint8)
+    prev_cfd = np.zeros((height, width), np.float32)
+    cur_cfd = scoremap.copy()
+    if is_init:
+        is_init = False
+        if height <= 64 or width <= 64:
+            disflow.setFinestScale(1)
+        elif height <= 160 or width <= 160:
+            disflow.setFinestScale(2)
+        else:
+            disflow.setFinestScale(3)
+        fusion_cfd = cur_cfd
+    else:
+        weights = np.ones((width, height), np.float32) * 0.3
+        track_cfd, is_track, weights = humanseg_tracking(
+            prev_gray, cur_gray, prev_cfd, weights, disflow)
+        fusion_cfd = humanseg_track_fuse(track_cfd, cur_cfd, weights, is_track)
+    fusion_cfd = cv2.GaussianBlur(fusion_cfd, (3, 3), 0)
+    return fusion_cfd
+
+
+class HumanSeg:
+    """人像分割类
+    封装了人像分割模型的加载，数据预处理，预测，后处理等
+    """
+
+    def __init__(self, model_dir, mean, scale, long_size, use_gpu=False):
+
+        self.mean = np.array(mean).reshape((3, 1, 1))
+        self.scale = np.array(scale).reshape((3, 1, 1))
+        self.long_size = long_size
+        self.load_model(model_dir, use_gpu)
+
+    def load_model(self, model_dir, use_gpu):
+        """加载模型并创建predictor
+        Args:
+            model_dir: 预测模型路径, 包含 `__model__` 和 `__params__`
+            use_gpu: 是否使用GPU加速
+        """
+        prog_file = os.path.join(model_dir, '__model__')
+        params_file = os.path.join(model_dir, '__params__')
+        config = fluid.core.AnalysisConfig(prog_file, params_file)
+        if use_gpu:
+            config.enable_use_gpu(100, 0)
+            config.switch_ir_optim(True)
+        else:
+            config.disable_gpu()
+        config.disable_glog_info()
+        config.switch_specify_input_names(True)
+        config.enable_memory_optim()
+        self.predictor = fluid.core.create_paddle_predictor(config)
+
+    def preprocess(self, image):
+        """图像预处理
+        hwc_rgb 转换为 chw_bgr，并进行归一化
+        输入参数:
+            image: 原始图像
+        返回值:
+            经过预处理后的图片结果
+        """
+        origin_h, origin_w = image.shape[0], image.shape[1]
+        scale = float(self.long_size) / max(origin_w, origin_h)
+        resize_w = int(round(origin_w * scale))
+        resize_h = int(round(origin_h * scale))
+        img_mat = cv2.resize(
+            image, (resize_w, resize_h), interpolation=cv2.INTER_LINEAR)
+        pad_h = self.long_size - resize_h
+        pad_w = self.long_size - resize_w
+        img_mat = cv2.copyMakeBorder(
+            img_mat,
+            0,
+            pad_h,
+            0,
+            pad_w,
+            cv2.BORDER_CONSTANT,
+            value=[127.5, 127.5, 127.5])
+
+        # HWC -> CHW
+        img_mat = img_mat.swapaxes(1, 2)
+        img_mat = img_mat.swapaxes(0, 1)
+        # Convert to float
+        img_mat = img_mat[:, :, :].astype('float32')
+        img_mat = (img_mat / 255. - self.mean) / self.scale
+        img_mat = img_mat[np.newaxis, :, :, :]
+        return img_mat
+
+    def postprocess(self, image, output_data):
+        """对预测结果进行后处理
+        Args:
+             image: 原始图，opencv 图片对象
+             output_data: Paddle预测结果原始数据
+        Returns:
+             原图和预测结果融合并做了光流优化的结果图
+        """
+        scoremap = output_data[0, 1, :, :]
+        scoremap = (scoremap * 255).astype(np.uint8)
+        # 光流处理
+        cur_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        origin_h, origin_w = image.shape[0], image.shape[1]
+        scale = float(self.long_size) / max(origin_w, origin_h)
+        resize_w = int(round(origin_w * scale))
+        resize_h = int(round(origin_h * scale))
+        cur_gray = cv2.resize(
+            cur_gray, (resize_w, resize_h), interpolation=cv2.INTER_LINEAR)
+        pad_h = self.long_size - resize_h
+        pad_w = self.long_size - resize_w
+        cur_gray = cv2.copyMakeBorder(
+            cur_gray, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=127.5)
+        optflow_map = optflow_handle(cur_gray, scoremap, False)
+        optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
+        optflow_map = threshold_mask(optflow_map, thresh_bg=0.2, thresh_fg=0.8)
+        optflow_map = optflow_map[0:resize_h, 0:resize_w]
+        optflow_map = cv2.resize(optflow_map, (origin_w, origin_h))
+        optflow_map = np.repeat(optflow_map[:, :, np.newaxis], 3, axis=2)
+        bg_im = np.ones_like(optflow_map) * 255
+        comb = (optflow_map * image + (1 - optflow_map) * bg_im).astype(
+            np.uint8)
+        return comb
+
+    def run_predict(self, image):
+        """运行预测并返回可视化结果图
+        输入参数:
+            image: 需要预测的原始图, opencv图片对象
+        返回值:
+            可视化的预测结果图
+        """
+        im_mat = self.preprocess(image)
+        im_tensor = fluid.core.PaddleTensor(im_mat.copy().astype('float32'))
+        output_data = self.predictor.run([im_tensor])[1]
+        output_data = output_data.as_ndarray()
+        return self.postprocess(image, output_data)
+
+    def image_segment(self, path):
+        """对图片文件进行分割
+            结果保存到`result.jpeg`文件中
+            """
+        img_mat = cv2.imread(path)
+        img_mat = self.run_predict(img_mat)
+        cv2.imwrite('result.jpeg', img_mat)
+
+    def video_segment(self, path=None):
+        """
+        对视屏流进行分割，
+        path为None时默认打开摄像头。
+        """
+        if path is None:
+            cap = cv2.VideoCapture(0)
+        else:
+            cap = cv2.VideoCapture(path)
+        if not cap.isOpened():
+            raise IOError("Error opening video stream or file")
+            return
+
+        if path is not None:
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fps = cap.get(cv2.CAP_PROP_FPS)
+            # 用于保存预测结果视频
+            out = cv2.VideoWriter('result.avi',
+                                  cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'),
+                                  fps, (width, height))
+            # 开始获取视频帧
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if ret:
+                    img_mat = self.run_predict(frame)
+                    out.write(img_mat)
+                else:
+                    break
+            cap.release()
+            out.release()
+
+        else:
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if ret:
+                    img_mat = self.run_predict(frame)
+                    cv2.imshow('HumanSegmentation', img_mat)
+                    if cv2.waitKey(1) & 0xFF == ord('q'):
+                        break
+                else:
+                    break
+            cap.release()
+
+
+def main(args):
+    """预测程序入口
+    完成模型加载, 对视频、摄像头、图片文件等预测过程
+    """
+    model_dir = args.model_dir
+    use_gpu = args.use_gpu
+
+    # 加载模型
+    mean = [0.5, 0.5, 0.5]
+    scale = [0.5, 0.5, 0.5]
+    long_size = 192
+    model = HumanSeg(model_dir, mean, scale, long_size, use_gpu)
+    if args.use_camera:
+        # 开启摄像头
+        model.video_segment()
+    elif args.video_path:
+        # 使用视频文件作为输入
+        model.video_segment(args.video_path)
+    elif args.img_path:
+        # 使用图片文件作为输入
+        model.image_segment(args.img_path)
+    else:
+        raise ValueError(
+            'One of (--model_dir, --video_path, --use_camera) should be given.')
+
+
+def parse_args():
+    """解析命令行参数
+    """
+    parser = argparse.ArgumentParser('Realtime Human Segmentation')
+    parser.add_argument(
+        '--model_dir',
+        type=str,
+        default='',
+        help='path of human segmentation model')
+    parser.add_argument(
+        '--img_path', type=str, default='', help='path of input image')
+    parser.add_argument(
+        '--video_path', type=str, default='', help='path of input video')
+    parser.add_argument(
+        '--use_camera',
+        type=bool,
+        default=False,
+        help='input video stream from camera')
+    parser.add_argument(
+        '--use_gpu', type=bool, default=False, help='enable gpu')
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

