diff --git a/contrib/HumanSeg/README.md b/contrib/HumanSeg/README.md index c5ba095749e592bbb3d866935bfde4a904323862..52a57590dd6bce71c785cc10d37b74357d0f15ce 100644 --- a/contrib/HumanSeg/README.md +++ b/contrib/HumanSeg/README.md @@ -18,9 +18,9 @@ $ pip install -r requirements.txt HumanSeg开放了在大规模人像数据上训练的三个预训练模型，满足多种使用场景的需求 | 模型类型 | Checkpoint | Inference Model | Quant Inference Model | 备注 | | --- | --- | --- | --- | --- | -| HumanSeg-server | [humanseg_server_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server.zip) | [humanseg_server_export](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_export.zip) | -- | 高精度模型，适用于服务端GPU且背景复杂的人像场景 | -| HumanSeg-mobile | [humanseg_mobile_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile.zip) | [humanseg_mobile_export](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_export.zip) | [humanseg_mobile_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_quant.zip) | 轻量级模型, 适用于移动端或服务端CPU的前置摄像头场景 | -| HumanSeg-lite | [humanseg_lite](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite.zip) | [humanseg_lite_export](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_export.zip) | [humanseg_lite_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_quant.zip) | 超轻量级模型, 适用于手机自拍人像，且有移动端实时分割场景 | +| HumanSeg-server | [humanseg_server_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_ckpt.zip) | [humanseg_server_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_inference.zip) | -- | 高精度模型，适用于服务端GPU且背景复杂的人像场景 | +| HumanSeg-mobile | [humanseg_mobile_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_ckpt.zip) | [humanseg_mobile_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_inference.zip) | [humanseg_mobile_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_quant.zip) | 轻量级模型, 适用于移动端或服务端CPU的前置摄像头场景 | +| HumanSeg-lite | [humanseg_lite_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_ckpt.zip) | [humanseg_lite_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_inference.zip) | [humanseg_lite_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_quant.zip) | 超轻量级模型, 适用于手机自拍人像，且有移动端实时分割场景 | **NOTE:** 其中Checkpoint为模型权重，用于Fine-tuning场景。 diff --git a/contrib/HumanSeg/datasets/ade20k.py b/contrib/HumanSeg/datasets/ade20k.py deleted file mode 100644 index ddd6876c2b5ba6f116b0c304cb3521e585be2034..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/datasets/ade20k.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License" -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/contrib/HumanSeg/datasets/cityscapes.py b/contrib/HumanSeg/datasets/cityscapes.py deleted file mode 100644 index ddd6876c2b5ba6f116b0c304cb3521e585be2034..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/datasets/cityscapes.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License" -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/contrib/HumanSeg/datasets/pascalvoc.py b/contrib/HumanSeg/datasets/pascalvoc.py deleted file mode 100644 index ddd6876c2b5ba6f116b0c304cb3521e585be2034..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/datasets/pascalvoc.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License" -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/contrib/HumanSeg/deploy/README.md b/contrib/HumanSeg/deploy/README.md deleted file mode 100644 index b5baf00a62b53109d201b3263ae2acd03c0cec43..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/deploy/README.md +++ /dev/null @@ -1,30 +0,0 @@ -# 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 | -| --- | --- | -|[humanseg_lite_quant]() | 小模型, 适合轻量级计算环境 | -|[humanseg_lite]()| 小模型，适合轻量级计算环境 | -|[humanseg_mobile_quant]() | 小模型, 适合轻量级计算环境 | -|[humanseg_mobile]()| 小模型，适合轻量级计算环境 | -|[humanseg_server_quant]() | 服务端GPU环境 | -|[humanseg_server]() | 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 - -
- - -