contrib/HumanSeg/deploy/python/requirements.txt

+opencv-python==4.1.2.30
+opencv-contrib-python==4.2.0.32

contrib/HumanSeg/human_infer.py

+import argparse
+import os
+import os.path as osp
+import cv2
+import numpy as np
+import tqdm
+
+import HumanSeg
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='HumanSeg inference and visualization')
+    parser.add_argument(
+        '--test_model',
+        dest='test_model',
+        help='model path for inference',
+        type=str)
+    parser.add_argument(
+        '--data_dir',
+        dest='data_dir',
+        help='the root directory of dataset',
+        type=str)
+    parser.add_argument(
+        '--file_list', dest='file_list', help='file list for test', type=str)
+    parser.add_argument(
+        '--save_dir',
+        dest='save_dir',
+        help='the directory for saveing the inferenc results',
+        type=str,
+        default='./result')
+    return parser.parse_args()
+
+
+def mkdir(path):
+    sub_dir = osp.dirname(path)
+    if not osp.exists(sub_dir):
+        os.makedirs(sub_dir)
+
+
+def process(args):
+    model = HumanSeg.models.load_model(args.test_model)
+    added_saveed_path = osp.join(args.save_dir, 'added')
+    mat_saved_path = osp.join(args.save_dir, 'mat')
+    scoremap_saved_path = osp.join(args.save_dir, 'scoremap')
+
+    with open(args.file_list, 'r') as f:
+        files = f.readlines()
+
+    for file in tqdm.tqdm(files):
+        file = file.strip()
+        im_file = osp.join(args.data_dir, file)
+        im = cv2.imread(im_file)
+        result = model.predict(im)
+
+        # save added image
+        added_image = HumanSeg.utils.visualize(im_file, result, weight=0.6)
+        added_image_file = osp.join(added_saveed_path, file)
+        mkdir(added_image_file)
+        cv2.imwrite(added_image_file, added_image)
+
+        # save score map
+        score_map = result['score_map'][:, :, 1]
+        score_map = (score_map * 255).astype(np.uint8)
+        score_map_file = osp.join(scoremap_saved_path, file)
+        mkdir(score_map_file)
+        cv2.imwrite(score_map_file, score_map)
+
+        # save mat image
+        score_map = np.expand_dims(score_map, axis=-1)
+        mat_image = np.concatenate([im, score_map], axis=2)
+        mat_file = osp.join(mat_saved_path, file)
+        ext = osp.splitext(mat_file)[-1]
+        mat_file = mat_file.replace(ext, '.png')
+        mkdir(mat_file)
+        cv2.imwrite(mat_file, mat_image)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    process(args)

contrib/HumanSeg/models/__init__.py

 from .humanseg import HumanSegMobile
+from .humanseg import HumanSegServer
+from .humanseg import HumanSegLite
+from .humanseg import HRNet
+from .load_model import load_model

contrib/HumanSeg/models/humanseg.py

@@ -24,6 +24,7 @@ import time
 import tqdm
 import cv2
 import yaml
+import paddleslim as slim
 
 import HumanSeg
 import HumanSeg.utils.logging as logging
@@ -42,16 +43,10 @@ def dict2str(dict_input):
     return out.strip(', ')
 
 
-class HumanSegMobile(object):
+class SegModel(object):
     # DeepLab mobilenet
     def __init__(self,
                  num_classes=2,
-                 backbone='MobileNetV2_x1.0',
-                 output_stride=16,
-                 aspp_with_sep_conv=True,
-                 decoder_use_sep_conv=True,
-                 encoder_with_aspp=False,
-                 enable_decoder=False,
                  use_bce_loss=False,
                  use_dice_loss=False,
                  class_weight=None,
@@ -63,19 +58,6 @@ class HumanSegMobile(object):
                 "dice loss and bce loss is only applicable to binary classfication"
             )
 
-        self.output_stride = output_stride
-
-        if backbone not in [
-                'Xception65', 'Xception41', 'MobileNetV2_x0.25',
-                'MobileNetV2_x0.5', 'MobileNetV2_x1.0', 'MobileNetV2_x1.5',
-                'MobileNetV2_x2.0'
-        ]:
-            raise ValueError(
-                "backbone: {} is set wrong. it should be one of "
-                "('Xception65', 'Xception41', 'MobileNetV2_x0.25', 'MobileNetV2_x0.5',"
-                " 'MobileNetV2_x1.0', 'MobileNetV2_x1.5', 'MobileNetV2_x2.0')".
-                format(backbone))
-
         if class_weight is not None:
             if isinstance(class_weight, list):
                 if len(class_weight) != num_classes:
@@ -91,16 +73,11 @@ class HumanSegMobile(object):
                     'Expect class_weight is a list or string but receive {}'.
                     format(type(class_weight)))
 
-        self.backbone = backbone
         self.num_classes = num_classes
         self.use_bce_loss = use_bce_loss
         self.use_dice_loss = use_dice_loss
         self.class_weight = class_weight
         self.ignore_index = ignore_index
-        self.aspp_with_sep_conv = aspp_with_sep_conv
-        self.decoder_use_sep_conv = decoder_use_sep_conv
-        self.encoder_with_aspp = encoder_with_aspp
-        self.enable_decoder = enable_decoder
         self.sync_bn = sync_bn
 
         self.labels = None
@@ -118,6 +95,9 @@ class HumanSegMobile(object):
         self.train_outputs = None
         self.test_outputs = None
         self.train_data_loader = None
+        self.eval_metrics = None
+        # 当前模型状态
+        self.status = 'Normal'
 
     def _get_single_car_bs(self, batch_size):
         if batch_size % len(self.places) == 0:
@@ -130,29 +110,7 @@ class HumanSegMobile(object):
 
     def build_net(self, mode='train'):
         """应根据不同的情况进行构建"""
-        model = HumanSeg.nets.DeepLabv3p(
-            self.num_classes,
-            mode=mode,
-            backbone=self.backbone,
-            output_stride=self.output_stride,
-            aspp_with_sep_conv=self.aspp_with_sep_conv,
-            decoder_use_sep_conv=self.decoder_use_sep_conv,
-            encoder_with_aspp=self.encoder_with_aspp,
-            enable_decoder=self.enable_decoder,
-            use_bce_loss=self.use_bce_loss,
-            use_dice_loss=self.use_dice_loss,
-            class_weight=self.class_weight,
-            ignore_index=self.ignore_index)
-        inputs = model.generate_inputs()
-        model_out = model.build_net(inputs)
-        outputs = OrderedDict()
-        if mode == 'train':
-            self.optimizer.minimize(model_out)
-            outputs['loss'] = model_out
-        else:
-            outputs['pred'] = model_out[0]
-            outputs['logit'] = model_out[1]
-        return inputs, outputs
+        pass
 
     def build_program(self):
         # build training network
@@ -276,27 +234,134 @@ class HumanSegMobile(object):
             if osp.exists(save_dir):
                 os.remove(save_dir)
             os.makedirs(save_dir)
-
-        fluid.save(self.train_prog, osp.join(save_dir, 'model'))
         model_info = self.get_model_info()
+
+        if self.status == 'Normal':
+            fluid.save(self.train_prog, osp.join(save_dir, 'model'))
+        elif self.status == 'Quant':
+            float_prog, _ = slim.quant.convert(
+                self.test_prog, self.exe.place, save_int8=True)
+            test_input_names = [
+                var.name for var in list(self.test_inputs.values())
+            ]
+            test_outputs = list(self.test_outputs.values())
+            fluid.io.save_inference_model(
+                dirname=save_dir,
+                executor=self.exe,
+                params_filename='__params__',
+                feeded_var_names=test_input_names,
+                target_vars=test_outputs,
+                main_program=float_prog)
+
+            model_info['_ModelInputsOutputs'] = dict()
+            model_info['_ModelInputsOutputs']['test_inputs'] = [
+                [k, v.name] for k, v in self.test_inputs.items()
+            ]
+            model_info['_ModelInputsOutputs']['test_outputs'] = [
+                [k, v.name] for k, v in self.test_outputs.items()
+            ]
+
+        model_info['status'] = self.status
         with open(
                 osp.join(save_dir, 'model.yml'), encoding='utf-8',
                 mode='w') as f:
             yaml.dump(model_info, f)
+
+        # The flag of model for saving successfully
         open(osp.join(save_dir, '.success'), 'w').close()
         logging.info("Model saved in {}.".format(save_dir))
 
     def export_inference_model(self, save_dir):
-        pass
+        test_input_names = [var.name for var in list(self.test_inputs.values())]
+        test_outputs = list(self.test_outputs.values())
+        fluid.io.save_inference_model(
+            dirname=save_dir,
+            executor=self.exe,
+            params_filename='__params__',
+            feeded_var_names=test_input_names,
+            target_vars=test_outputs,
+            main_program=self.test_prog)
+        model_info = self.get_model_info()
+        model_info['status'] = 'Infer'
 
-    def export_quant_model(self):
-        pass
+        # Save input and output descrition of model
+        model_info['_ModelInputsOutputs'] = dict()
+        model_info['_ModelInputsOutputs']['test_inputs'] = [
+            [k, v.name] for k, v in self.test_inputs.items()
+        ]
+        model_info['_ModelInputsOutputs']['test_outputs'] = [
+            [k, v.name] for k, v in self.test_outputs.items()
+        ]
+
+        with open(
+                osp.join(save_dir, 'model.yml'), encoding='utf-8',
+                mode='w') as f:
+            yaml.dump(model_info, f)
+
+        # The flag of model for saving successfully
+        open(osp.join(save_dir, '.success'), 'w').close()
+        logging.info("Model for inference deploy saved in {}.".format(save_dir))
+
+    def export_quant_model(self,
+                           dataset,
+                           save_dir,
+                           batch_size=1,
+                           batch_nums=10,
+                           cache_dir="./temp"):
+        self.arrange_transform(transforms=dataset.transforms, mode='quant')
+        dataset.num_samples = batch_size * batch_nums
+        try:
+            from HumanSeg.utils import HumanSegPostTrainingQuantization
+        except:
+            raise Exception(
+                "Model Quantization is not available, try to upgrade your paddlepaddle>=1.7.0"
+            )
+        is_use_cache_file = True
+        if cache_dir is None:
+            is_use_cache_file = False
+        post_training_quantization = HumanSegPostTrainingQuantization(
+            executor=self.exe,
+            dataset=dataset,
+            program=self.test_prog,
+            inputs=self.test_inputs,
+            outputs=self.test_outputs,
+            batch_size=batch_size,
+            batch_nums=batch_nums,
+            scope=None,
+            algo='KL',
+            quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
+            is_full_quantize=False,
+            is_use_cache_file=is_use_cache_file,
+            cache_dir=cache_dir)
+        post_training_quantization.quantize()
+        post_training_quantization.save_quantized_model(save_dir)
+        model_info = self.get_model_info()
+        model_info['status'] = 'Quant'
+
+        # Save input and output descrition of model
+        model_info['_ModelInputsOutputs'] = dict()
+        model_info['_ModelInputsOutputs']['test_inputs'] = [
+            [k, v.name] for k, v in self.test_inputs.items()
+        ]
+        model_info['_ModelInputsOutputs']['test_outputs'] = [
+            [k, v.name] for k, v in self.test_outputs.items()
+        ]
+
+        with open(
+                osp.join(save_dir, 'model.yml'), encoding='utf-8',
+                mode='w') as f:
+            yaml.dump(model_info, f)
+
+        # The flag of model for saving successfully
+        open(osp.join(save_dir, '.success'), 'w').close()
+        logging.info("Model for quant saved in {}.".format(save_dir))
 
     def default_optimizer(self,
                           learning_rate,
                           num_epochs,
                           num_steps_each_epoch,
-                          lr_decay_power=0.9):
+                          lr_decay_power=0.9,
+                          regularization_coeff=4e-5):
         decay_step = num_epochs * num_steps_each_epoch
         lr_decay = fluid.layers.polynomial_decay(
             learning_rate,
@@ -307,7 +372,7 @@ class HumanSegMobile(object):
             lr_decay,
             momentum=0.9,
             regularization=fluid.regularizer.L2Decay(
-                regularization_coeff=4e-05))
+                regularization_coeff=regularization_coeff))
         return optimizer
 
     def train(self,
@@ -323,7 +388,9 @@ class HumanSegMobile(object):
               optimizer=None,
               learning_rate=0.01,
               lr_decay_power=0.9,
-              use_vdl=False):
+              regularization_coeff=4e-5,
+              use_vdl=False,
+              quant=False):
         self.labels = train_dataset.labels
         self.train_transforms = train_dataset.transforms
         self.train_init = locals()
@@ -335,13 +402,27 @@ class HumanSegMobile(object):
                 learning_rate=learning_rate,
                 num_epochs=num_epochs,
                 num_steps_each_epoch=num_steps_each_epoch,
-                lr_decay_power=lr_decay_power)
+                lr_decay_power=lr_decay_power,
+                regularization_coeff=regularization_coeff)
         self.optimizer = optimizer
         self.build_program()
         self.net_initialize(
             startup_prog=fluid.default_startup_program(),
             pretrain_weights=pretrain_weights,
             resume_weights=resume_weights)
+
+        # 进行量化
+        if quant:
+            # 当 for_test=False ，返回类型为 fluid.CompiledProgram
+            # 当 for_test=True ，返回类型为 fluid.Program
+            self.train_prog = slim.quant.quant_aware(
+                self.train_prog, self.exe.place, for_test=False)
+            self.test_prog = slim.quant.quant_aware(
+                self.test_prog, self.exe.place, for_test=True)
+            # self.parallel_train_prog = self.train_prog.with_data_parallel(
+            #     loss_name=self.train_outputs['loss'].name)
+            self.status = 'Quant'
+
         if self.begin_epoch >= num_epochs:
             raise ValueError(
                 ("begin epoch[{}] is larger than num_epochs[{}]").format(
@@ -374,11 +455,19 @@ class HumanSegMobile(object):
                 build_strategy.sync_batch_norm = self.sync_bn
             exec_strategy = fluid.ExecutionStrategy()
             exec_strategy.num_iteration_per_drop_scope = 1
-            self.parallel_train_prog = fluid.CompiledProgram(
-                self.train_prog).with_data_parallel(
+            if quant:
+                build_strategy.fuse_all_reduce_ops = False
+                build_strategy.sync_batch_norm = False
+                self.parallel_train_prog = self.train_prog.with_data_parallel(
                     loss_name=self.train_outputs['loss'].name,
                     build_strategy=build_strategy,
                     exec_strategy=exec_strategy)
+            else:
+                self.parallel_train_prog = fluid.CompiledProgram(
+                    self.train_prog).with_data_parallel(
+                        loss_name=self.train_outputs['loss'].name,
+                        build_strategy=build_strategy,
+                        exec_strategy=exec_strategy)
 
         total_num_steps = math.floor(
             train_dataset.num_samples / train_batch_size)
@@ -478,8 +567,6 @@ class HumanSegMobile(object):
                         eval_dataset=eval_dataset,
                         batch_size=eval_batch_size,
                         epoch_id=i + 1)
-                    logging.info('[EVAL] Finished, Epoch={}, {} .'.format(
-                        i + 1, dict2str(self.eval_metrics)))
                     # 保存最优模型
                     current_miou = self.eval_metrics['miou']
                     if current_miou > best_miou:
@@ -567,18 +654,25 @@ class HumanSegMobile(object):
             zip(['miou', 'category_iou', 'macc', 'category_acc', 'kappa'],
                 [miou, category_iou, macc, category_acc,
                  conf_mat.kappa()]))
+
+        logging.info('[EVAL] Finished, Epoch={}, {} .'.format(
+            epoch_id, dict2str(metrics)))
         return metrics
 
     def predict(self, im_file, transforms=None):
         """预测。
         Args:
-            img_file(str): 预测图像路径。
+            img_file(str|np.ndarray): 预测图像。
             transforms(paddlex.cv.transforms): 数据预处理操作。
 
         Returns:
             dict: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图，
                 像素值表示对应的类别，'score_map'存储各类别的概率，shape=(h, w, num_classes)
         """
+        if isinstance(im_file, str):
+            if not osp.exists(im_file):
+                raise ValueError(
+                    'The Image file does not exist: {}'.format(im_file))
 
         if transforms is None and not hasattr(self, 'test_transforms'):
             raise Exception("transforms need to be defined, now is None.")
@@ -612,18 +706,231 @@ class HumanSegMobile(object):
         return {'label_map': pred, 'score_map': logit}
 
 
-class HumanSegLite(object):
-    # DeepLab ShuffleNet
-    def train(self):
-        pass
+class HumanSegMobile(SegModel):
+    # DeepLab mobilenet
+    def __init__(self,
+                 num_classes=2,
+                 backbone='MobileNetV2_x1.0',
+                 output_stride=16,
+                 aspp_with_sep_conv=True,
+                 decoder_use_sep_conv=True,
+                 encoder_with_aspp=False,
+                 enable_decoder=False,
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255,
+                 sync_bn=True):
+        super().__init__(
+            num_classes=num_classes,
+            use_bce_loss=use_bce_loss,
+            use_dice_loss=use_dice_loss,
+            class_weight=class_weight,
+            ignore_index=ignore_index,
+            sync_bn=sync_bn)
+        self.init_params = locals()
 
-    def evaluate(self):
-        pass
+        self.output_stride = output_stride
 
-    def predict(self):
-        pass
+        if backbone not in [
+                'MobileNetV2_x0.25', 'MobileNetV2_x0.5', 'MobileNetV2_x1.0',
+                'MobileNetV2_x1.5', 'MobileNetV2_x2.0'
+        ]:
+            raise ValueError(
+                "backbone: {} is set wrong. it should be one of "
+                "('MobileNetV2_x0.25', 'MobileNetV2_x0.5',"
+                " 'MobileNetV2_x1.0', 'MobileNetV2_x1.5', 'MobileNetV2_x2.0')".
+                format(backbone))
+
+        self.backbone = backbone
+        self.aspp_with_sep_conv = aspp_with_sep_conv
+        self.decoder_use_sep_conv = decoder_use_sep_conv
+        self.encoder_with_aspp = encoder_with_aspp
+        self.enable_decoder = enable_decoder
+        self.sync_bn = sync_bn
+
+    def build_net(self, mode='train'):
+        model = HumanSeg.nets.DeepLabv3p(
+            self.num_classes,
+            mode=mode,
+            backbone=self.backbone,
+            output_stride=self.output_stride,
+            aspp_with_sep_conv=self.aspp_with_sep_conv,
+            decoder_use_sep_conv=self.decoder_use_sep_conv,
+            encoder_with_aspp=self.encoder_with_aspp,
+            enable_decoder=self.enable_decoder,
+            use_bce_loss=self.use_bce_loss,
+            use_dice_loss=self.use_dice_loss,
+            class_weight=self.class_weight,
+            ignore_index=self.ignore_index)
+        inputs = model.generate_inputs()
+        model_out = model.build_net(inputs)
+        outputs = OrderedDict()
+        if mode == 'train':
+            self.optimizer.minimize(model_out)
+            outputs['loss'] = model_out
+        else:
+            outputs['pred'] = model_out[0]
+            outputs['logit'] = model_out[1]
+        return inputs, outputs
 