diff --git a/contrib/HumanSeg/deploy/python/README.md b/contrib/HumanSeg/deploy/python/README.md deleted file mode 100644 index e5aa6f2f6804cd006933abb5f163771ea03ac670..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/deploy/python/README.md +++ /dev/null @@ -1,55 +0,0 @@ -# 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。 diff --git a/contrib/HumanSeg/deploy/python/infer.py b/contrib/HumanSeg/deploy/python/infer.py deleted file mode 100644 index 74efbe1b170fcd47e2720050a426c066e80c8c89..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/deploy/python/infer.py +++ /dev/null @@ -1,342 +0,0 @@ -# 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) diff --git a/contrib/HumanSeg/deploy/python/requirements.txt b/contrib/HumanSeg/deploy/python/requirements.txt deleted file mode 100644 index 953dae0cf5e2036ad093907b30ac9a3a10858d27..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/deploy/python/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -opencv-python==4.1.2.30 -opencv-contrib-python==4.2.0.32 diff --git a/contrib/HumanSeg/nets/unet.py b/contrib/HumanSeg/nets/unet.py deleted file mode 100644 index 681202a6dfbba575e0d05f819116580d5738256b..0000000000000000000000000000000000000000 --- a/contrib/HumanSeg/nets/unet.py +++ /dev/null @@ -1,257 +0,0 @@ -# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License" -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from collections import OrderedDict -import paddle.fluid as fluid - -from .libs import scope -from .libs import bn_relu, conv, max_pool, deconv -from .libs import sigmoid_to_softmax -from .seg_modules import softmax_with_loss -from .seg_modules import dice_loss, bce_loss - - -class UNet(object): - """实现Unet模型 - `"U-Net: Convolutional Networks for Biomedical Image Segmentation" - ` - - Args: - num_classes (int): 类别数 - mode (str): 网络运行模式，根据mode构建网络的输入和返回。 - 当mode为'train'时，输入为image(-1, 3, -1, -1)和label (-1, 1, -1, -1) 返回loss。 - 当mode为'train'时，输入为image (-1, 3, -1, -1)和label (-1, 1, -1, -1)，返回loss， - pred (与网络输入label 相同大小的预测结果，值代表相应的类别），label，mask（非忽略值的mask， - 与label相同大小，bool类型）。 - 当mode为'test'时，输入为image(-1, 3, -1, -1)返回pred (-1, 1, -1, -1)和 - logit (-1, num_classes, -1, -1) 通道维上代表每一类的概率值。 - upsample_mode (str): UNet decode时采用的上采样方式，取值为'bilinear'时利用双线行差值进行上菜样， - 当输入其他选项时则利用反卷积进行上菜样，默认为'bilinear'。 - use_bce_loss (bool): 是否使用bce loss作为网络的损失函数，只能用于两类分割。可与dice loss同时使用。 - use_dice_loss (bool): 是否使用dice loss作为网络的损失函数，只能用于两类分割，可与bce loss同时使用。 - 当use_bce_loss和use_dice_loss都为False时，使用交叉熵损失函数。 - class_weight (list/str): 交叉熵损失函数各类损失的权重。当class_weight为list的时候，长度应为 - num_classes。当class_weight为str时， weight.lower()应为'dynamic'，这时会根据每一轮各类像素的比重 - 自行计算相应的权重，每一类的权重为：每类的比例 * num_classes。class_weight取默认值None是，各类的权重1， - 即平时使用的交叉熵损失函数。 - ignore_index (int): label上忽略的值，label为ignore_index的像素不参与损失函数的计算。 - - Raises: - ValueError: use_bce_loss或use_dice_loss为真且num_calsses > 2。 - ValueError: class_weight为list, 但长度不等于num_class。 - class_weight为str, 但class_weight.low()不等于dynamic。 - TypeError: class_weight不为None时，其类型不是list或str。 - """ - - def __init__(self, - num_classes, - mode='train', - upsample_mode='bilinear', - 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 Exception( - "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.upsample_mode = upsample_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 _double_conv(self, data, out_ch): - param_attr = fluid.ParamAttr( - name='weights', - regularizer=fluid.regularizer.L2DecayRegularizer( - regularization_coeff=0.0), - initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.33)) - with scope("conv0"): - data = bn_relu( - conv( - data, out_ch, 3, stride=1, padding=1, - param_attr=param_attr)) - with scope("conv1"): - data = bn_relu( - conv( - data, out_ch, 3, stride=1, padding=1, - param_attr=param_attr)) - return data - - def _down(self, data, out_ch): - # 下采样：max_pool + 2个卷积 - with scope("down"): - data = max_pool(data, 2, 2, 0) - data = self._double_conv(data, out_ch) - return data - - def _up(self, data, short_cut, out_ch): - # 上采样：data上采样(resize或deconv), 并与short_cut concat - param_attr = fluid.ParamAttr( - name='weights', - regularizer=fluid.regularizer.L2DecayRegularizer( - regularization_coeff=0.0), - initializer=fluid.initializer.XavierInitializer(), - ) - with scope("up"): - if self.upsample_mode == 'bilinear': - short_cut_shape = fluid.layers.shape(short_cut) - data = fluid.layers.resize_bilinear(data, short_cut_shape[2:]) - else: - data = deconv( - data, - out_ch // 2, - filter_size=2, - stride=2, - padding=0, - param_attr=param_attr) - data = fluid.layers.concat([data, short_cut], axis=1) - data = self._double_conv(data, out_ch) - return data - - def _encode(self, data): - # 编码器设置 - short_cuts = [] - with scope("encode"): - with scope("block1"): - data = self._double_conv(data, 64) - short_cuts.append(data) - with scope("block2"): - data = self._down(data, 128) - short_cuts.append(data) - with scope("block3"): - data = self._down(data, 256) - short_cuts.append(data) - with scope("block4"): - data = self._down(data, 512) - short_cuts.append(data) - with scope("block5"): - data = self._down(data, 512) - return data, short_cuts - - def _decode(self, data, short_cuts): - # 解码器设置，与编码器对称 - with scope("decode"): - with scope("decode1"): - data = self._up(data, short_cuts[3], 256) - with scope("decode2"): - data = self._up(data, short_cuts[2], 128) - with scope("decode3"): - data = self._up(data, short_cuts[1], 64) - with scope("decode4"): - data = self._up(data, short_cuts[0], 64) - return data - - def _get_logit(self, data, num_classes): - # 根据类别数设置最后一个卷积层输出 - param_attr = fluid.ParamAttr( - name='weights', - regularizer=fluid.regularizer.L2DecayRegularizer( - regularization_coeff=0.0), - initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=0.01)) - with scope("logit"): - data = conv( - data, - num_classes, - 3, - stride=1, - padding=1, - param_attr=param_attr) - return data - - 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): - # 在两类分割情况下，当loss函数选择dice_loss或bce_loss的时候，最后logit输出通道数设置为1 - if self.use_dice_loss or self.use_bce_loss: - self.num_classes = 1 - - image = inputs['image'] - encode_data, short_cuts = self._encode(image) - decode_data = self._decode(encode_data, short_cuts) - logit = self._get_logit(decode_data, 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) - - elif self.mode == 'eval': - label = inputs['label'] - mask = label != self.ignore_index - loss = self._get_loss(logit, label, mask) - return loss, pred, label, mask - else: - if self.num_classes == 1: - logit = sigmoid_to_softmax(logit) - else: - logit = fluid.layers.softmax(logit, axis=1) - return pred, logit diff --git a/contrib/HumanSeg/pretrained_weights/download_pretrained_weights.py b/contrib/HumanSeg/pretrained_weights/download_pretrained_weights.py index 9947bbdeaa57d1200514f5659194a0e37deb5a6a..e573df05f94f5a612ef6c2f5a2eb2c9cd55cc2f1 100644 --- a/contrib/HumanSeg/pretrained_weights/download_pretrained_weights.py +++ b/contrib/HumanSeg/pretrained_weights/download_pretrained_weights.py @@ -22,20 +22,20 @@ sys.path.append(TEST_PATH) from test_utils import download_file_and_uncompress model_urls = { - "humanseg_server": - "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server.zip", - "humanseg_server_export": - "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_export.zip", - "humanseg_mobile": - "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile.zip", - "humanseg_mobile_export": - "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_export.zip", + "humanseg_server_ckpt": + "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_ckpt.zip", + "humanseg_server_inference": + "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_inference.zip", + "humanseg_mobile_ckpt": + "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_ckpt.zip", + "humanseg_mobile_inference": + "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_inference.zip", "humanseg_mobile_quant": "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_quant.zip", - "humanseg_lite": - "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite.zip", - "humanseg_lite_export": - "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_export.zip", + "humanseg_lite_ckpt": + "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_ckpt.zip", + "humanseg_lite_inference": + "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_inference.zip", "humanseg_lite_quant": "https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_quant.zip", }