 
-class HumanSegServer(object):
+class HumanSegLite(SegModel):
+    # DeepLab ShuffleNet
+    def build_net(self, mode='train'):
+        """应根据不同的情况进行构建"""
+        model = HumanSeg.nets.ShuffleSeg(
+            self.num_classes,
+            mode=mode,
+            use_bce_loss=self.use_bce_loss,
+            use_dice_loss=self.use_dice_loss,
+            class_weight=self.class_weight,
+            ignore_index=self.ignore_index)
+        inputs = model.generate_inputs()
+        model_out = model.build_net(inputs)
+        outputs = OrderedDict()
+        if mode == 'train':
+            self.optimizer.minimize(model_out)
+            outputs['loss'] = model_out
+        else:
+            outputs['pred'] = model_out[0]
+            outputs['logit'] = model_out[1]
+        return inputs, outputs
+
+
+class HumanSegServer(SegModel):
     # DeepLab Xception
-    pass
+    def __init__(self,
+                 num_classes=2,
+                 backbone='Xception65',
+                 output_stride=16,
+                 aspp_with_sep_conv=True,
+                 decoder_use_sep_conv=True,
+                 encoder_with_aspp=True,
+                 enable_decoder=True,
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255,
+                 sync_bn=True):
+        super().__init__(
+            num_classes=num_classes,
+            use_bce_loss=use_bce_loss,
+            use_dice_loss=use_dice_loss,
+            class_weight=class_weight,
+            ignore_index=ignore_index,
+            sync_bn=sync_bn)
+        self.init_params = locals()
+
+        self.output_stride = output_stride
+
+        if backbone not in ['Xception65', 'Xception41']:
+            raise ValueError("backbone: {} is set wrong. it should be one of "
+                             "('Xception65', 'Xception41')".format(backbone))
+
+        self.backbone = backbone
+        self.aspp_with_sep_conv = aspp_with_sep_conv
+        self.decoder_use_sep_conv = decoder_use_sep_conv
+        self.encoder_with_aspp = encoder_with_aspp
+        self.enable_decoder = enable_decoder
+        self.sync_bn = sync_bn
+
+    def build_net(self, mode='train'):
+        model = HumanSeg.nets.DeepLabv3p(
+            self.num_classes,
+            mode=mode,
+            backbone=self.backbone,
+            output_stride=self.output_stride,
+            aspp_with_sep_conv=self.aspp_with_sep_conv,
+            decoder_use_sep_conv=self.decoder_use_sep_conv,
+            encoder_with_aspp=self.encoder_with_aspp,
+            enable_decoder=self.enable_decoder,
+            use_bce_loss=self.use_bce_loss,
+            use_dice_loss=self.use_dice_loss,
+            class_weight=self.class_weight,
+            ignore_index=self.ignore_index)
+        inputs = model.generate_inputs()
+        model_out = model.build_net(inputs)
+        outputs = OrderedDict()
+        if mode == 'train':
+            self.optimizer.minimize(model_out)
+            outputs['loss'] = model_out
+        else:
+            outputs['pred'] = model_out[0]
+            outputs['logit'] = model_out[1]
+        return inputs, outputs
+
+
+class HRNet(SegModel):
+    def __init__(self,
+                 num_classes=2,
+                 stage1_num_modules=1,
+                 stage1_num_blocks=[4],
+                 stage1_num_channels=[64],
+                 stage2_num_modules=1,
+                 stage2_num_blocks=[4, 4],
+                 stage2_num_channels=[18, 36],
+                 stage3_num_modules=4,
+                 stage3_num_blocks=[4, 4, 4],
+                 stage3_num_channels=[18, 36, 72],
+                 stage4_num_modules=3,
+                 stage4_num_blocks=[4, 4, 4, 4],
+                 stage4_num_channels=[18, 36, 72, 144],
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255,
+                 sync_bn=True):
+        super().__init__(
+            num_classes=num_classes,
+            use_bce_loss=use_bce_loss,
+            use_dice_loss=use_dice_loss,
+            class_weight=class_weight,
+            ignore_index=ignore_index,
+            sync_bn=sync_bn)
+        self.init_params = locals()
+
+        self.stage1_num_modules = stage1_num_modules
+        self.stage1_num_blocks = stage1_num_blocks
+        self.stage1_num_channels = stage1_num_channels
+        self.stage2_num_modules = stage2_num_modules
+        self.stage2_num_blocks = stage2_num_blocks
+        self.stage2_num_channels = stage2_num_channels
+        self.stage3_num_modules = stage3_num_modules
+        self.stage3_num_blocks = stage3_num_blocks
+        self.stage3_num_channels = stage3_num_channels
+        self.stage4_num_modules = stage4_num_modules
+        self.stage4_num_blocks = stage4_num_blocks
+        self.stage4_num_channels = stage4_num_channels
+
+    def build_net(self, mode='train'):
+        """应根据不同的情况进行构建"""
+        model = HumanSeg.nets.HRNet(
+            self.num_classes,
+            mode=mode,
+            stage1_num_modules=self.stage1_num_modules,
+            stage1_num_blocks=self.stage1_num_blocks,
+            stage1_num_channels=self.stage1_num_channels,
+            stage2_num_modules=self.stage2_num_modules,
+            stage2_num_blocks=self.stage2_num_blocks,
+            stage2_num_channels=self.stage2_num_channels,
+            stage3_num_modules=self.stage3_num_modules,
+            stage3_num_blocks=self.stage3_num_blocks,
+            stage3_num_channels=self.stage3_num_channels,
+            stage4_num_modules=self.stage4_num_modules,
+            stage4_num_blocks=self.stage4_num_blocks,
+            stage4_num_channels=self.stage4_num_channels,
+            use_bce_loss=self.use_bce_loss,
+            use_dice_loss=self.use_dice_loss,
+            class_weight=self.class_weight,
+            ignore_index=self.ignore_index)
+        inputs = model.generate_inputs()
+        model_out = model.build_net(inputs)
+        outputs = OrderedDict()
+        if mode == 'train':
+            self.optimizer.minimize(model_out)
+            outputs['loss'] = model_out
+        else:
+            outputs['pred'] = model_out[0]
+            outputs['logit'] = model_out[1]
+        return inputs, outputs

contrib/HumanSeg/models/load_model.py

+# copyright (c) 2020 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.
+
+import yaml
+import os.path as osp
+import six
+import copy
+from collections import OrderedDict
+import paddle.fluid as fluid
+import HumanSeg
+import HumanSeg.utils.logging as logging
+
+
+def load_model(model_dir):
+    if not osp.exists(osp.join(model_dir, "model.yml")):
+        raise Exception("There's not model.yml in {}".format(model_dir))
+    with open(osp.join(model_dir, "model.yml")) as f:
+        info = yaml.load(f.read(), Loader=yaml.Loader)
+    status = info['status']
+
+    if not hasattr(HumanSeg.models, info['Model']):
+        raise Exception("There's no attribute {} in HumanSeg.models".format(
+            info['Model']))
+    model = getattr(HumanSeg.models, info['Model'])(**info['_init_params'])
+    if status == "Normal":
+        startup_prog = fluid.Program()
+        model.test_prog = fluid.Program()
+        with fluid.program_guard(model.test_prog, startup_prog):
+            with fluid.unique_name.guard():
+                model.test_inputs, model.test_outputs = model.build_net(
+                    mode='test')
+        model.test_prog = model.test_prog.clone(for_test=True)
+        model.exe.run(startup_prog)
+        import pickle
+        with open(osp.join(model_dir, 'model.pdparams'), 'rb') as f:
+            load_dict = pickle.load(f)
+        fluid.io.set_program_state(model.test_prog, load_dict)
+
+    elif status in ['Infer', 'Quant']:
+        [prog, input_names, outputs] = fluid.io.load_inference_model(
+            model_dir, model.exe, params_filename='__params__')
+        model.test_prog = prog
+        test_outputs_info = info['_ModelInputsOutputs']['test_outputs']
+        model.test_inputs = OrderedDict()
+        model.test_outputs = OrderedDict()
+        for name in input_names:
+            model.test_inputs[name] = model.test_prog.global_block().var(name)
+        for i, out in enumerate(outputs):
+            var_desc = test_outputs_info[i]
+            model.test_outputs[var_desc[0]] = out
+    if 'test_transforms' in info:
+        model.test_transforms = build_transforms(info['test_transforms'])
+        model.eval_transforms = copy.deepcopy(model.test_transforms)
+
+    if '_Attributes' in info:
+        for k, v in info['_Attributes'].items():
+            if k in model.__dict__:
+                model.__dict__[k] = v
+
+    logging.info("Model[{}] loaded.".format(info['Model']))
+    return model
+
+
+def build_transforms(transforms_info):
+    import HumanSeg.transforms as T
+    transforms = list()
+    for op_info in transforms_info:
+        op_name = list(op_info.keys())[0]
+        op_attr = op_info[op_name]
+        if not hasattr(T, op_name):
+            raise Exception(
+                "There's no operator named '{}' in transforms".format(op_name))
+        transforms.append(getattr(T, op_name)(**op_attr))
+    eval_transforms = T.Compose(transforms)
+    return eval_transforms

contrib/HumanSeg/nets/__init__.py

 from .backbone import mobilenet_v2
-from .backbone import shufflenet_slim
 from .backbone import xception
 from .unet import UNet
 from .deeplabv3p import DeepLabv3p
+from .shufflenet_slim import ShuffleSeg
+from .hrnet import HRNet

contrib/HumanSeg/nets/hrnet.py

+# coding: utf8
+# copyright (c) 2020 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
+
+from collections import OrderedDict
+
+import paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+from .seg_modules import softmax_with_loss
+from .seg_modules import dice_loss
+from .seg_modules import bce_loss
+from .libs import sigmoid_to_softmax
+
+
+class HRNet(object):
+    def __init__(self,
+                 num_classes,
+                 mode='train',
+                 stage1_num_modules=1,
+                 stage1_num_blocks=[4],
+                 stage1_num_channels=[64],
+                 stage2_num_modules=1,
+                 stage2_num_blocks=[4, 4],
+                 stage2_num_channels=[18, 36],
+                 stage3_num_modules=4,
+                 stage3_num_blocks=[4, 4, 4],
+                 stage3_num_channels=[18, 36, 72],
+                 stage4_num_modules=3,
+                 stage4_num_blocks=[4, 4, 4, 4],
+                 stage4_num_channels=[18, 36, 72, 144],
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255):
+        # dice_loss或bce_loss只适用两类分割中
+        if num_classes > 2 and (use_bce_loss or use_dice_loss):
+            raise ValueError(
+                "dice loss and bce loss is only applicable to binary classfication"
+            )
+
+        if class_weight is not None:
+            if isinstance(class_weight, list):
+                if len(class_weight) != num_classes:
+                    raise ValueError(
+                        "Length of class_weight should be equal to number of classes"
+                    )
+            elif isinstance(class_weight, str):
+                if class_weight.lower() != 'dynamic':
+                    raise ValueError(
+                        "if class_weight is string, must be dynamic!")
+            else:
+                raise TypeError(
+                    'Expect class_weight is a list or string but receive {}'.
+                    format(type(class_weight)))
+
+        self.num_classes = num_classes
+        self.mode = mode
+        self.use_bce_loss = use_bce_loss
+        self.use_dice_loss = use_dice_loss
+        self.class_weight = class_weight
+        self.ignore_index = ignore_index
+        self.stage1_num_modules = stage1_num_modules
+        self.stage1_num_blocks = stage1_num_blocks
+        self.stage1_num_channels = stage1_num_channels
+        self.stage2_num_modules = stage2_num_modules
+        self.stage2_num_blocks = stage2_num_blocks
+        self.stage2_num_channels = stage2_num_channels
+        self.stage3_num_modules = stage3_num_modules
+        self.stage3_num_blocks = stage3_num_blocks
+        self.stage3_num_channels = stage3_num_channels
+        self.stage4_num_modules = stage4_num_modules
+        self.stage4_num_blocks = stage4_num_blocks
+        self.stage4_num_channels = stage4_num_channels
+
+    def build_net(self, inputs):
+        image = inputs['image']
+        logit = self._high_resolution_net(image, self.num_classes)
+        if self.num_classes == 1:
+            out = sigmoid_to_softmax(logit)
+            out = fluid.layers.transpose(out, [0, 2, 3, 1])
+        else:
+            out = fluid.layers.transpose(logit, [0, 2, 3, 1])
+
+        pred = fluid.layers.argmax(out, axis=3)
+        pred = fluid.layers.unsqueeze(pred, axes=[3])
+
+        if self.mode == 'train':
+            label = inputs['label']
+            mask = label != self.ignore_index
+            return self._get_loss(logit, label, mask)
+
+        else:
+            if self.num_classes == 1:
+                logit = sigmoid_to_softmax(logit)
+            else:
+                logit = fluid.layers.softmax(logit, axis=1)
+            return pred, logit
+
+        return logit
+
+    def generate_inputs(self):
+        inputs = OrderedDict()
+        inputs['image'] = fluid.data(
+            dtype='float32', shape=[None, 3, None, None], name='image')
+        if self.mode == 'train':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        elif self.mode == 'eval':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        return inputs
+
+    def _get_loss(self, logit, label, mask):
+        avg_loss = 0
+        if not (self.use_dice_loss or self.use_bce_loss):
+            avg_loss += softmax_with_loss(
+                logit,
+                label,
+                mask,
+                num_classes=self.num_classes,
+                weight=self.class_weight,
+                ignore_index=self.ignore_index)
+        else:
+            if self.use_dice_loss:
+                avg_loss += dice_loss(logit, label, mask)
+            if self.use_bce_loss:
+                avg_loss += bce_loss(
+                    logit, label, mask, ignore_index=self.ignore_index)
+
+        return avg_loss
+
+    def _conv_bn_layer(self,
+                       input,
+                       filter_size,
+                       num_filters,
+                       stride=1,
+                       padding=1,
+                       num_groups=1,
+                       if_act=True,
+                       name=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=num_groups,
+            act=None,
+            param_attr=ParamAttr(initializer=MSRA(), name=name + '_weights'),
+            bias_attr=False)
+        bn_name = name + '_bn'
+        bn = fluid.layers.batch_norm(
+            input=conv,
+            param_attr=ParamAttr(
+                name=bn_name + "_scale",
+                initializer=fluid.initializer.Constant(1.0)),
+            bias_attr=ParamAttr(
+                name=bn_name + "_offset",
+                initializer=fluid.initializer.Constant(0.0)),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+        if if_act:
+            bn = fluid.layers.relu(bn)
+        return bn
+
+    def _basic_block(self,
+                     input,
+                     num_filters,
+                     stride=1,
+                     downsample=False,
+                     name=None):
+        residual = input
+        conv = self._conv_bn_layer(
+            input=input,
+            filter_size=3,
+            num_filters=num_filters,
+            stride=stride,
+            name=name + '_conv1')
+        conv = self._conv_bn_layer(
+            input=conv,
+            filter_size=3,
+            num_filters=num_filters,
+            if_act=False,
+            name=name + '_conv2')
+        if downsample:
+            residual = self._conv_bn_layer(
+                input=input,
+                filter_size=1,
+                num_filters=num_filters,
+                if_act=False,
+                name=name + '_downsample')
+        return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
+
+    def _bottleneck_block(self,
+                          input,
+                          num_filters,
+                          stride=1,
+                          downsample=False,
+                          name=None):
+        residual = input
+        conv = self._conv_bn_layer(
+            input=input,
+            filter_size=1,
+            num_filters=num_filters,
+            name=name + '_conv1')
+        conv = self._conv_bn_layer(
+            input=conv,
+            filter_size=3,
+            num_filters=num_filters,
+            stride=stride,
+            name=name + '_conv2')
+        conv = self._conv_bn_layer(
+            input=conv,
+            filter_size=1,
+            num_filters=num_filters * 4,
+            if_act=False,
+            name=name + '_conv3')
+        if downsample:
+            residual = self._conv_bn_layer(
+                input=input,
+                filter_size=1,
+                num_filters=num_filters * 4,
+                if_act=False,
+                name=name + '_downsample')
+        return fluid.layers.elementwise_add(x=residual, y=conv, act='relu')
+
+    def _fuse_layers(self, x, channels, multi_scale_output=True, name=None):
+        out = []
+        for i in range(len(channels) if multi_scale_output else 1):
+            residual = x[i]
+            shape = fluid.layers.shape(residual)[-2:]
+            for j in range(len(channels)):
+                if j > i:
+                    y = self._conv_bn_layer(
+                        x[j],
+                        filter_size=1,
+                        num_filters=channels[i],
+                        if_act=False,
+                        name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))
+                    y = fluid.layers.resize_bilinear(input=y, out_shape=shape)
+                    residual = fluid.layers.elementwise_add(
+                        x=residual, y=y, act=None)
+                elif j < i:
+                    y = x[j]
+                    for k in range(i - j):
+                        if k == i - j - 1:
+                            y = self._conv_bn_layer(
+                                y,
+                                filter_size=3,
+                                num_filters=channels[i],
+                                stride=2,
+                                if_act=False,
+                                name=name + '_layer_' + str(i + 1) + '_' +
+                                str(j + 1) + '_' + str(k + 1))
+                        else:
+                            y = self._conv_bn_layer(
+                                y,
+                                filter_size=3,
+                                num_filters=channels[j],
+                                stride=2,
+                                name=name + '_layer_' + str(i + 1) + '_' +
+                                str(j + 1) + '_' + str(k + 1))
+                    residual = fluid.layers.elementwise_add(
+                        x=residual, y=y, act=None)
+
+            residual = fluid.layers.relu(residual)
+            out.append(residual)
+        return out
+
+    def _branches(self, x, block_num, channels, name=None):
+        out = []
+        for i in range(len(channels)):
+            residual = x[i]
+            for j in range(block_num[i]):
+                residual = self._basic_block(
+                    residual,
+                    channels[i],
+                    name=name + '_branch_layer_' + str(i + 1) + '_' +
+                    str(j + 1))
+            out.append(residual)
+        return out
+
+    def _high_resolution_module(self,
+                                x,
+                                blocks,
+                                channels,
+                                multi_scale_output=True,
+                                name=None):
+        residual = self._branches(x, blocks, channels, name=name)
+        out = self._fuse_layers(
+            residual,
+            channels,
+            multi_scale_output=multi_scale_output,
+            name=name)
+        return out
+
+    def _transition_layer(self, x, in_channels, out_channels, name=None):
+        num_in = len(in_channels)
+        num_out = len(out_channels)
+        out = []
+        for i in range(num_out):
+            if i < num_in:
+                if in_channels[i] != out_channels[i]:
+                    residual = self._conv_bn_layer(
+                        x[i],
+                        filter_size=3,
+                        num_filters=out_channels[i],
+                        name=name + '_layer_' + str(i + 1))
+                    out.append(residual)
+                else:
+                    out.append(x[i])
+            else:
+                residual = self._conv_bn_layer(
+                    x[-1],
+                    filter_size=3,
+                    num_filters=out_channels[i],
+                    stride=2,
+                    name=name + '_layer_' + str(i + 1))
+                out.append(residual)
+        return out
+
+    def _stage(self,
+               x,
+               num_modules,
+               num_blocks,
+               num_channels,
+               multi_scale_output=True,
+               name=None):
+        out = x
+        for i in range(num_modules):
+            if i == num_modules - 1 and multi_scale_output == False:
+                out = self._high_resolution_module(
+                    out,
+                    num_blocks,
+                    num_channels,
+                    multi_scale_output=False,
+                    name=name + '_' + str(i + 1))
+            else:
+                out = self._high_resolution_module(
+                    out, num_blocks, num_channels, name=name + '_' + str(i + 1))
+
+        return out
+
+    def _layer1(self, input, num_modules, num_blocks, num_channels, name=None):
+        # num_modules 默认为1,是否增加处理，官网实现为[1]，是否对齐。
+        conv = input
+        for i in range(num_blocks[0]):
+            conv = self._bottleneck_block(
+                conv,
+                num_filters=num_channels[0],
+                downsample=True if i == 0 else False,
+                name=name + '_' + str(i + 1))
+        return conv
+
+    def _high_resolution_net(self, input, num_classes):
+        x = self._conv_bn_layer(
+            input=input,
+            filter_size=3,
+            num_filters=self.stage1_num_channels[0],
+            stride=2,
+            if_act=True,
+            name='layer1_1')
+        x = self._conv_bn_layer(
+            input=x,
+            filter_size=3,
+            num_filters=self.stage1_num_channels[0],
+            stride=2,
+            if_act=True,
+            name='layer1_2')
+
+        la1 = self._layer1(
+            x,
+            self.stage1_num_modules,
+            self.stage1_num_blocks,
+            self.stage1_num_channels,
+            name='layer2')
+        tr1 = self._transition_layer([la1],
+                                     self.stage1_num_channels,
+                                     self.stage2_num_channels,
+                                     name='tr1')
+        st2 = self._stage(
+            tr1,
+            self.stage2_num_modules,
+            self.stage2_num_blocks,
+            self.stage2_num_channels,
+            name='st2')
+        tr2 = self._transition_layer(
+            st2, self.stage2_num_channels, self.stage3_num_channels, name='tr2')
+        st3 = self._stage(
+            tr2,
+            self.stage3_num_modules,
+            self.stage3_num_blocks,
+            self.stage3_num_channels,
+            name='st3')
+        tr3 = self._transition_layer(
+            st3, self.stage3_num_channels, self.stage4_num_channels, name='tr3')
+        st4 = self._stage(
+            tr3,
+            self.stage4_num_modules,
+            self.stage4_num_blocks,
+            self.stage4_num_channels,
+            name='st4')
+
+        # upsample
+        shape = fluid.layers.shape(st4[0])[-2:]
+        st4[1] = fluid.layers.resize_bilinear(st4[1], out_shape=shape)
+        st4[2] = fluid.layers.resize_bilinear(st4[2], out_shape=shape)
+        st4[3] = fluid.layers.resize_bilinear(st4[3], out_shape=shape)
+
+        out = fluid.layers.concat(st4, axis=1)
+        last_channels = sum(self.stage4_num_channels)
+
+        out = self._conv_bn_layer(
+            input=out,
+            filter_size=1,
+            num_filters=last_channels,
+            stride=1,
+            if_act=True,
+            name='conv-2')
+        out = fluid.layers.conv2d(
+            input=out,
+            num_filters=num_classes,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            act=None,
+            param_attr=ParamAttr(initializer=MSRA(), name='conv-1_weights'),
+            bias_attr=False)
+
+        input_shape = fluid.layers.shape(input)[-2:]
+        out = fluid.layers.resize_bilinear(out, input_shape)
+
+        return out

contrib/HumanSeg/nets/shufflenet_slim.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from collections import OrderedDict
+import paddle.fluid as fluid
+from paddle.fluid.initializer import MSRA
+from paddle.fluid.param_attr import ParamAttr
+from .libs import sigmoid_to_softmax
+from .seg_modules import softmax_with_loss
+from .seg_modules import dice_loss
+from .seg_modules import bce_loss
+
+
+class ShuffleSeg(object):
+    # def __init__(self):
+    # self.params = train_parameters
+    def __init__(self,
+                 num_classes,
+                 mode='train',
+                 use_bce_loss=False,
+                 use_dice_loss=False,
+                 class_weight=None,
+                 ignore_index=255):
+        # dice_loss或bce_loss只适用两类分割中
+        if num_classes > 2 and (use_bce_loss or use_dice_loss):
+            raise ValueError(
+                "dice loss and bce loss is only applicable to binary classfication"
+            )
+
+        if class_weight is not None:
+            if isinstance(class_weight, list):
+                if len(class_weight) != num_classes:
+                    raise ValueError(
+                        "Length of class_weight should be equal to number of classes"
+                    )
+            elif isinstance(class_weight, str):
+                if class_weight.lower() != 'dynamic':
+                    raise ValueError(
+                        "if class_weight is string, must be dynamic!")
+            else:
+                raise TypeError(
+                    'Expect class_weight is a list or string but receive {}'.
+                    format(type(class_weight)))
+
+        self.num_classes = num_classes
+        self.mode = mode
+        self.use_bce_loss = use_bce_loss
+        self.use_dice_loss = use_dice_loss
+        self.class_weight = class_weight
+        self.ignore_index = ignore_index
+
+    def _get_loss(self, logit, label, mask):
+        avg_loss = 0
+        if not (self.use_dice_loss or self.use_bce_loss):
+            avg_loss += softmax_with_loss(
+                logit,
+                label,
+                mask,
+                num_classes=self.num_classes,
+                weight=self.class_weight,
+                ignore_index=self.ignore_index)
+        else:
+            if self.use_dice_loss:
+                avg_loss += dice_loss(logit, label, mask)
+            if self.use_bce_loss:
+                avg_loss += bce_loss(
+                    logit, label, mask, ignore_index=self.ignore_index)
+
+        return avg_loss
+
+    def generate_inputs(self):
+        inputs = OrderedDict()
+        inputs['image'] = fluid.data(
+            dtype='float32', shape=[None, 3, None, None], name='image')
+        if self.mode == 'train':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        elif self.mode == 'eval':
+            inputs['label'] = fluid.data(
+                dtype='int32', shape=[None, 1, None, None], name='label')
+        return inputs
+
+    def build_net(self, inputs, class_dim=2):
+        if self.use_dice_loss or self.use_bce_loss:
+            self.num_classes = 1
+        image = inputs['image']
+        ## Encoder
+        conv1 = self.conv_bn(image, 3, 36, 2, 1)
+        print('encoder 1', conv1.shape)
+        shortcut = self.conv_bn(
+            input=conv1, filter_size=1, num_filters=18, stride=1, padding=0)
+        print('shortcut 1', shortcut.shape)
+
+        pool = fluid.layers.pool2d(
+            input=conv1,
+            pool_size=3,
+            pool_type='max',
+            pool_stride=2,
+            pool_padding=1)
+        print('encoder 2', pool.shape)
+
+        # Block 1
+        conv = self.sfnetv2module(pool, stride=2, num_filters=72)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        print('encoder 3', conv.shape)
+
+        # Block 2
+        conv = self.sfnetv2module(conv, stride=2)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        conv = self.sfnetv2module(conv, stride=1)
+        print('encoder 4', conv.shape)
+
+        ### decoder
+        conv = self.depthwise_separable(conv, 3, 64, 1)
+        shortcut_shape = fluid.layers.shape(shortcut)[2:]
+        conv_b = fluid.layers.resize_bilinear(conv, shortcut_shape)
+        concat = fluid.layers.concat([shortcut, conv_b], axis=1)
+        decode_conv = self.depthwise_separable(concat, 3, 64, 1)
+        logit = self.output_layer(decode_conv, class_dim)
+
+        if self.num_classes == 1:
+            out = sigmoid_to_softmax(logit)
+            out = fluid.layers.transpose(out, [0, 2, 3, 1])
+        else:
+            out = fluid.layers.transpose(logit, [0, 2, 3, 1])
+
+        pred = fluid.layers.argmax(out, axis=3)
+        pred = fluid.layers.unsqueeze(pred, axes=[3])
+
+        if self.mode == 'train':
+            label = inputs['label']
+            mask = label != self.ignore_index
+            return self._get_loss(logit, label, mask)
+
+        else:
+            if self.num_classes == 1:
+                logit = sigmoid_to_softmax(logit)
+            else:
+                logit = fluid.layers.softmax(logit, axis=1)
+            return pred, logit
+
+        return logit
+
+    def conv_bn(self,
+                input,
+                filter_size,
+                num_filters,
+                stride,
+                padding,
+                channels=None,
+                num_groups=1,
+                act='relu',
+                use_cudnn=True):
+        parameter_attr = ParamAttr(learning_rate=1, initializer=MSRA())
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            act=None,
+            use_cudnn=use_cudnn,
+            param_attr=parameter_attr,
+            bias_attr=False)
+        return fluid.layers.batch_norm(input=conv, act=act)
+
+    def depthwise_separable(self, input, filter_size, num_filters, stride):
+        num_filters1 = int(input.shape[1])
+        num_groups = num_filters1
+        depthwise_conv = self.conv_bn(
+            input=input,
+            filter_size=filter_size,
+            num_filters=int(num_filters1),
+            stride=stride,
+            padding=int(filter_size / 2),
+            num_groups=num_groups,
+            use_cudnn=False,
+            act=None)
+
+        pointwise_conv = self.conv_bn(
+            input=depthwise_conv,
+            filter_size=1,
+            num_filters=num_filters,
+            stride=1,
+            padding=0)
+        return pointwise_conv
+
+    def sfnetv2module(self, input, stride, num_filters=None):
+        if stride == 1:
+            shortcut, branch = fluid.layers.split(
+                input, num_or_sections=2, dim=1)
+            if num_filters is None:
+                in_channels = int(branch.shape[1])
+            else:
+                in_channels = int(num_filters / 2)
+        else:
+            branch = input
+            if num_filters is None:
+                in_channels = int(branch.shape[1])
+            else:
+                in_channels = int(num_filters / 2)
+            shortcut = self.depthwise_separable(input, 3, in_channels, stride)
+        branch_1x1 = self.conv_bn(
+            input=branch,
+            filter_size=1,
+            num_filters=int(in_channels),
+            stride=1,
+            padding=0)
+        branch_dw1x1 = self.depthwise_separable(branch_1x1, 3, in_channels,
+                                                stride)
+        output = fluid.layers.concat(input=[shortcut, branch_dw1x1], axis=1)
+
+        # channel shuffle
+        # b, c, h, w = output.shape
+        shape = fluid.layers.shape(output)
+        c = output.shape[1]
+        b, h, w = shape[0], shape[2], shape[3]
+        output = fluid.layers.reshape(x=output, shape=[b, 2, in_channels, h, w])
+        output = fluid.layers.transpose(x=output, perm=[0, 2, 1, 3, 4])
+        output = fluid.layers.reshape(x=output, shape=[b, c, h, w])
+        return output
+
+    def output_layer(self, input, out_dim):
+        param_attr = fluid.param_attr.ParamAttr(
+            learning_rate=1.,
+            regularizer=fluid.regularizer.L2Decay(0.),
+            initializer=fluid.initializer.Xavier())
+        # deconv
+        output = fluid.layers.conv2d_transpose(
+            input=input,
+            num_filters=out_dim,
+            filter_size=2,
+            padding=0,
+            stride=2,
+            bias_attr=True,
+            param_attr=param_attr,
+            act=None)
+        return output

contrib/HumanSeg/setup.py

+# copyright (c) 2020 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.
+
+import setuptools
+
+long_descrition = 'HumanSeg'
+
+setuptools.setup(
+    name='HumanSeg',
+    version='1.0.0',
+    author='paddleseg',
+    description=long_descrition,
+    long_descrition=long_descrition,
+    packages='./',
+    setup_requires=['cython', 'numpy'],
+    install_requires=['pyyaml', 'tqdm', 'visualdl==1.3.0', 'paddleslim==1.0.1'],
+    license='Apache 2.0')

contrib/HumanSeg/transforms/__init__.py

@@ -12,5 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from . import transforms
+from .transforms import *
 from . import functional

contrib/HumanSeg/transforms/transforms.py

@@ -58,13 +58,14 @@ class Compose:
 
         if im_info is None:
             im_info = dict()
-        im = cv2.imread(im).astype('float32')
+        if isinstance(im, str):
+            im = cv2.imread(im).astype('float32')
+        if isinstance(label, str):
+            label = np.asarray(Image.open(label))
         if im is None:
             raise ValueError('Can\'t read The image file {}!'.format(im))
         if self.to_rgb:
             im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
-        if label is not None:
-            label = np.asarray(Image.open(label))
 
         for op in self.transforms:
             outputs = op(im, im_info, label)

contrib/HumanSeg/tutorial/export_demo.py

+import HumanSeg
+model = HumanSeg.models.load_model('output/best_model')
+model.export_inference_model('output/export')

contrib/HumanSeg/tutorial/infer_demo.py

+import HumanSeg
+from HumanSeg.utils import visualize
+
+im_file = '/ssd1/chenguowei01/dataset/humanseg/supervise.ly/pexel/img/person_detection__ds6/img/pexels-photo-704264.jpg'
+model = HumanSeg.models.load_model('output/best_model')
+result = model.predict(im_file)
+visualize(im_file, result, save_dir='output/')

contrib/HumanSeg/tutorial/train_demo.py

+import os
+import numpy as np
+from HumanSeg.datasets.dataset import Dataset
+from HumanSeg.models import HumanSegMobile
+from HumanSeg.transforms import transforms
+
+train_transforms = transforms.Compose([
+    transforms.RandomHorizontalFlip(),
+    transforms.Resize((192, 192)),
+    transforms.Normalize()
+])
+
+eval_transforms = transforms.Compose(
+    [transforms.Resize((192, 192)),
+     transforms.Normalize()])
+
+data_dir = '/ssd1/chenguowei01/dataset/humanseg/supervise.ly'
+train_list = '/ssd1/chenguowei01/dataset/humanseg/supervise.ly/train.txt'
+val_list = '/ssd1/chenguowei01/dataset/humanseg/supervise.ly/val.txt'
+
+train_dataset = Dataset(
+    data_dir=data_dir,
+    file_list=train_list,
+    transforms=train_transforms,
+    num_workers='auto',
+    buffer_size=100,
+    parallel_method='thread',
+    shuffle=True)
+
+eval_dataset = Dataset(
+    data_dir=data_dir,
+    file_list=val_list,
+    transforms=eval_transforms,
+    num_workers='auto',
+    buffer_size=100,
+    parallel_method='thread',
+    shuffle=False)
+
+model = HumanSegMobile(num_classes=2)
+
+model.train(
+    num_epochs=100,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    save_interval_epochs=5,
+    train_batch_size=256,
+    # resume_weights='/Users/chenguowei01/PycharmProjects/github/PaddleSeg/contrib/HumanSeg/output/epoch_20',
+    log_interval_steps=2,
+    save_dir='output',
+    use_vdl=True,
+)

contrib/HumanSeg/tutorial/val_demo.py

+import HumanSeg
+from HumanSeg.datasets.dataset import Dataset
+from HumanSeg.transforms import transforms
+
+eval_transforms = transforms.Compose(
+    [transforms.Resize((192, 192)),
+     transforms.Normalize()])
+
+data_dir = '/ssd1/chenguowei01/dataset/humanseg/supervise.ly'
+val_list = '/ssd1/chenguowei01/dataset/humanseg/supervise.ly/val.txt'
+
+eval_dataset = Dataset(
+    data_dir=data_dir,
+    file_list=val_list,
+    transforms=eval_transforms,
+    num_workers='auto',
+    buffer_size=100,
+    parallel_method='thread',
+    shuffle=False)
+
+model = HumanSeg.models.load_model('output/best_model')
+model.evaluate(eval_dataset, 2)

contrib/HumanSeg/utils/__init__.py

@@ -16,3 +16,4 @@ from . import logging
 from . import utils
 from .metrics import ConfusionMatrix
 from .utils import *
+from .post_quantization import HumanSegPostTrainingQuantization

contrib/HumanSeg/utils/post_quantization.py

+# copyright (c) 2020 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 paddle.fluid.contrib.slim.quantization.quantization_pass import QuantizationTransformPass
+from paddle.fluid.contrib.slim.quantization.quantization_pass import AddQuantDequantPass
+from paddle.fluid.contrib.slim.quantization.quantization_pass import _op_real_in_out_name
+from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
+import paddle.fluid as fluid
+import os
+
+import HumanSeg.utils.logging as logging
+
+
+class HumanSegPostTrainingQuantization(PostTrainingQuantization):
+    def __init__(self,
+                 executor,
+                 dataset,
+                 program,
+                 inputs,
+                 outputs,
+                 batch_size=10,
+                 batch_nums=None,
+                 scope=None,
+                 algo="KL",
+                 quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"],
+                 is_full_quantize=False,
+                 is_use_cache_file=False,
+                 cache_dir="./temp_post_training"):
+        '''
+        The class utilizes post training quantization methon to quantize the
+        fp32 model. It uses calibrate data to calculate the scale factor of
+        quantized variables, and inserts fake quant/dequant op to obtain the
+        quantized model.
+
+        Args:
+            executor(fluid.Executor): The executor to load, run and save the
+                quantized model.
+            dataset(Python Iterator): The data Reader.
+            program(fluid.Program): The paddle program, save the parameters for model.
+            inputs(dict): The input of prigram.
+            outputs(dict): The output of program.
+            batch_size(int, optional): The batch size of DataLoader. Default is 10.
+            batch_nums(int, optional): If batch_nums is not None, the number of
+                calibrate data is batch_size*batch_nums. If batch_nums is None, use
+                all data provided by sample_generator as calibrate data.
+            scope(fluid.Scope, optional): The scope of the program, use it to load
+                and save variables. If scope=None, get scope by global_scope().
+            algo(str, optional): If algo=KL, use KL-divergenc method to
+                get the more precise scale factor. If algo='direct', use
+                abs_max methon to get the scale factor. Default is KL.
+            quantizable_op_type(list[str], optional): List the type of ops
+                that will be quantized. Default is ["conv2d", "depthwise_conv2d",
+                "mul"].
+            is_full_quantized(bool, optional): If set is_full_quantized as True,
+                apply quantization to all supported quantizable op type. If set
+                is_full_quantized as False, only apply quantization to the op type
+                according to the input quantizable_op_type.
+            is_use_cache_file(bool, optional): If set is_use_cache_file as False,
+                all temp data will be saved in memory. If set is_use_cache_file as True,
+                it will save temp data to disk. When the fp32 model is complex or
+                the number of calibrate data is large, we should set is_use_cache_file
+                as True. Defalut is False.
+            cache_dir(str, optional): When is_use_cache_file is True, set cache_dir as
+                the directory for saving temp data. Default is ./temp_post_training.
+        Returns:
+            None
+        '''
+        self._executor = executor
+        self._dataset = dataset
+        self._batch_size = batch_size
+        self._batch_nums = batch_nums
+        self._scope = fluid.global_scope() if scope == None else scope
+        self._algo = algo
+        self._is_use_cache_file = is_use_cache_file
+        self._cache_dir = cache_dir
+        if self._is_use_cache_file and not os.path.exists(self._cache_dir):
+            os.mkdir(self._cache_dir)
+
+        supported_quantizable_op_type = \
+            QuantizationTransformPass._supported_quantizable_op_type + \
+            AddQuantDequantPass._supported_quantizable_op_type
+        if is_full_quantize:
+            self._quantizable_op_type = supported_quantizable_op_type
+        else:
+            self._quantizable_op_type = quantizable_op_type
+            for op_type in self._quantizable_op_type:
+                assert op_type in supported_quantizable_op_type + \
+                    AddQuantDequantPass._activation_type, \
+                    op_type + " is not supported for quantization."
+
+        self._place = self._executor.place
+        self._program = program
+        self._feed_list = list(inputs.values())
+        self._fetch_list = list(outputs.values())
+        self._data_loader = None
+
+        self._op_real_in_out_name = _op_real_in_out_name
+        self._bit_length = 8
+        self._quantized_weight_var_name = set()
+        self._quantized_act_var_name = set()
+        self._sampling_data = {}
+        self._quantized_var_scale_factor = {}
+
+    def quantize(self):
+        '''
+        Quantize the fp32 model. Use calibrate data to calculate the scale factor of
+        quantized variables, and inserts fake quant/dequant op to obtain the
+        quantized model.
+
+        Args:
+            None
+        Returns:
+            the program of quantized model.
+        '''
+        self._preprocess()
+
+        batch_id = 0
+        for data in self._data_loader():
+            self._executor.run(
+                program=self._program,
+                feed=data,
+                fetch_list=self._fetch_list,
+                return_numpy=False)
+            self._sample_data(batch_id)
+
+            if batch_id % 5 == 0:
+                logging.info("run batch: {}".format(batch_id))
+            batch_id += 1
+            if self._batch_nums and batch_id >= self._batch_nums:
+                break
+        logging.info("all run batch: ".format(batch_id))
+        logging.info("calculate scale factor ...")
+        self._calculate_scale_factor()
+        logging.info("update the program ...")
+        self._update_program()
+
+        self._save_output_scale()
+        return self._program
+
+    def save_quantized_model(self, save_model_path):
+        '''
+        Save the quantized model to the disk.
+
+        Args:
+            save_model_path(str): The path to save the quantized model
+        Returns:
+            None
+        '''
+        feed_vars_names = [var.name for var in self._feed_list]
+        fluid.io.save_inference_model(
+            dirname=save_model_path,
+            feeded_var_names=feed_vars_names,
+            target_vars=self._fetch_list,
+            executor=self._executor,
+            params_filename='__params__',
+            main_program=self._program)
+
+    def _preprocess(self):
+        '''
+        Load model and set data loader, collect the variable names for sampling,
+        and set activation variables to be persistable.
+        '''
+        feed_vars = [fluid.framework._get_var(var.name, self._program) \
+            for var in self._feed_list]
+
+        self._data_loader = fluid.io.DataLoader.from_generator(
+            feed_list=feed_vars, capacity=3 * self._batch_size, iterable=True)
+        self._data_loader.set_sample_list_generator(
+            self._dataset.generator(self._batch_size, drop_last=True),
+            places=self._place)
+
+        # collect the variable names for sampling
+        persistable_var_names = []
+        for var in self._program.list_vars():
+            if var.persistable:
+                persistable_var_names.append(var.name)
+
+        for op in self._program.global_block().ops:
+            op_type = op.type
+            if op_type in self._quantizable_op_type:
+                if op_type in ("conv2d", "depthwise_conv2d"):
+                    self._quantized_act_var_name.add(op.input("Input")[0])
+                    self._quantized_weight_var_name.add(op.input("Filter")[0])
+                    self._quantized_act_var_name.add(op.output("Output")[0])
+                elif op_type == "mul":
+                    if self._is_input_all_not_persistable(
+                            op, persistable_var_names):
+                        op._set_attr("skip_quant", True)
+                        logging.warning(
+                            "Skip quant a mul op for two input variables are not persistable"
+                        )
+                    else:
+                        self._quantized_act_var_name.add(op.input("X")[0])
+                        self._quantized_weight_var_name.add(op.input("Y")[0])
+                        self._quantized_act_var_name.add(op.output("Out")[0])
+                else:
+                    # process other quantizable op type, the input must all not persistable
+                    if self._is_input_all_not_persistable(
+                            op, persistable_var_names):
+                        input_output_name_list = self._op_real_in_out_name[
+                            op_type]
+                        for input_name in input_output_name_list[0]:
+                            for var_name in op.input(input_name):
+                                self._quantized_act_var_name.add(var_name)
+                        for output_name in input_output_name_list[1]:
+                            for var_name in op.output(output_name):
+                                self._quantized_act_var_name.add(var_name)
+
+        # set activation variables to be persistable, so can obtain
+        # the tensor data in sample_data
+        for var in self._program.list_vars():
+            if var.name in self._quantized_act_var_name:
+                var.persistable = True

contrib/HumanSeg/utils/utils.py

@@ -20,6 +20,7 @@ import numpy as np
 import six
 import yaml
 import math
+import cv2
 from . import logging
 
 
@@ -218,3 +219,58 @@ def load_pretrain_weights(exe, main_prog, weights_dir, fuse_bn=False):
             len(vars_to_load), weights_dir))
     if fuse_bn:
         fuse_bn_weights(exe, main_prog, weights_dir)
+
+
+def visualize(image, result, save_dir=None, weight=0.6):
+    """
+    Convert segment result to color image, and save added image.
+    Args:
+        image: the path of origin image
+        result: the predict result of image
+        save_dir: the directory for saving visual image
+        weight: the image weight of visual image, and the result weight is (1 - weight)
+    """
+    label_map = result['label_map']
+    color_map = get_color_map_list(256)
+    color_map = np.array(color_map).astype("uint8")
+    # Use OpenCV LUT for color mapping
+    c1 = cv2.LUT(label_map, color_map[:, 0])
+    c2 = cv2.LUT(label_map, color_map[:, 1])
+    c3 = cv2.LUT(label_map, color_map[:, 2])
+    pseudo_img = np.dstack((c1, c2, c3))
+
+    im = cv2.imread(image)
+    vis_result = cv2.addWeighted(im, weight, pseudo_img, 1 - weight, 0)
+
+    if save_dir is not None:
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        image_name = os.path.split(image)[-1]
+        out_path = os.path.join(save_dir, image_name)
+        cv2.imwrite(out_path, vis_result)
+    else:
+        return vis_result
+
+
+def get_color_map_list(num_classes):
+    """ Returns the color map for visualizing the segmentation mask,
+        which can support arbitrary number of classes.
+    Args:
+        num_classes: Number of classes
+    Returns:
+        The color map
+    """
+    num_classes += 1
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
+    color_map = color_map[1:]
+    return color_map